DLSI - Capítulos de Libros
https://hdl.handle.net/10481/15210
2024-03-28T14:18:34ZFractal Analysis in MATLAB: A Tutorial for Neuroscientists
https://hdl.handle.net/10481/89946
Fractal Analysis in MATLAB: A Tutorial for Neuroscientists
Ruiz de Miras, Juan
MATLAB is one of the software platforms most widely used for scientific computation. MATLAB includes a large set of functions, packages, and toolboxes that make it simple and fast to obtain complex mathematical and statistical computations for many applications. In this chapter, we review some tools available in MATLAB for performing fractal analyses on typical neuroscientific data in a practical way. We provide detailed examples of how to calculate the fractal dimension of 1D, 2D, and 3D data in MATLAB. Furthermore, we review other software packages for fractal analysis.
This work is part of the research project PID2019-105145RB-I00 supported by the Spanish
Government (MCIN/AEI/10.13039/501100011033).
On the Combination of Pairwise and Granularity Learning for Improving Fuzzy Rule-Based Classification Systems: GL-FARCHD-OVO
https://hdl.handle.net/10481/88852
On the Combination of Pairwise and Granularity Learning for Improving Fuzzy Rule-Based Classification Systems: GL-FARCHD-OVO
Villar Castro, Pedro; Fernández, Alberto; Herrera Triguero, Francisco
Fuzzy rule-based systems constitute a very spread tool for classi cation problems, but several proposals may decrease its performance when dealing with multi-class problems. Among existing approaches, the FARC-HD algorithm has excelled as it has shown to achieve accurate and compact classi ers, even in the context of multi-class problems. In this work, we aim to go one step further to improve the behavior of the former algorithm by means of a "divide-and-conquer" approach, via binarization in a one-vs-one scheme. Besides, we will contextualize each binary classi er by adapting the data base for each subproblem by means of a granularity learning process to adapt the number of fuzzy labels per variable. Our experimental study, using several data-sets from KEEL data-set repository, shows the goodness of the proposed methodology.
An Introduction to GPU Computing for Numerical Simulation
https://hdl.handle.net/10481/88352
An Introduction to GPU Computing for Numerical Simulation
Mantas Ruiz, José Miguel; de la Asunción, Marc; Castro, Manuel Jesús
Graphics Processing Units (GPUs) have proven to be a powerful accelerator for intensive numerical computations. The massive parallelism of these platforms makes it possible to achieve dramatic runtime reductions over a standard CPU in many numerical applications at a very affordable price. Moreover, several programming environments, such as NVIDIA’s Compute Unified Device Architecture (CUDA) have shown a high effectiveness in the mapping of numerical algorithms to GPUs. These notes provide an introduction to the development of CUDA programs for numerical simulation using CUDA C/C++, the most popular GPU programming toolkit. An overview of CUDA programming will be illustrated through the CUDA implementation of simple numerical examples for PDEs. These CUDA implementations will be studied and run on modern GPU-based platforms.; Las unidades de procesamiento gráfico (GPU) han demostrado ser un potente acelerador para cálculos numéricos intensivos. El paralelismo masivo de estas plataformas permite conseguir drásticas reducciones del tiempo de ejecución con respecto a una CPU estándar en muchas aplicaciones numéricas a un precio muy asequible. Además, varios entornos de programación, como la plataforma Compute Unified Device Architecture (CUDA) de NVIDIA, han demostrado una gran eficacia en la adaptación de algoritmos numéricos a GPU. Estas notas proporcionan una introducción al desarrollo de programas CUDA para simulación numérica utilizando CUDA C/C++, el kit de herramientas de programación para GPU más popular. Se ilustrará una visión general de la programación en CUDA a través de la implementación en CUDA de sencillos ejemplos numéricos para resolver EDPs. Estas implementaciones CUDA se estudiarán y ejecutarán en modernas plataformas basadas en GPU.
Clustering Study of Vehicle Behaviors Using License Plate Recognition
https://hdl.handle.net/10481/87226
Clustering Study of Vehicle Behaviors Using License Plate Recognition
Bolaños Martinez, Daniel; Bermúdez Edo, María del Campo; Garrido Bullejos, José Luis
Ubiquitous computing and artificial intelligence contribute to deploying intelligent environments. Sensor networks in cities generate large amounts of data that can be analyzed to provide relevant information in different fields, such as traffic control. We propose an analysis of vehicular behavior based on license plate recognition (LPR) in a rural region of three small villages. The contribution is twofold. First, we extend an existing taxonomy of the most widely used clustering algorithms in machine learning with additional classes. Second, we compare the performance of algorithms from each class of the taxonomy, extracting behavioral patterns. Partitional and hierarchical algorithms obtain the best results, while density-based algorithms have poor results. The results show four differentiated patterns in vehicular behavior, distinguishing different patterns in both residents and tourists. Our work can help policymakers develop strategies to improve services in rural villages, and developers choose the correct algorithm for a similar study.