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In the m-out-of-n oblivious transfer model, Alice has n messages, Bob has m choices. After the interaction between the two parties, Bob can get m but only m messages from the n messages, Alice cannot know which m messages Bob gets. In this paper, for the first time, we construct a m-out-of-n OT protocol based on the general secure public key system. Keywords: oblivious transfer, secure protocol, public key system. [ABSTRACT FROM AUTHOR]

The security for electronic payments is very important in electronic commerce. Many electronic payment protocols have been proposed recently to meet the security requirements. Since the design of a protocol is a difficult and error-prone task, the use of formal methods that allow for the verification of such protocols has received increasing attention. In this paper we take a look at the iKP from the point of view of the applied pi calculus. The iKP is described in the calculus and some security properties, mainly the authentication and anonymity properties, are verified. [ABSTRACT FROM AUTHOR]

Avatar techniques have rapidly progressed in recent years, and will be widely adopted to various applications. The paper proposes hierarchical approach for representation and control techniques for avatar behavior for simpler avatar control in various domains. We proposed three-layered architecture: task-level behavior, high-level motion, and primitive motion. Thus, the user controls avatar at task-level layer and does not have to concern about low-level animation data. Our goal is to support flexible and extensible representation and control of avatar behavior by hierarchical approach separating application domains and implementation tools. [ABSTRACT FROM AUTHOR]

This paper presents a Min-Max modular k-nearest neighbor (M3-k-NN) classification method for massively parallel text categorization. The basic idea behind the method is to decompose a large-scale text categorization problem into a number of smaller two-class subproblems and combine all of the individual modular k-NN classifiers trained on the smaller two-class subproblems into an M3-k-NN classifier. Our experiments in text categorization demonstrate that M3-k-NN is much faster than conventional k-NN, and meanwhile the classification accuracy of M3-k-NN is slightly better than that of the conventional k-NN. In practical, M3-k-NN has intimate relationship with high order k-NN algorithm; therefore, in theoretical sense, the reliability of M3-k-NN has been supported to some extend. [ABSTRACT FROM AUTHOR]

Recently, interests on the home network, that requires new services and new computing paradigms, have enormously been increased. Applying the mobile agent to the home network is expected to provide a new computing model. The mobile agent authentication is a preceding technology to apply the mobile agent concept to the home network environments. The existing public key based authentication scheme is not suitable to the home network devices which has the limited computation capability. In this paper, we propose a lightweight mobile agent authentication scheme based on the shared key and public key infrastructures. [ABSTRACT FROM AUTHOR]

A supply chain refers to any system which consists of multiple entities (companies or business units within an enterprise), that depend on each other in some way in conducting their businesses. In a supply chain, sales forecasting is highly complex due to the influence of internal and external environments. However, a reliable prediction of sales can improve the business strategy. Agent-based technology is considered suitable in providing near-optimal adaptive business and knowledge management strategies to help managers reduce both mental effort and search costs. This paper presents an agent-based approach which supports mobile agents as mediators between system entities. The proposed mobile agent-based system uses the publish/subscribe communication mechanism; therefore, system entities (like customers and suppliers) can dynamically connect and disconnect to the system at any time. The system uses a "Two-Leveled Mobile Agent Structure" and some design details of the sales forecasting process of the system based on a neural network approach are presented. [ABSTRACT FROM AUTHOR]

Scientific bibliographies in online databases provide a rich source of information for scientists in support of their research. In this paper, we propose a new method to predict possible relations between a starting, known concept of interest and other concepts by mining scientific literature databases like Medline. The central novel feature of our method is to predict new relations based on finding brothers of middle concepts within the concept hierarchy. The method can help researchers explore new research directions from current scientific literature. [ABSTRACT FROM AUTHOR]

This paper describes a two-phase method for filtering spam mails based on textual information and hyperlinks. Since the body of a spam mail has little text information, it provides insufficient hints to distinguish spam mails from legitimate mails. To resolve this problem, we follows hyperlinks contained in the email body, fetches contents of a remote webpage, and extracts hints (i.e., features) from original email body and fetched webpages. We divided hints into two kinds of information: definite information and less definite textual information. In our experiment, the method of fetching web pages achieved an improvement of F-measure by 9.4% over the method of using an original email header and body only. [ABSTRACT FROM AUTHOR]

The Internet is one of the most significant and efficient ways for communication technology which has the potential to revolutionize business and marketing techniques. To understand efficiently who is visiting our Web site and how they are using it is the primary key for the success of the marketing based on Internet. The traffic analysis systems via Web server log files only provide partial and non-real time information about customer activities on your Web site, it's insufficient to support better business decisions which need detailed and real time information about customers' activities on your Web site. In this paper, we provide our experiences on an implementation of the sophisticated analysis system based on click stream analysis technologies. A case study is also provided to show practical values of this system. [ABSTRACT FROM AUTHOR]

The judgment of forged passports plays an important role in the immigration control system and requires the automatic recognition of passports as the pre-phase processing. This paper, for the recognition of passports, proposed a novel method using the enhanced RBF network based on ART2. The proposed method extracts code sequence blocks and individual codes by applying the Sobel masking, the smearing and the contour tracking algorithms in turn to passport images. The enhanced RBF network was proposed and used for the recognition of individual codes, which applies the ART2 algorithm to the learning structure of the middle layer. The experiment results showed that the proposed method has superior in performance in the recognition of passport. [ABSTRACT FROM AUTHOR]

Image representation has been a fundamental problem for many real world applications, such as image database visualization, browsing, retrieval, etc. In this paper, we investigate the use of Laplacian Eigenmap (LE) for image representation and retrieval. Conventional, Principal Component Analysis (PCA) has been considered effective as to discovering the low dimensional structure of the image space. However, PCA can only discover the linear structure. It fails when the images are sampled from a low dimensional nonlinear manifold which is embedded in the high dimensional Euclidean space. By using Laplacian Eigenmap, we first build a nearest neighbor graph which models the local geometrical structure of the image space. A locality preserving mapping is then obtained to respect the graph structure. We compared the PCA and LE based image representations in the context of image retrieval. Experimental results show the effectiveness of the LE based representation. [ABSTRACT FROM AUTHOR]

A lot of clustering technologies are being applied to websites these days. A webserver cluster can be configured with either a high performance hardware switch or LVS (Linux Virtual Server) software. A high performance hardware switch has good performance but costs a great deal when constructing small and middle-sized websites. LVS, which is free of charge and has good performance, has commonly been used to construct webserver clusters. LVS is hampered by having a single front-end as it can raise a bottleneck with increased requests, and can result in the cluster system being unable to function. In this paper, we suggest new architecture for webserver clusters based on LVS with multiple front-ends which can also act as back-ends. This architecture removes the bottleneck, and is useful in constructing small and middle-sized websites. We also propose a scheduling algorithm to distribute requests equally to servers by considering their load. With this scheduling algorithm, a server will be able to respond directly to a client's request when its load is not too large. Otherwise, the server will redirect the request to the selected back-end with the lowest load. Through our experiments, we show that a webserver cluster with multiple front-ends increases the throughput linearly, while a webserver cluster with a single front-end increases the throughput curvedly. We hope that a webserver cluster with multiple front-ends will be suitable and efficient for constructing small and middle-sized websites in terms of cost and performance. [ABSTRACT FROM AUTHOR]

Dense regions discovery is an important knowledge discovery process for finding distinct and meaningful patterns from given data. The challenge in dense regions discovery is how to find informative patterns from various types of data stored in structured or unstructured databases, such as mining user patterns from Web data. Therefore, novel approaches are needed to integrate and manage these multi-type data repositories to support new generation information management systems. In this paper, we focus on discussing and purposing several discretization methods for large matrices. The experiments suggest that the discretization methods can be employed in practical Web applications, such as user patterns discovery. Keywords: Discretization, Dense regions discovery, Web mining, Web information system. [ABSTRACT FROM AUTHOR]

Instant messenger (IM) has grown rapidly to involve billions of users. It can eliminate long trails of voice mails and e-mails, and it is especially valuable to link remote individuals. The previous researches of the bad influences of IM are focused on interruption of desktop computing tasks. We take the security problems of IM into consideration because IM has many security risks that may have severed impacts. In this paper, we provide the requirements analysis of security systems to control IM's risks and our development efforts to implement these security systems in technical aspects. [ABSTRACT FROM AUTHOR]

Many group key agreement protocols (GKA) for secure group communication (SGC) based on the Diffie-Hellman key exchange principle have been proposed in literature. All of these protocols require member serialization and/or existence of a central entity. In this paper, we propose a simple group Diffie-Hellman key agreement protocol which removes these two limitations. Moreover, the new protocol needs minimum (two) rounds of rekeying process and efficiently support high dynamics. [ABSTRACT FROM AUTHOR]

This paper presents a four-step training method for increasing the efficiency of support vector machine (SVM) by simplifying the shape of separation hypersurface. First, a SVM is initially trained by all the training samples, thereby producing a number of support vectors. Second, the support vectors, which make the hypersurface highly convoluted, are excluded from the training set. Third, the SVM is re-trained only by the remaining samples in the training set. Finally, the complexity of the trained SVM is further reduced by approximating the separation hypersurface with a subset of the support vectors. Compared to the initially trained SVM by all samples, the efficiency of the finally-trained SVM is highly improved, without system degradation. [ABSTRACT FROM AUTHOR]

Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning approach or ensemble classifiers approach. However, both of them can not make a prediction at any time exactly. In this paper, we propose a novel strategy for the maintenance of knowledge. Our approach stores and maintains knowledge in ambiguous decision table with current statistical indicators. With our disambiguation algorithm, a decision tree without any time problem can be synthesized on the fly efficiently. Our experiment results have shown that the accuracy rate of our approach is higher and smoother than other approaches. So, our algorithm is demonstrated to be a real anytime approach. [ABSTRACT FROM AUTHOR]

This paper proposes a novel cell nucleus segmentation method for color esophageal biopsy image. For each nucleus of cell image, based on color characteristics of cell nucleus, a threshold separating the nucleus can be detected automatically in each RGB color component. According to the thresholds, two fuzzy domains are established for each color component with bell-curve and S-curve membership functions. Then we propose a novel growing snake to extract cell nucleus boundary. Described in polar coordinates, the proposed snake is driven by the potential energy and the growing energy integrating the fuzzification information of tristimulus components. The proposed model has low computation cost and strong anti-noise ability. The experiments on a number of cell images show encouraging results. [ABSTRACT FROM AUTHOR]

Prediction of protein structural class from primary structure is studied in this paper. Wavelet packet transform is used to decompose the corresponding numerical signal of protein into several sub-signals at different resolution scales. The auto-correlation functions based on the sub-signals are used as feature vectors of the protein. The Bayes decision rule is used as classification algorithm. Experiments show that for the same datasets, the prediction accuracy is improved compared with the existed methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a color image segmentation algorithm based on a finite mixture model and examine its application to natural scene segmentation. Gaussian mixture model (GMM) is first adopted to represent the statistical distribution of multi-colored objects. Then a deterministic annealing Expectation Maximization (DAEM) formula is used to estimate the parameters of the GMM. The experimental results show that the proposed DAEM can avoid the initialization problem unlike the standard EM algorithm during the maximum likelihood (ML) parameter estimation and natural scenes containing texts are segmented more efficiently than the existing EM technique. [ABSTRACT FROM AUTHOR]

Decision trees (DTs) and multi-layer perceptron (MLP) neural networks have long been successfully used to various pattern classification problems. Those two classification models have been applied to a number of diverse areas for the identification of ‘biologically relevant' molecules. Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDITOF MS) is a novel approach to biomarker discovery and has been successfully used in projects ranging from rapid identification of potential maker proteins to segregation of abnormal cases from normal cases. SELDI-TOF MS can contain thousands of data points. This high dimensional data causes a more complex neural network architecture and slow training procedure. In the approach we proposed in this paper, a decision tree is first applied to select possible biomarker candidates from the SELDI-TOF MS data. At this stage, the decision tree selects a small number of discriminatory biomarker proteins. This candidate mass data defined by the peak amplitude values is then provided as input patterns to the MLP neural network which is trained to classify the mass spectrometry patterns. The key feature of this hybrid approach is to take advantage of both models: use the neural network for classification with significantly lowdimensional mass data obtained by the decision tree. We applied this bioinformatics tool to identify proteomic patterns in serum that distinguish prostate cancer samples from normal or benign ones. The results indicate that the proposed method for mass spectrometry analysis is a promising approach to classify the proteomic patterns and is applicable for the significant clinical diagnosis and prognosis in the fields of cancer biology. [ABSTRACT FROM AUTHOR]

We have developed a software tool, called PASL, which predicts the transmembrane region and its topology by pruning the subcellular location. The main virtues of PASL are that it discriminates the integral proteins of the plasma membrane from the intracellular membranes, and it eliminates the possibility of misrecognition of the signal peptide as a transmembrane region. The transmembrane region prediction algorithm, which is based on the Hidden Markov Model, and the ER signal peptide detection architecture, which is based on neural networks, have been used for the actual implementation of a prototype. This paper mainly describes the prototype and how it works. [ABSTRACT FROM AUTHOR]

This paper presents a new method for automatically segmenting brain parenchyma and cerebrospinal fluid in routine single-echo magnetic resonance (MR) images. Our method is based on the weak membrane model. Weak membrane models can model intensity measurement at each voxel site to implement piecewise smoothness constraint, and at the same time model discontinuities to control the interaction between each pair of the neighboring pixel. Segmentation is obtained by seeking for the maximum a posteriori estimation of the regions and the boundaries by using Bayesian inference and neighborhood constraints based on Markov random fields (MRFs) or Gibbs random fields (GRFs) models. Our approach has the following desirable properties: (1) brain voxels can be accurately classified into white matter, grey matter and cerebrospinal fluid (CSF), and (2) relatively insensitive to noise and intensity inhomogeneity. [ABSTRACT FROM AUTHOR]

According to the progress of an information-oriented society, more human friendly systems are required. Such systems can be implemented by a kind of much more intelligent algorithms. In this paper we propose the possibility of the implementation of an intelligent algorithm from gene behavior of human beings, which has some properties such as self organization and self regulation. The regulation of gene behavior was widely analyzed by Boolean network. Also the SORE (Self Organizable and Regulating Engine) is one of those algorithms. We here describe the concepts of the implementation of an intelligent algorithm through the analysis of both gene regulatory network. [ABSTRACT FROM AUTHOR]

In order to use parallel computers in specific applications, algorithms need to be developed and mapped onto parallel computer architectures. Main memory access for shared memory system or global communication in message passing system deteriorate the computation speed. In this paper, it is found that the m-step generalization of the block Lanczos method enhances parallel properties by forming m simultaneous search direction vector blocks. QR factorization, which lowers the speed on parallel computers, is not necessary in the m-step block Lanczos method. The m-step method has the minimized synchronization points, which resulted in the minimized global communications and main memory accesses compared to the standard method. [ABSTRACT FROM AUTHOR]

This paper presents an improved adaptive neighborhood contrast enhancement (ANCE) method for improvement of medical image quality. The ANCE method provides the advantages of enhancing or preserving image contrast while suppressing noise. However, it has a drawback. The performance of the ANCE method largely depends on how to determine the parameters used in the processing steps. The present study proposes a novel method for optimal and automatic determination of threshold-value and neighborhood-size parameters using entropy. To quantitatively compare the performance of the proposed method with that of the ANCE method, computer-simulated images are generated. The output-to-input SNR ratio and the mean squared error are used as comparison criteria. Results demonstrate the superiority of the proposed method. Moreover, we have applied our new algorithm to echocardiograms. Our results show that the proposed method has the potential to become useful for improvement of image quality of medical images. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new automated approach to extract the centerlines from 2-D angiography. The centerline extraction is the basis of 3-D reconstruction of the blood vessels, so the accurate localization of centerlines counts for much. The characteristics of multiscale Gabor even filter, flexible frequency bands and enhancement effects, are fully utilized to detect centerlines of the blood vessels in various size. [ABSTRACT FROM AUTHOR]

In positron emission tomography (PET) image reconstruction, classical regularization methods are usually used to overcome the slow convergence of the expectation maximization (EM) methods and to reduce the noise in reconstructed images. In this paper, the fuzzy set theory was employed into the reconstruction procedure. The observations of emission counts were viewed as Poisson random variables with fuzzy mean values. And the fuzziness of these mean values was modelled through choosing an appropriate fuzzy membership function with several adjustable parameters. Coupled with this fuzzy method, the new fuzzy penalized likelihood expectation maximization (FPL-EM) method was proposed for PET image reconstruction. Simulation results showed that the proposed method might perform better in both the image quality and the convergence rate compared with the classical maximum likelihood expectation-maximization (ML-EM). [ABSTRACT FROM AUTHOR]

Recent progress in biology and medical sciences has led to an explosive growth of biomedical data. Extracting relevant knowledge from such volumes of data represents an enormous challenge and opportunity. This paper assesses several approaches to improving neural network-based biomedical pattern discovery and visualization. It focuses on unsupervised classification problems, as well as on interactive and iterative methods to display, identify and validate potential relevant patterns. Clustering and pattern visualization models were based on the adaptation of a self-adaptive neural network known as Growing Self Organizing Maps. These models provided the basis for the implementation of hierarchical clustering, cluster validity assessment and a method for monitoring learning processes (cluster formation). This framework was tested on an electrocardiogram beat data set and data consisting of DNA splice-junction sequences. The results indicate that these techniques may facilitate knowledge discovery tasks by improving key factors such as predictive effectiveness, learning efficiency and understandability of outcomes. [ABSTRACT FROM AUTHOR]

This paper proposes the algorithm that additional term is added to an objective function of noise clustering algorithm to define fuzzy subspaces in a fuzzy regression manner to identify fuzzy subspaces and parameters of the consequent parts simultaneously and obtain robust performance against outliers. [ABSTRACT FROM AUTHOR]

This paper proposes a new 3D visualization method for MRI head data based upon direct volume rendering. Surface rendering has difficulties in displaying speckles due to information loss during the surface construction procedure, whereas direct volume rendering does not have this problem, though managing MR head image data is not an easy task. In our method, brain structures are extracted from MR images, and then embedded back into the remaining regions. To extract the brain structure, we use a combination of thresholding, morphology and SNAKES operations. Experimental results show that our method makes it possible to simultaneously visualize all the anatomical organs of human brains in three dimensions. [ABSTRACT FROM AUTHOR]

Both volume and shape of the organs within the brain such as hippocampus indicate their abnormal neurological states such as epilepsy, schizophrenia, and Alzheimer's diseases. This paper proposes a new method for the analysis of hippocampal shape using an integrated Octree-based representation, consisting of meshes, voxels, and skeletons. Initially, we create multi-level meshes by applying the Marching Cube algorithm to the hippocampal region segmented from MR images. Then, we convert the polygonal model to intermediate binary voxel representation by a depth-buffer based voxelization, which makes it easier to extract a 3-D skeleton as well as relate to original MR images. As a similarity measure between the shapes, we compute L2 norm and Hausdorff distance for each sampled mesh by shooting the rays fired from the extracted skeleton. It also allows an interactive analysis because of the octree-based data structure. Moreover, it increases the speed of analysis without degrading accuracy by using a hierarchical level-of-detail approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-stage level set segmentation framework to extract vascular tree from magnetic resonance angiography(MRA). First, we smooth the isosurface of MRA by anisotropic diffusion filter. Then this smoothed surface is treated as the initial localization of the desired contour, and used in the following geodesic active contours method, which provides accurate vascular structure. Results on cases demonstrate the effectiveness and accuracy of the approach. [ABSTRACT FROM AUTHOR]

In taxonomy, frog is a member of the Anura order under the class of Amphibia with many species. Some species of frog share a common biological function with other species, some have unique biological activities. Peptides contained in the frog species play an important role in these biological functions. In this paper, we investigate the degree of similarity of skin secretion peptide profiles between species. After the ESI-MS spectra from a sample of amphibian skin secretions are interpreted and deconvoluted by a heuristic deconvolution algorithm [1], a mass spectral profile is established. An overlap comparison algorithm is proposed and applied for the comparison of the peptide profiles between species. Results from the interspecies comparison and intraspecies comparison show that the peptide profiles of frog skin secretion can reflect their taxonomy classification. [ABSTRACT FROM AUTHOR]

Microcalcifications in digital mammogram images can be an early sign of breast cancer. It is very challenging, however, to detect all microcalcifications since they appear as slightly brighter spots than their backgrounds. In this paper, a new method is proposed to efficiently detect all microcalcifications using morphological bandpass filters. Morphological bandpass filters, each with two different structuring elements, are tuned to isolate frequency components of microcalcifications in this method. Experimental results show that the proposed method with bandpass filters can recognize microcalcifications with a higher visibility and more accurate positions and sizes comparing to the well-known wavelet transform method. [ABSTRACT FROM AUTHOR]

The stability of non-autonomous delayed cellular neural networks is studied in this paper. By applying a delay differential inequality, a new sufficient condition which guarantees the global asymptotic stability is established. Since the condition does not impose differentiability on delay, it is less conservative than some established in the earlier references. [ABSTRACT FROM AUTHOR]

This paper presents a formal extension to describe simple chain-like biomolecular structures and related operations based on the calculus of BioAmbients which serves as a basic framework to model compartments. In our extension, we represent these biomolecules by means of an abstract chain structure possibly with paired molecules at each node, including the effect between molecules, the intra interaction and movement inside linked molecules, and the inter interaction and movement between linked molecules and general compartments. Moreover, An example is given to model the one of the major phases during the process of protein synthesis. [ABSTRACT FROM AUTHOR]

In this paper, an influence of moment arms on the lumbar spine subjected to the follower load was investigated. A two-dimensional finite element model of the lumbar spine including idealized trunk muscles was developed to evaluate the muscle force activation generating the follower load when the lengths of moment arms varied. The follower forces, the shear forces and the resultant joint moments were also calculated to confirm the generation of the follower load. Finally, the dependence of the lengths of moment arms on the deformed shape of the lumbar spine model concerning the spinal stability was examined. [ABSTRACT FROM AUTHOR]

Feature selection is widely used to reduce dimension and remove irrelevant features. In this paper, particle swarm optimization is employed to select feature subset for classification task and train RBF neural network simultaneously. One advantage is that both the number of features and neural network configuration are encoded into particles, and in each iteration of PSO there is no iterative neural network training sub-algorithm. Another is that the fitness function considers three factors: mean squared error between neural network outputs and desired outputs, the complexity of network and the number of features, which guarantees strong generalization ability of RBF network. Furthermore, our approach could select as small-sized feature subset as possible to satisfy high accuracy requirement with rational training time. Experimental results on four datasets show that this method is attractive. [ABSTRACT FROM AUTHOR]

This paper proposes a method of combining two n-gram language models to construct a single language model. One of the corpora is constructed from a very small corpus of the right domain of interest, and the other is constructed from a large but less adequate corpus. This method is based on the observation that a small corpus from the right domain has high quality n-grams but suffers from sparseness problem, while a large corpus from another domain is inadequately biased, but easy to obtain bigger size. The basic idea behind dual-source backoff is basically the same with Katz's backoff. We ran experiments with 3-gram language models constructed from newspaper corpora of several millions to tens of millions words together with models from smaller broadcast news corpora. The target domain was broadcast news. We obtained significant improvement by incorporating a small corpus around one thirtieth size of the newspaper corpus. [ABSTRACT FROM AUTHOR]

The Differential Evolution (DE) algorithm is a floating-point encoded Evolutionary Algorithm for global optimization. It has been demonstrated to be an efficient, effective, and robust optimization method especially for problems containing continuous variables. The paper concerns applying a DE-based method to perform function approximation using Gaussian Radial Basis Function (RBF) networks with variable widths. This method selects centres and decides weights of the networks heuristically, then uses the Differential Evolution algorithm for local and global tuning iteratively to find the widths of RBFs. The method is demonstrated by training networks that approximate a set of functions. The Mean Square Error from the desired outputs to the actual network outputs is applied as the objective function to be minimized. A comparison of the net performances with other approaches reported in the literature has been performed. The proposed approach effectively overcomes the problem of how many radial basis functions to use. The obtained initial results suggest that the Differential Evolution based method is an efficient approach in approximating functions with growing radial basis function networks and the resulting network generally improves the approximation results reported for continuous mappings. Keywords: Radial Basis Function, Neural Network, Differential Evolution, Evolutionary Algorithm. [ABSTRACT FROM AUTHOR]

Previous mechanical heart models were mainly constructed for analysis of the left ventricle behavior, and the right ventricle was almost not included because of its complex geometry structure. The motion of the healthy right ventricle and its alteration due to disease are not currently well understood. In this paper, a 3-D finite element biventricular model with real geometric shape and fiber structure has been constructed and the right ventricular wall motion and deformation during systole phase have been simulated. The results show that: 1) The right ventricular free wall moves towards the septum, and at the same time, the base and middle of the free wall move towards the apex, which reduce the volume of right ventricle; 2) The minimum principal strain is largest at the apex, then at the middle of free wall, and its direction is in the approximate direction of the epicardial muscle fibers. The results are in good accordance with solutions obtained from MR tagging images. [ABSTRACT FROM AUTHOR]

This paper discusses an attempt to improve the resolution of a seismic record by using balanced biorthogonal multi-wavelet transform. Wavelet transform offers a lot of local information of seismic signals relevant to time, space, frequency and wave number. Compensation for the loss of high frequency information allows an improvement of the resolution ratio of the seismic record. Numerical results show that the suggested method has several advantages over single wavelet in feasibility and effectiveness and the practice shows some satisfactory results. [ABSTRACT FROM AUTHOR]

The paper investigates into the early and late congruences on the asymmetric χ≠-processes. The two bisimilarities are defined and their closure properties are given. Sound and complete equational systems are constructed for both congruences. [ABSTRACT FROM AUTHOR]

This paper proposes a QoS routing algorithm (called MIS-LB), which extends the definition of nonlinear path distances into the link-sharing and load balancing. Based on the extension, it can find feasible paths by the shortest-first criterion, but also adjust the link-sharing and the loads among multiple feasible paths. Finally, the simulation results show that it outperforms other multiple-constraints routing, such as TAMCRA and H_MCOP. [ABSTRACT FROM AUTHOR]

In this paper, we propose a modified estimation of distribution algorithm HCFA (History information based Constraint Factorization Algorithm) to solve the optimization problem of additivelydecomposed function with constraints. It is based on factorized distribution instead of penalty function and any transformation to a linear model or others. The history information is used and good results can be achieved with small population size. The feasibility of the new algorithm is also given. [ABSTRACT FROM AUTHOR]

This paper presents a high performance service based mobile agent computation model. The scalability and reliability of this model is secured through the service clone policy and access privilege policy. With the introduction of service density of group, we can further decrease resource waiting and balance the service occupancy for the whole network. [ABSTRACT FROM AUTHOR]

The notion of program equivalence plays a fundamental role in the understanding of programming related issues in the framework of concurrent/distributive/mobile/global/grid computing. Many observational equivalences have been proposed in the literature. These equivalences are based on the intuition that different classes of environments have different observing powers. The paper provides a formalization of the observations of environments. This formalization leads to an equivalence relation called global bisimulation. We examine this relation in some well known computing models. [ABSTRACT FROM AUTHOR]

It is difficult to satisfy most of the performance targets by using the PID control law only, if the plants are the processes with uncertain time-delay, varying parameters and non-linearity. For this reason a genetic algorithm based neuro-fuzzy network adaptive PID controller is proposed in this paper. The neuro-fuzzy network is used to amend the parameters of the PID controller online, the global optimal parameters of the network are found with a high speed, and the improved genetic algorithm is introduced to overcome the local optimum defect of the BP algorithm. Finally, the simulation experiment of the control method on the tobacco-drying control process is performed. The simulation results demonstrate that this kind of control method is effective. [ABSTRACT FROM AUTHOR]

The constraint problem can be transformed to an optimization problem. In this paper a new hybrid algorithm—MSCGA was introduced which mixes genetic algorithm with chaos optimization method. The character of this new method is that the mechanism of the GA was not changed but the search space and the coefficient of adjustment was reduced continually and this can lead generation to evolve to the next generation in order to produce better optimization individuals. It can improve the performance of the GA and get over the disadvantage of the GA. The examination indicates that this algorithm can show a better performance than the normal GA and other hybrid methods in solving a geometric constraint and acquires a satisfying result. [ABSTRACT FROM AUTHOR]