Research Center en Computacion

San Juan de Dios (Naranjas de Dios), Mexico

Research Center en Computacion

San Juan de Dios (Naranjas de Dios), Mexico
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Vazquez R.A.,La Salle University at Cuauhtémoc | Sossa H.,Research Center en Computacion
Neurocomputing | Year: 2011

Median associative memories (MED-AMs) are a special type of associative memory that substitutes the maximum and minimum operators of a morphological associative memory with the median operator. This associative model has been applied to restore grey scale images and provided a better performance than morphological associative memories when the patterns are altered with mixed noise. Despite their power, MED-AMs have not been adopted in problems related with true-colour patterns. In this paper, we describe how MED-AMs can be applied to problems involving true-colour patterns. Furthermore, a complete study of the behaviour of this associative model in the restoration of true-colour images is performed using a benchmark of 16,000 images altered by different noise types. © 2011 Elsevier B.V.

Garro B.A.,Research Center en Computacion | Sossa H.,Research Center en Computacion | Vazquez R.A.,La Salle University at Cuauhtémoc
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

Due to their efficiency and adaptability, bio-inspired algorithms have shown their usefulness in a wide range of different non-linear optimization problems. In this paper, we compare two ways of training an artificial neural network (ANN): Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms. The main contribution of this paper is to show which of these two algorithms provides the best accuracy during the learning phase of an ANN. First of all, we explain how the ANN training phase could be seen as an optimization problem. Then, we explain how PSO and DE could be applied to find the best synaptic weights of the ANN. Finally, we perform a comparison between PSO and DE approaches when used to train an ANN applied to different non-linear problems. © 2011 Springer-Verlag.

Song X.-D.,Dalian Jiaotong University | Sun G.-H.,Research Center en Computacion | Dong S.-H.,Escuela Superior de Fisica y Matematicas
Physics Letters, Section A: General, Atomic and Solid State Physics | Year: 2015

We study the position Sr and momentum Sp Shannon entropies of the infinite circular well and find that the Sr increases with the radius R for a given m, but first increases and then decreases with the m for a given R. The variation Sp on radius R is from the first increment to the final decrement, but its general tendency first decreases with m and then increases with it. We also note that the variation of Sr on radius R is almost independent of n. Finally, BBM inequality is tested and hold for this system. © 2015 Elsevier B.V. All rights reserved.

Medel Juarez J.J.,Research Center en Computacion | Zagaceta Alvarez M.T.,Research Center en Ciencia
Revista Mexicana de Fisica | Year: 2010

The digital filter theory with identification process allows knowing internal states dynamics, with respect to a reference system, commonly considered as a black box or base system. This gives the identifier its input and output signals as essential information; so that the identifier is formed by identification actions. The actions developed by the identifier consider the transition matrix "described by the exponential function respect to internal parameters for the unknown black box reference system", identifying states delayed, with gain matrix "formatted by correlation convergence error" and the innovation process "build by the output base system noise and the identification result." Unfortunately, in the black box concept the internal parameters have the same problem, which means, neither observed nor transition matrix, because it is a description function depicted exponentially. Thus, the identifier structure considers that the transition matrix is an essential problem. This paper proposes the estimator as internal parameter descriptor "this is a technique required to describe the internal gains with respect to the black box system" thus generating the transition matrix. With respect to the gain matrix, the identification error "expressed by a second probability moment in recursive forms" affects the identifier as an adaptive algorithm. This allows having a sufficient convergence rate. The filter is built with two actions: estimator and identifier, this considers the adaptive properties with respect to the gain and dynamically adjusts the identifier convergence levels, observed in simulation results.

Peredo R.,Research Center en Computacion | Canales A.,Coordinadora Of University Abierta ucacion stancia Unam | Menchaca A.,Research Center en Computacion | Peredo I.,Research Center en Computacion
Expert Systems with Applications | Year: 2011

This paper depicts a set of integrated tools to build an intelligent Web-based education system. Our purpose is to create a Web learning environment that can be tailored to the Learners' needs. The Web learning environment is composed of Authoring Tool, Evaluation System, Interactive Voice System and a Virtual Laboratory for programming in Java. All tools use Web Services and have the characteristics of powerful adaptability for the management, authoring, delivery and monitoring of learning content. Part of the decision-making inside the intelligent Web-based education system was made with a multi-agent system. © 2011 Elsevier Ltd. All rights reserved.

Cuevas E.,University of Guadalajara | Oliva D.,University of Guadalajara | Zaldivar D.,University of Guadalajara | Perez-Cisneros M.,University of Guadalajara | Sossa H.,Research Center en Computacion
Information Sciences | Year: 2012

Nature-inspired computing has yielded remarkable applications of collective intelligence which considers simple elements for solving complex tasks by common interaction. On the other hand, automatic circle detection in digital images has been considered an important and complex task for the computer vision community that has devoted a tremendous amount of research, seeking for an optimal circle detector. This paper presents an algorithm for the automatic detection of circular shapes embedded into cluttered and noisy images without considering conventional Hough transform techniques. The approach is based on a nature-inspired technique known as the Electro-magnetism Optimization (EMO). It follows the electro-magnetism principle regarding a collective attraction-repulsion mechanism which manages particles towards an optimal solution. Each particle represents a solution by holding a charge which is related to the objective function to be optimized. The algorithm uses the encoding of three non-collinear points embedded into an edge-only image as candidate circles. Guided by the values of the objective function, the set of encoded candidate circles (charged particles) are evolved using an EMO algorithm so that they can fit into actual circular shapes over the edge-only map of the image. Experimental evidence from several tests on synthetic and natural images which provide a varying range of complexity validates the efficiency of our approach regarding accuracy, speed and robustness. © 2011 Elsevier Inc. All rights reserved.

Cuevas E.,University of Guadalajara | Zaldivar D.,University of Guadalajara | Perez-Cisneros M.,University of Guadalajara | Sossa H.,Research Center en Computacion | Osuna V.,Research Center en Computacion
Applied Soft Computing Journal | Year: 2013

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost. © 2012 Elsevier B.V. All rights reserved.

Osuna-Enciso V.,Research Center en Computacion | Cuevas E.,University of Guadalajara | Sossa H.,Research Center en Computacion
Expert Systems with Applications | Year: 2013

In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class is labeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selection problems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates the histogram, representing a pixel class and therefore a threshold point. Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem. © 2012 Elsevier Ltd. All rights reserved.

Yu W.,CINVESTAV | Moreno-Armendariz M.A.,Research Center en Computacion | Rodriguez F.O.,CINVESTAV
Information Sciences | Year: 2011

In order to control mechanical systems, this paper proposes a novel fast control strategy. The controller includes a normal proportional and derivative (PD) regulator and a fuzzy cerebellar model articulation controller (CMAC). For an overhead crane, this control can realize both position tracking and anti-swing. Using a Lyapunov method and an input-to-state stability technique, the PD control with CMAC compensation is proven to be robustly stable with bounded uncertainties. Real-time experiments are presented comparing this new stable control strategy with regular crane controllers. © 2011 Elsevier Inc. All rights reserved.

Aguilar-Ibanez C.,Research Center en Computacion | Suarez-Castanon M.S.,Escuela Superior de Computo | Cruz-Cortes N.,Research Center en Computacion
Nonlinear Dynamics | Year: 2012

In this work, we present an output feedback stabilization method for the Inverted Pendulum Cart (IPC) system around its unstable equilibrium point. The pendulum is initialized in the upper-half plane, and the position of the cart and the pendulum angular positions are always available. Our strategy was accomplished introducing a suitable coordinate change to obtain a nonlinear version of the original system, which is affine in the unmeasured velocities state. This fact allows us to adapt an observer based controller devoted to render the closed-loop system to the origin. The proposed observer based controller was designed using the direct Lyapunov method. This allows estimating the corresponding attraction domain for the whole system, which can be as large or as small as desired. While the corresponding closed-loop stability analysis was made using the LaSalle Invariance Theorem. Convincing numerical simulations were included to show the performance of the closed-loop system. © 2012 Springer Science+Business Media B.V.

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