José Mira

Bio: José Mira is an academic researcher from National University of Distance Education. The author has contributed to research in topics: Artificial neural network & Expert system. The author has an hindex of 29, co-authored 190 publications receiving 2618 citations. Previous affiliations of José Mira include Spanish National Research Council & ETSI.

Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system

Abstract: A preliminary study to approach the problem of reliably detecting life threatening ventricular arrhythmias in real time is described. An algorithm (DIAGNOSIS) has been developed in order to classify ECG signal records on the basis of the computation of four simple parameters calculated from a representation in the frequency domain. This algorithm uses a set of rules constituting an operative classification scheme based on the comparison of the parameters with a set of pre-established thresholds. This allows us to differentiate four general categories: ventricular fibrillation-flutter, ventricular rhythms, imitative artefacts and predominant sinus rhythm.

DIAVAL, a Bayesian expert system for echocardiography

Abstract: DIAVAL is an expert system for the diagnosis of heart diseases, including several kinds of data, mainly from echocardiography. The first part of this paper is devoted to the causal probabilistic model which constitutes the knowledge base of the expert system in the form of a Bayesian network, emphasizing the importance of the OR gate. The second part deals with the process of diagnosis, which consists of computing the a posteriori probabilities, selecting the most probable and most relevant diagnoses, and generating a written report. It also describes the results of the evaluation of the program.

A model and a language for the fuzzy representation and handling of time

Abstract: In this paper we present a model for the representation and handling of fuzzy temporal references. We define the concepts of date, time extent, and interval, according to the formalism of possibility theory. We introduce relations between the temporal entities dates and intervals, interpreted as constraints on the distance between dates and projected onto Fuzzy Temporal Constraint Satisfaction Networks. We introduce a language for the representation and manipulation of temporal entities and relations, which captures some of the terms we use in our expressions in the natural language and therefore, it is a flexible and powerful interface for those systems in which the representation of fuzzy temporal information is necessary. Our approach permits a common interpretation of qualitative and quantitative temporal relations, facilitating the relativization of the meaning of the temporal relations to each specific application context and the verification of relations between temporal entities. © 1994.

Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach

Abstract: Evolutionary Computation.- Cultural Operators for a Quantum-Inspired Evolutionary Algorithm Applied to Numerical Optimization Problems.- New Codification Schemas for Scheduling with Genetic Algorithms.- Solving the Multidimensional Knapsack Problem Using an Evolutionary Algorithm Hybridized with Branch and Bound.- Cryptanalysis of Substitution Ciphers Using Scatter Search.- Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification.- Convergence Analysis of a GA-ICA Algorithm.- An Evolutionary Strategy for the Multidimensional 0-1 Knapsack Problem Based on Genetic Computation of Surrogate Multipliers.- An Evolutionary Approach to Designing and Solving Fuzzy Job-Shop Problems.- Memetic Algorithms with Partial Lamarckism for the Shortest Common Supersequence Problem.- 2D and 3D Pictural Networks of Evolutionary Processors.- Analysing Sentences with Networks of Evolutionary Processors.- Simulating Evolutionary Algorithms with Eco-grammar Systems.- Timed Accepting Hybrid Networks of Evolutionary Processors.- A New Immunotronic Approach to Hardware Fault Detection Using Symbiotic Evolution.- A Basic Approach to Reduce the Complexity of a Self-generated Fuzzy Rule-Table for Function Approximation by Use of Symbolic Regression in 1D and 2D Cases.- Parallel Evolutionary Computation: Application of an EA to Controller Design.- MEPIDS: Multi-Expression Programming for Intrusion Detection System.- A Study of Heuristic Techniques Inspired in Natural Process for the Solution of the Container Fill Problem.- Attribute Grammar Evolution.- Evolution and Evaluation in Knowledge Fusion System.- The Allele Meta-model - Developing a Common Language for Genetic Algorithms.- Using Bees to Solve a Data-Mining Problem Expressed as a Max-Sat One.- A Comparison of GA and PSO for Excess Return Evaluation in Stock Markets.- Nonlinear Robust Identification Using Multiobjective Evolutionary Algorithms.- Genetic Algorithms for Multiobjective Controller Design.- Grammar Based Crossover Operator in Genetic Programming.- GA-Selection Revisited from an ES-Driven Point of View.- Agent WiSARD in a 3D World.- One Generalization of the Naive Bayes to Fuzzy Sets and the Design of the Fuzzy Naive Bayes Classifier.- Towards a Methodology to Search for Near-Optimal Representations in Classification Problems.- Playing a Toy-Grammar with GCS.- A Genetic Approach to Data Dimensionality Reduction Using a Special Initial Population.- Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms.- Solving Partitioning Problem in Codesign with Ant Colonies.- Electronics and Robotics.- A Neuromimetic Integrated Circuit for Interactive Real-Time Simulation.- A FPGA Architecture of Blind Source Separation and Real Time Implementation.- Description and Simulation of Bio-inspired Systems Using VHDL-AMS.- Transistor-Level Circuit Experiments Using Evolvable Hardware.- An Electronic Reconfigurable Neural Architecture for Intrusion Detection.- Construction and VHDL Implementation of a Fully Local Network with Good Reconstruction Properties of the Inputs.- Reconfigurable Hardware Implementation of Neural Networks for Humanoid Locomotion.- An Associative Cortical Model of Language Understanding and Action Planning.- Neural Clustering Analysis of Macroevolutionary and Genetic Algorithms in the Evolution of Robot Controllers.- Induced Behavior in a Real Agent Using the Multilevel Darwinist Brain.- Landscaping Model for Virtual Environment.- Other Applications.- Sensitivity from Short-Term Memory vs. Stability from Long-Term Memory in Visual Attention Method.- Visual Attention, Visual Saliency, and Eye Movements During the Inspection of Natural Scenes.- Model Performance for Visual Attention in Real 3D Color Scenes.- On the Evolution of Formal Models and Artificial Neural Architectures for Visual Motion Detection.- Estimation of Fuel Moisture Content Using Neural Networks.- Adjustment of Surveillance Video Systems by a Performance Evaluation Function.- Application of Machine Learning Techniques for Simplifying the Association Problem in a Video Surveillance System.- A Neurocalibration Model for Autonomous Vehicle Navigation.- Some Remarks on the Application of Artificial Neural Networks to Optical Character Recognition.- Using Fuzzy Clustering Technique for Function Approximation to Approximate ECG Signals.- Information Retrieval and Classification with Wavelets and Support Vector Machines.- A New Approach to Clustering and Object Detection with Independent Component Analysis.- Bispectra Analysis-Based VAD for Robust Speech Recognition.- On-line Training of Neural Networks: A Sliding Window Approach for the Levenberg-Marquardt Algorithm.- Boosting Parallel Perceptrons for Label Noise Reduction in Classification Problems.- On the Connection Between the Human Visual System and Independent Component Analysis.- Image Classifier for the TJ-II Thomson Scattering Diagnostic: Evaluation with a Feed Forward Neural Network.- Computerized Adaptive Tests and Item Response Theory on a Distance Education Platform.- Stochastic Vs Deterministic Traffic Simulator. Comparative Study for Its Use Within a Traffic Light Cycles Optimization Architecture.

Forecasting with artificial neural networks: the state of the art

Abstract: Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous surge in research activities in the past decade. While ANNs provide a great deal of promise, they also embody much uncertainty. Researchers to date are still not certain about the effect of key factors on forecasting performance of ANNs. This paper presents a state-of-the-art survey of ANN applications in forecasting. Our purpose is to provide (1) a synthesis of published research in this area, (2) insights on ANN modeling issues, and (3) the future research directions.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

Robot vision

Abstract: A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >