Optimización de cuantificadores vectoriales basada en algoritmos genéticos y técnicas heurísticas

El intrincado problema del diseño de cuantificadores vectoriales, esto es, la obtención de librerias de código con las que la codificación de señales tenga las menores distorsiones posibles, se hace aun mas complejo cuando son considerados los efectos del ruido en el canal, plasmados en la Tasa de Error por Bit (Bit Error Rate 0 BER). Muchos de los algoritmos de diseño de cuantificadores vectoriales, entre los que destaca el GLA (Algoritmo de Lloyd Generalizado), no son capaces de sortear los numerosos minimos locales subóptimos que presenta la funci6n de distorsión media de la cuantificaci6n en el espacio de las librerias de c6digo. Por ello es preciso ejecutarlos repetidas veces, partiendo de puntos de inicio diferentes. En esta Tesis se han querido explorar las posibilidades que brindan los Algoritmos Genéticos y otras tecnicas heuristicas en el diseño óptimo de cuantificadores vectoriales sujetos a errores de canal. Los Algoritmos Geneticos (AG) son procedimientos de optimizaci6n global iterativos y estocasticos inspirados en algunos mecanismos que rigen la dinamica de la Naturaleza, en particular la selección natural, la codificación genética y la reproducción heterosexual. Un AG contiene una población de individuos pertenecientes al espacio de posibles soluciones, que compiten entre si y evolucionan tratando de maximizar alguna función de prestaciones o minimizar alguna función de coste definida sobre ese espacio. Esta evoluci6n se basa en la selección de los mejores individuos y la eliminación de los peores, junto con diversos mecanismos para procrear nuevos individuos (hijos) a partir de los anteriormente seleccionados (padres). Se plantean tres metodos distintos: - El AGCV (Algoritmo Genetico para la Cuantificaci6n Vectorial): es un genetico en el que los individuos de la poblaci6n son tentativas librerias de vectores código. Para facilitar su evolución hacia puntos de minima distorsión, se incorpora, como mecanismo de busqueda local, el algoritmo GLA. - EI ARL (Algoritmo Refinado de Lloyd): es un algoritmo heuristico en el que se ejecuta sucesivas veces el algoritmo GLA, preservando siempre los mejores vectores c6digo hallados hasta el momento. A medida que el algoritmo progresa, el numero de vectores nuevos (no preservados) se va haciendo menor, con el objeto de que la busqueda vaya siendo progresivamente mas local. - El AHCV (Algoritmo Hibrido para la Cuantificación Vectorial): es otro genético en el que se parte de una libreria de códigos ya conocido, y lo que se optimiza es la asignación de los códigos binarios disponibles, a los vectores código de la libreria. Los dos primeros son sometidos a extensas pruebas de simulación y contrastados con tres algoritmos de reputado nombre, corrobonindose su adecuación al disefio de cuantificadores vectoriales. EI tercero se plantea como una tecnica posible, aun sin explorar ni probar exhaustivamente, que abre el camino a una nueva manera de utilizar los AG en este problema. Al margen de estos metodos, una segunda cuestión abordada en esta Tesis es la reformulación de los principios de la Cuantificación Vectorial cuando las condiciones del canal no se suponen fijas o bien conocidas, sino que son descritas mediante la función densidad de probabilidad del BER. A este respecto se determina analiticamente la nueva función de distorsión y las reglas de optimalidad para el diseño de cuantificadores óptimos. Esto constituye un nuevo punto de partida para el diseño de cuantificadores vectoriales con planteamientos mas realistas que los normalmente considerados.The involved problem of Vector Quantization (VQ) design, i. e. the search for codebooks which yield as minimum distortions as possible, turns even more complicated when channel noise effects, characterised by the Bit Error Rate (BER), are considered. Many VQ design techniques, included the most famous GLA (Generalized Lloyd Algorithm), are unable to avoid the sub-optimum local minima present in the quantification distortion function. Thus repeated executions, with different starting points are needed. In this Thesis the possibilities offered by Genetic Algorithms and other heuristic techniques for noisy channel VQ design are explored. Genetic Algorithms (GA) are stochastic and iterative global optimisation procedures, inspired on various mechanisms which rule Nature dynamics, such as natural selection, genetic coding and heterosexual reproduction. A GA contains a population of individuals belonging to the solution space, which compete each other and evolve towards maximisation of some performance function or minimisation of some cost function defined throughout this space. This evolution is carried out by means of selecting the fittest individuals in the population while removing the worst ones, as well as by several mechanisms for creating new individuals (offsprings) from selected ones (parents). Three new methods are proposed: - AGCV (Vector Quantization Genetic Algorithm): a GA in which individuals are tentative codebooks of the VQ scheme. To ease the evolution towards minimum distortion points, the GLA is included as a local search mechanism. - ARL (Lloyd Refinement Algorithm): an heuristic algorithm in which GLA is run several times, preserving the best codevectors encountered so far. As the algorithm progresses, the number of codevectors removed and replaced by new ones is decreases, making the search progressively local. - AHCV (Vector Quantization Hybrid Algorithm): another GA which starts from an initial fixed code book and tries to optimise the assignment of the available binary codes to the vectors in the code book. The two first ones are submitted to extensive simulation tests and compared to three well-reputed methods in the field, confirming their adequacy to the problem under study. The third one is given as a possible technique, without having been exhaustively explored or tested so far; it only leads the way to a new manner of using GA for VQ design. Apart from this, a second question faced in this Thesis is the reformulation of the VQ principles when channel conditions are not supposed fixed or well known, but are described by the probability density function of the BER. To this respect, a new distortion function and optimality laws are analytically determined. This constitutes a new starting point for VQ design with more realistic basis than normally considered

EMG Modeling

The aim of this chapter is to describe the approaches used for modelling electromyographic (EMG) signals as well as the principles of electrical conduction within the muscle. Sections are organized into a progressive, step-by-step EMG modeling of structures of increasing complexity. First, the basis of the electrical conduction that allows for the propagation of the EMG signals within the muscle is presented. Second, the models used for describing the electrical activity generated by a single fibre described. The third section is devoted to modeling the organization of the motor unit and the generation of motor unit potentials. Based on models of the architectural organization of motor units and their activation and firing mechanisms, the last section focuses on modeling the electrical activity of a complete muscle as recorded at the surface

New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

10 pages, 6 figures.-- PMID: 20217264.This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time–frequency distributions (TFDs). Particularly, the method extracts features from the Smoothed Pseudo Wigner-Ville distribution using tracks estimated from the McAulay-Quatieri sinusoidal model. The proposed features are the length, frequency, and energy of the principal track. We evaluate the proposed scheme using several datasets and we compute sensitivity, specificity, F-score, receiver operating characteristics (ROC) curve, and percentile bootstrap confidence to conclude that the proposed scheme generalizes well and is a suitable approach for automatic seizure detection at a moderate cost, also opening the possibility of formulating new criteria to detect, classify or analyze abnormal EEGs.This work has been funded by the Spain CICYT grant TEC2008-02473.Publicad

The filling factor of the sEMG signal at low contraction forces in the quadriceps muscles is influenced by the thickness of the subcutaneous layer

Introduction: It has been shown that, for male subjects, the sEMG activity at low contraction forces is normally “pulsatile”, i.e., formed by a few large-amplitude MUPs, coming from the most superficial motor units. The subcutaneous layer thickness, known to be greater in females than males, influences the electrode detection volume. Here, we investigated the influence of the subcutaneous layer thickness on the type of sEMG activity (pulsatile vs. continuous) at low contraction forces.Methods: Voluntary surface EMG signals were recorded from the quadriceps muscles of healthy males and females as force was gradually increased from 0% to 40% MVC. The sEMG filling process was examined by measuring the EMG filling factor, computed from the non-central moments of the rectified sEMG signal.Results: 1) The sEMG activity at low contraction forces was “continuous” in the VL, VM and RF of females, whereas this sEMG activity was “pulsatile” in the VL and VM of males. 2) The filling factor at low contraction forces was lower in males than females for the VL (p = 0.003) and VM (p = 0.002), but not for the RF (p = 0.54). 3) The subcutaneous layer was significantly thicker in females than males for the VL (p = 0.001), VM (p = 0.001), and RF (p = 0.003). 4) A significant correlation was found in the vastus muscles between the subcutaneous layer thickness and the filling factor (p < 0.05).Discussion: The present results indicate that the sEMG activity at low contraction forces in the female quadriceps muscles is “continuous” due to the thick subcutaneous layer of these muscles, which impedes an accurate assessment of the sEMG filling process