ダウンロード Signal Processing and Machine Learning with Applications 本

Signal processing captures, interprets, describes and manipulates physical phenomena. Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information. This book presents the different kinds of signals that humans and machines use to communicate, and their treatments and applications. The main topic is stochastic processes of signals that are useful for applications.  In Part A the authors present the fundamentals of signal processing, signal transformation, and spectral analysis. The chapters in Part B cover machine learning and recognition issues such as general learning, stochastic processes, feature extraction, probability theory, unsupervised learning, Markov models, fuzzy logic and rough sets, and neural networks. Part C addresses practical implementation aspects, in particular noise and audio and speech recognition, and then the authors give insights on how to apply the techniques explained in domains such as biomedicine, seismology, visual analytics, visual storytelling, emergency medicine, and interactive communications.  The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engaged with signal processing and its applications.