The specification of a Multi-Agent System (MAS) involves the identification of a large number of entities and their relationships. This is a non-trivial task that requires managing different views of the system. Many problems concerning this issue originate in the presence of contradictory goals and tasks, inconsistencies, and unexpected behaviours. Such troublesome configurations should be detected and prevented during the development process in order to study alternative ways to cope with them. In this paper, we present methods and tools that support the management of contradictions during the analysis and design of MAS. Contradiction management in MAS has to consider both individual (i.e. agent) and social (i.e. organization) aspects, and their dynamics. Such issues have already been considered in social sciences, and more concretely in the Activity Theory, a social framework for the study of interactions in activity systems. Our approach applies knowledge from Activity Theory in MAS, especially its base of contradiction patterns. That requires a formalization of this social theory in order to be applicable in a software engineering context and its adaptation to agent-oriented methodologies. Then, it will be possible to check the occurrence of contradiction patterns in a MAS specification and provide solutions to those situations. This technique has been validated by implementing an assistant for the INGENIAS Development Kit and has been tested with several case studies. This paper shows part of one of these experiments for a web application.

We propose, in this article, the Hierarchical Behavior-Knowledge Space as an extension of Behavior-Knowledge Space. Hierarchical BKS utilizes ranked level individual classifiers, and automatically expands its behavioral knowledge in order to satisfy given reliability requirement. From the statistical view point, its decisions are as optimal as those of original BKS, and the reliability threshold is a lower bound of estimated reliability. Several comparisons with original BKS and unanimous voting are shown with some experiments.

In statistical pattern recognition, it is important to estimate the distribution of patterns precisely to achieve high recognition accuracy. In general, precise estimation of the parameters of the distribution requires a great number of sample patterns, especially when the feature vector obtained from the pattern is high-dimensional. For some pattern recognition problems, such as face recognition or character recognition, very high-dimensional feature vectors are necessary and there are always not enough sample patterns for estimating the parameters. In this paper, we focus on estimating the distribution of high-dimensional feature vectors with small number of sample patterns. First, we define a function, called simplified quadratic discriminant function (SQDF). SQDF can be estimated with small number of sample patterns and approximates the quadratic discriminant function (QDF). SQDF has fewer parameters and requires less computational time than QDF. The effectiveness of SQDF is confirmed by three types of experiments. Next, as an application of SQDF, we propose an algorithm for estimating the parameters of the normal mixture. The proposed algorithm is applied to face recognition and character recognition problems which require high-dimensional feature vectors.

We propose a new genetic fuzzy discretization method with feature selection for the pattern classification problems. Traditional discretization methods categorize a continuous attribute into a number of bins. Because they are made on crisp discretization, there exists considerable information loss. Fuzzy discretization allows overlapping intervals and reflects linguistic classification. However, the number of intervals, the boundaries of intervals, and the degrees of overlapping are intractable to get optimized and a discretization process increases the total amount of data being transformed. We use a genetic algorithm with feature selection not only to optimize these parameters but also to reduce the amount of transformed data by filtering the unconcerned attributes. Experimental results showed considerable improvement on the classification accuracy over a crisp discretization and a typical fuzzy discretization with feature selection.

An age-group classification method based on a fusion of different classifiers with different two-dimensional feature extraction algorithms is proposed. Theoretically, an integration of multiple classifiers can provide better performance compared to a single classifier. In this paper, we extract effective features from one sample image using different dimensional reduction methods, construct multiple classifiers in each subspace, and combine them to reduce age-group classification errors. As for the dimensional reduction methods, two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) are used. These algorithms are antisymmetric in the treatment of the rows and the columns of the images. We prepared the row-based and column-based algorithms to make two different classifiers with different error tendencies. By combining these classifiers with different errors, the performance can be improved. Experimental results show that our fusion-based age-group classification method achieves better performance than existing two-dimensional algorithms alone.

MIMO systems using a space division multiplexing (SDM) technique in which each transmit antenna sends an independent signal substream have been studied as one of the successful applications to increase data rates in wireless communications. The throughput of a MIMO channel can be maximized by using an eigenbeam-SDM (E-SDM) technique, and this paper investigates the practical performance of 22 and 44 MIMO E-SDM based on indoor measurements. The channel capacity and bit error rate obtained in various uniform linear array configurations are evaluated and are compared with the corresponding values for conventional SDM. Analysis results show that the bit error rate performance of E-SDM is better than that of SDM and that E-SDM gives better performance in line-of-sight (LOS) conditions than in non-LOS ones. They also show that the performance of E-SDM in LOS conditions depends very much on the array configuration.

The realization of k-nearest-matches search capability in fully-parallel mixed digital-analog associative memories by a sequential autonomous search mode is reported. The proposed concept and circuit implementation can be applied with all types of distance measures such as Hamming, Manhattan or Euclidean distance search, and the k value can be freely selected during operation. A test chip for concept verification has been designed in 0.35 µm CMOS technology with two-poly, three-metal layers, realizes k-nearest-matches Euclidean distance search and consumes 5.12 mm2 of the chip area for 64 reference patterns each with 16 units of 5-bit.

This letter presents a simple scheme to transform colors to some representative classes for color information reduction. Then, the weighted distributions of color index histogram (CIH) and local binary pattern (LBP) are applied to measure the similarity of adjacent texture regions during the segmentation process. In addition, for improving the segmentation accuracy, an efficient boundary checking algorithm is proposed. The proposed method not only saves execution time but also segments the distinct texture regions correctly.

This paper proposes a two-dimensional self-matching receiver for Free Space Optics (FSO) communication system using chaotic spatial synchronization. This system is able to obtain the information of two-dimensional code from received pattern. This paper considers that proposed system is applied to two applications. The first application is image transmission. This paper shows that applying proposed system to image transmission enables to restore the desired image, which doesn't require strict alignment of receiver, and evaluates transmission optical power. The second application is Code Division Multiplexing (CDM). This paper shows that applying proposed system to CDM system enables to demodulate desired digital signals regardless of the uncertainty of received position. Moreover, the required transmission optical power and bit error rate performance are obtained by computer simulation.

In this paper, we propose a new region extraction method using chroma key with a two-tone checker pattern background. The method solves the problem in conventional chroma key techniques that foreground objects become transparent if their colors are the same as the background color. The method utilizes the adjacency condition between two-tone regions of the background and the geometrical information of the background grid line. The procedure of the proposed method consists of four steps: 1) background color extraction, 2) background grid line extraction, 3) foreground extraction, and 4) image composition. As to background color extraction, a color space approach is used. As to background grid line extraction, it is difficult to extract background grid line by a color space approach because the color of this region may be a composite of two background colors and different from them. Therefore, the background grid line is extracted from adjacency conditions between two background colors. As to foreground extraction, the boundary between the foreground and the background is detected to recheck the foreground region whose color is same as the background, and the background region whose color is same as the foreground. To detect regions whose colors are same as the background, the adjacency conditions with the background grid line are utilized. As to image composition, the process that smoothes the color of the foreground's boundary against the new background is carried out to create natural images. Experimental results show that the foreground objects can be segmented exactly from the background regardless of the colors of the foreground objects.

In vector analysis, it is important to classify three flow primitives as translation, rotation and divergence. These three primitives can be detected utilizing line integral and surface integral according to the knowledge of vector analysis. In this paper, we introduce a method for extracting these three primitives utilizing edges in an image based on vector analysis, namely edge field analysis. The edge has the information of inclination. However, the edge has no information of the direction unlike vector. Hence, line integral and surface integral can not be directly applied to detect these three primitives utilizing edges. We firstly formulate the problem and describe the algorithm for detecting the three primitives in vector analysis. We then propose an algorithm for estimating three primitives regarding edge image as pseudo-vector field. For illustration, we apply edge field analysis to quasi-motion extraction and feature extraction. We also show the experimental results in terms of estimating the center of the flowers, the cell body of neuron, the eye of the storm, the center of the explosion and so on.

This paper describes a flexible strategy to generate candidate answers for factoid questions in Question Answering (QA) systems. Most QA systems have predefined the conceptual categories for candidate answers. But if the conceptual category of answers to any question is not prepared in the QA system, it is hard to extract correct answers to that question. Therefore, we propose an extraction method for IS-A relation patterns which describe relations between the nominal target concepts of question and candidate answers. The extracted IS-A relation patterns can be used for questions with an unexpected target concept.

This letter considers an iterative decoding algorithm for non-binary linear block codes in which erasure and error decoding is performed for input words given by the sums of a hard-decision received sequence and given test patterns. We have proposed a new selection method of test patterns for the iterative decoding algorithm. Simulation results have shown that the decoding algorithm with test patterns by the proposed selection method provides better error performance than a conventional iterative decoding algorithm with the same number of the error and erasure decoding iterations over an additive white Gaussian noise channel using binary phase-shift keying modulation.

A network based on the Inverse Function Delayed (ID) model which can recall a temporal sequence of patterns, is proposed. The classical problem that the network is forced to make long distance jumps due to strong attractors that have to be isolated from each other, is solved by the introduction of the ID neuron. The ID neuron has negative resistance in its dynamics which makes a gradual change from one attractor to another possible. It is then shown that a network structure consisting of paired conventional and ID neurons, perfectly can recall a sequence.

A general method for generating multiple two-dimensional frequency-hopping pilot signals with limited mutual interference, for propagation channel estimation in time and frequency with equidistant sampling, is presented. Each pilot signal uses a different generic frequency-hopping pilot pattern that is repeated in frequency domain, with repetition period equal to the desired sampling interval in frequency domain. Some interesting special cases of the general construction are considered as well. The practical applicability and usefulness of the proposed solution are demonstrated by the numerical evaluation of a set of frequency-hopping pilot patterns in a typical multi-cell scenario of the future evolved third generation cellular systems.

To clarify the transmission characteristics and near magnetic field on the angle pattern for the parallel transmission lines, the authors investigate how influence the angled pattern on the transmission lines by experiment and calculation. The angled patterns on the transmission lines are straight, right angle and curve. It shows that the suppression of EMI radiation at the angled pattern on the parallel transmission lines of the magnetic head is essential. In addition, it is suggested that angle pattern might be one of cause for the signal distortion and specific EMI radiation at high frequency.

Point pattern matching (PPM) arises in areas such as pattern recognition, digital video processing and computer vision. In this study, a novel Genetic Algorithm (GA) based method for matching affine-related point sets is described. Most common techniques for solving the PPM problem, consist in determining the correspondence between points localized spatially within two sets and then find the proper transformation parameters, using a set of equations. In this paper, we use this fact that the correspondence and transformation matrices are two unitary polar factors of Grammian matrices. We estimate one of these factors by the GA's population and then evaluate this estimation by computing an error function using another factor. This approach is an easily implemented one and because of using the GA in it, its computational complexity is lower than other known methods. Simulation results on synthetic and real point patterns with varying amount of noise, confirm that the algorithm is very effective.

Activity patterns of metabolic subnetworks, each of which can be regarded as a biological function module, were focused on in order to clarify biological meanings of observed deviation patterns of gene expressions induced by various chemical stimuli. We tried to infer association structures of genes by applying the multivariate statistical method called graphical Gaussian modeling to the gene expression data in a subnetwork-wise manner. It can be expected that the obtained graphical models will provide reasonable relationships between gene expressions and macroscopic biological functions. In this study, the gene expression patterns in nematodes under various conditions (stresses by chemicals such as heavy metals and endocrine disrupters) were observed using DNA microarrays. The graphical models for metabolic subnetworks were obtained from these expression data. The obtained models (independence graph) represent gene association structures of cooperativities of genes. We compared each independence graph with a corresponding metabolic subnetwork. Then we obtained a pattern that is a set of characteristic values for these graphs, and found that the pattern of heavy metals differs considerably from that of endocrine disrupters. This implies that a set of characteristic values of the graphs can representative a macroscopic biological meaning.

An improved algorithm for fast motion estimation based on the block matching algorithm (BMA) is presented for use in a block-based video coding system. To achieve enhanced motion estimation performance, we propose an adaptive search pattern length for each iteration for the current macro block (MB). In addition, search points that must be checked are determined by means of directional information from the error surface, thus reducing intermediate searches. The proposed algorithm is tested with several sequences and excellent performance is verified.

Refactoring is one of the promising techniques for improving program design by means of program transformation with preserving behavior, and is widely applied in practice. However, it is difficult for engineers to identify how and where to refactor programs, because proper knowledge and skills of a high order are required of them. In this paper, we propose the technique to instruct how and where to refactor a program by using a sequence of its modifications. We consider that the histories of program modifications reflect developers' intentions, and focusing on them allows us to provide suitable refactoring guides. Our technique can be automated by storing the correspondence of modification patterns to suitable refactoring operations. By implementing an automated supporting tool, we show its feasibility. The tool is implemented as a plug-in for Eclipse IDE. It selects refactoring operations by matching between a sequence of program modifications and modification patterns.