Signal Processing - Candy, James Vincent
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Separate signals from noise with this valuable introduction to signal processing by applied decomposition The decomposition of complex signals into their sub-signals or individual components is a crucial tool in signal processing. It allows each component of a signal to be analyzed individually and enables the signal to be isolated from noise and processed in full. Decomposition processes have not always been widely adopted due to the difficult underlying mathematics and complex applications. This text simplifies these obstacles. Signal Processing: An Applied Decomposition Approach demystifies…mehr

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Produktbeschreibung
Separate signals from noise with this valuable introduction to signal processing by applied decomposition The decomposition of complex signals into their sub-signals or individual components is a crucial tool in signal processing. It allows each component of a signal to be analyzed individually and enables the signal to be isolated from noise and processed in full. Decomposition processes have not always been widely adopted due to the difficult underlying mathematics and complex applications. This text simplifies these obstacles. Signal Processing: An Applied Decomposition Approach demystifies these tools with a model-based perspective. Addressing each major decomposition approach in turn, it offers a mathematically-informed step-by-step analysis of the process by which it breaks a composite signal/system down into its constituent parts. Introducing both fundamental concepts and advanced applications, it is an indispensable addition to any library regarding signal processing. Signal Processing readers will find: Signal decomposition techniques developed from the data-based, spectral-based and model-based perspectives incorporate: statistical approaches (PCA, ICA, Singular Spectrum); spectral approaches (MTM, PHD, MUSIC); and model-based approaches (EXP, LATTICE,SSP)In depth discussion of topics includes signal/system estimation and decomposition, time domain and frequency domain techniques, systems theory, modal decompositions, applications and many moreNumerous figures, examples, and tables illustrating key concepts and algorithms are developed throughout the textIncludes problem sets, case studies, real-world applications as well as MATLAB notes highlighting applicable commands Signal Processing is ideal for engineering and scientific professionals, as well as graduate students seeking a focused text on signal/system decomposition with performance metrics and real-world applications.
Autorenporträt
James Vincent Candy, PhD, is the Chief Scientist for Engineering, a Distinguished Member of the Technical Staff, founder and former Director of the Center for Advanced Signal & Image Sciences (CASIS) at the Lawrence Livermore National Laboratory and an Adjunct Full-Professor at the University of California, Santa Barbara. He received his his BSEE from the University of Cincinnati along with his MSE and PhD in Electrical Engineering from the University of Florida. Dr. Candy is a Life-Fellow of the IEEE and a 25-Year-Fellow of the Acoustical Society of America (ASA). He was elected as a Life Member at the University of Cambridge (Clare Hall College). Dr. Candy has been awarded the Interdisciplinary Helmholtz-Rayleigh Silver Medal in Signal Processing/Underwater Acoustics by the Acoustical Society of America, the IEEE Distinguished Technical Achievement Award for the development of model-based signal processing as well as an elected IEEE Distinguished Lecturer in Oceanic Signal Processing. He also received the R&D100 award for his innovative invention in radiation threat detection. He has published over 250 journal articles, book chapters, and technical reports as well as written six texts in signal processing: Signal Processing: the Model-Based Approach, (McGraw-Hill, 1986), Signal Processing: the Modern Approach, (McGraw-Hill, 1988), Model-Based Signal Processing, (Wiley/IEEE Press, 2006), Bayesian Signal Processing: Classical, Modern and Particle Filtering (Wiley/IEEE Press, 2009), Bayesian Signal Processing: Classical, Modern and Particle Filtering, 2nd Ed. (Wiley/IEEE Press, 2016), and Model-Based Processing: An Applied Subspace Identification Approach (Wiley, 2019).