Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc.

Provides quick tutorial reviews of important and emerging topics of research in signal processing-based tools
Presents core principles in signal processing theory and shows their applications
Discusses some emerging signal processing tools applied in machine learning methods
References content on core principles, technologies, algorithms and applications
Includes references to journal articles and other literature on which to build further, more specific, and detailed knowledge

Informazioni sull?autore

Paulo S. R. Diniz’s teaching and research interests are in analog and digital signal processing, adaptive signal processing, digital communications, wireless communications, multirate systems, stochastic processes, and electronic circuits. He has published over 300 refereed papers in some of these areas and wrote two textbooks and a research book. He has received awards for best papers and technical achievements

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