Signal Processing Techniques for Knowledge Extraction and Information Fusion [electronic resource] /
Mandic, Danilo.editor.
Golz, Martin.editor.
Kuh, Anthony.editor.
Obradovic, Dragan.editor.
Tanaka, Toshihisa.editor.
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.
Description based upon print version of record.
Includes bibliographical references and index.
Collaborative Signal Processing Algorithms -- Collaborative Adaptive Filters for Online Knowledge Extraction and Information Fusion -- Wind Modelling and its Possible Application to Control of Wind Farms -- Hierarchical Filters in a Collaborative Filtering Framework for System Identification and Knowledge Retrieval -- Acoustic Parameter Extraction From Occupied Rooms Utilizing Blind Source Separation -- Signal Processing for Source Localization -- Sensor Network Localization Using Least Squares Kernel Regression -- Adaptive Localization in Wireless Networks -- Signal Processing Methods for Doppler Radar Heart Rate Monitoring -- Multimodal Fusion for Car Navigation Systems -- Information Fusion in Imaging -- Cue and Sensor Fusion for Independent Moving Objects Detection and Description in Driving Scenes -- Distributed Vision Networks for Human Pose Analysis -- Skin Color Separation and Synthesis for E-Cosmetics -- ICA for Fusion of Brain Imaging Data -- Knowledge Extraction in Brain Science -- Complex Empirical Mode Decomposition for Multichannel Information Fusion -- Information Fusion for Perceptual Feedback: A Brain Activity Sonification Approach -- Advanced EEG Signal Processing in Brain Death Diagnosis -- Automatic Knowledge Extraction: Fusion of Human Expert Ratings and Biosignal Features for Fatigue Monitoring Applications.
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.
Signal processing.
Image processing.
Speech processing systems.
Electrical engineering.
Data mining.
Signal, Image and Speech Processing.
Communications Engineering, Networks.
Data Mining and Knowledge Discovery.