Este libro se ha realizado con los objetivos de brindar los conocimientos básicos de programación y funcionamiento de un sistema embebido en plataformas libres. Arduino se ha convertido en una herramienta muy importante para el prototipado de innovaciones tecnológicas, su bajo costo y amplias referencias en el internet. Esto permite disminuir la curva de aprendizaje de los estudiantes en áreas de ingeniería. Se encuentra estructurado de la forma que cada capítulo sea las bases de aprendizaje para los siguientes contenidos.

Es un texto guía para comprender el manejo de diodos y transistores para aplicarlos en la rectificación de voltaje alterno a continuo, y las formas de amplificar la corriente según las diferentes polarizaciones de transistores y sus métodos de conmutación. El uso de diodos de silicio y germanio, transistores BJT, MOSFET y FET con ejemplos y ejercicios para comprender su funcionamiento, siendo este la base de la electrónica moderna.

The analysis of dynamic or time-varying data has emerged as an issue of great interest taking increasingly an important place in scientific community, especially in automation, pattern recognition and machine learning. There exists a broad range of important applications such as video analysis, motion identification, segmentation of human motion and airplane tracking, among others. Spectral matrix analysis is one of the approaches to address this issue. Spectral techniques, mainly those based on kernels, have proved to be a suitable tool in several aspects of interest in pattern recognition and machine learning even when data are time-varying, such as the estimation of the number of clusters, clustering and classification. Most of spectral clustering approaches have been designed for analyzing static data, discarding the temporal information, i.e. the evolutionary behavior along time. Some works have been developed to deal with the time varying effect. Nonetheless, an approach able to accurately track and cluster time-varying data in real time applications remains an open issue. This thesis describes the design of a kernel-based dynamic spectral clustering using a primaldual approach so as to carry out the grouping task involving the dynamic information, that is to say, the changes of data frames along time. To this end, a dynamic kernel framework aimed to extend a clustering primal formulation to dynamic data analysis is introduced. Such framework is founded on a multiple kernel learning (MKL) approach. Proposed clustering approach, named dynamic kernel spectral clustering (DKSC) uses a linear combination of kernels matrices as a MKL model. Kernel matrices are computed from an input frame sequence represented by data matrices. Then, a cumulative kernel is obtained, being the model coefficients or weighting factors obtained by ranking each sample contained in the frame. Such ranking corresponds to a novel tracking approach that takes advantages of the spectral decomposition of a generalized kernel matrix. Finally, to get the resultant cluster assignments, data are clustered using the cumulative kernel matrix. Experiments are done over real databases (human motion and moon covered by clouds) as well as artificial data (moving-Gaussian clouds). As a main result, proposed spectral clustering method for dynamic data proved to be able for grouping underlying events and movements and detecting hidden objects as well. The proposed approach may represent a contribution to the pattern recognition field, mainly, for solving problems involving dynamic information aimed to either tracking or clustering of data.

This work presents a comparative study of different partitional and spectral clustering techniques to cluster heartbeats patterns of long-term ECG signals. Due to the nature of signals and since, in many cases, it is not feasible labeling thereof, clustering is preferred for analysis. The use of a generic model of partitional clustering and the appropriate estimation of initialization parameters via spectral techniques represent some of the most important contributions of this research. The experiments are done with a standard arrhythmia database of MIT (Massachusetts Institute of Technology) and the feature extraction is carried out using techniques recommended by literature. Another important contribution is the design of a sequential analysis method which reduces the computational cost and improves clustering performance compared to traditional analysis that is, analyzing the whole data set in one iteration. Additionally, it suggests a complete system for unsupervised analysis of ECG signals, including feature extraction, feature selection, initialization and clustering stages. Also, some appropriate performance measures based on groups analysis were designed, which relate the clustering performance with the number of resultants groups and computational cost. This study is done taking into account the AAMI standard (Association for the Advance of Medical Instrumentation).