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Licea G.,Autonomous University of Baja California | Castro J.J.,University of Tijuana
IEEE Latin America Transactions | Year: 2017

Perception is the process of understanding our environment. If perception is not accurate or incorrect, problems with reading, spelling, handwriting, math and comprehension can be present. Eye care professionals must apply several tests to children to evaluate perceptual skills and suggest the most appropriate therapies to increase learning capacities. This paper presents PSR (Perceptual Skills Registry), a software application designed to support the storage, calculation and interpretation of results of perceptual skills tests. PSR allows an eye care professional to reduce significantly the process to evaluate the perceptual skills of a patient. © 2003-2012 IEEE.


Hidalgo D.,University of Tijuana | Melin P.,Tijuana Institute of Technology | Castillo O.,Tijuana Institute of Technology
Expert Systems with Applications | Year: 2012

This paper proposes an optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty (FOU) of the membership functions, considering three different cases to reduce the complexity problem of searching the parameter space of solutions. For the optimization method, we propose the use of a genetic algorithm (GA) to optimize the type-2 fuzzy inference systems, considering different cases for changing the level of uncertainty of the membership functions to reach the optimal solution at the end. © 2011 Elsevier Ltd. All rights reserved.


Murillo-Escobar M.A.,Autonomous University of Baja California | Cruz-Hernandez C.,CICESE | Abundiz-Perez F.,Autonomous University of Baja California | Lopez-Gutierrez R.M.,Autonomous University of Baja California | Acosta Del Campo O.R.,University of Tijuana
Signal Processing | Year: 2014

Currently, color image encryption is important to ensure its confidentiality during its transmission on insecure networks or its storage. The fact that chaotic properties are related with cryptography properties in confusion, diffusion, pseudorandom, etc., researchers around the world have presented several image (gray and color) encryption algorithms based on chaos, but almost all them with serious security problems have been broken with the powerful chosen/known plain image attack. In this work, we present a color image encryption algorithm based on total plain image characteristics (to resist a chosen/known plain image attack), and 1D logistic map with optimized distribution (for fast encryption process) based on Murillo-Escobars algorithm (Murillo-Escobar et al. (2014) [38]). The security analysis confirms that the RGB image encryption is fast and secure against several known attacks; therefore, it can be implemented in real-time applications where a high security is required. © 2014 Published by Elsevier B.V.


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.


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.


Hidalgo D.,University of Tijuana | Melin P.,Tijuana Institute of Technology | Castillo O.,Tijuana Institute of Technology
Proceedings - 2010 IEEE International Conference on Granular Computing, GrC 2010 | Year: 2010

In this paper we describe an evolutionary method for the optimization of type-2 fuzzy systems based on the level of uncertainty. The proposed evolutionary method produces the best fuzzy inference systems (based on the memberships functions) for particular applications. The optimization of membership functions of the type-2 fuzzy systems is based on the level of uncertainty considering three different cases to reduce the complexity problem of searching the solution space. © 2010 IEEE.


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.


Huertas C.,University of Tijuana | Ramirez R.J.,University of Tijuana
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Public health is a critical issue, therefore we can find a great research interest to find faster and more accurate methods to detect diseases. In the particular case of cancer, the use of mass spectrometry data has become very popular but some problems arise due to that the number of mass-to-charge ratios exceed by a huge margin the number of patients in the samples. In order to deal with the high dimensionality of the data, most works agree with the necessity to use pre-processing. In this work we propose an algorithm called Heat Map Based Feature Selection (HmbFS) that can work with huge data without the need of pre-processing, thanks to a built-in compression mechanism based on color quantization. Results shows that our proposal is very competitive against some of the most popular algorithms and succeeds where other methodologies may fail due to the high dimensionality of the data. © Springer International Publishing Switzerland 2015.


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.


Castillo O.,Tijuana Institute of Technology | Castro J.R.,University of Tijuana | Melin P.,Tijuana Institute of Technology | Rodriguez-Diaz A.,University of Tijuana
Soft Computing | Year: 2014

Neural networks (NNs), type-1 fuzzy logic systems and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to be important methods in real world applications, which range from pattern recognition, time series prediction, to intelligent control. Recent research shows that embedding an IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect or incomplete information. In this paper we are presenting several models of interval type-2 fuzzy neural networks (IT2FNNs) that use a set of rules and interval type-2 membership functions for that purpose. Simulation results of non-linear function identification using the IT2FNN for one and three variables and for the Mackey-Glass chaotic time series prediction are presented to illustrate that the proposed models have potential for real world applications. © 2013 Springer-Verlag Berlin Heidelberg.

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