Librería para el procesamiento de señales digitales con computadora

In this work the basic characteristics of the development and the functionality of a specific software are exposed for the teaching in any matter that includes among their contents the "digital signal processing". The software has allowed that the students of the University of Cordoba that study the studies of Engineer in Automatic ad Electronic Industrial, Technical Engineering in Computer science of Systems and Technical Engineering in Computer science of Administration, they can simulate the theoretical contents corresponding to matters with the thematic one commented previously, without necessity of requiring additional computer of the University. The main advantage of the developed product rests in the limitation of the time doesn't exceed the three hours, keeping in mind that for these ends the software can be used without necessity of having programming knowledge. In the environment of the investigation, it can be used as development platform, beings necessary to have programming knowledge in the language C++.En este trabajo se exponen las características básicas del desarrollo y la funcionalidad de un software específico para la enseñanza en cualquier materia qeu incluya entre sus contenidos el "procesamiento digital de señales". El software ha permitido que los alumnos de la Universidad de Córdoba que cursan los estudios de Ingeniero en Automática y Electrónica Industrial, Ingeniería Técnica en Informática de Sistemas e Ingeniería Técnica en Informática de Gestión, puedan simular los contenidos teóricos correspondientes a materias con la temática comentada anteriormente, sin necesidad de requerir adicionales medios informáticos de la Universidad.La principal ventaja del producto desarrollado estriba en la limitación del tiempo requerido para su aprendizaje. En el ámbito de la enseñanza práctica, ha sido comprobado que este tiempo no excede las tres horas, teniendo en cuenta que para estos fines el software puede ser utilizado sin necesidad de tener conocimientos de programación. En el ámbito de la investigación, puede ser utilizado como plataforma de desarrollo, siendo necesario tener conocimientos de programación en el lenguaje C++

Optimización de la predicción de demanda de agua mediante algoritmos neuro-genéticos para un conjunto de datos reducido

La predicción de la demanda de agua es uno de los factores principales en el diseño y gestión de sistemas de abastecimiento y distribución de agua. Recientemente, avanzadas técnicas en inteligencia computacional como las Redes Neuronales Artificiales (RNAs) han sido aplicadas para la predicción de series temporales con importantes resultados. En este trabajo se ha desarrollado una metodología híbrida que combina RNAs y Algoritmos Genéticos multiobjetivo para la predicción a corto plazo de la demanda de agua en una Comunidad de Regantes cuando la disponibilidad de datos es escasa. El modelo fue desarrollado utilizando datos de series temporales del Sector VII de la Zona Regable Bembézar M.D. Tras el proceso de optimización con un algoritmo genético multiobjetivo se obtuvo una RNA de tipo perceptrón multicapa entrenada mediante el algoritmo Regularización Bayesiana con 24 neuronas en la primera capa oculta y 21 en la segunda. El modelo desarrollado fue capaz de explicar el 95 % de la varianza total de los datos observados con un Error Estándar de Predicción del 9.38 % (periodo de test).Ministerio de Economía y Competitivida

Comparing Evolutionary Algorithms and Particle Filters for Markerless Human Motion Capture

Markerless Human Motion Capture is the problem of determining the joints’ angles of a three-dimensional articulated body model that best matches current and past observations acquired by video cameras. The problem of Markerless Human Motion Capture is high-dimensional and requires the use of models with a considerable number of degrees of freedom to appropriately adapt to the human anatomy. Particle filters have become the most popular approach for Markerless Human Motion Capture, despite their difficulty to cope with high-dimensional problems. Although several solutions have been proposed to improve their performance, they still suffer from the curse of dimensionality. As a consequence, it is normally required to impose mobility limitations in the body models employed, or to exploit the hierarchical nature of the human skeleton by partitioning the problem into smaller ones. Evolutionary algorithms, though, are powerful methods for solving continuous optimization problems, specially the high-dimensional ones. Yet, few works have tackled Markerless Human Motion Capture using them. This paper evaluates the performance of three of the most competitive algorithms in continuous optimization – Covariance Matrix Adaptation Evolutionary Strategy, Differential Evolution and Particle Swarm Optimization – with two of the most relevant particle filters proposed in the literature, namely the Annealed Particle Filter and the Partitioned Sampling Annealed Particle Filter. The algorithms have been experimentally compared in the public dataset HumanEva-I by employing two body models with different complexities. Our work also analyzes the performance of the algorithms in hierarchical and holistic approaches, i.e., with and without partitioning the search space. Non-parametric tests run on the results have shown that: (i) the evolutionary algorithms employed outperform their particle filter counterparts in all the cases tested; (ii) they can deal with high-dimensional models thus leading to better accuracy; and (iii) the hierarchical strategy surpasses the holistic one

New memory-based hybrid model for middle-term water demand forecasting in irrigated areas

The energy demand and their associated costs in pressurized irrigation networks together with water scarcity are currently causing serious challenges for irrigation district’s (ID) managers. Additionally, most of the new water distribution networks in IDs have been designed to be operated on-demand complexing ID managers the daily decision-making process. The knowledge of the water demand several days in advance would facilitate the management of the system and would help to optimize the water use and energy costs. For an efficient management and optimization of the water-energy nexus in IDs, longer term forecasting models are needed. In this work, a new hybrid model (called LSTMHybrid) combining Fuzzy Logic (FL), Genetic Algorithm (GA), LSTM encoder-decoder and dense or full connected neural networks (DNN) for the one-week forecasting of irrigation water demand at ID scale has been developed. LSTMHybrid was developed in Python and applied to a real ID. The optimal input variables for LSTMHydrid were mean temperature (°C), reference evapotranspiration (mm), solar radiation (MJ m−2) and irrigation water demand of the ID (m3) from 1 to 7 days prior to the first day of prediction. The optimal LSTMHybrid model selected consisted of 50 LSTM cells in the encoder submodel, 409 LSTM cells in the decoder submodel and three hidden layers in the DNN submodel with 31, 96 and 128 neurons in each hidden layer, respectively. Thus, LSTMHybrid had a total of 1.5 million parameters, obtaining a representativeness higher than 94 % and an accuracy around of 20 %

Phylogenetic relationships of Chanidae (Teleostei: Gonorynchiformes) as impacted by Dastilbe moraesi, from the Sanfranciscana basin, Early Cretaceous of Brazil

Fossil gonorynchiform fishes range from the Lower Cretaceous to the early Miocene, and are represented by a few dozen living species. The order is currently divided into two major clades: Gonorynchoidei, which includes the families Gonorynchidae and Kneriidae, and Chanoidei, encompassing a single family, Chanidae, with a single recent species, the Indo-Pacific Chanos chanos, and several fossil taxa. Chanidae includes some poorly known taxa, such as Dastilbe moraesi, described from the Aptian (Lower Cretaceous) of the Areado Formation, Sanfranciscana basin, Brazil. This species is currently considered to be a junior synonym of the type species of its genus, Dastilbe crandalli, from Santana Formation, Aptian, northeastern Brazil. The analysis of abundant D. moraesi specimens revealed several new morphological features, many of which had previously been misinterpreted. Dastilbe moraesi was incorporated into a gonorynchiform character matrix as revised and modified for the Chanidae. We obtained a single most parsimonious tree in which D. moraesi is distinct and phylogenetically apart from D. crandalli. According our analysis, D. moraesi forms a sister pair with Chanos, a clade which is closely related to Tharrhias, all composing the tribe ChaniniGonorynchiformes fósseis ocorrem desde do Cretáceo inferior ao Mioceno inferior, e são representados por alguns representantes viventes. A ordem está dividida atualmente em dois clados principais: Gonorynchoidei, que inclui as famílias Gonorynchidae e Kneriidae, e Chanoidei, compreendendo uma única família, Chanidae, com uma única espécie vivente, Chanos chanos, do Indo-Pacífico, além de vários representantes fósseis. Chanidae inclui alguns táxons problemáticos, tais como Dastilbe moraesi, descrito do Aptiano (Cretáceo Inferior) da Formação Areado, bacia Sanfranciscana, Brasil. Esta espécie é atualmente considerada um sinônimo júnior da espécie-tipo de seu gênero, Dastilbe crandalli, da Formação Santana, Aptiano do nordeste do Brasil. A análise de abundante material de D. moraesi revelou várias novas características anatômicas, muitas das quais haviam sido previamente mal interpretadas. Dastilbe moraesi foi incorporado em uma matriz revisada de caracteres da família Chanidae. Nós obtivemos uma única árvore mais parcimoniosa na qual D. moraesi é distinto e filogeneticamente distante de D. crandalli. De acordo com nossa análise, D. moraesi é o grupo-irmão de Chanos, um clado intimamente relacionado a Tharrhias, com todos compondo a tribo ChaniniThis study was supported by CNPq (process # 401818/2010-1) and project CGL2013-42643P, Ministerio de Ciencia e Innovación de Españ

Multi-view gait recognition on curved

Appearance changes due to viewing angle changes cause difficulties for most of the gait recognition methods. In this paper, we propose a new approach for multi-view recognition, which allows to recognize people walking on curved paths. The recognition is based on 3D angular analysis of the movement of the walking human. A coarse-to-fine gait signature represents local variations on the angular measurements along time. A Support Vector Machine is used for classifying, and a sliding temporal window for majority vote policy is used to smooth and reinforce the classification results. The proposed approach has been experimentally validated on the publicly available “Kyushu University 4D Gait Database”