Catálogo de publicaciones - libros

Mobile robot localization is the problem of determining the pose (position and orientation) of a mobile robot under complex measurement uncertainties. The Soft-reduced Hypotheses Tracking algorithm introduced here is based on the modified multiple model and exploits a soft gating of the measurements to reduce the computational requirements of the approach. The position part is based on an x- and y-histograms scan matching procedure, where x- and y-histograms are extracted directly from local occupancy grid maps using probability scalar transformation. The orientation part is based on the proposed obstacle vector transformation combined with polar histograms. Proposed algorithms are tested using a Pioneer 2DX mobile robot.

This paper proposes an Arabic character recognition system. The system focuses on employing Support Vector Machines (SVMs) as a promising pattern recognition tool. In addition to applying SVM classification which is a novel feature in arabic character recognition systems, the problem of dots and holes is solved in a completely different way from the ones previously employed. The proposed system proceeds in several phases. The first phase involves image acquisition and character extraction, the second phase performs image binarization where a character image is converted into white with black background, while the next phase involves smoothing and noise removal. In the fourth phase a thinning algorithm is used to thin the character body. The fifth phase involves feature extraction where statistical features, such as moment invariants, and structural features, such as number and positions of dots and number of holes, are extracted. Finally, the classification phase takes place using SVMs, by applying a one-against-all technique to classify 58 Arabic character shapes. The proposed system has been tested using different sets of characters, achieving a nearly 99% recognition rate.

We present a method that extracts structural features of images. The method is based on both a region-based analysis and a contour-based analysis. The image is first segmented, based on its pixels’ information. Color information of each segmented region is performed by using the hue-saturation-value color space. Area of each region is also extracted by counting the number of bound pixels. Location of each region is computed as a center of the region’s convex hull. A contour of the region is approximated by a B-spline approximation to obtain its control polygon and curve in the limit. The region’s convex hull is obtained from the control polygon. For multi-scale features, we apply Chaikin’s algorithm to the control polygon for finer level of control polygons, which could be used in a coarse to fine comparison. Curvature information of the B-spline curve fitting could also be used in the comparison. Our method could be used in many interesting applications including image retrieval, image classification, image clustering, image manipulation, image understanding, pattern recognition, and machine vision.

This paper presents some studies regarding the functional model of a robot system that has a visual servoing control, and also the simulation results for the system model. The information given by a visual system of the position of a target, materialized by a moving light source is used to realize the robot control.

Test Methodologies for Globally Asynchronous Locally Synchronous (GALS) System On a Chip (SOC) are a subject of growing research interest since they appear to offer benefits in low power applications and promise greater design modularity. Pre-designed cores and reusable modules are popularly used in the design of large and complex Systems. As the size and complexity of System increase, the test effort, including test development effort, test data volume, and test application time, has also significantly increased. Available techniques for testing of core-based systems on a chip do not provide a systematic means for compact test solutions. A test solution for a complex system requires good optimization of Test Scheduling and Test Access Mechanism (TAM). In this paper, we provide a Test Scheduling Optimization for Globally Asynchronous Locally Synchronous System-On-Chip Using Genetic Algorithm that gives compact test scheduling.

Red light running is a leading cause of urban crashes that often results in injury and death. The main reason for this is when the automobile driver fails to stop at the intersections when the signal light is red, and runs over other road users. In this research a computer vision-based low cost system is proposed for continuously monitoring the vehicles crossing the intersections when the signal light is “red” with the help of video cameras placed at the intersections, and penalizing careless drivers accordingly. This monitoring system finds application at all busy intersections. By using this setup people would be more conscious of getting penalty tickets which in turn will discourage them from running red lights. This research is intended to provide a support tool for the law enforcement agencies to proactively ensure that intersections are engineered to discourage red light running.

– The procedure for approximate analytical determination of describing function of nonlinear systems with odd static characteristics is presented in the paper. Generalized mathematical expressions for determining such describing function with error estimation are given. The procedure is illustrated on determination of describing function, and corresponding error estimation, of Mamdani fuzzy element and nonlinear system with fuzzy element and saturation nonlinearity.

A method of computerized tongue image analysis based on image processing for the purpose of digitizing the tongue properties in traditional Chinese medical diagnosis is presented. A hybrid method which uses Support Vector Machine to extract the semantic object, and a combination kernal function is selection after many compare. Finite Mixture Model and many image process methods is applied into diagnosis system. The experiment of the system shows that methods proposed are effective. The following results are presented in the article: 1) A multiply semantic image model is built our literature, which contributes abundant character to determine disease. 2) The SVM classifications are applied to transaction from the lower level to the top ones. The complex of the SVM classifications depends on the sample number rather than the characteristic dimension, which can satisfy the requirement of the system. 3) An application implements the approaches mentioned by the literature is introduced, through which the effect of the model are proved.

Grid and P2P systems have become popular options for large-scale distributed computing, but their popularity has led to a number of varying definitions that are often conflicting. Taxonomies developed to aid the decision process are also quite limited in their applicability. While some researchers have argued that the two technologies are converging [1], in this paper, we develop a unified taxonomy along two necessary distributed computing dimensions and present a framework for identifying the right alternative between P2P and Grid Computing for the development of distributed computing applications.

Abstract- Urdu is a widely used language in South Asia and is spoken in more than 20 countries. In writing, Urdu is traditionally written in Nastaliq script. Though this script is defined by well-formed rules, passed down mainly through generations of calligraphers, than books etc, these rules have not been quantitatively examined and published in enough detail. The extreme context sensitive nature of Nastaliq is generally accepted by its writers without the need to actually explore this hypothesis. This paper aims to show both. It first performs a quantitative analysis of Nastaliq and then explains its contextual behavior. This behavior is captured in the form of a context sensitive grammar. This computational model could serve as a first step towards electronic Typography of Nastaliq.