New Advances in Intelligent Signal Processing [recurso electrónico] / edited by António E. Ruano, Annamária R. Várkonyi-Kóczy.

Formulation of Fuzzy Random Regression Model -- Evolutionary Multiobjective Neural Network Models Identification: Evolving Task-Optimised Models -- Structural Learning Model of the Neural Network and its Application to LEDs Signal Retrofit -- Robustness of DNA-based Clustering -- Advances in automated neonatal seizure detection -- Design of Fuzzy Relation-Based Image Sharpeners -- Application of Fuzzy Logic and Lukasiewicz Operators for Image Contrast Control -- Low Complexity Situational Models in Image Quality Improvement -- A flexible representation and invertible transformations for images on quantum computers -- Weakly supervised learning: application to fish school recognition -- Intelligent Spaces as Assistive Environments: Visual fall detection using an evolutive algorithm.

The current volume “New Advances in Intelligent Signal Processing” contains extended works based on a careful selection of papers presented originally at the jubilee sixth IEEE International Symposium on Intelligent Signal Processing (WISP’2009), held in Budapest Hungary, August 26-28, 2009 - celebrating the 10 years anniversary of the WISP event series. The present book does not intent to be an overall survey on the fields of interest of the area, but tries to find topics which represent new, hot, and challenging problems. The book begins with papers investigating selected problems of Modeling, Identification, and Clustering such as  fuzzy random variables, evolutionary multi-objective neural network models, a structural learning model of neural networks within a Boltzmann machine, a robust DNA-based clustering techniques, and the advances of combining multi-criteria analysis of signals and pattern recognition using machine learning principles.  In the second part of the book Image Processing is treated. The carefully edited chapters deal with fuzzy relation based image enhancement, image contrast control technique based on the application of Lukasiewicz algebra operators, low complexity situational models of image quality mprovement, flexible representation of map images to quantum computers, and object recognition in images.The last chapter presents an image processing application for elderly care, performing real-time 3D tracking based on a new evolutive multi-modal algorithm. The present book does not intent to be an overall survey on the fields of interest of the area, but tries to find topics which represent new, hot, and challenging problems. The book begins with papers investigating selected problems of Modeling, Identification, and Clustering such as  fuzzy random variables, evolutionary multi-objective neural network models, a structural learning model of neural networks within a Boltzmann machine, a robust DNA-based clustering techniques, and the advances of combining multi-criteria analysis of signals and pattern recognition using machine learning principles.  In the second part of the book Image Processing is treated. The carefully edited chapters deal with fuzzy relation based image enhancement, image contrast control technique based on the application of Lukasiewicz algebra operators, low complexity situational models of image quality mprovement, flexible representation of map images to quantum computers, and object recognition in images.The last chapter presents an image processing application for elderly care, performing real-time 3D tracking based on a new evolutive multi-modal algorithm.

19