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In this paper, a novel Fisher criterion is introduced and shown to be equivalent to the traditional Fisher criterion. Based on this new Fisher criterion and simultaneous diagonalization technique, a St-subspace Fisher discriminant (St-SFD) method is developed to deal with the small sample size (S3) problem in face recognition. The proposed method overcomes some drawbacks of existing LDA based algorithms. Also, our method has good computational complexity. Two public available databases, namely ORL and FERET databases, are exploited to evaluate the proposed algorithm. Comparing with existing LDA-based methods in solving the S3 problem, the proposed St-SFD method gives the best performance. [ABSTRACT FROM AUTHOR]

Although genetic algorithm (GA) has the ability to do quick and stochastic global search, it can't efficiently use the output information for systems. Ant algorithm (AA), on the other hand, is a parallel-proceed and distributive-forward system with a relatively slow speed for carrying out its solution. Incorporating GA and AA can improve their merits one for another. In this paper, the model and the method from the combination of GA and AA are proposed. The convergence of the method based on the Markov theory is analyzed. The experiment and analysis are conducted on the NP-hard problems for the cases of TSP30 (Travel Salesman Problem 30 cities) and CHN144 (China 144 cities). This work proves that the satisfactory solution sequence is monotonically decreasing and convergent. The results of simulations show that not only this combined algorithm is a step-by-step convergent process, but also its speed and effect of solving are quite satisfactory. [ABSTRACT FROM AUTHOR]

In this paper, a new line-based camera calibration technique is presented. Firstly, the relation between dimensional line parameters and projective line parameters is deduced. Using more than three dimensional lines which are not parallel with each other, the camera intrinsic parameters can be determined by solving linear equations. Experiment results show that in same noise level the mean attained by this method is more closer to factual value and MSE is smaller by about 65% than classic point-based technique. [ABSTRACT FROM AUTHOR]

LKH(Logical Key Hierarchy) is a basic method in secure multicast group rekeying. It does not distinguish the behavior of group members even they have different probabilities (join or leave). When members have diverse changing probability or different changing mode, the gap between LKH and the optimal rekeying algorithm will become bigger. If the probabilities of members have been known, LKH can be improved someway, but it can not be known exactly the changing probabilities of members. Based on the basic knowledge of group members' behavior (ex. active or inactive), the active members and inactive members are partitioned in the new method, and they are set on the different location in logical key tree firstly. Then the concept "Dirty Path" is introduced in order to reduce the repeat rekeying overhead in the same path. All these can decrease the number of encryptions in the Group Manager and the network communication overhead. The simulation results indicate that the new method has a better improvement over traditional LKH method even if the multicast group members' behavior could be distinguished "approximately". [ABSTRACT FROM AUTHOR]

A multi-objective evolutionary algorithm (MOEA) based approach to Power System Stabilizer (PSS) and Static Var Compensators (SVC) tuning has been investigated in this paper. The coordinated design problem of PSS and SVC is formulated as a multi-objective optimization problem, in which the system response is optimized by minimizing several system-behavior measure criterions. MOEA is employed to search optimal controller parameters.Design of the multi-objective optimization aims to find out the Pareto optimal solution which is a set of possible optimal solutions for controller parameters. And effectiveness of the proposed control scheme has been demonstrated in a multiple power system. [ABSTRACT FROM AUTHOR]

For real-time computer-controlled systems, a control task does not have a fixed period but a range of periods in which control performance varies. Hence for multiple tasks scheduled on a single processor, to consider the optimization design of sampling periods in the co-design of control and scheduling is necessary to improve the control performance and use limited computing resource efficiently. In this paper, the mathematic description of the optimization problem of designing periods is presented, and the optimization solution using evolution strategy is proposed. The performances of proposed solution are revealed via simulation studies. Simulation shows that the optimization design of sampling periods can be implemented by using the evolution strategy method. [ABSTRACT FROM AUTHOR]

Both a concept of the optimal set of elementary siphons and a deadlock prevention policy based on integer programming are presented to solve deadlock problems arising in flexible manufacturing systems(FMS). Furthermore, an algorithm based on modified particle swarm optimization(PSO) is illustrated to show its efficiency to deal with such problems. Numerical simulation shows that this policy can minimize the number of newly additional control places and arcs while improving the dynamic performance of the resultant system. [ABSTRACT FROM AUTHOR]

Filled function method is a type of efficient methods to solve global optimization problems arisen in non-convex programming. In this paper, a new class of filled functions is proposed. This class of filled functions has only one adjustable parameter a. Several examples of this class of filled functions with specified parameter values are given, which contain the filled functions proposed in [3] and [4]. These examples show this class of filled functions contains more simple functions, therefore this class of filled functions have better computability. An algorithm employing the proposed filled function is presented, and numerical experiments show that the proposed filled functions are efficient. [ABSTRACT FROM AUTHOR]

In this study, we will employ a multi-agent for searching and extraction of data. We will use Integrator Agent based on CORBA architecture for the proposed model on hierarchical Clustering and Association Rule Discovery (HCARD). The model will address the inadequacy of other data mining tools in processing performance and efficiency when use for knowledge discovery. The result revealed faster searching using the agents. Our experiment also shows that the HCARD generated isolated but imperative association rules which in return could be practically explained for decision making purposes. Shorter processing time had been noted in computing for smaller clusters implying ideal processing period than dealing with the entire dataset. [ABSTRACT FROM AUTHOR]

One of the greatest challenges for dependable service-oriented software systems of next generation is coping with the complexity of required adaptation or reaction to the detected unforeseen vulnerability attacks. To this end, autonomic system[1] has been advocated as a way to design self-protective systems to defend against malicious attacks or cascading failures. However, other initiatives such as the self-regenerative system[2] adopt the biological-inspired [2, 3]notions such as natural diversity and self-immune as a main strategy to achieve the robust and adaptable self-protection. Based on an ongoing research into self-regenerative programming model, this paper presents a knowledge-centric approach for supporting the runtime automated generation of software adapters for cross-standard service activation; and argues the importance of application of a semantic knowledge to extract the notion of self-regenerative adaptation from the previous polyarchical middleware implementation. The benefit of this will be the production of a customizable self-regenerative adaptation service; and also, support for abstraction integration between domain of similar interests or others in a high-level management directed towards building autonomic systems in a large domain of interest. [ABSTRACT FROM AUTHOR]

This paper addresses how intelligent characters, having learning capability based on the neural network technology, automatically adapt to environmental changes in computer games. Our adaptation solution includes an autonomous adaptation scheme and a cooperative adaptation scheme. With the autonomous adaptation scheme, each intelligent character steadily assesses changes of its game environment while taking into consideration recently earned scores, and initiates a new learning process when a change is detected. Intelligent characters may confront various opponents in many computer games. When each intelligent character has fought with just part of the opponents, the cooperative adaptation scheme, based on a genetic algorithm, creates new intelligent characters by composing their partial knowledge of the existing intelligent characters. The experimental results show that intelligent characters can properly accommodate to the changes with the proposed schemes. [ABSTRACT FROM AUTHOR]

A novel technique for shape coding of a digital object based on its inner and/or outer isothetic polygonal shape, is proposed. The method uses a multigrid background for several applications including digital image visualization and retrieval with varying levels of accuracy. The elegance of the proposed shape code lies in capturing the shape of the objects present in an image from their gross appearances to finer details with a set of hierarchical isothetic polygons that tightly envelop (cover) the objects from outside (inside). Experimental results demonstrate the strength and efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents an improved method which is suitable for gradient-threshold edge detectors. The proposed method takes into account the basic characteristics of the human visual system (HVS) and precisely determines the local masking regions for the edges with arbitrary shape according to the image content. Then the gradient image is masked with the luminance and the activity of local image before edge labelling. The experimental results show that the edge images obtained by our algorithm are more consistent with the perceptive edge images. [ABSTRACT FROM AUTHOR]

This paper presents a microcalcification patterns recognition method based autoassociator and classifier to detect the breast cancer. It studies the autoassociative and classification abilities of a neural network approach to classify the microcalcification patterns into Benign and Malignant using some certain image structure features. The proposed technique used the combination of two kinds of neural networks, autoassociator and classifier to analyze the microcalcification. It could obtain 88% classification rate for testing dataset and 100% classification rate for training dataset. [ABSTRACT FROM AUTHOR]

Human gait recognition is the process of identifying individuals by their walking manners. The gait as one of newly coming biometrics has recently gained more and more interests from computer vision researchers. In this paper, we propose a new method for model-free recognition of gait based on silhouette in computer vision sequences. The silhouette shape is represented by a novel approach which includes not only the spatial body contour but also the temporal information. First, a background subtraction is used to separate objects from background. Then, we represent the spatial shape of walker and their motion by the temporal matrix, and use Discrete Fourier analysis to analyze the gait feature. The nearest neighbor classifier is used to distinguish the different gaits of human. The performance of our approach is tested using different gait databases. Recognition results show this approach is efficient. [ABSTRACT FROM AUTHOR]

Fingerprint recognition and verification are always the key issues in intelligent technology and information security. Extraction of fingerprint ridge lines is a critical pre-processing step in fingerprint identification applications. Although existing algorithms for fingerprint extraction work well on good-quality images. Their performance decrease when handling poor-quality images. This paper addresses the ridge line extraction problem as curve tracking processes under the framework of probabilistic tracking. Each ridge line is modeled as sequential frames of a continuous curve and then traced by standard CONDENSATION algorithm in the area of computer vision. Additionally, local directional image is rectified with a feedback technique after each tracking step to improve the accuracy. The experimental results are compared with those obtained through existing well-known algorithms, such as local-binarization and sampling-tracing methods. In spite of greater computational complexity, the method proposed performs better both in terms of efficiency and robustness. [ABSTRACT FROM AUTHOR]

The paper introduces the method of probabilistic neural network (PNN) and its classifying principle. It constructs two PNN structures which are used to recognize both the two patterns and the three patterns respectively. The structure of the two patterns classification of PNN is used to classify the 106 listed companies of China in 2000 into two groups. The classification accuracy rate is 87.74%. The structure of the three patterns classification of PNN is used to classify the 96 listed companies of China in 2000 into three groups. The classification accuracy rate is 85.42%. [ABSTRACT FROM AUTHOR]

In this paper, a Devanagari script recognition scheme based on a novel algorithm is proposed. Devanagari script poses new challenges in the field of pattern recognition primarily due to the highly cursive nature of the script seen across its diverse character set. In the proposed algorithm, the character is initially subjected to a simple noise removal filter. Based on a reference co-ordinate system, the significant contours of the character are extracted and characterized as a contour set. The recognition of the character involves comparing these contour sets with those in the enrolled database. The matching of these contour sets is achieved by characterizing each contour based on its length, its relative position in the reference co-ordinate system and an interpolation scheme which eliminates displacement errors. In the Devanagari script, similar contour sets are observed among few characters, hence this method helps to filter out disparate characters and narrow down the possibilities to a limited set. The next step involves focusing on the subtle yet vital differences between the similar contours in this limited set. This is done by a prioritization scheme which concentrates only on those portions of character which reflect its uniqueness. The major challenge in developing the proposed scheme lay in striking the right balance between definiteness and flexibility to derive optimal solutions for out of sample data. Experimental results show the validity and efficiency of the developed scheme for recognition of characters of this script. Keywords: Devanagari script recognition, feature extraction, interpolation, contour sets, horizontal scanning, vertical scanning, priority matching. [ABSTRACT FROM AUTHOR]

Video shot boundary detection (SBD) is an important step in many video applications. In this paper, previous temporal multi-resolution analysis (TMRA) framework was extended by first using SVM (Supported Vector Machines) classify the video frames within a sliding window into normal frames, gradual transition frames and CUT frames, then clustering the classified frames into different shot categories. The experimental result on ground truth, which has about 26 hours (13,344 shots) news video clips, shows that the new framework has relatively good accuracy for the detection of shot boundaries. It basically solves the difficulties of shot boundaries detection caused by sub-window technique in video. The framework also greatly improves the accuracy of gradual transitions. [ABSTRACT FROM AUTHOR]

Locality preserving projections (LPP) can find an embedding that preserves local information and discriminates data well. However, only one projection matrix over the whole data is not enough to discriminate complex data. In this paper, we proposed locality preserving projections mixture models (LPP mixtures), where the set of all data were partitioned into several clusters and a projection matrix for each cluster was obtained. In each cluster, We performed LPP via QR-decomposition, which is efficient computationally in under-sampled situations. Its theoretical foundation was presented. Experiments on a synthetic data set and the Yale face database showed the superiority of LPP mixtures. [ABSTRACT FROM AUTHOR]

According to drawbacks of available algorithms,a new hierarchical and multiresolution approach to the detection and classification of scene breaks in video sequences is presented in this paper. This method gives a unified framework for different shots by using the multi-resolution analysis as mainstay. Firstly, the video clips are cut by FCM clustering method, then the fade and dissolve are detected using ECR and SCD algorithm in the high components and the low components respectively by integer-to-integer wavelet transform. Finally, according to the cut and the former detection of gradual changes, we use motion vectors in high-components of 3D-WT to detect the wipe transition. Experimental results with real video clips about 7h demonstrate that our method can detect and classify a variety of scene breaks, including cuts, fades and dissolves, even in sequences involving significant motions and flash. [ABSTRACT FROM AUTHOR]

An R-functions based classification approach along with a regularization framework is proposed. The abnormal software process detection problem was used as the test bed. The R-functions based classification method is termed as the R-cloud method. The approach was validated both on synthetic and real-world data. Regularization allows to achieve good generalization and classification performance. In addition, the R-cloud approach gives the benefit of the analytical representation of the decision boundary. The introductory study on practical use of the R-cloud classifiers yielded promising results. The prototyping has shown that application of the R-functions based pattern recognition technique is a significant and practical tool for fault detection in providing fault tolerant computing. [ABSTRACT FROM AUTHOR]

This paper presents a method that automatically recognizes the shoe's outsole products into model type, which flows through the conveyor belts from right to left. The interlaced pixels are displayed when we use the NTSC based camera in experiments. So, we require a suitable post-processing. For the purpose of this processing, it decides to find rectangle region of object by thresholding after removing interlaced pixels using de-interlacing method. And then, after rectangle region is separated into blocks through edge detection, we calculates pixel number per each block, re-classifies using its average, and classifies products into model type. [ABSTRACT FROM AUTHOR]

This paper presents a framework of HMM parameter adaptation technique for improving automatic speech recognition (ASR) performance in the noisy environments, which online combines the clean hidden Markov models (HMMs) with the noise model. Based on the given composite HMM corresponding to the initial recognition pass result and truncated vector Taylor series, the noise model in the cepstral domain is updated and refined using iterative Expectation-Maximization (EM) algorithm under maximum likelihood (ML) criterion. Experiments results show that the presented approach in this paper is found to greatly improve recognition performance under mismatched conditions. [ABSTRACT FROM AUTHOR]