Titre :	Signal Processing for Intelligent Sensor Systems with MATLAB
Type de document :	texte imprimé
Auteurs :	David C. Swanson, Auteur
Mention d'édition :	2nd. ed.
Editeur :	Boca Raton; London; New York : CRC Press/Taylor & Françis Group
Année de publication :	2012
Importance :	665 p.
Présentation :	couv. ill.,ill.
Format :	26 cm.
ISBN/ISSN/EAN :	978-1-420-04304-4
Langues :	Anglais (eng)
Catégories :	AUTOMATISME
Index. décimale :	25-05 Application du traitement numérique du signal
Résumé :	Signal Processing for Intelligent Sensors with MATLAB(R), Second Edition once again presents the key topics and salient information required for sensor design and application. Organized to make it accessible to engineers in school as well as those practicing in the field, this reference explores a broad array of subjects and is divided into sections: Fundamentals of Digital Signal Processing, Frequency Domain Processing, Adaptive System Identification and Filtering, Wavenumber Sensor Systems, and Signal Processing Applications. Taking an informal, application-based approach and using a tone that is more engineer-to-engineer than professor-to-student, this revamped second edition enhances many of the features that made the original so popular. This includes retention of key algorithms and development methodologies and applications, which are creatively grouped in a way that differs from most comparable texts, to optimize their use. New for the Second Edition: Inclusion of more solved problems Web access to a large collection of MATLAB(R) scripts used to support data graphs presented throughout the book Additional coverage of more audio engineering, transducers, and sensor networking technology A new chapter on Digital Audio processing reflects a growing interest in digital surround sound (5.1 audio) techniques for entertainment, home theaters, and virtual reality systems New sections on sensor networking, use of meta-data architectures using XML, and agent-based automated data mining and control Serving dual roles as both a learning resource and a field reference on sensor system networks, this book progressively reveals digestible nuggets of critical information to help readers quickly master presented algorithms and adapt them to meet their requirements. It illustrates the current trend toward agile development of web services for wide area sensor networking and intelligent processing in the sensor system networks that are employed in homeland security, business, and environmental and demographic information systems.
Note de contenu :	Contents Part I: Fundamentals of Digital Signal Processing Chapter 1 Sampled Data Systems Chapter 2 Z-Transform Chapter 3 Digital Filtering Chapter 4 Digital Audio Processing Chapter 5 Linear Filter Applications Part II: Frequency Domain Processing Chapter 6 Fourier Transform Chapter 7 Spectral Density Chapter 8 Wavenumber Transforms Part III: Adaptive System Identification and Filtering Chapter 9 Linear Least-Squared Error Modeling Chapter 10 Recursive Least-Squares Techniques Chapter 11 Recursive Adaptive Filtering Part IV: Wavenumber Sensor Systems Chapter 12 Signal Detection Techniques Chapter 13 Wavenumber and Bearing Estimation Chapter 14 Adaptive Beamforming and Localization Part V: Signal Processing Applications Chapter 15 Noise Reduction Techniques Chapter 16 Sensors and Transducers Chapter 17 Intelligent Sensor Systems -Index

Signal Processing for Intelligent Sensor Systems with MATLAB [texte imprimé] / David C. Swanson, Auteur  . -  2nd. ed. . - Boca Raton; London; New York : CRC Press/Taylor & Françis Group, 2012 . - 665 p. : couv. ill.,ill. ; 26 cm.
ISBN : 978-1-420-04304-4
Langues : Anglais (eng)

Catégories :	AUTOMATISME
Index. décimale :	25-05 Application du traitement numérique du signal
Résumé :	Signal Processing for Intelligent Sensors with MATLAB(R), Second Edition once again presents the key topics and salient information required for sensor design and application. Organized to make it accessible to engineers in school as well as those practicing in the field, this reference explores a broad array of subjects and is divided into sections: Fundamentals of Digital Signal Processing, Frequency Domain Processing, Adaptive System Identification and Filtering, Wavenumber Sensor Systems, and Signal Processing Applications. Taking an informal, application-based approach and using a tone that is more engineer-to-engineer than professor-to-student, this revamped second edition enhances many of the features that made the original so popular. This includes retention of key algorithms and development methodologies and applications, which are creatively grouped in a way that differs from most comparable texts, to optimize their use. New for the Second Edition: Inclusion of more solved problems Web access to a large collection of MATLAB(R) scripts used to support data graphs presented throughout the book Additional coverage of more audio engineering, transducers, and sensor networking technology A new chapter on Digital Audio processing reflects a growing interest in digital surround sound (5.1 audio) techniques for entertainment, home theaters, and virtual reality systems New sections on sensor networking, use of meta-data architectures using XML, and agent-based automated data mining and control Serving dual roles as both a learning resource and a field reference on sensor system networks, this book progressively reveals digestible nuggets of critical information to help readers quickly master presented algorithms and adapt them to meet their requirements. It illustrates the current trend toward agile development of web services for wide area sensor networking and intelligent processing in the sensor system networks that are employed in homeland security, business, and environmental and demographic information systems.
Note de contenu :	Contents Part I: Fundamentals of Digital Signal Processing Chapter 1 Sampled Data Systems Chapter 2 Z-Transform Chapter 3 Digital Filtering Chapter 4 Digital Audio Processing Chapter 5 Linear Filter Applications Part II: Frequency Domain Processing Chapter 6 Fourier Transform Chapter 7 Spectral Density Chapter 8 Wavenumber Transforms Part III: Adaptive System Identification and Filtering Chapter 9 Linear Least-Squared Error Modeling Chapter 10 Recursive Least-Squares Techniques Chapter 11 Recursive Adaptive Filtering Part IV: Wavenumber Sensor Systems Chapter 12 Signal Detection Techniques Chapter 13 Wavenumber and Bearing Estimation Chapter 14 Adaptive Beamforming and Localization Part V: Signal Processing Applications Chapter 15 Noise Reduction Techniques Chapter 16 Sensors and Transducers Chapter 17 Intelligent Sensor Systems -Index