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£172.80

Taylor & Francis Learning Approaches in Signal Processing (Jenny Stanford Series on Digital Signal Processing)

Name: Learning Approaches in Signal Processing (Jenny Stanford Series on Digital Signal Processing)
Brand: Taylor & Francis
Price: 172.8 GBP
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Architecture & Microprocessors

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Description

Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security, and bio-informatics applications has witnessed explosive growth. This has been made possible via fast algorithms on data, speech, image, and video processing with advanced GPU technology. This book presents an up-to-date tutorial and overview on learning technologies such as random forests, sparsity, and low-rank matrix estimation and 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. The applications include super-resolution imaging, fringe projection profilometry, human activities detection/capture, gesture recognition, spoken language processing, cooperative networks, bioinformatics, DNA, and healthcare. About the Author Wan-Chi Siu, a PhD graduate of Imperial College London, is emeritus professor and was chair professor, head (Electronic and Information Engineering), and dean of the Engineering Faculty of the Hong Kong Polytechnic University. He was the convener of the First Engineering/IT Panel of the 1993 Research Assessment Exercise in Hong Kong and vice president, conference board chair, and core member of the Board of Governors of the IEEE Signal Processing Society (2012–2014). Prof. Siu is a life-fellow of the IEEE, fellow of the IET and the HKIE, and president (2017–2018) of the Asia Pacific Signal and Information Processing Association. He has been a guest editor/subject editor/associate editor for IEEE Transactions on Circuits and Systems, Image Processing, Circuits and Systems for Video Technology, and Electronics Letters, published over 500 research papers, and organized IEEE-sponsored flagship conferences as the TPC chair (ISCAS1997) and general chair (ICASSP2003 and ICIP2010). Lap-Pui Chau works in the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, and is a fellow of the IEEE. He was chair of the Technical Committee on Circuits and Systems for Communications of the IEEE Circuits and Systems Society (2010–2012) and has served as an associate editor for five IEEE journals. Dr. Chau has also been an IEEE Distinguished Lecturer (2009–2016). Liang Wang is full professor at the Institute of Automation, Chinese Academy of Sciences; deputy director of the National Laboratory of Pattern Recognition, China; secretary-general of the Technical Committee on Computer Vision, China Computer Federation; and director of the Technical Committee on Visual Big Data, China Society of Image and Graphics. He is a senior member of the IEEE and a fellow of the International Association of Pattern Recognition. Tieniu Tan, a PhD graduate of Imperial College London, joined the Institute of Automation, Chinese Academy of Sciences, as a full professor in 1998. He is director of the Center for Research on Intelligent Perception and Computing at CASIA and deputy director of the Liaison Office of the Central People’s Government in the Hong Kong S.A.R. He has published 14 edited books and monographs and more than 600 research papers. Prof. Tan is a fellow of The World Academy of Sciences, Chinese Academy of Sciences, IEEE, and IAPR, an international fellow of the Royal Academy of Engineering, UK, and a corresponding member of the Brazilian Academy of Sciences.

Product Specifications

Brand: Taylor & Francis
Format: Hardcover
ASIN: 9814800503
Category: Books > Subjects > Computing & Internet > Computer Science > Architecture & Microprocessors
Domain: Amazon UK
Release Date: 28 November 2018
Listed Since: 31 October 2018

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Taylor & Francis Learning Approaches in Signal Processing (Jenny Stanford Series on Digital Signal Processing)

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