Learning Approaches in Signal Processing

Nonfiction, Computers, Advanced Computing, Theory, Programming, Systems Analysis, Engineering, Computer Architecture
Cover of the book Learning Approaches in Signal Processing by , Jenny Stanford Publishing
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Author: ISBN: 9780429590320
Publisher: Jenny Stanford Publishing Publication: December 7, 2018
Imprint: Jenny Stanford Publishing Language: English
Author:
ISBN: 9780429590320
Publisher: Jenny Stanford Publishing
Publication: December 7, 2018
Imprint: Jenny Stanford Publishing
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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