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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]

This paperThis work is supported in part by KTU (Grant No:KTU-2002.112.009.1). presents a new approach for human identification at a distance using gait recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four view directions to silhouette. Based on normalized correlation on the distance vectors, gait cycle estimation is first performed to extract the gait cycle. Second, eigenspace transformation based on PCA is applied to time-varying distance vectors and then Mahalanobis and normalized Euclidean distances based supervised pattern classification is finally performed in the lower-dimensional eigenspace for human identification. Experimental results on two main database demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the walking styles for data set of 25 people, and other data set of 22 people. [ABSTRACT FROM AUTHOR]

Patchouli is a common used traditional Chinese herbal medicine for flatulence or vomit with a long history in China and some other Asian countries. Patchoulis of different geographical origins usually have different curative effects. In order to evaluate their qualities, it's very necessary to discriminate them. Our objective of this study is to develop a nondestructive and accurate identification method. The results in this paper showed that it's difficult to use conventional infrared spectra and second derivative spectra directly to distinguish them. But it's quite easy to use two-dimensional infrared (2D IR) correlation spectra, especially after applying wavelet transform process, to discriminate them. The resolution of the 2D IR spectrum is improved obviously after wavelet transform process, more peaks appear and the peaks become quite clear and separate. The differences of the 2D IR spectra become rather remarkable. In this way, it's not difficult to discriminate the patchoulis of different geographical origins. This will be a nondestructive, economical and rapid way to distinguish complicated mixture like traditional Chinese medicines. Combined 2D IR and wavelet transform, Fourier transform infrared spectroscopy would become more powerful in analysis and discrimination. [ABSTRACT FROM AUTHOR]

When a radial-basis function neural network (RBFNN) is used for pattern classification, the problem involves designing the topology of RBFNN and also its centers and widths. In this paper, we present a particle swarm optimization (PSO) learning algorithm to automate the design of RBF networks, to solve pattern classification problems. Simulation results for benchmark problems in the pattern classification area show that the PSO-RBF outperforms two other learning algorithms in terms of network size and generalization performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose ubiFloorII, a novel floor-based user identification system to recognize humans based on their walking pattern such as stride length, dynamic range, foot angle, and stance and swing time. To obtain users walking pattern from their gait, we deployed photo interrupter sensors instead of switch sensors used in ubiFloorI. We developed a software module to extract walking pattern from users' gait. For user identification, we employed neural network trained with users' walking samples. We achieved about 96% recognition accuracy using this floor-based approach. The ubiFloorII system may be used to automatically and transparently identify users in home-like environments. [ABSTRACT FROM AUTHOR]

In this paper, an emotion recognition system for mandarin speech is presented. Five basic human emotions including angry, fear, happy, neutral and sad are investigated. The recognizer is based on neural network with OCON and ACON architecture. Some novel feature selection methods are also added as optional tool to enhance the efficiency and classification accuracy. The system can train speaker dependent emotion speech model through online emotional utterance recording. Experiment results show that emotion can be recognized through neural network model, the best mean accuracy is 86.7%. In addition, the feature selection module is effective to reduce the compute load and increase the generalization ability of the recognizer. [ABSTRACT FROM AUTHOR]

This paper presents an outcome-space branch and bound algorithm for globally solving the linear multiplicative programming problems, in which we use a new two-level partition technique on rectangles and solve a simple relaxed quasiconcave programming problem at each branch node over outcome-space.We prove that the proposed algorithm is convergent. It can be shown by the numerical results that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

Using genetic programming (GP) integrated with nonlinear parameter estimation we can identify the model for avermectin process. In order to reduce the effect caused by bloating which appears when a GP run stagnates in the later period, a fitness function with a double penalty strategy is proposed. GP with this penalty strategy is less sensitive to the choice of penalty parameters and compromises the fitness and the complexity of an individual, so the method can save considerable amounts of computational effort and find models with better quality. In addition, we combine the mechanism knowledge of the fermentation in GP to increase the quality of population and the convergence speed. Experiments prove that this method outperforms standard GP in reducing computational effort and finding better models more quickly. [ABSTRACT FROM AUTHOR]

Computational methods detecting the transcription factor binding sites (TFBS) remain one of the most intriguing and challenging subjects in bioinformatics. Gibbs sampling is essentially a heuristic method, and it is easy to trap into a nonoptimal "local maximum". To overcome this problem and to improve the accuracy and sensitivity of the algorithm, we present an improved Gibbs sampling strategy MPWMGMS to search for TFBS. We have tested MPWMGMS and other existing Gibbs sampling algorithms on simulated data and real biological data sets with regulatory elements. The results indicate that MPWMGMS has better performance than other methods to a great extent in accuracy and sensitivity of finding true TFBS. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA) has been applied in many fields of signal processing and many ICA learning algorithms have been proposed from different perspectives. However, there is still a lack of a deep mathematical theory to describe the ICA learning algorithm or problem, especially in the cases of both super- and sub-Gaussian sources. In this paper, from the point of view of the one-bit-matching principle, we propose two adaptive matching learning algorithms for the general ICA problem. It is shown by the simulation experiments that the adaptive matching learning algorithms can efficiently solve the ICA problem with both super- and sub-Gaussian sources and outperform the typical existing ICA algorithms in certain aspects. [ABSTRACT FROM AUTHOR]

Multi-layer perceptrons (MLPs) have been widely used in classification and regression task. How to improve the training speed of MLPs has been an interesting field of research. Instead of the classical method, we try to train MLPs by a MiniMin model which can ensure that the weights of the last layer are optimal at each step. Significant improvement on training speed has been made using our method for several big benchmark data sets. [ABSTRACT FROM AUTHOR]

This paper introduces a prediction method for time series that is based on the multi-resolution analysis of wavelets (MRA). The MRA is better able to decompose the non-stationary time series of nonlinear systems into different components, allowing a better separation of the general trend terms, the periodic terms and the random fluctuation terms. By applying the most suitable prediction methods(for example, the neural networks method, cosine approximation, or the ARMA model) to the components under different resolutions, this new prediction method produces more accurate prediction results. The new approach is then applied to a real example - the BRENT oil price time series - to demonstrates its usefulness and validity. [ABSTRACT FROM AUTHOR]

Global exponential stability of non-autonomous delayed neural networks is discussed. A new sufficient condition ensuring the exponential stability for this type of neural networks by constructing a new suitable Lyapunov functionals. Since the condition does not impose boundedness on activation functions, it is less restrictive than some established in the earlier literature. [ABSTRACT FROM AUTHOR]

In this paper, a new approach on image registration is presented. We introduce a novel conception- normal vector information (NVI) - to evaluate the similarity between two images. NVI method takes advantage of the relationship between voxels in the image to extract the normal vector (NV) information of each voxel. Firstly, NVI criterion is presented. Then, based on the criterion, we find that NVI related metric has a quite perfect global optimal value on transformation parameter ranges. Finally, registration examples which are based on NVI criterion are provided. The result implies that the feature of smooth value distribution and one global optimal value that NVI metric has makes the optimization procedure much easier to be implemented in image registration. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel multi-stage algorithm for 3D medical image segmentation that is inspired by an improved Fast Marching method and a morphological reconstruction algorithm. The segmentation procedure consists of three steps: Connectivity Reduction, Hybrid segmentation, and Region recovery. The approach is tested on CT cardiac and MRI brain images, to demonstrate the effectiveness and accuracy of the technique. In order to validate this segmentation algorithm, a novel Radial Distance Based Validation (RDBV) method is proposed that provides a global accuracy (GA) measure. GA is calculated based on Local Radial Distance Errors (LRDE), where measured errors are along radii emitted from points along the skeleton of the object rather than the centroid, in order to accommodate more complicated organ structures. Using this GA measure, our results demonstrate that this multi-stage segmentation is fast and accurate, achieving approximately the same segmentation result as the watershed method, but with a processing speed of 3-5 times faster. [ABSTRACT FROM AUTHOR]

This paper expatiated on the process of numerical computing of brain electric field in electroencephalogram (EEG). Based on boundary element method (BEM), a 3D reconstruction of computing model is presented first, which is the premise of BEM computing in EEG. A simple but efficient triangular mesh generation method with constrained points is developed, furthermore, a mesh subdivision method is also put forward. Forward computation of EEG is investigated and acceptable results are obtained with the simulated experiments by using these methods. [ABSTRACT FROM AUTHOR]

Vascular stent is a small tubular device expanded into stenotic artery to restore natural blood flow. This paper introduces application background and research overview of medical vascular stent. A tubular mini stent was specially designed and manufactured for the small diameter coronary vessels. A computational and experimental method of research on biomechanics of vascular stent was presented. Computational simulation of stent deployment expanded by the balloon based on nonlinear finite element analysis was performed including large displacement and deformation, geometric and material nonlinearity. The experimental platform of in vitro stent expansion based on the machine vision technology was established and the image processing software was developed. The fabricated stent was tested on the assembled experimental equipments. Matching between the computational and experimental results was quite satisfactory. The experimental scheme provides powerful support for the computational analysis of stent biomechanics. [ABSTRACT FROM AUTHOR]

In this paper, we propose a topology control algorithm for constructing an energy-efficient spanning subgraph for a wireless multi-hop network. The constructed topology has the following properties: (1) it preserves a minimum-energy path between every pair of nodes; (2) it is biconnected, i.e., it can tolerate any one node failure and avoid network partition. Simulation results show that the constructed topology has a small average node degree, a small average transmission range and a constant power stretch factor. [ABSTRACT FROM AUTHOR]

Based on the Antibody Clonal Selection Theory of immunology, four immunologic regulation operators inspired by immune regulation mechanism of biology immune system are presented in this paper, and a corresponding algorithm, Immunologic Regulation Clonal Selection Algorithm (IRCSA), is put forward. The essential of immunologic regulation operators is to make fine adjustment among the candidates of the algorithm so as to make interrelations between antibodies more complicated and improve the stability, robustness and accuracy of the algorithm. Numeric experiments of function optimization indicate that the new algorithm is effective and useful. [ABSTRACT FROM AUTHOR]

Key frame extraction has been recognized as one of the important research issues in video retrieval. Key Frame Extraction based on Evolutionary Artificial Immune Network (KFE-EAIN) is proposed in this paper. To describe the distribution of video frame data, an artificial immune network is first evolved by video frame data. Then, key frame can be selected by minimal spanning tree of the network. KFE-EAIN does not require the number of clusters to be known beforehand. Otherwise, it can apply to both single shot and video sequence. Experimental results show that KFE-EAIN can effectively summarize content of a video with acceptable complexity. [ABSTRACT FROM AUTHOR]

Based on the clonal selection theory, a new Dynamic Multiobjective Optimization (DMO) algorithm termed as Clonal Selection Algorithm for DMO (CSADMO) is presented. The clonal selection, the nonuniform mutation and the distance method are three main operators in the algorithm. CSADMO is designed for solving continuous DMO and is tested on two test problems. The simulation results show that CSADMO outperforms another Dynamic Evolutionary Multiobjective Optimization (EMO) Algorithm: a Direction-Based Method (DBM ) in terms of finding a diverse set of solutions and in converging near the true Pareto-optimal front (POF) in each time step. [ABSTRACT FROM AUTHOR]

An Intelligent Particle Swarm Optimization (IPSO) for MO problems is proposed based on AER (Agent-Environment-Rules) model, in which Competition and Clonal Selection operator are designed to provide an appropriate selection pressure to propel the swarm population towards the Pareto-optimal front. Simulations and comparison with NSGA-II and MOPSO indicate that IPSO is highly competitive. [ABSTRACT FROM AUTHOR]

In this paper, a novel fusing method for fundus retinal images based on robust registration techniques is proposed. In order to construct precise fusion map, we apply a ‘coarse-to-fine' mapping strategy to accurately align pairs of identified vascular trees of retinas. A coarse mapping algorithm that exploits rigid model is first performed to maximize the goodness of fit between the vascular features over two time periods. However, the results suffer from local misalignment due to the inherent imprecise characteristics of the simplified model. A fine mapping algorithm is employed to eliminate ‘ghost vessels' based on a local elastic matching technique. The transformed vectors for pixels in the registered fundus image are conveniently calculated by combining the local move vector and the global model transformed vector. Experiment results demonstrate nearly perfect fusion maps of several retinal fundus images in terms of visual inspection. [ABSTRACT FROM AUTHOR]

There are two important factors to visualize ultrasound datasets for volume ray casting method. The first is an efficient method to skip over empty space and the second is an adequate noise filtering method. We propose a noise-insensitive hierarchical min-max octree. In preprocessing stage, we generate a filtered dataset and make a hierarchical min-max octree from the dataset. In rendering step, we exploit the hierarchical min-max octree only when rays skip over transparent region. If rays reach meaningful object, color and opacity values are computed from the original volume dataset. By adaptively using two datasets, our method increases image quality while reducing rendering time. [ABSTRACT FROM AUTHOR]

We present a novel algorithm SASAlignSimulated annealing with star-alignment. In the SASAlign, instead of starting with an initial solution chosen at random, we use the results formed by star-alignment to give a good starting point as the initial solution to the SA for further refinement. The time required by the algorithm scales linearly with the number of sequences in S, linearly with the number of iterations, and cube with the length of the sequences, that is O(Nnl3). Experiments on the BAliBASE benchmark database also show that the proposed algorithm is efficient, and prove to be competitive with and better than the other method HMMT. [ABSTRACT FROM AUTHOR]

A method to estimate long distance navigation of a mobile robot is proposed. The method uses the dead reckoning,sonar and infrared sensors to detect the landmarks. A corridor environment with equal spaced convex edges is applied as the mobile robot's moving space and the convex edges are used as landmarks for the robot mounted with the combined sensor system to estimate its position. The robot detects the convex edges using combined sensor system, and navigates in this corridor by using the information obtained from dead reckoning and combined sensor system based on the Extended Kalman. Experiment result show the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Microarray technology has been widely used in biological and medical research to observe a large number of gene expressions. However, such experiments are usually carried out with few replica or instances, which may lead to poor modelling and analysis. This paper suggests an approach to improve classification by using synthetic data. A new algorithm is proposed to estimate synthetic data value and the generated data are labelled by ensemble methods. Experiments with artificial data and real world data demonstrate that the proposed algorithm is able to generate synthetic data on uncertain regions of classifiers to improve effectiveness and efficiency of classification on microarray data sets. [ABSTRACT FROM AUTHOR]