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Full 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.
Contents
Tutorial And Overview Of Learning Approaches Introduction to Random Tree and Random Forests for Fast Signal Processing and Object Classification. Sparsity Based Dictionary Learning Techniques. A Comprehensive Survey of Persistent Homology for Pattern Recognition. Low-Rank Matrix Estimation and Its Applications in Signal Processing and Machine Learning. Introduction to Face Recognition and Its Recent Work. Filter Design And Multirate Signal Processing The Ensemble Kalman Filter. Teaching Programming and Debugging Techniques for Multirate Signal Processing. Imaging Technologies Learning Approaches for Super-Resolution Imaging. Non-Contact Three-Dimensional Measurement Using the Learning Approach. Biometrics And Health Applications Computational and Learning Aspects of DNA Sequences. Visual Food Recognition for Dietary Logging and Health Monitoring. Motions In Videos, Pose Recognition And Human Activity Analysis Learning Randomized Decision Trees for Human Behavior Capture. Deep Learning in Gesture Recognition Based on sEMG Signals. Tutorial And Overview Of Learning Approaches Introduction to Random Tree and Random Forests for Fast Signal Processing and Object Classification. Sparsity Based Dictionary Learning Techniques. A Comprehensive Survey of Persistent Homology for Pattern Recognition. Low-Rank Matrix Estimation and Its Applications in Signal Processing and Machine Learning. Introduction to Face Recognition and Its Recent Work. Filter Design And Multirate Signal Processing The Ensemble Kalman Filter. Teaching Programming and Debugging Techniques for Multirate Signal Processing. Imaging Technologies Learning Approaches for Super-Resolution Imaging. Non-Contact Three-Dimensional Measurement Using the Learning Approach. Biometrics And Health Applications Computational and Learning Aspects of DNA Sequences. Visual Food Recognition for Dietary Logging and Health Monitoring. Motions In Videos, Pose Recognition And Human Activity Analysis Learning Randomized Decision Trees for Human Behavior Capture. Deep Learning in Gesture Recognition Based on sEMG Signals. 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. Language Processing, Cooperative Network And Communications Multitask Cooperative Networks and their Diverse Applications. Spoken Language Processing: From Isolated Word Recognition to Neural Representation of Syntactical Structures, Based upon Kernel Memory. Discussion On Ai For Healthcare The Brave New World of Machine Paulina Chan and Stephen K. NgLearning in AI and Medicine. 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. Language Processing, Cooperative Network And Communications Multitask Cooperative Networks and their Diverse Applications. Spoken Language Processing: From Isolated Word Recognition to Neural Representation of Syntactical Structures, Based upon Kernel Memory. Discussion On Ai For Healthcare The Brave New World of Machine Learning in AI and Medicine.
