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Digital Signal Processing with Kernel Methods

Author: Jose Luis Rojo-Alvarez

Publisher: John Wiley & Sons

Published: 2018-02-05

Total Pages: 665

ISBN-13: 1118611799

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A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.

Book Synopsis Digital Signal Processing with Kernel Methods by : Jose Luis Rojo-Alvarez

Download or read book Digital Signal Processing with Kernel Methods written by Jose Luis Rojo-Alvarez and published by John Wiley & Sons. This book was released on 2018-02-05 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.

Kernel Methods in Bioengineering, Signal and Image Processing

Author: Gustavo Camps-Valls

Publisher: IGI Global

Published: 2007-01-01

Total Pages: 431

ISBN-13: 1599040425

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"This book presents an extensive introduction to the field of kernel methods and real world applications. The book is organized in four parts: the first is an introductory chapter providing a framework of kernel methods; the others address Bioegineering, Signal Processing and Communications and Image Processing"--Provided by publisher.

Book Synopsis Kernel Methods in Bioengineering, Signal and Image Processing by : Gustavo Camps-Valls

Download or read book Kernel Methods in Bioengineering, Signal and Image Processing written by Gustavo Camps-Valls and published by IGI Global. This book was released on 2007-01-01 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents an extensive introduction to the field of kernel methods and real world applications. The book is organized in four parts: the first is an introductory chapter providing a framework of kernel methods; the others address Bioegineering, Signal Processing and Communications and Image Processing"--Provided by publisher.

Digital Signal Processing with Kernel Methods

Author: Jose Luis Rojo-Alvarez

Publisher: Wiley

Published: 2018-01-05

Total Pages: 672

ISBN-13: 9781118705841

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Book Synopsis Digital Signal Processing with Kernel Methods by : Jose Luis Rojo-Alvarez

Download or read book Digital Signal Processing with Kernel Methods written by Jose Luis Rojo-Alvarez and published by Wiley. This book was released on 2018-01-05 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Kernel Methods for Remote Sensing Data Analysis

Author: Gustau Camps-Valls

Publisher: John Wiley & Sons

Published: 2009-09-03

Total Pages: 434

ISBN-13: 0470749008

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Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods. Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection. Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification. Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.

Book Synopsis Kernel Methods for Remote Sensing Data Analysis by : Gustau Camps-Valls

Download or read book Kernel Methods for Remote Sensing Data Analysis written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2009-09-03 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods. Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection. Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification. Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.

Digital Alias-free Signal Processing

Author: Ivars Bilinskis

Publisher: John Wiley & Sons

Published: 2007-09-27

Total Pages: 454

ISBN-13: 9780470511626

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As demand for applications working in extended frequency ranges increases, classical Digital signal processing (DSP) techniques, not protected against aliasing, are becoming less effective. Digital alias-free signal processing (DASP) is a technique for overcoming the problems of aliasing at extended frequency ranges. Based on non-uniform or randomised sampling techniques and the development of novel algorithms, it creates the capacity to suppress potential aliasing crucial for high frequency applications and to reduce the complexity of designs. This book provides practical and comprehensive coverage of the theory and techniques behind alias-free digital signal processing. Key features: Analyses issues of sampling, randomised and pseudo-randomised quantisation and direct and indirectly randomised sampling. Examines periodic and hybrid sampling, including information on processing algorithms and potential limitations imposed by signal dynamics. Sets out leading methods and techniques for complexity reduced designs, in particular designs of large aperture sensor arrays, massive data acquisition and compression from a number of signal sources and complexity-reduced processing of non-uniform data. Presents examples of engineering applications using these techniques including spectrum analysis, waveform reconstruction and the estimation of various parameters, emphasising the importance of the technique for developing new technologies. Links DASP and traditional technologies by mapping them into embedded systems with standard inputs and outputs. Digital Alias-free Signal Processing is ideal for practising engineers and researchers working on the development of digital signal processing applications at extended frequencies. It is also a valuable reference for electrical and computer engineering graduates taking courses in signal processing or digital signal processing.

Book Synopsis Digital Alias-free Signal Processing by : Ivars Bilinskis

Download or read book Digital Alias-free Signal Processing written by Ivars Bilinskis and published by John Wiley & Sons. This book was released on 2007-09-27 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: As demand for applications working in extended frequency ranges increases, classical Digital signal processing (DSP) techniques, not protected against aliasing, are becoming less effective. Digital alias-free signal processing (DASP) is a technique for overcoming the problems of aliasing at extended frequency ranges. Based on non-uniform or randomised sampling techniques and the development of novel algorithms, it creates the capacity to suppress potential aliasing crucial for high frequency applications and to reduce the complexity of designs. This book provides practical and comprehensive coverage of the theory and techniques behind alias-free digital signal processing. Key features: Analyses issues of sampling, randomised and pseudo-randomised quantisation and direct and indirectly randomised sampling. Examines periodic and hybrid sampling, including information on processing algorithms and potential limitations imposed by signal dynamics. Sets out leading methods and techniques for complexity reduced designs, in particular designs of large aperture sensor arrays, massive data acquisition and compression from a number of signal sources and complexity-reduced processing of non-uniform data. Presents examples of engineering applications using these techniques including spectrum analysis, waveform reconstruction and the estimation of various parameters, emphasising the importance of the technique for developing new technologies. Links DASP and traditional technologies by mapping them into embedded systems with standard inputs and outputs. Digital Alias-free Signal Processing is ideal for practising engineers and researchers working on the development of digital signal processing applications at extended frequencies. It is also a valuable reference for electrical and computer engineering graduates taking courses in signal processing or digital signal processing.

Intelligent Computing for Interactive System Design

Author: Parisa Eslambolchilar

Publisher: ACM Books

Published: 2021-02-25

Total Pages: 472

ISBN-13: 9781450390262

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Intelligent Computing for Interactive System Design provides a comprehensive resource on what has become the dominant paradigm in designing novel interaction methods, involving gestures, speech, text, touch and brain-controlled interaction, embedded in innovative and emerging human-computer interfaces. These interfaces support ubiquitous interaction with applications and services running on smartphones, wearables, in-vehicle systems, virtual and augmented reality, robotic systems, the Internet of Things (IoT), and many other domains that are now highly competitive, both in commercial and in research contexts. This book presents the crucial theoretical foundations needed by any student, researcher, or practitioner working on novel interface design, with chapters on statistical methods, digital signal processing (DSP), and machine learning (ML). These foundations are followed by chapters that discuss case studies on smart cities, brain-computer interfaces, probabilistic mobile text entry, secure gestures, personal context from mobile phones, adaptive touch interfaces, and automotive user interfaces. The case studies chapters also highlight an in-depth look at the practical application of DSP and ML methods used for processing of touch, gesture, biometric, or embedded sensor inputs. A common theme throughout the case studies is ubiquitous support for humans in their daily professional or personal activities. In addition, the book provides walk-through examples of different DSP and ML techniques and their use in interactive systems. Common terms are defined, and information on practical resources is provided (e.g., software tools, data resources) for hands-on project work to develop and evaluate multimodal and multi-sensor systems. In a series of in-chapter commentary boxes, an expert on the legal and ethical issues explores the emergent deep concerns of the professional community, on how DSP and ML should be adopted and used in socially appropriate ways, to most effectively advance human performance during ubiquitous interaction with omnipresent computers. This carefully edited collection is written by international experts and pioneers in the fields of DSP and ML. It provides a textbook for students and a reference and technology roadmap for developers and professionals working on interaction design on emerging platforms.

Book Synopsis Intelligent Computing for Interactive System Design by : Parisa Eslambolchilar

Download or read book Intelligent Computing for Interactive System Design written by Parisa Eslambolchilar and published by ACM Books. This book was released on 2021-02-25 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Computing for Interactive System Design provides a comprehensive resource on what has become the dominant paradigm in designing novel interaction methods, involving gestures, speech, text, touch and brain-controlled interaction, embedded in innovative and emerging human-computer interfaces. These interfaces support ubiquitous interaction with applications and services running on smartphones, wearables, in-vehicle systems, virtual and augmented reality, robotic systems, the Internet of Things (IoT), and many other domains that are now highly competitive, both in commercial and in research contexts. This book presents the crucial theoretical foundations needed by any student, researcher, or practitioner working on novel interface design, with chapters on statistical methods, digital signal processing (DSP), and machine learning (ML). These foundations are followed by chapters that discuss case studies on smart cities, brain-computer interfaces, probabilistic mobile text entry, secure gestures, personal context from mobile phones, adaptive touch interfaces, and automotive user interfaces. The case studies chapters also highlight an in-depth look at the practical application of DSP and ML methods used for processing of touch, gesture, biometric, or embedded sensor inputs. A common theme throughout the case studies is ubiquitous support for humans in their daily professional or personal activities. In addition, the book provides walk-through examples of different DSP and ML techniques and their use in interactive systems. Common terms are defined, and information on practical resources is provided (e.g., software tools, data resources) for hands-on project work to develop and evaluate multimodal and multi-sensor systems. In a series of in-chapter commentary boxes, an expert on the legal and ethical issues explores the emergent deep concerns of the professional community, on how DSP and ML should be adopted and used in socially appropriate ways, to most effectively advance human performance during ubiquitous interaction with omnipresent computers. This carefully edited collection is written by international experts and pioneers in the fields of DSP and ML. It provides a textbook for students and a reference and technology roadmap for developers and professionals working on interaction design on emerging platforms.

Regularization, Optimization, Kernels, and Support Vector Machines

Author: Johan A.K. Suykens

Publisher: CRC Press

Published: 2014-10-23

Total Pages: 528

ISBN-13: 1482241390

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Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

Book Synopsis Regularization, Optimization, Kernels, and Support Vector Machines by : Johan A.K. Suykens

Download or read book Regularization, Optimization, Kernels, and Support Vector Machines written by Johan A.K. Suykens and published by CRC Press. This book was released on 2014-10-23 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

An Introduction to Digital Signal Processing

Author: Stanley Mneney

Publisher: River Publishers

Published: 2009-01-10

Total Pages: 284

ISBN-13: 8792329128

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Mneney's text focuses on basic concepts of digital signal processing, MATLAB simulation, and implementation on selected DSP hardware.

Book Synopsis An Introduction to Digital Signal Processing by : Stanley Mneney

Download or read book An Introduction to Digital Signal Processing written by Stanley Mneney and published by River Publishers. This book was released on 2009-01-10 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mneney's text focuses on basic concepts of digital signal processing, MATLAB simulation, and implementation on selected DSP hardware.

Real-time Digital Signal Processing

Author: Sen-Maw Kuo

Publisher: 清华大学出版社有限公司

Published: 2003

Total Pages: 524

ISBN-13: 9787302077008

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Book Synopsis Real-time Digital Signal Processing by : Sen-Maw Kuo

Download or read book Real-time Digital Signal Processing written by Sen-Maw Kuo and published by 清华大学出版社有限公司. This book was released on 2003 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt:

C++ Algorithms for Digital Signal Processing

Author: Paul Embree

Publisher: Pearson Education

Published: 1998-11-13

Total Pages: 697

ISBN-13: 0132442051

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Bring the power and flexibility of C++ to all your DSP applications The multimedia revolution has created hundreds of new uses for Digital Signal Processing, but most software guides have continued to focus on outdated languages such as FORTRAN and Pascal for managing new applications. Now C++ Algorithms for Digital Signal Processing applies object-oriented techniques to this growing field with software you can implement on your desktop PC. C++ Algorithms for Digital Signal Processing's programming methods can be used for applications as diverse as: Digital audio and video Speech and image processing Digital communications Radar, sonar, and ultrasound signal processing Complete coverage is provided, including: Overviews of DSP and C++ Hands-on study with dozens of exercises Extensive library of customizable source code Import and Export of Microsoft WAV and Matlab data files Multimedia professionals, managers, and even advanced hobbyists will appreciate C++ Algorithms for Digital Signal Processing as much as students, engineers, and programmers. It's the ideal bridge between programming and signal processing, and a valuable reference for experts in either field. Source code for all of the DSP programs and DSP data associated with the examples discussed in this book and Appendix B and the file README.TXT which provide more information about how to compile and run the programs can be downloaded from www.informit.com/title/9780131791442

Book Synopsis C++ Algorithms for Digital Signal Processing by : Paul Embree

Download or read book C++ Algorithms for Digital Signal Processing written by Paul Embree and published by Pearson Education. This book was released on 1998-11-13 with total page 697 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bring the power and flexibility of C++ to all your DSP applications The multimedia revolution has created hundreds of new uses for Digital Signal Processing, but most software guides have continued to focus on outdated languages such as FORTRAN and Pascal for managing new applications. Now C++ Algorithms for Digital Signal Processing applies object-oriented techniques to this growing field with software you can implement on your desktop PC. C++ Algorithms for Digital Signal Processing's programming methods can be used for applications as diverse as: Digital audio and video Speech and image processing Digital communications Radar, sonar, and ultrasound signal processing Complete coverage is provided, including: Overviews of DSP and C++ Hands-on study with dozens of exercises Extensive library of customizable source code Import and Export of Microsoft WAV and Matlab data files Multimedia professionals, managers, and even advanced hobbyists will appreciate C++ Algorithms for Digital Signal Processing as much as students, engineers, and programmers. It's the ideal bridge between programming and signal processing, and a valuable reference for experts in either field. Source code for all of the DSP programs and DSP data associated with the examples discussed in this book and Appendix B and the file README.TXT which provide more information about how to compile and run the programs can be downloaded from www.informit.com/title/9780131791442