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LSMS 2007
Plenary speakers

Plenary speakers

Prof. George W Irwin, FREng, MRIA, FIEEE Queen's University Belfast, UK

Prof. Cheng Wu, Tsinghua University, China, Member of Chinese Academy of Engineering, Former National Chief Scientist on Automation Technology


Prof. Tong Heng Lee, The National University of Singapore, Singapore


Dr. Gordon J. Harris, Director of 3D Imaging Service and Radiology Computer Aided Diagnostics Laboratory, Massachusetts General Hospital


Prof. John V McCanny, UK


Prof. Er-Wei Bai, The University of Iowa, Iowa City, USA


Professor Tom Heskes, Radboud University Nijmegen, Netherland

Professor Shuzhi Sam Ge, Department of Electrical and Computer Engineering, the National University of Singapore


keynote speech



Prof George W Irwin

Intelligent Systems and Control

School of Electronics, Electrical Engineering and Computer Science

Queen's University Belfast UK

Professor George Irwin leads the Intelligent Systems and Control Research group and is Director of the University Virtual Engineering Centre at Queen University Belfast. He has been elected Fellow of the Royal Academy of Engineering and Member of the Royal Irish Academy and is a Chartered Engineer, an IEEE Fellow, a Fellow of the IEE and a Fellow of the Institute of Measurement and Control. Prof Irwin's research covers identification, monitoring, and control, including neural networks, fuzzy neural systems and multivariate statistics and has published over 350 research papers and 12 edited books. He is currently working on wireless networked control systems, fault diagnosis of internal combustion engines and novel techniques for fast temperature measurement and was Technical Director of Anex6 Ltd, a spin out company from his group specialising in process monitoring. He has been awarded a number of prizes including four IEE Premiums, a Best Paper award from the Czech Academy of Sciences and the 2002 Honeywell International Medal from the Institute of Measurement and Control. International recognitions include Honorary Professor at Harbin Institute of Technology and Shandong University, and Visiting Professor at Shanghai University. George Irwin is a former Editor-in-Chief of the IFAC Journal Control Engineering Practice and past chair of the UK Automatic Control Council. He currently chairs the IFAC Publications Committee and serves on the editorial boards of several journals.



Prof. Cheng Wu

Tsinghua University, China, Member of Chinese Academy of Engineering, Former National Chief Scientist on Automation Technology

Academic Posts
Professor, Department of Automation, Tsinghua University
Member, Chinese Academy of Engineering
Director, China National Engineering Research Center for Contemporary Integrated Manufacturing Systems
Member, Advisory Board of IEEE Transactions on Automation Science and Engineering
Member, Editorial Board of International Journal of Robotics and Computer Integrated Manufacturing
Member, Editorial Board of International Journal of Industrial and Management Optimization
Research Interests
System Integration
Modeling, Planning, Scheduling and Optimization of Complex Industrial Systems
Professional Experiences
1967- Teaching Assistant, lecturer, associate professor, professor, Tsinghua University,Beijing
1962, Bachelor, Department of Electrical Engineering, Tsinghua University
1966, Master, Department of Electrical Engineering, Tsinghua University
Awards & Honors
1991, 1996, 2001, First Prize of 863 National Scheme
1999, Second Prize of Science & Technology Progress Award, Ministry of Education
1997, Award of National Outstanding Teacher
1994, Science & Technology Progress Prize, Ho Leung Ho Lee Foundation
1994, "University LEAD Award" for CIM excellence by the SME (Society of Manufacturing Engineering) of USA for his achievements on CIMS research and applications


Inventing and Developing an Automated Haematopoietic Stem Cells Harvesting Machine; and other Recent Advances in Intelligent Precision Modeling, Simulation & Control for Life Sciences Developments

T.H.Lee, B.A. (Hons I) Cambridge; M.Engrg NUS; Ph.D. Yale

Professor & Cluster Head (Control Systems)

Professor in the Graduate School, NUS NGS

Dept of ECE, NUS

Dy Editor-in-Chief, IFAC Mechatronics Int Jnl

E-mail: ELELEETH@nus.edu.sg


The human placenta and umbilical cord blood (UCB) provide a rich source of highly-proliferative haematopoietic stem cells (HSCs) for many clinical uses with advantages over traditional sources like the bone marrow and periphery blood. However, the current constraint with this source of HSCs is the inadequate number of HSCs cells which can be harvested in a single collection using current approaches which render a large number of collections unusable on their own, even for paediatric patients. The large reservoir of useful HSCs within the placenta has to be discarded upon the delivery of the placenta out of the maternal body. A novel design, involving mechanical, electronics and control components, seeks to create an artificial uterus force to harvest the HSCs. This paper will present the development of this automated device to enable more effective harvesting of HSCs from placentas, upon the discharge of placentas after deliveries. Comprehensive results, in terms of mononucleated cells (MNCs) count and CD34+ cells count, will be furnished to verify the effectiveness of the developed system, over the other current approaches. (Note: This invention was a winner of the IEEE ICMA 2009 Best Paper in Automation Award. It is patented in U.S.A., Europe, Japan and Singapore. A company, Dynamed Hi-Tech Medical Instruments, has licensed it and will market it in 2010.)

Additionally, the paper will also present recent advances in Intelligent Precision Modeling, Simulation & Control for Life Sciences developments; including recent research and development work in developing portable precision Tissue Micro-Arrayers for Tissue Repositories.


Prof. Tong Heng Lee

 The National University of Singapore, Singapore

T. H. Lee received the B.A. degree with First Class Honours in the Engineering Tripos from Cambridge University, England, in 1980; and the Ph.D. degree from Yale University in 1987. He is a Professor in the Department of Electrical and Computer Engineering at the National University of Singapore (NUS); and also a Professor in the NUS Graduate School, NUS NGS. He was a Past Vice-President (Research) of NUS.

Dr. Lee's research interests are in the areas of adaptive systems, knowledge-based control, intelligent mechatronics and computational intelligence. He currently holds Associate Editor appointments in the IEEE Transactions in Systems, Man and Cybernetics; IEEE Transactions in Industrial Electronics; Control Engineering Practice (an IFAC journal); and the International Journal of Systems Science (Taylor and Francis, London). In addition, he is the Deputy Editor-in-Chief of IFAC Mechatronics journal.

Dr. Lee was a recipient of the Cambridge University Charles Baker Prize in Engineering; the 2004 ASCC (Melbourne) Best Industrial Control Application Paper Prize; the 2009 IEEE ICMA Best Paper in Automation Prize; and the 2009 ASCC Best Application Paper Prize. He has also co-authored five research monographs (books), and holds four patents (two of which are in the technology area of adaptive systems, and the other two are in the area of intelligent mechatronics). He has published more than 300 international journal papers.

Dr. Lee was an Invited Panelist at the World Automation Congress, WAC2000 Maui U.S.A.; an Invited Keynote Speaker for IEEE International Symposium on Intelligent Control, IEEE ISIC 2003 Houston U.S.A.; an Invited Keynote Speaker for LSMS 2007, Shanghai China; an Invited Expert Panelist for IEEE AIM2009; and an Invited Plenary Speaker for IASTED RTA 2009.


The 3D Imaging Service at Massachusetts General Hospital: 11 Years Experience

Dr. Gordon J. Harris
Director, 3D Imaging Service, and Radiology Computer Aided Diagnostics Laboratory (RAD CADx LAB), Massachusetts General Hospital
Associate Professor of Radiology, Harvard Medical School.


In 1999, we set out to create a radiology three-dimensional (3D) imaging service at Massachusetts General Hospital (MGH). Our goal was two-fold: first, to integrate 3D image post-processing capabilities, computer-aided diagnosis (CAD), and quantitative analysis into the routine clinical workflow; and to create an infrastructure generally more conducive to the transfer of new image-processing technologies from the research realm into clinical use. Initially, we found that although our institution possessed several 3D imaging workstations, they were used only occasionally for research purposes and, when a clinical request for 3D post-processing was made, the staff lacked the expertise and experience to fulfill those requests.
Three-dimensional image processing begins with a stack of 2-dimensional images, assembles them into a 3-D volume, and then manipulates them in a variety of ways. There are numerous techniques for image manipulation that can be performed at a 3D workstation, including maximum intensity projection (MIP), volume rendering (VR), endoluminal views, segmentation, and functional imaging; however, the challenge is selecting the technique that provides the most clinical value. To that end, the staff of our 3D Imaging Service has undergone extensive training. In addition, together with radiologists and referring physicians, our staff has crafted 3D protocols with standard views for each imaging modality (magnetic resonance [MR], computed tomography [CT], ultrasound [US]) and clinical application, which have been selected in order to provide reliable consistency and optimal clinical value, both important features for any clinical service.
Selection of an appropriate 3D image analysis technique often depends on the perspective of the physician: for diagnosis, radiologists may prefer a technique such as MIP in which all of the information is present and none has been removed by the computer, whereas a surgeon may prefer a more anatomically realistic view for surgical planning, such as a VR image in which some of the information has been segmented out (Figure 1). For applications such as vascular imaging, it is not uncommon to pair more than one technique: for example, VR to assess the geometry of any vascular lesions together with curved multiplanar reformatting (MPR) to assess for stenoses or occlusions (Figure 2).
For diagnostic vascular imaging, 3D image analysis has allowed us to almost completely replace the more expensive and invasive catheter angiogram with CT angiography (CTA) or MR angiography (MRA). Moreover, with CTA and MRA, it is possible to view not only the vessels, but also the surrounding parenchyma and other nearby structures. One example of the benefit of 3D vascular imaging is in evaluation of living renal donors, where the transplant surgeon requires a complete picture of the number and geometry of the renal arteries, veins, and ureters of the donor. For this application, we have been able to replace two more expensive and invasive exams, catheter angiography (involving anesthesia and higher risk of complications) plus intravenous pyelography (IVP), with a single outpatient CT exam involving CTA plus delayed-phase CT urography. The healthy donor is spared from expensive, invasive procedures and, instead, receives a simple, outpatient, contrast-enhanced multiphasic CT scan capable of gathering all of the necessary information with minimal risk.
Computer-aided segmentations are used to disarticulate structures within an image, which can greatly assist in pre-surgical planning, as in the case of the repair of complex fractures (Figure 3). Segmentation can also facilitate the assessment of vessels; for example, in cardiac imaging, segmenting out some of the adjacent structures can provide a clear view of the vessels from their origin to the apex. Segmentation can also be useful for accurate determination of brain tumor volumes, particularly in the case of irregularly shaped tumors where a linear measure provides insufficient information. Another use of quantitative segmentation is in the accurate determination of the volume of a donor liver to determine if it is large enough to supply both the donor and the recipient. Previously, this determination was based on a fairly rough estimation; however, it is now possible to more precisely determine the volume of the liver and perform a virtual resection, potentially increasing the success rate of liver transplantation. Moreover, this technique has been automated and can now be performed in less than 10 minutes.
In the 3D Imaging Service, we also perform functional imaging using CT and MR perfusion, and functional MRI (fMRI). CT perfusion can be used to assess patients for stroke by measuring various hemodynamic parameters: for example, an increased mean transit time and decreased cerebral blood flow indicate the presence of an infarcted area. Functional MRI plays a role in neurosurgical planning, helping the surgeons to determine the proximity of the surgical target to critical sensory, motor, and language areas. The use of fMRI in this way can reduce the amount of time the surgeon spends doing intraoperative cortical mapping.
At MGH, our full-time 3D Imaging Service is now capable of performing 3D imaging upon request with rapid turnaround time. We are fully integrated with the hospital's picture archiving and communications systems, billing, and information systems. Our volume has continued to grow each year: When we started in February 1999, we performed an average of two exams per day, and now we perform approximately 120 exams per day, or 2,500 per month. Our clinical staff is currently comprised of approximately 13 individuals, including 3-D technologists, image analysts, operations and technical managers, and billing coordinators, and we utilize a wide variety of different types of workstations from many different vendors for different applications. We primarily perform CTA and MRA, nonvascular CT and MR exams, and 3D US, with approximately half being neuro-based and the remainder being vascular, as well as other applications. We currently process approximately 10% of the CT examinations and 20% of the MRI examinations at MGH.
In summary, 3D image analysis provides more comprehensive and realistic patient evaluation. Quantitative analysis with CAD can provide more accurate, reliable assessment, staging, and treatment planning, ultimately improving patient care, increasing clinical confidence, and reducing the time, cost, and invasiveness of procedures. We recognize that the level of commitment of resources needed to develop an in-house 3D imaging service may not be practical for all imaging centers; therefore, through improvements in networking and communications, we are hoping to expand our CAD and 3D services to help support the needs of many hospitals and imaging centers.



Dr. Gordon J. Harris
Director, 3D Imaging Service, and Radiology Computer Aided Diagnostics Laboratory (RAD CADx LAB), Massachusetts General Hospital
Associate Professor of Radiology, Harvard Medical School.

Dr. Harris is Director of the 3D Imaging Service, and the Radiology Computer Aided Diagnostics Laboratory (RAD CADx LAB) at the Massachusetts General Hospital, and the Tumor Imaging Metrics Core of the Dana-Farber/Harvard Cancer Center, and Associate Professor of Radiology at Harvard Medical School. Dr. Harris received his Ph.D. from Johns Hopkins Medical Institutions in Radiation Health Sciences and a B.S. in Electrical Engineering from Lafayette College. After graduate school, Dr. Harris spent one post-doctoral year and two years as junior faculty at Johns Hopkins School of Medicine. After four subsequent years as Director of the Neuroimaging Research Laboratory at New England Medical Center, Dr. Harris joined the faculty at MGH in 1997 and began a new 3D Imaging Service for clinically-oriented imaging. His primary research interests include structural and functional brain imaging research in psychiatric and neurologic illnesses including stroke and alcoholism, as well as quantitative tracking of tumors for clinical care and clinical trials. Dr. Harris has published and lectured extensively on medical imaging.



Professor John V McCanny
Institute of Electronics Communications and Information Technology (ECIT),
Queen's University Belfast,
Northern Ireland Science Park,
Belfast BT3 9DT, UK


Competitiveness in a global economy is highly dependent on our ability to create new knowledge that in turn drives new innovations and new market opportunities. A key aspect of this is a nation's ability to captilalise on and create new products and services from its research base and the role that business focused Research Centres, can play a key role in enabling effective Knowledge Transfer, between the academia and industry. This plenary presentation will give an overview of how these challenges are being addressed at Queen's University's Institute of Electronics Communications and Information Technology - ECIT.

ECIT, which opened in 2004 as a result of a $70M investment, is the research flagship for the Northern Ireland Science Park and brings together specialists in complementary fields of Electronics and Computer Science. It has a unique environment that couples internationally leading research with very strong industrial engagement locally, nationally and internationally. Its major themes are (a) Digital and Wireless Communications Technology and (b) Secure Information Technology, following a recent $50M investment funded by the UK's national Engineering and Physical Sciences Research Council (EPSRC) and by the UK's national Technology Strategy Board (TSB)

This talk will present an overview of ECIT's activities including new "Open Innovation" models that juxtapose speculative academic research with engineering staff that have many years industrial experience. An overview will also be given of ECIT's wider role in helping to create new high technology industry on the Northern Ireland Science Park and its transfer to technology to national and international industry. This includes and environment that provides access to national and international entrepreneurs, business angels, Venture Capitalists s and Intellectual Property Lawyers as well as to leading researchers nationally and internationally.


Professor John V McCanny  


Professor John McCanny is an international authority on special purpose silicon architectures for Signal and Video Processing. He is a Fellow of the Royal Society (of London), the UK Royal Academy of Engineering, the Irish Academy of Engineering, the IEEE and Engineers Ireland. He is also a Member of the Royal Irish Academy.

He is recipient of numerous honours/awards including a UK Royal Academy of Engineering Silver Medal (1996), an IEEE Millennium Medal, the Royal Dublin Society/Irish Times Boyle medal (2004) and the IET's Faraday medal (2006). He has co-founded two successful high technology companies, Amphion Semiconductor Ltd. (later acquired by Conexant, then NXP) and Audio Processing Technology Ltd. In 2002 he was awarded a CBE (Commander of the Order of the British Empire) for his "Contributions to Engineering and Higher Education".

He has published 5 research books, 350 peer reviewed research papers and holds over 20 patents. He is currently Director of the Institute of Electronics, Communications and Information Technology (ECIT) at Queen's University Belfast and also Head of the School of Electronics, Electrical Engineering and Computer Science.

He has served on numerous Royal Society committees and chaired of Sectional Committee 4 (Engineering) during 2005 and 2006. He is currently is a Member of the Council of the Royal Academy of Engineering and also serves on its International Committee. He has been a board member for Ireland's Tyndall National ICT research centre since its was established in 2004, is currently a member of EPSRC's ICT Strategic Advisory Team and on the advisory board of the German Excellence Centre on "Ultra High-Speed Mobile Information and Communication" (UMIC) based at the University of Aachen.

He was heavily involved in developing the vision that led to the creation of the Northern Ireland Science Park and the creation of its ECIT research flagship. He also led the initiative that created the £30M Centre for Secure Information Technology (CSIT) which is based at ECIT.

He holds a Bachelors degree in Physics from the University of Manchester, a PhD in Physics from the University of Ulster and was awarded a DSc (higher doctorate) in 1998 in Electrical and Electronics Engineering from Queen's University Belfast.


Adaptive Bolus Chasing Computed Tomography Angiography


Er-Wei Bai

The University of Iowa, Iowa City


This talk focuses on how control, identification and signal processing techniques are used to solve an bio-medical engineering problem. The problem considered is to improve imaging quality and to reduce contrast dose and radiation exposure of a modern CT scanner. To combat mismatch of the bolus peak density and the imaging aperture in a modern CT, an optimal adaptive bolus chasing controller is proposed and experimentally tested. The controller estimates and predicts the unknown two dimensional bolus density on line and then determines the optimal control actions. Tracking errors are mathematically quantified in terms of estimation errors. The test results not only support the analytical analysis and exhibit its superior performance over the current constant velocity controller, but also demonstrate the clinical feasibility.


Professor Er-Wei Bai
4316 Seamans Center for the Engineering Arts and Sciences 
The University of Iowa 
Iowa City, IA 52242-1527 
Telephone: (319) 335-5949

Er-Wei Bai received his PhD degree from the University of California at Berkeley and is Professor of Electrical and Computer Engineering at University of Iowa. Professor Bai is a Fellow of IEEE, and a leading expert on system identification and parameter estimation. Prof Bai has written over 140 journal papers as well as a number of conference papers and book chapters on identification, adaptive systems, signal processing and their applications to medicine and engineering. He has served as an associate editor or editorial board member for a number of journals including IEEE Trans on Automatic Control and Automatica and as a panel member for US National Science Foundation (NSF) and the US National Institute of Health.  Prof Bai currently serves on the IFAC technical committee on Modelling, Identification and Signal processing, and IEEE CSS technical committee on System Identification and Adaptive Control. He is a recipient of the President's Award for Teaching Excellence and the (State of Iowa Board of ) Regents Award for Faculty Excellence.


Bayesian machine learning for brains, genes, and hearing aids


Professor Tom Heskes

Radboud University Nijmegen, Netherland


Professor Tom Heskes

Head of Machine Learning Group, Intelligent Systems
Institute for Computing and Information Sciences (iCIS)
Faculty of Science
Radboud University Nijmegen

Dr Tom Heskes is a Professor in Artificial Intelligence, and he leads the Machine Learning Group, at the Institute for Computing and Information Sciences, Radboud University Nijmegen, the Netherlands. He is further affiliated Principal Investigator at the Donders Centre for Neuroscience.

Prof Heskes' research is on artificial intelligence, in particular (Bayesian) machine learning. He works on Bayesian inference (approximate inference, hierarchical modeling, dynamic Bayesian networks, preference elicitation); machine learning (multi-task learning, bias-variance decompositions); and neural networks (on-line learning, self-organizing maps, time-series prediction). In a nutshell, he and the members of his group use probability calculus and statistics to design and understand "intelligent" systems that can learn from data. He is also involved in several projects that concern applications in, among others, brain-computer interfaces, adaptive personalization of hearing aids, and bioinformatics. Prof Heskes has published over 100 research papers and books in the above area.

Prof Heskes is the Editor-in-Chief of Neurocomputing. He has served in various prestigious committees of over 40 international conferences since 2004 onwards.


Nonlinear Control and Its Applications

Shuzhi Sam Ge
Professor, IEEE Fellow, PhD, DIC, BSc, PEng
Director, Institute of Intelligent Systems and Information Technology (ISIT), and Robotics Institute
University of Electronic Science and Technology of China
Chengdu, 611731, China
Director, Social Robotics Lab, Interactive Digital Media Institute (IDMI) &
Professor, Department of Electrical & Computer Engineering
National University of Singapore
Singapore 117576


Many complex systems are usually difficult to model and governed by general (non-affine) nonlinear systems. The well developed control schemes for affine nonlinear systems find of little use. By elegantly utilizing the Mean value and implicit function theorems, the existence of ideal stabilizing control laws are first established for non-affine nonlinear systems. Then, by combining the adaptive control and neural network parametrizition techniques, stable adaptive neural network control is presented rigorously, which demonstrate that intelligent control can do what traditional adaptive control could not, and intelligent control provides the fundamentals for further development of advanced adaptive control for complex industrial systems. Because of the inherent differences of operators, adaptive controls are presented for nonlinear systems in both continuous time and discrete-time.

Finally, a new control design is presented for a class of nonlinear systems in strict feedback form with output constraint, though our newly introduced - Barrier Lyapunov Function - which grows to infinity when its arguments approaches certain limiting values. The key principle is that, by ensuring boundedness of the Barrier Lyapunov Function in the closed loop, we also ensure that the barriers are not transgressed. Asymptotic tracking is achieved without violation of constraint, and all closed loop signals remain bounded, under a mild condition on the initial output.


Professor Shuzhi Sam Ge

EEE Fellow, PhD, DIC, BSc, PEng
Director, Institute of Intelligent Systems and Information Technology (ISIT), and Robotics Institute
University of Electronic Science and Technology of China
Chengdu, 611731, China
Director, Social Robotics Lab, Interactive Digital Media Institute (IDMI) &
Professor, Department of Electrical & Computer Engineering
National University of Singapore
Singapore 117576

Professor Shuzhi Sam Ge, IEEE Fellow, IET Fellow, is the founding director of Institute of Intelligent Systems and Information Technology, University of Electronic Science and Technology of China, and the founding Director of Social Robotics Lab of Interactive Digital Media Institute, and Professor of the Department of Electrical and Computer Engineering, the National University of Singapore.

He is the founding Editor-in-Chief, International Journal of Social Robotics, Springer. He has served/been serving as an Associate Editor for a number of flagship journals including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Neural Networks, and Automatica. He also serves as a book Editor of the Taylor & Francis Automation and Control Engineering Series. At IEEE Control Systems Society, he served/serves as Vice President for Technical Activities, 2009-2010, Member of Board of Governors of IEEE Control Systems Society, 2007-2009, and Chair of Technical Committee on Intelligent Control,  2005-2008. He served as the inaugural General Chair of IEEE Multi-conference on Systems and Control, Singapore 2007, and the General Chair of the IEEE International Symposium on Intelligent Control, Taipei, 2004. He was the founding General Chair of IEEE Conference on Robotics, Automation and Mechatronics, & IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 2004.

He was the recipient of inaugural Thousand Talent Scheme (TTS) Professor, China, 2008; Changjiang Guest Professor, Ministry of Education, China, 2008; Outstanding Overseas Young Research Award, National Science Foundation, China, 2004; Inaugural Temasek Young Investigator Award, Defence Science and Technology Agency (DSTA), Singapore, 2002; National Technology Award of the National Science & Technology Board, Singapore,1999. He provides technical consultancy to industrial and government agencies. His current research interests include social robotics, multimedia fusion, adaptive control, intelligent systems and artificial intelligence.