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In many situations found both in Nature and in human-built systems, a set of mixed signals is observed (frequently also with noise), and it is of great scientific and technological relevance to be able to isolate or separate them so that the information in each of the signals can be utilized. Blind source separation (BSS) research is one of the more interesting emerging fields now a days in the field of signal processing. It deals with the algorithms that allow the recovery of the original sources from a set of mixtures only. The adjective “blind” is applied because the purpose is to estimate the original sources without any a priori knowledge about either the sources or the mixing system. Most of the models employed in BSS assume the hypothesis about the independence of the original sources. Under this hypothesis,a BSS problem can be considered as a particular case of independent component analysis(ICA),a linear transformation technique that, starting from a multivariate representation of the data, minimizes the statistical dependence between the components of the representation. It can be claimed that most of the advances in ICA have been motivated by the search for solutions to the BSS problem and, the other way around,advances in ICA have been immediately applied to BSS. ICA and BSS algorithms start from a mixture model, whose parameters are estimated from the observed mixtures. Separation is achieved by applying the inverse mixture model to the observed signals(separating or unmixing model).Mixturem- els usually fall into three broad categories: instantaneous linear models, convolutive models and nonlinear models ,the ?rstone being the simplest but,in general,not near realistic applications. The development and test of the algorithms can be accomplished through synthetic data or with real-world data.Obviously, the most important aim(and most difficult) is the separation of real-world mixtures. BSS and ICA have strong relations also, apart from signal processing, with other fields such as statistics and artificial neural networks. As long as we can find a system that emits signals propagated through a mean, andthosesignalsarereceivedbyasetofsensorsandthereisaninterestinrecovering the original sources,we have a potential field of application for BSS and ICA. Inside that wide range of applications we can find, for instance: noise reduction applications, biomedical applications,audio systems,telecommunications,and many others. This volume comes out just 20 years after the first contributions in ICA and BSS 1 appeared . Therein after,the number of research groups working in ICA and BSS has been constantly growing, so that nowadays we can estimate that far more than 100 groups are researching in these fields. As proof of the recognition among the scientific community of ICA and BSS developments there have been numerous special sessions and special issues in several well- 1 J.Herault, B.Ans,“Circuits neuronaux à synapses modi?ables: décodage de messages composites para apprentissage non supervise”, C.R. de l’Académie des Sciences, vol. 299, no. III-13,pp.525–528,1984.

Signal processing --- Neural networks (Computer science) --- Electronic noise --- Independent component analysis --- Digital techniques --- ICA (Independent component analysis) --- Mathematics. --- Special purpose computers. --- Coding theory. --- Computers. --- Algorithms. --- Mathematical analysis. --- Analysis (Mathematics). --- Statistics. --- Analysis. --- Special Purpose and Application-Based Systems. --- Algorithm Analysis and Problem Complexity. --- Computation by Abstract Devices. --- Coding and Information Theory. --- Statistics and Computing/Statistics Programs. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- 517.1 Mathematical analysis --- Mathematical analysis --- Algorism --- Algebra --- Arithmetic --- Data compression (Telecommunication) --- Digital electronics --- Information theory --- Machine theory --- Signal theory (Telecommunication) --- Computer programming --- Special purpose computers --- Computers --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace --- Math --- Science --- Foundations --- Multivariate analysis --- Global analysis (Mathematics). --- Software engineering. --- Computer software. --- Computer science. --- Mathematical statistics. --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Informatics --- Software, Computer --- Computer software engineering --- Engineering --- Analysis, Global (Mathematics) --- Differential topology --- Functions of complex variables --- Geometry, Algebraic --- Information theory. --- Statistics . --- Communication theory --- Communication

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Latin America --- United States --- United States --- Amérique latine --- Etats-Unis --- Etats-Unis --- Foreign relations --- Foreign relations --- Foreign relations --- Chronology. --- Relations extérieures --- Relations extérieures --- Relations extérieures --- Chronologie

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Complex analysis --- Mathematical statistics --- Molecular biology --- Computer science --- Programming --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- patroonherkenning --- neuronale netwerken --- beeldverwerking --- factoranalyse --- complexe analyse (wiskunde) --- informatica --- KI (kunstmatige intelligentie) --- robots --- moleculaire biologie --- AI (artificiële intelligentie)

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Neural computation arises from the capacity of nervous tissue to process information and accumulate knowledge in an intelligent manner. Conventional computational machines have encountered enormous difficulties in duplicatingsuch functionalities. This has given rise to the development of Artificial Neural Networks where computation is distributed over a great number of local processing elements with a high degree of connectivityand in which external programming is replaced with supervised and unsupervised learning. The papers presented in this volume are carefully reviewed versions of the talks delivered at the International Workshop on Artificial Neural Networks (IWANN '93) organized by the Universities of Catalonia and the Spanish Open University at Madrid and held at Barcelona, Spain, in June 1993. The 111 papers are organized in seven sections: biological perspectives, mathematical models, learning, self-organizing networks, neural software, hardware implementation, and applications (in five subsections: signal processing and pattern recognition, communications, artificial vision, control and robotics, and other applications).

681.3*I26 --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- Neural networks (Computer science) --- Congresses --- Artificial intelligence. --- Optical pattern recognition. --- Computer vision. --- Software engineering. --- Computer science. --- Electronics. --- Artificial Intelligence. --- Pattern Recognition. --- Image Processing and Computer Vision. --- Special Purpose and Application-Based Systems. --- Processor Architectures. --- Electronics and Microelectronics, Instrumentation. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Electrical engineering --- Physical sciences --- Informatics --- Science --- Computer software engineering --- Engineering --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems

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This book constitutes the refereed proceedings of the 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, held in Salamanca, Spain in June 2009. The 167 revised full papers presented together with 3 invited lectures were carefully reviewed and selected from over 230 submissions. The papers are organized in thematic sections on theoretical foundations and models; learning and adaptation; self-organizing networks, methods and applications; fuzzy systems; evolutionary computation and genetic algoritms; pattern recognition; formal languages in linguistics; agents and multi-agent on intelligent systems; brain-computer interfaces (bci); multiobjetive optimization; robotics; bioinformatics; biomedical applications; ambient assisted living (aal) and ambient intelligence (ai); other applications.

Computational intelligence --- Biologically-inspired computing --- Artificial intelligence --- Neural networks (Computer science) --- Computer Science --- Biology - General --- Engineering & Applied Sciences --- Biology --- Health & Biological Sciences --- Artificial intelligence. --- Bioinformatics. --- Computer science. --- Data mining. --- Optical pattern recognition. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Informatics --- Bio-informatics --- Biological informatics --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Computers. --- Pattern recognition. --- Computer Science. --- Computational Biology/Bioinformatics. --- Pattern Recognition. --- Artificial Intelligence (incl. Robotics). --- Data Mining and Knowledge Discovery. --- Models and Principles. --- Database searching --- Science --- Information science --- Computational biology --- Systems biology --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace --- Data processing --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Artificial Intelligence. --- Pattern recognition systems. --- Computational and Systems Biology. --- Automated Pattern Recognition. --- Models of Computation. --- Pattern classification systems --- Pattern recognition computers --- Computer vision