M. I. M. Chacon

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This paper presents a comparative study between a feedforward neural network and a SOM network. The paper also proposes the incorporation of a new spatial feature, face feature lines, FFL, to represent the faces. FFL are considered as new features based on previous studies related to face recognition tasks on newborns. Besides the face feature lines, the(More)
This paper presents a methodology to incorporate cutting edge technologies, Matlab/Simulink, Code Composer Study and DSK. The purpose of this methodology is to provide the instructors and students in DSP related courses an efficient method to develop and test DSP applications. The methodology involves the current and very important professional vision used(More)
In this paper a method to detect and classify typical electric power disturbances is presented. Voltage sags, swells, momentary outage and capacitor switching transient events (CSTs) are the electric disturbances considered in this work. Disturbance detection and some disturbance features are obtained by the discrete wavelet transform. These features are(More)
This paper presents a case of study where two of the most used fuzzy clustering algorithms in pattern recognition tasks are analyzed under a classification problem that involves a high degree of subjectivity. The problem consists on the classification of seven types of wood defects called knots. The algorithms are the Abonyi-Szeifert modification of the(More)
Computational intelligence theories offer, individually, different potentials to solve real world problems. However, fusion of these potentials provides opportunities to generate more real world robust systems. Cosmetic inspection of possible non-uniform surfaces found in manufacturing is a challenge to human inspectors. This paper deals with the proposal(More)
This paper address the dust aerosol detection problem based on a probabilistic multispectral image analysis. Two classifiers are designed. First the Maximum Likelihood classifier is adapted to mode different types of atmospheric components. The second is a Probabilistic Neural Network (PNN) model. The data sets are MODIS multispectral bands from NASA Terra(More)
This paper address the problem of dust storm detection based on multispectral image analysis from a probabilistic point of view. Two classifiers are designed, one based on classic probability theory and other based on a probabilistic computational intelligence approach. The first classifier is designed under the Maximum Likelihood Estimation (MLE) model,(More)
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