Search Word(s) in Title, Keywords, Authors, and Abstract: OCA

An Improved Level Set Method to Image Segmentation Based on Saliency
Yan Wang and Xianfa Xu
Page: 7~21, Vol. 15, No.1, 2019
10.3745/JIPS.02.0105
Keywords: Canny Operator, Edge Energy, Level Set Method, Local Renyi Entropy, Saliency Map
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In order to improve the edge segmentation effect of the level set image segmentation and avoid the influence of the initial contour on the level set method, a saliency level set image segmentation model based on local Renyi entropy is proposed. Firstly, the saliency map of the original image is extracted by using saliency detection algorithm. And the outline of the saliency map can be used to initialize the level set. Secondly, the local energy and edge energy of the image are obtained by using local Renyi entropy and Canny operator respectively. At the same time, new adaptive weight coefficient and boundary indication function are constructed. Finally, the local binary fitting energy model (LBF) as an external energy term is introduced. In this paper, the contrast experiments are implemented in different image database. The robustness of the proposed model for segmentation of images with intensity inhomogeneity and complicated edges is verified.


Hierarchical Graph based Segmentation and Consensus based Human Tracking Technique
Sunitha Madasi Ramachandra, Haradagere Siddaramaiah Jayanna and Ramegowda
Page: 67~90, Vol. 15, No.1, 2019
10.3745/JIPS.04.0100
Keywords: Consensus Based Framework, Hierarchical Graph Based Segmentation, SIFT Keypoint Descriptor
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Accurate detection, tracking and analysis of human movement using robots and other visual surveillance systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which involved scanning of various sizes of windows across an image. This paper concentrates on employing a stateof- the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme with validation phase. Localization of human region in each frame is performed by keypoints by casting votes for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based framework is used to detect voting behavior. The designed methodology is tested on the video sequences having 3 to 4 persons.


An Improved PSO Algorithm for the Classification of Multiple Power Quality Disturbances
Liquan Zhao and Yan Long
Page: 116~126, Vol. 15, No.1, 2019
10.3745/JIPS.04.0102
Keywords: Classification Accuracy, Classification of Power Quality Disturbance, Particle Swarm Optimization, Support Vector Machine
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In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the outset and effectively search locally later in a study, which improves the overall classification accuracy. The experimental results show that the improved particle swarm optimization method is more accurate than a grid search algorithm optimization and other improved particle swarm optimizations with regard to its classification of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.


Wavelet-based Digital Image Watermarking by using Lorenz Chaotic Signal Localization
Jantana Panyavaraporn and Paramate Horkaew
Page: 169~180, Vol. 15, No.1, 2019
10.3745/JIPS.03.0109
Keywords: Binary Image, Chaotic Signal, QR Code, Watermarking, Wavelet Analysis
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Transmitting visual information over a broadcasting network is not only prone to a copyright violation but also is a forgery. Authenticating such information and protecting its authorship rights call for more advanced data encoding. To this end, electronic watermarking is often adopted to embed inscriptive signature in imaging data. Most existing watermarking methods while focusing on robustness against degradation remain lacking of measurement against security loophole in which the encrypting scheme once discovered may be recreated by an unauthorized party. This could reveal the underlying signature which may potentially be replaced or forged. This paper therefore proposes a novel digital watermarking scheme in temporal-frequency domain. Unlike other typical wavelet based watermarking, the proposed scheme employed the Lorenz chaotic map to specify embedding positions. Effectively making this is not only a formidable method to decrypt but also a stronger will against deterministic attacks. Simulation report herein highlights its strength to withstand spatial and frequent adulterations, e.g., lossy compression, filtering, zooming and noise.


Health and Wellness Monitoring Using Intelligent Sensing Technique
Yao Meng, Sang-Hoon Yi and Hee-Cheol Kim
Page: 478~491, Vol. 15, No.3, 2019
10.3745/JIPS.04.0115
Keywords: Accelerometer, Electrocardiogram, Healthcare, Persuasive Technology, Real-Time Monitoring
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This work develops a monitoring system for the population with health concerns. A belt integrated with an onbody circuit and sensors measures a wearer’s selected vital signals. The electrocardiogram sensors monitor heart conditions and an accelerometer assesses the level of physical activity. Sensed signals are transmitted to the circuit module through digital yarns and are forwarded to a mobile device via Bluetooth. An interactive application, installed on the mobile device, is used to process the received signals and provide users with realtime feedback about their status. Persuasive functions are designed and implemented in the interactive application to encourage users’ physical activity. Two signal processing algorithms are developed to analyze the data regarding heart and activity. A user study is conducted to evaluate the performance and usability of the developed system.


Adaptive Cooperative Spectrum Sensing Based on SNR Estimation in Cognitive Radio Networks
Shuiping Ni, Huigang Chang and Yuping Xu
Page: 604~615, Vol. 15, No.3, 2019
10.3745/JIPS.03.0122
Keywords: Adaptive Spectrum Sensing, Cognitive Radio, Detection Time, Fusion Center, SNR Estimation, Voting Rule
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Single-user spectrum sensing is susceptible to multipath effects, shadow effects, hidden terminals and other unfavorable factors, leading to misjudgment of perceived results. In order to increase the detection accuracy and reduce spectrum sensing cost, we propose an adaptive cooperative sensing strategy based on an estimated signal-to-noise ratio (SNR). Which can adaptive select different sensing strategy during the local sensing phase. When the estimated SNR is higher than the selection threshold, adaptive double threshold energy detector (ED) is implemented, otherwise cyclostationary feature detector is performed. Due to the fact that only a better sensing strategy is implemented in a period, the detection accuracy is improved under the condition of low SNR with low complexity. The local sensing node transmits the perceived results through the control channel to the fusion center (FC), and uses voting rule to make the hard decision. Thus the transmission bandwidth is effectively saved. Simulation results show that the proposed scheme can effectively improve the system detection probability, shorten the average sensing time, and has better robustness without largely increasing the costs of sensing system.


Shape Description and Retrieval Using Included-Angular Ternary Pattern
Guoqing Xu, Ke Xiao and Chen Li
Page: 737~747, Vol. 15, No.4, 2019
10.3745/JIPS.02.0114
Keywords: Image Retrieval, Included-Angular Ternary Pattern, Multiscale, Shape Description
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Shape description is an important and fundamental issue in content-based image retrieval (CBIR), and a number of shape description methods have been reported in the literature. For shape description, both global information and local contour variations play important roles. In this paper a new included-angular ternary pattern (IATP) based shape descriptor is proposed for shape image retrieval. For each point on the shape contour, IATP is derived from its neighbor points, and IATP has good properties for shape description. IATP is intrinsically invariant to rotation, translation and scaling. To enhance the description capability, multiscale IATP histogram is presented to describe both local and global information of shape. Then multiscale IATP histogram is combined with included-angular histogram for efficient shape retrieval. In the matching stage, cosine distance is used to measure shape features’ similarity. Image retrieval experiments are conducted on the standard MPEG-7 shape database and Swedish leaf database. And the shape image retrieval performance of the proposed method is compared with other shape descriptors using the standard evaluation method. The experimental results of shape retrieval indicate that the proposed method reaches higher precision at the same recall value compared with other description method.


Comprehensive Survey on Internet of Things, Architecture, Security Aspects, Applications, Related Technologies, Economic Perspective, and Future Directions
Khusanbek Gafurov and Tai-Myoung Chung
Page: 797~819, Vol. 15, No.4, 2019
10.3745/JIPS.03.0125
Keywords: Cloud, Edge, IoT, IoT Security, MEC/MCC, RFID, WSN, 5G
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Internet of Things (IoT) is the paradigm of network of Internet-connected things as objects that constantly sense the physical world and share the data for further processing. At the core of IoT lies the early technology of radio frequency identification (RFID), which provides accurate location tracking of real-world objects. With its small size and convenience, RFID tags can be attached to everyday items such as books, clothes, furniture and the like as well as to animals, plants, and even humans. This phenomenon is the beginning of new applications and services for the industry and consumer market. IoT is regarded as a fourth industrial revolution because of its massive coverage of services around the world from smart homes to artificial intelligence-enabled smart driving cars, Internet-enabled medical equipment, etc. It is estimated that there will be several dozens of billions of IoT devices ready and operating until 2020 around the world. Despite the growing statistics, however, IoT has security vulnerabilities that must be addressed appropriately to avoid causing damage in the future. As such, we mention some fields of study as a future topic at the end of the survey. Consequently, in this comprehensive survey of IoT, we will cover the architecture of IoT with various layered models, security characteristics, potential applications, and related supporting technologies of IoT such as 5G, MEC, cloud, WSN, etc., including the economic perspective of IoT and its future directions.


Robust Ultrasound Multigate Blood Volume Flow Estimation
Yi Zhang, Jinkai Li, Xin Liu and Dong Chyuan Liu
Page: 820~832, Vol. 15, No.4, 2019
10.3745/JIPS.01.0046
Keywords: Blood Volume Flow Estimation, Flow Velocity Estimation, Ultrasound
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Estimation of accurate blood volume flow in ultrasound Doppler blood flow spectrograms is extremely important for clinical diagnostic purposes. Blood volume flow measurements require the assessment of both the velocity distribution and the cross-sectional area of the vessel. Unfortunately, the existing volume flow estimation algorithms by ultrasound lack the velocity space distribution information in cross-sections of a vessel and have the problems of low accuracy and poor stability. In this paper, a new robust ultrasound volume flow estimation method based on multigate (RMG) is proposed and the multigate technology provides detail information on the local velocity distribution. In this method, an accurate double iterative flow velocity estimation algorithm (DIV) is used to estimate the mean velocity and it has been tested on in vivo data from carotid. The results from experiments indicate a mean standard deviation of less than 6% in flow velocities when estimated for a range of SNR levels. The RMG method is validated in a custom-designed experimental setup, Doppler phantom and imitation blood flow control system. In vitro experimental results show that the mean error of the RMG algorithm is 4.81%. Low errors in blood volume flow estimation make the prospect of using the RMG algorithm for real-time blood volume flow estimation possible.


Selection of Machine Learning Techniques for Network Lifetime Parameters and Synchronization Issues in Wireless Networks
Nimmagadda Srilakshmi and Arun Kumar Sangaiah
Page: 833~852, Vol. 15, No.4, 2019
10.3745/JIPS.04.0125
Keywords: Congestion, Energy Harvesting, Machine Learning Algorithms, Network Lifetime, Wireless Networks
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In real time applications, due to their effective cost and small size, wireless networks play an important role in receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisation of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault detection, and quality of service, among others. Conventional methods were programmed, for static networks which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less human intervention and reprogramming. In this paper, we present a wireless networks survey based on different machine learning algorithms and network lifetime parameters, and include the advantages and drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion, synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief discussion on the challenges inherent in this area of research.


Mobile User Interface Pattern Clustering Using Improved Semi-Supervised Kernel Fuzzy Clustering Method
Wei Jia, Qingyi Hua, Minjun Zhang, Rui Chen, Xiang Ji and Bo Wang
Page: 986~1016, Vol. 15, No.4, 2019
10.3745/JIPS.04.0131
Keywords: Intuitionistic Fuzzy Entropy Measure, Mobile User Interface Pattern, Particle Swarm Optimization, Population Search Strategy, Semi-Supervised Kernel Fuzzy C-Means
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Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.


A Tracking-by-Detection System for Pedestrian Tracking Using Deep Learning Technique and Color Information
Mai Thanh Nhat Truong and Sanghoon Kim
Page: 1017~1028, Vol. 15, No.4, 2019
10.3745/JIPS.04.0132
Keywords: Color Distribution, Convolutional Neural Network, Pedestrian Tracking, Tracking-by-Detection
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Pedestrian tracking is a particular object tracking problem and an important component in various visionbased applications, such as autonomous cars and surveillance systems. Following several years of development, pedestrian tracking in videos remains challenging, owing to the diversity of object appearances and surrounding environments. In this research, we proposed a tracking-by-detection system for pedestrian tracking, which incorporates a convolutional neural network (CNN) and color information. Pedestrians in video frames are localized using a CNN-based algorithm, and then detected pedestrians are assigned to their corresponding tracklets based on similarities between color distributions. The experimental results show that our system is able to overcome various difficulties to produce highly accurate tracking results.


A Comparative Analysis of Music Similarity Measures in Music Information Retrieval Systems
Kuldeep Gurjar and Yang-Sae Moon
Page: 32~55, Vol. 14, No.1, 2018
10.3745/JIPS.04.0054
Keywords: Content-Based Music Retrieval, MIR System, Music Information Retrieval Survey, Music Similarity Measures
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The digitization of music has seen a considerable increase in audience size from a few localized listeners to a wider range of global listeners. At the same time, the digitization brings the challenge of smoothly retrieving music from large databases. To deal with this challenge, many systems which support the smooth retrieval of musical data have been developed. At the computational level, a query music piece is compared with the rest of the music pieces in the database. These systems, music information retrieval (MIR systems), work for various applications such as general music retrieval, plagiarism detection, music recommendation, and musicology. This paper mainly addresses two parts of the MIR research area. First, it presents a general overview of MIR, which will examine the history of MIR, the functionality of MIR, application areas of MIR, and the components of MIR. Second, we will investigate music similarity measurement methods, where we provide a comparative analysis of state of the art methods. The scope of this paper focuses on comparative analysis of the accuracy and efficiency of a few key MIR systems. These analyses help in understanding the current and future challenges associated with the field of MIR systems and music similarity measures


A Survey on Automatic Twitter Event Summarization
Dwijen Rudrapal, Amitava Das and Baby Bhattacharya
Page: 79~100, Vol. 14, No.1, 2018
10.3745/JIPS.02.0079
Keywords: ROUGE, Social Media Text, Tweet Stream, Tweet Summarization
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Twitter is one of the most popular social platforms for online users to share trendy information and views on any event. Twitter reports an event faster than any other medium and contains enormous information and views regarding an event. Consequently, Twitter topic summarization is one of the most convenient ways to get instant gist of any event. However, the information shared on Twitter is often full of nonstandard abbreviations, acronyms, out of vocabulary (OOV) words and with grammatical mistakes which create challenges to find reliable and useful information related to any event. Undoubtedly, Twitter event summarization is a challenging task where traditional text summarization methods do not work well. In last decade, various research works introduced different approaches for automatic Twitter topic summarization. The main aim of this survey work is to make a broad overview of promising summarization approaches on a Twitter topic. We also focus on automatic evaluation of summarization techniques by surveying recent evaluation methodologies. At the end of the survey, we emphasize on both current and future research challenges in this domain through a level of depth analysis of the most recent summarization approaches.


A Development of LDA Topic Association Systems Based on Spark-Hadoop Framework
Kiejin Park and Limei Peng
Page: 140~149, Vol. 14, No.1, 2018
10.3745/JIPS.04.0057
Keywords: Association Analysis, Hadoop, LDA (Latent Dirichlet Allocation), Spark, Topic Model
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Social data such as users’ comments are unstructured in nature and up-to-date technologies for analyzing such data are constrained by the available storage space and processing time when fast storing and processing is required. On the other hand, it is even difficult in using a huge amount of dynamically generated social data to analyze the user features in a high speed. To solve this problem, we design and implement a topic association analysis system based on the latent Dirichlet allocation (LDA) model. The LDA does not require the training process and thus can analyze the social users’ hourly interests on different topics in an easy way. The proposed system is constructed based on the Spark framework that is located on top of Hadoop cluster. It is advantageous of high-speed processing owing to that minimized access to hard disk is required and all the intermediately generated data are processed in the main memory. In the performance evaluation, it requires about 5 hours to analyze the topics for about 1 TB test social data (SNS comments). Moreover, through analyzing the association among topics, we can track the hourly change of social users’ interests on different topics.


Face Recognition Based on the Combination of Enhanced Local Texture Feature and DBN under Complex Illumination Conditions
Chen Li, Shuai Zhao, Ke Xiao and Yanjie Wang
Page: 191~204, Vol. 14, No.1, 2018
10.3745/JIPS.04.0060
Keywords: Deep Belief Network, Enhanced Local Texture Feature, Face Recognition, Illumination Variation
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To combat the adverse impact imposed by illumination variation in the face recognition process, an effective and feasible algorithm is proposed in this paper. Firstly, an enhanced local texture feature is presented by applying the central symmetric encode principle on the fused component images acquired from the wavelet decomposition. Then the proposed local texture features are combined with Deep Belief Network (DBN) to gain robust deep features of face images under severe illumination conditions. Abundant experiments with different test schemes are conducted on both CMU-PIE and Extended Yale-B databases which contain face images under various illumination condition. Compared with the DBN, LBP combined with DBN and CSLBP combined with DBN, our proposed method achieves the most satisfying recognition rate regardless of the database used, the test scheme adopted or the illumination condition encountered, especially for the face recognition under severe illumination variation.


Vocal Effort Detection Based on Spectral Information Entropy Feature and Model Fusion
Hao Chao, Bao-Yun Lu, Yong-Li Liu and Hui-Lai Zhi
Page: 218~227, Vol. 14, No.1, 2018
10.3745/JIPS.04.0063
Keywords: Gaussian Mixture Model, Model Fusion, Multilayer Perceptron, Spectral Information Entropy, Support Vector Machine, Vocal Effort
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Vocal effort detection is important for both robust speech recognition and speaker recognition. In this paper, the spectral information entropy feature which contains more salient information regarding the vocal effort level is firstly proposed. Then, the model fusion method based on complementary model is presented to recognize vocal effort level. Experiments are conducted on isolated words test set, and the results show the spectral information entropy has the best performance among the three kinds of features. Meanwhile, the recognition accuracy of all vocal effort levels reaches 81.6%. Thus, potential of the proposed method is demonstrated


Sustaining Low-Carbon Emission Development: An Energy Efficient Transportation Plan for CPEC
Asma Zubedi, Zeng Jianqiu, Qasim Ali Arain, Imran Memon, Sehrish Khan, Muhammad Saad Khan and Ying Zhang
Page: 322~345, Vol. 14, No.2, 2018
10.3745/JIPS.04.0067
Keywords: Carbon Emission, Climate Change, CPEC, Green ICT, ITS
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Climate change has become a major challenge for sustainable development of human society. This study is an attempt to analyze existing literature to identify economic indicators that hamper the process of global warming. This paper includes case studies based on various countries to examine the nexus for environment and its relationship with Foreign Direct Investment, transportation, economic growth and energy consumption. Furthermore, the observations are analyzed from the perspective of China-Pakistan Economic Corridor (CPEC) and probable impact on carbon emission of Pakistan. A major portion of CPEC investment is allocated for transportation. However, it is evident that transportation sector is substantial emitter of carbon dioxide (CO2) gas. Unfortunately, there is no empirical work on the subject of CPEC and carbon emission for vehicular transportation. This paper infers that empirical results from various other countries are ambiguous and inconclusive. Moreover, the evidence for the pollution haven hypothesis and the halo effect hypothesis is limited in general and inapplicable for CPEC in particular. The major contribution of this study is the proposal of an energy efficient transportation model for reducing CO2 emission. In the end, the paper suggests strategies to climate researchers and policymakers for adaptation and mitigation of greenhouse gases (GHG).


Fingerprint Identification Based on Hierarchical Triangulation
Meryam Elmouhtadi, Sanaa El fkihi and Driss Aboutajdine
Page: 435~447, Vol. 14, No.2, 2018
10.3745/JIPS.02.0084
Keywords: Biometric, Fingerprint Identification, Delaunay Triangulation, Fingerprint Matching, Minutiae Extraction
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Fingerprint-based biometric identification is one of the most interesting automatic systems for identifying individuals. Owing to the poor sensing environment and poor quality of skin, biometrics remains a challenging problem. The main contribution of this paper is to propose a new approach to recognizing a person’s fingerprint using the fingerprint’s local characteristics. The proposed approach introduces the barycenter notion applied to triangles formed by the Delaunay triangulation once the extraction of minutiae is achieved. This ensures the exact location of similar triangles generated by the Delaunay triangulation in the recognition process. The results of an experiment conducted on a challenging public database (i.e., FVC2004) show significant improvement with regard to fingerprint identification compared to simple Delaunay triangulation, and the obtained results are very encouraging.


On Modification and Application of the Artificial Bee Colony Algorithm
Zhanxiang Ye, Min Zhu and Jin Wang
Page: 448~454, Vol. 14, No.2, 2018
10.3745/JIPS.01.0025
Keywords: Artificial Bee Colony, Bees’ Number Reallocation, Search Equation
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Artificial bee colony (ABC) algorithm has attracted significant interests recently for solving the multivariate optimization problem. However, it still faces insufficiency of slow convergence speed and poor local search ability. Therefore, in this paper, a modified ABC algorithm with bees’ number reallocation and new search equation is proposed to tackle this drawback. In particular, to enhance solution accuracy, more bees in the population are assigned to execute local searches around food sources. Moreover, elite vectors are adopted to guide the bees, with which the algorithm could converge to the potential global optimal position rapidly. A series of classical benchmark functions for frequency-modulated sound waves are adopted to validate the performance of the modified ABC algorithm. Experimental results are provided to show the significant performance improvement of our proposed algorithm over the traditional version.


GLIBP: Gradual Locality Integration of Binary Patterns for Scene Images Retrieval
Salah Bougueroua and Bachir Boucheham
Page: 469~486, Vol. 14, No.2, 2018
10.3745/JIPS.02.0081
Keywords: CBIR, Elliptic-Region, Global Information, LBP, Local Information, Texture
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We propose an enhanced version of the local binary pattern (LBP) operator for texture extraction in images in the context of image retrieval. The novelty of our proposal is based on the observation that the LBP exploits only the lowest kind of local information through the global histogram. However, such global Histograms reflect only the statistical distribution of the various LBP codes in the image. The block based LBP, which uses local histograms of the LBP, was one of few tentative to catch higher level textural information. We believe that important local and useful information in between the two levels is just ignored by the two schemas. The newly developed method: gradual locality integration of binary patterns (GLIBP) is a novel attempt to catch as much local information as possible, in a gradual fashion. Indeed, GLIBP aggregates the texture features present in grayscale images extracted by LBP through a complex structure. The used framework is comprised of a multitude of ellipse-shaped regions that are arranged in circular-concentric forms of increasing size. The framework of ellipses is in fact derived from a simple parameterized generator. In addition, the elliptic forms allow targeting texture directionality, which is a very useful property in texture characterization. In addition, the general framework of ellipses allows for taking into account the spatial information (specifically rotation). The effectiveness of GLIBP was investigated on the Corel-1K (Wang) dataset. It was also compared to published works including the very effective DLEP. Results show significant higher or comparable performance of GLIBP with regard to the other methods, which qualifies it as a good tool for scene images retrieval.


Efficient Hybrid Transactional Memory Scheme using Near-optimal Retry Computation and Sophisticated Memory Management in Multi-core Environment
Yeon-Woo Jang, Moon-Hwan Kang and Jae-Woo Chang
Page: 499~509, Vol. 14, No.2, 2018
10.3745/JIPS.01.0026
Keywords: Bloom Filter, Concurrency Control, Hybrid Transactional Memory, Multi-core in-Memory Databases
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Recently, hybrid transactional memory (HyTM) has gained much interest from researchers because it combines the advantages of hardware transactional memory (HTM) and software transactional memory (STM). To provide the concurrency control of transactions, the existing HyTM-based studies use a bloom filter. However, they fail to overcome the typical false positive errors of a bloom filter. Though the existing studies use a global lock, the efficiency of global lock-based memory allocation is significantly low in multicore environment. In this paper, we propose an efficient hybrid transactional memory scheme using nearoptimal retry computation and sophisticated memory management in order to efficiently process transactions in multi-core environment. First, we propose a near-optimal retry computation algorithm that provides an efficient HTM configuration using machine learning algorithms, according to the characteristic of a given workload. Second, we provide an efficient concurrency control for transactions in different environments by using a sophisticated bloom filter. Third, we propose a memory management scheme being optimized for the CPU cache line, in order to provide a fast transaction processing. Finally, it is shown from our performance evaluation that our HyTM scheme achieves up to 2.5 times better performance by using the Stanford transactional applications for multi-processing (STAMP) benchmarks than the state-of-the-art algorithms.


Review on Self-embedding Fragile Watermarking for Image Authentication and Self-recovery
Chengyou Wang, Heng Zhang and Xiao Zhou
Page: 510~522, Vol. 14, No.2, 2018
10.3745/JIPS.02.0082
Keywords: Image Authentication and Self-recovery, Least Significant Bit (LSB), Peak Signal-to-Noise Ratio (PSNR), Self-embedding Fragile Watermarking
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As the major source of information, digital images play an indispensable role in our lives. However, with the development of image processing techniques, people can optionally retouch or even forge an image by using image processing software. Therefore, the authenticity and integrity of digital images are facing severe challenge. To resolve this issue, the fragile watermarking schemes for image authentication have been proposed. According to different purposes, the fragile watermarking can be divided into two categories: fragile watermarking for tamper localization and fragile watermarking with recovery ability. The fragile watermarking for image tamper localization can only identify and locate the tampered regions, but it cannot further restore the modified regions. In some cases, image recovery for tampered regions is very essential. Generally, the fragile watermarking for image authentication and recovery includes three procedures: watermark generation and embedding, tamper localization, and image self-recovery. In this article, we make a review on self-embedding fragile watermarking methods. The basic model and the evaluation indexes of this watermarking scheme are presented in this paper. Some related works proposed in recent years and their advantages and disadvantages are described in detail to help the future research in this field. Based on the analysis, we give the future research prospects and suggestions in the end.


Load Balancing in Cloud Computing Using Meta-Heuristic Algorithm
Youssef Fahim, Hamza Rahhali, Mohamed Hanine, El-Habib Benlahmar, El-Houssine Labriji, Mostafa Hanoune and Ahmed Eddaoui
Page: 569~589, Vol. 14, No.3, 2018
10.3745/JIPS.01.0028
Keywords: Bat-Algorithm, Cloud Computing, Load Balancing, Pre-scheduling, Virtual Machines
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Cloud computing, also known as country as you go”, is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders’ number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software’s profitability. Our axis of research is the load balancing between a data center’s virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels (‘odd levels’ or ‘even levels’) in ascending order based on the meta-heuristic “Bat-algorithm”. The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.


Analysis of a Third-Party Application for Mobile Forensic Investigation
Jung Hyun Ryu, Nam Yong Kim, Byoung Wook Kwon, Sang Ki Suk, Jin Ho Park and Jong Hyuk Park
Page: 680~693, Vol. 14, No.3, 2018
10.3745/JIPS.03.0097
Keywords: Digital Investigation, Mobile, Forensics, Third-Party Applications
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Nowadays, third-party applications form an important part of the mobile environment, and social networking applications in particular can leave a variety of user footprints compared to other applications. Digital forensics of mobile third-party applications can provide important evidence to forensics investigators. However, most mobile operating systems are now updated on a frequent basis, and developers are constantly releasing new versions of them. For these reasons, forensic investigators experience difficulties in finding the locations and meanings of data during digital investigations. Therefore, this paper presents scenario-based methods of forensic analysis for a specific third-party social networking service application on a specific mobile device. When applied to certain third-party applications, digital forensics can provide forensic investigators with useful data for the investigation process. The main purpose of the forensic analysis proposed in the present paper is to determine whether the general use of third-party applications leaves data in the mobile internal storage of mobile devices and whether such data are meaningful for forensic purposes.


A New Approach for Hierarchical Dividing to Passenger Nodes in Passenger Dedicated Line
Chanchan Zhao, Feng Liu and Xiaowei Hai
Page: 694~708, Vol. 14, No.3, 2018
10.3745/JIPS.04.0074
Keywords: Hierarchical Dividing, K-Means, Passenger Nodes, Passenger Dedicated line, Self-Organizing Map
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China possesses a passenger dedicated line system of large scale, passenger flow intensity with uneven distribution, and passenger nodes with complicated relations. Consequently, the significance of passenger nodes shall be considered and the dissimilarity of passenger nodes shall be analyzed in compiling passenger train operation and conducting transportation allocation. For this purpose, the passenger nodes need to be hierarchically divided. Targeting at problems such as hierarchical dividing process vulnerable to subjective factors and local optimum in the current research, we propose a clustering approach based on self-organizing map (SOM) and k-means, and then, harnessing the new approach, hierarchical dividing of passenger dedicated line passenger nodes is effectuated. Specifically, objective passenger nodes parameters are selected and SOM is used to give a preliminary passenger nodes clustering firstly; secondly, Davies–Bouldin index is used to determine the number of clusters of the passenger nodes; and thirdly, k-means is used to conduct accurate clustering, thus getting the hierarchical dividing of passenger nodes. Through example analysis, the feasibility and rationality of the algorithm was proved.


QP-DTW: Upgrading Dynamic Time Warping to Handle Quasi Periodic Time Series Alignment
Imen Boulnemour and Bachir Boucheham
Page: 851~876, Vol. 14, No.4, 2018
10.3745/JIPS.02.0090
Keywords: Alignment, Comparison, Diagnosis, DTW, Motif Discovery, Pattern Recognition, SEA, Similarity Search, Time Series
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Dynamic time warping (DTW) is the main algorithms for time series alignment. However, it is unsuitable for quasi-periodic time series. In the current situation, except the recently published the shape exchange algorithm (SEA) method and its derivatives, no other technique is able to handle alignment of this type of very complex time series. In this work, we propose a novel algorithm that combines the advantages of the SEA and the DTW methods. Our main contribution consists in the elevation of the DTW power of alignment from the lowest level (Class A, non-periodic time series) to the highest level (Class C, multiple-periods time series containing different number of periods each), according to the recent classification of time series alignment methods proposed by Boucheham (Int J Mach Learn Cybern, vol. 4, no. 5, pp. 537-550, 2013). The new method (quasi-periodic dynamic time warping [QP-DTW]) was compared to both SEA and DTW methods on electrocardiogram (ECG) time series, selected from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) public database and from the PTB Diagnostic ECG Database. Results show that the proposed algorithm is more effective than DTW and SEA in terms of alignment accuracy on both qualitative and quantitative levels. Therefore, QP-DTW would potentially be more suitable for many applications related to time series (e.g., data mining, pattern recognition, search/retrieval, motif discovery, classification, etc.).


Significant Motion-Based Adaptive Sampling Module for Mobile Sensing Framework
Muhammad Fiqri Muthohar, I Gde Dharma Nugraha and Deokjai Choi
Page: 948~960, Vol. 14, No.4, 2018
10.3745/JIPS.04.0082
Keywords: Adaptive Sampling, Android Mobile Sensing Framework, Significant Motion Sensor
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Many mobile sensing frameworks have been developed to help researcher doing their mobile sensing research. However, energy consumption is still an issue in the mobile sensing research, and the existing frameworks do not provide enough solution for solving the issue. We have surveyed several mobile sensing frameworks and carefully chose one framework to improve. We have designed an adaptive sampling module for a mobile sensing framework to help solve the energy consumption issue. However, in this study, we limit our design to an adaptive sampling module for the location and motion sensors. In our adaptive sampling module, we utilize the significant motion sensor to help the adaptive sampling. We experimented with two sampling strategies that utilized the significant motion sensor to achieve low-power consumption during the continuous sampling. The first strategy is to utilize the sensor naively only while the second one is to add the duty cycle to the naive approach. We show that both strategies achieve low energy consumption, but the one that is combined with the duty cycle achieves better result.


An Efficient Implementation of Mobile Raspberry Pi Hadoop Clusters for Robust and Augmented Computing Performance
Kathiravan Srinivasan, Chuan-Yu Chang, Chao-Hsi Huang, Min-Hao Chang, Anant Sharma and Avinash Ankur
Page: 989~1009, Vol. 14, No.4, 2018
10.3745/JIPS.01.0031
Keywords: Clusters, Hadoop, MapReduce, Mobile Raspberry Pi, Single-board Computer
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Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed ‘big data’. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.


A Multi-Level Integrator with Programming Based Boosting for Person Authentication Using Different Biometrics
Sumana Kundu and Goutam Sarker
Page: 1114~1135, Vol. 14, No.5, 2018
10.3745/JIPS.02.0094
Keywords: Accuracy, Back Propagation Learning, Biometrics, HBC, F-score, Malsburg Learning, Mega-Super-Classifier, MOCA, Multiple Classification System, OCA, Person Identification, Precision, Recall, RBFN, SOM, Super- Classifier
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A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual superclassifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.


A Hybrid Proposed Framework for Object Detection and Classification
Muhammad Aamir, Yi-Fei Pu, Ziaur Rahman, Waheed Ahmed Abro, Hamad Naeem, Farhan Ullah and Aymen Mudheher Badr
Page: 1176~1194, Vol. 14, No.5, 2018
10.3745/JIPS.02.0095
Keywords: Image Proposals, Feature Extraction, Object Classification, Object Detection, Segmentation
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The object classification using the images’ contents is a big challenge in computer vision. The superpixels’ information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it according to the region score. Further, this information is used to extract local and global features using a hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the classification accuracy, the feature fusion technique is applied to combine local and global features vectors through weight parameter. The support vector machine classifier is a supervised algorithm is used for classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007 (VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in high-quality class for independent objects’ locations with a mean average best overlap (MABO) of 0.833 at 1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it is proved that it gave the better classification results for the non-rigid classes.


A Study on the Design of Humane Animal Care System and Java Implementation
Hui-Su Gong, Sunghyun Weon and Jun-Ho Huh
Page: 1225~1236, Vol. 14, No.5, 2018
10.3745/JIPS.02.0096
Keywords: Animal Care, Artificial Intelligence, BPM, Design, Humane Animal Care, Intelligent Agent, Software Engineering
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Nowadays, the number of pets in the Republic of Korea (ROK) is continuously growing, and people’s perception of animals is changing. Accordingly, new systems and services for them are emerging. Despite such changes, there are still many serious problems such as animal cruelty, abandonment, and factory-type breeding places. In this study, we have conducted a research on the design of a humane animal care system and its implementation with Java. The methodology involved in the design will enable managing animals' safety and health by systematically categorizing and studying each health-related issue for protection. Moreover, with this methodology, animals can avert risks through periodic examinations, and the analyzed data will be useful in managing animals efficiently. Thus, this paper proposes a system that monitors whether the owners actually carry out such obligation. Authors expect this convenient, easily accessible system to lead to a more humane approach to the animals they own. The authors plan to establish an animal care network together with local animal associations for the active promotion of the system implemented in this study, in the hope that the network will be extended nationwide.


Triqubit-state Measurement-based Image Edge Detection Algorithm
Zhonghua Wang and Faliang Huang
Page: 1331~1346, Vol. 14, No.6, 2018
10.3745/JIPS.04.0095
Keywords: Edge Detection, Partial Differential Equation, Pixel Saliency, Qubit State, Quantum Measurement
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Aiming at the problem that the gradient-based edge detection operators are sensitive to the noise, causing the pseudo edges, a triqubit-state measurement-based edge detection algorithm is presented in this paper. Combing the image local and global structure information, the triqubit superposition states are used to represent the pixel features, so as to locate the image edge. Our algorithm consists of three steps. Firstly, the improved partial differential method is used to smooth the defect image. Secondly, the triqubit-state is characterized by three elements of the pixel saliency, edge statistical characteristics and gray scale contrast to achieve the defect image from the gray space to the quantum space mapping. Thirdly, the edge image is outputted according to the quantum measurement, local gradient maximization and neighborhood chain code searching. Compared with other methods, the simulation experiments indicate that our algorithm has less pseudo edges and higher edge detection accuracy.


A CPU-GPU Hybrid System of Environment Perception and 3D Terrain Reconstruction for Unmanned Ground Vehicle
Wei Song, Shuanghui Zou, Yifei Tian, Su Sun, Simon Fong, Kyungeun Cho and Lvyang Qiu
Page: 1445~1456, Vol. 14, No.6, 2018
10.3745/JIPS.02.0099
Keywords: Driving Awareness, Environment Perception, Unmanned Ground Vehicle, 3D Reconstruction
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Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.


Path Generation Method of UAV Autopilots using Max-Min Algorithm
Jeonghoon Kwak and Yunsick Sung
Page: 1457~1463, Vol. 14, No.6, 2018
10.3745/JIPS.02.0100
Keywords: Autopilot, Max-Min Algorithm, Path Generation, Unmanned Aerial Vehicle
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In recent times, Natural User Interface/Natural User Experience (NUI/NUX) technology has found widespread application across a diverse range of fields and is also utilized for controlling unmanned aerial vehicles (UAVs). Even if the user controls the UAV by utilizing the NUI/NUX technology, it is difficult for the user to easily control the UAV. The user needs an autopilot to easily control the UAV. The user needs a flight path to use the autopilot. The user sets the flight path based on the waypoints. UAVs normally fly straight from one waypoint to another. However, if flight between two waypoints is in a straight line, UAVs may collide with obstacles. In order to solve collision problems, flight records can be utilized to adjust the generated path taking the locations of the obstacles into consideration. This paper proposes a natural path generation method between waypoints based on flight records collected through UAVs flown by users. Bayesian probability is utilized to select paths most similar to the flight records to connect two waypoints. These paths are generated by selection of the center path corresponding to the highest Bayesian probability. While the K-means algorithm-based straight-line method generated paths that led to UAV collisions, the proposed method generates paths that allow UAVs to avoid obstacles.


LBP and DWT Based Fragile Watermarking for Image Authentication
Chengyou Wang, Heng Zhang and Xiao Zhou
Page: 666~679, Vol. 14, No.3, 2018
10.3745/JIPS.03.0096
Keywords: Discrete Wavelet Transform (DWT), Fragile Watermarking, Image Authentication, Local Binary Pattern (LBP), Semi-blind Detection
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The discrete wavelet transform (DWT) has good multi-resolution decomposition characteristic and its low frequency component contains the basic information of an image. Based on this, a fragile watermarking using the local binary pattern (LBP) and DWT is proposed for image authentication. In this method, the LBP pattern of low frequency wavelet coefficients is adopted as a feature watermark, and it is inserted into the least significant bit (LSB) of the maximum pixel value in each block of host image. To guarantee the safety of the proposed algorithm, the logistic map is applied to encrypt the watermark. In addition, the locations of the maximum pixel values are stored in advance, which will be used to extract watermark on the receiving side. Due to the use of DWT, the watermarked image generated by the proposed scheme has high visual quality. Compared with other state-of-the-art watermarking methods, experimental results manifest that the proposed algorithm not only has lower watermark payloads, but also achieves good performance in tamper identification and localization for various attacks.


An Embedded Multifunctional Media System for Mobile Devices in Terrestrial DTV Relaying
Jun Huang and Haibing Yin
Page: 1272~1285, Vol. 14, No.5, 2018
10.3745/JIPS.03.0100
Keywords: DTV, Media Relaying, Media Server, Mobile Devices, Terrestrial Broadcasting
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The paper presents a novel embedded multifunctional media sever (EMMS) for mobile devices to receive various media programs. Being different from other contemporary system research, the paper mainly studies how to design a reception solution for terrestrial digital television (DTV) on mobile devices and how to enable mobile devices can receive DTV program, enjoy video-on-demand (VOD), achieve video surveillance and relay Internet video program via local Wi-Fi simultaneously. In the system design, we integrate broadcasting-terrestrial DTV tuner, streaming media re-transmission system, VOD disk, video camera and access interface to the Internet into EMMS, which can either receive terrestrial DTV radio signals and demodulate out digital transport stream (TS), or can read streaming media bit-stream from VOD disk, surveillance camera or access interface to the Internet. The experimental results show the proposed system is stable and quality-efficient. Comparing with the other systems, the proposed system has the least packet loss rate and response time.


Texture Image Retrieval Using DTCWT-SVD and Local Binary Pattern Features
Dayou Jiang and Jongweon Kim
Page: 1628~1639, Vol. 13, No.6, 2017
10.3745/JIPS.02.0077
Keywords: Dual-Tree Complex Wavelet Transform, Image Retrieval, Local Binary Pattern, SVD, Texture Feature
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The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.


Beacon-Based Indoor Location Measurement Method to Enhanced Common Chord-Based Trilateration
Jeonghoon Kwak and Yunsick Sung
Page: 1640~1651, Vol. 13, No.6, 2017
10.3745/JIPS.04.0053
Keywords: Beacon, Chord-Based Trilateration, Indoor Location, Trilateration, Unmanned Aerial Vehicle
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To make an unmanned aerial vehicle (UAVs) fly in indoor environments, the indoor locations of the UAV are required. One of the approaches to calculate the locations of an UAV in indoor environments is enhanced trilateration using one Bluetooth-based beacon and three or more access points (APs). However, the locations of the UAV calculated by the common chord-based trilateration has errors due to the distance errors of the beacon measured at the multiple APs. This paper proposes a method that corrects the errors that occur in the process of applying the common chord-based trilateration to calculate the locations of an UAV. In the experiments, the results of measuring the locations using the proposed method in an indoor environment was compared and verified against the result of measuring the locations using the common chord-based trilateration. The proposed method improved the accuracy of location measurement by 81.2% compared to the common chord-based trilateration.


An Improved Cat Swarm Optimization Algorithm Based on Opposition-Based Learning and Cauchy Operator for Clustering
Yugal Kumar and G. Sahoo
Page: 1000~1013, Vol. 13, No.4, 2017
10.3745/JIPS.02.0022
Keywords: Cat Swarm Optimization, Cauchy Mutation Operator, Clustering, Opposition-Based Learning, Particle Swarm Optimization
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Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta- heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.


Joint Estimation of Near-Field Source Parameters and Array Response
Han Cui and Wenjuan Peng
Page: 83~94, Vol. 13, No.1, 2017
10.3745/JIPS.03.0060
Keywords: Array Calibration, Gain/Phase Response, Near-Field Source Localization
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Near-field source localization algorithms are very sensitive to sensor gain/phase response errors, and so it is important to calibrate the errors. We took into consideration the uniform linear array and are proposing a blind calibration algorithm that can estimate the directions-of-arrival and range parameters of incident signals and sensor gain/phase responses jointly, without the need for any reference source. They are estimated separately by using an iterative approach, but without the need for good initial guesses. The ambiguities in the estimations of 2-D electric angles and sensor gain/phase responses are also analyzed in this paper. We show that the ambiguities can be remedied by assuming that two sensor phase responses of the array have been previously calibrated. The behavior of the proposed method is illustrated through simulation experiments. The simulation results show that the convergent rate is fast and that the convergent precision is high


Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network
Sanjeev Kumar and Mahesh Chandra
Page: 703~715, Vol. 13, No.4, 2017
10.3745/JIPS.01.0007
Keywords: Cascade-Forward Back Propagation Technique, Computer-Aided Diagnosis (CAD), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gray-Level Co-Occurrence Matrix (GLCM), Mammographic Image Analysis Society (MIAS) Database, Modified Sigmoid Function
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Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray- level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.


Self-Identification of Boundary’s Nodes in Wireless Sensor Networks
Kouider Elouahed Moustafa and Haffaf Hafid
Page: 128~140, Vol. 13, No.1, 2017
10.3745/JIPS.03.0062
Keywords: Boundary Recognition, Military Applications, Military Surveillance, Wireless Sensor Network
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The wireless sensor networks (WSNs) became a very essential tool in borders and military zones surveillance, for this reason specific applications have been developed. Surveillance is usually accomplished through the deployment of nodes in a random way providing heterogeneous topologies. However, the process of the identification of all nodes located on the network’s outer edge is very long and energy-consuming. Before any other activities on such sensitive networks, we have to identify the border nodes by means of specific algorithms. In this paper, a solution is proposed to solve the problem of energy and time consumption in detecting border nodes by means of node selection. This mechanism is designed with several starter nodes in order to reduce time, number of exchanged packets and then, energy consumption. This method consists of three phases: the first one is to detect triggers which serve to start the mechanism of boundary nodes (BNs) detection, the second is to detect the whole border, and the third is to exclude each BN from the routing tables of all its neighbors so that it cannot be used for the routing.


Secure Authentication Approach Based New Mobility Management Schemes for Mobile Communication
Ghazli Abdelkader, Hadj Said Naima and Ali Pacha Adda
Page: 152~173, Vol. 13, No.1, 2017
10.3745/JIPS.03.0064
Keywords: Authentication, GSM, Location Update, Mobility Management, Paging, Security
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Mobile phones are the most common communication devices in history. For this reason, the number of mobile subscribers will increase dramatically in the future. Therefore, the determining the location of a mobile station will become more and more difficult. The mobile station must be authenticated to inform the network of its current location even when the user switches it on or when its location is changed. The most basic weakness in the GSM authentication protocol is the unilateral authentication process where the customer is verified by the system, yet the system is not confirmed by the customer. This creates numerous security issues, including powerlessness against man-in-the-middle attacks, vast bandwidth consumption between VLR and HLR, storage space overhead in VLR, and computation costs in VLR and HLR. In this paper, we propose a secure authentication mechanism based new mobility management method to improve the location management in the GSM network, which suffers from a lot off drawbacks, such as transmission cost and database overload. Numerical analysis is done for both conventional and modified versions and compared together. The numerical results show that our protocol scheme is more secure and that it reduces mobility management costs the most in the GSM network.


An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight
Ingyu Lee and Byungin Moon
Page: 256~267, Vol. 13, No.2, 2017
10.3745/JIPS.02.0057
Keywords: Adaptive Census Transform, Adaptive Support Weight, Local Matching, Multiple Sparse Windows, Stereo Matching
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An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.


Self-adaptive and Bidirectional Dynamic Subset Selection Algorithm for Digital Image Correlation
Wenzhuo Zhang, Rong Zhou and Yuanwen Zou
Page: 305~320, Vol. 13, No.2, 2017
10.3745/JIPS.02.0056
Keywords: Digital Image Correlation, Dynamic Subset Size, Image Processing, Information Amount, Self-adaptive
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The selection of subset size is of great importance to the accuracy of digital image correlation (DIC). In the traditional DIC, a constant subset size is used for computing the entire image, which overlooks the differences among local speckle patterns of the image. Besides, it is very laborious to find the optimal global subset size of a speckle image. In this paper, a self-adaptive and bidirectional dynamic subset selection (SBDSS) algorithm is proposed to make the subset sizes vary according to their local speckle patterns, which ensures that every subset size is suitable and optimal. The sum of subset intensity variation (?) is defined as the assessment criterion to quantify the subset information. Both the threshold and initial guess of subset size in the SBDSS algorithm are self-adaptive to different images. To analyze the performance of the proposed algorithm, both numerical and laboratory experiments were performed. In the numerical experiments, images with different speckle distribution, different deformation and noise were calculated by both the traditional DIC and the proposed algorithm. The results demonstrate that the proposed algorithm achieves higher accuracy than the traditional DIC. Laboratory experiments performed on a substrate also demonstrate that the proposed algorithm is effective in selecting appropriate subset size for each point.


Unsupervised Segmentation of Images Based on Shuffled Frog-Leaping Algorithm
Amel Tehami* and Hadria Fizazi
Page: 370~384, Vol. 13, No.2, 2017
10.3745/JIPS.02.0055
Keywords: Image, K-means, Meta-Heuristic, Optimization, SFLA, Unsupervised Segmentation
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The image segmentation is the most important operation in an image processing system. It is located at the joint between the processing and analysis of the images. Unsupervised segmentation aims to automatically separate the image into natural clusters. However, because of its complexity several methods have been proposed, specifically methods of optimization. In our work we are interested to the technique SFLA (Shuffled Frog-Leaping Algorithm). It’s a memetic meta-heuristic algorithm that is based on frog populations in nature searching for food. This paper proposes a new approach of unsupervised image segmentation based on SFLA method. It is implemented and applied to different types of images. To validate the performances of our approach, we performed experiments which were compared to the method of K-means.


Fragile Watermarking Based on LBP for Blind Tamper Detection in Images
Heng Zhang, Chengyou Wang and Xiao Zhou
Page: 385~399, Vol. 13, No.2, 2017
10.3745/JIPS.03.0070
Keywords: Fragile Watermarking, Local Binary Pattern (LBP), Least Significant Bit (LSB), Tamper Detection and Localization
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Nowadays, with the development of signal processing technique, the protection to the integrity and authenticity of images has become a topic of great concern. A blind image authentication technology with high tamper detection accuracy for different common attacks is urgently needed. In this paper, an improved fragile watermarking method based on local binary pattern (LBP) is presented for blind tamper location in images. In this method, a binary watermark is generated by LBP operator which is often utilized in face identification and texture analysis. In order to guarantee the safety of the proposed algorithm, Arnold transform and logistic map are used to scramble the authentication watermark. Then, the least significant bits (LSBs) of original pixels are substituted by the encrypted watermark. Since the authentication data is constructed from the image itself, no original image is needed in tamper detection. The LBP map of watermarked image is compared to the extracted authentication data to determine whether it is tampered or not. In comparison with other state-of-the-art schemes, various experiments prove that the proposed algorithm achieves better performance in forgery detection and location for baleful attacks.


A Power Allocation Algorithm Based on Variational Inequality Problem for Cognitive Radio Networks
Ming-Yue Zhou and Xiao-Hui Zhao
Page: 417~427, Vol. 13, No.2, 2017
10.3745/JIPS.03.0068
Keywords: Cognitive Radio, Power Allocation, Variational Inequality
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Power allocation is an important factor for cognitive radio networks to achieve higher communication capacity and faster equilibrium. This paper considers power allocation problem to each cognitive user to maximize capacity of the cognitive systems subject to the constraints on the total power of each cognitive user and the interference levels of the primary user. Since this power control problem can be formulated as a mixed-integer nonlinear programming (NP) equivalent to variational inequality (VI) problem in convex polyhedron which can be transformed into complementary problem (CP), we utilize modified projection method to solve this CP problem instead of finding NP solution and give a power control allocation algorithm with a subcarrier allocation scheme. Simulation results show that the proposed algorithm performs well and effectively reduces the system power consumption with almost maximum capacity while achieve Nash equilibrium.


Granular Bidirectional and Multidirectional Associative Memories: Towards a Collaborative Buildup of Granular Mappings
Witold Pedrycz
Page: 435~447, Vol. 13, No.3, 2017
10.3745/JIPS.02.0058
Keywords: Allocation of Information Granularity and Optimization, Bidirectional Associative Memory, Collaborative Clustering, Granular Computing, Multi-directional Associative Memory, Prototypes
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Associative and bidirectional associative memories are examples of associative structures studied intensively in the literature. The underlying idea is to realize associative mapping so that the recall processes (one- directional and bidirectional ones) are realized with minimal recall errors. Associative and fuzzy associative memories have been studied in numerous areas yielding efficient applications for image recall and enhancements and fuzzy controllers, which can be regarded as one-directional associative memories. In this study, we revisit and augment the concept of associative memories by offering some new design insights where the corresponding mappings are realized on the basis of a related collection of landmarks (prototypes) over which an associative mapping becomes spanned. In light of the bidirectional character of mappings, we have developed an augmentation of the existing fuzzy clustering (fuzzy c-means, FCM) in the form of a so- called collaborative fuzzy clustering. Here, an interaction in the formation of prototypes is optimized so that the bidirectional recall errors can be minimized. Furthermore, we generalized the mapping into its granular version in which numeric prototypes that are formed through the clustering process are made granular so that the quality of the recall can be quantified. We propose several scenarios in which the allocation of information granularity is aimed at the optimization of the characteristics of recalled results (information granules) that are quantified in terms of coverage and specificity. We also introduce various architectural augmentations of the associative structures.


Improvement of OPW-TR Algorithm for Compressing GPS Trajectory Data
Qingbin Meng, Xiaoqiang Yu, Chunlong Yao, Xu Li, Peng Li and Xin Zhao
Page: 533~545, Vol. 13, No.3, 2017
10.3745/JIPS.03.0073
Keywords: ASED, GPS Trajectory, SED, Trajectory Compression
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Massive volumes of GPS trajectory data bring challenges to storage and processing. These issues can be addressed by compression algorithm which can reduce the size of the trajectory data. A key requirement for GPS trajectory compression algorithm is to reduce the size of the trajectory data while minimizing the loss of information. Synchronized Euclidean distance (SED) as an important error measure is adopted by most of the existing algorithms. In order to further reduce the SED error, an improved algorithm for open window time ratio (OPW-TR) called local optimum open window time ratio (LO-OPW-TR) is proposed. In order to make SED error smaller, the anchor points are selected by calculating point’s accumulated synchronized Euclidean distance (ASED). A variety of error metrics are used for the algorithm evaluation. The experimental results show that the errors of our algorithm are smaller than the existing algorithms in terms of SED and speed errors under the same compression ratio


Copyright Protection for Digital Image by Watermarking Technique
Suhad A. Ali, Majid Jabbar Jawad and Mohammed Abdullah Naser
Page: 599~617, Vol. 13, No.3, 2017
10.3745/JIPS.03.0074
Keywords: Digital Watermarking, Discrete Cosine Transform (DCT), Normalized Correlation (NC), PSNR
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Due to the rapid growth and expansion of the Internet, the digital multimedia such as image, audio and video are available for everyone. Anyone can make unauthorized copying for any digital product. Accordingly, the owner of these products cannot protect his ownership. Unfortunately, this situation will restrict any improvement which can be done on the digital media production in the future. Some procedures have been proposed to protect these products such as cryptography and watermarking techniques. Watermarking means embedding a message such as text, the image is called watermark, yet, in a host such as a text, an image, an audio, or a video, it is called a cover. Watermarking can provide and ensure security, data authentication and copyright protection for the digital media. In this paper, a new watermarking method of still image is proposed for the purpose of copyright protection. The procedure of embedding watermark is done in a transform domain. The discrete cosine transform (DCT) is exploited in the proposed method, where the watermark is embedded in the selected coefficients according to several criteria. With this procedure, the deterioration on the image is minimized to achieve high invisibility. Unlike the traditional techniques, in this paper, a new method is suggested for selecting the best blocks of DCT coefficients. After selecting the best DCT coefficients blocks, the best coefficients in the selected blocks are selected as a host in which the watermark bit is embedded. The coefficients selection is done depending on a weighting function method, where this function exploits the values and locations of the selected coefficients for choosing them. The experimental results proved that the proposed method has produced good imperceptibility and robustness for different types of attacks


HESnW: History Encounters-Based Spray-and-Wait Routing Protocol for Delay Tolerant Networks
Shunyi Gan, Jipeng Zhou and Kaimin Wei
Page: 618~629, Vol. 13, No.3, 2017
10.3745/JIPS.03.0075
Keywords: Delivery Cost, DTNs, History Node, Multiple Probability, Spray-and-Wait
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Mobile nodes can't always connect each other in DTNs (delay tolerant networks). Many DTN routing protocols that favor the “multi-hop forwarding” are proposed to solve these network problems. But they also lead to intolerant delivery cost so that designing a overhead-efficient routing protocol which is able to perform well in delivery ratio with lower delivery cost at the same time is valuable. Therefore, we utilize the small-world property and propose a new delivery metric called multi-probability to design our relay node selection principles that nodes with lower delivery predictability can also be selected to be the relay nodes if one of their history nodes has higher delivery predictability. So, we can find more potential relay nodes to reduce the forwarding overhead of successfully delivered messages through our proposed algorithm called HESnW. We also apply our new messages copies allocation scheme to optimize the routing performance. Comparing to existing routing algorithms, simulation results show that HESnW can reduce the delivery cost while it can also obtain a rather high delivery ratio.


An Improved Secure Semi-fragile Watermarking Based on LBP and Arnold Transform
Heng Zhang, Chengyou Wang and Xiao Zhou
Page: 1382~1396, Vol. 13, No.5, 2017
10.3745/JIPS.02.0063
Keywords: Digital Image Watermarking, Semi-fragile Watermarking, False Detection, Local Binary Pattern (LBP), Arnold Transform
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In this paper, we analyze a recently proposed semi-fragile watermarking scheme based on local binary pattern (LBP) operators, and note that it has a fundamental flaw in the design. In this work, a binary watermark is embedded into image blocks by modifying the neighborhood pixels according to the LBP pattern. However, different image blocks might have the same LBP pattern, which can lead to false detection in watermark extraction process. In other words, one can modify the host image intentionally without affecting its watermark message. In addition, there is no encryption process before watermark embedding, which brings another potential security problem. To illustrate its weakness, two special copy-paste attacks are proposed in this paper, and several experiments are conducted to prove the effectiveness of these attacks. To solve these problems, an improved semi-fragile watermarking based on LBP operators is presented. In watermark embedding process, the central pixel value of each block is taken into account and Arnold transform is adopted to guarantee the security of watermark. Experimental results show that the improved watermarking scheme can overcome the above defects and locate the tampered region effectively.


Achievable Rate Analysis for Opportunistic Non-orthogonal Multiple Access-Based Cooperative Relaying Systems
In-Ho Lee and Howon Lee
Page: 630~642, Vol. 13, No.3, 2017
10.3745/JIPS.03.0076
Keywords: Achievable Rate Analysis, Decode-and-Forward Relaying, Non-orthogonal Multiple Access, Opportunistic Transmission, Rayleigh Fading Channels, Superposition Coding
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In this paper, we propose the opportunistic non-orthogonal multiple access (NOMA)-based cooperative relaying system (CRS) with channel state information (CSI) available at the source, where CSI for the source- to-destination and source-to-relay links is used for opportunistic transmission. Using the CSI, for opportunistic transmission, the source instantaneously chooses between the direct transmission and the cooperative NOMA transmission. We provide an asymptotic expression for the average achievable rate of the opportunistic NOMA-based CRS under Rayleigh fading channels. We verify the asymptotic analysis through Monte Carlo simulations, and compare the average achievable rates of the opportunistic NOMA-based CRS and the conventional one for various channel powers and power allocation coefficients used for NOMA


Using Semantic Knowledge in the Uyghur-Chinese Person Name Transliteration
Alim Murat, Turghun Osman, Yating Yang, Xi Zhou, Lei Wang and Xiao Li
Page: 716~730, Vol. 13, No.4, 2017
10.3745/JIPS.02.0065
Keywords: Gender, Language Origin, Semantic Knowledge-based Model, Transliteration of Person Name
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In this paper, we propose a transliteration approach based on semantic information (i.e., language origin and gender) which are automatically learnt from the person name, aiming to transliterate the person name of Uyghur into Chinese. The proposed approach integrates semantic scores (i.e., performance on language origin and gender detection) with general transliteration model and generates the semantic knowledge-based model which can produce the best candidate transliteration results. In the experiment, we use the datasets which contain the person names of different language origins: Uyghur and Chinese. The results show that the proposed semantic transliteration model substantially outperforms the general transliteration model and greatly improves the mean reciprocal rank (MRR) performance on two datasets, as well as aids in developing more efficient transliteration for named entities.


Weighted Local Naive Bayes Link Prediction
JieHua Wu, GuoJi Zhang, YaZhou Ren, XiaYan Zhang and Qiao Yang
Page: 914~927, Vol. 13, No.4, 2017
10.3745/JIPS.04.0040
Keywords: Complex Network, Link Prediction, Naive Bayes Model, Weighted Network
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Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model—local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.


CPU Scheduling with a Round Robin Algorithm Based on an Effective Time Slice
Mohammad M. Tajwar, Md. Nuruddin Pathan, Latifa Hussaini and Adamu Abubakar
Page: 941~950, Vol. 13, No.4, 2017
10.3745/JIPS.01.0018
Keywords: Average Turnaround Time, Average Waiting Time, CPU Processing Time, Round Robin Algorithm, Quantum Time
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The round robin algorithm is regarded as one of the most efficient and effective CPU scheduling techniques in computing. It centres on the processing time required for a CPU to execute available jobs. Although there are other CPU scheduling algorithms based on processing time which use different criteria, the round robin algorithm has gained much popularity due to its optimal time-shared environment. The effectiveness of this algorithm depends strongly on the choice of time quantum. This paper presents a new effective round robin CPU scheduling algorithm. The effectiveness here lies in the fact that the proposed algorithm depends on a dynamically allocated time quantum in each round. Its performance is compared with both traditional and enhanced round robin algorithms, and the findings demonstrate an improved performance in terms of average waiting time, average turnaround time and context switching.


Traffic Information Service Model Considering Personal Driving Trajectories
Homin Han and Soyoung Park
Page: 951~969, Vol. 13, No.4, 2017
10.3745/JIPS.03.0078
Keywords: GPS-to-Road Mapping Strategy, Personal Trajectory, Traffic Information System, Trajectory Estimation
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In this paper, we newly propose a traffic information service model that collects traffic information sensed by an individual vehicle in real time by using a smart device, and which enables drivers to share traffic information on all roads in real time using an application installed on a smart device. In particular, when the driver requests traffic information for a specific area, the proposed driver-personalized service model provides him/her with traffic information on the driving directions in advance by predicting the driving directions of the vehicle based on the learning of the driving records of each driver. To do this, we propose a traffic information management model to process and manage in real time a large amount of online-generated traffic information and traffic information requests generated by each vehicle. We also propose a road node- based indexing technique to efficiently store and manage location-based traffic information provided by each vehicle. Finally, we propose a driving learning and prediction model based on the hidden Markov model to predict the driving directions of each driver based on the driver's driving records. We analyze the traffic information processing performance of the proposed model and the accuracy of the driving prediction model using traffic information collected from actual driving vehicles for the entire area of Seoul, as well as driving records and experimental data.


Regularization Parameter Selection for Total Variation Model Based on Local Spectral Response
Yuhui Zheng, Kai Ma, Qiqiong Yu, Jianwei Zhang and Jin Wang
Page: 1168~1182, Vol. 13, No.5, 2017
10.3745/JIPS.02.0072
Keywords: Image Denoising, Local Spectral Response, Regularization Parameter Selection
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In the past decades, various image regularization methods have been introduced. Among them, total variation model has drawn much attention for the reason of its low computational complexity and well-understood mathematical behavior. However, regularization parameter estimation of total variation model is still an open problem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposed in this paper, by means of using the local spectral response, which has the capability of locally selecting the regularization parameters in a content-aware way and therefore adaptively adjusting the weights between the two terms of the total variation model. Experiment results on simulated and real noisy image show the good performance of our proposed method, in visual improvement and peak signal to noise ratio value.


Modeling and Simulation of Scheduling Medical Materials Using Graph Model for Complex Rescue
Ming Lv, Jingchen Zheng, Qingying Tong, Jinhong Chen, Haoting Liu and Yun Gao
Page: 1243~1258, Vol. 13, No.5, 2017
10.3745/JIPS.04.0042
Keywords: Bipartite Graph, BSCS, Drug Scheduling, Medical Rescue, Optimization Matching
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A new medical materials scheduling system and its modeling method for the complex rescue are presented. Different from other similar system, first both the BeiDou Satellite Communication System (BSCS) and the Special Fiber-optic Communication Network (SFCN) are used to collect the rescue requirements and the location information of disaster areas. Then all these messages will be displayed in a special medical software terminal. After that the bipartite graph models are utilized to compute the optimal scheduling of medical materials. Finally, all these results will be transmitted back by the BSCS and the SFCN again to implement a fast guidance of medical rescue. The sole drug scheduling issue, the multiple drugs scheduling issue, and the backup-scheme selection issue are all utilized: the Kuhn-Munkres algorithm is used to realize the optimal matching of sole drug scheduling issue, the spectral clustering-based method is employed to calculate the optimal distribution of multiple drugs scheduling issue, and the similarity metric of neighboring matrix is utilized to realize the estimation of backup-scheme selection issue of medical materials. Many simulation analysis experiments and applications have proved the correctness of proposed technique and system.


Sector Based Multiple Camera Collaboration for Active Tracking Applications
Sangjin Hong, Kyungrog Kim and Nammee Moon
Page: 1299~1319, Vol. 13, No.5, 2017
10.3745/JIPS.02.0074
Keywords: Active Tracking, Master-Slave, Object Dynamics, Sector-Based Representation
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This paper presents a scalable multiple camera collaboration strategy for active tracking applications in large areas. The proposed approach is based on distributed mechanism but emulates the master-slave mechanism. The master and slave cameras are not designated but adaptively determined depending on the object dynamic and density distribution. Moreover, the number of cameras emulating the master is not fixed. The collaboration among the cameras utilizes global and local sectors in which the visual correspondences among different cameras are determined. The proposed method combines the local information to construct the global information for emulating the master-slave operations. Based on the global information, the load balancing of active tracking operations is performed to maximize active tracking coverage of the highly dynamic objects. The dynamics of all objects visible in the local camera views are estimated for effective coverage scheduling of the cameras. The active tracking synchronization timing information is chosen to maximize the overall monitoring time for general surveillance operations while minimizing the active tracking miss. The real-time simulation result demonstrates the effectiveness of the proposed method


Content-based Image Retrieval Using Texture Features Extracted from Local Energy and Local Correlation of Gabor Transformed Images
Hee-Hyung Bu, Nam-Chul Kim, Bae-Ho Lee and Sung-Ho Kim
Page: 1372~1381, Vol. 13, No.5, 2017
10.3745/JIPS.02.0075
Keywords: Content-based Image Retrieval, Gabor Transformation, Local Energy, Local Correlation, Texture Feature
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In this paper, a texture feature extraction method using local energy and local correlation of Gabor transformed images is proposed and applied to an image retrieval system. The Gabor wavelet is known to be similar to the response of the human visual system. The outputs of the Gabor transformation are robust to variants of object size and illumination. Due to such advantages, it has been actively studied in various fields such as image retrieval, classification, analysis, etc. In this paper, in order to fully exploit the superior aspects of Gabor wavelet, local energy and local correlation features are extracted from Gabor transformed images and then applied to an image retrieval system. Some experiments are conducted to compare the performance of the proposed method with those of the conventional Gabor method and the popular rotation-invariant uniform local binary pattern (RULBP) method in terms of precision vs recall. The Mahalanobis distance is used to measure the similarity between a query image and a database (DB) image. Experimental results for Corel DB and VisTex DB show that the proposed method is superior to the conventional Gabor method. The proposed method also yields precision and recall 6.58% and 3.66% higher on average in Corel DB, respectively, and 4.87% and 3.37% higher on average in VisTex DB, respectively, than the popular RULBP method.


Hierarchical Location Caching Scheme for Mobile Object Tracking in the Internet of Things
Youn-Hee Han, Hyun-Kyo Lim and Joon-Min Gil
Page: 1410~1429, Vol. 13, No.5, 2017
10.3745/JIPS.03.0081
Keywords: Internet of Things, Location Caching Scheme, Location Tracking, Mobile Computing, Mobile Object
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Mobility arises naturally in the Internet of Things networks, since the location of mobile objects, e.g., mobile agents, mobile software, mobile things, or users with wireless hardware, changes as they move. Tracking their current location is essential to mobile computing. To overcome the scalability problem, hierarchical architectures of location databases have been proposed. When location updates and lookups for mobile objects are localized, these architectures become effective. However, the network signaling costs and the execution number of database operations increase particularly when the scale of the architectures and the numbers of databases becomes large to accommodate a great number of objects. This disadvantage can be alleviated by a location caching scheme which exploits the spatial and temporal locality in location lookup. In this paper, we propose a hierarchical location caching scheme, which acclimates the existing location caching scheme to a hierarchical architecture of location databases. The performance analysis indicates that the adjustment of such thresholds has an impact on cost reduction in the proposed scheme.


Inter-Domain Mobility Management Based on the Proxy Mobile IP in Mobile Networks
Moneeb Gohar and Seok-Joo Koh
Page: 196~213, Vol. 12, No.2, 2016
10.3745/JIPS.03.0037
Keywords: Comparison, HIP, LTE, LISP, MIP, Mobility Management, PMIP, SAE
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System Architecture Evolution (SAE) with Long Term Evolution (LTE) has been used as the key technology for the next generation mobile networks. To support mobility in the LTE/SAE-based mobile networks, the Proxy Mobile IPv6 (PMIP), in which the Mobile Access Gateway (MAG) of the PMIP is deployed at the Serving Gateway (S-GW) of LTE/SAE and the Local Mobility Anchor (LMA) of PMIP is employed at the PDN Gateway (P-GW) of LTE/SAE, is being considered. In the meantime, the Host Identity Protocol (HIP) and the Locator Identifier Separation Protocol (LISP) have recently been proposed with the identifier-locator separation principle, and they can be used for mobility management over the global-scale networks. In this paper, we discuss how to provide the inter-domain mobility management over PMIP-based LTE/SAE networks by investigating three possible scenarios: mobile IP with PMIP (denoted by MIP-PMIP-LTE/SAE), HIP with PMIP (denoted by HIP-PMIP-LTE/SAE), and LISP with PMIP (denoted by LISP-PMIP-LTE/SAE). For performance analysis of the candidate inter-domain mobility management schemes, we analyzed the traffic overhead at a central agent and the total transmission delay required for control and data packet delivery. From the numerical results, we can see that HIP-PMIP-LTE/SAE and LISP-PMIP-LTE/SAE are preferred to MIP-PMIP-LTE/SAE in terms of traffic overhead; whereas, LISP-PMIP-LTE/SAE is preferred to HIP-PMIP-LTE/SAE and MIP-PMIP-LTE/SAE in the viewpoint of total transmission delay.


Geohashed Spatial Index Method for a Location-Aware WBAN Data Monitoring System Based on NoSQL
Yan Li, Dongho Kim and Byeong-Seok Shin
Page: 263~274, Vol. 12, No.2, 2016
10.3745/JIPS.04.0025
Keywords: Location-Aware, NoSQL Database System, WBAN Monitoring System
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The exceptional development of electronic device technology, the miniaturization of mobile devices, and the development of telecommunication technology has made it possible to monitor human biometric data anywhere and anytime by using different types of wearable or embedded sensors. In daily life, mobile devices can collect wireless body area network (WBAN) data, and the co-collected location data is also important for disease analysis. In order to efficiently analyze WBAN data, including location information and support medical analysis services, we propose a geohash-based spatial index method for a location-aware WBAN data monitoring system on the NoSQL database system, which uses an R-tree-based global tree to organize the real-time location data of a patient and a B-tree-based local tree to manage historical data. This type of spatial index method is a support cloud-based location-aware WBAN data monitoring system. In order to evaluate the proposed method, we built a system that can support a JavaScript Object Notation (JSON) and Binary JSON (BSON) document data on mobile gateway devices. The proposed spatial index method can efficiently process location-based queries for medical signal monitoring. In order to evaluate our index method, we simulated a small system on MongoDB with our proposed index method, which is a document-based NoSQL database system, and evaluated its performance.


Landmark-Guided Segmental Speech Decoding for Continuous Mandarin Speech Recognition
Hao Chao and Cheng Song
Page: 410~421, Vol. 12, No.3, 2016
10.3745/JIPS.03.0052
Keywords: Decoding, Landmark, Mandarin, Speech Recognition, Segment Model
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In this paper, we propose a framework that attempts to incorporate landmarks into a segment-based Mandarin speech recognition system. In this method, landmarks provide boundary information and phonetic class information, and the information is used to direct the decoding process. To prove the validity of this method, two kinds of landmarks that can be reliably detected are used to direct the decoding process of a segment model (SM) based Mandarin LVCSR (large vocabulary continuous speech recognition) system. The results of our experiment show that about 30% decoding time can be saved without an obvious decrease in recognition accuracy. Thus, the potential of our method is demonstrated.


Bilingual Multiword Expression Alignment by Constituent-Based Similarity Score
Hyeong-Won Seo, Hongseok Kwon, Min-Ah Cheon and Jae-Hoon Kim
Page: 455~467, Vol. 12, No.3, 2016
10.3745/JIPS.02.0044
Keywords: Bilingual Lexicon, Compositionality, Context Vector, Multiword Expression, MWE Alignment, Pivot Language
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This paper presents the constituent-based approach for aligning bilingual multiword expressions, such as noun phrases, by considering the relationship not only between source expressions and their target translation equivalents but also between the expressions and constituents of the target equivalents. We only considered the compositional preferences of multiword expressions and not their idiomatic usages because our multiword identification method focuses on their collocational or compositional preferences. In our experimental results, the constituent-based approach showed much better performances than the general method for extracting bilingual multiword expressions. For our future work, we will examine the scoring method of the constituent-based approach in regards to having the best performance. Moreover, we will extend target entries in the evaluation dictionaries by considering their synonyms.


Evaluation of Histograms Local Features and Dimensionality Reduction for 3D Face Verification
Chouchane Ammar*, Belahcene Mebarka, Ouamane Abdelmalik and Bourennane Salah
Page: 468~488, Vol. 12, No.3, 2016
10.3745/JIPS.02.0037
Keywords: 3D Face Verification, Depth Image, Dimensionality Reduction, Histograms Local Features, Local Descriptors, Support Vector Machine
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The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four- Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.


A Multiple Features Video Copy Detection Algorithm Based on a SURF Descriptor
Yanyan Hou, Xiuzhen Wang and Sanrong Liu
Page: 502~510, Vol. 12, No.3, 2016
10.3745/JIPS.02.0042
Keywords: Local Invariant Feature, Speeded-Up Robust Features, Video Copy Detection
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Considering video copy transform diversity, a multi-feature video copy detection algorithm based on a Speeded-Up Robust Features (SURF) local descriptor is proposed in this paper. Video copy coarse detection is done by an ordinal measure (OM) algorithm after the video is preprocessed. If the matching result is greater than the specified threshold, the video copy fine detection is done based on a SURF descriptor and a box filter is used to extract integral video. In order to improve video copy detection speed, the Hessian matrix trace of the SURF descriptor is used to pre-match, and dimension reduction is done to the traditional SURF feature vector for video matching. Our experimental results indicate that video copy detection precision and recall are greatly improved compared with traditional algorithms, and that our proposed multiple features algorithm has good robustness and discrimination accuracy, as it demonstrated that video detection speed was also improved.


SDN-Based Enterprise and Campus Networks: A Case of VLAN Management
Van-Giang Nguyen and Young-Han Kim
Page: 511~524, Vol. 12, No.3, 2016
10.3745/JIPS.03.0039
Keywords: Campus Network, Enterprise Network, OpenFlow, Software Defined Networking (SDN), VLAN Management
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The Virtual Local Area Network (VLAN) has been used for a long time in campus and enterprise networks as the most popular network virtualization solution. Due to the benefits and advantages achieved by using VLAN, network operators and administrators have been using it for constructing their networks up until now and have even extended it to manage the networking in a cloud computing system. However, their configuration is a complex, tedious, time-consuming, and error-prone process. Since Software Defined Networking (SDN) features the centralized network management and network programmability, it is a promising solution for handling the aforementioned challenges in VLAN management. In this paper, we first introduce a new architecture for campus and enterprise networks by leveraging SDN and OpenFlow. Next, we have designed and implemented an application for easily managing and flexibly troubleshooting the VLANs in this architecture. This application supports both static VLAN and dynamic VLAN configurations. In addition, we discuss the hybrid-mode operation where the packet processing is involved by both the OpenFlow control plane and the traditional control plane. By deploying a real test-bed prototype, we illustrate how our system works and then evaluate the network latency in dynamic VLAN operation.


Audio Data Hiding Based on Sample Value Modification Using Modulus Function
Mohammed Hatem Ali Al-Hooti, Supeno Djanali and Tohari Ahmad
Page: 525~537, Vol. 12, No.3, 2016
10.3745/JIPS.03.0054
Keywords: Audio, Data Hiding, Modulus Function, Information Security, Network Security
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Data hiding is a wide field that is helpful to secure network communications. It is common that many data hiding researchers consider improving and increasing many aspects such as capacity, stego file quality, or robustness. In this paper, we use an audio file as a cover and propose a reversible steganographic method that is modifying the sample values using modulus function in order to make the reminder of that particular value to be same as the secret bit that is needed to be embedded. In addition, we use a location map that locates these modified sample values. This is because in reversible data hiding it needs to exactly recover both the secret message and the original audio file from that stego file. The experimental results show that, this method (measured by correlation algorithm) is able to retrieve exactly the same secret message and audio file. Moreover, it has made a significant improvement in terms of the following: the capacity since each sample value is carrying a secret bit. The quality measured by peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), Pearson correlation coefficient (PCC), and Similarity Index Modulation (SIM). All of them have proven that the quality of the stego audio is relatively high.


A New Image Clustering Method Based on the Fuzzy Harmony Search Algorithm and Fourier Transform
Ibtissem Bekkouche and Hadria Fizazi
Page: 555~576, Vol. 12, No.4, 2016
10.3745/JIPS.02.0047
Keywords: Fourier Transform, Fuzzy Clustering, Harmony Search, Processing Image, Remote Sensing
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In the conventional clustering algorithms, an object could be assigned to only one group. However, this is sometimes not the case in reality, there are cases where the data do not belong to one group. As against, the fuzzy clustering takes into consideration the degree of fuzzy membership of each pixel relative to different classes. In order to overcome some shortcoming with traditional clustering methods, such as slow convergence and their sensitivity to initialization values, we have used the Harmony Search algorithm. It is based on the population metaheuristic algorithm, imitating the musical improvisation process. The major thrust of this algorithm lies in its ability to integrate the key components of population-based methods and local search-based methods in a simple optimization model. We propose in this paper a new unsupervised clustering method called the Fuzzy Harmony Search-Fourier Transform (FHS-FT). It is based on hybridization fuzzy clustering and the harmony search algorithm to increase its exploitation process and to further improve the generated solution, while the Fourier transform to increase the size of the image's data. The results show that the proposed method is able to provide viable solutions as compared to previous work


Community Model for Smart TV over the Top Services
Suman Pandey, Young Joon Won, Mi-Jung Choi and Joon-Min Gil
Page: 577~590, Vol. 12, No.4, 2016
10.3745/JIPS.03.0057
Keywords: Community Formation, Datamining, HbbTV, Smart TV
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We studied the current state-of-the-art of Smart TV, the challenges and the drawbacks. Mainly we discussed the lack of end-to-end solution. We then illustrated the differences between Smart TV and IPTV from network service provider point of view. Unlike IPTV, viewer of Smart TV’s over-the-top (OTT) services could be global, such as foreign nationals in a country or viewers having special viewing preferences. Those viewers are sparsely distributed. The existing TV service deployment models over Internet are not suitable for such viewers as they are based on content popularity, hence we propose a community based service deployment methodology with proactive content caching on rendezvous points (RPs). In our proposal, RPs are intermediate nodes responsible for caching routing and decision making. The viewer’s community formation is based on geographical locations and similarity of their interests. The idea of using context information to do proactive caching is itself not new, but we combined this with “in network caching” mechanism of content centric network (CCN) architecture. We gauge the performance improvement achieved by a community model. The result shows that when the total numbers of requests are same; our model can have significantly better performance, especially for sparsely distributed communities


Image Deblocking Scheme for JPEG Compressed Images Using an Adaptive-Weighted Bilateral Filter
Liping Wang, Chengyou Wang, Wei Huang and Xiao Zhou
Page: 631~643, Vol. 12, No.4, 2016
10.3745/JIPS.02.0046
Keywords: Image Deblocking, Adaptive-Weighted Bilateral Filter, Blind Image Quality Assessment (BIQA), Local Entropy
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Due to the block-based discrete cosine transform (BDCT), JPEG compressed images usually exhibit blocking artifacts. When the bit rates are very low, blocking artifacts will seriously affect the image’s visual quality. A bilateral filter has the features for edge-preserving when it smooths images, so we propose an adaptiveweighted bilateral filter based on the features. In this paper, an image-deblocking scheme using this kind of adaptive-weighted bilateral filter is proposed to remove and reduce blocking artifacts. Two parameters of the proposed adaptive-weighted bilateral filter are adaptive-weighted so that it can avoid over-blurring unsmooth regions while eliminating blocking artifacts in smooth regions. This is achieved in two aspects: by using local entropy to control the level of filtering of each single pixel point within the image, and by using an improved blind image quality assessment (BIQA) to control the strength of filtering different images whose blocking artifacts are different. It is proved by our experimental results that our proposed image-deblocking scheme provides good performance on eliminating blocking artifacts and can avoid the over-blurring of unsmooth regions


Image-Centric Integrated Data Model of Medical Information by Diseases: Two Case Studies for AMI and Ischemic Stroke
Meeyeon Lee, Ye-Seul Park and Jung-Won Lee
Page: 741~753, Vol. 12, No.4, 2016
10.3745/JIPS.04.0027
Keywords: Acute Myocardial Infarction, Data Model, Hospital Information System, Ischemic Stroke, Medical Image, Medical Information, Ontology
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In the medical fields, many efforts have been made to develop and improve Hospital Information System (HIS) including Electronic Medical Record (EMR), Order Communication System (OCS), and Picture Archiving and Communication System (PACS). However, materials generated and used in medical fields have various types and forms. The current HISs separately store and manage them by different systems, even though they relate to each other and contain redundant data. These systems are not helpful particularly in emergency where medical experts cannot check all of clinical materials in the golden time. Therefore, in this paper, we propose a process to build an integrated data model for medical information currently stored in various HISs. The proposed data model integrates vast information by focusing on medical images since they are most important materials for the diagnosis and treatment. Moreover, the model is disease-specific to consider that medical information and clinical materials including images are different by diseases. Two case studies show the feasibility and the usefulness of our proposed data model by building models about two diseases, acute myocardial infarction (AMI) and ischemic stroke


An Experimental Implementation of a Cross-Layer Approach for Improving TCP Performance over Cognitive Radio Networks
Sang-Seon Byun
Page: 73~82, Vol. 12, No.1, 2016
10.3745/JIPS.03.0041
Keywords: Cognitive Radio Networks, Congestion Control, TCP, USRP
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In cognitive radio networks (CRNs), the performance of the transmission control protocol (TCP) at the secondary user (SU) severely drops due to the mistrigger of congestion control. A long disruption is caused by the transmission of primary user, leading to the mistrigger. In this paper, we propose a cross-layer approach, called a CR-aware scheme that enhances TCP performance at the SU. The scheme is a sender side addition to the standard TCP (i.e., TCP-NewReno), and utilizes an explicit cross-layer signal delivered from a physical (or link) layer and the signal gives an indication of detecting the primary transmission (i.e., transmission of the primary user). We evaluated our scheme by implementing it onto a software radio platform, the Universal Software Radio Peripheral (USRP), where many parts of lower layer operations (i.e., operations in a link or physical layer) run as user processes. In our implementation, we ran our CR-aware scheme over IEEE 802.15.4. Furthermore, for the purpose of comparison, we implemented a selective ACK-based local recovery scheme that helps TCP isolate congestive loss from a random loss in a wireless section.


Analysis of Semantic Relations Between Multimodal Medical Images Based on Coronary Anatomy for Acute Myocardial Infarction
Yeseul Park, Meeyeon Lee, Myung-Hee Kim and Jung-Won Lee
Page: 129~148, Vol. 12, No.1, 2016
10.3745/JIPS.04.0021
Keywords: Acute Myocardial Infarction, Coronary Anatomy, Coronary Angiography, Data Model, Echocardiography, Medical Images, Multimodality, Semantic Features
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Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multi- modal medical images with flat and unstructured data. It has a lack of semantic information between multi- modal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively.


An Energy Efficient Distributed Approach-Based Agent Migration Scheme for Data Aggregation in Wireless Sensor Networks
Govind P. Gupta, Manoj Misra and Kumkum Garg
Page: 148~164, Vol. 11, No.1, 2015
10.3745/JIPS.03.0018
Keywords: Agent Migration Protocol, Data Aggregation, Mobile Agent, WSN
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The use of mobile agents for collaborative processing in wireless sensor network has gained considerable attention. This is when mobile agents are used for data aggregation to exploit redundant and correlated data. The efficiency of agent-based data aggregation depends on the agent migration scheme. However, in general, most of the proposed schemes are centralized approach-based schemes where the sink node determines the migration paths for the agents before dispatching them in the sensor network. The main limitations with such schemes are that they need global network topology information for deriving the migration paths of the agents, which incurs additional communication overhead, since each node has a very limited communication range. In addition, a centralized approach does not provide fault tolerant and adaptive migration paths. In order to solve such problems, we have proposed a distributed approach-based scheme for determining the migration path of the agents where at each hop, the local information is used to decide the migration of the agents. In addition, we also propose a local repair mechanism for dealing with the faulty nodes. The simulation results show that the proposed scheme performs better than existing schemes in the presence of faulty nodes within the networks, and manages to report the aggregated data to the sink faster.


An Adaptive Superframe Duration Allocation Algorithm for Resource-Efficient Beacon Scheduling
Young-Ae Jeon, Sang-Sung Choi, Dae-Young Kim and Kwang-il Hwang
Page: 295~309, Vol. 11, No.2, 2015
10.3745/JIPS.03.0025
Keywords: Beacon Scheduling, Energy Efficient, IEEE802.15.4, IEEE802.15.4e, LR-WPAN, Superframe Duration Allocation
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Beacon scheduling is considered to be one of the most significant challenges for energy-efficient Low-Rate Wireless Personal Area Network (LR-WPAN) multi-hop networks. The emerging new standard, IEEE802.15.4e, contains a distributed beacon scheduling functionality that utilizes a specific bitmap and multi-superframe structure. However, this new standard does not provide a critical recipe for superframe duration (SD) allocation in beacon scheduling. Therefore, in this paper, we first introduce three different SD allocation approaches, LSB first, MSB first, and random. Via experiments we show that IEEE802.15.4e DSME beacon scheduling performs differently for different SD allocation schemes. Based on our experimental results we propose an adaptive SD allocation (ASDA) algorithm. It utilizes a single indicator, a distributed neighboring slot incrementer (DNSI). The experimental results demonstrate that the ASDA has a superior performance over other methods from the viewpoint of resource efficiency.


Simple Pyramid RAM-Based Neural Network Architecture for Localization of Swarm Robots
Siti Nurmaini and Ahmad Zarkasi
Page: 370~388, Vol. 11, No.3, 2015
10.3745/JIPS.01.0008
Keywords: Localization Process, RAM-Based Neural Network, Swarm Robots
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The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent’s position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.


Robust ROI Watermarking Scheme Based on Visual Cryptography: Application on Mammograms
Meryem Benyoussef, Samira Mabtoul, Mohamed El Marraki and Driss Aboutajdine
Page: 495~508, Vol. 11, No.4, 2015
10.3745/JIPS.02.0032
Keywords: Copyright Protection, Mammograms, Medical Image, Robust Watermarking, Visual Cryptography
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In this paper, a novel robust medical images watermarking scheme is proposed. In traditional methods, the added watermark may alter the host medical image in an irreversible manner and may mask subtle details. Consequently, we propose a method for medical image copyright protection that may remedy this problem by embedding the watermark without modifying the original host image. The proposed method is based on the visual cryptography concept and the dominant blocks of wavelet coefficients. The logic in using the blocks dominants map is that local features, such as contours or edges, are unique to each image. The experimental results show that the proposed method can withstand several image processing attacks such as cropping, filtering, compression, etc.


Rotational Wireless Video Sensor Networks with Obstacle Avoidance Capability for Improving Disaster Area Coverage
Nawel Bendimerad and Bouabdellah Kechar
Page: 509~527, Vol. 11, No.4, 2015
10.3745/JIPS.03.0034
Keywords: Coverage, Fault Tolerance, Field of View, Obstacles Avoidance, Scheduling, Simulation, Wireless Video Sensor Networks
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Wireless Video Sensor Networks (WVSNs) have become a leading solution in many important applications, such as disaster recovery. By using WVSNs in disaster scenarios, the main goal is achieving a successful immediate response including search, location, and rescue operations. The achievement of such an objective in the presence of obstacles and the risk of sensor damage being caused by disasters is a challenging task. In this paper, we propose a fault tolerance model of WVSN for efficient post-disaster management in order to assist rescue and preparedness operations. To get an overview of the monitored area, we used video sensors with a rotation capability that enables them to switch to the best direction for getting better multimedia coverage of the disaster area, while minimizing the effect of occlusions. By constructing different cover sets based on the field of view redundancy, we can provide a robust fault tolerance to the network. We demonstrate by simulating the benefits of our proposal in terms of reliability and high coverage.


Text Detection in Scene Images Based on Interest Points
Minh Hieu Nguyen and Gueesang Lee
Page: 528~537, Vol. 11, No.4, 2015
10.3745/JIPS.02.0026
Keywords: Connected Component, Interest Point, Tensor Voting, Text Detection
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Text in images is one of the most important cues for understanding a scene. In this paper, we propose a novel approach based on interest points to localize text in natural scene images. The main ideas of this approach are as follows: first we used interest point detection techniques, which extract the corner points of characters and center points of edge connected components, to select candidate regions. Second, these candidate regions were verified by using tensor voting, which is capable of extracting perceptual structures from noisy data. Finally, area, orientation, and aspect ratio were used to filter out non-text regions. The proposed method was tested on the ICDAR 2003 dataset and images of wine labels. The experiment results show the validity of this approach.


Spatial Interpolation and Assimilation Methods for Satellite and Ground Meteorological Data in Vietnam
Khac Phong Do, Ba Tung Nguyen, Xuan Thanh Nguyen, Quang Hung Bui, Nguyen Le Tran, Thi Nhat Thanh Nguyen, Van Quynh Vuong, Huy Lai Nguyen and Thanh Ha Le
Page: 556~572, Vol. 11, No.4, 2015
10.3745/JIPS.02.0030
Keywords: Assimilation, Interpolation, Meteorological Variables, Kriging, Vietnam
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This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.


Secured Telemedicine Using Whole Image as Watermark with Tamper Localization and Recovery Capabilities
Gran Badshah, Siau-Chuin Liew, Jasni Mohamad Zain and Mushtaq Ali
Page: 601~615, Vol. 11, No.4, 2015
10.3745/JIPS.03.0044
Keywords: Lossless Recovery, Tamper Localization, Telemedicine, Watermarking, Whole Image, WITALLOR
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Region of interest (ROI) is the most informative part of a medical image and mostly has been used as a major part of watermark. Various shapes ROIs selection have been reported in region-based watermarking techniques. In region-based watermarking schemes an image region of non-interest (RONI) is the second important part of the image and is used mostly for watermark encapsulation. In online healthcare systems the ROI wrong selection by missing some important portions of the image to be part of ROI can create problem at the destination. This paper discusses the complete medical image availability in original at destination using the whole image as a watermark for authentication, tamper localization and lossless recovery (WITALLOR). The WITALLOR watermarking scheme ensures the complete image security without of ROI selection at the source point as compared to the other region-based watermarking techniques. The complete image is compressed using the Lempel-Ziv-Welch (LZW) lossless compression technique to get the watermark in reduced number of bits. Bits reduction occurs to a number that can be completely encapsulated into image. The watermark is randomly encapsulated at the least significant bits (LSBs) of the image without caring of the ROI and RONI to keep the image perceptual degradation negligible. After communication, the watermark is retrieved, decompressed and used for authentication of the whole image, tamper detection, localization and lossless recovery. WITALLOR scheme is capable of any number of tampers detection and recovery at any part of the image. The complete authentic image gives the opportunity to conduct an image based analysis of medical problem without restriction to a fixed ROI.


Viewpoint Unconstrained Face Recognition Based on Affine Local Descriptors and Probabilistic Similarity
Yongbin Gao and Hyo Jong Lee
Page: 643~654, Vol. 11, No.4, 2015
10.3745/JIPS.02.0027
Keywords: Affine Scale Invariant Feature Transform, Face Recognition, Probabilistic Similarity
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Face recognition under controlled settings, such as limited viewpoint and illumination change, can achieve good performance nowadays. However, real world application for face recognition is still challenging. In this paper, we propose using the combination of Affine Scale Invariant Feature Transform (SIFT) and Probabilistic Similarity for face recognition under a large viewpoint change. Affine SIFT is an extension of SIFT algorithm to detect affine invariant local descriptors. Affine SIFT generates a series of different viewpoints using affine transformation. In this way, it allows for a viewpoint difference between the gallery face and probe face. However, the human face is not planar as it contains significant 3D depth. Affine SIFT does not work well for significant change in pose. To complement this, we combined it with probabilistic similarity, which gets the log likelihood between the probe and gallery face based on sum of squared difference (SSD) distribution in an offline learning process. Our experiment results show that our framework achieves impressive better recognition accuracy than other algorithms compared on the FERET database.


Stroke Width-Based Contrast Feature for Document Image Binarization
Le Thi Khue Van and Gueesang Lee
Page: 55~68, Vol. 10, No.1, 2014
10.3745/JIPS.2014.10.1.055
Keywords: Degraded Document Image, Binarization, Stroke Width, Contrast Feature, Text Boundary
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Automatic segmentation of foreground text from the background in degraded document images is very much essential for the smooth reading of the document content and recognition tasks by machine. In this paper, we present a novel approach to the binarization of degraded document images. The proposed method uses a new local contrast feature extracted based on the stroke width of text. First, a pre-processing method is carried out for noise removal. Text boundary detection is then performed on the image constructed from the contrast feature. Then local estimation follows to extract text from the background. Finally, a refinement procedure is applied to the binarized image as a post-processing step to improve the quality of the final results. Experiments and comparisons of extracting text from degraded handwriting and machine-printed document image against some well-known binarization algorithms demonstrate the effectiveness of the proposed method.


Cooperation-Aware VANET Clouds: Providing Secure Cloud Services to Vehicular Ad Hoc Networks
Rasheed Hussain and Heekuck Oh
Page: 103~118, Vol. 10, No.1, 2014
10.3745/JIPS.2014.10.1.103
Keywords: VANET Clouds, Security, Privacy, Traffic Information, Data Dissemination, Cloud Computing
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Over the last couple of years, traditional VANET (Vehicular Ad Hoc NETwork) evolved into VANET-based clouds. From the VANET standpoint, applications became richer by virtue of the boom in automotive telematics and infotainment technologies. Nevertheless, the research community and industries are concerned about the under-utilization of rich computation, communication, and storage resources in middle and high-end vehicles. This phenomenon became the driving force for the birth of VANET-based clouds. In this paper, we envision a novel application layer of VANET-based clouds based on the cooperation of the moving cars on the road, called CaaS (Cooperation as a Service). CaaS is divided into TIaaS (Traffic Information as a Service), WaaS (Warning as a Service), and IfaaS (Infotainment as a Service). Note, however, that this work focuses only on TIaaS and WaaS. TIaaS provides vehicular nodes, more precisely subscribers, with the fine-grained traffic information constructed by CDM (Cloud Decision Module) as a result of the cooperation of the vehicles on the roads in the form of mobility vectors. On the other hand, WaaS provides subscribers with potential warning messages in case of hazard situations on the road. Communication between the cloud infrastructure and the vehicles is done through GTs (Gateway Terminals), whereas GTs are physically realized through RSUs (Road-Side Units) and vehicles with 4G Internet access. These GTs forward the coarse-grained cooperation from vehicles to cloud and fine-grained traffic information and warnings from cloud to vehicles (subscribers) in a secure, privacy-aware fashion. In our proposed scheme, privacy is conditionally preserved wherein the location and the identity of the cooperators are preserved by leveraging the modified location-based encryption and, in case of any dispute, the node is subject to revocation. To the best of our knowledge, our proposed scheme is the first effort to offshore the extended traffic view construction function and warning messages dissemination function to the cloud.


Probabilistic Models for Local Patterns Analysis
Khiat Salim, Belbachir Hafida and Rahal Sid Ahmed
Page: 145~161, Vol. 10, No.1, 2014
10.3745/JIPS.2014.10.1.145
Keywords: Global Pattern, Maximum Entropy Method, Non-derivable Itemset, Itemset Inclusion-exclusion Model
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Recently, many large organizations have multiple data sources (MDS’) distributed over different branches of an interstate company. Local patterns analysis has become an effective strategy for MDS mining in national and international organizations. It consists of mining different datasets in order to obtain frequent patterns, which are forwarded to a centralized place for global pattern analysis. Various synthesizing models [2,3,4,5,6,7,8,26] have been proposed to build global patterns from the forwarded patterns. It is desired that the synthesized rules from such forwarded patterns must closely match with the mono-mining results (i.e., the results that would be obtained if all of the databases are put together and mining has been done). When the pattern is present in the site, but fails to satisfy the minimum support threshold value, it is not allowed to take part in the pattern synthesizing process. Therefore, this process can lose some interesting patterns, which can help the decider to make the right decision. In such situations we propose the application of a probabilistic model in the synthesizing process. An adequate choice for a probabilistic model can improve the quality of patterns that have been discovered. In this paper, we perform a comprehensive study on various probabilistic models that can be applied in the synthesizing process and we choose and improve one of them that works to ameliorate the synthesizing results. Finally, some experiments are presented in public database in order to improve the efficiency of our proposed synthesizing method.


Non-iterative Bit Loading Algorithm for OFDM in Independent and Correlated fading
John W. Manry and Santosh Nagaraj
Page: 163~175, Vol. 10, No.2, 2014
10.3745/JIPS.03.0001
Keywords: Adaptive Modulation, Orthogonal Frequency Division Multiplexing (OFDM), FadingAdaptive Modulation, Orthogonal Frequency Division Multiplexing (OFDM), Fading
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This paper will focus on improving the performance of orthogonal frequency division multiplexing (OFDM) in Rayleigh fading environments. The proposed technique will use a previously published method that has been shown to improve OFDM performance in independent fading, based on ordered sub-carrier selection. Then, a simple non-iterative method for finding the optimal bit-loading allocation was proposed. It was also based on ordered sub-carrier selection. We compared both of these algorithms to an optimal bit-loading solution to determine their effectiveness in a correlated fading environment. The correlated fading was simulated using the JTC channel models. Our intent was not to create an optimal solution, but to create a low complexity solution that can be used in a wireless environment in which the channel conditions change rapidly and that require a simple algorithm for fast bit loading.


Imputation of Medical Data Using Subspace Condition Order Degree Polynomials
Klaokanlaya Silachan and Panjai Tantatsanawong
Page: 395~411, Vol. 10, No.3, 2014
10.3745/JIPS.04.0007
Keywords: Imputation, Personal Temporal Data, Polynomial Interpolation
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Temporal medical data is often collected during patient treatments that require personal analysis. Each observation recorded in the temporal medical data is associated with measurements and time treatments. A major problem in the analysis of temporal medical data are the missing values that are caused, for example, by patients dropping out of a study before completion. Therefore, the imputation of missing data is an important step during pre-processing and can provide useful information before the data is mined. For each patient and each variable, this imputation replaces the missing data with a value drawn from an estimated distribution of that variable. In this paper, we propose a new method, called Newton’s finite divided difference polynomial interpolation with condition order degree, for dealing with missing values in temporal medical data related to obesity. We compared the new imputation method with three existing subspace estimation techniques, including the k-nearest neighbor, local least squares, and natural cubic spline approaches. The performance of each approach was then evaluated by using the normalized root mean square error and the statistically significant test results. The experimental results have demonstrated that the proposed method provides the best fit with the smallest error and is more accurate than the other methods.


Spectrum Sensing and Data Transmission in a Cognitive Relay Network Considering Spatial False Alarms
Tasnina A. Tishita, Sumiya Akhter, Md. Imdadul Islam and M. R. Amin
Page: 459~470, Vol. 10, No.3, 2014
10.3745/JIPS.03.0007
Keywords: Cognitive Network, Conventional False Alarms, Probability of Symbol Error Rate, Spatial False Alarms, Spectrum Sensing
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In this paper, the average probability of the symbol error rate (SER) and throughput are studied in the presence of joint spectrum sensing and data transmission in a cognitive relay network, which is in the environment of an optimal power allocation strategy. In this investigation, the main component in calculating the secondary throughput is the inclusion of the spatial false alarms, in addition to the conventional false alarms. It has been shown that there exists an optimal secondary power amplification factor at which the probability of SER has a minimum value, whereas the throughput has a maximum value. We performed a Monte-Carlo simulation to validate the analytical results.


On the Performance of Oracle Grid Engine Queuing System for Computing Intensive Applications
Vladi Kolici, Albert Herrero and Fatos Xhafa
Page: 491~502, Vol. 10, No.4, 2014
10.3745/JIPS.01.0004
Keywords: Benchmarking, Cloud Computing, Computing Intensive Applications, Genetic Algorithms, Grid Computing, Oracle Grid Engine, Scheduling, Simulation
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In this paper we present some research results on computing intensive applications using modern high performance architectures and from the perspective of high computational needs. Computing intensive applications are an important family of applications in distributed computing domain. They have been object of study using different distributed computing paradigms and infrastructures. Such applications distinguish for their demanding needs for CPU computing, independently of the amount of data associated with the problem instance. Among computing intensive applications, there are applications based on simulations, aiming to maximize system resources for processing large computations for simulation. In this research work, we consider an application that simulates scheduling and resource allocation in a Grid computing system using Genetic Algorithms. In such application, a rather large number of simulations is needed to extract meaningful statistical results about the behavior of the simulation results. We study the performance of Oracle Grid Engine for such application running in a Cluster of high computing capacities. Several scenarios were generated to measure the response time and queuing time under different workloads and number of nodes in the cluster.


Performance Evaluation of the WiMAX Network under a Complete Partitioned User Group with a Traffic Shaping Algorithm
Jesmin Akhter, Md. Imdadul Islam and M. R. Amin
Page: 568~580, Vol. 10, No.4, 2014
10.3745/JIPS.03.0016
Keywords: Blocking Probability, CAC, Complete Partition Scheme, Subscriber Station, Throughput
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To enhance the utilization of the traffic channels of a network (instead of allocating radio channel to an individual user), a channel or a group of channels are allocated to a user group. The idea behind this is the statistical distribution of traffic arrival rates and the service time for an individual user or a group of users. In this paper, we derive the blocking probability and throughput of a subscriber station of Worldwide Interoperability for Microwave Access (WiMAX) by considering both the connection level and packet-level traffic under a complete partition scheme. The main contribution of the paper is to incorporate the traffic shaping scheme onto the incoming turbulent traffic. Hence, we have also analyzed the impact of the drain rate of the buffer on the blocking probability and throughput.


A Step towards User Privacy while Using Location-Based Services
Fizza Abbas and Heekuck Oh
Page: 618~627, Vol. 10, No.4, 2014
10.3745/JIPS.01.0003
Keywords: Location Based Services, Location Privacy, Point of Interests
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Nowadays mobile users are using a popular service called Location-Based Services (LBS). LBS is very helpful for a mobile user in finding various Point of Interests (POIs) in their vicinity. To get these services, users must provide their personal information, such as user identity or current location, which severely risks the location privacy of the user. Many researchers are developing schemes that enable a user to use these LBS services anonymously, but these approaches have some limitations (i.e., either the privacy prevention mechanism is weak or the cost of the solution is too much). As such, we are presenting a robust scheme for mobile users that allows them to use LBS anonymously. Our scheme involves a client side application that interacts with an untrusted LBS server to find the nearest POI for a service required by a user. The scheme is not only efficient in its approach, but is also very practical with respect to the computations that are done on a client’s resource constrained device. With our scheme, not only can a client anonymously use LBS without any use of a trusted third party, but also a server’s database is completely secure from the client. We performed experiments by developing and testing an Android-based client side smartphone application to support our argument.


Region-Based Facial Expression Recognition in Still Images
Gawed M. Nagi, Rahmita Rahmat, Fatimah Khalid and Muhamad Taufik
Page: 173~188, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.173
Keywords: Facial Expression Recognition (FER), Facial Features Detection, Facial Features Extraction, Cascade Classifier, LBP, One-Vs-Rest SVM
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In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.


Round Robin with Server Affinity: A VM Load Balancing Algorithm for Cloud Based Infrastructure
Komal Mahajan, Ansuyia Makroo and Deepak Dahiya
Page: 379~394, Vol. 9, No.3, 2013
10.3745/JIPS.2013.9.3.379
Keywords: Virtual Machine (VM), Server affinity, VM load balancer, CloudAnalyst, Data center, Cloudlet
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Cloud computing is an evolving computing paradigm that has influenced every other entity in the globalized industry, whether it is in the public sector or the private sector. Considering the growing importance of cloud, finding new ways to improve cloud services is an area of concern and research focus. The limitation of the available Virtual Machine Load balancing policies for cloud is that they do not save the state of the previous allocation of a virtual machine to a request from a Userbase and the algorithm requires execution each time a new request for Virtual Machine allocation is received from the Userbase. This problem can be resolved by developing an efficient virtual machine load balancing algorithm for the cloud and by doing a comparative analysis of the proposed algorithm with the existing algorithms.


A New Approach for Information Security using an Improved Steganography Technique
Mamta Juneja and Parvinder Singh Sandhu
Page: 405~424, Vol. 9, No.3, 2013
10.3745/JIPS.2013.9.3.405
Keywords: Adaptive LSB Steganography, AES; Hybrid Feature Detection, Random Pixel Embeddin g, Steganography, Two Component based LSB Steganography
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This research paper proposes a secured, robust approach of information security using steganography. It presents two component based LSB (Least Significant Bit) steganography methods for embedding secret data in the least significant bits of blue components and partial green components of random pixel locations in the edges of images. An adaptive LSB based steganography is proposed for embedding data based on the data available in MSB’s (Most Significant Bits) of red, green, and blue components of randomly selected pixels across smooth areas. A hybrid feature detection filter is also proposed that performs better to predict edge areas even in noisy conditions. AES (Advanced Encryption Standard) and random pixel embedding is incorporated to provide two-tier security. The experimental results of the proposed approach are better in terms of PSNR and capacity. The comparison analysis of output results with other existing techniques is giving the proposed approach an edge over others. It has been thoroughly tested for various steganalysis attacks like visual analysis, histogram analysis, chi-square, and RS analysis and could sustain all these attacks very well.


An Architecture for Home-Oriented IPTV Service Platform on Residential Gateway
Pyung Soo Kim
Page: 425~434, Vol. 9, No.3, 2013
10.3745/JIPS.2013.9.3.425
Keywords: A Home Network, IPTV, Service Platform, Open Architecture, Home Electronic System (HES), Home Gateway Initiative (HGI)
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In order for end-users in home networks to receive opportunities for useful services that go beyond legacy Internet Protocol TV (IPTV) services, this paper proposes a service platform that resides on the residential gateway (RG) for interworking between the home network and IPTV. This proposed service platform is called the home-oriented IPTV service platform (HISP) on the RG (HISP-RG). The proposed HISP-RG provides open architecture and functionalities to enable 3rd party IPTV service providers to locally and directly deliver home-oriented IPTV services to end-users in home networks. The HISP-RG can be an “add-on” and not a “built-in” solution for the existing standard RG. This paper introduces several home-oriented IPTV services that can be executed and delivered locally through the HISP-RG. Then, the open architecture and functionalities of the HISP-RG are defined and their requirements are specified. Finally, use cases of the HISP-RG for home-oriented IPTV services are presented.


Self-Localized Packet Forwarding in Wireless Sensor Networks
Tarun Dubey and O. P. Sahu
Page: 477~488, Vol. 9, No.3, 2013
10.3745/JIPS.2013.9.3.477
Keywords: Localization, Node Density, Packet Forwarding, Redundancy, WSNs
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Wireless Sensor Networks (WSNs) are comprised of sensor nodes that forward data in the shape of packets inside a network. Proficient packet forwarding is a prerequisite in sensor networks since many tasks in the network, together with redundancy evaluation and localization, depend upon the methods of packet forwarding. With the motivation to develop a fault tolerant packet forwarding scheme a Self-Localized Packet Forwarding Algorithm (SLPFA) to control redundancy in WSNs is proposed in this paper. The proposed algorithm infuses the aspects of the gossip protocol for forwarding packets and the end to end performance of the proposed algorithm is evaluated for different values of node densities in the same deployment area by means of simulations.


Small Object Segmentation Based on Visual Saliency in Natural Images
Huynh Trung Manh and Gueesang Lee
Page: 592~601, Vol. 9, No.4, 2013
10.3745/JIPS.2013.9.4.592
Keywords: Gaussian Mixture Model (GMM), Visual Saliency, Segmentation, Object Detection.
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Object segmentation is a challenging task in image processing and computer vision. In this paper, we present a visual attention based segmentation method to segment small sized interesting objects in natural images. Different from the traditional methods, we first search the region of interest by using our novel saliency-based method, which is mainly based on band-pass filtering, to obtain the appropriate frequency. Secondly, we applied the Gaussian Mixture Model (GMM) to locate the object region. By incorporating the visual attention analysis into object segmentation, our proposed approach is able to narrow the search region for object segmentation, so that the accuracy is increased and the computational complexity is reduced. The experimental results indicate that our proposed approach is efficient for object segmentation in natural images, especially for small objects. Our proposed method significantly outperforms traditional GMM based segmentation.


A Computational Intelligence Based Online Data Imputation Method: An Application For Banking
Kancherla Jonah Nishanth and Vadlamani Ravi
Page: 633~650, Vol. 9, No.4, 2013
10.3745/JIPS.2013.9.4.633
Keywords: Data Imputation, General Regression Neural Network (GRNN), Evolving Clustering Method (ECM), Imputation, K-Medoids clustering, K-Means clustering, MLP
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All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing vlaues with cluster centers, as part of the local learnig strategy Stage 2 refines the resultant approximate values using a Genearal Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques emploly K-Means or K-Medoids and Multi Layer Perceptron (MLP) or GRNN in Stage-1 and Stage-2 respectively. Several experiments were conducted on 8 benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.


A New Approach to Fingerprint Detection Using a Combination of Minutiae Points and Invariant Moments Parameters
Sarnali Basak, Md. Imdadul Islam and M. R. Amin
Page: 421~436, Vol. 8, No.3, 2012
10.3745/JIPS.2012.8.3.421
Keywords: Random Variable, Skewness, Kurtosis, Invariant Moment, Termination And Bifurcation Points, Virtual Core Point
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Different types of fingerprint detection algorithms that are based on extraction of minutiae points are prevalent in recent literature. In this paper, we propose a new algorithm to locate the virtual core point/centroid of an image. The Euclidean distance between the virtual core point and the minutiae points is taken as a random variable. The mean, variance, skewness, and kurtosis of the random variable are taken as the statistical parameters of the image to observe the similarities or dissimilarities among fingerprints from the same or different persons. Finally, we verified our observations with a moment parameter-based analysis of some previous works.


Using an Adaptive Search Tree to Predict User Location
Sechang Oh
Page: 437~444, Vol. 8, No.3, 2012
10.3745/JIPS.2012.8.3.437
Keywords: Location Prediction, Learning System, Search Tree, Context-Awareness
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In this paper, we propose a method for predicting a user’s location based on their past movement patterns. There is no restriction on the length of past movement patterns when using this method to predict the current location. For this purpose, a modified search tree has been devised. The search tree is constructed in an effective manner while it additionally learns the movement patterns of a user one by one. In fact, the time complexity of the learning process for a movement pattern is linear. In this process, the search tree expands to take into consideration more details about the movement patterns when a pattern that conflicts with an existing trained pattern is found. In this manner, the search tree is trained to make an exact matching, as needed, for location prediction. In the experiments, the results showed that this method is highly accurate in comparison with more complex and sophisticated methods. Also, the accuracy deviation of users of this method is significantly lower than for any other methods. This means that this method is highly stable for the variations of behavioral patterns as compared to any other method. Finally, 1.47 locations were considered on average for making a prediction with this method. This shows that the prediction process is very efficient


An Adaptive Workflow Scheduling Scheme Based on an Estimated Data Processing Rate for Next Generation Sequencing in Cloud Computing
Byungsang Kim, Chan-Hyun Youn, Yong-Sung Park, Yonggyu Lee and Wan Choi
Page: 555~566, Vol. 8, No.4, 2012
10.3745/JIPS.2012.8.4.555
Keywords: Resource-Provisioning, Bio-Workflow Broker, Next-Generation Sequencing
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The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batchprocessing scheme in a local computing farm and data storage. In the case of a largescale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resourceprovisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.


Design and Simulation of a Flow Mobility Scheme Based on Proxy Mobile IPv6
Hyon-Young Choi, Sung-Gi Min, Youn-Hee Han and Rajeev Koodli
Page: 603~620, Vol. 8, No.4, 2012
10.3745/JIPS.2012.8.4.603
Keywords: Flow Mobility, Proxy Mobile IPv6
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Proxy Mobile IPv6 (PMIPv6) is a network-based mobility support protocol and it does not require Mobile Nodes (MNs) to be involved in the mobility support signaling. In the case when multiple interfaces are active in an MN simultaneously, each data flow can be dynamically allocated to and redirected between different access networks to adapt to the dynamically changing network status and to balance the workload. Such a flow redistribution control is called "flow mobility". In the existing PMIPv6-based flow mobility support, although the MN"'"s logical interface can solve the well-known problems of flow mobility in a heterogeneous network, some missing procedures, such as an MN-derived flow handover, make PMIPv6-based flow mobility incomplete. In this paper, an enhanced flow mobility support is proposed for actualizing the flow mobility support in PMIPv6. The proposed scheme is also based on the MN"'"s logical interface, which hides the physical interfaces from the network layer and above. As new functional modules, the flow interface manager is placed at the MN"'"s logical interface and the flow binding manager in the Local Mobility Anchor (LMA) is paired with the MN"'"s flow interface manager. They manage the flow bindings, and select the proper access technology to send packets. In this paper, we provide the complete flow mobility procedures which begin with the following three different triggering cases: the MN"'"s new connection/disconnection, the LMA"'"s decision, and the MN"'"s request. Simulation using the ns-3 network simulator is performed to verify the proposed procedures and we show the network throughput variation caused by the network offload using the proposed procedures.


Dynamic Load Balancing and Network Adaptive Virtual Storage Service for Mobile Appliances
Ivy Ong and Hyotaek Lim
Page: 53~62, Vol. 7, No.1, 2011
10.3745/JIPS.2011.7.1.053
Keywords: iATA Protocol, Load Balancing, Network Monitoring, Storage Network Solution, Write Replication
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With the steady growth of mobile technology and applications, demand for more storage in mobile devices has also increased. A lightweight block-level protocol, Internet Advanced Technology Attachment (iATA), has been developed to deliver a costeffective storage network solution for mobile devices to obtain more storage. This paper seeks to contribute to designing and implementing Load Balancing (LB), Network Monitoring (NM) and Write Replication (WR) modules to improve the protocol¡¯s scalability and data availability. LB and NM modules are invoked to collect system resources states and current network status at each associate node (server machine). A dynamic weight factor is calculated based on the collected information and sent to a referral server. The referral server is responsible to analyze and allocate the most ideal node with the least weight to serve the client. With this approach, the client can avoid connecting to a heavily loaded node that may cause delays in subsequent in-band I/O operations. Write replication is applied to the remaining nodes through a WR module by utilizing the Unison file synchronization program. A client initially connected to node IP A for write operations will have no hindrances in executing the relevant read operations at node IP B in new connections. In the worst case scenario of a node crashing, data remain recoverable from other functioning nodes. We have conducted several benchmark tests and our results are evaluated and verified in a later section.


Ensuring Anonymity for LBSs in Smartphone Environment
Mohammed Alzaabi, Chan Yeob Yeun and Thomas Anthony Martin
Page: 121~136, Vol. 7, No.1, 2011
10.3745/JIPS.2011.7.1.121
Keywords: Location Based Services, Anonymity, Location Information
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With the rapid growth of GPS-enable Smartphones, the interest on using Location Based Services (LBSs) has increased significantly. The evolution in the functionalities provided by those smartphones has enabled them to accurately pinpoint the location of a user. Because location information is what all LBSs depend on to process user¡¯s request, it should be properly protected from attackers or malicious service providers (SP). Additionally, maintaining user¡¯s privacy and confidentiality are imperative challenges to be overcome. A possible solution for these challenges is to provide user anonymity, which means to ensure that a user initiating a request to the SP should be indistinguishable from a group of people by any adversary who had access to the request. Most of the proposals that maintain user¡¯s anonymity are based on location obfuscation. It mainly focuses on adjusting the resolution of the user¡¯s location information. In this paper, we present a new protocol that is focused on using cryptographic techniques to provide anonymity for LBSs users in the smartphone environment. This protocol makes use of a trusted third party called the Anonymity Server (AS) that ensures anonymous communication between the user and the service provider.


Lifting a Metadata Model to the Semantic Multimedia World
Gaetan Martens, Ruben Verborgh, Chris Poppe and Rik Van de Walle
Page: 199~208, Vol. 7, No.1, 2011
10.3745/JIPS.2011.7.1.199
Keywords: Multimedia, Metadata Annotation, Semantic Web Technologies
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This paper describes best-practices in lifting an image metadata standard to the Semantic Web. We provide guidelines on how an XML-based metadata format can be converted into an OWL ontology. Additionally, we discuss how this ontology can be mapped to the W3C¡¯s Media Ontology. This ontology is a standardization effort of the W3C to provide a core vocabulary for multimedia annotations. The approach presented here can be applied to other XML-based metadata standards.


The Principle of Justifiable Granularity and an Optimization of Information Granularity Allocation as Fundamentals of Granular Computing
Witold Pedrycz
Page: 397~412, Vol. 7, No.3, 2011
10.3745/JIPS.2011.7.3.397
Keywords: Information Granularity, Principle of Justifiable Granularity, Knowledge Management, Optimal Granularity Allocation
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Granular Computing has emerged as a unified and coherent framework of designing, processing, and interpretation of information granules. Information granules are formalized within various frameworks such as sets (interval mathematics), fuzzy sets, rough sets, shadowed sets, probabilities (probability density functions), to name several the most visible approaches. In spite of the apparent diversity of the existing formalisms, there are some underlying commonalities articulated in terms of the fundamentals, algorithmic developments and ensuing application domains. In this study, we introduce two pivotal concepts: a principle of justifiable granularity and a method of an optimal information allocation where information granularity is regarded as an important design asset. We show that these two concepts are relevant to various formal setups of information granularity and offer constructs supporting the design of information granules and their processing. A suite of applied studies is focused on knowledge management in which case we identify several key categories of schemes present there.


Partial Bicasting with Buffering for Proxy Mobile IPv6 Handover in Wireless Networks
Ji-In Kim and Seok-Joo Koh
Page: 627~634, Vol. 7, No.4, 2011
10.3745/JIPS.2011.7.4.627
Keywords: Proxy Mobile IPv6, Handover, Partial Bicasting, Buffering, Simulation Analysis
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This paper addresses the Proxy Mobile IPv6 (PMIP) handover using bicasting in mobile/wireless networks. The bicasting scheme can be used to support the PMIP handover, which tends to waste the network resources of wireless links and incurs data losses during handover. We propose an enhanced scheme of PMIP handover, called the partial bicasting with buffering for PMIP (PBB-PMIP). In the PBB-PMIP handover, the bicasting is performed in the “partial” region between the Local Mobility Anchor (LMA) and the Mobile Access Gateway (MAG), when a mobile node is in the handover area. The data packets are buffered at the new MAG during handover to reduce data losses and are then forwarded to mobile nodes after handover. By ns-2 simulations, the proposed PBB-PMIP scheme is compared with the existing schemes of PMIP and PMIP with bicasting. The proposed scheme can benefit from the reduction of handover delay and packet loss, and the effective use of the network resources of wireless links, as compared to the existing handover schemes.


Performance Evaluation of Finite Queue Switching Under Two-Dimensional M/G/1(m) Traffic
Md. Syeful Islam, Md. Rezaur Rahman, Anupam Roy, Md. Imdadul Islam and M. R. Amin
Page: 679~690, Vol. 7, No.4, 2011
10.3745/JIPS.2011.7.4.679
Keywords: Carried Traffic, LST, Two-Dimensional Traffic, Cell Dropping Probability, M/G/1 Model
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In this paper we consider a local area network (LAN) of dual mode service where one is a token bus and the other is a carrier sense multiple access with a collision detection (CSMA/CD) bus. The objective of the paper is to find the overall cell/packet dropping probability of a dual mode LAN for finite length queue M/G/1(m) traffic. Here, the offered traffic of the LAN is taken to be the equivalent carried traffic of a one-millisecond delay. The concept of a tabular solution for two-dimensional Poisson’s traffic of circuit switching is adapted here to find the cell dropping probability of the dual mode packet service. Although the work is done for the traffic of similar bandwidth, it can be extended for the case of a dissimilar bandwidth of a circuit switched network.


GML Map Visualization on Mobile Devices
Eun-Ha Song and Young-Sik Jeong
Page: 33~42, Vol. 6, No.1, 2010
10.3745/JIPS.2010.6.1.033
Keywords: Map Visualization, DXF, DWG, SHP, GML, POI, Trace Monitoring
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GIS can only be applied to certain areas by storing format. It is subordinate to a system when displaying geographic information data. It is therefore inevitable for GIS to use GML that supports efficient usage of various geographic information data and interoperability for integration and sharing. The paper constructs VisualGML that translates currently-used geographic information such as DXF (Drawing Exchange Format), DWG (DraWinG), or SHP (Shapefile) into GML format for visualization. VisualGML constructs an integrated map pre-process module, which filters geographic information data according to its tag and properties, to provide the flexibility of a mobile device. VisualGML also provides two major GIS services for the user and administrator. It can enable visualizing location search. This is applied with a 3-Layer POI structure for the user. It has trace monitoring visualization through moving information of mobile devices for the administrator.


A Hexagon Tessellation Approach for the Transmission Energy Efficiency in Underwater Wireless Sensor Networks
Sungun Kim, Hyunsoo Cheon, Sangbo Seo, Seungmi Song and Seonyeong Park
Page: 53~66, Vol. 6, No.1, 2010
10.3745/JIPS.2010.6.1.053
Keywords: UWSN, Hexagon Tessellation, Energy Efficiency, Hybrid
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The energy efficiency is a key design issue to improve the lifetime of the underwater sensor networks (UWSN) consisting of sensor nodes equipped with a small battery of limited energy resource. In this paper, we apply a hexagon tessellation with an ideal cell size to deploy the underwater sensor nodes for two-dimensional UWSN. Upon this setting, we propose an enhanced hybrid transmission method that forwards data packets in a mixed transmission way based on location dependent direct transmitting or uniform multi-hop forwarding. In order to select direct transmitting or uniform multi-hop forwarding, the proposed method applies the threshold annulus that is defined as the distance between the cluster head node and the base station (BS). Our simulation results show that the proposed method enhances the energy efficiency compared with the existing multi-hop forwarding methods and hybrid transmission methods


A Fine-grained Localization Scheme Using A Mobile Beacon Node for Wireless Sensor Networks
Kezhong Liu and Ji Xiong
Page: 147~162, Vol. 6, No.2, 2010
10.3745/JIPS.2010.6.2.147
Keywords: Localization Algorithm, Mobile Beacon Node, Sensor Network, RS?
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In this paper, we present a fine-grained localization algorithm for wireless sensor networks using a mobile beacon node. The algorithm is based on distance measurement using RSSI. The beacon node is equipped with a GPS sender and RF (radio frequency) transmitter. Each stationary sensor node is equipped with a RF. The beacon node periodically broadcasts its location information, and stationary sensor nodes perceive their positions as beacon points. A sensor node’s location is computed by measuring the distance to the beacon point using RSSI. Our proposed localization scheme is evaluated using OPNET 8.1 and compared with Ssu’s and Yu’s localization schemes. The results show that our localization scheme outperforms the other two schemes in terms of energy efficiency (overhead) and accuracy.


On the Handling of Node Failures: Energy-Efficient Job Allocation Algorithm for Real-time Sensor Networks
Hamid Karimi, Mehdi Kargahi and Nasser Yazdani
Page: 413~434, Vol. 6, No.3, 2010
10.3745/JIPS.2010.6.3.413
Keywords: Failure Recovery, Job Allocation, Quality of Service, Real-Time Scheduling, Wireless Sensor Network
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Wireless sensor networks are usually characterized by dense deployment of energy constrained nodes. Due to the usage of a large number of sensor nodes in uncontrolled hostile or harsh environments, node failure is a common event in these systems. Another common reason for node failure is the exhaustion of their energy resources and node inactivation. Such failures can have adverse effects on the quality of the real-time services in Wireless Sensor Networks (WSNs). To avoid such degradations, it is necessary that the failures be recovered in a proper manner to sustain network operation. In this paper we present a dynamic Energy efficient Real-Time Job Allocation (ERTJA) algorithm for handling node failures in a cluster of sensor nodes with the consideration of communication energy and time overheads besides the nodes’ characteristics. ERTJA relies on the computation power of cluster members for handling a node failure. It also tries to minimize the energy consumption of the cluster by minimum activation of the sleeping nodes. The resulting system can then guarantee the Quality of Service (QoS) of the cluster application. Further, when the number of sleeping nodes is limited, the proposed algorithm uses the idle times of the active nodes to engage a graceful QoS degradation in the cluster. Simulation results show significant performance improvements of ERTJA in terms of the energy conservation and the probability of meeting deadlines compared with the other studied algorithms.


Mining Spatio-Temporal Patterns in Trajectory Data
Juyoung Kang and Hwan-Seung Yong
Page: 521~536, Vol. 6, No.4, 2010
10.3745/JIPS.2010.6.4.521
Keywords: Data Mining, Spatio-Temporal Data Mining, Trajectory Data, Frequent Spatio-Temporal Patterns
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Spatio-temporal patterns extracted from historical trajectories of moving objects reveal important knowledge about movement behavior for high quality LBS services. Existing approaches transform trajectories into sequences of location symbols and derive frequent subsequences by applying conventional sequential pattern mining algorithms. However, spatio-temporal correlations may be lost due to the inappropriate approximations of spatial and temporal properties. In this paper, we address the problem of mining spatio-temporal patterns from trajectory data. The inefficient description of temporal information decreases the mining efficiency and the interpretability of the patterns. We provide a formal statement of efficient representation of spatio-temporal movements and propose a new approach to discover spatio-temporal patterns in trajectory data. The proposed method first finds meaningful spatio-temporal regions and extracts frequent spatio-temporal patterns based on a prefix-projection approach from the sequences of these regions. We experimentally analyze that the proposed method improves mining performance and derives more intuitive patterns.


An Optimized Approach of Fault Distribution for Debugging in Parallel
Maneesha Srivasatav, Yogesh Singh and Durg Singh Chauhan
Page: 537~552, Vol. 6, No.4, 2010
10.3745/JIPS.2010.6.4.537
Keywords: Clustering, Debugging, Fault Localization, Optimization, Software Testing
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Software Debugging is the most time consuming and costly process in the software development process. Many techniques have been proposed to isolate different faults in a program thereby creating separate sets of failing program statements. Debugging in parallel is a technique which proposes distribution of a single faulty program segment into many fault focused program slices to be debugged simultaneously by multiple debuggers. In this paper we propose a new technique called Faulty Slice Distribution (FSD) to make parallel debugging more efficient by measuring the time and labor associated with a slice. Using this measure we then distribute these faulty slices evenly among debuggers. For this we propose an algorithm that estimates an optimized group of faulty slices using as a parameter the priority assigned to each slice as computed by value of their complexity. This helps in the efficient merging of two or more slices for distribution among debuggers so that debugging can be performed in parallel. To validate the effectiveness of this proposed technique we explain the process using example.


SVD-LDA: A Combined Model for Text Classification
Nguyen Cao Truong Hai, Kyung-Im Kim and Hyuk-Ro Park
Page: 5~10, Vol. 5, No.1, 2009
10.3745/JIPS.2009.5.1.005
Keywords: Latent Dirichlet Allocation, Singular Value Decomposition, Input Filtering, Text Classification, Data Preprocessing.
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Text data has always accounted for a major portion of the world¡¯s information. As the volume of information increases exponentially, the portion of text data also increases significantly. Text classification is therefore still an important area of research. LDA is an updated, probabilistic model which has been used in many applications in many other fields. As regards text data, LDA also has many applications, which has been applied various enhancements. However, it seems that no applications take care of the input for LDA. In this paper, we suggest a way to map the input space to a reduced space, which may avoid the unreliability, ambiguity and redundancy of individual terms as descriptors. The purpose of this paper is to show that LDA can be perfectly performed in a ¡°clean and clear¡± space. Experiments are conducted on 20 News Groups data sets. The results show that the proposed method can boost the classification results when the appropriate choice of rank of the reduced space is determined.


Bidding Strategically for Scheduling in Grid Systems
Babak-Naddaf and Jafar-Habibi
Page: 87~96, Vol. 5, No.2, 2009
10.3745/JIPS.2009.5.2.087
Keywords: Grid Computing, Grid Scheduling, Resource Allocation, Auction Model
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Grid computing is a new technology which involves efforts to create a huge source of processing power by connecting computational resources throughout the world. The key issue of such environments is their resource allocation and the appropriate job scheduling strategy. Several approaches to scheduling in these environments have been proposed to date. Market driven scheduling as a decentralized solution for such complicated environments has introduced new challenges. In this paper the bidding problem with regard to resources in the reverse auction resource allocation model has been investigated and the new bidding strategies have been proposed and investigated.


Topological Boundary Detection in Wireless Sensor Networks
Thanh Le Dinh
Page: 145~150, Vol. 5, No.3, 2009
10.3745/JIPS.2009.5.3.145
Keywords: Wireless sensor network, Hole, Boundary detection, 2-neighbor graph
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The awareness of boundaries in wireless sensor networks has many benefits. The identification of boundaries is especially challenging since typical wireless sensor networks consist of low-capability nodes that are unaware of their geographic location. In this paper, we propose a simple, efficient algorithm to detect nodes that are near the boundary of the sensor field as well as near the boundaries of holes. Our algorithm relies purely on the connectivity information of the underlying communication graph and does not require any information on the location of nodes. We introduce the 2-neighbor graph concept, and then make use of it to identify nodes near boundaries. The results of our experiment show that our algorithm carries out the task of topological boundary detection correctly and efficiently.


Utility-based Rate Allocation Scheme for Mobile Video Streaming over Femtocell Networks
Shan Guo Quan, Jian Xu and Young Yong Kim
Page: 151~158, Vol. 5, No.3, 2009
10.3745/JIPS.2009.5.3.151
Keywords: Utility, femtocell network, backhaul, cross-talk, video streaming
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This paper proposes a utility-based data rate allocation algorithm to provide high-quality mobile video streaming over femtocell networks. We first derive a utility function to calculate the optimal data rates for maximizing the aggregate utilities of all mobile users in the femtocell. The total sum of optimal data rates is limited by the link capacity of the backhaul connections. Furthermore, electromagnetic cross-talk poses a serious problem for the backhaul connections, and its influence passes on to mobile users, as well as causing data rate degradation in the femtocell networks. We also have studied a fixed margin iterative water-filling algorithm to achieve the target data rate of each backhaul connection as a counter-measure to the cross-talk problem. The results of our simulation show that the algorithm is capable of minimizing the transmission power of backhaul connections while guaranteeing a high overall quality of service for all users of the same binder. In particular, it can provide the target data rate required to maximize user satisfaction with the mobile video streaming service over the femtocell networks.


Spatial Query Processing Based on Minimum Bounding in Wireless Sensor Networks
Sun Ok Yang and SungSuk Kim
Page: 229~236, Vol. 5, No.4, 2009
10.3745/JIPS.2009.5.4.229
Keywords: Notification Message, Parent Selection Message, Spatial Query Process, Minimum Bounding Area
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Sensors are deployed to gather physical, environmental data in sensor networks. Depending on scenarios, it is often assumed that it is difficult for batteries to be recharged or exchanged in sensors. Thus, sensors should be able to process users¡¯ queries in an energy-efficient manner. This paper proposes a spatial query processing scheme- Minimum Bounding Area Based Scheme. This scheme has a purpose to decrease the number of outgoing messages during query processing. To do that, each sensor has to maintain some partial information locally about the locations of descendent nodes. In the initial setup phase, the routing path is established. Each child node delivers to its parent node the location information including itself and all of its descendent nodes. A parent node has to maintain several minimum bounding boxes per child node. This scheme can reduce unnecessary message propagations for query processing. Finally, the experimental results show the effectiveness of the proposed scheme.


Developing Protege Plug-in: OWL Ontology Visualization using Social Network
Minsoo Kim and Minkoo Kim
Page: 61~66, Vol. 4, No.2, 2008
10.3745/JIPS.2008.4.2.061
Keywords: OWL visualization, Protege, Protege plug-in
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In recent years, numerous studies have been attempted to exploit ontology in the area of ubiquitous computing. Especially, some kinds of ontologies written in OWL are proposed for major issues in ubiquitous computing such like context-awareness. OWL is recommended by W3C as a descriptive language for representing ontology with rich vocabularies. However, developers struggle to design ontology using OWL, because of the complex syntax of OWL. The research for OWL visualization aims to overcome this problem, but most of the existing approaches unfortunately do not provide efficient interface to visualize OWL ontology. Moreover, as the size of ontology grows bigger, each class and relation are difficult to represent on the editing window due to the small size limitation of screen. In this paper, we present OWL visualization scheme that supports class information in detail. This scheme is based on concept of social network, and we implement OWL visualization plug-in on Protégé that is the most famous ontology editor.


Membership Management based on a Hierarchical Ring for Large Grid Environments
Tae-Wan Gu, Seong-Jun Hong, Saangyong Uhmn and Kwang-Mo Lee
Page: 8~15, Vol. 3, No.1, 2007
None
Keywords: P2P, Membership Overlay, Membership Management, Hierarchical Ring
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Grid environments provide the mechanism to share heterogeneous resources among nodes. Because of the similarity between grid environments and P2P networks, the structures of P2P networks can be adapted to enhance scalability and efficiency in deployment and to search for services. In this paper, we present a membership management based on a hierarchical ring which constructs P2P-like Grid environments. The proposed approach uses only a limited number of connections, reducing communication cost. Also, it only keeps local information for membership, which leads to a further reduction in management cost. This paper analyzes the performance of the approach by simulation and compares it with other approaches.


Static Type Assignment for SSA Form in CTOC
Ki-Tae Kim and Weon-Hee Yoo
Page: 26~32, Vol. 3, No.1, 2007
None
Keywords: Bytecode, control flow graph, Static Single Assignment, Static Type Assignment
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Although the Java bytecode has numerous advantages, it also has certain shortcomings such as its slow execution speed and difficulty of analysis. In order to overcome such disadvantages, a bytecode analysis and optimization must be performed. The control flow of the bytecode should be analyzed; next, information is required regarding where the variables are defined and used to conduct a dataflow analysis and optimization. There may be cases where variables with an identical name contain different values at different locations during execution, according to the value assigned to a given variable in each location. Therefore, in order to statically determine the value and type, the variables must be separated according to allocation. In order to achieve this, variables can be expressed using a static single assignment form. After transformation into a static single assignment form, the type information of each node expressed by each variable and expression must be configured to perform a static analysis and optimization. Based on the basic type information, this paper proposes a method for finding the related equivalent nodes, setting nodes with strong connection components, and efficiently assigning each node type


Use of Mobile Devices in the Performance of Group Decision-Making under Contextual Pressure
Oh Byung Kwon, Tae Kyung Kim and Choong Rhyun Kim
Page: 64~72, Vol. 3, No.2, 2007
None
Keywords: Group Decision Making, Mobile Technology, Mobile Devices, Group Decision Support
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Mobile technology appears promising as a method to promote group performance in circumstances dependent on time, but not member proximity. However, the success of mobile technology in group decision-making situations has not yet been proven. This paper aims to see how mobile technology affects the performance of group decision-making tasks that should be resolved urgently and/or sources of idea are disconnected with on-line network. Laboratory experiment was used to investigate the effects of mobile factors on group decision-making. The results from the experiment supported the proposition that pressures of time and location play a significant role in the assessment of group decision performance measures. We found that the adoption of mobile technology to group decision-making procedures might be competitive when group decision-making tasks are urgent and sources of idea are disconnected with on-line network, even though mobile technology is not a panacea on which to depend when designing group decision-making.


A Universal Model for Policy-Based Access Control-enabled Ubiquitous Computing
Yixin Jing, Jinhyung Kim and Dongwon Jeong
Page: 28~33, Vol. 2, No.1, 2006
None
Keywords: Access control, Ubiquitous computing, Task computing, Context-awareness
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The initial research of Task Computing in the ubiquitous computing (UbiComp) environment revealed the need for access control of services. Context-awareness of service requests in ubiquitous computing necessitates a well-designed model to enable effective and adaptive invocation. However, nowadays little work is being undertaken on service access control under the UbiComp environment, which makes the exposed service suffer from the problem of ill-use. One of the research focuses is how to handle the access to the resources over the network. Policy-Based Access Control is an access control method. It adopts a security policy to evaluate requests for resources but has a lightweight combination of the resources. Motivated by the problem above, we propose a universal model and an algorithm to enhance service access control in UbiComp. We detail the architecture of the model and present the access control implementation.


Determination of Optimal Cell Capacity for Initial Cell Planning in Wireless Cellular Networks
Young Ha Hwang, Sung-Kee Noh and Sang-Ha Kim
Page: 88~94, Vol. 2, No.2, 2006
None
Keywords: QoS, optimal cell capacity, cell planning, wireless cellular networks
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In wireless cellular networks, previous researches on admission control policies and resource allocation algorithm considered the QoS (Quality of Service) in terms of CDP (Call Dropping Probability) and CBP (Call Blocking Probability). However, since the QoS was considered only within a predetermined cell capacity, the results indicated a serious overload problem of systems not guaranteeing both CDP and CBP constraints, especially in the hotspot cell. That is why a close interrelationship between CDP, CBP and cell capacity exists. Thus, it is indispensable to consider optimal cell capacity guaranteeing multiple QoS (CDP and CBP) at the time of initial cell planning for networks deployment. In this paper, we will suggest a distributed determination scheme of optimal cell capacity guaranteeing both CDP and CBP from a long-term perspective for initial cell planning. The cell-provisioning scheme is performed by using both the two-dimensional continuous-time Markov chain and an iterative method called the Gauss-Seidel method. Finally, numerical and simulation results will demonstrate that our scheme successfully determines an optimal cell capacity guaranteeing both CDP and CBP constraints for initial cell planning.


Selection of a Competent Wireless Access Point for High Wireless Bandwidth
Ji Yeon Park and Kitae Hwang
Page: 159~162, Vol. 2, No.3, 2006
None
Keywords: WLAN, AP, SNMP, Network Utilization
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Wireless LANs are becoming more widespread because of the rapid advance of wireless technologies and mobile computers. In this paper, we present the design and implementation of a system to help mobile users to select the most competent AP. By monitoring the network traffic of APs within the local LAN in real time, this system offers the mobile user the network utilizations, locations, and signal strengths of APs online. Based on the information, the user can select a competent AP with a high wireless bandwidth. Finally, we verified the accuracy of monitoring and calculating with regard to the utilizations of APs through real experiments.


A Light-weight and Dynamically Reconfigurable RMON Agent System
Jun-Hyung Lee, Zin-Won Park and Myung-Kyun Kim
Page: 183~188, Vol. 2, No.3, 2006
None
Keywords: Network management, RMON agent system, Dynamic reconfiguration.
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A RMON agent system, which locates on a subnet, collects the network traffic information for management by retrieving and analyzing all of the packets on the subnet. The RMON agent system can miss some packets due to the high packet analyzing overhead when the number of packets on the subnet is huge. In this paper, we have developed a light-weight RMON agent system that can handle a large amount of packets without packet loss. Our RMON agent system has also been designed such that its functionality can be added dynamically when needed. To demonstrate the dynamic reconfiguration capability of our RMON agent system, a simple port scanning attack detection module is added to the RMON agent system. We have also evaluated the performance of our RMON agent system on a large network that has a huge traffic. The test result has shown our RMON agent system can analyze the network packets without packet loss.


A Cluster-Based Energy-Efficient Routing Protocol without Location Information for Sensor Networks
Giljae Lee, Jonguk Kong, Minsun Lee and Okhwan Byeon
Page: 49~54, Vol. 1, No.1, 2005
None
Keywords: Wireless sensor networks, ubiquitous sensor networks, cluster-based routing protocol, energy-efficient routing
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With the recent advances in Micro Electro Mechanical System (MEMS) technology, low cost and low power consumption wireless micro sensor nodes have become available. However, energy-efficient routing is one of the most important key technologies in wireless sensor networks as sensor nodes are highly energy-constrained. Therefore, many researchers have proposed routing protocols for sensor networks, especially cluster-based routing protocols, which have many advantages such as reduced control messages, bandwidth re-usability, and improved power control. Some protocols use information on the locations of sensor nodes to construct clusters efficiently. However, it is rare that all sensor nodes know their positions. In this article, we propose another cluster-based routing protocol for sensor networks. This protocol does not use information concerning the locations of sensor nodes, but uses the remaining energy of sensor networks and the desirable number of cluster heads according to the circumstances of the sensor networks. From performance simulation, we found that the proposed protocol shows better performance than the low-energy adaptive clustering hierarchy (LEACH).


Performance Analysis of the Distributed Location Management Scheme in Large Mobile Networks
Dong Chun Lee, Hong-Jin Kim, Jong Chan Lee and Yi Bing Lin
Page: 55~61, Vol. 1, No.1, 2005
None
Keywords: Distributed Location Management, LMN, Performance Analysis, IMT-2000
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In this paper we propose a distributed location management scheme to reduce the bottleneck problem of HLR in Large Mobile Networks (LMN). Using analytical modeling and numerical simulation, we show that replicating location information is both appropriate and efficient for small mobile networks. Then, we extend the scheme in a hierarchical environment to reduce the overhead traffic and scale to LMN. In numerical results, we show the superiority of our scheme compared to the current IS-95 standard scheme in IMT-2000 networks.


Trusted Certificate Validation Scheme for Open LBS Application Based on XML Web Services
Kiyoung Moon, Namje Park, Kyoil Chung, Sungwon Sohn and Jaecheol Ryou
Page: 86~95, Vol. 1, No.1, 2005
None
Keywords: Location-based service, Open LBS security, XKMS, XML security, XML web services
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Location-based services or LBS refer to value-added service by processing information utilizing mobile user location. With the rapidly increasing wireless Internet subscribers and world LBS market, the various location based applications are introduced such as buddy finder, proximity and security services. As the killer application of the wireless Internet, the LBS have reconsidered technology about location determination technology, LBS middleware server for various application, and diverse contents processing technology. However, there are fears that this new wealth of personal location information will lead to new security risks, to the invasion of the privacy of people and organizations. This paper describes a novel security approach on open LBS service to validate certificate based on current LBS platform environment using XKMS (XML Key Management Specification) and SAML (Security Assertion Markup Language), XACML (extensible Access Control Markup Language) in XML security mechanism.


A Statistic Correlation Analysis Algorithm Between Land Surface Temperature and Vegetation Index
Hyung Moo Kim, Beob Kyun Kim and Kang Soo You
Page: 102~106, Vol. 1, No.1, 2005
None
Keywords: LST, NDVI, Correlation Analysis, Landsat ETM+
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As long as the effective contributions of satellite images in the continuous monitoring of the wide area and long range of time period, Landsat TM and Landsat ETM+ satellite images are surveyed. After quantization and classification of the deviations between TM and ETM+ images based on approved thresholds such as gains and biases or offsets, a correlation analysis method for the compared calibration is suggested in this paper. Four time points of raster data for 15 years of the highest group of land surface temperature and the lowest group of vegetation of the Kunsan city Chollabuk_do Korea located beneath the Yellow sea coast, are observed and analyzed their correlations for the change detection of urban land cover. This experiment based on proposed algorithm detected strong and proportional correlation relationship between the highest group of land surface temperature and the lowest group of vegetation index which exceeded R=(+)0.9478, so the proposed Correlation Analysis Model between the highest group of land surface temperature and the lowest group of vegetation index will be able to give proof an effective suitability to the land cover change detection and monitoring.


Privacy-Preservation Using Group Signature for Incentive Mechanisms in Mobile Crowd Sensing
Mihui Kim, Younghee Park and Pankaj Balasaheb Dighe
Page: 0~0, Vol. 0, No.0, 0
10.3745/JIPS.01.0045
Keywords: Incentive Method, Internet of Things (IoT) Model, Mobile Crowd Sensing (MCS), Privacy-Preserving, Using Group Signature
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Recently, concomitant with a surge in numbers of Internet of Things (IoT) devices with various sensors, mobile crowdsensing (MCS) has provided a new business model for IoT. For example, a person can share road traffic pictures taken with their smartphone via a cloud computing system and the MCS data can provide benefits to other consumers. In this service model, to encourage people to actively engage in sensing activities and to voluntarily share their sensing data, providing appropriate incentives is very important. However, the sensing data from personal devices can be sensitive to privacy, and thus the privacy issue can suppress data sharing. Therefore, the development of an appropriate privacy protection system is essential for successful MCS. In this study, we address this problem due to the conflicting objectives of privacy preservation and incentive payment. We propose a privacy-preserving mechanism that protects identity and location privacy of sensing users through an on-demand incentive payment and group signatures methods. Subsequently, we apply the proposed mechanism to one example of MCS—an intelligent parking system—and demonstrate the feasibility and efficiency of our mechanism through emulation.


Saturation Prediction for Crowdsensing Based Smart Parking System
Mihui Kim and Junhyeok Yun
Page: 0~0, Vol. 0, No.0, 0
10.3745/JIPS.03.0123
Keywords: Crowdsensing, Regression Model, Saturation Prediction, Smart Parking System
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Crowdsensing technologies can improve the efficiency of smart parking system in comparison with present sensor based smart parking system because of low install price and no restriction caused by sensor installation. A lot of sensing data is necessary to predict parking lot saturation in real-time. However in real world, it is hard to reach the required number of sensing data. In this paper, we model a saturation predication combining a time-based prediction model and a sensing data-based prediction model. The time-based model predicts saturation in aspects of parking lot location and time. The sensing data-based model predicts the degree of saturation of the parking lot with high accuracy based on the degree of saturation predicted from the first model, the saturation information in the sensing data, and the number of parking spaces in the sensing data. We perform prediction model learning with real sensing data gathered from a specific parking lot. We also evaluate the performance of the predictive model and show its efficiency and feasibility.