Earticle

Perhaps one of the most enabling features of Long Term Evolution (LTE) standard and its successors of the third Generation Partnership Project (3GPP) family is the addition of multiple transmission and reception antennas at one transmitting or receiving side or both for improving data throughput and network quality of service (QoS). This paper addresses the impact of employing MIMO in LTE base stations on coexistence requirements of deploying LTE cells in the band 2.6 GHz. Based on the developed coexistence model and simulation results, the adoption of MIMO can dramatically increase separation requirements, and therefore requires more careful site engineering and isolation measures to ensure peaceful coexisted systems.

Quality of Service (QoS) support in Mobile Ad hoc NETworks (MANETs) is a challenging task due to bandwidth and delay constraints, varying channel conditions, power limitations, node mobility and dynamic topology. This paper proposes an intelligent agent based on-demand (source initiated) delay aware QoS routing scheme in MANETs by using software agents that employ neuro-fuzzy logic supported by Q-learning. The proposed scheme operates in following steps. (1) Source node uses an agent that uses Dynamic Source Routing (DSR) to find various paths, their bandwidth and packet loss rate to reach a destination. (2) Parallely, a static neuro-fuzzy agent at the source node is used to optimize membership functions of fuzzy parameters according to user delay requirement of the fuzzy inference system (FIS); also, a fuzzy Q-learning static agent at the source node is employed to optimize the consequent part of if-then rules of FIS. (3) Fuzzy static agent at the source node decides whether node on a path satisfies delay requirement for an application according to the user by considering fuzzy parameters, bandwidth, packet loss rate and delay. (4) A path with QoS satisfied nodes will be selected by fuzzy QoS static agent and (5) mobile agents are used to maintain and repair the path. The scheme has been simulated in various network scenarios to test operation effectiveness and observed that proposed scheme performs better than the existing fuzzy based DSR routing methods.

MIMO-OFDM technology is a combination of multiple-input multiple-output (MIMO) wireless technology with orthogonal frequency division multiplexing (OFDM) that has been recognized as one of the most promising techniques to support high data rate and high performance in different channel conditions. Again, Alamouti’s space time block coding scheme for MIMO system has drawn much attention in 4G wireless technologies just because of its decoding simplicity. This paper presents the performance evaluation of Alamouti’s space-time block coded (ASTBC) MIMO-OFDM systems covering channel model, channel capacity, coding scheme and diversity gain. The mathematical model of capacity is derived for deterministic, random and correlated Rayleigh fading channels. The channel capacity per unit bandwidth is evaluated as a function of SNR. It is observed that the channel capacity increases with the number of antenna added to the system due to the more diversity gain of Alamouti’s code. Finally we investigate the correlated MIMO fading channel with different correlation matrix. At the higher SNR value, independent and identically distributed (i.i.d) channel capacity outperforms the correlated channel capacity. But At very low SNR value correlated channel capacity outperforms the i.i.d channel capacity.

Integer Discrete Cosine Transform (DCT) is among the techniques used to improve the performance of the H.264/AVC Standard. All the profiles in the H.264/AVC standard support 4×4 integer DCT and the high profiles of this standard support 8×8 integer DCT as well as the 4×4 integer DCT. Various hardware realizations have been proposed for forward and inverse integer DCT in the literature because they are among the computational intensive units in the H.264/AVC standard. In this paper we propose a unified pipelined architecture to realize of the entire forward and inverse DCTs as well as the Hadamard transforms in the H.264/AVC encoder. The synthesis results indicate that our architecture achieves higher clock rate and relatively lower gate count compared to the other published architectures that realize only a number of the transforms in the H.264/AVC encoder.

A novel algorithm for enhancing colour images is proposed. Since DWT is used in image compression and image compression reduces storage space, an algorithm is developed to work directly in DWT space. DWT separates image into high and low frequency components. High frequency components define contrast of an image. Enhancement is done in two steps namely local enhancement and global enhancement. Local enhancement is based on contrast measure and improves contrast. Global enhancement is needed for overall image quality improvement. HSV colour space is used of which V components will undergo enhancement, S component will undergo histogram equalization, and H remains the same for preserving the colour.

Today, getting an exact result from the huge raw data on the internet is not an easy task. Utilizing search engines is a good way, but still there is a need for a tool for personalization and having the adaptive capability according to the users’ interests. In this paper, we propose a new algorithm which uses the positive and negative feedback from the user for filtering the information. We will use ART1 classifier to generate the dynamic profile of the users and utilize the genetic algorithm to make the most suitable query to give a better result according to user’s criteria search.

In this paper, we propose a new painterly rendering technique for visualizing sound, based on correlation between sight and auditory. We analyze the sound feature to create an image of an intense atmosphere or a relaxing atmosphere which is dependent upon the input sound. We analyze the tempo and the frequency which are the basic characteristics of sound features. Sound features are used to determine strokes shape and color which are the crucial elements to complete the pictorial rendering. In addition, during the rendering process, we use a multiple layer system to overlap the layers. From this study, we can create a non-photorealistic image which is suitable for the mood of the music.

Dempster-Shafer Evidence Theory (DST) plays an important role in multi-sensor data fusion. In this paper, we propose weighting method based on event information sensor of aggregation for context awareness in wireless sensor networks. Context inference in wireless sensor networks has to use weighting method, and context inference based on sensor aggregation due to structure feature of wireless sensor network. We propose weight method based on event frequency of the sensor and the repetitive of event report sensor at the continuous time slot. The validity of reasoning through context inference in wireless sensor network can improve.

This paper presents the significant trends on the technological developments that hold relevance to the evolution information and telecommunication systems to allow faster communication and immediate actions concerning the different enterprises, organizations (both public and private), and businesses, hence, it will ensure thru high productivity. Some of these developments in digital revolution are cloud computing, mobilization and miniaturization of computing, and the evolution of wireless technologies. The convergence of these technological advancements will be integrated to information and telecommunication systems.

This paper introduces an optimized method to improve the accuracy of content based image retrieval systems (CBIR). CBIR systems classify the images according to low and higher features. This research improves both feature selection and classifier partition of a CBIR system. This paper normalizes and descretizes the feature vectors. A pair-wise classifier is employed to improve the accuracy of classification. Results show high accuracy of proposed classifier in an image database.

Stone skipping is an example of rigid-fluid coupling, which typically need much compu- tation. In this paper, we present a real-time method for visually plausible stone skipping simulation. Based on the Newtonian physics, an intuitive dynamics model is presented to simulate the linear and rotational motions of the stone. We also present a modied water surface model for the waves due to the bounces of the stone. For more realistic user ex- periences, the real-world stone is substituted by the Wii Remote connected to the host PC. User just throws the controller (strapped to his/her wrist), and our implementation shows the real-time simulation of the bouncing process of the virtual stone.

Recently, user authentication scheme in e-commerce and m-commerce has been becoming one of important security issues. In 2008, Bindu et al. proposed an improved remote user authentication scheme preserving user anonymity. In this paper, we analyze the security of Bindu et al.’s authentication scheme, and we demonstrate that their scheme is still insecure against the man-in-the-middle attack, the password guessing attack, and does not provide the user anonymity. Also, we propose an enhanced scheme to withstand the security weaknesses of Bindu et al.’s scheme, even if the secret information stored in the smart card is revealed. As a result of security analysis, we prove that the enhanced scheme is secure for the various attacks known by literatures, and provides the user anonymity, the session key agreement, and the mutual authentication between the user and the server.

In many elds of computer science and other engineering areas, we often need to balance multi-articulated structures. In this paper, we formalize this kind of balancing problem from a more physical and theoretical point of view. Through describing details of all the solution steps, we nally represent a set of algorithms to automatically balance multi-articulated ob- jects with tree topologies. Given the geometric congurations and masses at the leaf nodes of target multi-articulated objects, our algorithms achieve their balanced state through adjust- ing the mass of each node. To minimize the mass changes from the initial conguration, we use constraints of minimizing the norms of the mass dierences between the initial masses and the nal balanced masses. Actually, we use three dierent metrics, l1, l2 and l1 norms. These norms show slightly dierent behaviors in the minimization process, and users can select one of them according to their preferences and application purposes. We show all the details of algorithms, their time complexity analyses, and experimental results. keywords: balancing, tree-topology, minimization

Evolution of IT and computer has made e-books popular day by day. In this paper, we are interested in searching a word in e-books. However, it is impossible to search a word in digitized e-books if they consist of image files such as JPG and PDF. Our solution to this problem is to transform the image file based e-books into text files based e-books to enable searching a word in e-books. We use EPUB, a XML-based text file, which is defined by IDPF(International Digital Publishing Forum). That is, we convert the image file based e-books into EPUB format e-books, so that searching a word in e-books can be done without any problem. The converting job should deal with very big data usually and require a lot of computing power. If we do the conversion in an usual personal computer, it would take a lot of processing time or it might be impossible for us to complete it. We used MapReduce model with a cluster system which enables us to perform the conversion successfully and reduce the processing time. This paper presents our Hadoop-based e-book Conversion System which is a distributed computing framework to transform the image based e-books into EPUB format e-books. Our experimental system consists of up to 15 cluster nodes. This paper evaluates the performance of the experimental system which processes the conversion of up to 2TB(Terra Byte) image files into EPUB files with a 15 nodes cluster system. We analyzed the processing time when the number of nodes in the cluster system was varied. We also analyzed the improvement effect when the dpi of the image file was varied. The performance evaluation confirmed us that the Hadoop-based e-book Conversion System successfully processed the big data for e-book.

The recent trend in Internet traffic indicates the proliferation of multimedia streaming services over wireless networks. The multimedia streaming over wireless access in unison with great mobility brings challenges to sustain the mobile user video quality high. Up to the present, several studies have been performed in order to provide prioritization of stations or services over WLAN IEEE 802.11 for improving the quality of multimedia service. The IEEE 802.11e draft specification aims to extend the original IEEE 802.11 MAC protocol to support QoS. In this paper, we propose a dynamic video frame mapping scheme to improve the streaming quality of SVC(Scalable Video Coding) bitstream over IEEE 802.11e. Our scheme dynamically maps the video packets to appropriate access categories based on both the significance of the video data and the client buffer occupancy. Simulation results show that the proposed scheme improves the streaming quality by improving the receiving rate of important data to decode a SVC bitstream.

IEEE 802.11x CSMA/CA DCF MAC protocol supports that wireless nodes have statistically impartial probabilities of wireless channel access through fair competition. However, there is greedy node problem that maliciously increasing the transmission rates of mobile nodes altering their MAC operation disturbs fair transmissions between wireless nodes. This paper addresses how to find misbehavior greedy nodes. Previous works inspect the operation of DCF MAC protocol by the MAC frame to detect greedy nodes. In this paper, a greedy node detection algorithm using Kolmogorov-Smirnov test is proposed. The algorithm classifies wireless nodes with similar probability distributions of transmission intervals and draws a comparison between groups to find a group of greedy nodes. This paper evaluates the proposed algorithm through simulation and the simulation results shows that the algorithm can accurately detect greedy nodes in the congestion condition.

In this paper, we propose a new research trend analysis using important word clusters and its relationship. Journals published many papers every month or week and new scientific contributions were exponentially cumulated to their database. If can analysis important words and related relationships of the papers, a change of research trend in a domain is an interesting topic in text mining. We use a Term Frequency Inverse Document Frequency (TFIDF) to extract meaningful words, the similarity of words measures using WordNet information and a document comparison approach. To measure the similarity from word lists extracted by TFIDF and differences of important word clusters and weights, the approach analyzes the research trend and visualizes the differences of research interest in same research fields. To show usefulness of proposed approach, we illustrate simulations and various results.

Frequent graph mining is one of famous data mining fields that receive the most attention, and its importance has been raised continually as recent databases in the real world become more complicated. Weighted frequent graph mining is an approach for applying importance of objects in the real world to the graph mining, and numerous studies related to this have been conducted so far. However, all of the results obtained from this approach do not become actually useful information, and a significant portion of them may be meaningless ones even though they are weighted frequent sub-graph patterns. To overcome this problem, in this paper, we propose a novel method which can consider whether any sub-graph pattern has close correlation among elements in the pattern, called MSCG (Mining Strongly Correlated sub-Graph). In experimental results, we demonstrate that our MSCG outperforms a state-of-the-art method with respect to runtime and memory usage.

As aging trend and high standard of living in our contemporary society have been proliferating, it is critical for people to sustain healthy life during the whole lifetime. People become more and more interested in wellness health in which they want to in advance prevent illness as well as to care about health before some illness occurs. In this paper, we present a user-centered method rather than a medical expert that allows users to monitor some foods in real-time by providing quantity of calorie, protein, fat, sodium and so on that the foods contain. A user simply takes an image of a pizza. The algorithm extracts features of the pizza and matches to figure out nutrition of it using SIFT. In addition, a user-friendly interface can also be considered by displaying iconic metaphor rather than text-oriented information. The matching method can be also extended to a food rendering task if some rendering features for the matched food such as absorption, scattering and reflectance coefficients are provided. We have carried out some experiments with sets of various pizzas to validate the method as well.

Current, so many TV program contents are being provided to viewers on various channels through internet protocol televisions called IPTVs. Therefore the development of an effective searching scheme to enable viewers find and consume their preferred contents has been being important issue. This paper proposes a TV program contents searching scheme based on contents ontologies, viewer category, and usage examination. Our scheme search TV programs by computing the similarity between contents ontologies, filter the candidates with preferences of viewers, and return the list of top-N ranked program contents. As results of experiments, we know that our scheme has about 80% precision.

In this paper, transmission rate enhancement schemes for DRM (Digital Radio Mondiale), which is a worldwide standard for digital AM and FM broadcasting in the AM and FM bands below 170 MHz, are presented and compared. The data rate of the legacy DRM system is supported up-to 186kbps with 16QAM and MLC (Multi-level Coding) at 100 kHz bandwidth. However, this rate is not sufficient to achieve new high quality services such as loss-less audio broadcasting and mobile TV. In order to increase transmission rates of the DRM system, two different types of FEC (Forward Error Correction) codes are used with 64QAM in the proposed schemes. One is the MLC (Multi-level Coding) derived straightforwardly from the legacy DRM system while the other is the TC (Turbo Code) designed for an enhanced DRM. The proposed schemes can achieve transmission rates higher than 300kbps. Also, compared with MLC, TC has 1.6 dB – 5 dB SNR gain with code rate 1/2 in different channel environments for Band-II.

The emergence of new technologies is permitting malicious users to collect big data in unexpected way. In particular, privacy accidents related to invasion of privacy and breach of confidentiality happen for many reasons including mere curiosity, and deliberate. However, the hackers today are no longer amateur teenagers who try to break into some data base just to prove they can do it. We are to deal with more organized professional cyber criminals who have enough economic incentive to steal large scale data. Large scale data hijacking affects privacy information breach problem to a large number of people at once. Further it can pull back the big data and related industry to grow. To solve the privacy problem with big data, we considered it necessary to develop the architecture for privacy protection. In order to protect the stored data more efficiently, we need to design the system where we can link the protection algorithm closely with the data protection law and regulation. Our proposed architecture helps to prevent the data loss or hijacking especially in a large scale.

This paper proposes a method for capturing the drawing style of cartoon from the original comic book. Given a concept drawing image of the specific comic book, our approach recovers not only the color scheme, but also the color ratio. The main technical contribution of this paper is a shading model capture and a stylized cartoon rendering. For the shading model capture, we collect RGB statistics from the original drawing and sort the RGB data using its frequency and extract the shading model using dynamic threshold. We also propose the stylized cartoon rendering to represent 3d object using 2D texture according to the original cartoon style. We show on various cartoons that our approach can capture the drawing styles even in the case of rapid color changes and wide color schemes.

Identification of analysis classes is a critical design decision to be made early in the design phase in software development. Although incorrect identification of analysis classes can diminish the quality of a whole software design, it still heavily relies on the expertise and experience of the developer and has been ad-hoc. The majority existing works on identification of analysis classes focus on the rule-based approaches. However, the rule-based approaches which are used for analyzing sentence structures cannot be adopted for the language, which has very flexible word order like Korean. In this paper, we proposed a statistical learning method for identification of analysis classes from requirements sentences in Korean. The approach is evaluated using the precision and recall of the automatically extracted candidate classes from real requirements sentences in Korean. The result shows that we can promise numerically measurable enhancement of performance on solving the automatic identification problem of analysis classes using statistical methods, in the real use case specifications of a banking system.