In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts.
Inhaltsverzeichnis
BASICS. Significance of Intelligent Sensor Networks. Elements of Intelligent Sensor Networks. Recent Advances and Applications. SENSING AND SAMPLING. Sensors for Multi-format Signals. Sampling Principle and Architecture. Bio-inspired Sensing. Compressive Sensing (CS) Principle. CS Signal Recovery. Hardware and Software Design for Compressive Sensing. DISTRIBUTED SIGNAL PROCESSING. Sensing Signal Features. Sensing Signal Processing. Networked Processing. Distributed Estimation. Distributed Prediction. INTELLIGENT SIGNAL LEARNING. Machine Learning Basics. Supervise Sensor Signal Learning. Unsupervised Sensor Signal Learning. Variational Bayes for Sensor Signal Learning. Information Geometry for Intelligent Sensor Networking.
