2019 IEEE CAS Singapore Chapter Talks and Seminars

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Gradient-free Learning based on the Kernel and the Range Space

Prof. Kar-Ann Toh, Yonsei University, Seoul, Korea

Organized by EXQUISITUS, Centre for System Intelligence and Efficiency & IEEE Industrial Electronics Singapore Chapter & IEEE Circuits and Systems Singapore Chapter

Date : 23 January 2019 (Wednesday)
Time : 3.00 PM
Venue : Meeting Room C (S1-B1c-111), School of EEE, NTU

Abstract

In this talk, we show that solving the system of linear equations by manipulating the kernel and the range space is equivalent to solving the problem of least squares error approximation. This establishes the ground for a gradient-free learning search when the system can be expressed in the form of a linear matrix equation. When the nonlinear activation function is invertible, the learning problem of a fully-connected multilayer feedforward neural network can be adapted for this novel learning framework. By a series of kernel and range space manipulations, it turns out that such a network learning boils down to solving a set of cross-coupling equations. By having the weights deterministically or randomly initialized, the equations can be decoupled and the network solution shows relatively good learning capability for real world data sets of small to moderate dimensions. Based on the structural information of the matrix equation, the network representation is found to be dependent on the number of data samples and the output dimension.

Speaker Biography

http://mi.yonsei.ac.kr/_/rsrc/1472851203655/professor/KA_Toh.gifKar-Ann Toh is a Professor in the School of Electrical and Electronic Engineering at Yonsei University, South Korea. He received the PhD degree from Nanyang Technological University (NTU), Singapore. Since then, he worked for two years in the aerospace industry prior to his post-doctoral appointments at research centers in NTU from 1998 to 2002. He was affiliated with the Institute for Infocomm Research in Singapore from 2002 to 2005 prior to his current appointment in Korea. His research interests include pattern classification, machine learning, neural networks and biometrics. He is a co-inventor of two US patents and has made several PCT filings related to biometric applications. Besides being active in publications, Dr. Toh has served as an advisor/co-chair/member of technical program committee for international conferences related to biometrics and artificial intelligence. He is/has serving/served as an Associate Editor of several international journals including IEEE Transactions on Biometrics, Behavior and Identity Science, IEEE Transactions on Information Forensics and Security, Journal of Franklin Institute, Pattern Recognition Letters, and IET Biometrics.

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Further Optimization of FRM Filters

Prof. Tapio Saramaki, Tampere University of Technology, Finland

Organized by IEEE Circuits and Systems Singapore Chapter & IEEE Signal Processing Singapore Chapter & Centre for Infocomm Technology (INFINITUS), School of EEE, NTU

Date : 22 May 2019 (Wednesday)
Time : 3.00 PM - 4.00 PM
Venue : Executive Seminar Room (S2.2-B2-53), School of EEE, NTU

Abstract

Various approaches to synthesizing finite impulse response (FIR) filters based on the frequency response masking (FRM) technique are considered. Among these FRM FIR filters, interpolated FIR (IFIR) are treated as special cases.

Originally, FRM FIR filters have been synthesized by first designing the masking filters using either linear programming or the Remez algorithm and then the periodic filter. The second approach is based on iteratively designing the masking filter pair (each masking filter pair for multistage designs) and the periodic filter until the difference between successive overall filters is within the given tolerance limits. The third approach is to apply nonlinear optimization. These three approaches are compared with each other in terms of achievable filter complexities and the ability of implementing the resulting filters efficiently in practice.

For IFIR filters, the design is drastically simplified and the overall filter synthesis can be done based on iteratively designing the sub filters using the efficient Remez algorithm.

Finally, it is shown that both FRM and IFIR filters can be designed so that their overall order differs only slightly from those of the direct-form FIR filters, but at the same time, the computational complexity in the filter implementation is significantly reduced

Speaker Biography

Tapio Saramaki was born in Orivesi, Finland, in 1953. He has received the Diploma Engineer (with honors) and Doctor of Technology (with honors) degrees in electrical engineering from the Tampere University of Technology (TUT), Tampere, Finland, in 1978 and 1981, respectively. From 1977, he has held various research and teaching positions at TUT. He was a Professor of Digital Signal Processing and a Docent (Adjunct Professor) of Communications until becoming an Emeritus Professor in 2016.

Dr. Saramaki is also a Co-Founder and a System-Level Designer of VLSI Solution, Finland, specializing in efficient VLSI implementations of both analog and digital signal processing algorithms for various applications. His research interests are in digital signal processing, especially in filter and filter bank design, efficient VLSI implementations of DSP algorithms, and communications application as well as approximation and optimization theories. He has contributed to more than 300 international journals and conference articles as well as more than 10 international book chapters. He holds three worldwide used patents.

Dr. Saramaki is the Fellow of IEEE and the Russian A. S. Popov Society for Radio-Engineering, Electronics, and Communications. He was a recipient of the 1987 and 2007 IEEE Circuits and Systems Society’s Guillemin-Cauer Awards as well as two other best paper awards. He is a founding member of the Median-Free Group International.

He has paid numerous research visits to many international universities. The countries include Argentina, Brazil, Canada, China, India, Norway, Mexico, Singapore, Sweden, and USA. He is a founding member of the Median-Free Group International.

Dr. Saramaki has been actively taking part in many duties to the IEEE Circuits and Systems Society’s DSP Committee by being a Chairman (2002-2004), a Distinguished Lecturer (2002-2003), and a Track or a Co-Track Chair for many ISCAS symposiums (2003-2005 and 2011-2019). Furthermore, he has been on the technical committee of several international conferences including, among others, DSP, DSPA, EUSIPCO, ECCTD, IASTED, ICECS, ISPA, and NORCAS.

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Iterative Learning Control and Its Application on Artificial Pancreas

Prof. Youqing Wang, Beijing University of Chemical Technology, China

Organized by IEEE Circuits and Systems Singapore Chapter & IEEE Industrial Electronics Singapore Chapter & Centre for Bio Devices and Signal Analysis (VALENS), School of EEE, NTU

Date : 07 June 2019 (Friday)
Time : 2.30 PM - 3.30 PM
Venue : Meeting Room b2 (S2-B2b-777), School of EEE, NTU

Abstract

This presentation proposes to utilize advanced control algorithms with insulin pumps and CGM sensors to improve glycemic performance in persons with type 1 diabetes mellitus (T1DM). It is evident that there exist repetitive cycles in glucose-meal-insulin dynamics, e.g., dietary habit and circadian variation of hormone levels. To exploit the repetitive nature of glucose-meal-insulin dynamics, a novel combination of iterative learning control (ILC) and model predictive control (MPC), referred to as learning-type MPC (L-MPC), was recently proposed for closed-loop control of an artificial pancreas by the applicant and his colleagues. Clinical trial results show that L-MPC can learn from an individual’s lifestyle, inducing the glucose control performance to improve from day to day. Theoretically, L-MPC belongs to indirect ILC. This presentation also introduces some stability analysis results for general indirect ILC.

Speaker Biography

证件2Youqing Wang (M’09-SM’12) received the B.S. degree from Shandong University, Jinan, Shandong, China, in 2003, and PhD degree in Control Science and Engineering from Tsinghua University, Beijing, China, in 2008. From 2006 to 2007, he was a Research Assistant at the Department of Chemical Engineering, The Hong Kong University of Science and Technology, Hong Kong. From 2008 to 2010, he was a Senior Investigator at the Department of Chemical Engineering, University of California, Santa Barbara, CA, USA. In 2015, he joined the University of Alberta in Edmonton, AB, Canada, as a Visiting Professor. He is currently a Professor at Beijing University of Chemical Technology, and also Shandong University of Science and Technology and. His research interests include fault-tolerant controls, state monitoring, modeling and control of biomedical processes (e.g., artificial pancreas system), and iterative learning controls. Dr. Wang was a recipient of several research awards, including the Journal of Process Control Survey Paper Prize and ADCHEM2015 Young Author Prize.

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