CENTRE FOR SIGNAL PROCESSING

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CENTRE FOR SIGNAL

PROCESSING

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Introduction

CSP was first formed as a research centre in the School of EEE in 1991. From August 1995 to March 2002, CSP receivedits core funding from A*STAR (then NSTB) and operated as a national research centre. Since April 2002, CSP has been re-organized as a research centre in the School of EEE, with the mission of spearheading the research and development ofsignal processing technologies for the future.

The Centre is organized into five Research Programmes with a total of about 150 research staff (including about 45professors and more than 100 researchers and research students). With the vision that future systems will become moreintelligent and connected, CSP strives to create significant research impact on the enabling DSP technologies. Specifically,three thrust areas have been identified as the focus for our research projects: (1) biomedical signal processing, (2) homelandsecurity, and (3) intelligent human-machine interface. Collaboration with local and overseas universities, research institutesand industry will continue to be a very important part of the activities for CSP. Much of the research undertaken in theCentre is supported by external research funding amounting to about $3.8m (and by NTU funding of about $1.4m) overthe past one year. Highlights of the research activities undertaken in 2003-2004 are summarised below.

MULTIMEDIA SIGNAL PROCESSING

CENTRE FOR SIGNAL PROCESSING

In response to the increased concern for homeland securityin the face of global terrorism and outbreaks of infectiousviruses, one research effort of the content analysis groupfocuses on automated video surveillance for enhancedsafety in human living and work places. In particular, aprobabilistic reasoning approach has been developed tomonitor people in a closed environment by exploiting bothlow-level visual features and high-level domain knowledge.For the former, the research of automatic video objectsegmentation using spatio-temporal methodology has beencontinuously conducted in the last year. For the latter, thenovelty of this work lies in the systematic approach toincorporating the correlations and constraints amongpeople observations such that the optimality of recognitioncan be achieved by maximizing a joint posterior probabilityof the observations.

Research activities are also undertaken in the areas of videocoding, super-resolution, and transcoding. On the researchof digital video coding, we have been working on encodingoptimization; in particular, on the aspect of error resilience,new packetization scheme and two-way variable lengthdata block coding method are developed. For videoprocessing, one project has developed a unified videosegmentation approach to partitioning video into shots andscenes by using sequential change detection methodology.

Super-resolution imaging overcomes the inherent limitationof the image acquisition equipment to create high-resolution images from low-resolution images. A novel state-space approach is under investigation for improving thesuper-resolution imaging performance.

Figure 1: Super-resolution imaging

Another research activity focuses on exploring machinelearning algorithms to learn the user’s perception and henceto bridge the semantic gap in Content Based Image Retrieval(CBIR), between the high-level understanding of imagecontent by human beings and the representation of the

Figure 2: Video object segmentation

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image contents by low level image features. Current workfocuses on the use of Support Vector Machine (SVM) as aclassifier that classifies the images into relevant andirrelevant classes through relevant feedback. Efforts havebeen made to optimize the learning of Kernel-BiasDiscriminant Analysis for CBIR and incorporating priorknowledge into the SVM learning.

In the audio area, the main focus is in the following areas:(i) speech enhancement for mobile applications, (ii)acoustical signal processing for sound control, (iii)directional sound system using parametric array, and (iv)real-time signal processing on the latest audio processors.

Two US patents were issued in 2004:

1. Yap-Peng Tan, Wenjun Zeng, and Shawmin Lei, “Methodfor Removing Ringing Artifacts from Locations NearDominant Edges of an Image Reconstructed afterCompression,” U.S. Patent 6,707,952, Mar 2004.

2. Yap-Peng Tan, Ping-Sing Tsai, and Tinku Acharya,“Method and Apparatus for Adaptively Sharpening LocalImage Content of an Image,” U.S. Patent 6,697,534, Feb2004.

Assoc Prof Xue PingProgram [email protected]

Staff Members

Prof Er Meng Hwa Assoc Prof Tan Yap Peng Asst Prof Yap Kim Hui

Assoc Prof Ma Kai Kuang Assoc Prof Chan Kap Luk Assoc Prof Chau Lap Pui Assoc Prof Gan Woon Seng

Assoc Prof Soon Inn Yann Assoc Prof Chong Yong Kim Assoc Prof Foo Say Wei Prof Koh Soo Ngee

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Asst Prof Farook Sattar Assoc Prof Saman SAbeysekera

Assoc Prof Soong Boon Hee Asst Prof Zhu Ce

Asst Prof Yang Jun Dr Wang LeiResearch Fellow

Dr Gao ShaoshuaiResearch Fellow

Dr Xia JiantaoResearch Fellow

Dr Gao HaiResearch Fellow

Ms Gan LuResearch Associate

Mr Yu XiaodongResearch Associate

Mr Lee Kong AikProject Officer

Mr Tan Ee LengProject Officer

Mr Furi KarnapiResearch Associate

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Ongoing Research Projects

Project Title Principal Investigator Funding

Single-Channel Speech Enhancement Koh Soo Ngee DSTA ($180k)NTU ($150k)

DSP University Programme Gan Woon Seng Analog Device Int’lInc ($122k)

Power Efficient Algorithms for the Micro Signal Architecture Gan Woon Seng Analog Device Int’lInc ($36k)

Biobridge: Design and Implementation of a Context-aware Gan Woon Seng I2R ($41k)Middleware for Smart Laboratory

Design Tools and Techniques to support Embedded Designs Gan Woon Seng Texas Instrumentson T1 OMAP Platform ($13.7k)

Digital Video Coding, Streaming and Retrieval Ma Kai-Kuang A*Star SERC ($448k)

Quality-of-Service Multicast Routing in Mobile Ad Hoc Soong Boon Hee DSO ($71k)Networks NTU ($60k)

Mobile Wireless Communications Capability and Differential Soong Boon Hee STA ($130k)Global Positioning System Correction Signal Interface for NTU($130k)Unmanned Aerial Vehicles

Time-Reversal Mirror and its Application for Audible Yang Jun NTU ($3k)Range Acoustics

Perception/ Content-Based Analysis for Video Coding Xue Ping I2R ($300k)and Indexing NTU ($211k)

Research Projects Completed in 2004


Image and Multimedia Processing Using Computational Yap Kim Hui NTU ($6k)Intelligence

Audio Beam: A New Type of Loudspeaker Design Gan Woon Seng NTU ($164k)A*Star ($500k)

Bluetooth & DSP Enable Headsets Gan Woon Seng Excelpoint Sys (P) Ltd

MEng and PhD Theses Completed in 2004

Project Title Degree Student Supervisor(s)

Lattice Factorizations of Filter Banks: Theory, Structure and PhD Gan Lu Ma Kai-KuangParameterization

The Small Sample Problem in Content-based Image Retrieval with PhD Wang Lei Chan Kap LukRelevance Feedback

Mixture Analysis and Kernel Density Estimation with PhD Yang Xiangyu Liu Jun /Applications to Image Segmentation and Retrieval Tan Yap Peng

Information Hiding for Intellectual Property Protection of Digital PhD Yu Dan Farook SattarMedia

Joint Source-channel Decoding and Its Application to MELP PhD Liu Xiaobei Koh Soo NgeeEncoded Speech

Real-time MP3 Transmission over Bluetooth ACL Link MEng Chen Feng Farook Sattar

An Efficient Video Streaming Method for Multimedia Delivery MEng Gao Yunlong Chau Lap Pui

Fast Motion Estimation for Video Coding MEng Qi Weisong Zhu Ce

Just-Noticeable Distortion Estimation for Images MEng Zhang Xiaohui Xue Ping

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Courses

Name(s) of Instructor(s) Title of Course Course Conducted for

Ling Nam Video & Image Compression: Principles & Standards Centre for Signal Processing(including MPEG-4 AVC/H.264)

1. S.S. Abeysekera, K.P. Padhi, J. Absar, and S. George, “An audiosignal scaling technique using harmonic grouping and shifting”, Proc.ISCAS’04, Canada, May 2004.

2. S. S. Abeysekera, “Multiple pitch estimation of poly-phonic audiosignals in a frequency-lag domain using the bispectrum”, ISCAS,Canada, May 2004.

3. S.S. Abeysekera, K.P. Padhi, J. Absar, and S. George, “An investigationof window effects on the frequency estimation using the phasevocoder”, accepted, IEEE Trans. Speech & Audio Processing, 2004.

4. J. Wei, B.H. Soong, and Z.G. Li, “A new rate distortion model forvideo transmission using multiple logarithmic functions”, IEEE SignalProc. Letters, vol. 11, no. 8, pp. 694-697, Aug. 2004.

5. Y.P. Tan, K.H. Yap, and L. Wang, Eds., Intelligent MultimediaProcessing with Soft Computing, in press, Springer-Verlag Heidelberg,2004.

6. Y.P. Tan, “Content-based multimedia analysis and retrieval”, inInformation Technology: Principles and Applications, A. K. Ray andT. Acharya, Eds., Prentice Hall India, New Delhi, 2004.

7. W. Lu, and Y.P. Tan, “A vision-based approach to early detection ofdrowning incidents in swimming pools”, IEEE Trans. Circuits Syst.Video Technol., Special Issue on Image- and Video-Based Biometrics-II, vol. 14, no. 2, pp. 159-178, Feb. 2004.

8. Y.P. Tan, Y. Liang, and H. Sun, “On the methods and performances ofrational downsizing video transcoding”, Signal Processing: ImageCommunication, vol. 19, no. 1, pp. 47-65, Jan. 2004.

9. L. Wang, K.L. Chan, and P. Xue, “A criterion for optimizing kernelparameters in KBDA for image retrieval with relevance feedback”,in press, IEEE Trans. System Man and Cybernetics-Part B, SpecialIssue in Machine Leaning for Content-Based Image Retrieval.

10. L. Wang, P. Xue, and K. L. Chan, “Incorporating prior knowledgeinto SVM for image retrieval”, Proc. 17th International Conferenceon Pattern Recognition (ICPR’04), U. K., Aug. 2004.

11. H. Gao, X.D. Yu, L. Wang, P. Xue, and Q. Tian, “Robust multi-levelvideo representation using mean shift analysis”, Proc. IEEE ICME’04,Taiwan, Jun. 2004.

12. X.D. Yu, P. Xue, and Q. Tian, “A statistical approach for object motionestimation with MPEG motion vectors”, Proc. IEEE ICME’04, Taiwan,Jun. 2004.

13. H. Gao, P. Xue, and W.S. Lin, “A new marker-based watershedalgorithm”, Proc. IEEE ISCAS’04, Vancouver, Canada, May. 2004.

14. X. D. Yu, L. Wang, Q. Tian, and P. Xue, “Multi- layer videorepresentation with application to key-frame extraction”, Proc. 10thInternational Multi-Media Modeling Conference, Australia, Jan. 2004.

Selected Publications in 2004

15. D. Yu, and F. Sattar, “Intellectual property protection for multimediadistribution through digital watermarking”, Digital Watermarking forDigital Media, Juergen Seitz (ed.), publisher: Idea Group, Inc, 2004.

16. F. Sattar, R. Doraiswami, and M. Pwint, “A new segmentationtechnique for noisy multi-component signals using wavelettransform”, Proc. IEEE ISCAS’04, Canada, May 2004.

17. D. Yu, F. Sattar, and S.G. Razul, “Transparent robust information hidingfor ownership verification”, Proc. IEEE ISCAS’04, Canada, May 2004.

18. C. Charayaphan, and F. Sattar, “Hardware for real-time ICA-basedblind source separation”, Proc. IEEE ASIC/SOC, U.S.A., Sept. 2004.

19. F. Sattar, D. Yu, S.G. Razul, and S. M. Krishnan, “Transparentinformation hiding with automatic embedding range selection forownership verification”, Proc. IEEE ICIP’04 , Singapore, Oct. 2004.

20. D. Yu, F. Sattar, and B. Barkat, “Multiresolution fragile watermarkingusing complex chirp signal for content integrity verification”, Proc.IEEE ICIP’04 , Singapore, Oct. 2004.

21. X. Gao, F. Sattar, and R. Venkateswarlu, “Corner detection of graylevel images using Gabor wavelets”, Proc. IEEE ICIP’04 , Singapore,Oct. 2004.

22. X. Gao, Z. Yu, F. Sattar, and R. Venkateswarlu, “Multiscale cornerdetection of gray-scale images using plessey method”, Proc. ICARCV,China, Dec. 2004.

23. W. Zhang, X. Gao, E. Sung, F. Sattar, and R. Venkateswarlu, “A robustfeature-based matching of two uncalibrated images”, Proc. ICARCV,China, Dec. 2004.

24. L. Chen, and K.H. Yap, “A parametric double regularization approachto adaptive blind image deconvolution”, accepted, IEEE Trans. ImageProcessing.

25. K. Wu, and K.H. Yap, “A perceptual subjectivity notion in interactivecontent-based image retrieval systems”, in press, IntelligentMultimedia Processing with Soft Computing, Springer-Verlag, 2004.

26. Y.P. Tan, K.H. Yap, and L. Wang, Eds., Intelligent MultimediaProcessing with Soft Computing, in press, Springer-Verlag, 2004.

27. H.Q. Wang, S.N. Koh, and W.W. Chang, “Application of reversiblevariable-length codes in robust speech coding”, accepted, IEEProceedings-Communications, 2004.

28. C.H. You, S.N. Koh, and R. Susanto, “Beta-order MMSE spectralamplitude estimation for speech enhancement”, accepted, IEEE Trans.Speech and Audio Processing, 2004.

29. C.H. You, S.N. Koh, and R. Susanto, “An MMSE speechenhancement approach incorporating masking properties”, Proc. IEEEICASSP’04, Montreal, Canada, May 2004.

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30. C.H. You, S.N. Koh, and R. Susanto, “Kalman filtering speechenhancement incorporating masking properties for mobilecommunication in a car environment”, Proc. IEEE ICME’04, Taiwan,Jun. 2004.

31. K.A. Furi, W. S. Gan, and Y. K. Chong, “FPGA implementation ofparametric loudspeaker system”, Microsystem and MicroprocessorJournal, vol 28, pp. 261-272, May 2004.

32. K.A. Lee, and W.S. Gan, “Improving convergence of the NLMSalgorithm using constrained subband updates”, accepted, IEEE SignalProcessing Letters, Feb. 2004.

33. K.S. Tan, W.S. Gan, J. Yang, and M.H. Er, “‘An efficient digitalbeamsteering system for difference frequency in parametric array”,Proc. ICASSP’04, Canada, May 2004.

34. K.A. Lee, and W.S. Gan, “Subband adaptive filtering using a multiple-constraint optimization criterion”, Proc. 12th European SignalProcessing Conference, EUSIPCO’04, Austria, Sep. 2004.

35. J. Yang, K. Sha, W.S. Gan, and J. Tian, “Nonlinear wave propagationfor a parametric loudspeaker”, IEICE Trans. Fundamentals. vol. E87-A, no.9. Sep. 2004.

36. J. Yang, W.S. Gan, and S. E. Tan, “Development of virtual soundimaging system using triple elevated speakers”, IEEE Trans. ConsumerElectron., vol. 50, no. 3, Aug. 2004.

37. J. Yang, K. Sha, W.S. Gan, and J. Tian, “Radiation impedancecalculation for arbitrary shaped piston”, Jpn. J. Appl. Phys., vol. 43,no. 9A, Sep. 2004.

38. C. Zhu, W.S. Qi, and W. Ser, “Predictive fine granularity successiveelimination for fast optimal block matching motion estimation”,accepted, IEEE Trans. Image Processing.

39. C. Zhu, X. Lin, L.P. Chau, and L.M. Po, “Enhanced hexagonal searchfor fast block motion estimation”, accepted, IEEE Trans. Circuits Syst.Video Technol., Oct. 2004.

40. C. Zhu, X. Lin, L.P. Chau, H. Ang, and C. Ong, “Efficient inner searchfor faster diamond search”, Signal Processing, EURASIP, vol. 84, no.3, pp. 527-533, Mar. 2004.

41. X.K. Yang, C. Zhu, Z.G. Li, X. Lin, and N. Ling, “An unequal packetloss resilience scheme for video over the internet”, accepted, IEEETrans. on Multimedia.

42. C. Zhu, W. Qi, and W. Ser, “A new successive elimination algorithmfor fast block matching in motion estimation”, Proc. IEEE ISCAS’04,Canada, May 2004.

43. C. Zhu, Y. Gao, and L.P. Chau, “Reducing drift for FGS coding basedon multiframe motion compensation”, Proc. IEEE ISCAS’04, Canada,May 2004.

SIGNAL CLASSIFICATION AND PATTERN RECOGNITION

Objective

The objective of the programme is to excel in research intwo important areas, i.e., (1) Neural Networks, and (2)Computer Vision Technology.

The Neural Network Research Group emphasizes the theoryof neural networks, evolutionary computation, and fuzzylogic, as well as applications to various strategic areas, suchas multimedia, communications, and data mining.

The research focus for the Computer Vision TechnologyResearch Group is on extracting intelligent information fromimages or sequences of images. Some of the current R&Dactivities include:

a) 2D/3D face recognition, expression recognition andmodeling

b) Activity recognition using HMMsc) 2D object tracking from a moving platformd) Smart surveillance

Highlights of Research Activities

Image data can be modeled as a realization of a stochasticprocess and analyzed using Bayesian inference methods.We focused on the grouping of data, including image datausing a fully Bayesian inference algorithm known asReversible Jump Markov Chain Monte Carlo algorithm. Thealgorithm was extended to a class of multidimensional

Gaussian mixture model in which the eigenvectors areidentical. We then developed an RJMCMC algorithm thatsolves the parameter estimation and model selectionproblems jointly. The RJMCMC algorithm was furtherapplied to image segmentation by treating the image pixellabels as a Markov random field and the image data as a3D Gaussian mixture. Soft computing method based onfuzzy clustering is also being investigated. The fuzzyclustering algorithm begins with over-clustering of the datafollowed by fast cluster merging through the newlydeveloped cluster-merging criterion. This algorithm hasbeen applied to image segmentation as well as image

Figure 1: Querying of time-dependent data

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categorization. We also studied the pattern space andconcentrated on the learning of a manifold and itsapplication to face recognition. We extended the ISOMAPalgorithm to handle a fragmented manifold that is oftenencountered in many multi-class pattern recognitionproblems, such as in face recognition. We also developedan ISOMAP-like algorithm with an improved neighborhoodconstruction and relevant component analysis.

In our model, any time-dependent data are interpretedgeometrically as multidimensional points with timedimension. A geometric query is a query solid of any shapespecified by its parameters, location and time. These queriesare formulated with geometric objects and operations overthem to form the query solid changing through time. Thegeometric objects and operations are described withimplicit functions. With the uniform geometric model weintegrate visual mining and querying of time-dependentdata employing 3D visualization tools (see Figures 1-2).We work with the data resulting from numerical simulationstudying physics of DNA in interaction with different solventions and various proteins. Various rules and conditions arespecified, such as strength of bonding forces, charges ofions and other factors that affect the simulation. With thisnumerical model, we are able to obtain data changing Figure 2: Result of the query at one time point

through time that we use as input for our geometric querysystem. It improves the exploration of structures inmolecular dynamics and allows biologists to discoverhidden results.

Assoc Prof Wang LipoProgram Director

[email protected]

Staff Members

Assoc Prof Chua Chin Seng Assoc Prof Chen Lihui Assoc Prof Eric Sung Assoc Prof Chan CheeKeong

Asst Prof Olga Sourina Asst Prof Wan Chunru Asst Prof Yap Kim Hui Assoc Prof Tan Boon Tiong Assoc Prof Chan Kap Luk

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Studies on Vehicle Tracking and Surveillance Chua Chin Seng NTU ($139k)



Vehicle Tracking and Surveillance Chua Chin Seng DSO ($218K)

Image and Multimedia Processing using Computational Yap Kim Hui NTU ($6k)Intelligence



Robust Motion Detection and Tracking For Automatic Surveillance PhD Ren Ying Chua Chin Seng

Face Recognition From 2D and 3D Images PhD Wang Yingjie Chua Chin Seng

3D Human Face Modelling for Dynamic Facial Expression PhD Zhang Yu Eric SungSynthesis

Image Analysis using Random Fields and Bayesian Methods PhD Chen Chibiao Chan Kap Luk

iSearch Framework: Using Web Structure and Summarization MEng Chue WaiLian Chen LihuiTechniques for Web Content Mining

Feature Dimension Reduction for Microarray Data Analysis MEng Shi Chao Chen Lihui

1. L. Wang, S. Li, F. Tian, and X. Fu, “A noisy chaotic neural network forsolving combinatorial optimization problems: stochastic chaoticsimulated annealing”, IEEE Trans. System, Man, Cybern, Part B -Cybernetics, pp. 2119-2125, Aug. 2004.

2. Y. Peng, B.H. Soong, and L. Wang, “Broadcast scheduling in packetradio networks using a mixed tabu-greedy algorithm”, ElectronicsLetters, vol.40, no.6, pp.375-376, Mar. 2004.

3. L. Wang, Ed., Support Vector Machines: Theory and Applications,Springer-Verlag, 2004.

4. Y.P. Tan, K.H. Yap, and L. Wang, Eds., Intelligent MultimediaProcessing with Soft Computing, in press, Springer-Verlag, 2004.

5. L. Chen, and K.H. Yap, “A parametric double regularization approachto adaptive blind image deconvolution”, accepted, IEEE Trans. ImageProcessing.

6. K.H. Yap, “A contextual Hebbian neural learning framework toadaptive blind multiuser detection”, Neural Information Processing-Letters and Reviews, vol. 3, No. 1, pp. 11-17, Apr. 2004.

7. L. Chen, and W. Chue, “Using Web structure and summarizationtechniques for Web content mining”, accepted, Journal of InformationProcessing and Management, 2004.

8. Y. He, and L. Chen, “A threshold criterion, auto-detection and itsuse in MST-based clustering”, accepted, Journal of Intelligent DataAnalysis, 2004.

9. H.P. Lu, Y.C. Shi, A. Kot, and L. Chen, “Binary image watermarkingthrough blurring and biased binarization”, accepted, InternationalJournal of Image and Graphics (IJIG), 2004.

10. X.H. Liu, and C.S. Chua, “Multi-agent activity recognition withobservation decomposed hidden Markov model”, Proc. ACCV’04,Korea, Jan. 2004.

11. Y. Wang, and C.S. Chua, “Face recognition across views from 2Dand 3D images”, Proc. ACCV’04, Korea, Jan. 2004.


12. H. Yu, and C.S. Chua, “Image segmentation and tracking under max/min flow framework,” Proc. ACCV’04, Korea, Jan. 2004.

13. O. Sourina, and D. Liu, “Visual interactive 3-dimensional clustering”,accepted, Computer Graphics & Geometry, ISSN 1811-8992, 2004.

14. O. Sourina, and N. Korolev, ”Geometric querying of time-dependentdata for data mining in molecular dynamics”, Proc. Cyberworlds,Japan, Nov. 2004.

15. O. Sourina, and D. Liu, “Visual interactive 3-dimensional clusteringwith implicit functions”, Proc. IEEE CIS’04, Singapore, Dec. 2004.

16. Y. Yuan, and C. Wan, “Sports content analysis based on edge andcolor information”, Proc. ACCV’04, Korea, Jan. 2004.

17. B. Liu, C.R. Wan, and L. Wang, “Unsupervised gene selection viaspectral biclustering”, Proc. Int. Conf. Neural Networks (IJCNN’04),Hungary, Jul. 2004.

18. J.G. Wang, E. Sung, and R. Venkateswarlu, “EM enhancement of 3Dhead pose estimated by perspective invariance”, Proc. ECCV’04Workshop in Human-Computer Interaction, Czech Republic, May2004.

19. Z. Zhang, K.L. Chan, Y. Wu, and C. Chen, “Learning a multivariateGaussian mixture with reversible jump MCMC algorithm”, in press,Journal of Statistics and Computing, Kluwer Academic Publishers,Netherlands, 2004.

20. X. Xiong, K.L. Chan, and K.L. Tan, “Similarity-driven cluster mergingmethod for unsupervised fuzzy clustering”, Proc. 20th Conf.Uncertainty in Artificial Intelligence (UAI’04), Canada, Jul. 2004.

21. Y. Wu, K.L. Chan, and L. Wang, “Face recognition based ondiscriminative manifold learning”, Proc. 17th Int. Conf. PatternRecognition (ICPR’04), U.K., Aug. 2004.

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BIO-SIGNAL PROCESSING

Objective

The objective of this research programme is to conductresearch in new areas in bio-signal processing. Our researchinterests include genomic signal processing, bioinformatics,and medical imaging.


The research activities of the bio-signal processingprogramme are currently focused mainly on medicalimaging, including functional imaging, fluorescent imageprocessing and ultrasonic data processing.

Dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI) offers the possibility for a more comprehensiveassessment of breast tumors, as it allows for the estimationof parameters that are indicative of the pathophysiology ofthe tumor. A DCE-MRI research protocol has been set upwith collaborators in the National Cancer Center, andpatient studies are ongoing. Initial results show that thehemodynamic maps generated from the DCE-MRI imagescan reveal the extent and location of the breast tumors. Forsuch hemodynamic maps to be directly useful for clinicaldiagnosis, further validation and correlation studies willbe carried out with results from histopathology. Oncevalidated, these hemodyanmic maps obtained from DCE-MRI can be a valuable clinical tool, as they can be used forthe assessment of treatment outcome and for testing of theeffects of drugs.

Figure 1: Example of a parametric map generated from DCE-MRI

Noise suppression is an important aspect in the analysis of3D fluorescent microscopy image sets, as noise is a seriousobstacle to the use of 3D fluorescent microscopy imagesin many image processing algorithms such as deconvolutionalgorithms. Gaussian filter is a standard method for

smoothing 3D image sets, but its use will cause the loss ofsharp details in 3D image sets. A state space approach isdeveloped for noise reduction of 3D fluorescent microscopyimages.

The new method consists of two parts: the decompositionof a three-dimensional image set, and the balanced statespace realizations of finite one-dimensional sequences. Thenew algorithm has been successfully applied to a 3Dfluorescent microscopy image set of a mouse T cell.

Figure 2. Comparison of the exact estimate and the approximated estimateof the image set obtained via the proposed algorithm. (a1),(a2),(a3),(a4)show the 12th frame of the exact estimate and the approximated estimate,and the difference between them and the 12th frame of the original imageset in the X-Y plane.

Ultrasound is the second most widely used medical imagingmodality (next to X-day) and has advantages in non-invasiveimage formation, real time data acquisition, portability andlow cast. However, ultrasound imaging suffers from a kindof noise called speckle caused by the backscatteredinterference due to unresolved tissue inhomogeneity. Theobjective of this project is to investigate and developmethods and algorithms for speckle reduction and highquality ultrasound images. We carry out both theoreticaland experimental investigations. In contrast to the existingalgorithms which process ultrasound envelope images andhave difficulties in separating the tissue information fromspeckles, the novel concepts and methods of this projectare to apply multidimensional and optimal deconvolutionand multiresolution decomposition techniques to radiofrequency echo signal processing for speckles separationand reduction.

Figure 3: Experimentalultrasound signals.

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Assoc Prof Lin ZhipingProgram [email protected]

Staff Members

Assoc Prof Ser Wee Assoc Prof Ma Kai-Kuang Assoc Prof Zhang Cishen Assoc Prof Chua Chin Seng

Assoc Prof Wang Lipo Assoc Prof Chen Lihui Asst Prof Mao Kezhi Asst Prof Wan Chunru Asst Prof Koh Tong San



Development of Algorithms for High Performance Digital Zhang Cishen NTU ($97k)Image Processing with Applications to Medical Imaging

In-Vivo Assessment of Neo-Angiogenesis and Microcirculatory Koh Tong San BMRC ($158.5k)Characteristics of Breast Carcinoma using DynamicContrast-Enhanced Magnetic Resonance Imaging withCorrelations to Histopathology



Multidimensional Signal Processing for NMR Data Lin Zhiping AcRF ($28k)



Feature Dimension Reduction for Microarray Data Analysis MEng Shi Chao Chen Lihui

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Courses

Name(s) of Instructor(s) Title of Course Course Conducted for

Lin Zhiping Introduction to Biomedical Signal Processing Centre for ContinuingEducation, NTU


1. D.L.H. Cheong, T.C.C. Lim, and T. S. Koh, “Dynamic contrast-enhanced CT imaging of intracranial meningioma: a comparison ofdistributed and compartmental tracer-kinetic models – initial results”,in press, Radiology, 2004.

2. R.J. Ober, X. Lai, Z. Lin, and E. S. Ward, “State space realization of athree-dimensional image set with application to noise reduction offluorescent microscopy images of cells”, accepted, MultidimensionalSystems and Signal Processing.

3. F. Chu, and L. Wang, “Bio-medical data mining using neuralnetworks,” accepted, Encyclopedia of Data Warehousing and Mining,J. Wang, Ed., Information Sciences Publishing.

4. F. Chu, G. Jin, and L. Wang, “Cancer diagnosis and protein secondarystructure prediction using support vector machine”, in L. Wang, Ed.,Support Vector Machines: Theory and Applications, Springer-Verlag,2004.

5. B. Liu, C. Wan, and L. Wang, “Unsupervised gene selection viaspectral biclustering”, Proc. Int. Conf. Neural Networks, Hungary,Jul. 2004.

6. C. Shi, and L. Chen. “High dimensional gene expression datadimension reduction”, Proc. IEEE Conf. Cybernetics and IntelligentSystems, Singapore, Dec. 2004.

7. W. S. Yeoh, and C. Zhang, “Subband characteristics of ultrasoundecho envelope”, Proc. 8th Int. Conf. Control, Automation, Roboticsand Vision, China, Dec., 2004.

8. M. Yan, and C. Zhang, “Tilted plane Feldkamp type reconstructionalgorithm for spiral cone beam CT”, Proc. 8th Int. Conf. Control,Automation, Robotics and Vision, China, Dec., 2004.M. Yan, and C. Zhang, “Evaluation of slice sensitivity profiles forTPRF algorithm”, Proc. 1st Int. Bioengineering Conf., Singapore, Sep.2004.

STATISTICAL AND ADAPTIVE SIGNAL PROCESSING

Objective

The group’s research focus is in a broad range of statisticaland adaptive signal processing applications. Primarily, themembers in the research group work on areas related tocommunication systems. Other areas of research in whichthe members are active include signal processing for activeSONAR, active noise control, FPGA in high speed signalprocessing systems, and radar signal processing for maritimesecurity applications. The objectives of the programme arein exploring statistical and adaptive signal processingtechniques, conducting research and development workin related areas using state-of-the-art DSP technology, andpromoting collaborations with local/overseas universities,research institutes, and industries.


Space-Time Signal Processing using Sensor ArrayDOA estimation is one of the position location techniquesrelated to Digital beamforming. In this project, a 3-elementspace-time signal processing test-bed was constructed withthe objectives of (a) developing new algorithms which arerobust and efficient to implement, (b) using the platform toverify performance of the new algorithms, and (c)

developing application prototypes. We have demonstratedthe use of the three-channel Smart antenna system test-bed to perform real-time direction of arrival (DOA)estimation with consideration of system imperfection.Currently, conventional DOA algorithms such as Caponand MUSIC (Multiple Signal Classification) areimplemented in the system. The project roadmap includesthe implementation of adaptive beamforming and space-time block codes algorithms on the system’s Xilinx Virtex IIFPGAs.

The smart antenna system testbed: (left) RF subsystem, (Right) basebandsubsystem

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Microphone Array ResearchSmart e-Textile Uniform: An intriguing example of makingthe human-machine interface a “natural” one would bewearable microphone arrays used for the detection andclassification of acoustic signals in visually impaired orcluttered environments. Another example would be arapidly deployable conformal array for the detection andlocalization of distant acoustic sources. All this wouldpreferably be embedded in electronic textiles making thesesensor systems highly flexible and mobile. However, as themicrophones are embedded in a flexible fabric, variousfactors would contribute to the degradation of theirperformance. The project has focused on the developmentof a mathematical model to simulate the perturbations ofthe microphone locations. This has produced benchmarkingof the standard delay and sum beamformer.

Microphone array for iRoom: The objective is to developan array of microphones, which can be used to track thelocation of a desired speaker and form a beam to the desiredspeaker to pick up his/her voice and suppress theinterference. The design aims for real-time implementationin places such as auditoriums and conference rooms, andin healthcare applications by overcoming some inherentdifficulties, e.g. reverberation, sensor random mismatch,signal source movement, etc.. Robust methods for weaksignal detection and identification for health caring Microphone Array Setup for iRoom

Assoc Prof Saman SAbeysekera

Program [email protected]

Staff Members

Asst Prof Braham Barkat Assoc Prof Bi Guoan Asst Prof CharoensakCharayaphan

applications were investigated. A real-time system foriRoom which automatically tracks and steers a videocamera to the speaker has been developed.

FPGA development for Signal ProcessingAn efficient single FPGA chip for Software Defined Radio(SDR) FM demodulator has been developed. The systemconsists of a band-pass Sigma-Delta (Σ-∆) based IF stageseparating the inphase and quadrature (I and Q) signalsusing a single circuit path and eliminating I-Q imbalancedue to component mismatch. FM demodulator has beenefficiently implemented using a recursive Kalman filter, andthe pulse-pair technique.

Assoc Prof Foo Say Wei Assoc Prof Lew Henry Assoc Prof Li Gang Assoc Prof Li Kwok Hung

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Assoc Prof SirajudeenGulam Razul

Assoc Prof Ser WeeDirector, CSP

Assoc Prof Soon Ing Yann Asst Prof Teh Kah Chan

Asst Prof Wan Chunru Asst Prof Yang Jun Mr Yu ZhuliangResearch Engineer

Mr Zou QiyueResearch Engineer



Space-Time Signal Processing using Sensor Array Ng Boon Poh (NTU) I2R ($996k)Susanto Rahardja (I2R) NTU ($210k)

Advanced Detection, Localization and Classification Using Wan Chunru DSTA ($100k)Randomly Distributed Acoustic Sensor Network

Wideband Array Signal Processing Ng Boon Poh DSO ($103k)NTU ($103k)

Location Awareness, Human Behaviour and Event Ser Wee A*STAR ($50k)Analysis for a Health Care Based Intelligent Room

Time-reversal Mirror and its Application for Audible Yang Jun NTU ($3k)Range Acoustics

Smart e-Textile Uniform: Microphone Array Signal Processing Lew Henry I2R ($62k)

Wireless Communication Laboratory, NICT Singapore Rep Office Ser Wee NICT ($22k)

Topics in Audio-Visual Enhancement and Understanding Systems Ser Wee I2R ($221k)



Advanced Signal Processing for Active Sonar Systems Wan Chunru DSO ($77k)NTU ($77k)

Simulation of Targets & Backgrounds in Sonar/Radar Systems Lew Henry NTU ($35k)

Assoc Prof Lin Zhiping Assoc Prof Lu Yilong Assoc Prof Ng Boon Poh Assoc Prof Ong Patrick

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Space-time Communication over Wireless Multiple Access PhD Ling Chong Li Kwok HungChannels

Efficient Solutions for High-Resolution Direction-of-Arrival PhD Li Minghui Lu YilongEstimation of Multiple Emitters

Optimum DOA Algorithms for Narrowband and Wideband MEng Bao Haoji Ng Boon PohApplications

Optimal Detection and Parameter Estimation for Sinusoidal PhD Wang Qing Wan ChunruSignals

Seminars

Name(s) of Speaker(s) Title of Seminar Affiliation

Ask Law Smart Antenna SensorCom Inc., Annapolis MD, USA.

Ge Hongya Transceiver Design with Space Time Block New Jersey Institute of Technology, NJ,Codes for Wireless Communications USA.

1. S.S. Abeysekera, “Implementation of a zero-forcing residue equalizerusing a laguerre filter architecture”, Proc. ISCAS’04, Canada, May2004.

2. S.S. Abeysekera, and M. R. Sharif, “Efficient time delay and dopplerstretch estimation of wideband sonar signals using the radonambiguity transform”, Proc. 8th World Multiconference on Systemics,Cybernetics and Informatics (SCI’04) USA, Jul. 2004.

3. S.S. Abeysekera, “Comparison of various threshold modulationtechniques used in pulse stuffing synchronizers”, accepted, SignalProcessing, Elsevier – Netherlands, 2004.

4. D. Yu, F. Sattar, and B. Barkat, “Multiresolution fragile watermarkingusing complex chirp signal for content integrity verification”, Proc.ICIP’04, Singapore, Oct. 2004.

5. Y. Ju, and B. Barkat, “A new efficient chirp modulation technique formulti-user access communications systems”, Proc. ICASSP’04,Canada, May, 2004.

6. B. Barkat, and K. Abed-Meraim, “Algorithms for blind componentsseparation and extraction from the time-frequency distribution oftheir mixture”, accepted, EURASIP Journal of Applied SignalProcessing.

7. F. Zhang, and G. Bi, “Harmonic transform”, Proc. IEE Vision, Imageand Signal Processing, 2004.

8. G. Zhang, G. Bi, and L. Zhang, “Blind multiuser detection forasynchronous MC-CDMA system without channel estimation”, IEEETrans. Vehicular Technology, vol. 53, no. 4, pp. 1001 – 1013, Jul.2004.

9. G. Zhang, G. Bi, and L. Zhang, “Group-blind intersymbol multiuserdetection for downlink CDMA with multipath”, accepted, IEEE Trans.Wireless Commun., 2004.

10. C. Charoensak, and S. S. Abeysekera, “FPGA implementation ofefficient Kalman band-pass sigma-delta filter for application in FMdemodulation”, Proc. IEEE Int. SOC Conf., USA, Sep. 2004.

11. S.W. Foo, Y. Lian, and L. Dong, “Recognition of visual speechelements using adaptively boosted hidden Markov models”, IEEETrans. Circuits Syst. Video Technol., vol. 14, no. 5, pp. 693 - 705,May 2004.

12. L. Dong, S.W. Foo, and Y. Lian, “A two-channel training algorithmfor hidden Markov model and its application to lip reading”,accepted, EURASIP Journal on Applied Signal Processing, 2004.

13. L. Dong, S.W. Foo, and Y. Lian, “Level-building on AdaBoost HMMclassifiers and the application to visual speech processing”, Proc.IEICE Trans. Information and Systems, Nov. 2004.


14. H. Lew, “Broadband echoes from underwater targets”, accepted,International Journal of Computational Engineering Science, 2004.

15. H. Lew, Z.Y. Yan, and K.C. Hung, “Application of target modelling todoppler and FM processing”, Proc. Int. Conf. Scientific andEngineering Computation 2004, Singapore, Jun. 2004.

16. J. Zhou, and G. Li, “Plain gradient based direct frequency estimationusing 2nd order constrained adaptive IIR notch filter”, Electron.Letters, vol. 40, no. 5, pp. 351 - 352, Mar. 2004.

17. C. Ling, K.H. Li, and A.C. Kot, “On decision-feedback detection ofdifferential space-time modulation in continuous fading”, accepted,IEEE Trans. Commun., 2004.

18. C. Ling, K.H. Li, and A.C. Kot, “Gallager bounds for space-timecodes”, Proc. IEEE Communication Theory Workshop 2004, Italy,May 2004.

19. Y. Liu, and Z. Lin, “Active array beamforming using the frequency-response masking technique”, Proc. ISCAS, Canada, May 2004.

20. M.H. Li, and Y.L. Lu, “Improving the performance of GA-ML DOAestimator with a re-samping scheme”, accepted, Signal Processing,2004.

21. M.H. Li, and Y.L. Lu, “Accurate localization of multiple sources usinggenetic approach”, accepted, Wireless Communications and MobileComputing, 2004.

22. C.Z. Ma, B.P. Ng, H. Bao, and X.B. Yang, “Robust adaptivebeamforming for large steering angle error”, Proc. ISCAS’04, Canada,May 2004.

23. A.G. Feng, B.P. Ng, and Q.Y. Yin, “Space-time coding/decoding withadaptive transmit beamforming in semi-correlated MIMO wirelesscommunication systems”, Proc. IEEE Vehicular TechnologyConference, U.S.A., Sep. 2004.

24. X.B. Yang, and B.P. Ng, “Microstrip yagi array on air substrate”, Proc.IEEE APS’04, USA, Jun. 2004.

25. C. Sun, N.C. Karmakar, K.S. Lim, and A.G. Feng, “Combiningbeamforming with alamouti scheme for multiuser MIMOcommunications”, Proc. IEEE Vehicular Technology Conference, USA,Sep. 2004.

26. P. Wang, and W. Ser, “A novel pseudo-error monitor”, EURASIPJournal on Applied Signal Processing, vol. 2004, no. 3, pp. 412-417,Mar. 2004.

27. M. Zhang, H. Lan, and W. Ser, “On comparison of online secondarypath methods with auxiliary noise”, accepted, IEEE Trans. Speechand Audio Processing, 2004.

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28. F. Yang, and W. Ser, “Adaptive semi-blind channel estimation forOFDM systems,” Proc. VTC’04-Spring, Italy, May 2004.

29. H.W. Fung, A.C. Kot, K.H. Li, and K.C. Teh, “Parameter estimation ofa real single tone from short data records”, Signal Processing, vol.84, no. 3, pp. 601-617, Mar. 2004.

30. Y. Yang, C. Sun, and C. R. Wan, “Broadband constant beamwidthbeamforming for circular acoustic sensor arrays”, accepted, AppliedAcoustics (Elsevier), 2004.

31. Q. Wang, and C.R. Wan, “A novel CFAR tonal detector using phasecompensation”, accepted, IEEE Journal of Oceanic Engineering, 2004.

32. M.C. Liu, C.R. Wan, and L. Wang, “Intelligent content-based audioclassification and retrieval for web applications”, in press, Y.Q. Zhang,A. Kandel, T.Y. Lin, and Y.Y. Yao, Eds., Computational WebIntelligence: Intelligent Technology for Web Applications, Series inMachine Perception and Artificial Intelligence, vol. 58, WorldScientific, 2004.

33. J. Yang, K. Sha, W.S. Gan, and J. Tian, “Nonlinear wave propagationfor a parametric loudspeaker”, IEICE Trans. Fundamentals. vol. E87-A, no.9. Sep. 2004.

34. J. Yang, W.S. Gan, and S.E. Tan, “Development of virtual soundimaging system using triple elevated speakers”, IEEE Trans. ConsumerElectron. vol. 50, issue 3, Aug. 2004.

35. J. Yang, K. Sha, W.S. Gan, and J. Tian, “Radiation impedancecalculation for arbitrary shaped piston”, Jpn. J. Appl. Phys., 43 (9A),Sept. 2004.

36. Q. Zou, Z.L. Yu, and Z. Lin, “A robust algorithm for linearlyconstrained adaptive beamforming”, IEEE Signal Processing Letters,vol. 11, pp. 26-29, Jan. 2004.

37. Z.L. Yu and M.H. Er, “A robust adaptive blind multichannelidentification algorithm for acoustic applications”, Proc. IEEEICASSP’04, Canada, May 2004.

38. Z.L. Yu and M.H. Er, “Blind multichannel identification for speechdereverberation and enhancement”, Proc. IEEE ICASSP’04, Canada,May 2004.

DSP SYSTEM DESIGN & DEVELOPMENT

Objective

The objective of this research programme is to design anddevelop real-time signal processing system prototypes andprocessing cores of select signal processing technologiesfor intelligent system, biomedical, homeland security andminiaturisation applications.


The group continues the development efforts to miniaturisethe multi-channel video graphics rendition engine into asmall form factor module. Application programme interfacewith the embedded processor host and real-time operatingsystem driver development are in progress.

The group has completed the development of the firstdemonstration system for the embedded reconfigurablemultimedia processor. The demonstration system wasintegrated successfully with I2R’s application software tomeet the milestone requirement for the Smart E-textileUniform project funded by DSTA. The development workfor the next stage platform is progressing.

The group continues to develop the real-time smart antennasystem to provide a test-bed for research in signalenhancement and interference suppression, directionfinding and MIMO channel estimation and tracking forOFDM system. A collaboration project with NICT (formallyCRL) is ongoing for software-defined radio application usingthe real-time smart antenna system.

A Fingerprint Quality Analysis engine has been developedto analyse the fingerprint image obtained from a typicalfingerprint identification system. It can automatically rejecta low-quality image capture and can also be used toevaluate the performance of the fingerprint sensorsproduced by different manufacturers. A draft report on

fingerprint analysis is also being done together with I2Rand the completed report will be submitted to ITSC/ISOfor their consideration to be used as a standard document.

On human thermogram analysis project, the infraredimaging group is customising the imaging setup anddevising processing techniques for the intended biomedicalapplication.

Figure 1: Fusion of infrared and visible video imagery for biomedicalapplication

Figure 2: Human traffic estimator & analyser prototype system hTea1deployed in a park

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Mr Toh Hock LyeProgram [email protected]

Staff Members

Mr Lu DaweiResearch Engineer

Mr Wang PengResearch Engineer

Mr Yang FanResearch Engineer

Mr Lim EyungResearch Engineer

Mr Zou QiyueResearch Engineer



Real-time Multi-channel Display Processor Toh Hock Lye MNC ($1m)

Embedded Re-configurable Multimedia Signal Processor Toh Hock Lye I2R ($34k)

Distributed Video Processing Engine Toh Hock Lye TTCS ($68k)

Receiver Node for Wireless Temperature Tracking System Toh Hock Lye Cadi ($19k)



Development of hTea1 Prototype System Toh Hock Lye NParks ($50k)

Selected Publications in 20041. P. Wang, and W. Ser, “A novel pseudoerror monitor”, EURASIP Journal

on Applied Signal Processing, vol. 2004, no. 3, pp. 412-417, Mar.2004.

2. E. Lim, K.A. Toh, P.N. Suganthan, X. Jiang, and W.Y. Yau, “Fingerprintimage quality analysis”, Proc. IEEE Int. Conf. Signal Processing,Singapore, Oct. 2004.

3. F. Yang, and W. Ser, “Adaptive semi-blind channel estimation forOFDM based WLAN”, Proc. VTC’04-Spring, Milan, May 2004.

CENTRE FOR SIGNAL PROCESSING

Documents

Transcript of CENTRE FOR SIGNAL PROCESSING