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We consider the problem of recognizing face or object when only single training image per class is available, which is typically encountered in law enforcement, passport or identification card verification, etc. In such cases, many discriminant subspace methods such as Linear Discriminant Analysis (LDA) fail because of the non-existence of intra-class variation. In this paper, we propose a novel framework called 2-Dimensional Kernel PCA (2D-KPCA) for face or object recognition from a single image. In contrast to conventional KPCA, 2D-KPCA is based on 2D image matrices and hence can effectively utilize the intrinsic spatial structure information of the images. On the other hand, in contrast to 2D-PCA, 2D-KPCA is capable of capturing part of the higher-order statistics information. Moreover, this paper reveals that the current 2D-PCA algorithm and its many variants consider only the row information or column information, which has not fully exploited the information contained in the image matrices. So, besides proposing the unilateral 2D-KPCA, this paper also proposes the bilateral 2D-KPCA which could exploit more information concealed in the image matrices Furthermore, some approximation techniques are developed for improving the computational efficiency. Experimental results on the FERET face database and the COIL-20 object database show that: 1) the performance of KPCA is not necessarily better than that of PCA; 2) 2D-KPCA almost always outperforms 2D-PCA significantly; 3) the kernel methods are more appropriate on 2D pattern than on 1D patterns. [ABSTRACT FROM AUTHOR]

For high-dimensional classification tasks such as face recognition, the number of samples is smaller than the dimensionality of the samples. In such cases, a problem encountered in Linear Discriminant Analysis-based (LDA) methods for dimension reduction is what is known as the Small Sample Size (SSS) problem. Recently, a number of approaches that attempt to solve the SSS problem have been proposed in the literature. In this paper, a different way of solving the SSS problem compared to these is proposed. It is one that employs a dissimilarity representation method where an object is represented based on the dissimilarity measures among representatives extracted from training samples instead of from the feature vector itself. Thus, by appropriately selecting representatives and by defining the dissimilarity measure, it is possible to reduce the dimensionality and achieve a better classification performance in terms of both speed and accuracy. Apart from utilizing the dissimilarity representation, in this paper simultaneously employing a fusion technique is also proposed in order to increase the classification accuracy. The rationale for this is explained in the paper. The proposed scheme is completely different from the conventional ones in terms of the computation of the transformation matrix, as well as in controlling the number of dimensions. The present experimental results, which to the best of the authors' knowledge, are the first such reported results, demonstrate that the proposed mechanism achieves nearly identical efficiency results in terms of the classification accuracy compared with the conventional LDA-extension approaches for well-known face databases involving AT&T and Yale databases. [ABSTRACT FROM AUTHOR]

In this paper, we present a scheme that segments triangular meshes into several meaningful patches using multi-scale normal variation. In differential geometry, there is a traditional scheme that segments smooth surfaces into several patches such as elliptic, hyperbolic, or parabolic regions, with several curves such as ridge, valley, and parabolic curve between these regions, by means of the principal curvatures of the surface. We present a similar segmentation scheme for triangular meshes. For this purpose, we develop a simple and robust scheme that approximates the principal curvatures on triangular meshes by multi-scale normal variation scheme. Using these approximated principal curvatures and modifying the classical segmentation scheme for triangular meshes, we design a scheme that segments triangular meshes into several meaningful regions. This segmentation scheme is implemented by evaluating a feature weight at each vertex, which quantifies the likelihood that each vertex belongs to one of the regions. We test our scheme on several face models and demonstrate its capability by segmenting them into several meaningful regions. [ABSTRACT FROM AUTHOR]

Semiconductor Technology and multimedia communication has been developed to interest in high quality video and multi channels audio. Especially, it is trend to important technology so that DVD market grows rapidly. Mpeg audio standard is available support for any bit rate. In this paper, the design of Field Programmable Gate Array(FPGA) is presented mpeg audio synthesis filter by Distributed Arithmetic Look Up Table(DALUT) method. In order to design for high speed filter that operated from 10MHz to 30MHz and DALUT is used instead of multiplier. In this paper, the output is designed by VHDL and is verification using C language. VHDL simulation used Model-sim SE 5.7f and synthesis is used Synplify Pro 7.2Ver. Target library is used XC4010E, XC4020EX, XC4052XL and P&R tool used XACT M1.4. [ABSTRACT FROM AUTHOR]

There are restrictions of complete rationality and information symmetry, which is difficult to work in reality, in the traditional game theory. However, the evolutionary game theory, based on bounded rationality, can compensate these defects. In this paper, we set up an asymmetric model of the evolutionary game between a supplier and a retailer with asymmetric information. The evolutionary stable strategies and their premises were obtained with the replicator dynamics mechanism. The analysis in this paper has significance of explanation and direction for the supply chain management. [ABSTRACT FROM AUTHOR]

The variance minimization dual adaptive control approach with unknown parameters for stochastic system is studied in this paper. First,the problem is proposed for solving the optimal dual control law. Furthermore, the differential equation is transferred into state space model, the suboptimal dual control law is obtained through DUL algorithm. Finally, the example is given to verify the method developed in this paper. It is shown that the method is effective and practical. Keywords: dual control; adaptive control; stochastic systems. [ABSTRACT FROM AUTHOR]

The paper presents a new framework of oligopolistic market simulation based on coevolutionary computation. The coevolutionary compu-tation architecture can be regarded as a special model of the agent-based computational economics (ACE), which is a computational study of economies modeled as dynamic systems of interacting agents. The supply function equilibrium (SFE) model of an oligopolistic market is used in simulation. The piece-wise affine and continuous supply functions which have a large number of pieces are used to numerically estimate the equilibrium supply functions of any shapes. An example based on the cost data from the real-world electricity industry is used to validate the approach presented in this paper. Simulation results show that the coevolutionary approach robustly converges to SFE in different cases. The approach is robust and flexible and has the potential to be used to solve the complicated equilibrium problems in real-world oligopolistic markets. [ABSTRACT FROM AUTHOR]

This paper proposes a new blind watermarking scheme in which a watermark is embedded into the discrete wavelet transform (DWT) domain. The method uses the HVS model, and radial basis function neural networks (RBF). RBF will be implemented while embedding and extracting watermark. The human visual system (HVS) model is used to determine the watermark insertion strength. The inserted watermark is a random sequence. The secret key determines the beginning position of the image where the watermark is embedded. This process prevents possible pirates from removing the watermark easily. Experimental results show that the proposed method has good imperceptibility and high robustness to common image processing attacks. [ABSTRACT FROM AUTHOR]

According to the basic emotions theory, the paper presents personality,mood, and emotion space. The mapping relationship among personality , mood and emotion is built. The equations for updating the affective and mood states are induced and a generic computing model for personality ,mood and emotion simulation for virtual human is constructed. The simulation results demonstrate that the affective model can better simulate the dynamic process of emotion and mood change under various environment stimulus. The model provides a valid method to the emotional robot modelling and affective decision system. [ABSTRACT FROM AUTHOR]

A new robust controller based on linear-paremeter-neural-networks is designed for a class of nonlinear unkonwn chaotic systems which could be turned to "standard block control type" by using backstepping method. It was proved by constructing Lyapunov function step by step that all signals of the system are bounded and exponentially converge to the neighborhood of the origin globally and the weights of neural network converge to the optimal weights eventually. The assumption for unknown control function is reduced which stand for the innovation of our method compared with the traditional method. Also the unknown control function needn't to be positive or negative strictly in our paper. This assumption in the other papers is so strict that it couldn't be satisfied by many practical systems. So our method can be applied to a more extensive nonlinear systems. At last, take the unknown Duffing chaotic system for example, simulation study is given to demonstrate that the proposed method is effective. [ABSTRACT FROM AUTHOR]

The paper proposes the use of the multilayer perceptron model to the problem of detecting ham and spam e-mail patterns. It also proposes an intensive use of data pre-processing and feature selection methods to simplify the task of the multilayer perceptron in classifying ham and spam e-mails. The multilayer perceptron is trained and assessed on patterns extracted from the SpamAssassin Public Corpus. It is required to classify novel types of ham and spam patterns. The results are presented and evaluated in the paper. [ABSTRACT FROM AUTHOR]

In singular models, the Bayes estimation, commonly, has the advantage of the generalization performance over the maximum likelihood estimation, however, its accurate approximation using Markov chain Monte Carlo methods requires huge computational costs. The variational Bayes (VB) approach, a tractable alternative, has recently shown good performance in the automatic relevance determination model (ARD), a kind of hierarchical Bayesian learning, in brain current estimation from magnetoencephalography (MEG) data, an ill-posed linear inverse problem. On the other hand, it has been proved that, in three-layer linear neural networks (LNNs), the VB approach is asymptotically equivalent to a positive-part James-Stein type shrinkage estimation. In this paper, noting the similarity between the ARD in a linear problem and an LNN, we analyze a simplified version of the VB approach in the ARD. We discuss its relation to the shrinkage estimation and how ill-posedness affects learning. We also propose the algorithm that requires simpler computation than, and will provide similar performance to, the VB approach. [ABSTRACT FROM AUTHOR]

Effective automatic summarization usually requires simulating human reasoning such as abstraction or relevance reasoning. In this paper we describe a solution for this type of reasoning in the particular case of surveillance of the behavior of a dynamic system using sensor data. The paper first presents the approach describing the required type of knowledge with a possible representation. This includes knowledge about the system structure, behavior, interpretation and saliency. Then, the paper shows the inference algorithm to produce a summarization tree based on the exploitation of the physical characteristics of the system. The paper illustrates how the method is used in the context of automatic generation of summaries of behavior in an application for basin surveillance in the presence of river floods. [ABSTRACT FROM AUTHOR]

This paper reports a snapshot of our on-going experiments in which a common target we call a-tiny-island-in-a-huge-lake is explored with different methods ranging from a data-mining technique to an artificial immune system. Our implicit interest is a network intrusion detection, and we assume data floating in the huge lake are normal while ones found on the tiny island are abnormal. Our goal here is twofold. One is to know (i) whether or not it is possible to train a system using just normal data alone. The other is to study (ii) a limit of the size of the detectable area, when we decrease the size of the island eventually shrinking to zero, equivalently so-called a-needle-in-a-haystack which is still an open and worth while tuckling problem. To learn these two issues, a fuzzy rule extraction system - one with fixed triangle/trapezoid membership functions for our first goal, and for the second goal with Gaussian membership functions whose shape is adaptively determined for the second goal, are exploited in this paper. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have been widely used in the power industry for applications such as fault classification, protection, fault diagnosis, relaying schemes, load forecasting, power generation and optimal power flow etc. Most of NNs are built upon the environment of real numbers. However, it is well known that in computations related to electric power systems, such as load-flow analysis and fault level estimation etc., complex numbers are extensively involved. The reactive power drawn from a substation, the impedance, busbar voltages and currents are all expressed in complex numbers. Hence, NNs in the complex domain must be adopted for these applications. This paper proposes the complex-valued neural network (CVNN) and a new fuzzy clustering complex-valued neural network (FC-CVNN) to estimate busbar voltages in a load-flow problem. The aim of this paper is to present a comparative study of estimation busbar voltages in load-flow analysis using the conventional neural network (real-valued neural network, RVNN), the CVNN and the new FC-CVNN. The results suggest that a new proposed FC-CVNN and CVNN architecture can generalize better than ordinary RVNN and the FC-CVNN is also learn faster. [ABSTRACT FROM AUTHOR]

This paper deals with the application of segment procedure neural networks to predict harm status of horsetail-pine worm. A novel procedure neural networks is proposed to solve those problems which are related to certain distinct segments of procedure. It is indicated that this model is a generalized form of the known procedure neural networks, and it owns all properties of the known model. This paper also presents learning algorithms for the segment procedure neural networks. Horsetail-pine worm forecast is a hard work for forest experts, but it is a typical segment procedure problem. In this paper a segment procedure neural networks is applied to deal with this issue, and some simulation experiment results are presented. Keywords: Neural networks, topological structure, procedure neural networks, segment, algorithm, learning, prediction. [ABSTRACT FROM AUTHOR]

The classical rough set theory based on complete information systems stems from the observation that objects with the same characteristics are indiscernible according to available information. With respect to upper-approximation and lower-approximation defined on an indiscernibility relation it classifies objects into different equivalent classes. But in some cases such a rigid indiscernibility relation is far from applications in the real world. Therefore, several generalizations of the rough set theory have been proposed some of which extend the indiscernibility relation using more general similarity or tolerance relations. For example, Kryszkiewicz [4] studied a tolerance relation, and Stefanowski [7] explored a non-symmetric, similarity relation and valued tolerance relation. Unfortunately, All the extensions mentioned above have their inherent limitations. In this paper, after discussing several extension models based on rough sets for incomplete information, a concept of constrained dissymmetrical similarity relation is introduced as a new extension of the rough set theory, the upper-approximation and the lower-approximation defined on constrained similarity relation are proposed as well. Furthermore, we present the comparison between the performance of these extended relations. Analysis of results shows that this relation works effectively in incomplete information and generates rational object classification. Keywords: Rough sets, incomplete information, constrained dissymmetrical similarity relation. [ABSTRACT FROM AUTHOR]

It is a very demanding task to design a reliable fingerprint matching approach with high accuracy and speed. An algorithm based on the novel structure combining the singular point with its neighborhood minutiae proposed in this paper can solve this problem efficiently. The structure introduced in this paper has two novel ideas as follows: First, we give an efficient singular points detection method by the inter-relationship between the singular points and the minutiae around them. It can reject the spurious singular points detected by the Poincare index. Second, an improvement of minutiae pairing strategy is introduced, which can sharply decrease the number of candidate minutiae pairs. Experiment results show that these improvements can highly speed up the matching with a preferable accuracy. This algorithm can be used in the one-to-many matching of the on-line fingerprint identification system. Keywords: Fingerprint; Matching; Singular Point; Minutiae Pairing. [ABSTRACT FROM AUTHOR]

Ring artifacts can occur in reconstructed images from X-ray microtomography as full or partial circles centred on the rotation axis. In this paper, a 2D method is proposed that reduces these ring artifacts in the reconstructed images. The method consists of two main parts. First, the artifacts are localised in the image using local orientation estimation of the image structures and filtering to find ring patterns in the orientation information. Second, the map of the located artifacts is used to calculate a correction image using normalised convolution. The method is evaluated on 2D images from volume data of paper fibre imaged at the European Synchrotron Radiation Facility (ESRF) with high resolution X-ray microtomography. The results show that the proposed method reduces the artifacts and restores the pixel values for all types of partial and complete ring artifacts where the signal is not completely saturated. [ABSTRACT FROM AUTHOR]

With the growth of ontology scale and complexity, the query performance of Ontology Repository Management System (ORMS) becomes more and more important. The paper proposes materialized relations technique which speeds up query processing in ORMS by making the implicit derived relations of ontology explicit. Here the selection of materialized relations is a key problem, because the materialized relations technique trades off required inference time against maintenance cost and storage space. However, the problem has not been discussed formally before. So the paper proposes a QSS model to describe the queries set of ontology formally and gives the benefit evaluation model and the selection algorithm of materialized relations based on QSS model. The method in this paper not only considers the benefit in query response of the materialization technique, but also the storage and maintenance cost of it. In the end, an application case is introduced to prove the selection method of materialized relations is effective. [ABSTRACT FROM AUTHOR]

This paper summarizes a new concept to determine principal curves for nonlinear principal component analysis (PCA). The concept is explained within the framework of the Hastie and Stuetzle algorithm and utilizes spline functions. The paper proposes a new algorithm and shows that it provides an efficient method to extract underlying information from measured data. The new method is geometrically simple and computationally expedient, as the number of unknown parameters increases linearly with the analyzed variable set. The utility of the algorithm is exemplified in two examples. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a fast affine transform method for real-time machine vision applications. Inspection of parts by machine vision requires accurate, fast, reliable, and consistent operations, where the transform of visual images plays an important role. Image transform is generally expensive in computation for real-time applications. For example, a transform including rotation and scaling would require four multiplications and four additions per pixel, which is going to be a great burden to process a large image. Our proposed method reduces the complexity substantially by removing four multiplications per pixel, which exploits the relationship between two neighboring pixels. In addition, this paper shows that the affine transform can be performed by fixed point operations with marginal error. Two interpolation methods are also tried on top of the proposed method in order to test the feasibility of fixed point operations. Experimental results indicated that the proposed algorithm was about six times faster than conventional ones without any interpolation and five times faster with bilinear interpolation. [ABSTRACT FROM AUTHOR]

The Although a variety of standardization and Learning Management System (LMS) were produced to develop and effectively manage web contents in response to active diffusion of internet application, practical changes to assist online learners are not yet to be found. The core purpose of the LMS system proposed in this paper is to help learners pursue proactive and self-oriented education by allowing learners to proactively configure their own content, that is, learners no longer have to be restricted by prescribed sequence of lectures. In this paper, I would like to introduce a LMS that can support self-leading education by providing various types of learners at Virtual University with delicately organized educational contents for maximum efficiency. The system allows a learner to select a lecture or a chapter which has been presorted to meet his educational needs and intellectual ability. [ABSTRACT FROM AUTHOR]

The face images are varying environment database from external illumination. Therefore we propose parallel cascade face detector. We define context image illumination and distinguish context using unsupervised learning. Many unsupervised method is available to distinguish varying illuminant images. This approach can be distribution face image and we can make the classifier for face image context. Therefore, in this paper, we parallel classifier that is strutted cascade classifier of two methods. In first classifier, we use sub-sampling feature extraction and in second classifier we use wavelet transformation method. We achieved very encouraging experimental results. Our method is enhancement varying illumination environment. [ABSTRACT FROM AUTHOR]

This paper considers the capacities of the elements in a set E efficiently so that the total cost for the increment of capacity can be decrease to maximum extent while the final expansion capacity of a given family F of subsets of E is with a given limit bound. The paper supposes the cost w is a fuzzy variable. Network bottleneck capacity expansion problem with fuzzy cost is originally formulated as Expected value model according to some criteria. For solving the fuzzy model efficiently, network bottleneck capacity algorithm, fuzzy simulation, neural network(NN) and genetic algorithm(GA) are integrated to produce a hybrid intelligent algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the global exponential stability of Takagi-Sugeno Fuzzy cellular neural networks with time-varying delays. Using the reduction to absurdity, a less conservative delay-independent stability criterion is derived to guarantee the exponential stability of Takagi-Sugeno Fuzzy cellular neural networks with time-varying delays. Since our model is more general than some existing works, the results presented in this paper are the improvement and extension of the existed ones. [ABSTRACT FROM AUTHOR]

Gastric cancer is a great part of the cancer occurrence and the mortality from cancer in Korea, and the early detection of gastric cancer is very important in the treatment and convalescence. This paper, for the early detection of gastric cancer, proposes the analysis system of an endoscopic image of the stomach, which detects the abnormal region by using the change of color in the image and by providing the surface tissue information to the detector. While advanced inflammation and cancer may be easily detected, early inflammation and cancer are difficult to detect and requires more attention to be detected. This paper, at first, converts the endoscopic image to the image of the IHb(Index of Hemoglobin) model and removes noises incurred by illumination and, automatically detects the regions suspected as cancer and provides the related information to the detector, or provides the surface tissue information for the regions appointed by the detector. This paper does not intend to provide the final diagnosis of the detected abnormal regions as gastric cancer, but it intends to provide a supplementary mean to reduce the load and mistaken diagnosis of the detector, by automatically detecting the abnormal regions not easily detected by the human eye and this provides additional information for the diagnosis. The experiments using practical endoscopic images for performance evaluation showed that the proposed system is effective in the analysis of endoscopic image of the stomach. [ABSTRACT FROM AUTHOR]

Automatic classification of objects based on their visual appearance is often performed based on clustering algorithms, which can be based on soft computing techniques. One of the most used methods is fuzzy clustering. However, this method can converge to local minima. This problem has been addressed very recently by applying ant colony optimization to tackle this problem. This paper proposed the use of this fuzzy-ant clustering approach to derive fuzzy models. These models are used to classify marbles based on their visual appearance; color and vein classification is performed. The proposed fuzzy modeling approach is compared to other soft computing classification algorithms, namely: fuzzy, neural, simulated annealing, genetic and combinations of these approaches. Fuzzy-ant models presented higher classification rates than the other soft computing techniques. [ABSTRACT FROM AUTHOR]

This paper presents a simple and practical solution to the 6-point 2-view focal-length estimation problem. Based on the hidden-variable technique we have derived a 15th degree polynomial in the unknown focal-length. During this course, a simple and constructive algorithm is established. To make use of multiple redundant measurements and then select the best solution, we suggest a kernel-voting scheme. The algorithm has been tested on both synthetic data and real images. Satisfactory results are obtained for both cases. For reference purpose we include our Matlab implementation in the paper, which is quite concise, consisting of 20 lines of code only. The result of this paper will make a small but useful module in many computer vision systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to unsupervised texture segmentation that relies on a very general nonparametric statistical model of image neighborhoods. The method models image neighborhoods directly, without the construction of intermediate features. It does not rely on using specific descriptors that work for certain kinds of textures, but is rather based on a more generic approach that tries to adaptively capture the core properties of textures. It exploits the fundamental description of textures as images derived from stationary random fields and models the associated higher-order statistics nonparametrically. This general formulation enables the method to easily adapt to various kinds of textures. The method minimizes an entropy-based metric on the probability density functions of image neighborhoods to give an optimal segmentation. The entropy minimization drives a very fast level-set scheme that uses threshold dynamics, which allows for a very rapid evolution towards the optimal segmentation during the initial iterations. The method does not rely on a training stage and, hence, is unsupervised. It automatically tunes its important internal parameters based on the information content of the data. The method generalizes in a straightforward manner from the two-region case to an arbitrary number of regions and incorporates an efficient multi-phase level-set framework. This paper presents numerous results, for both the two-texture and multiple-texture cases, using synthetic and real images that include electron-microscopy images. [ABSTRACT FROM AUTHOR]

One of the most exciting advances in early vision has been the development of efficient energy minimization algorithms. Many early vision tasks require labeling each pixel with some quantity such as depth or texture. While many such problems can be elegantly expressed in the language of Markov Random Fields (MRF's), the resulting energy minimization problems were widely viewed as intractable. Recently, algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: for example, such methods form the basis for almost all the top-performing stereo methods. Unfortunately, most papers define their own energy function, which is minimized with a specific algorithm of their choice. As a result, the tradeoffs among different energy minimization algorithms are not well understood. In this paper we describe a set of energy minimization benchmarks, which we use to compare the solution quality and running time of several common energy minimization algorithms. We investigate three promising recent methods—graph cuts, LBP, and tree-reweighted message passing—as well as the well-known older iterated conditional modes (ICM) algorithm. Our benchmark problems are drawn from published energy functions used for stereo, image stitching and interactive segmentation. We also provide a general-purpose software interface that allows vision researchers to easily switch between optimization methods with minimal overhead. We expect that the availability of our benchmarks and interface will make it significantly easier for vision researchers to adopt the best method for their specific problems. Benchmarks, code, results and images are available at http://vision.middlebury.edu/MRF. [ABSTRACT FROM AUTHOR]

This paper analyses the chaotic characteristics of violent abnormal vibration signals of a large rolling mill, and studies phase space reconstruction techniques of the signals. On this basis, the vibration model of wavelet neural networks and the model of backpropagation neural networks are set up, respectively, through inversion methods. The properties of these two models are tested and compared with each other. The result shows that the wavelet neural networks have an advantage over the backpropagation neural networks in rapid convergence and high accuracy. [ABSTRACT FROM AUTHOR]

How face pose (rotating from right to left) influence the fusion authentication accuracy in face and fingerprint identity authentication system? This paper firstly tries to answer this question. The maximum rotating degree that fusion system can bear is given out by experiment. Furthermore, theoretical analysis deals with how face pose influence the fusion performance is proposed in this paper. Experiment results show that faces with big rotated degree can not be helpful but harmful to fusion system. And the maximum rotated angle of face that fusion system can bear is 20 degree. On the other hand, theoretical analysis proved that the mathematical inherence of influence of face pose on fusion system is not only the reduction of variance but also the decrease of distance between the genuine and imposter classes. [ABSTRACT FROM AUTHOR]

For the purpose of extending the Web that is able to understand and process information by machine, Semantic Web shared knowledge in the ontology form. For exquisite query processing, this paper proposes a method to use semantic relations in the ontology as relevance feedback information to query expansion. We made experiment on pharmacy domain. And in order to verify the effectiveness of the semantic relation in the ontology, we compared a keyword based document retrieval system that gives weights by using the frequency information compared with an ontology based document retrieval system that uses relevant information existed in the ontology to a relevant feedback. From the evaluation of the retrieval performance, we knew that search engine used the concepts and relations in ontology for improving precision effectively. Also it used them for the basis of the inference for improvement the retrieval performance. [ABSTRACT FROM AUTHOR]

The paper presents the application of the wavelet transform of the frequency histogram of connected elements to the detection of very thin cracks in used pallets. First, the paper presents this novel concept and introduces the parameters that define a connected element, showing that the conventional grayscale intensity histogram of a digital image is a particular case of the histogram of connected elements. Then, the discriminant capability of the wavelet transform of this generalized histogram is analyzed. In particular, the information conveyed by the histogram of connected elements is exploited to detect very thin cracks in used pallets. An artificial neural network classifier to discriminate sound wood from defective wood with very thin cracks has been designed. The exhaustive experimental test carried out with numerous boards of used pallets has validated the proposed method, in particular its remarkably low ratio of false alarms. [ABSTRACT FROM AUTHOR]

This paper presents and application of Genetic Programming (GP) for time series forecast. Although this kind of application has been carried out with a wide range of techniques and with very good results, this paper presents a different approach. In most of the experiments done in time series forecasting the objective is, from a consecutive set of samples or time interval, to obtain the value of the sample in the next time step. The aim of this paper is to study the forecasting not only on the next sample, but in general several samples forward. This will allow the building of more complete prediction systems. With this objective, one of the most widely used series for this kind of application has been used, the Mackey-Glass series. [ABSTRACT FROM AUTHOR]

As shown in the bibliography, training an ensemble of networks is an interesting way to improve the performance with respect to a single network. However there are several methods to construct the ensemble. In this paper we present some new results in a comparison of twenty different methods. We have trained ensembles of 3, 9, 20 and 40 networks to show results in a wide spectrum of values. The results show that the improvement in performance above 9 networks in the ensemble depends on the method but it is usually low. Also, the best method for a ensemble of 3 networks is called "Decorrelated" and uses a penalty term in the usual Backpropagation function to decorrelate the network outputs in the ensemble. For the case of 9 and 20 networks the best method is conservative boosting. And finally for 40 networks the best method is Cels. [ABSTRACT FROM AUTHOR]

A novel immunotronic approach to fault detection in hardware based on symbiotic evolution is proposed in this paper. In the immunotronic system, the generation of tolerance conditions corresponds to the generation of antibodies in the biological immune system. In this paper, the principle of antibody diversity, one of the most important concepts in the biological immune system, is employed and it is realized through symbiotic evolution. Symbiotic evolution imitates the generation of antibodies in the biological immune system more than the standard genetic algorithm(SGA) does. It is demonstrated that the suggested method outperforms the previous immunotronic methods with less running time. The suggested method is applied to fault detection in a decade counter (typical example of finite state machines) and MCNC finite state machines and its effectiveness is demonstrated by the computer simulation. Keywords: immunotronic system, hardware fault detection, tolerance conditions, antibody diversity, symbiotic evolution. [ABSTRACT FROM AUTHOR]

This paper is concerned with time series of graphs and compares two novel schemes that are able to predict the presence or absence of nodes in a graph. Our work is motivated by applications in computer network monitoring. However, the proposed prediction methods are generic and can be used in other applications as well. Experimental results with graphs derived from real computer networks indicate that a correct prediction rate of up to 97% can be achieved. [ABSTRACT FROM AUTHOR]

In this paper we describe a study carried out with SenToy: a tangible interface that has the shape of a doll and is used to capture emotions from its user whilst performing specific gestures. SenToy was used with an application named Fearnot!, which is a virtual storytelling environment, where characters act autonomously and in character, so that stories emerge from those autonomous actions. The integration of SenToy in FearNot! was evaluated in two ways: (1) if users were able to manipulate the tangible interface appropriately, even if engaged in storytelling situation and (2) if the emotions expressed by the users with SenToy were the same as the ones reported to have been felt after the session. The results of the study show that Sentoy can be used to understand how the viewers reacted to the stories portrayed, and at the same time that the emotions that were expressed with SenToy (apart from some exceptions, reported in the paper) are indeed similar as the ones reported to have been felt by the users. Keywords: Affective tangible interface, affective computing, synthetic characters. [ABSTRACT FROM AUTHOR]

Reticular Activating system which has a form of small neural networks in the brain is closely related system with the automatic nervous system. It takes charge of the function that distinguishes between memorizing one and the others, accepts the only selected information and discards the unnecessary things.In this paper, we propose Reticular Activating system which has Knowledge acquisition, selection, storing, reconfiguration and retrieving part. In this paper, On/Off Switching mechanism for flexible memory is specially designed and tested. [ABSTRACT FROM AUTHOR]

Human emotion is related to stimulus and cognitive appraisal. Emotion is very important to entertainment application of virtual reality. Emotion model of 3D virtual characters is a challenging branch of Intelligent Virtual Environment (IVE). A believable 3D character should be provided with emotion and perception. In general, a virtual character is regarded as an autonomous agent with sense, perception, behavior and action. An emotion model of 3D virtual characters on the basis of psychology theory is presented in this paper. Our work is to construct 3D virtual characters that have internal sensor and perception for external stimulus, and express emotion autonomously in real time. Firstly, architecture of a virtual character is set up by cognitive model; Secondly, emotion class is set up by OCC and Plutchik's emotion theory; Thirdly, some new concepts about emotion are presented with a general mathematical model which is relation among emotion, stimulus, motivation variable, personality variable. Fourthly, a perception model of 3D characters by Gibson's theory is introduced. As a result, an emotional animation demo system of 3D virtual character is implemented on PC. [ABSTRACT FROM AUTHOR]

The paper first builds a novel affective model based on rough sets, presents the static description of affective space. Meanwhile, the paper creatively combines rough sets with Markov chain, gives the dynamic forecast of human affective change. In this affective model, some concepts and states are defined such as affective description precision and so on. It is a fundamental work to more research. Simulations are done using Matlab software, and simulation results show that this affective model can well simulate the human emotion. The results of this paper are innovative. It is a new research direction of affective computing that rough sets and affective computing infiltrate each other. [ABSTRACT FROM AUTHOR]

In this paper, combined features of global and time-sequence were used as the characteristic parameters for speech emotional recognition. A new method based on formula of MMD (Modified Mahalanobis Distance) was proposed to decrease the estimated errors and simplify the calculation. Four emotions including happiness, anger, surprise and sadness are considered in the paper. 1000 recognizing sentences collected from 10 speakers were used to demonstrate the effectiveness of the new method. The average emotion recognition rate reached at 95%. Comparison with method of MQDF [1] (Modified quadratic discriminant function), Data analysis also displayed that the MMD is better than MQDF. [ABSTRACT FROM AUTHOR]

This paper describes our work on practical stereo vision for mobile robots using commodity hardware. The approach described in this paper is based on line segments, since those provide a lot of information about the environment, provide more depth information than point features, and are robust to image noise and colour variations. However, stereo matching with line segments is a difficult problem due to poorly localized end points and perspective distortion. Our algorithm uses integral images and Haar features for line segment extraction. Dynamic programming is used in the line segment matching phase. The resulting line segments track accurately from one frame to the next, even in the presence of noise. [ABSTRACT FROM AUTHOR]

Based on three dimensional echocardiography sequences, a new technique using connected equi-length curve angle chain (CELCAC) model for reconstruction and dynamic analysis of mitral annulus is proposed in this paper. Firstly, the boundary points of mitral annulus of the mitral annulus is extracted by interactive method and ordered according to their positions. Then, the three dimensional mitral annulus visualization model is established based on non-uniform rational B - spline algorithm. Finally, dynamic analysis of mitral annulus presented by the CELCAC model is suggested. The presentation is invariant to rotation, scaling, and translation. Results show that the reconstruction and analysis method proposed in this paper is feasible to assess the geometry and analyze the motion of mitral annulus. [ABSTRACT FROM AUTHOR]

Now a days all medical imaging equipments give output as digital image and non-invasive techniques are becoming cheaper, the database of images is becoming larger. This archive of images increases up to significant size and in telemedicine-based applications the storage and transmission requires large memory and bandwidth respectively. There is a need for compression to save memory space and fast transmission over internet and 3G mobile with good quality decompressed image, even though compression is lossy. This paper presents a novel approach for designing enhanced vector quantizer, which uses Kohonen's Self Organizing neural network. The vector quantizer (codebook) is designed by training with a neatly designed training image and by selective training approach .Compressing; images using it gives better quality. The quality analysis of decompressed images is evaluated by using various quality measures along with conventionally used PSNR. [ABSTRACT FROM AUTHOR]

Dual-energy mammography can suppress the contrast between adipose and glandular tissues and improve the detectability of microcalcifications (MCs). In the published papers, MCs were calibrated by aluminum and identified by their thickness. However, the variety of compositions of MCs causes the variety of attenuation differences between MCs and MC calibration material which bring about huge calculation errors. In our study, we selected calcium carbonate and calcium phosphate as the most suitable MC calibration materials and the correction coefficient was reasonably determined. Area density was used as MC indicator instead of thickness. Therefore, the calculation errors from MC calibration materials can be reduced a lot and the determination of MCs will become possible. [ABSTRACT FROM AUTHOR]

In a health care context, the use of RFID (Radio Frequency Identi- fication) technology can be employed for not only bringing down health care costs but also to facilitate automatic streamlining patient identification processes in health centers and assist medical practitioners in quick and accurate diagnosis and treatments. In this paper, we outline a describe design and application of RFID-based Real-time Intelligent Clinical Diagnosis and Treatment Support System (ICDTS) in health care. [ABSTRACT FROM AUTHOR]

Breast cancer is the leading cancer among females, the key technology of preventing breast cancers is early detection. Based on the advantage of support vector machine (SVM), finding global solution and possessing higher generalization capability on dealing with the small sample, a new method of diagnosing breast cancer by CAD is proposed in this paper. Firstly, a principal component analysis is used to represent the information of ROI image, which account for most of the variance of the original data set while significantly reducing the data dimension. After the extraction of principal components, only those data of which account for most part of variance were retained as the feature vector and input into a SVM classification and BP neural network classification to classify. Finally, the results of experiment show that the accuracy and specificity for the diagnosis of breast cancer using SVM classification is good. [ABSTRACT FROM AUTHOR]