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Learning Approaches in Signal Processing
edited by Wan-Chi Siu, Lap-Pui Chau, Liang Wang and Tieniu Tan
- Format: eBook
- ISBN: 9780429061141
- Subject: Signal Processing
- Published: November -0001
For Course Instructors: Inspection Copies
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.
Chapter 1
Introduction to Random Tree and Random Forests for Fast Signal Processing and Object Classification
Wan-Chi Siu, Xue-Fei Yang, Li-Wen Wang, Jun-Jie Huang, and Zhi-Song Liu
Pages: 1-52
USD $34.95
Add to cartChapter 2
Sparsity Based Dictionary Learning Techniques
Raju Ranjan, Sumana Gupta, and K. S. Venkatesh
Pages: 53-78
USD $34.95
Add to cartChapter 3
A Comprehensive Survey of Persistent Homology for Pattern Recognition
Zhen Zhou, Yongzhen Huang, Rocio Gonzalez-Diaz, Liang Wang, and Tieniu Tan
Pages: 79-128
USD $34.95
Add to cartChapter 4
Low-Rank Matrix Estimation and Its Applications in Signal Processing and Machine Learning
Aimin Jiang, Hon Keung Kwan, and Yanping Zhu
Pages: 129-154
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Add to cartChapter 5
Introduction to Face Recognition and Recent Work
Tianrui Liu, Wan-Chi Siu, Cigdem Turan, Shun-Cheung Lai, Kin-Man Lam, and Tania Stathaki
Pages: 155-200
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Add to cartChapter 6
The Ensemble Kalman Filter
Pedro A. M. Fonini and Paulo S. R. Diniz
Pages: 201-228
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Add to cartChapter 7
Teaching Programming and Debugging Techniques for Multirate Signal Processing
Fred Harris and Chris Dick
Pages: 229-258
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Add to cartChapter 8
Learning Approaches for Super-Resolution Imaging
Wan-Chi Siu, Zhi-Song Liu, Jun-Jie Huang, and Kwok-Wai Hung
Pages: 259-328
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Add to cartChapter 9
Non-Contact Three-Dimensional Measurement Using the Learning Approach
Daniel P. K. Lun and B. Budianto
Pages: 329-378
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Add to cartChapter 10
Computational and Learning Aspects of DNA Sequences
Ngai-Fong Law
Pages: 379-406
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Add to cartChapter 11
Visual Food Recognition for Dietary Logging and Health Monitoring
Sharmili Roy, Zhao Heng, Kim-Hui Yap, Alex Kot, and Lingyu Duan
Pages: 407-434
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Add to cartChapter 12
Learning Randomized Decision Trees for Human Behavior Capture
Zhen-Peng Bian, Cheen-Hau Tan, Junhui Hou, and Lap-Pui Chau
Pages: 435-470
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Add to cartChapter 13
Deep Learning in Gesture Recognition Based on sEMG Signals
Panagiotis Tsinganos, Athanassios Skodras, Bruno Cornelis, and Bart Jansen
Pages: 471-496
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Add to cartChapter 14
Measuring Precise Inter-Person Physiological Synchrony and Its Trends through Adaptive, Data-Driven Algorithms: Combining NA-MEMD and the Synchrosqueezing Transform to Identify Synchronised Respiratory and HRV Frequencies
Apit Hemakom, Katarzyna Powezka, Valentin Goverdovsky, Usman Jaffer, Jonathon Chambers, and Danilo P. Mandic
Pages: 497-542
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Add to cartChapter 15
Multitask Cooperative Networks and Their Diverse Applications
Saeid Sanei, Sadaf Monajemi, Amir Rastegarnia, Oana Geman, and Ong Sim Heng
Pages: 543-578
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Add to cartChapter 16
Spoken Language Processing: From IsolatedWord Recognition to Neural Representation of Syntactical Structures, Based upon Kernel Memory
Tetsuya Hoya
Pages: 579-614
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Add to cartChapter 17
The Brave New World of Machine Learning in AI and Medicine
Paulina Y. Chan and Stephen K. Ng
Pages: 615-644
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Add to cartWan-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.