Signal Processing for Intelligent Sensor Systems with Matlab(r)

Signal Processing for Intelligent Sensors with MATLAB®, 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® 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.

David C. Swanson

Fundamentals of Digital Signal Processing:Sampled Data Systems. Z-Transform. Digital Filtering. Digital Audio Processing. Linear Filter Applications. Frequency Domain Processing: Fourier Transform. Spectral Density. Wavenumber Transforms. Adaptive System Identification and Filtering: Linear Least-Squared Error Modeling. Recursive Least-Squares Techniques. Recursive Adaptive Filtering. Wavenumber Sensor Systems: Signal Detection Techniques. Wavenumber and Bearing Estimation. Adaptive Beamforming and Localization. Signal Processing Applications: Noise Reduction Techniques. Sensors and Transducers. Intelligent Sensor Systems.