Learning Approaches in Signal Processing
Signal Processing

Learning Approaches in Signal Processing

Edited by Wan-Chi Siu , Lap-Pui Chau , Liang Wang and Tieniu Tan

653 pages, 153.00 x 229.00 mm

  • Hardcover -
  • ISBN: 9789814800501
  • Published: November 2018

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Coupled with machine learning, there has been an explosive growth in the use of signal processing techniques for big data analysis, internet of things, smart city, security and bio-informatics applications. All these make possible via fast algorithms on data, speech, image, video processing with advanced GPU technology. This book provides readers with (i) an up-to-date tutorial and overview on learning technologies such as random forests, sparsity and low-rank matrix estimation, and (ii) cutting edge visual/signal processing techniques including face recognition, Kalman filtering and multirate DSP. Research and applications making use of deep learning, convolutional neural networks (CNN), random forests, etc. are discussed. They include super-resolution imaging, fringe projection profilometry (FPP), human activities detection/capture, gesture recognition, spoken language processing, cooperative networks, bioinformatics, DNA and healthcare. The book is the result of a collective effort from a group of Imperial College London PhD alumnus/friends/colleagues who have been under the supervision or mentorship of Professor A.G. Constantinides, a pioneer and a world leader in digital signal processing. The group follows the traditional spirit of Imperial College London to give the most innovative ideas and practical applications to readers. 

This book presents (i) an up-to-date tutorial and overview on learning technologies such as random forests, sparsity, and low-rank matrix estimation and (ii) cutting-edge visual/signal processing techniques, including face recognition, Kalman filtering, and multirate DSP. It discusses the applications that make use of deep learning, convolutional neural networks, random forests, etc.