The acoustic propagation in the human vocal tract using the Transmission Line Matrix method (TLM) has been simulated. Tri-dimensional meshes are built from segmented magnetic resonance images. Initially, simple models of one and two tubes were constructed to then simulate the same signal in a human vocal tract model whose shape represents the production of the open vowel /a/, simulating at the same time a human glottal signal. For the vocal tract model, the voice signals generated by the simulations were compared with voice samples from the same subject from whom the mesh was extracted. A compensation factor for the topology-dependent numerical dispersion for improving formant determination is proposed. It is concluded that the proposed compensation factor improves the effectiveness and accuracy of the TLV method.

Se ha simulado la propagación acústica en el tracto vocal humano usando el Método de La Línea de Transmisión (TLM). Se construyeron mallas tridimensionales a partir de segmentaciones de imágenes de resonancia magnética. Inicialmente se construyen modelos simples de uno y dos tubos y luego se simula la misma señal en un modelo de tracto vocal humano cuya forma representa la producción de la vocal abierta /a/, simulando también la propagación de una señal glotal. Para el modelo del tracto vocal, las señales de voz generadas mediante las simulaciones fueron comparadas con muestras de voz del mismo sujeto a quien se extrajo la malla. Se propone un factor de compensación de la dispersión numérica dependiente de la topología para mejorar la determinación de los formantes. Se concluye que el factor de compensación mejora la eficacia y exactitud del método TLM.

A methodology for analyzing the roughness based on surface characteristics of the images obtained from optical and electronic microscopes, is presented. The features that describe textures and are also used to classify them derive from the Haralick descriptors, which are based on co-occurrence matrices. The primary roughness patterns are evaluated and classified according to several features which use the values of these descriptors. The values extracted from the patterns are fed to artificial neural network of the multi-layer perceptron type. It is concluded that it is possible to start implementing the control of metal parts for industrial quality control of manufactured products through this system of roughness recognition.

Se presenta una metodología para análisis de la rugosidad basada en las características superficiales de las imágenes obtenidas de microscopios ópticos y electrónicos. Se muestra un método de análisis basado en las características de rugosidad de textura. Las características que describen las texturas y que se utilizan para clasificarlas, provienen de los descriptores Haralick, que también se basan en matrices de co-ocurrencia. Los patrones de rugosidad primaria son evaluados y clasificados de acuerdo con varias características que utilizan los valores de estos descriptores. Los valores extraídos de los patrones se incorporan después a una red neuronal artificial de perceptrón multicapa. Se concluye que es posible iniciar la implementación del control de piezas metálicas para inspección de la calidad industrial de productos manufacturados a partir de ese sistema de reconocimiento de la rugosidad.

This paper presents a system for detecting angular position of targets, using feature extraction techniques in digital imaging and artificial neural networks. Military ships images graphically generated by three-dimensional solid modeling software are used. Several tests using artificial neural networks applied to the set of geometric features were performed. The results show the important contribution of recognition algorithms in determining the ship angular position, regardless of their distance from the observer. The results encourage future applications for tracking targets using infrared images.

Este trabajo presenta un sistema de detección de posición angular de buques, utilizando técnicas de extracción de características en imágenes digitales y redes neurales artificiales. Se utilizan imágenes de embarcaciones militares generadas gráficamente. Se realizaron diferentes pruebas usando redes neuronales artificiales aplicadas al conjunto de características geométricas. Los resultados de las pruebas comprueban la importante contribución de la utilización de algoritmos de reconocimiento en la determinación de posicionamiento angular de embarcaciones, independiente del alejamiento del observador. Los resultados favorecen aplicaciones futuras en el seguimiento de buques (tracking) utilizando imágenes infrarrojas.

This paper describes a mathematical and graphical model for face aging. It considers the possibility of predicting the aging process by offering an initial quantification of this process as it applies to the face. It is concerned with physical measurements and a general law of time dependence. After measuring and normalizing a photograph of a person, one could predict, with a known amount of error, the appearance of that person at a different age. The technique described has served its purpose successfully, with a representative amount of patient data behaving sufficiently near the general aging curve of each parameter. That model uses a warping technique to emulate the aging changes on the face of women. Frequently the warping methods are based on the interpolation between images or general mathematical functions to calculate the pixel attributes. The implemented process considers the age features of selected parts of a face such as the face outline and the shape of the lips. These age features were obtained by measuring the facial regions of women that have been photographed throughout their lives. The present work is first concerned with discussing a methodology to define the aging parameters that can be measured, and second with representing the age effects graphically.