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E-grāmata: Trends in Digital Signal Processing: A Festschrift in Honour of A.G. Constantinides

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Edited by (Nanyang Technological University, Singapore), Edited by (Hong Kong Polytechnic University), Edited by (University of Windsor, Canada)
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Digital signal processing is ubiquitous. It is an essential ingredient in many of today’s electronic devices, ranging from medical equipment to weapon systems. It makes the difference between dumb and intelligent systems. This book is organized into five parts: (1) Introduction, which contains an account of Prof. Constantinides’ contribution to the field and brief summaries of the remaining chapters of this festschrift, (2) Digital Filters and Transforms, which covers efficient digital filtering techniques for improving signal quality, (3) Signal Processing, which provides an insight into fundamental theories, (4) Communications, which deals with some important applications of signal processing techniques, and (5) Finale, which contains a discussion on the impact of digital signal processing on our society and the closing remarks on this festschrift.

Recenzijas

"This book is a very valuable addition to the signal processing literature. All chapters are written by internationally acclaimed researchers in the field addressing hot topics of current interest. Most of these renowned experts carry the technical DNA inherited from Prof. Constantinides, a living proof of how outstanding genetic information can lead to fruitful contributions to society development.

The book includes eighteen survey chapters on the general areas of digital filters and transforms, signal processing, and communications, beautifully written by the technical heirs of Prof. Constantinides and his contemporary colleagues. Indeed this book reflects some of the many advances that Prof. Constantinides and his generation brought about to the ubiquitous DSP technology that benefits the current generation." Prof. Paulo S. R. Diniz, Federal University of Rio de Janeiro, Brazil

"A fitting tribute to a world-renowned pioneer in digital filter design, DSP, and communications, this book offers panoramic views of the past, present, and future of the theory and applications of these fields. The continual evolutions in time-interleaved A/D converters, all-pass filters, and multi-rate filters have pushed the envelopes of their applications, ranging from wireless communications to human face recognition and seismic signal processing. Authored by leading researchers in these fields, this book provides valuable insights into what have been made possible and potential future developments."

Prof. Yih-Fang Huang. University of Notre Dame, USA

"Professor Constantinides has made worldwide impact on DSP through his novel contributions to this area. With his unique energy, he has successfully pushed forward DSP scientists in the fields of education, research, and services. His personal inspiration to attract young scientists to work on DSP, especially in Europe, already started in the 70s with the establishment of the European Association for Signal Processing and the European Journal for Signal Processing as well as the organization of the first international series of meetings on DSP."

Prof. Tapio Saramäki, Tampere University of Technology, Finland

"This festschrift covers a number of most recent and important DSP research works, including DSP theories, filter designs, and applications, and is very valuable for the DSP community."

Prof. Soo-Chang Pei, National Taiwan University, Taiwan

Foreword xxi
Part I Introduction
1 Introduction
3(10)
Yong Ching Lim
Hon Keung Kwan
Wan-Chi Siu
Part II Digital Filters And Transforms
2 A Review on Time-Interleaved Analog-to-Digital Converters and Mismatch Compensation Techniques
13(52)
Saihua Xu
Yong Ching Lim
2.1 Introduction
14(1)
2.2 TIADC Architecture
15(3)
2.2.1 Ideal ADC
15(2)
2.2.2 Ideal TIADC
17(1)
2.3 Sources of Mismatch Errors and Their Effects
18(9)
2.3.1 Offset Mismatches
19(1)
2.3.2 Gain Mismatches
19(1)
2.3.3 Timing Mismatches
20(1)
2.3.4 Frequency Response Mismatches
21(1)
2.3.5 Effect of the Mismatches in the frequency domain
22(5)
2.4 Mismatch Estimation and Compensation
27(24)
2.4.1 Identification and Correction of Offset Mismatch
28(3)
2.4.2 Identification and Correction of Gain Mismatch
31(2)
2.4.3 Identification and Correction of Timing Mismatch
33(1)
2.4.3.1 Correction of timing mismatch
34(1)
2.4.3.2 Identification of time skews
43(4)
2.4.4 Correction of Frequency Response Mismatch
47(4)
2.5 Conclusion
51(14)
3 How to Perform Very Wideband Digital Filtering in Modern Software Defined Radios
65(44)
Fred Harris
Elettra Venosa
Xiaofei Chen
3.1 Introduction
66(5)
3.2 NMDFBs Model
71(4)
3.2.1 Non-Maximally Decimated Filter Banks and Perfect Reconstruction Property
72(2)
3.2.2 Low-Pass Prototype Filter Design
74(1)
3.3 PR Property
75(3)
3.4 Practical Implementation of PR-NMDFBs
78(7)
3.4.1 Polyphase Analysis Channelizer
79(4)
3.4.2 Polyphase Synthesis Channelizer
83(2)
3.5 Spectral Shaping Approximation via Intermediate Processing Elements
85(6)
3.5.1 Piecewise Constant Spectral Approximation
87(2)
3.5.2 Straight Line Spectral Approximation
89(2)
3.6 Design Options
91(7)
3.6.1 Rectangular Low-Pass Prototype Filter Design
93(3)
3.6.2 Triangular Low-Pass Prototype Filter Design
96(2)
3.7 Application Example
98(6)
3.8 Conclusions
104(5)
4 A Survey of Digital All-Pass Filter-Based Real and Complex Adaptive Notch Filters
109(36)
P.T. Wheeler
J.A. Chambers
P.A. Regalia
4.1 Introduction
109(1)
4.2 Evaluation of Four Adaptive Notch Filters
110(11)
4.2.1 Synthesising the Four Structures
112(1)
4.2.1.1 Chambers and Constantinides' NFB all-pass structure
113(1)
4.2.1.2 Regalia's all-pass solution
114(1)
4.2.1.3 Cho, Choi and Lee's all-pass method
114(1)
4.2.1.4 Kwan and Martin's DCS solution
115(2)
4.2.2 Tracking Two Real Sinusoid Signals
117(1)
4.2.3 Tracking Three Real Sinusoid Signals
118(2)
4.2.4 Summary
120(1)
4.3 Evaluating the Two Complex Adaptive Notch Filters
121(7)
4.3.1 Filter Realisation
122(1)
4.3.2 The Learning Algorithm
123(1)
4.3.3 Tracking Two Complex Sinusoid Signals
124(1)
4.3.4 Simulation Results and Comparison
125(3)
4.3.5 Summary
128(1)
4.4 Tracking a Complex-Valued Chirp Signal
128(4)
4.4.1 Convergence of the Update of the Frequency Parameters
128(2)
4.4.2 Comparison of Two Methods for Tracking a CVCS
130(2)
4.4.3 Summary
132(1)
4.5 Bandwidth Parameter Adaptation in a Complex Adaptive Notch Filter
132(9)
4.5.1 The Full Gradient Term for the Update of α
134(1)
4.5.2 Simulations and Results
135(1)
4.5.2.1 Tracking a single CSS whilst also updating α
136(1)
4.5.2.2 Tracking two CSSs, whilst adapting individual α values for each CSS being tracked
137(1)
4.5.2.3 Tracking a CVCS and a frequency hopping CSS
138(1)
4.5.3 Computational Complexity of the Algorithms used to Track CSSs
139(1)
4.5.4 Summary
140(1)
4.6 Overall Conclusions
141(4)
5 Recent Advances in Sparse FIR Filter Design Using to and Optimization Techniques
145(30)
Aimin Jiang
Hon Keung Kwan
5.1 Classical FIR Filter Designs
146(2)
5.2 Sparse FIR Filter Designs
148(22)
5.2.1 Hard Thresholding Method
152(1)
5.2.2 Minimum 1-Norm Method
153(1)
5.2.3 Successive Thinning Method
154(1)
5.2.4 Iterative Shrinkage/Thresholding (1ST) Method
155(5)
5.2.5 Joint Optimization of Coefficient Sparsity and Filter Order
160(10)
5.3 Summary
170(5)
6 Sparse Models in Echo Cancellation: When the Old Meets the New
175(26)
Yannis Kopsinis
Symeon Chouvardas
Sergios Theodoridis
6.1 Introduction
175(3)
6.2 Sparse Adaptive Filtering: The Proportionate Updating Approach
178(3)
6.3 Sparse Adaptive Filtering: Sparsity-Induced Regularization/Thresholding Approach
181(3)
6.4 Adaptive Sparsity Promotion: A Geometrical Point of View
184(1)
6.5 Sparse Adaptive Filtering: Set Theoretic Approach
185(6)
6.5.1 Adaptive Thresholding
189(2)
6.6 Robust Online Learning: The Double-Talk Scenario
191(2)
6.7 Experimental Validation
193(8)
7 Transform Domain Processing for Recent Signal and Video Applications
201(60)
Wan-Chi Siu
7.1 Introduction
202(2)
7.1.1 DSP Operation Basis: Cyclic Convolutions and Discrete Fourier Transforms
202(2)
7.1.2 Number Theoretic Transforms
204(1)
7.2 Theory of a General Transform
204(7)
7.2.1 Circular Convolution Property
205(6)
7.3 Transform in a Ring of Integers Modulo an Integer, M
211(6)
7.3.1 Mersenne Number and Fermat Number Transforms
213(4)
7.4 Very Fast Discrete Fourier Transform Using Number Theoretic Transform
217(4)
7.5 Discrete Cosine Transform
221(10)
7.5.1 Length-4 DCT
227(1)
7.5.2 Inverse DCT
228(3)
7.6 Integer Cosine Transform
231(8)
7.6.1 Length-4 DCT Again
231(2)
7.6.2 Orthogonal Requirement for Length-4 DCT
233(2)
7.6.3 New Integer Cosine Kernels
235(4)
7.7 Application to Interpolation and Super-Resolution Videos/Images
239(9)
7.7.1 Interpolation
239(1)
7.7.2 Methodology
240(1)
7.7.3 Video Up-Sampling with the Transform Domain
240(8)
7.8 Conclusion
248(13)
Part III Signal Processing
8 Ramanujan-Sums and the Representation of Periodic Sequences
261(26)
Palghat P. Vaidyanathan
8.1 Introduction
261(3)
8.1.1 Notations
263(1)
8.2 Periodic Signals and DFT
264(2)
8.3 Ramanujan Sums
266(4)
8.4 Ramanujan Subspaces
270(3)
8.4.1 Properties of Ramanujan subspaces
272(1)
8.5 A Second Ramanujan Sum Basis Using Subspaces Sqi
273(4)
8.5.1 Properties of the Representation
274(1)
8.5.2 Finding Period Using Decomposition
275(1)
8.5.3 Justification of the Representation
276(1)
8.6 Examples of Use of Ramanujan Representations
277(3)
8.7 Dictionary Approaches
280(3)
8.8 Concluding Remarks
283(4)
9 High-Dimensional Kernel Regression: A Guide for Practitioners
287(24)
Felipe Tobar
Danilo P. Mandic
9.1 Introduction
287(2)
9.2 Background on Kernel Estimation
289(5)
9.2.1 Support Vector Regression
289(2)
9.2.2 Sparsification Criteria
291(1)
9.2.3 Finding the Optimal Mixing Parameters: Ridge Regression and Least Mean Square
292(2)
9.3 Complex-Valued Kernels
294(4)
9.3.1 Complexification of Real-Valued Kernels
294(2)
9.3.2 Online Wind Prediction Using Complex-Valued Kernels
296(2)
9.4 Quaternion-Valued Kernels
298(4)
9.4.1 Quaternion Reproducing Kernel Hilbert Spaces
299(1)
9.4.2 Body Motion Tracking Using Quaternion Kernels
300(2)
9.5 Vector-Valued Kernels
302(5)
9.5.1 A Vector-Valued Reproducing Kernel Hilbert Space
303(2)
9.5.2 Nonlinear Function Approximation Using Multikernel Ridge Regression
305(2)
9.6 Discussion
307(4)
10 Linear Microphone Array TDE via Generalized Gaussian Distribution
311(22)
Theodoros Petsatodis
Fotios Talantzis
10.1 Introduction
311(2)
10.2 System Model Description
313(5)
10.3 Information Theoretical Time Delay Estimation
318(4)
10.3.1 Mutual Information-Based TDE
318(4)
10.4 Employing Generalized Gaussian Distribution
322(6)
10.5 Conclusions
328(5)
11 Recognition of Human Faces under Different Degradation Conditions
333(24)
Soodeh Nikan
Majid Ahmadi
11.1 Introduction
333(1)
11.2 Illumination Variation Challenge
334(9)
11.2.1 Illumination-Insensitive Image Processing
335(1)
11.2.1.1 Intensity-level transformation
335(1)
11.2.1.2 Gradient-based techniques
336(1)
11.2.1.3 Reflection component estimation
337(2)
11.2.2 Illumination-Invariant Image Descriptor
339(1)
11.2.3 Block-Based Illumination-Invariant Pattern Recognition
340(3)
11.3 Partial Occlusion Challenges
343(9)
11.3.1 Excluding Occluded Face Regions or Reducing Their Effect
345(7)
11.4 Conclusion
352(5)
12 Semantic Representation, Enrichment, and Retrieval of Audiovisual Film Content
357(50)
Alexandros Chortaras
Stefanos Kollias
Kostas Rapantzikos
Giorgos Stamou
12.1 Introduction
357(4)
12.2 Film Data Description
361(4)
12.2.1 Metadata
361(1)
12.2.2 Scripts and Post-Production Scripts
362(3)
12.3 Knowledge-Based Representation of Data
365(12)
12.3.1 Semantic Technologies
365(4)
12.3.2 Overview of the Semantic Representation
369(2)
12.3.3 Film Ontologies and Metadata Representation
371(1)
12.3.4 Video Content Representation
372(1)
12.3.5 Script Representation
373(4)
12.4 Visual Analysis
377(8)
12.4.1 The Analysis Subsystem
377(1)
12.4.2 The Main Components
378(1)
12.4.2.1 Local visual characteristics and descriptors
378(1)
12.4.2.2 Quantization and codebook
379(1)
12.4.2.3 Visual matching and geometry
379(1)
12.4.3 The Visual Analysis Scheme
380(1)
12.4.3.1 Constructing visual dictionaries
381(1)
12.4.3.2 Geometry consistency checking
382(1)
12.4.3.3 Feature selection
383(1)
12.4.3.4 Geo-location exploitation
384(1)
12.4.3.5 Feature extraction
384(1)
12.5 Film Metadata and Content Enrichment
385(9)
12.5.1 Using IMDb Data
385(3)
12.5.2 Named Entity Recognition
388(2)
12.5.3 Linking to WordNet
390(3)
12.5.4 Visual Analysis Results
393(1)
12.6 Query Answering
394(6)
12.6.1 Query Rewriting
395(1)
12.6.2 Evaluation
396(4)
12.7 Conclusions
400(7)
13 Modeling the Structures of Complex Systems: Data Representations, Neural Learning, and Artificial Mind
407(28)
Tetsuya Hoya
13.1 Introduction
408(3)
13.2 Holistic Representation of Complex Data Structures—by Way of Graph Theoretic Methods
411(3)
13.2.1 Edge Detection of an Image
411(1)
13.2.2 Pruning the Dataset Used for Training Neural Networks
412(2)
13.3 Incremental Training Using a Probabilistic Neural Network
414(2)
13.3.1 A Pattern Correction Scheme Using a PNN
415(1)
13.3.2 Accommodation of New Classes within a PNN
416(1)
13.4 The Concept of Kernel Memory
416(7)
13.4.1 Simultaneous Pattern Classification and Association by Kernel Memory
420(1)
13.4.2 Temporal Data Processing by Way of Kernel Memory
421(1)
13.4.3 Application of Kernel Units for Detecting Sequential Patterns to Spoken Word Recognition
421(2)
13.5 Artificial Mind System: Toward Drawing a Blueprint of Artificial Intelligence
423(6)
13.5.1 A Hierarchical Network Model of Short-and Long Term Memory, Attention, and Intuition
424(2)
13.5.2 The Artificial Mind System
426(2)
13.5.3 Ongoing Research Activities Relevant to the Artificial Mind System
428(1)
13.6 Conclusion
429(6)
Part IV Communications
14 Markov Chain Monte Carlo Statistical Detection Methods for Communication Systems
435(26)
Behrouz Farhang-Boroujeny
14.1 Introduction
435(2)
14.2 Channel Model
437(1)
14.3 Iterative Multiuser/MIMO Receiver
437(2)
14.4 Monte Carlo Statistical Methods
439(3)
14.4.1 Monte Carlo Integration
439(1)
14.4.2 Importance Sampling
440(1)
14.4.3 Connection with LLR Computation
441(1)
14.4.4 MCMC Simulation and Gibbs Sampler
441(1)
14.4.5 Symbol-wise and Bit-wise Gibbs Samplers
442(1)
14.5 Implementation of Multiuser/MIMO Detector
442(6)
14.5.1 Monte Carlo Summations
443(1)
14.5.2 Computation of L-Values
444(2)
14.5.3 Statistical Inference
446(1)
14.5.4 Max-log Approximation
447(1)
14.5.5 Discussion
447(1)
14.6 Implementation of MCMC Detector
448(13)
14.6.1 Reformulation of the Channel Model
449(1)
14.6.2 Bit-wise Gibbs Sampler
449(3)
14.6.3 L-Values Calculator
452(1)
14.6.4 Hardware Architectures
453(8)
15 Multiple Antennas for Physical Layer Secrecy
461(20)
Jiangyuan Li
Athina P. Petropulu
15.1 Physical Layer Secrecy
461(2)
15.2 Secrecy Capacity Concept of the Wiretap Channel
463(2)
15.3 Secrecy Capacity of MIMO Wiretap Channels
465(16)
15.3.1 Conditions for Positive Secrecy Capacity Convexity, and Solution
466(2)
15.3.2 MISO Wiretap Channels
468(1)
15.3.3 Single-Antenna Eavesdropper
469(1)
15.3.4 Two-Antenna Transmitter
470(2)
15.3.5 HR HR - HE HE Has Exactly One Positive Eigenvalue
472(2)
15.3.6 Conditions for Optimality of Beamforming
474(2)
15.3.7 Algorithms for General Non-Convex Cases
476(5)
16 Radio Frequency Localization for IoT Applications
481(28)
Antonis Kalis
Anastasis Kounoudes
16.1 Indoor Localization Challenges and Applications
481(2)
16.2 Basic Measurement-Based Methods
483(9)
16.2.1 Time-of-Flight Measuring Methods
485(4)
16.2.2 Received Signal Strength-Based Measurements
489(2)
16.2.3 Angle of Arrival-Based Measurements
491(1)
16.3 The Special Case of Wireless Sensor Networks
492(4)
16.4 Smart Antennas for WSN
496(9)
16.4.1 Direction Finding
498(2)
16.4.2 Localization
500(5)
16.5 Summary
505(4)
17 Classification and Prediction Techniques for Localization in IEEE 802.11 Networks
509(26)
Kelong Cong
Kin K. Leung
17.1 Introduction
510(1)
17.2 Background
511(4)
17.2.1 Path-Loss and Log-Normal Shadowing
511(1)
17.2.2 Location Estimation: Fingerprinting
512(2)
17.2.3 Machine Learning
514(1)
17.2.4 Existing Work
514(1)
17.2.4.1 RADAR and Horus
514(1)
17.2.4.2 COMPASS
515(1)
17.2.4.3 Ekahau
515(1)
17.3 Methodology
515(5)
17.3.1 Simulated Data Generation
516(1)
17.3.2 Experimental Testbed
517(1)
17.3.3 Data Collection and Processing
518(1)
17.3.4 Classification Algorithms
518(1)
17.3.4.1 Input data
518(1)
17.3.4.2 Algorithms and their implementation
519(1)
17.4 Results
520(8)
17.4.1 Analysis on Simulated Data
520(1)
17.4.1.1 Analysis on the number of measurements per fingerprinted location used in the training phase
520(1)
17.4.1.2 Analysis on the number of measurements used to perform a single prediction
522(2)
17.4.2 Analysis on Real Data
524(1)
17.4.2.1 Analysis on the number of measurements per fingerprinted location used in the training phase
524(1)
17.4.2.2 Monitor subset analysis using confusion matrix
526(2)
17.5 Conclusion
528(7)
Part V Finale
18 Our World Is Better Served by DSP Technologies and Their Innovative Solutions
535(32)
Paulina Chan
18.1 Synopsis
535(1)
18.2 DSP Offers Solutions to Societal Needs and Sets Technology Trends
536(2)
18.3 Area 1: Universal and Personalised Healthcare
538(5)
18.3.1 The Synergy of Engineering and I-Healthcare in Global Healthcare Innovation
540(1)
18.3.2 Big Data for I-Care Systems
540(1)
18.3.3 I-Training and Education
541(1)
18.3.4 Apply Omics Science to Clinical Applications
542(1)
18.4 Area 2: Internet of Data/Things/People in Communications
543(5)
18.4.1 DSP in ICT Industry
543(1)
18.4.2 Making the Digital Economy Smarter
544(1)
18.4.2.1 Fifth-Generation Mobile Networks and Systems
544(1)
18.4.2.2 Internet of Things
545(1)
18.4.2.3 Internet of Big Data and Cloud Computing
545(1)
18.4.2.4 Internet of People and Online Gaming
547(1)
18.5 Area 3: Smart City Applications and Sustainable Ecology
548(6)
18.5.1 Smart Grids
549(1)
18.5.2 Modelling Some Smart Applications in Smart Cities in the USA and at Imperial College London
550(1)
18.5.2.1 A Wireless Mesh Network to monitor Traffic
551(1)
18.5.2.2 The All Traffic Solutions
551(1)
18.5.2.3 A High-Performance Building Programme
552(1)
18.5.2.4 At Imperial College London
553(1)
18.6 Area 4: Green Technologies and Renewable Energy
554(8)
18.6.1 Multidisciplinary Teams at Imperial College London on Green
555(1)
18.6.1.1 Wind Turbine Industry Analysis at the Business School
555(1)
18.6.1.2 The Energy Futures Lab at the Engineering Faculty
556(1)
18.6.2 Green Architecture in the UK
557(1)
18.6.3 Green Mobility in the USA
558(1)
18.6.4 Solar Farms and Solar Grids
559(1)
18.6.4.1 Sahara Desert: The Source of Solar Energy
559(1)
18.6.4.2 Manufacturing Solar Panels in China
560(1)
18.6.5 Waste Water to Electricity Generation
561(1)
18.7 A Path from R&D to Product and Service Releases for DSP
562(5)
Closing Remarks 567(4)
Lajos Hanzo
Index 571
Yong Ching Lim received his ACGI and BSc degrees in 1977 and DIC and PhD in 1980, all in electrical engineering, from Imperial College London, United Kingdom. From 1980 to 1982, he was with the Naval Postgraduate School, California. From 1982 to 2003, he was with the Department of Electrical Engineering, National University of Singapore. Since 2003, he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. His research interests include digital signal processing and VLSI circuits and systems design. Dr. Lim was a recipient of the 1996 IEEE Circuits and Systems Societys Guillemin-Cauer Best Paper Award, the 1990 IREE (Australia) Norman Hayes Memorial Best Paper Award, 1977 IEE (UK) Prize, and the 197477 Siemens Memorial (Imperial College) Award. He is a fellow of the IEEE. He served as a lecturer for the IEEE Circuits and Systems Society under the distinguished lecturer program from 2001 to 2002; as an associate editor for the IEEE Transactions on Circuits and Systems from 1991 to 1993 and from 1999 to 2001; as an associate editor for Circuits, Systems and Signal Processing from 1993 to 2000; as chairman of the DSP Technical Committee of the IEEE Circuits and Systems Society from 1998 to 2000; and in the Technical Program Committees DSP Track as the chairman in IEEE ISCAS97 and IEEE ISCAS00 and as a co-chairman in IEEE ISCAS99. He was the general chairman for IEEE APCCAS 2006, a co-general chairman for IEEE ISCAS 2009, ICGCS 2010, and IEEE ICDSP 2015.

Hon Keung Kwan received his DIC and PhD degrees in electrical engineering (signal processing) in 1981 with Prof. Tony Constantinides as his supervisor from Imperial College London, United Kingdom. His previous experience includes working as a design engineer in the electronics and computer memory industry in Hong Kong during 19771978 and serving as a faculty member (lecturer) in the Department of Electronic Engineering in the Hong Kong Polytechnic University in 1981 and then in the Department of Electrical and Electronic Engineering in the University of Hong Kong during 198188. He subsequently joined the University of Windsor as an associate professor and holds the rank of professor in electrical and computer engineering since 1989. Dr. Kwan has published extensively on digital filters and intelligent systems in refereed journals and conference proceedings, and eBooks. The innovations described in his research papers have benefited (and/or been utilized by) industry in developing various inventions described in eight US and six European patents. He has taught a variety of undergraduate and graduate courses and his recent teaching covers subject areas of digital filters and systems, multimedia signals and systems, intelligent signal processing, intelligent computing and systems, and computational intelligence. He has served in a variety of capacities, including chair and member in various undergraduate, graduate, administrative, faculty, and university committees; advisor, appraiser, assessor, reviewer, external examiner, and editorial board member of various academic and professional tasks; chair, organizer, and member of technical program committees and sessions in various international, national, and regional conferences, symposia, and workshops; and the past-chair, chair, chair-elect, secretary, secretary-elect, and member of the Digital Signal Processing Technical Committee and the Neural Systems and Applications Technical Committee of the IEEE Circuits and Systems Society. Dr. Kwan has been a recipient of research grants from sources including NSERC, Auto21, and Micronet; a recipient of research professorship, research excellence, research fellowship, and outstanding paper awards; a licensed Professional Engineer (Ontario), a Chartered Electrical Engineer (UK), and was elected in 1996 a fellow of the Institution of Engineering and Technology (UK).
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