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This paper proposes a novel approach for directly tuning the gaussian kernel matrix for one class learning. The popular gaussian kernel includes a free parameter, σ, that requires tuning typically performed through validation. The value of this parameter impacts model performance significantly. This paper explores an automated method for tuning this kernel based upon a hill climbing optimization of statistics obtained from the kernel matrix. [ABSTRACT FROM AUTHOR]

This paper discusses an alternative approach to parameter optimization of prototype-based learning algorithms that aim to minimize an objective function based on gradient search. The proposed approach is a stochastic optimization method called the Cross Entropy (CE) method. The CE method is used to tackle the initialization sensitiveness problem associated with the original generalized Learning Vector Quantization (GLVQ) algorithm and its variants and to locate the globally optimal solutions. We will focus our study on a variant which deals with a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, highly competitive numerical results on real world data sets are reported. [ABSTRACT FROM AUTHOR]

Multidimensional Scaling algorithms (MDS) are useful tools that help to discover high dimensional object relationships. They have been applied to a wide range of practical problems and particularly to the visualization of the semantic relations among documents or terms in textual databases. The MDS algorithms proposed in the literature often suffer from a low discriminant power due to its unsupervised nature and to the ‘curse of dimensionality'. Fortunately, textual databases provide frequently a manually created classification for a subset of documents that may help to overcome this problem. In this paper we propose a semi-supervised version of the Torgerson MDS algorithm that takes advantage of this document classification to improve the discriminant power of the word maps generated. The algorithm has been applied to the visualization of term relationships. The experimental results show that the model proposed outperforms well known unsupervised alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we propose an evolutionary approach to deal with shortcomings on conventional focused crawling systems in semantic web environment. Thereby, meta-evolution strategy for collaboration among multiple crawlers has to be efficiently carried out. It is based on incremental aggregation of partial semantic structures extracted from web resources, which are in advance annotated with local ontologies. To do this, we employ similarity-based matching algorithm, so that fitness function is formulated by summing all possible semantic similarities. As a result, the best mapping condition (i.e., the fitness is maximized) is obtained for efficiently i) reconciling semantic conflicts between multiple crawlers, and ii) evolving semantic structures of web spaces over time. [ABSTRACT FROM AUTHOR]

In this paper, we examine analysis of clusters of labeled samples to identify their underlying hierarchical structure. The key in this identification is to select a suitable measure of dissimilarity among clusters characterized by subpopulations of the samples. Accordingly, we introduce a dissimilarity measure suitable for measuring a hierarchical structure of subpopulations that fit the mixture model. Glass identification is used as a practical problem for hierarchical cluster analysis, in the experiments in this paper. In the experimental results, we exhibit the effectiveness of the introduced measure, compared to several others. [ABSTRACT FROM AUTHOR]

In this paper we present a well designed method that makes use of edge information to extract textual blocks from gray scale document images. It aims at detecting textual regions on heavy noise infected newspaper images and separate them from graphical regions. The algorithm traces the feature points in different entities and then groups those edge points of textual regions. Finally feature based connected component merging was introduced to gather homogeneous textual regions together within the scope of its bounding rectangles. The proposed method can be used to locate text in-group of newspaper images with multiple page layouts. Initial results are encouraging, then they are experimented with considerable number of newspaper images with different layout structures and promising results were obtained. This finds its major application in digital libraries for OCR where information can be of different quality depending on the age of the scanned paper. [ABSTRACT FROM AUTHOR]

In this paper, we present a HMM based system that is used to recognize ultra low resolution text such as those frequently embedded in images available on the web. We propose a system that takes specifically the challenges of recognizing text in ultra low resolution images into account. In addition to this, we show in this paper that word models can be advantageously built connecting together sub-HMM-character models and inter-character state. Finally we report on the promising performance of the system using HMM topologies which have been improved to take into account the presupposed minimum length of each character. [ABSTRACT FROM AUTHOR]

An automated approach for computation of the frontal plane QRS vector and an important observation of its clinical significance is described in this paper. Frontal plane QRS vector is computed from the six frontal plane leads ( Standard leads I, II, III , AVR, AVL and AVF). The R-R interval of each ECG wave is detected by square derivative technique. The baseline or isoelectric level of every ECG wave is determined. After that the net positive or net negative deflection (NQD) of QRS complex is detected. Net positive or net negative deflection in any lead is obtained by subtracting the smaller deflection (+ve or -ve) from the larger deflection (-ve or +ve). An algorithm is developed for computation of the exact angle,amplitude and direction of the frontal plane QRS vector from maximum and minimum NQD. In the present work, the PTB diagnostic ECG database of normal and Myocardial Infarction (MI) subjects is used for computation of the QRS vector. An interesting clinical observation that, the rotation of QRS axis for MI data may significantly detect the region of the infarcted cardiac wall, is reported in this paper. [ABSTRACT FROM AUTHOR]

Automatic tracking of references involves aggregating and synthesizing references through World Wide Web, thereby introducing greater efficiency and granularity to the task of finding publication information. This paper discusses the design and implementation of crawler-based reference tracking system, which has the advantage of online reference filtering. The system automatically analyses the semantic relevance of the reference article by harvesting keywords and their meanings, from title and abstract of the respective article. Indirectly this attempts to improve the performance of the reference database by reducing the articles that are actually being downloaded thereby improving the performance of the system. The number of levels for recursive downloads of reference articles are specified by the user. According to user's interest the system tracks up the references required for the understanding of the seed article, stores them in the databases and projects the information by threshold based view filtering. [ABSTRACT FROM AUTHOR]

This paper presents a framework for using a case-based reasoning system for stock analysis in financial market. The unique aspect of this paper is the use of a hierarchical structure for case representation. The system further incorporates a multi-criteria decision-making algorithm which furnishes the most suitable solution with respect to the current market scenario. Two important aspects of financial market are addressed in this paper: stock evaluation and investment planning. CBR and multi-criteria when used in conjunction offer an effective tool for evaluating goodness of a particular stock based on certain factors. The system also suggests a suitable investment plan based on the current assets of a particular investor. Stock evaluation maps to a flat case structure, but investment planning offers a scenario more suited for structuring the case into successive detailed layers of information related to different facets. This naturally leads to a hierarchical case structure. [ABSTRACT FROM AUTHOR]

This paper presents a computer vision guidance system for agricultural vehicles. This system is based on a segmentation algorithm that uses an optimum threshold function in terms of minimum quadratic value over a discriminant based on the Fisher lineal discriminant. This system has achieved not only very interesting results in the sense of segmentation, but it has also guided successfully a vehicle in a real world environment. [ABSTRACT FROM AUTHOR]

Transmission of images and video over unreliable channels produces visual artifacts due to either loss or corruption of bit streams. Error concealment schemes aim at reducing such degradation and improving the visual quality. Error concealment schemes usually conceal errors of a specific type. This points towards the possibility of combining more than one scheme, for efficient error concealment and better visual effects. In this paper, we propose a scheme for error concealment using a combination of watermarking and Projection Onto Convex Sets (POCS). Watermarking an image using the information derived from the host data itself conceals errors initially and those which are prominent even after this stage are concealed through POCS. The proposed method can be used for concealing errors in images and intra coded video by preserving average values through watermarking and edge information through POCS. Simulation results of POCS, watermarking and watermarking with POCS are given in the paper to show the advantage of combining both the methods. [ABSTRACT FROM AUTHOR]

Applications in various domains often lead to very large and frequently high-dimensional data. Successful algorithms must avoid the curse of dimensionality but at the same time should be computationally efficient. Finding useful patterns in large datasets has attracted considerable interest recently. The primary goal of the paper is to implement an efficient Hybrid Tree based clustering method based on CF-Tree and KD-Tree, and combine the clustering methods with KNN-Classification. The implementation of the algorithm involves many issues like good accuracy, less space and less time. We will evaluate the time and space efficiency, data input order sensitivity, and clustering quality through several experiments. [ABSTRACT FROM AUTHOR]

Focusing on the application of Intelligent Security Supervisory Control System, this paper proposes a new human activity recognition approach in which the Background Subtraction and the Time-stepping are averaged by weights to implement the precise extraction of moving human contour. In this way, the incompleteness of the extracting objects contour resulting from the color comparability between the human and the background can be resolved. Moreover, an ant colony clustering algorithm is applied to estimate and classify the body posture. Finally, Discrete Hidden Markov Models is used for human posture training, modeling and activity matching to recognize the human motion. Experiment results have shown that this method gives stable performances and good robustness. [ABSTRACT FROM AUTHOR]

This paper addresses a service-mining technique and applies this technique to improve the services of vehicle service centers. We propose a service-mining system and its data structure to discover the most important services required through analyzing service records, feedback records and the available products. The system can improve the quality of mining automatically by updating mining strategies regularly. [ABSTRACT FROM AUTHOR]

Visual analysis of human motion from video sequences is one of the most active research topics in the field of computer vision. This research has certain practical value and can be widely applied in some places such as: bank,hotel,garage,government department,large public facility. In this paper we present a novel method for judging irregular behavior based on treading track. Firstly, we use an object detection method in which the background subtraction method and the time difference method are averaged by weights to detect moving body, and then we judge whether someone is suspicious or not on the basis of treading track, it improves the judgment ability of irregular behavior recognition and experiment results have shown that this method gives static performances and good robustness. [ABSTRACT FROM AUTHOR]

Formalized ontology model and its semantic consistency checking has become one of the highlights in knowledge engineering research. This paper constructs an ontology model based on description logics. The term consistency checking of this model is divided into four types: term satisfiability checking, term subsumption checking, term equivalence checking and term disjointness checking. Then the proof that the four types of checking can be resolved into subsumption checking of term extension is provided in this paper. Moreover, the ontology term consistency algorithm using inference of description logics is proposed and tested by examples. [ABSTRACT FROM AUTHOR]

The emphasis nowadays on individual information needs and personalization of content as well as the level of complexity involved in some domains necessitates the use of Knowledge Engineering approaches. In particular, product and performance support is a complex and knowledge-intensive domain which despite the advances in the last few years, still suffers from the lack of information and knowledge models. This paper addresses this problem by offering definitions the for the product and performance support domain suggesting a problem-solving approach which uses the proposed knowledge models to adapt existing and construct entirely new solutions. [ABSTRACT FROM AUTHOR]

The AMP-activated protein kinase (AMPK) acts as a metabolic master switch regulating several intracellular systems. The effect of AMPK on muscle cellular energy status makes this protein a promising pharmacological target for disease treatment. With increasingly available AMPK regulation data, it is critical to develop an efficient way to analyze the data since this assists in further understanding AMPK pathways. Bayesian networks can play an important role in expressing the dependency and causality in the data. This paper aims to analyse the regulation data using B-Course, a powerful analysis tool to exploit several theoretically elaborate results in the fields of Bayesian and causal modelling, and discover a certain type of multivariate probabilistic dependencies. The identified dependency models are easier to understand in comparison with the traditional frequent patterns. [ABSTRACT FROM AUTHOR]

Discovering resource descriptions and merging results obtained from remote search engines are two key issues in distributed information retrieval studies. In uncooperative environments, query-based sampling and normalizing scores based merging strategies are well-known approaches to solve such problems. However, such approaches only consider the content of the remote database and do not consider the retrieval performance. In this paper, we address the problem that in peer to peer information systems and argue that the performance of search engine should also be considered. We also proposed a collection profiling strategy which can discover not only collection content but also retrieval performance. Web-based query classification and two collection fusion approaches based on the collection profiling are also introduced in this paper. Our experiments show that our merging strategies are effective in merging results on uncooperative environment. [ABSTRACT FROM AUTHOR]

One of the issues for Web-based learning applications is to adaptively provide personalized instructions for different learning activities. This paper proposes a high level colored timed Petri Net based approach to providing some level of adaptation for different users and learning activities. Examples are given to demonstrate how to realize adaptive interfaces and personalization. Future directions are also discussed at the end of this paper. [ABSTRACT FROM AUTHOR]

It has been widely recognized that the relative priority of requirements can help developers to resolve inconsistencies and make some necessary trade-off decisions. However, for most distributed development such as Viewpoints-based approaches, different stakeholders may assign different levels of priority to the same shared requirements statement from their own perspectives. The disagreement in the local priorities assigned to the same shared requirements statement often puts developers into a dilemma during inconsistency handling process. As a solution to this problem, we present a merging-based approach to handling inconsistency in the Viewpoints framework in this paper. In the Viewpoints framework, each viewpoint is a requirements collection with local prioritization. Informally, we transform such a requirements collection with local prioritization into a stratified knowledge base. Moreover, the relationship between viewpoints is considered as integrity constraints. By merging these stratified knowledge bases, we then construct a merged knowledge base with a global prioritization, which may be viewed as an overall belief in these viewpoints. Finally, proposals for inconsistency handling are derived from the merged result. The global prioritization as well as the local prioritization may be used to argue these proposals and to help developers make a reasonable trade-off decision on handling inconsistency. [ABSTRACT FROM AUTHOR]

Automatic segmentation of sub-cortical structures has great use in studying various neurodegentative diseases. In this paper, we propose a fully automatic solution to this problem through the utilization of a distribution atlas built from a set of training MR images. Our model consists of two major components: a local likelihood based active contour (LLAC) model and a guiding probabilistic atlas. The former has a very strong ability in standing out the structures that are in low contrast with the surrounding tissues. The latter has the functionality of defining and leading the segmentation procedure to capture the structure of interest. Formulated under the maximum a posterior framework, probabilistic atlas for the structure of interest, e.g. caudate, putamen, can be seamlessly integrated into the level set evolution procedure, and no thresholding step is needed for capturing the target. [ABSTRACT FROM AUTHOR]

The ability to recognize the strokes drawn by the user, is central to most sketch-based interfaces. However, very few solutions that rely on recognition are robust enough to make sketching a definitive alternative to traditional WIMP user interfaces. In this paper, we propose an approach based on classification that given an unconstrained sketch, can robustly assign a label to each stroke that comprises the sketch. A key contribution of our approach is a technique for grouping strokes that eliminates outliers and enhances the robustness of the classification. We also propose a set of features that capture important attributes of the shape and mutual relationship of strokes. These features are statistically well-behaved and enable robust classification with Support Vector Machines (SVM). We conclude by presenting a concrete implementation of these techniques in an interface for driving facial expressions. [ABSTRACT FROM AUTHOR]

Visualization is a technique used to facilitate the understanding of scientific results such as large data sets and maps. Provenance techniques can also aid in increasing the understanding and acceptance of scientific results by providing access to information about the sources and methods which were used to derive them. Visualization and provenance techniques, although rarely used in combination, may further increase scientists' understanding of results since the scientists may be able to use a single tool to see and evaluate result derivation processes including any final or partial result. In this paper we introduce Probe-It!: a visualization tool for scientific provenance information that enables scientists to move the visualization focus from intermediate and final results to provenance back and forth. To evaluate the benefits of Probe-It!, in the context of maps, this paper presents a quantitative user study on how the tool was used by scientists to discriminate between quality results and results with known imperfections. The study demonstrates that only a very small percentage of the scientists tested can identify imperfections using maps without the help of knowledge provenance and that most scientists, whether GIS experts, subject matter experts (i.e., experts on gravity data maps) or not, can identify and explain several kinds of map imperfections when using maps together with knowledge provenance visualization. [ABSTRACT FROM AUTHOR]

Academia and search engine industry followers consider personalization as the future of search engines, and this fact is well supported by the tremendous amount of research in this field. However the impact of technological advancement seems to be focused towards bringing more relevant results to the users - not the way it is usually presented to the users. User archives are useful resources which can be exploited more efficiently if reusability is promoted appropriately. In this paper, we present a theoretical framework which can sit on top of existing search technologies and deliver visually enhanced user experience and archival reusability. Contribution of this paper is two fold; first - visual interface for personal search engine setup, self-updating user interests, and session mapping based on modified spider graph; and secondly - enabling better archival reusability through user archival maps, session maps, interest specific maps and visual bookmarking. [ABSTRACT FROM AUTHOR]

In this paper, a hierarchical shape decomposition algorithm is proposed, which integrates the advantages of skeleton-based and minima-rule-based meaningful segmentation algorithms. The method makes use of new geometrical and topological functions of skeleton to define initial cutting critical points, and then employs salient contours with negative minimal principal curvature values to determine natural final boundary curves among parts. And sufficient experiments have been carried out on many meshes, and shown that our framework can provide more reasonable perceptual results than single skeleton-based [8] or minima-rule-based [15] algorithm. In addition, our algorithm not only can divide a mesh of any genus into a collection of genus zero, but also partition level-of-detail meshes into similar parts. [ABSTRACT FROM AUTHOR]

Face recognition is one of the most successful applications in biometric authentication. However, methods reported in the literature still suffer from some problems which prevent the further development in face recognition. This paper presents a novel robust method for face recognition under near infrared (NIR) lighting condition based on Extended Local Binary Pattern (ELBP), which solves the problems produced by variations of illumination rightly, since the NIR images are insensitive to variations of ambient lighting, and ELBP can extract adequate texture features form the NIR images. By combining the local feature vectors, a global feature vector is formed and as the global feature vectors extracted by ELBP operator often have very high dimensions, a classifier has been trained using the AdaBoost algorithm to select the most representative features for better performance and dimensionality reduction. Compared with the huge number of features produced by ELBP operator, only a small part of the features are selected in this paper, which saves much computation and time cost. The comparison with the results of classic algorithms proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Content based image retrieval (CBIR), a technique which uses visual contents to search images from the large scale image databases, is an active area of research for the past decade. It is increasingly evident that an image retrieval system has to be domain specific. In this paper, we present an algorithm for retrieving images with respect to a database consisting of engineering/computer-aided design (CAD) models. The algorithm uses the shape information in an image along with its 3D information. A linear approximation procedure that can capture the depth information using the idea of shape from shading has been used. Retrieval of objects is then done using a similarity measure that combines shape and the depth information. Plotted precision/recall curves show that this method is very effective for an engineering database. [ABSTRACT FROM AUTHOR]

Schlick's approximation of the term xp is used primarily to reduce the complexity of specular lighting calculations in graphics applications. Since moment functions have a kernel defined using a monomial xpyp, the same approximation could be effectively used in the computation of normalized geometric moments and invariants. This paper outlines a framework for computing moments of various orders of an image using a simplified kernel, and shows the advantages provided by the approximating function through a series of experimental results. [ABSTRACT FROM AUTHOR]

Floating mines are a significant threat to the safety of ships in theatres of military or terrorist conflict. Automating mine detection is difficult, due to the unpredictable environment and high requirements for robustness and accuracy. In this paper, a floating mine detection algorithm using motion analysis methods is proposed. The algorithm aims to locate suspicious regions in the scene using contrast and motion information, specifically regions that exhibit certain predefined motion patterns. Throughput of the algorithm is improved with a parallel pipelined data flow. Moreover, this data flow enables further computational performance improvements though special hardware such as field programmable gate arrays (FPGA) or Graphics Processing Units (GPUs). Experimental results show that this algorithm is able to detect mine regions in the video with reasonable false positive and minimum false negative rates. [ABSTRACT FROM AUTHOR]

A tensor-based optical flow algorithm is presented in this paper. This algorithm uses a cost function that is an indication of tensor certainty to adaptively adjust weights for tensor computation. By incorporating a good initial value and an efficient search strategy, this algorithm is able to determine optimal weights in a small number of iterations. The weighting mask for the tensor computation is decomposed into rings to simplify a 2D weighting into 1D. The devised algorithm is well-suited for real-time implementation using a pipelined hardware structure and can thus be used to achieve real-time optical flow computation. This paper presents simulation results of the algorithm in software, and the results are compared with our previous work to show its effectiveness. It is shown that the proposed new algorithm automatically achieves equivalent accuracy to that previously achieved via manual tuning of the weights. [ABSTRACT FROM AUTHOR]

This paper shows a strategy based on simulated annealing for the optimal tuning of a PID controller to deal with time-varying delay. The main goal is to minimize the integral time absolute error (ITAE) performance index and the overshoot for a drilling-force control system. The proposed strategy is compared with other classic tuning rules (the Ziegler-Nichols and Cohen-Coon tuning formulas). Other tuning laws derived from genetic algorithms and the Simplex search algorithm for unconstrained optimization are also included in the comparative study. The results demonstrate that simulated annealing provides an optimal tuning of the PID controller, which means better transient response (less overshoot) and less ITAE than with other methods. [ABSTRACT FROM AUTHOR]

This paper presents an approach to extracting stock market trading rules from stock market data. Trading rules are based on two multi-layer perceptrons, one generating buy signals and one generating sell signals. Inputs of these perceptrons are fed with values of technical indicators computed on historical stock quotations. Results of a large number of experiments on real-life data from the Paris Stock Exchange confirm that the model of trading rules is reasonable and the trading rules are able to generate reasonable trading signals, not only over a training period, used in the training process, but also over a test period, unknown during constructing trading rules. Moreover, trading strategies defined by such trading rules are profitable and often outperform the simple Buy&Hold strategy. [ABSTRACT FROM AUTHOR]

This paper describes an integrated clinical support system combining data entry and access with prognostic modelling, for use by the clinician, complemented by a patient information system tailored to the particular characteristics of the individual patient. The core of the system comprises a modelling methodology based on the PLANN-ARD neural network which combines risk staging with automatic rule generation to derive an explanation facility for the risk group allocation of each patient. The aim of the system is to promote better informed decision making on the part of both the clinician and the patient, exploiting the combined potential of analytical methodologies and the internet. [ABSTRACT FROM AUTHOR]

In this paper the utility of using the Self Organizing Maps (SOM), in conjunction with U-matrix, to visualize the evolution of a social network community formed by a set of blogs is shown. Weblogs are dynamic websites updated via easy-to-use content management systems whose links tend to mirror or in some cases establish new types of social relations, thereby creating a social network. Analyzing the evolution of this network allows the discovery of emerging social structures and their trends in growth. Here we apply this method to Blogalia, a blog hosting site from which we have a complete set of data. The proposed procedure not only gives some insight on how communities form and evolve, but would also enable to predict the future paths that their members will take. [ABSTRACT FROM AUTHOR]

In this paper we explore the interest of computational intelligence tools in the management of heterogeneous communication networks, specifically to predict congestion, failures and other anomalies in the network that may eventually lead to degradation of the quality of offered services. We show two different applications based on neural and neurofuzzy systems for Quality of Service (QoS) management in next generation networks for V2oIP services. The two examples explained in this paper attempt to predict the communication network resources for new incoming calls, and visualizing by means of self-organizing maps the QoS of a communication network. [ABSTRACT FROM AUTHOR]

The topology verification is an important problem in power system engineering. The paper presents the solution for this problem with use of the method, that is independent of state estimation. The method combines utilization of knowledge about power system and radial basis function networks. It allows to perform the power system topology verification as a series of verification processes for particular nodes of a power network. In the paper the possibility of such decomposition is proved. A principle of the method is described. Next, the computational example of topology verification with use of the characterized method is presented. At the end, the features of the method are analyzed, paying special attention to its efficiency. [ABSTRACT FROM AUTHOR]

This paper describes a new platform for FPGA implementation of the multilayer perceptron (MLP) back propagation algorithm (BP). The three implementation figures of the algorithm are considered. These are the off type implementation, the on chip global implementation and the dynamic reconfiguration of the ANN. To achieve our goal, a design for reuse strategy has been applied. To validate our approach, three case studies are considered using the Virtex-II and Virtex-4 FPGA devices. A comparative study is done and new conclusions are given. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology. [ABSTRACT FROM AUTHOR]

All neural networks, both natural and artificial, are characterized by two kinds of dynamics. The first one is concerned with what we would call "learning dynamics", in fact the sequential (discrete time) dynamics of the choice of synaptic weights. The second one is the intrinsic dynamics of the neural network viewed as a dynamical system after the weights have been established via learning. The paper deals with the second kind of dynamics. Since the emergent computational capabilities of a recurrent neural network can be achieved provided it has suitable dynamical properties when viewed as a system with several equilibria, the paper deals with those qualitative properties connected to the achievement of such dynamical properties, more precisely the gradient like behavior. In the case of the neural networks with delays, these aspects are reformulated in accordance with the state of the art of the theory of delay dynamical systems. [ABSTRACT FROM AUTHOR]

First this paper considers a Common Due Window (CDW) scheduling problem of n jobs on a single machine to minimize the sum of common weighted earliness and weighted number of tardy jobs when only one manufacturer processes these jobs. Two dynamic algorithms are designed for two cases respectively and each case is proved to be ordinary NP-hard. Successively the scenario, where two manufacturers jointly process these jobs due to the insufficient production facilities or techniques of each party, is investigated. A novel dynamic programming algorithm is proposed to obtain an existing reasonable set of processing utility distributions on the bi-partition of these jobs. [ABSTRACT FROM AUTHOR]

This paper presents efficient algorithms for the minimum risk-sum path problem which arises in a variety of applications. Given a source-destination node pair in a network G = (V,E), where each link e in G can be traveled using time xe in a prespecified interval [le,ue] while taking risk $\frac{u_e-x_e}{u_e-l_e}$, the minimum risk-sum path problem is to find a path in G from source to destination, together with an assignment of travel times along each link on the path, so that the total travel time of the path is no more than a given constant and the risk sum over the links on the path is minimized. In this paper, we solve the minimum risk-sum path problem optimally in O(V3E) time. [ABSTRACT FROM AUTHOR]

Based on some results of k-server problem and k-taxi problem, the on-line weighted k-taxi problem is originally proposed and studied by our team. In the weighted k-taxi problem, the cost of every taxi on the same distance is different whereas that is same in the traditional k-taxi problem. In this paper, the formulation of the on-line weighted k-taxi problem is presented first. Following that, some preliminary results which used to get the main theorems are given. Thirdly, some on-line algorithms are designed to address the problem and the competitive analysis are given in detail. Furthermore, the lower bound of competitive ratio for some special cases is obtained. Finally, some conclusions are made and some future research directions are pointed out. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach that achieves shape registration by optimizing shape representation and transformation simultaneously, which are modeled by a constrained Gaussian Mixture Model (GMM) and a regularized thin plate spline respectively. The problem is formulated within a Bayesian framework and solved by an expectation-maximum (EM) algorithm. Compared with the popular methods based on landmarks-sliding, its advantages include: (1) It can naturally deal with shapes of complex topologies and 3D dimension; (2) It is more robust against data noise; (3) The registration performance is better in terms of the generalization error of the resultant statistical shape model. These are demonstrated on both synthetic and biomedical shapes. [ABSTRACT FROM AUTHOR]

Dual-energy mammography can suppress the contrast between adipose and glandular tissues and improve the detectability of microcalcifications (MCs). In clinical dual-energy mammography, imaging object is human breast, while in calibration measurements, only phantoms of breast-tissue-equivalent material can be used. The composition and density differences between calibration materials and human breast bring the differences of linear attenuation coefficient which lead to the calculation errors in dual-energy imaging. In this paper, the magnitude of MC thickness error from calibration materials has been analyzed using a first-order propagation of error analysis. This analysis shows that the thickness error from calibration materials ranges from dozens to thousands of microns which can not be ignored when carrying out dual-energy calculations. The evaluation of several popular phantoms shows that it is of great importance to adopt the phantom materials approaching human breast most. [ABSTRACT FROM AUTHOR]

Our paper summarizes experiments for measuring the accuracy of deformable 2D-3D registration between sets of simulated x-ray images (DRR's) and a statistical shape model of the pelvis bones, which includes x-ray attenuation information ("density"). In many surgical scenarios, the images contain a truncated view of the pelvis anatomy. Our work specifically addresses this problem by examining different selections of truncated views as target images. Our atlas is derived by applying principal component analysis to a population of up to 110 instance shapes. The experiments measure the registration error with a large and truncated FOV. A typical accuracy of about 2 mm is achieved in the 2D-3D registration, compared with about 1.4 mm of an "optimal" 3D-3D registration. [ABSTRACT FROM AUTHOR]

Segmenting cortical and sub-cortical structures from 3D brain images is of significant practical importance. However, various anatomical structures have similar intensity patterns in MRI, and the automatic segmentation of them is a challenging task. In this paper, we present a new brain segmentation algorithm using a hybrid model. (1) A multi-class classifier, PBT.M2, is proposed for learning/computing multi-class discriminative models. The PBT.M2 handles multi-class patterns more easily than the original probabilistic boosting tree (PBT) [11], and it facilitates the process, eventually, toward whole brain segmentation. (2) We use an edge field, by learning, to constraint the region boundaries. We show the improvements due to the two new aspects both numerically and visually, and also compare the results with those by FreeSurfer [2]. Our algorithm is general and easy to use, and the results obtained are encouraging. [ABSTRACT FROM AUTHOR]

Finite mixture models can be used in estimating complex, unknown probability distributions and also in clustering data. The parameters of the models form a complex representation and are not suitable for interpretation purposes as such. In this paper, we present a methodology to describe the finite mixture of multivariate Bernoulli distributions with a compact and understandable description. First, we cluster the data with the mixture model and subsequently extract the maximal frequent itemsets from the cluster-specific data sets. The mixture model is used to model the data set globally and the frequent itemsets model the marginal distributions of the partitioned data locally. We present the results in understandable terms that reflect the domain properties of the data. In our application of analyzing DNA copy number amplifications, the descriptions of amplification patterns are represented in nomenclature used in literature to report amplification patterns and generally used by domain experts in biology and medicine. [ABSTRACT FROM AUTHOR]

Depth estimation is an important parameter for three-dimensional shape recovery. There are many factors affecting the depth estimation including luminance, texture reflectance, noise etc. In this paper, we limit our discussion to noise. We present noise analysis by first pre-filtering the noisy images using well known Wiener filter and then using a robust focus measure for depth estimation. That depth map can further be used in techniques and algorithms leading to recovery of three dimensional structure of the object. The focus measure is based on an optical transfer function implemented in the Fourier domain and its results are compared with the earlier focus measures and presented in this paper. The additive Gaussian noise is considered for noise analysis. [ABSTRACT FROM AUTHOR]