Independent Component Analysis and Signal Separation

Independent Component Analysis and Signal Separation
Author: Mike E. Davies,Christopher C. James,Samer A. Abdallah,Mark D. Plumbley
Publsiher: Springer Science & Business Media
Total Pages: 847
Release: 2007-08-28
ISBN 10: 3540744932
ISBN 13: 9783540744931
Language: EN, FR, DE, ES & NL

Independent Component Analysis and Signal Separation Book Review:

This book constitutes the refereed proceedings of the 7th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2007, held in London, UK, in September 2007. It covers algorithms and architectures, applications, medical applications, speech and signal processing, theory, and visual and sensory processing.

Blind Source Separation

Blind Source Separation
Author: Ganesh R. Naik,Wenwu Wang
Publsiher: Springer
Total Pages: 551
Release: 2014-05-21
ISBN 10: 3642550169
ISBN 13: 9783642550164
Language: EN, FR, DE, ES & NL

Blind Source Separation Book Review:

Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). The book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications. Furthermore, the research results previously scattered in many journals and conferences worldwide are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms and applications of BSS. Dr. Ganesh R. Naik works at University of Technology, Sydney, Australia; Dr. Wenwu Wang works at University of Surrey, UK.

Independent Component Analysis

Independent Component Analysis
Author: Dr. James V. Stone
Publsiher: MIT Press
Total Pages: 193
Release: 2004
ISBN 10: 9780262693158
ISBN 13: 0262693151
Language: EN, FR, DE, ES & NL

Independent Component Analysis Book Review:

A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation.

Handbook of Blind Source Separation

Handbook of Blind Source Separation
Author: Pierre Comon,Christian Jutten
Publsiher: Academic Press
Total Pages: 856
Release: 2010-02-17
ISBN 10: 9780080884943
ISBN 13: 0080884946
Language: EN, FR, DE, ES & NL

Handbook of Blind Source Separation Book Review:

Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. Covers the principles and major techniques and methods in one book Edited by the pioneers in the field with contributions from 34 of the world’s experts Describes the main existing numerical algorithms and gives practical advice on their design Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications

Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy

Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy
Author: Dajiang Zhu,Jingwen Yan,Heng Huang,Li Shen,Paul M. Thompson,Carl-Fredrik Westin,Xavier Pennec,Sarang Joshi,Mads Nielsen,Tom Fletcher,Stanley Durrleman,Stefan Sommer
Publsiher: Springer Nature
Total Pages: 230
Release: 2019-10-10
ISBN 10: 3030332268
ISBN 13: 9783030332266
Language: EN, FR, DE, ES & NL

Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy Book Review:

This book constitutes the refereed joint proceedings of the 4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 16 full papers presented at MBAI 2019 and the 7 full papers presented at MFCA 2019 were carefully reviewed and selected. The MBAI papers intend to move forward the state of the art in multimodal brain image analysis, in terms of analysis methodologies, algorithms, software systems, validation approaches, benchmark datasets, neuroscience, and clinical applications. The MFCA papers are devoted to statistical and geometrical methods for modeling the variability of biological shapes. The goal is to foster the interactions between the mathematical community around shapes and the MICCAI community around computational anatomy applications.

Blind Source Separation

Blind Source Separation
Author: Xianchuan Yu,Dan Hu,Jindong Xu
Publsiher: John Wiley & Sons
Total Pages: 416
Release: 2013-12-13
ISBN 10: 1118679873
ISBN 13: 9781118679876
Language: EN, FR, DE, ES & NL

Blind Source Separation Book Review:

A systematic exploration of both classic and contemporaryalgorithms in blind source separation with practical casestudies The book presents an overview of Blind Source Separation, arelatively new signal processing method. Due to themultidisciplinary nature of the subject, the book has been writtenso as to appeal to an audience from very different backgrounds.Basic mathematical skills (e.g. on matrix algebra and foundationsof probability theory) are essential in order to understand thealgorithms, although the book is written in an introductory,accessible style. This book offers a general overview of the basics of BlindSource Separation, important solutions and algorithms, and in-depthcoverage of applications in image feature extraction, remotesensing image fusion, mixed-pixel decomposition of SAR images,image object recognition fMRI medical image processing, geochemicaland geophysical data mining, mineral resources prediction andgeoanomalies information recognition. Firstly, the background andtheory basics of blind source separation are introduced, whichprovides the foundation for the following work. Matrix operation,foundations of probability theory and information theory basics areincluded here. There follows the fundamental mathematical model andfairly new but relatively established blind source separationalgorithms, such as Independent Component Analysis (ICA) and itsimproved algorithms (Fast ICA, Maximum Likelihood ICA, OvercompleteICA, Kernel ICA, Flexible ICA, Non-negative ICA, Constrained ICA,Optimised ICA). The last part of the book considers the very recentalgorithms in BSS e.g. Sparse Component Analysis (SCA) andNon-negative Matrix Factorization (NMF). Meanwhile, in-depth casesare presented for each algorithm in order to help the readerunderstand the algorithm and its application field. A systematic exploration of both classic and contemporaryalgorithms in blind source separation with practical casestudies Presents new improved algorithms aimed at differentapplications, such as image feature extraction, remote sensingimage fusion, mixed-pixel decomposition of SAR images, image objectrecognition, and MRI medical image processing With applications in geochemical and geophysical data mining,mineral resources prediction and geoanomalies informationrecognition Written by an expert team with accredited innovations in blindsource separation and its applications in natural science Accompanying website includes a software system providing codesfor most of the algorithms mentioned in the book, enhancing thelearning experience Essential reading for postgraduate students and researchersengaged in the area of signal processing, data mining, imageprocessing and recognition, information, geosciences, lifesciences.

Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes

Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes
Author: Karâa, Wahiba Ben Abdessalem
Publsiher: IGI Global
Total Pages: 335
Release: 2015-11-03
ISBN 10: 1466688122
ISBN 13: 9781466688124
Language: EN, FR, DE, ES & NL

Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes Book Review:

Every second, users produce large amounts of image data from medical and satellite imaging systems. Image mining techniques that are capable of extracting useful information from image data are becoming increasingly useful, especially in medicine and the health sciences. Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes addresses major techniques regarding image processing as a tool for disease identification and diagnosis, as well as treatment recommendation. Highlighting current research intended to advance the medical field, this publication is essential for use by researchers, advanced-level students, academicians, medical professionals, and technology developers. An essential addition to the reference material available in the field of medicine, this timely publication covers a range of applied research on data mining, image processing, computational simulation, data visualization, and image retrieval.

Blind Source Separation

Blind Source Separation
Author: Yong Xiang,Dezhong Peng,Zuyuan Yang
Publsiher: Springer
Total Pages: 94
Release: 2014-09-16
ISBN 10: 9812872272
ISBN 13: 9789812872272
Language: EN, FR, DE, ES & NL

Blind Source Separation Book Review:

This book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest development in this field. The book gives an overview on blind source separation where three promising blind separation techniques that can tackle mutually correlated sources are presented. The book further focuses on the non-negativity based methods, the time-frequency analysis based methods, and the pre-coding based methods, respectively.

Adaptive Blind Signal and Image Processing

Adaptive Blind Signal and Image Processing
Author: Andrzej Cichocki,Shun-ichi Amari
Publsiher: John Wiley & Sons
Total Pages: 586
Release: 2002-06-14
ISBN 10: 9780471607915
ISBN 13: 0471607916
Language: EN, FR, DE, ES & NL

Adaptive Blind Signal and Image Processing Book Review:

With solid theoretical foundations and numerous potential applications, Blind Signal Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume unifies and extends the theories of adaptive blind signal and image processing and provides practical and efficient algorithms for blind source separation: Independent, Principal, Minor Component Analysis, and Multichannel Blind Deconvolution (MBD) and Equalization. Containing over 1400 references and mathematical expressions Adaptive Blind Signal and Image Processing delivers an unprecedented collection of useful techniques for adaptive blind signal/image separation, extraction, decomposition and filtering of multi-variable signals and data. Offers a broad coverage of blind signal processing techniques and algorithms both from a theoretical and practical point of view Presents more than 50 simple algorithms that can be easily modified to suit the reader's specific real world problems Provides a guide to fundamental mathematics of multi-input, multi-output and multi-sensory systems Includes illustrative worked examples, computer simulations, tables, detailed graphs and conceptual models within self contained chapters to assist self study Accompanying CD-ROM features an electronic, interactive version of the book with fully coloured figures and text. C and MATLAB user-friendly software packages are also provided MATLAB is a registered trademark of The MathWorks, Inc. By providing a detailed introduction to BSP, as well as presenting new results and recent developments, this informative and inspiring work will appeal to researchers, postgraduate students, engineers and scientists working in biomedical engineering, communications, electronics, computer science, optimisations, finance, geophysics and neural networks.

Unsupervised Signal Processing

Unsupervised Signal Processing
Author: João Marcos Travassos Romano,Romis Attux,Charles Casimiro Cavalcante,Ricardo Suyama
Publsiher: CRC Press
Total Pages: 340
Release: 2018-09-03
ISBN 10: 1420019465
ISBN 13: 9781420019469
Language: EN, FR, DE, ES & NL

Unsupervised Signal Processing Book Review:

Unsupervised Signal Processing: Channel Equalization and Source Separation provides a unified, systematic, and synthetic presentation of the theory of unsupervised signal processing. Always maintaining the focus on a signal processing-oriented approach, this book describes how the subject has evolved and assumed a wider scope that covers several topics, from well-established blind equalization and source separation methods to novel approaches based on machine learning and bio-inspired algorithms. From the foundations of statistical and adaptive signal processing, the authors explore and elaborate on emerging tools, such as machine learning-based solutions and bio-inspired methods. With a fresh take on this exciting area of study, this book: Provides a solid background on the statistical characterization of signals and systems and on linear filtering theory Emphasizes the link between supervised and unsupervised processing from the perspective of linear prediction and constrained filtering theory Addresses key issues concerning equilibrium solutions and equivalence relationships in the context of unsupervised equalization criteria Provides a systematic presentation of source separation and independent component analysis Discusses some instigating connections between the filtering problem and computational intelligence approaches. Building on more than a decade of the authors’ work at DSPCom laboratory, this book applies a fresh conceptual treatment and mathematical formalism to important existing topics. The result is perhaps the first unified presentation of unsupervised signal processing techniques—one that addresses areas including digital filters, adaptive methods, and statistical signal processing. With its remarkable synthesis of the field, this book provides a new vision to stimulate progress and contribute to the advent of more useful, efficient, and friendly intelligent systems.

Neural Networks and Statistical Learning

Neural Networks and Statistical Learning
Author: Ke-Lin Du,M. N. S. Swamy
Publsiher: Springer Science & Business Media
Total Pages: 824
Release: 2013-12-09
ISBN 10: 1447155718
ISBN 13: 9781447155713
Language: EN, FR, DE, ES & NL

Neural Networks and Statistical Learning Book Review:

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

Parallel and Distributed Computing Applications and Technologies

Parallel and Distributed Computing  Applications and Technologies
Author: Kim-Meow Liew,Hong Shen,Simon See,Wentong Cai,Pingzhi Fan,Susumu Horiguchi
Publsiher: Springer Science & Business Media
Total Pages: 891
Release: 2004-12-02
ISBN 10: 3540240136
ISBN 13: 9783540240136
Language: EN, FR, DE, ES & NL

Parallel and Distributed Computing Applications and Technologies Book Review:

This book constitutes the refereed proceedings of the 5th International Conference on Parallel and Distributed Computing, Applications and Technologies; PDCAT 2004, held in Singapore in December 2004. The 173 papers presented were carefully reviewed and selected from 242 submissions. The papers focus on parallel and distributed computing from the perspectives of algorithms, networking and architecture, software systems and technologies, and applications. Besides classical topics from high performance computing, major recent developments are addressed, such as molecular computing, date mining, knowledge discovery, optical networks, secure computing and communications, wireless networks, mobile computing, component-based systems, Internet computing, and Web Technologies.

Independent Component Analysis and Signal Separation

Independent Component Analysis and Signal Separation
Author: Tulay Adali,Christian Jutten,Joao Marcos Travassos Romano,Allan Kardec Barros
Publsiher: Springer Science & Business Media
Total Pages: 785
Release: 2009-02-25
ISBN 10: 3642005985
ISBN 13: 9783642005985
Language: EN, FR, DE, ES & NL

Independent Component Analysis and Signal Separation Book Review:

This volume contains the papers presented at the 8th International Conf- ence on Independent Component Analysis (ICA) and Source Separation held in Paraty, Brazil, March 15–18, 2009. This year's event resulted from scienti?c collaborations between a team of researchers from ?ve di?erent Brazilian u- versities and received the support of the Brazilian Telecommunications Society (SBrT) as well as the ?nancial sponsorship of CNPq, CAPES and FAPERJ. Independent component analysis and signal separation is one of the most - citing current areas of research in statistical signal processing and unsupervised machine learning. The area has received attention from severalresearchcom- nities including machine learning, neural networks, statistical signal processing and Bayesian modeling. Independent component analysis and signal separation has applications at the intersection of many science and engineering disciplines concerned with understanding and extracting useful information from data as diverse as neuronal activity and brain images, bioinformatics, communications, the World Wide Web, audio, video, sensor signals, and time series.

Advances in Neural Information Processing Systems 12

Advances in Neural Information Processing Systems 12
Author: Sara A. Solla,Todd K. Leen,Klaus-Robert Müller
Publsiher: MIT Press
Total Pages: 1080
Release: 2000
ISBN 10: 9780262194501
ISBN 13: 0262194503
Language: EN, FR, DE, ES & NL

Advances in Neural Information Processing Systems 12 Book Review:

The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The broad range of interdisciplinary research areas represented includes computer science, neuroscience, statistics, physics, cognitive science, and many branches of engineering, including signal processing and control theory. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented.

Modern Multivariate Statistical Techniques

Modern Multivariate Statistical Techniques
Author: Alan J. Izenman
Publsiher: Springer Science & Business Media
Total Pages: 733
Release: 2009-03-02
ISBN 10: 9780387781891
ISBN 13: 0387781897
Language: EN, FR, DE, ES & NL

Modern Multivariate Statistical Techniques Book Review:

This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.

Independent Component Analysis and Blind Signal Separation

Independent Component Analysis and Blind Signal Separation
Author: Justinian Rosca,Deniz Erdogmus,Jose C. Principe,Simon Haykin
Publsiher: Springer Science & Business Media
Total Pages: 980
Release: 2006-02-13
ISBN 10: 9783540326304
ISBN 13: 3540326308
Language: EN, FR, DE, ES & NL

Independent Component Analysis and Blind Signal Separation Book Review:

This book constitutes the refereed proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2006, held in Charleston, SC, USA, in March 2006. The 120 revised papers presented were carefully reviewed and selected from 183 submissions. The papers are organized in topical sections on algorithms and architectures, applications, medical applications, speech and signal processing, theory, and visual and sensory processing.

Towards a New Cognitive Neuroscience Modeling Natural Brain Dynamics

Towards a New Cognitive Neuroscience  Modeling Natural Brain Dynamics
Author: Klaus Gramann,Tzyy-Ping Jung,Daniel P. Ferris,Chin-Teng Lin,Scott Makeig
Publsiher: Frontiers E-books
Total Pages: 166
Release: 2014-10-03
ISBN 10: 2889192717
ISBN 13: 9782889192717
Language: EN, FR, DE, ES & NL

Towards a New Cognitive Neuroscience Modeling Natural Brain Dynamics Book Review:

Decades of brain imaging experiments have revealed important insights into the architecture of the human brain and the detailed anatomic basis for the neural dynamics supporting human cognition. However, technical restrictions of traditional brain imaging approaches including functional magnetic resonance tomography (fMRI), positron emission tomography (PET), and magnetoencephalography (MEG) severely limit participants’ movements during experiments. As a consequence, our knowledge of the neural basis of human cognition is rooted in a dissociation of human cognition from what is arguably its foremost, and certainly its evolutionarily most determinant function, organizing our behavior so as to optimize its consequences in our complex, multi-scale, and ever-changing environment. The concept of natural cognition, therefore, should not be separated from our fundamental experience and role as embodied agents acting in a complex, partly unpredictable world. To gain new insights into the brain dynamics supporting natural cognition, we must overcome restrictions of traditional brain imaging technology. First, the sensors used must be lightweight and mobile to allow monitoring of brain activity during free participant movements. New hardware technology for electroencephalography (EEG) and near infrared spectroscopy (NIRS) allows recording electrical and hemodynamic brain activity while participants are freely moving. New data-driven analysis approaches must allow separation of signals arriving at the sensors from the brain and from non-brain sources (neck muscles, eyes, heart, the electrical environment, etc.). Independent component analysis (ICA) and related blind source separation methods allow separation of brain activity from non-brain activity from data recorded during experimental paradigms that stimulate natural cognition. Imaging the precisely timed, distributed brain dynamics that support all forms of our motivated actions and interactions in both laboratory and real-world settings requires new modes of data capture and of data processing. Synchronously recording participants’ motor behavior, brain activity, and other physiology, as well as their physical environment and external events may be termed mobile brain/body imaging ('MoBI'). Joint multi-stream analysis of recorded MoBI data is a major conceptual, mathematical, and data processing challenge. This Research Topic is one result of the first international MoBI meeting in Delmenhorst Germany in September 2013. During an intense workshop researchers from all over the world presented their projects and discussed new technological developments and challenges of this new imaging approach. Several of the presentations are compiled in this Research Topic that we hope may inspire new research using the MoBI paradigm to investigate natural cognition by recording and analyzing the brain dynamics and behavior of participants performing a wide range of naturally motivated actions and interactions.

Independent Component Analysis and Blind Signal Separation

Independent Component Analysis and Blind Signal Separation
Author: Anonim
Publsiher: Anonim
Total Pages: 329
Release: 2004
ISBN 10:
ISBN 13: UOM:39015061764240
Language: EN, FR, DE, ES & NL

Independent Component Analysis and Blind Signal Separation Book Review:

Advances in Neural Information Processing Systems 16

Advances in Neural Information Processing Systems 16
Author: Sebastian Thrun,Lawrence K. Saul,Bernhard Schölkopf
Publsiher: MIT Press
Total Pages: 1621
Release: 2004
ISBN 10: 9780262201520
ISBN 13: 0262201526
Language: EN, FR, DE, ES & NL

Advances in Neural Information Processing Systems 16 Book Review:

Papers presented at the 2003 Neural Information Processing Conference by leading physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees -- physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.

Artificial Intelligence and Soft Computing ICAISC 2004

Artificial Intelligence and Soft Computing     ICAISC 2004
Author: Leszek Rutkowski,Jörg Siekmann,Ryszard Tadeusiewicz,Lotfi A. Zadeh
Publsiher: Springer
Total Pages: 1210
Release: 2004-05-18
ISBN 10: 3540248447
ISBN 13: 9783540248446
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Soft Computing ICAISC 2004 Book Review:

This book constitutes the refereed proceedings of the 7th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2004, held in Zakopane, Poland in June 2004. The 172 revised contributed papers presented together with 17 invited papers were carefully reviewed and selected from 250 submissions. The papers are organized in topical sections on neural networks, fuzzy systems, evolutionary algorithms, rough sets, soft computing in classification, image processing, robotics, multiagent systems, problems in AI, intelligent control, modeling and system identification, medical applications, mechanical applications, and applications in various fields.