This state-of-the-art resource brings together the latest findings from the cross-fertilization of signal processing, machine learning and computer science. The emphasis is on demonstrating synergy of different signal processing methods with knowledge extraction and heterogeneous information fusion. Issues related to the processing of signals with low signal-to-noise ratio, solving real-world multi-channel problems, and using adaptive techniques where nonstationarity, uncertainty and complexity play major roles are addressed. Particular methods include Independent Component Analysis, Support Vector Machines, Distributed and Collaborative Adaptive Filtering, Empirical Mode Decomposition, Self Organizing Maps, Fuzzy Logic, Evolutionary Algorithms and several others used frequently in these fields. Also included are both important and novel applications from telecommunications, renewable energy and biomedical engineering.
Signal Processing Techniques for Knowledge Extraction and Information Fusion which proposes new techniques for extracting knowledge based on combining heterogeneous information sources is an excellent reference for professionals in signal and image processing, machine learning, data and sensor fusion, computational intelligence, knowledge discovery, pattern recognition, and environmental science and engineering.