Description
This book brings together the latest research achievements from various areas of signal processing and related disciplines in order to consolidate the existing and proposed new directions in DSP based knowledge extraction and information fusion. Within the book contributions presenting both novel algorithms and existing applications, especially those (but not restricted to) on-line processing of real world data are included. The areas of Knowledge Extraction and Information Fusion are naturally linked and aim at detecting and estimating the signal of interest and its parameters, and further at combining measurements from multiple sensors (and associated databases if appropriate) to achieve improved accuracies and more specific inferences which cannot be achieved by using only a single signal modality.The subject therefore is of major interest for modern biomedical, environmental, and industrial applications to provide a state of the art and propose new techniques in order to combine heterogeneous information sources. 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.
