Prof. Chong-Yung Chi, IEEE Life Fellow
National Tsing Hua University, Taiwan

Chong-Yung Chi (Life Fellow, IEEE)received the B.S. degree from Tatung Institute of Technology, Taipei, Taiwan in 1975, the master’s degree from National Taiwan University, Taipei, Taiwan in 1977, and the Ph.D. degree from the University of Southern California, Los Angeles, CA, USA, in 1983, all in electrical engineering.
He is currently a Professor of National Tsing Hua University, Hsinchu, Taiwan. He has published more than 240 technical papers (with citations more than 7300 times by Google-Scholar), including more than 90 journal papers (mostly in IEEE TRANSACTIONS ON SIGNAL PROCESSING), more than 140 peer-reviewed conference papers, 3 book chapters, and 2 books, including a textbook, Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications, CRC Press, 2017 (which has been popularly used in a series of invited intensive short courses at 10 top-ranking universities in Mainland China since 2010 before its publication). His current research interests include signal processing for wireless communications, convex analysis and optimization for blind source separation, biomedical and hyperspectral image analysis, graph based learning and signal processing, and data security and privacy protection in machine learning.
Dr. Chi received 2018 IEEE Signal Processing Society Best Paper Award, entitled “Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization,” IEEE Transactions on Signal Processing, vol. 62, no. 21, Nov. 2014. He has been a Technical Program Committee member for many IEEE sponsored and cosponsored workshops, symposiums and conferences on signal processing and wireless communications, including Co-Organizer and General Co-Chairman of 2001 IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC). He was an Associate Editor (AE) for four IEEE Journals, including IEEE TRANSACTIONS ON SIGNAL PROCESSING for 9 years (5/2001-4/2006, 1/2012-12/2015), and he was a member of Signal Processing Theory and Methods Technical Committee (SPTM-TC) (2005-2010), a member of Signal Processing for Communications and Networking Technical Committee (SPCOM-TC) (2011-2016), and a member of Sensor Array and Multichannel Technical Committee (SAM-TC) (2013-2018), IEEE Signal Processing Society.

Online Speech Title: Convex Optimization Aided Artificial Intelligence

Abstract: Mathematical optimization, such as convex optimization (CVX-opt), that has been extensively applied in sciences and engineering over the last decades. Artificial Intelligence (AI), such as Machine Learning (ML) and Deep Learning (DL), has been pervasive not only in sciences and engineering, but also in our daily life. Both CVX-opt and AI currently perform outstandingly and independently by themselves, in spite of some existing challenges yet to be resolved. For the former, with no need of training data, a specific mathematical model and problem formulation are required, while optimal solutions or acceptable approximate solutions can always be practically obtained, together with insightful performance characteristics and unique properties that may be disclosed, and used as the guidelines for the practical algorithm implementation and development. For the latter, big training dataset and tremendous computing complexity are frequently required, though neither math model nor intricate mathematics involved. In this speech, we will address their intriguing fusion or combination, potentially with fantastic benefits in learning performance, running time saving, and problem scalability, etc. Specifically, we will present CVX-opt aided AI by the following application instances:
1. Computing Resource Allocation in Space-Aerial Integrated Network
2. Hyperspectral Image Denoising
Finally, we draw some conclusions as well as some future research explorations.

Prof. Paolo Mercorelli, Leuphana University of Lueneburg, Germany

Mercorelli (Member of IEEE) received a Ph.D. degree in Systems Engineering from the University of Bologna, Bologna, Italy, in 1998. In 1997, he was a Visiting Researcher for one year at the Department of Mechanical and Environmental Engineering, University of California, Santa Barbara, CA, USA. From 1998 to 2001, he was a Postdoctoral Researcher with Asea Brown Boveri Corporate Research, Heidelberg, Germany. From 2002 to 2005, he was a Senior Researcher at the Institute of Automation and Informatics, Wernigerode, Germany, where he was the leader of the control group. From 2005 to 2011, he was an Associate Professor of Process Informatics at the Ostfalia University of Applied Sciences, Wolfsburg, Germany. Since 2012, he has been a Full Professor and a Chair of Control and Drive systems at the Institute for Production Technology and Systems, Leuphana University of Lueneburg, Lueneburg, Germany. Since 2018, he has been an International Distinguished Visiting Professor at the Institute of Automatic Control, Lodz University of Technology, Lodz, Poland, where he has been responsible for different courses in the field of control of robotic systems. His current research interests include applications of Kalman filters, robotics, wavelets, geometric control, and Sliding Mode Control. Dr. Mercorelli was the recipient of a three-year scholarship from the Marie Curie Actions Research Fellowship Program which is one of the most competitive and prestigious European Awards sponsored by the European Commission from 1998 to 2001. He received seven best international conference paper awards: IECON 2013, IECON 2014, CoDIT 2014, ICCC 2017, FedCSIS 2019, ACD 2019, ICCC 2020. In the years 2019, 2020, 2021 and 2022 he was on the list of the top 2% scientists of Elsevier Database and (METRICS) University of Stanford (USA) (Ioannidis, J.; Boyack, K. & Baas,J., 2020, Updated science-wide author databases of standardized citation indicators, PLOS, October 16). Since 2022 he has been Editor-in-Chief for the Section „Engineering Mathematics“ in „Mathematics“, an Open Access Journal of MDPI, Basel, Switzerland. In summer semester 2023 he has been Visiting Professor at Chandigarh University (India) and responsible for the course of Control Systems.

Speech Title: Wavelet Packets for Applications in Signal Processing and Control Systems
Wavelets and wavelet packets represent a relatively new subject in applied mathematics, signal processing and control systems. Research on wavelet transformation is an extension of the current theory to include Fourier transformation as a useful tool in the areas of time-limited frequency analysis, filtering of signals and data compression. In a classic Fourier analysis, signals are represented as a superposition of trigonometric functions. Methods using Fourier analysis are particularly useful for describing stationary properties of signals. For variable signals, on the other hand, methods using wavelets or time-frequency analysis are usually much better suited. These methods represent the signals as superimpositions of signal modules that are limited in the time and frequency domain and are obtained from a basic function by means of scaling, shifting and modulation. Therefore, these methods are much more flexible and can be better adapted to special applications.

Starting from a short historical introduction and from the concept of wavelets, and in particular, starting from the concept of wavelet packets and their trees, applications on the context of de-noise in signal processing are shown. The de-noise problem is one of the most important problems in signal processing and control. In this context, concepts of coherent and incoherent parts of signals are introduced, and once a particular seminorm is defined, a structural property of the wavelet packets is shown. These concepts allow for obtainment of a de-noise threshold-free algorithm. Wavelet packets can also be applied in control problems. Also, in this case, the tree of the wavelet packets plays a crucial role in the adaptation of the parameters of the controllers. An example of the application of the wavelet packets can be seen in the context of the control of a flexible joint in which an adaptive proportional integral (PI) controller is proposed, and its effectiveness is validated.