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Tijuana, Mexico

Licea G.,University of Tijuana
Computer Applications in Engineering Education | Year: 2011

COCHI is a flexible and easy to use software pattern system that supports the development of groupware applications. This paper describes COCHI and the experiences using this pattern system to enhance project complexity in undergraduate and graduate groupware courses in three Mexican universities. Copyright © 2010 Wiley Periodicals, Inc. Source


Melin P.,Tijuana Institute of Technology | Mendoza O.,University of Tijuana | Castillo O.,Tijuana Institute of Technology
Expert Systems with Applications | Year: 2010

In this paper, a method for edge detection in digital images based on the morphological gradient and fuzzy logic is described. A basic method for edge detection was improved using fuzzy logic. An advantage of the improved method is that there is no need of applying filtering to the image. The simulation results were obtained with a type-1 fuzzy inference system (T1FIS) and with an interval type-2 fuzzy inference system (IT2FIS) for improving the edge detection method. We show that the images obtained with fuzzy logic are better than the ones obtained with only the morphological gradient method. In particular the IT2FIS achieved the best results, because of the flexibility to model the uncertainty in the gradient values and the gray ranges for the edge images. In both TIFIS and IT2FIS the membership function parameters were obtained directly from the images; this allows application of the proposed method to images with different gray scales. © 2010 Elsevier Ltd. All rights reserved. Source


Castillo O.,Tijuana Institute of Technology | Melin P.,Tijuana Institute of Technology | Castro J.R.,University of Tijuana
Computer Applications in Engineering Education | Year: 2013

A software tool for interval type-2 fuzzy logic is presented in this article. The software tool includes a graphical user interface for construction, edition, and observation of the fuzzy systems. The Interval Type-2 Fuzzy Logic System Toolbox (IT2FLS) has a user-friendly environment for interval type-2 fuzzy logic inference system development. Tools that cover the different phases of the fuzzy system design process, from the initial description phase, to the final implementation phase, are presented as part of the Toolbox. The Toolbox's best properties are the capacity to develop complex systems and the flexibility that permits the user to extend the availability of functions for working with type-2 fuzzy operators, linguistic variables, interval type-2 membership functions, defuzzification methods, and the evaluation of interval type-2 fuzzy inference systems. The toolbox can be used for educational and research purposes. © 2011 Wiley Periodicals, Inc. Source


Melin P.,Tijuana Institute of Technology | Mendoza O.,University of Tijuana | Castillo O.,Tijuana Institute of Technology
IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans | Year: 2011

In this paper, a modification of the Sugeno integral with interval type-2 fuzzy logic is proposed. The modification includes changing the original equations of the Sugeno Measures and the Sugeno integral that were initially proposed for type-1 fuzzy logic. The proposed modification enables calculation of the interval type-2 Sugeno integral for combining multiple source of information with a higher degree of uncertainty than with the traditional type-1 Sugeno integral. The advantages of the interval type-2 Sugeno integral are illustrated by reporting improved recognition rates in benchmark face databases. This new concept could also be a useful tool in other areas of applications. Also, the improvement provided by the type-2 integral is verified to be statistically significant in the recognition results for complex face databases (like the FERET database) when compared with the type-1 Sugeno integral. The proposed Sugeno integral is used to combine the modules' outputs of a modular neural network for face recognition. Simulation results show that the interval type-2 Sugeno integral is able to improve the recognition rate for the benchmark face databases. Recognition results are better or comparable to results produced by alternative approaches present in the literature reported for the same benchmark problems. © 2011 IEEE. Source


Martinez-Soto R.,University of Tijuana | Rodriguez A.,University of Tijuana | Castillo O.,Tijuana Institute of Technology | Aguilar L.T.,National Polytechnic Institute of Mexico
International Journal of Innovative Computing, Information and Control | Year: 2012

We describe in this paper the optimization of the gains of a PID controller to stabilize the inertia wheel pendulum (IWP) using bio-inspired and evolutionary methods. Particle swarm optimization and genetic algorithms are used tond the optimal gain values of the PID controller. Computer simulations and experiments are presented showing the control results using the optimal gain values to stabilize the inertia wheel pendulum. Both particle swarm optimization (PSO) and genetic algorithms (GAs) are shown to be effective tools for gain optimization of the inertia wheel. © 2012 ICIC International. Source

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