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Collaborative Signal Processing Algorithms | |
Collaborative Adaptive Filters for Online Knowledge Extraction and Information Fusion | p. 3 |
Introduction | p. 3 |
Previous Online Approaches | p. 5 |
Collaborative Adaptive Filters | p. 6 |
Derivation of The Hybrid Filter | p. 7 |
Detection of the Nature of Signals: Nonlinearity | p. 8 |
Tracking Changes in Nonlinearity of Signals | p. 10 |
Detection of the Nature of Signals: Complex Domain | p. 12 |
Split-Complex vs. Fully-Complex | p. 13 |
Complex Nature of Wind | p. 17 |
Conclusions | p. 19 |
References | p. 20 |
Wind Modelling and its Possible Application to Control of Wind Farms | p. 23 |
Formulating Yaw Control for a Wind Turbine | p. 23 |
Characteristics for Time Series of the Wind | p. 25 |
Surrogate Data | p. 25 |
Results | p. 25 |
Modelling and Predicting the Wind | p. 27 |
Multivariate Embedding | p. 27 |
Radial Basis Functions | p. 28 |
Possible Coordinate Systems | p. 30 |
Direct vs. Iterative Methods | p. 30 |
Measurements of the Wind | p. 30 |
Results | p. 32 |
Applying the Wind Prediction to the Yaw Control | p. 34 |
Conclusions | p. 34 |
References | p. 35 |
Hierarchical Filters in a Collaborative Filtering Framework for System Identification and Knowledge Retrieval | p. 37 |
Introduction | p. 37 |
Hierarchical Structures | p. 39 |
Generalised Structures | p. 40 |
Equivalence with FIR | p. 41 |
Multilayer Adaptive Algorithms | p. 43 |
The Hierarchical Least Mean Square Algorithm | p. 43 |
Evaluation of the Performance of HLMS | p. 44 |
The Hierarchical Gradient Descent Algorithm | p. 45 |
Applications | p. 46 |
Standard Filtering Applications | p. 46 |
Knowledge Extraction | p. 47 |
Conclusions | p. 49 |
Mathematical Analysis of the HLMS | p. 50 |
References | p. 53 |
Acoustic Parameter Extraction From Occupied Rooms Utilizing Blind Source Separation | p. 55 |
Introduction | p. 55 |
Blind Estimation of Room RT in Occupied Rooms | p. 57 |
MLE-Based RT Estimation Method | p. 57 |
Proposed Noise Reducing Preprocessing | p. 59 |
A Demonstrative Study | p. 60 |
Blind Source Separation | p. 62 |
Adaptive Noise Cancellation | p. 65 |
Simulation Results | p. 67 |
Discussion | p. 72 |
Conclusion | p. 73 |
References | p. 74 |
Signal Processing for Source Localization | |
Sensor Network Localization Using Least Squares Kernel Regression | p. 77 |
Introduction | p. 77 |
Sensor Network Model | p. 80 |
Localization Using Classification Methods | p. 81 |
Least Squares Subspace Kernel Regression Algorithm | p. 82 |
Least Squares Kernel Subspace Algorithm | p. 82 |
Recursive Kernel Subspace Least Squares Algorithm | p. 84 |
Localization Using Kernel Regression Algorithms | p. 85 |
Centralized Kernel Regression | p. 85 |
Kernel Regression for Mobile Sensors | p. 86 |
Distributed Kernel Regression | p. 87 |
Simulations | p. 89 |
Stationary Motes | p. 90 |
Mobile Motes | p. 91 |
Distributed Algorithm | p. 92 |
Summary and Further Directions | p. 93 |
References | p. 94 |
Adaptive Localization in Wireless Networks | p. 97 |
Introduction | p. 97 |
RF Propagation Modelling | p. 98 |
Characteristics of the Indoor Propagation Channel | p. 99 |
Parametric Channel Models | p. 99 |
Geo Map-Based Models | p. 100 |
Non-Parametric Models | p. 102 |
Localization Solution | p. 103 |
Simultaneous Localization and Learning | p. 104 |
Kohonen SOM | p. 105 |
Main Algorithm | p. 106 |
Comparison Between SOM and SLL | p. 107 |
Convergence Properties of SLL | p. 107 |
Statistical Conditions for SLL | p. 113 |
Results on 2D Real-World Scenarios | p. 116 |
Conclusions | p. 118 |
References | p. 119 |
Signal Processing Methods for Doppler Radar Heart Rate Monitoring | p. 121 |
Introduction | p. 121 |
Signal Model | p. 123 |
Physiological Signal Model | p. 125 |
Single Person Signal Processing | p. 126 |
Demodulation | p. 126 |
Detection of Heartbeat and Estimation of Heart Rate | p. 127 |
Multiple People Signal Processing | p. 132 |
Heartbeat Signal | p. 133 |
Algorithm | p. 133 |
Results | p. 134 |
Conclusion | p. 138 |
References | p. 139 |
Multimodal Fusion for Car Navigation Systems | p. 141 |
Introduction | p. 141 |
Kalman Filter-Based Sensor Fusion for Dead Reckoning Improvement | p. 143 |
Map Matching Improvement by Pattern Recognition | p. 146 |
Generation of Feature Vectors by State Machines | p. 147 |
Evaluation of Certainties of Road Alternatives Based on Feature Vector Comparison | p. 150 |
Fuzzy Guidance | p. 154 |
Conclusions | p. 157 |
References | p. 157 |
Information Fusion in Imaging | |
Cue and Sensor Fusion for Independent Moving Objects Detection and Description in Driving Scenes | p. 161 |
Introduction | p. 161 |
Vision Sensor Data Processing | p. 164 |
Vision Sensor Setup | p. 164 |
Independent Motion Stream | p. 165 |
Recognition Stream | p. 167 |
Training | p. 168 |
Visual Streams Fusion | p. 170 |
IMO Detection and Tracking | p. 171 |
Classification and Description of the IMOs | p. 171 |
LIDAR Sensor Data Processing | p. 172 |
LIDAR Sensor Setup | p. 172 |
Ground Plane Estimation | p. 173 |
LIDAR Obstacles Projection | p. 175 |
Vision and LIDAR Fusion | p. 175 |
Results | p. 176 |
Conclusions and Future Steps | p. 177 |
References | p. 178 |
Distributed Vision Networks for Human Pose Analysis | p. 181 |
Introduction | p. 181 |
A Unifying Framework | p. 183 |
Smart Camera Networks | p. 184 |
Opportunistic Fusion Mechanisms | p. 185 |
Human Posture Estimation | p. 187 |
The 3D Human Body Model | p. 189 |
In-Node Feature Extraction | p. 190 |
Collaborative Posture Estimation | p. 192 |
Towards Behavior Interpretation | p. 195 |
Conclusions | p. 198 |
References | p. 199 |
Skin Color Separation and Synthesis for E-Cosmetics | p. 201 |
Introduction | p. 201 |
Image-Based Skin Color Analysis and Synthesis | p. 203 |
Shading Removal by Color Vector Space Analysis: Simple Inverse Lighting Technique | p. 205 |
Imaging Model | p. 205 |
Finding the Skin Color Plane in the Face and Projection Technique for Shading Removal | p. 208 |
Validation of the Analysis | p. 210 |
Image-Based Skin Color and Texture Analysis/Synthesis | p. 211 |
Data-Driven Physiologically Based Skin Texture Control | p. 212 |
Conclusion and Discussion | p. 218 |
References | p. 219 |
ICA for Fusion of Brain Imaging Data | p. 221 |
Introduction | p. 221 |
An Overview of Different Approaches for Fusion | p. 223 |
A Brief Description of Imaging Modalities and Feature Generation | p. 224 |
Functional Magnetic Resonance Imaging | p. 224 |
Structural Magnetic Resonance Imaging | p. 226 |
Diffusion Tensor Imaging | p. 226 |
Electroencephalogram | p. 227 |
Brain Imaging Feature Generation | p. 228 |
Feature-Based Fusion Framework Using ICA | p. 228 |
Application of the Fusion Framework | p. 230 |
Multitask fMRI | p. 231 |
Functional Magnetic Resonance Imaging-Structural Functional Magnetic Resonance Imaging | p. 231 |
Functional Magnetic Resonance Imaging-Event-Related Potential | p. 233 |
Structural Magnetic Resonance Imaging-Diffusion Tensor Imaging | p. 233 |
Parallel Independent Component Analysis | p. 235 |
Selection of Joint Components | p. 235 |
Conclusion | p. 237 |
References | p. 237 |
Knowledge Extraction in Brain Science | |
Complex Empirical Mode Decomposition for Multichannel Information Fusion | p. 243 |
Introduction | p. 243 |
Data Fusion Principles | p. 244 |
Empirical Mode Decomposition | p. 244 |
Ensemble Empirical Mode Decomposition | p. 247 |
Extending EMD to the Complex Domain | p. 249 |
Complex Empirical Mode Decomposition | p. 251 |
Rotation Invariant Empirical Mode Decomposition | p. 254 |
Complex EMD as Knowledge Extraction Tool for Brain Prosthetics | p. 254 |
Empirical Mode Decomposition as a Fixed Point Iteration | p. 257 |
Discussion and Conclusions | p. 258 |
References | p. 259 |
Information Fusion for Perceptual Feedback: A Brain Activity Sonification Approach | p. 261 |
Introduction | p. 261 |
Principles of Brain Sonification | p. 263 |
Empirical Mode Decomposition | p. 264 |
EEG and EMD: A Match Made in Heaven? | p. 265 |
Time-Frequency Analysis of EEG and MIDI Representation | p. 269 |
Experiments | p. 271 |
Conclusions | p. 272 |
References | p. 273 |
Advanced EEG Signal Processing in Brain Death Diagnosis | p. 275 |
Introduction | p. 275 |
Background and EEG Recordings | p. 276 |
Diagnosis of Brain Death | p. 276 |
EEG Preliminary Examination and Diagnosis System | p. 276 |
EEG Recordings | p. 278 |
EEG Signal Processing | p. 279 |
A Model of EEG Signal Analysis | p. 280 |
A Robust Prewhitening Method for Noise Reduction | p. 280 |
Independent Component Analysis | p. 283 |
Fourier Analysis and Time-Frequency Analysis | p. 285 |
EEG Preliminary Examination with ICA | p. 285 |
Extracted EEG Brain Activity from Comatose Patients | p. 286 |
The Patients Without EEG Brain Activities | p. 287 |
Quantitative EEG Analysis with Complexity Measures | p. 288 |
The Approximate Entropy | p. 289 |
The Normalized Singular Spectrum Entropy | p. 290 |
The C[subscript 0] Complexity | p. 291 |
Detrended Fluctuation Analysis | p. 292 |
Quantitative Comparison Results | p. 292 |
Classification | p. 295 |
Conclusion and Future Study | p. 296 |
References | p. 297 |
Automatic Knowledge Extraction: Fusion of Human Expert Ratings and Biosignal Features for Fatigue Monitoring Applications | p. 299 |
Introduction | p. 299 |
Fatigue Monitoring | p. 301 |
Problem | p. 301 |
Human Expert Ratings | p. 302 |
Experiments | p. 303 |
Feature Extraction | p. 305 |
Feature Fusion and Classification | p. 306 |
Learning Vector Quantization | p. 307 |
Automatic Relevance Determination | p. 308 |
Support Vector Machines | p. 309 |
Results | p. 310 |
Feature Fusion | p. 310 |
Feature Relevance | p. 312 |
Intra-Subject and Inter-Subject Variability | p. 313 |
Conclusions and Future Work | p. 314 |
References | p. 315 |
Index | p. 317 |
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