The Journal of Information Processing Systems
(JIPS) is the official international journal of the Korea Information Processing Society.
As information processing systems are progressing at a rapid pace, the Korea Information Processing Society is committed to providing researchers and other professionals
with the academic information and resources they need to keep abreast with ongoing developments. The JIPS aims to be a premier source that enables researchers and professionals
all over the world to promote, share, and discuss all major research issues and developments in the field of information processing systems and other related fields.
ISSN: 1976-913X (Print), ISSN: 2092-805X (Online)
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Journal of Information Processing Systems, Vol. 14, No.5, 2018
In this paper, we consider the delay partial differential equation of three dimensions with constant
coefficients. We established the alternating direction difference scheme by the standard finite difference
method, gave the order of convergence of the format and the expression of the difference scheme truncation
In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in
order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small
and medium sized databases but they suffer from serious problems when it comes to using them with large
databases like those encountered in biometrics applications. To address this issue, we proposed in this work a
new training algorithm for PNNs to reduce the hidden layer’s size and avoid over-fitting at the same time.
The proposed training algorithm generates networks with a smaller hidden layer which contains only
representative examples in the training data set. Moreover, adding new classes or samples after training does
not require retraining, which is one of the main characteristics of this solution. Results presented in this work
show a great improvement both in the processing speed and generalization of the proposed classifier. This
improvement is mainly caused by reducing significantly the size of the hidden layer.
As cloud computing has become a widespread technology, malicious attackers can obtain the private
information of users that has leaked from the service provider in the outsourced databases. To resolve the
problem, it is necessary to encrypt the database prior to outsourcing it to the service provider. However, the
most existing data encryption schemes cannot process a query without decrypting the encrypted databases.
Moreover, because the amount of the data is large, it takes too much time to decrypt all the data. For this,
Programmable Order-Preserving Secure Index Scheme (POPIS) was proposed to hide the original data while
performing query processing without decryption. However, POPIS is weak to both order matching attacks
and data count attacks. To overcome the limitations, we propose a group order-preserving data encryption
scheme (GOPES) that can support efficient query processing over the encrypted data. Since GOPES can
preserve the order of each data group by generating the signatures of the encrypted data, it can provide a high
degree of data privacy protection. Finally, it is shown that GOPES is better than the existing POPIS, with
respect to both order matching attacks and data count attacks.
Vibrotactile feedback is widely used in designing non-visual interactions on mobile phones, such as message
notification, non-visual reading, and blind use. In this work, novel vibrotactile codes are presented to
implement a non-visual text reading system for mobile phones. The 26 letters of the English alphabet are
formed in an index table with four rows and seven columns, and each letter is mapped using the codes of
vibrations. Two kinds of vibrotactile codes are designed with the actuator’s on and off states and with specific
lengths (short and long) assigned to each state. To improve the efficiency of tactile perception and user
satisfaction, three user experiments are conducted. The first experiment explores the maximum number of
continuous vibrations and minimum vibration time of the actuator’s on and off states that the human can
perceive. The second experiment determines the minimum interval between continuous vibrations. The
vibrotactile reading system is designed and evaluated in the third experiment according to the results of the
two preceding experiments. Results show that the character reading accuracy reaches 91.7% and the character
reading speed is approximately 617.8 ms. Our method has better reading efficiency and is easier to learn than
the traditional Braille coding method.
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.
With the increasing of urban electricity demand, making the most use of the power cable carrying capacity has become an important task in power grid system. Contrary to the rated ampacity obtained under extremely conservative conditions, this paper presents the various steady value of cable ampacity by using the changing surrounding parameters under operation, which is based on cable ampacity calculation equation under the IEC-60287 standard. To some degree, the cable ampacity analysis of actual surroundings improves the transmission capacity of cables. This paper reveals the factors that influence cable ampacity such as insulating layer thickness, allowable long-term conductor temperature, the ambient temperature, soil thermal resistance coefficient, and so on, then gives the class of the influence of these parameters on the ampacity, which plays a great role in accurately calculating the real-time ampacity and improving the utilization rate of cable in the complex external environment condition. Furthermore, the transient thermal rating of the cable is analyzed in this paper, and temperature variation of the conductor under different overload conditions is discussed, which provides effective information for the operation and control of the system.
This paper presents a physical storage design method for image access structures using transformation
techniques of multidimensional file organizations in image information systems. Physical storage design is the
process of determining the access structures to provide optimal query processing performance for a given set
of queries. So far, there has been no such attempt in the image information system. We first show that the
number of pages to be accessed decreases as the shape of the given retrieval query region and that of the data
page region become similar in the transformed domain space. Using these properties, we propose a method
for finding an optimal image access structure by controlling the shapes of the page regions. For the
performance evaluation, we have performed many experiments with a multidimensional file organization
using transformation techniques. The results indicate that our proposed method is at least one to maximum
five times faster than the conventional method according to the query pattern within the scope of the
experiments. The result confirms that the proposed physical storage design method is useful in a practical way.
Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the
examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed
simultaneously. However, microarray data have very little sample data and high data dimensionality.
Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction
can eliminate redundancy of data; thus, features used in classification are features that only have a high
correlation with their class. There are two types of dimensional reduction, namely feature selection and
feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection.
The proposed approach can be used to categorize features that have the same characteristics in one cluster, so
that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm
such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected
and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the
simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate
Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is
therefore higher than the approach using Random Forest without clustering.
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.
Typical search engines may not be the most efficient means of returning images in accordance with user
requirements. With the help of semantic web technology, it is possible to search through images more
precisely in any required domain, because the images are annotated according to a custom-built ontology.
With appropriate annotations, a search can then, return images according to the context. This paper reports
on the design of a tourism ontology relevant to touristic images. In particular, the image features and the
meaning of the images are described using various properties, along with other types of information relevant
to tourist attractions using the OWL language. The methodology used is described, commencing with
building an image and tourism corpus, creating the ontology, and developing the search engine. The system
was tested through a case study involving the western region of Thailand. The user can search specifying the
specific class of image or they can use text-based searches. The results are ranked using weighted scores based
on kinds of properties. The precision and recall of the prototype system was measured to show its efficiency.
User satisfaction was also evaluated, was also performed and was found to be high.
Interval-valued neutrosophic hesitant fuzzy set (IVNHFS) is an extension of neutrosophic set (NS) and
hesitant fuzzy set (HFS), each element of which has truth membership hesitant function, indeterminacy
membership hesitant function and falsity membership hesitant function and the values of these functions lie
in several possible closed intervals in the real unit interval [0,1]. In contrast with NS and HFS, IVNHFS can be
more flexibly used to deal with uncertain, incomplete, indeterminate, inconsistent and hesitant information.
In this study, I propose the novel correlation coefficient of IVNHFSs and my paper discusses its properties.
Then, based on the novel correlation coefficient, I develop an approach to deal with multi-attribute decisionmaking
problems within the framework of IVNHFS. In the end, a practical example is used to show that the
approach is reasonable and effective in dealing with decision-making problems.
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.
Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the
music improvisation process and can solve different optimization problems. In order to further improve the
performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters
including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are
optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved
method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four
complex function optimization and pressure vessel optimization problems were simulated using the
optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and
exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to
find global search and evolutionary speed. Optimization effect simulation results are satisfactory.
Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many textmining
applications such as document summarization and clustering. In this paper, we propose to use two
novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns
that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set
to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share
very few similar patterns and stylistic features while non-keyphrase candidates often share many similar
patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence
graph that could not connect two words that are syntactically related and placed far from each other in a
sentence while the dependency graph can do so. In experiments, we have compared the performances with
different settings of the graphs (co-occurrence and dependency), and with the existing method results.
Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the
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.
To support and protect firefighters in the scene of disaster, this research suggests an Internet of Things (IoT)
system architecture that can be configurable and applicable to firefighting and rescue in various disaster
situations. The proposed approach provides increased adaptability and reusability of systems compared to
existing approaches. To validate the feasibility of the approach, a system of systems based on the architecture
was developed and successfully tested for a specific firefighting and rescue scenario in a given test
For the disadvantages of multi-scale geometric analysis methods such as loss of definition and complex
selection of rules in image fusion, an improved multi-focus image fusion method is proposed. First, the initial
fused image is quickly obtained based on the lifting stationary wavelet transform, and a simple normalized cut
is performed on the initial fused image to obtain different segmented regions. Then, the original image is
subjected to NSCT transformation and the absolute value of the high frequency component coefficient in
each segmented region is calculated. At last, the region with the largest absolute value is selected as the postfusion
region, and the fused multi-focus image is obtained by traversing each segment region. Numerical
experiments show that the proposed algorithm can not only simplify the selection of fusion rules, but also
overcome loss of definition and has validity.
Quorum-based algorithms are widely used for solving several problems in mobile ad hoc networks (MANET) and wireless sensor networks (WSN). Several quorum-based protocols are proposed for multi-hop ad hoc networks that each one has its pros and cons. Quorum-based protocol (QEC or QPS) is the first study in the asynchronous sleep scheduling protocols. At the time, most of the proposed protocols were non-adaptive ones. But nowadays, adaptive quorum-based protocols have gained increasing attention, because we need protocols which can change their quorum size adaptively with network conditions. In this paper, we first introduce the most popular quorum systems and explain quorum system properties and its performance criteria. Then, we present a comparative and comprehensive survey of the non-adaptive and adaptive quorum-based protocols which are subsequently discussed in depth. We also present the comparison of different quorum systems in terms of the expected quorum overlap size (EQOS) and active ratio. Finally, we summarize the pros and cons of current adaptive and non-adaptive quorum-based protocols.
The significant advances in information and communication technologies are changing the process of how information is accessed. The internet is a very important source of information and it influences the development of other media. Furthermore, the growth of digital content is a big problem for academic digital libraries, so that similar tools can be applied in this scope to provide users with access to the information. Given the importance of this, we have reviewed and analyzed several proposals that improve the processes of disseminating information in these university digital libraries and that promote access to information of interest. These proposals manage to adapt a user’s access to information according to his or her needs and preferences. As seen in the literature one of the techniques with the best results, is the application of recommender systems. These are tools whose objective is to evaluate and filter the vast amount of digital information that is accessible online in order to help users in their processes of accessing information. In particular, we are focused on the analysis of the fuzzy linguistic recommender systems (i.e., recommender systems that use fuzzy linguistic modeling tools to manage the user’s preferences and the uncertainty of the system in a qualitative way). Thus, in this work, we analyzed some proposals based on fuzzy linguistic recommender systems to help researchers, students, and teachers access resources of interest and thus, improve and complement the services provided by academic digital libraries.
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.
Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.
This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.
The recent advent of increasingly affordable and powerful 3D scanning devices capable of capturing high resolution range data about real-world objects and environments has fueled research into effective 3D surface reconstruction techniques for rendering the raw point cloud data produced by many of these devices into a form that would make it usable in a variety of application domains. This paper, therefore, provides an overview of the existing literature on surface reconstruction from 3D point clouds. It explains some of the basic surface reconstruction concepts, describes the various factors used to evaluate surface reconstruction methods, highlights some commonly encountered issues in dealing with the raw 3D point cloud data and delineates the tradeoffs between data resolution/accuracy and processing speed. It also categorizes the various techniques for this task and briefly analyzes their empirical evaluation results demarcating their advantages and disadvantages. The paper concludes with a cross-comparison of methods which have been evaluated on the same benchmark data sets along with a discussion of the overall trends reported in the literature. The objective is to provide an overview of the state of the art on surface reconstruction from point cloud data in order to facilitate and inspire further research in this area.
Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.
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.
The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other trainingbased approaches.
Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.
The confinement problem was first noted four decades ago. Since then, a huge amount of efforts have been spent on defining and mitigating the problem. The evolution of technologies from traditional operating systems to mobile and cloud computing brings about new security challenges. It is perhaps timely that we review the work that has been done. We discuss the foundational principles from classical works, as well as the efforts towards solving the confinement problem in three domains: operating systems, mobile computing, and cloud computing. While common issues exist across all three domains, unique challenges arise for each of them, which we discuss.
Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user"'"s interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning” capabilities of the estimator used. Therefore, resorting to strong estimators that converge with a probability of 1 is inefficient since they rely on the assumption that the distribution of the user"'"s preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking a user"'"s time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art technology.
The most important criterion for achieving the maximum performance in a wireless mesh network (WMN) is to limit the interference within the network. For this purpose, especially in a multi-radio network, the best option is to use non-overlapping channels among different radios within the same interference range. Previous works that have considered non-overlapping channels in IEEE 802.11a as the basis for performance optimization, have considered the link quality across all channels to be uniform. In this paper, we present a measurement-based study of link quality across all channels in an IEEE 802.11a-based indoor WMN test bed. Our results show that the generalized assumption of uniform performance across all channels does not hold good in practice for an indoor environment and signal quality depends on the geometry around the me routers.
This paper describes different aspects of a typical RFID implementation. Section 1 provides a brief overview of the concept of Automatic Identification and compares the use of different technologies while Section 2 describes the basic components of a typical RFID system. Section 3 and Section 4 deal with the detailed specifications of RFID transponders and RFID interrogators respectively. Section 5 highlights different RFID standards and protocols and Section 6 enumerates the wide variety of applications where RFID systems are known to have made a positive improvement. Section 7 deals with privacy issues concerning the use of RFIDs and Section 8 describes common RFID system vulnerabilities. Section 9 covers a variety of RFID security issues, followed by a detailed listing of countermeasures and precautions in Section 10.
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.
In earlier days, most of the data carried on communication networks was textual data requiring limited bandwidth. With the rise of multimedia and network technologies, the bandwidth requirements of data have increased considerably. If a network link at any time is not able to meet the minimum bandwidth requirement of data, data transmission at that path becomes difficult, which leads to network congestion. This causes delay in data transmission and might also lead to packet drops in the network. The retransmission of these lost packets would aggravate the situation and jam the network. In this paper, we aim at providing a solution to the problem of network congestion in mobile ad hoc networks [1, 2] by designing a protocol that performs routing intelligently and minimizes the delay in data transmission. Our Objective is to move the traffic away from the shortest path obtained by a suitable shortest path calculation algorithm to a less congested path so as to minimize the number of packet drops during data transmission and to avoid unnecessary delay. For this we have proposed a protocol named as Congestion Aware Selection Of Path With Efficient Routing (CASPER). Here, a router runs the shortest path algorithm after pruning those links that violate a given set of constraints. The proposed protocol has been compared with two link state protocols namely, OSPF [3, 4] and OLSR [5, 6, 7, 8].The results achieved show that our protocol performs better in terms of network throughput and transmission delay in case of bulky data transmission.
Vehicular networks are a promising application of mobile ad hoc networks. In this paper, we introduce an efficient broadcast technique, called CB-S (Cell Broadcast for Streets), for vehicular networks with occlusions such as skyscrapers. In this environment, the road network is fragmented into cells such that nodes in a cell can communicate with any node within a two cell distance. Each mobile node is equipped with a GPS (Global Positioning System) unit and a map of the cells. The cell map has information about the cells including their identifier and the coordinates of the upper-right and lower-left corner of each cell. CB-S has the following desirable property. Broadcast of a message is performed by rebroadcasting the message from every other cell in the terrain. This characteristic allows CB-S to achieve an efficient performance. Our simulation results indicate that messages always reach all nodes in the wireless network. This perfect coverage is achieved with minimal overhead. That is, CB-S uses a low number of nodes to disseminate the data packets as quickly as probabilistically possible. This efficiency gives it the advantage of low delay. To show these benefits, we give simulations results to compare CB-S with four other broadcast techniques. In practice, CB-S can be used for information dissemination, or to reduce the high cost of destination discovery in routing protocols. By also specify the radius of affected zone, CB-S is also more efficient when broadcast to a subset of the nodes is desirable.
Cryptographic hash functions reduce inputs of arbitrary or very large length to a short string of fixed length. All hash function designs start from a compression function with fixed length inputs. The compression function itself is designed from scratch, or derived from a block cipher or a permutation. The most common procedure to extend the domain of a compression function in order to obtain a hash function is a simple linear iteration; however, some variants use multiple iterations or a tree structure that allows for parallelism. This paper presents a survey of 17 extenders in the literature. It considers the natural question whether these preserve the security properties of the compression function, and more in particular collision resistance, second preimage resistance, preimage resistance and the pseudo-random oracle property.
This paper proposes a novel reversible data hiding scheme based on a Vector Quantization (VQ) codebook. The proposed scheme uses the principle component analysis (PCA) algorithm to sort the codebook and to find two similar codewords of an image block. According to the secret to be embedded and the difference between those two similar codewords, the original image block is transformed into a difference number table. Finally, this table is compressed by entropy coding and sent to the receiver. The experimental results demonstrate that the proposed scheme can achieve greater hiding capacity, about five bits per index, with an acceptable bit rate. At the receiver end, after the compressed code has been decoded, the image can be recovered to a VQ compressed image.
The interconnection of mobile devices in urban environments can open up a lot of vistas for collaboration and content-based services. This will require setting up of a network in an urban environment which not only provides the necessary services to the user but also ensures that the network is secure and energy efficient. In this paper, we propose a secure, energy efficient dynamic routing protocol for heterogeneous wireless sensor networks in urban environments. A decision is made by every node based on various parameters like longevity, distance, battery power which measure the node and link quality to decide the next hop in the route. This ensures that the total load is distributed evenly while conserving the energy of battery-constrained nodes. The protocol also maintains a trusted population for each node through Dynamic Trust Factor (DTF) which ensures secure communication in the environment by gradually isolating the malicious nodes. The results obtained show that the proposed protocol when compared with another energy efficient protocol (MMBCR) and a widely accepted protocol (DSR) gives far better results in terms of energy efficiency. Similarly, it also outdoes a secure protocol (QDV) when it comes to detecting malicious nodes in the network.
The trend of Next Generation Networks’ (NGN) evolution is towards providing multiple and multimedia services to users through ubiquitous networks. The aim of IP Multimedia Subsystem (IMS) is to integrate mobile communication networks and computer networks. The IMS plays an important role in NGN services, which can be achieved by heterogeneous networks and different access technologies. IMS can be used to manage all service related issues such as Quality of Service (QoS), Charging, Access Control, User and Services Management. Nowadays, internet technology is changing with each passing day. New technologies yield new impact to IMS. In this paper, we perform a survey of IMS and discuss the different impacts of new technologies on IMS such as P2P, SCIM, Web Service and its security issues.
Due to the convergence of voice, data, and video, today’s telecom operators are facing the complexity of service and network management to offer differentiated value-added services that meet customer expectations. Without the operations support of well-developed Business Support System/Operations Support System (BSS/OSS), it is difficult to timely and effectively provide competitive services upon customer request. In this paper, a suite of NGOSS-based Telecom OSS (TOSS) is developed for the support of fulfillment and assurance operations of telecom services and IT services. Four OSS groups, TOSS-P (intelligent service provisioning), TOSS-N (integrated large-scale network management), TOSS-T (trouble handling and resolution), and TOSS-Q (end-to-end service quality management), are organized and integrated following the standard telecom operation processes (i.e., eTOM). We use IPTV and IP-VPN operation scenarios to show how these OSS groups co-work to support daily business operations with the benefits of cost reduction and revenue acceleration.
By providing ubiquitous Internet connectivity, wireless networks offer more convenient ways for users to surf the Internet. However, wireless networks encounter more technological challenges than wired networks, such as bandwidth, security problems, and handoff latency. Thus, this paper proposes new technologies to solve these problems. First, a Security Access Gateway (SAG) is proposed to solve the security issue. Originally, mobile terminals were unable to process high security calculations because of their low calculating power. SAG not only offers high calculating power to encrypt the encryption demand of SAG¡¯s domain, but also helps mobile terminals to establish a multiple safety tunnel to maintain a secure domain. Second, Robust Header Compression (RoHC) technology is adopted to increase the utilization of bandwidth. Instead of Access Point (AP), Access Gateway (AG) is used to deal with the packet header compression and de-compression from the wireless end. AG¡¯s high calculating power is able to reduce the load on AP. In the original architecture, AP has to deal with a large number of demands by header compression/de-compression from mobile terminals. Eventually, wireless networks must offer users ¡°Mobility¡± and ¡°Roaming¡±. For wireless networks to achieve ¡°Mobility¡± and ¡°Roaming,¡± we can use Mobile IPv6 (MIPv6) technology. Nevertheless, such technology might cause latency. Furthermore, how the security tunnel and header compression established before the handoff can be used by mobile terminals handoff will be another great challenge. Thus, this paper proposes to solve the problem by using Early Binding Updates (EBU) and Security Access Gateway (SAG) to offer a complete mechanism with low latency, low handoff mechanism calculation, and high security.
Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.
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