Catálogo de publicaciones - libros

This paper studies the problem of designing wide area networks. In the paper the web replica allocation and topology assignment problem with budget constraint is considered. The goal is select web replica allocation at nodes, network topology, channel capacities and flow routes in order to minimize average delay per packet and web replica connecting cost at nodes subject to budget constraint. The problem is NP-complete. Then, the branch and bound method is used to construct the exact algorithm. Also an approximate algorithm is presented. Some computational results are reported. Based on computational experiments, several properties of the considered problem are formulated.

In this paper, we described an optimal walking pattern generation using genetic-fuzzy algorithm that can assist walking robot avoid obstacles. In order to walk on an uneven terrain, a quadruped robot must recognize obstacles and take a trajectory that fits with the environment. In that respect, the robot should have two decision-making algorithms that will help its structural limitation. The first algorithm is to generate a body movement that can be related to the movement of the legs, and the other is to make legs’ movements smooth in order to reduce jerks. The research presented in this paper, using genetic-fuzzy algorithm, suggests how to find an optimal path movement and smooth walk for quadruped robots. To realize such movement, a relationship between body path and legs trajectory was defined, and a rule based on genetic-fuzzy algorithm was made. From that rule, the optimal legs’ trajectory could be determined and the body path generated. As a result, a quadruped robot could walk and avoid obstacles with smoothness.

This article discusses the modelling and the evaluation of process of electronic signature (ES). According to a certain point of view, this process can be shown as a class of Dynamic Discrete Event Systems (DDES). It is in this framework that we study this class with using Petri Nets (PN) and the theory of linear systems in (max, +) algebra. We introduce these two formalisms with the aim to describe the graphical and analytical behaviours of studied process. The resolution of (max, +) model which describes the system enables us to evaluate the process performances in terms of occurrence dates of various events that compose it (authentication, hashcoding, signature, timestamping). To illustrate our methodology, we finish this article with a numerical example.

By merging network and channel interfaces, resulting interfaces allow multiple computers to physically share storage devices. A storage area network (SAN) is a high-speed special-purpose network (or subnetwork) that interconnects different kinds of data storage devices with associated data servers on behalf of a larger network of users. In SAN, computers service local file requests directly from shared storage devices. Direct device access eliminates the server machines as bottlenecks to performance and availability. Communication is unnecessary between computers, since each machine views the storage as being locally attached. SAN provides us to very large physical storage up to 64-bit address space, but traditional file systems can’t adapt to the file system for SAN because they have the limitation of scalability.

In this paper, we present architectures and features of SANtopia that allows multiple machines to access and share disk and tape devices on a Fibre Channel or SCSI storage network in Linux system. It performs well as a local file system, as a traditional network file system running over IP environments, and as a high performance cluster file system running over storage area networks like Fibre Channel. SANtopia provides a key cluster enabling technology for Linux, helping to bring the availability, scalability, and load balancing benefits of clustering to Linux.

This paper proposes automatic boundary tumor segmentation for the computer aided liver diagnosis system. As pre-processing, the liver structure is first segmented using histogram transformation, multi-modal threshold, C-class maximum a posteriori decision, and binary morphological filtering. After binary transformation of the liver structure, the image based bounding box is created and convex deficiencies are segmented. Large convex deficiencies are selected by pixel area estimation and selected deficiencies are transformed to gray-level deficiencies. The boundary tumor is selected by estimating the variance of deficiencies. In order to test the proposed algorithm, 225 slices from nine patients were selected. Experimental results show that the proposed algorithm is very useful for diagnosis of the abnormal liver with the boundary tumor.

Image restoration problems are often solved by finding the minimizer of a suitable objective function. Usually this function consists of a data-fitting term and a regularization term. For the least squares solution, both the data-fitting and the regularization terms are in the ℓ2 norm. In this paper, we consider the least absolute deviation (LAD) solution and the least mixed norm (LMN) solution. For the LAD solution, both the data-fitting and the regularization terms are in the ℓ1 norm. For the LMN solution, the regularization term is in the ℓ1 norm but the data-fitting term is in the ℓ2 norm. The LAD and the LMN solutions are formulated as the solutions of a linear and a quadratic programming problems respectively, and solved by interior point methods. At each iteration of the interior point method, a structured linear system must be solved. The preconditioned conjugate gradient method with factorized sparse inverse preconditioners is employed to such structured inner systems. Experimental results are used to demonstrate the effectiveness of our approach. We also show the quality of the restored images using the minimization of ℓ1 norm/mixed ℓ1 and ℓ2 norms is better than that using ℓ2 norm approach.

This paper will present a new approach to the interpretation of semantic information retrieved from complex X-ray images. The tasks of the analysis and the interpretation of cognitive meaning of selected medical diagnostic images are made possible owing to the application of graph image languages based on tree grammars. One of the main problems in the fast accessing and analysis of information collected in various medical examinations is the way to transform efficiently the visual information into a form enabling intelligent recognition and understanding of semantic meaning of selected patterns. Another problem in accessing for useful information in multimedia databases is the creation of a method of representation and indexing of important objects constituting the data contents. In the paper we describe some examples presenting ways of applying picture languages techniques in the creation of intelligent cognitive multimedia systems for selected classes of medical images showing especially wrist structures.

We present an unsupervised segmentation algorithm combining the mean shift procedure and deterministic annealing expectation maximization (DAEM) called MS-DAEM algorithm. We use the mean shift procedure to determine the number of components in a mixture model and to detect their modes of each mixture component. Next, we have adopted the Gaussian mixture model (GMM) to represent the probability distribution of color feature vectors. A DAEM formula is used to estimate the parameters of the GMM which represents the multi-colored objects statistically. The experimental results show that the mean shift part of the proposed MS-DAEM algorithm is efficient to determine the number of components and initial modes of each component in mixture models. And also it shows that the DAEM part provides a global optimal solution for the parameter estimation in a mixture model and the natural color images are segmented efficiently by using the GMM with components estimated by MS-DAEM algorithm.

To date, most identity-based key agreement protocols are based on a single PKG (Private Key Generator) environment. In 2002, Chen and Kudla proposed an identity-based key agreement protocol for a multiple PKG environment, where each PKG shares identical system parameters but possesses a distinct master key. However, it is more realistic to assume that each PKG uses different system parameters. In this paper, we propose a new two party key agreement protocol between users belonging to different PKGs that do not share system parameters. We also extend this protocol to a tripartite key agreement protocol. Our two party protocol requires the same amount of pairing computation as Smart’s protocol for a single PKG environment and provides PKG forward secrecy. We show that the proposed key agreement protocols satisfy every security requirements of key agreement protocols.

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms (GAs) based Evolutionally optimized Fuzzy Neural Networks (EoFNN) are introduced and the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. The proposed EoFNN is based on the Fuzzy Neural Networks (FNN) with the extended structure of fuzzy rules being formed within the networks. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and modified quadratic are taken into consideration. The structure and parameters of the EoFNN are optimized by the dynamic search-based GAs.