Date:

2021-09-06 -- 2021-09-20

Time:

19:00-21:00, 21:00-23:00 (Beijing)

13:00-15:00, 15:00-17:00 (Berlin)

7:00-9:00, 9:00-11:00 (New York)

Chair:

Zhiguo Shi, Zhejiang University, China; Martin Haardt, Ilmenau University of Technology, Germany

Local Chair:

Chengwei Zhou, Zhejiang University, China; Qianqian Yang, Zhejiang University, China

Program:

Sponsorship:

Sponsor:

Zhejiang University

Co-sponsor:

Chinese Institute of Electronics on Radar Society

Hangzhou Future Sci-tech City

Organizer:

Graduate School of Zhejiang University

State Key Laboratory of Industrial Control Technology

Free registration:

Please click this link https://jinshuju.net/f/yBIj07  to sign up for the international workshop.

Please contact luninglin@zju.edu.cn if you have any questions.

P.S.  Detailed information for the invited speakers:

Hing Cheung So

City University of Hong Kong, China

Title of the talk:

Robust Matrix Recovery

Hing Cheung So (Fellow, IEEE) was born in Hong Kong. He received the B.Eng. degree in electronic engineering from the City University of Hong Kong, Hong Kong, in 1990, and the Ph.D. degree in electronic engineering from The Chinese University of Hong Kong, Hong Kong, in 1995.  

From 1990 to 1991, he was an Electronic Engineer with the Research and Development Division, Everex Systems Engineering Ltd., Hong Kong. From 1995 to 1996, he was a Post-Doctoral Fellow with The Chinese University of Hong Kong. From 1996 to 1999, he was a Research Assistant Professor with the Department of Electronic Engineering, City University of Hong Kong, where he is a professor. His research interests include detection and estimation, fast and adaptive algorithms, multidimensional harmonic retrieval, robust signal processing, source localization, and sparse approximation.

Dr. So was an Elected Member of the Signal Processing Theory and Methods Technical Committee of the IEEE Signal Processing Society, from 2011 to 2016, where he was the Chair of the Awards Subcommittee from 2015 to 2016. He has been on the editorial boards of IEEE Signal Processing Magazine from 2014 to 2017, the IEEE Transactions on Signal Processing from 2010 to 2014, Signal Processing since 2010, and Digital Signal Processing since 2011. He was also the Lead Guest Editor of the IEEE Journal of Selected Topics in Signal Processing, special issue on Advances in Time/Frequency Modulated Array Signal Processing in 2017.

Abstract of the Lecture

Many real-world signals such as textual, visual, audio and financial data lie near some low-dimensional subspace. Low-rank matrix approximation refers to extracting the low-dimensional or signal subspace from a two-dimensional array, while low-rank matrix completion aims to find a low-rank matrix from only a subset of possibly noisy entries. Conventional techniques for matrix recovery include the convex optimization approach, which minimizes the nuclear norm subject to a constraint on the Frobenius norm of the residual. However, they may not be robust to outliers and have a high computational complexity. In this talk, I will present two lp-norm based factorization methods to achieve computationally simpler and robust matrix recovery. Application examples will also be provided.

Xiaodong Wang

Columbia University, USA

Title of the talk:

Signal Processing for Radar Communication Coexistence

Xiaodong Wang received the Ph.D. degree in Electrical Engineering from Princeton University. He is a Professor of Electrical Engineering at Columbia University in New York.

Dr. Wang’s research interests fall in the general areas of signal processing and communications, and has published extensively in these areas. Among his publications is a book entitled “Wireless Communication Systems: Advanced Techniques for Signal Reception”, published by Prentice Hall in 2003. His current research interests include wireless communications, statistical signal processing, and genomic signal processing. Dr. Wang received the 1999 NSF CAREER Award, the 2001 IEEE Communications Society and Information Theory Society Joint Paper Award, and the 2011 IEEE Communication Society Award for Outstanding Paper on New Communication Topics. He has served as an Associate Editor for the IEEE Transactions on Communications, the IEEE Transactions on Wireless Communications, the IEEE Transactions on Signal Processing, and the IEEE Transactions on Information Theory. He is a Fellow of the IEEE and listed as an ISI Highly-cited Author.

Yuejie Chi

Carnegie Mellon University, USA

Title of the talk:

Scalable and robust nonconvex approaches for low-rank structure estimation

Yuejie Chi received the B.E. degree (Hons.) in electrical engineering from Tsinghua University, Beijing, China, in 2007, and the M.A. and Ph.D. degrees in electrical engineering from Princeton University, in 2009 and 2012, respectively. She was with The Ohio State University from 2012 to 2017. Since 2018, she has been an Associate Professor with the Department of Electrical and Computer Engineering, Carnegie Mellon University, where she held the Robert E. Doherty Early Career Development Professorship, from 2018 to 2020. Her research interests lie in the theoretical and algorithmic foundations of data science, signal processing, machine learning, and inverse problems, with applications in sensing systems, broadly defined. Among others, she was a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award for contributions to high-dimensional structured signal processing, and named the 2021 Goldsmith Lecturer by the IEEE Information Theory Society. She currently serves as an Associate Editor for IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing, and IEEE Transactions on Pattern Recognition and Machine Intelligence.

Abstract of the Lecture

Many inverse problems encountered in sensing and imaging can be formulated as estimating a low-rank object from incomplete linear measurements; examples include phase retrieval, matrix completion, blind deconvolution, low-rank tensor estimation, and so on. Through the lens of matrix and tensor factorization, one of the most popular approaches is to employ simple iterative algorithms such as gradient descent to recover the low-rank factors directly, which allow small memory and computation footprints. Despite wide empirical success, the theoretical underpinnings have remained elusive. In this talk, I will discuss our recent line of efforts in understanding the geometry of the nonconvex loss landscape with the aid of statistical reasoning, and how gradient descent harnesses such geometry in an implicit manner to achieve both computational and statistical efficiency all at once. Furthermore, I will discuss how to adjust vanilla gradient descent to make it provably robust to outliers and ill-conditioning without losing computational and statistical efficiency.

   Xiang-Gen Xia

University of Delaware, USA

Title of the talk:

Robust Remaindering for Real Numbers and Its Applications in Mod Sampling

Xiang-Gen Xia received his Ph.D. degree in electrical engineering from the University of Southern California, Los Angeles. He is currently the Charles Black Evans Professor at the Department of Electrical and Computer Engineering, University of Delaware, USA.  His current research interests include MIMO and OFDM communications systems, and SAR and ISAR imaging. Dr. Xia is the author of the book Modulated Coding for Intersymbol Interference Channels (New York, Marcel Dekker, 2000).

Dr. Xia received the National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program Award in 1997, the Office of Naval Research (ONR) Young Investigator Program (YIP) Award in 1998, and the Outstanding Overseas Young Investigator Award from the National Nature Science Foundation of China in 2001. He received the 2019 Information Theory Outstanding Overseas Chinese Scientist Award, The Information Theory Society of Chinese Institute of Electronics. Dr. Xia is the General Co-Chair of ICASSP 2005 in Philadelphia. He is a Fellow of IEEE. 

Abstract of the Lecture

A large integer can be reconstructed from its several much smaller remainders and Chinese Remainder Theorem (CRT) provides a solution. However, it is well-known that CRT is not robust in the sense that a small error in a remainder may cause a large error in the reconstruction. We have studied  robust CRT in the last decade such that a large integer can be robustly reconstructed from its erroneous remainders as long as the errors in the remainders are not too large. In this talk, I will briefly talk about the robust CRT not only for large integer reconstructions but also for large real number reconstructions from erroneous remainders. We then briefly introduce its applications in mod sampling, where a signal is reconstructed from its mod samplings. It is related to unlimited sampling.  

Antonio De Maio

University of Naples Federico II, Italy

Title of the talk:

Phased Array Radar Adaptive Beamforming

Antonio De Maio received the Dr. Eng. (Hons.) and Ph.D. degrees in information engineering from the University of Naples Federico II, Naples, Italy, in 1998 and 2002, respectively. From October to December 2004, he was a Visiting Researcher with the U.S. Air Force Research Laboratory, Rome, NY, USA. From November to December 2007, he was a Visiting Researcher with the Chinese University of Hong Kong, Hong Kong. He is currently a Professor with the University of Naples Federico II. His research interest lies in the field of statistical signal processing, with emphasis on radar detection, optimization theory applied to radar signal processing, and multiple-access communications. He is the recipient of the 2010 IEEE Fred Nathanson Memorial Award as the young (less than 40 years of age) AESS Radar Engineer 2010 whose performance is particularly noteworthy as evidenced by contributions to the radar art over a period of several years, with the following citation for “robust CFAR detection, knowledge-based radar signal processing, and waveform design and diversity”. He is the corecipient of the 2013 best paper award (entitled to B. Carlton) of the IEEE Transactions on Aerospace and Electronic Systems with the contribution “Knowledge-Aided (Potentially Cognitive) Transmit Signal and Receive Filter Design in Signal-Dependent Clutter”.

Abstract of the Lecture

This presentation is focused on adaptive digital beamforming (DBF) for phased array radar. First of all basic concepts about DBF are given.  Then challenges connected with the practical implementation and the computational issues are pinpointed. Architectures based on the use of subarrays are presented accounting for regular and irregular configurations. Thus a discussion on the adaptive implementation of the DBF is provided including issues connected with adaptive jamming cancellation and training data selection. In this respect, the effects of non-idealities encountered in practical environments as well as of a misguided training data choice are explained together with a machine learning-based approach to select the appropriate reference data for adaptation. Special architectures such as sidelobe canceller and adaptive beamspace cancellation are presented. Finally some evolutions of the phased array concept are discussed.

André de Almeida

Federal University of Ceará, Brazil

Title of the talk:

Introduction to Tensor Algebra (Part 1 & Part 2)

André Lima Férrer de Almeida received a double Ph.D. degree in Sciences and Teleinformatics Engineering from the University of Nice, Sophia Antipolis, France, and the Federal University of Ceará, Fortaleza, Brazil, in 2007. From 2007 to 2008, he held a one-year teaching position at the University of Nice Sophia Antipolis, France. In 2010, he joined the Teleinformatics Engineering Department of the Federal University of Ceará, where he holds the Signal Processing chair and currently is an Associate Professor. He was awarded multiple times visiting professor positions at the University of Nice Sophia Antipolis, France (2012, 2013, 2015, 2018, 2019). He served as an Associate Editor for several journals, such as the IEEE Transactions on Signal Processing (2012-2014 and 2014-2016), the IEEE Signal Processing Letters (2016-2018 and 2018-2020). He has served as Guest Editor for the EURASIP Journal on Advances in Signal Processing (2014), and the Wireless Communications and Mobile Computing (2018). He currently serves as a Senior Area Editor for the IEEE Signal Processing Letters and as an Associate Editor for the IEEE Transactions on Vehicular Technology. He also served in the Editorial Board of other journals, including Circuits, Systems & Signal Processing (2012-2018), Wireless Communications and Mobile Computing (2018-2020), the KSII Transactions on Internet and Information Systems (2012-2014), and the French journal Traitement du Signal (2016-2018).

Abstract of the Lecture

Tensor algebra provides a generalization of matrix algebra for dimensions greater than two, by operating on data arrays of three or more dimensions. It has a rich history that spans almost a century and spans several disciplines. But only recently have they become ubiquitous in analytics, signal processing, statistics, data mining, and machine learning. The broad success of tensor methods can be attributed to their ability to model, analyze, predict, recognize and learn from multimodal data. This short course, divided into two parts, provides a starting point for researchers and practitioners interested in manipulating and exploring tensor algebra tools, focusing on the fundamentals and motivation for tensor algebra. We conceptualize tensors, their main properties, and operators, overview the main tensor decompositions, and factor estimation algorithms. We also provide a brief look at some applications to selected communications and signal processing problems.

Z. Jane Wang

University of British Columbia, Canada

Title of the talk:

Adversarial Deep Learning in Digital Media Security & Forensics

Z. Jane Wang received the B.Sc. degree in electrical engineering from Tsinghua University, China, in 1996, and the M.Sc. and Ph.D. degrees in electrical engineering from the University of Connecticut, in 2000 and 2002, respectively. She has been a Research Associate with the Electrical and Computer Engineering Department, University of Maryland, College Park. Since 2004, she has been with the Department Electrical and Computer Engineering, The University of British Columbia, Canada, where she is currently a professor. Her research interests include statistical signal processing theory and applications, with focus on multimedia security and biomedical signal processing and modeling. While at the University of Connecticut, she received the Outstanding Engineering Doctoral Student Award. She co-received the EURASIP Journal on Applied Signal Processing (JASP) Best Paper Award in 2004, and the IEEE Signal Processing Society Best Paper Award in 2005. She is the Chair and Founder of the IEEE Signal Processing Chapter at Vancouver. She is serving as an Associate Editor for the IEEE Transactions on Signal Processing, the IEEE Transactions on Information Forensics and Security, and the IEEE Transactions on Biomedical Engineering.

Abstract of the Lecture

This talk gives a brief review of Wang group’s current research efforts at UBC, in the areas of Adversarial Deep Learning in Digital Media Security & Forensics. Deep learning has achieved state-of-the-art performances in many applications. Unfortunately, current deep learning models however could be sensitive to perturbations, giving rise to security, privacy and reliability issues in practical applications.

Under the paradigm of adversarial deep learning, as an attacker, we study potential adversarial attacks and explore novel approaches to scrutinize potential vulnerabilities of deep learning models in digital media security & forensics, by investigating three fundamental learning tasks: matching, classification and regression. Specifically, this talk presents novel attacks (both in the digital domain and in the physical domain) for several essential models belonging to the above three dominant tasks: 1) image hashing for image retrieval and authentication, as a typical matching task; 2) GAN-generated fake face imagery forensics, as a representative binary classification task; 3) multiclass image classification; 4) camera-LIDAR 3d object detection; and 5) single object tracking in videos, which is an important video surveillance model involving a combination of the matching task, the classification task and the regression task. We address security and privacy threats that arise in the above typical digital media problems and study how to fool deep learning models to make wrong decisions.

Wei Liu

University of Sheffield, UK

Title of the talk:

Basic Concepts and Techniques for Wideband Beamforming

Wei Liu received the B.Sc. degree in Space Physics (minor in Electronics) in 1996 and LLB in Intellectual Property Law in1997 from Peking University, China (passed the Lawyer Qualification Examination of China in 1997 and practised as a trainee lawyer and then received his full lawyer's licence in 1999), MPhil from the Department of Electrical and Electronic Engineering, University of Hong Kong, in 2001, PhD in 2003 from the School of Electronics and Computer Science, University of Southampton, U.K. He then worked as a postdoc first in Southampton and later in the Department of Electrical and Electronic Engineering, Imperial College London. In September 2005, He joined the Communications Research Group, Department of Electronic and Electrical Engineering, University of Sheffield as a lecturer, and then promoted to senior lecturer in January 2015.

His research interests cover a wide range of topics in signal processing, with a focus on sensor (antenna, hydrophone, microphone, seismometer, etc.) array signal processing (beamforming and source separation/extraction, direction of arrival estimation, target tracking and localization, etc.), and its various applications, such as robotics and autonomous vehicles, remote sensing, human computer interface, data analysis, radar, sonar, and wireless communications.

Abstract of the Lecture

This tutorial will focus on basic ideas and techniques for wideband beamforming. The topics covered include the difference between narrowband beamforming and wideband beamforming and their different approaches (sensor delay-line based and tapped delay-line based), fixed wideband beamformer design (in particular frequency invariant beamformers), adaptive wideband beamforming (such as reference signal based beamformer, the linearly constrained minimum variance beamformer and its variation, and subband/frequency-domain methods for wideband beamforming), and other issues in wideband beamforming such as robust design against various model errors and sensor location optimisation for improved performance.

Nuria Gonzalez Prelcic

North Carolina State University, USA

Title of the talk:

Integrated MIMO communication and sensing: the killer technology for future wireless networks

Nuria González Prelcic is currently an Associate Professor with the Electrical and Computer Engineering Department, North Carolina State University. Her main research interests include signal processing theory and signal processing and machine learning for wireless communications and sensing: filter banks, compressive sampling and estimation, multicarrier modulation, massive MIMO, MIMO processing for millimeter-wave communication and sensing, including vehicle-to-everything (V2X), air-to-everything (A2X), satellite MIMO communication, positioning, and joint radar and communication. She has published more than 80 articles in the topic of signal processing for millimeter-wave communications. She is a member of the IEEE Sensor Array and Multichannel Signal Processing Technical Committee. She was the founder Director of the Atlantic Research Center for Information and Communication Technologies (atlanTTic) at the University of Vigo, from July 2008 to January 2017. She is an Editor of the IEEE Transactions on Wireless Communications and an Area Editor of the IEEE Signal Processing Magazine.

Waheed U. Bajwa

Rutgers University, USA

Title of the talk:

High-dimensional Regression and Dictionary Learning: Some Recent Advances for Tensor Data

Waheed U. Bajwa has been with Rutgers since 2011, where he is currently an associate professor in the Dept. of ECE and an associate member of the graduate faculty of the Dept. of Statistics and Biostatistics. His research interests include statistical signal processing, highdimensional statistics, machine learning, harmonic analysis, inverse problems, and networked systems. He has received a number of awards including the Army Research Office Young Investigator Award (2014), the National Science Foundation CAREER Award (2015), Rutgers Presidential Merit Award (2016), Rutgers Presidential Fellowship for Teaching Excellence (2017), and Rutgers Engineering Governing Council ECE Professor of the Year Award (2016, 2017, 2019). He is a co-investigator on a work that received the Cancer Institute of New Jersey’s Gallo Award for Scientific Excellence in 2017, a co-author on papers that received Best Student Paper Awards at IEEE IVMSP 2016 and IEEE CAMSAP 2017 workshops, and a Member of the Class of 2015 National Academy of Engineering Frontiers of Engineering Education Symposium. Dr. Bajwa is currently serving as the Lead Guest Editor for a special issue of IEEE Signal Processing Magazine on “Distributed, Streaming Machine Learning, a Guest Editor for a special issue of Proceedings of the IEEE on “Optimization for Data-driven Learning and Control, a Senior Area Editor for IEEE Signal Processing Letters, an Associate Editor for IEEE Transactions on Signal and Information Processing over Networks, and an elected member of the Big Data Special Interest Group and Sensor Array and Multichannel (SAM) and Signal Processing for Communications and Networking (SPCOM) Technical Committees of the IEEE Signal Processing Society.

Elias Aboutanios

University of New South Wales, Australia

Title of the talk:

Dual Function Radar Communications: A Sibling Rivalry

Elias Aboutanios  received the bachelor’s degree in engineering from the University of New South Wales (UNSW), Kensington, NSW, Australia, in 1997, and the Ph.D. degree from the University of Technology Sydney, Ultimo, NSW, Australia, in 2003. From 2003 to 2007, he was a Research Fellow with the Institute for Digital Communications, University of Edinburgh, where he conducted research on space time adaptive processing for radar target detection. He is currently an Associate Professor with the School of Electrical Engineering and Telecommunications, University of New South Wales, Kensington, NSW, Australia. His research interests are in statistical signal processing, in particular signal detection and parameter estimation, for various applications such as radar, GNSS, smart grids, and nuclear magnetic resonance spectroscopy. He is the recipient of the Best Oral Presentation Award (CISPBMEI10), Teaching Excellence Award in 2011, Excellence in Research Supervision Award in 2014, the Australian Postgraduate Scholarship in 1998, Sydney Electricity Scholarship in 1994, and UNSW Co-Op Scholarship in 1993. He is a member of the IEEE SAM Technical Committee and is currently serving as an Associate Editor for the IEEE Transactions on Signal Processing and IET Signal Processing. He also runs various space activities and projects and has established and led the UNSW-EC0 CubeSat Project, which culminated in the launch of the satellite in 2017.

Yao Xie

Georgia Institute of Technology, USA

Title of the talk:

Learning point process network using discrete events data

 Yao Xie received the Ph.D. degree in electrical engineering from Stanford University, with a focus on mathematics. She is currently an Associate Professor and the Harold R. and Mary Anne Nash Early Career Professor with the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology and also an Associate Director of the Machine Learning Center. Her research areas are statistics, sequential analysis and sequential change-point detection, machine learning, and signal processing. She received the National Science Foundation (NSF) CAREER Award in 2017. She is also an Associate Editor for IEEE Transactions on Signal Processing.

Xiao-Ping (Steven) Zhang

Ryerson University, Canada

Title of the talk:

Foundations in Graph Signal Processing

Xiao-Ping (Steven) Zhang (xzhang@ryerson.ca) received the B.S. and Ph.D. degrees from Tsinghua University, Beijing, China, in electronic engineering and the M.B.A. degree in finance, economics, and entrepreneurship from the University of Chicago, Illinois. He is a professor of electrical and computer engineering and is cross appointed to the Finance Department at the Ted Rogers School of Management at Ryerson University, Toronto, Canada. His research interests include signal processing, electronic systems, machine learning, big data, finance, and marketing. He is the cofounder and chief executive officer for EidoSearch, an Ontario-based company offering a content-based search and analysis engine for financial big data.

Pierluigi Salvo Rossi

Norwegian University of Science and Technology, Norway

Title of the talk:

Signal Processing for IoT: Decision Fusion in Sensor Networks

P. Salvo Rossi was born in Naples, Italy, in April 1977. He received the Dr.Eng. degree in telecommunications engineering (summa cum laude) and the Ph.D. degree in computer engineering from the University of Naples “Federico II”, Italy, in 2002 and 2005, respectively. From 2005 to 2008, he has worked as a Post-Doctoral Researcher with the Department of Computer Science and Systems, University of Naples “Federico II”, the Department of Information Engineering, Second University of Naples, Italy, and the Department of Electronics and Telecommunications, Norwegian University of Science and Technology (NTNU), Norway. From 2008 to 2014, he was an Assistant Professor (tenured in 2011) in telecommunications with the Department of Industrial and Information Engineering, Second University of Naples. From 2014 to 2016, he was an Associate Professor in signal processing with the Department of Electronics and Telecommunications, NTNU. From 2016 to 2017, he was a Full Professor in signal processing with the Department of Electronic Systems, NTNU. From 2017 to 2019, he was a Principal Engineer with the Department of Advanced Analytics and Machine Learning, Kongsberg Digital AS, Norway. He held visiting appointments at the Department of Electrical and Computer Engineering, Drexel University, USA, the Department of Electrical and Information Technology, Lund University, Sweden, the Department of Electronics and Telecommunications, NTNU, and the Excellence Center for Wireless Sensor Networks, Uppsala University, Sweden. Since 2019, he has been a Full Professor in statistical machine learning with the Department of Electronic Systems, NTNU, where he is also the Director of IoT@NTNU. His research interests fall within the areas of communication theory, data fusion, machine learning, and signal processing. He was awarded as an Exemplary Senior Editor for IEEE Communications letters in 2018. He was an Associate Editor and a Senior Editor for IEEE Communication letters from 2012 to 2016 and 2016 to 2019, respectively. He has been serving as an Executive Editor for IEEE Communication Letters since 2019, an Area Editor for IEEE Open Journal of The Communications Society since 2019, an Associate Editor for IEEE Transactions on Signal and Information Processing Over Networks since 2019, and an Associate Editor for IEEE Transactions on Wireless Communication since 2015.

Abstract of the Lecture

 The digital transformation is pervading almost every aspect of human life, ranging from healthcare to industry, from entertainment to communications and security. In this respect, the Internet-of-Things (IoT) paradigm plays a crucial role, with a multitude of networked devices interacting with the physical world and providing services through data collection, communication, processing and control.

This lecture adopts a statistical signal processing perspective and focuses on the distributed version of the binary-hypothesis test which supports several energy-efficient IoT practical applications concerning the robust detection of a phenomenon of interest (e.g. environmental hazard, oil/gas leakage, forest fire). The objective of this lecture is to cover design and analysis of fusion approaches for future IoT setup.

Domenico Ciuonzo

University of Naples Federico II, Italy

Title of the talk:

Signal Processing for IoT: Decision Fusion in Sensor Networks

D. Ciuonzo received the Ph.D. degree in electronic engineering from the University of Campania “L. Vanvitelli,” Italy. Since 2011, he has been holding several visiting researcher appointments. He is currently an Assistant Professor with the University of Naples “Federico II,” Italy. His research interests include data fusion, statistical signal processing, wireless sensor networks, the Internet of Things, traffic analysis, and machine learning. Since 2014, he has been an Editor of several IEEE, IET, and ELSEVIER journals.