Organizers
Prof. Marcello Luiz Rodrigues de Campos (COPPE/UFRJ)
Prof. Charles Casimiro Cavalcante (DETI/UFC)
Sponsors
I Brazilian Signal Processing Forum: cooperating for a connected world
SPS Member Driven Initiative
June 20th and 21st, 2024
For registration, please send an email to bsp-forum@smt.ufrj.br
Information
Through a sequence of plenary talks and technical panels, the event will discuss distributed signal processing from different aspects and for different applications. These include, but are not limited to, distributed optimization and learning, cooperation, consensus update, fully distributed versus federated learning, distributed computations for communications applications, distributed monitoring systems, and privacy issues.
Named as the first Brazilian Signal Processing Forum, this event will target mainly an academic audience. All talks and panels will be recorded, and all the slides and video will be made available to the Signal Processing Society Resource Center, if approved. There will be no written proceedings, nor paper submission.
We believe the technical aspects discussed will bring the attention of students and lecturers to the possibilities and services brought by the Signal Processing Society. In particular, we target the academic audience pursuing advanced studies in Electrical Engineering.
There will be registration, but no registration fee for this event.
The event will be hybrid, therefore those who have registered and cannot attend at the venue will be allowed to participate remotely. All talks and panels will be recorded and made available to the SPS society.
Students (graduate and senior undergraduate), post-doctorate researchers, lecturers and professors.
The event will take place at the Federal University of Rio de Janeiro, in the Alberto Luiz Coimbra Institute, COPPE, at the Technological Center, Room G122
Program
All lectures and activities are set to happen in GMT-3 (Brazil time zone).
This Wheel’s on Fibre: using fibre optic sensing for traffic monitoring
Abstract: Telecom fibers are ubiquitous in urban environments. They are reliable, durable, and much of them can be upgraded to support smart cities applications in a cost-effective manner. Indeed, with Distributed Acoustic Sensing (DAS), one can turn existing roadside fiber-optic telecommunication cables into vibration sensors with metric spatial resolution over sensing range exceeding 100 km. Thanks to Artificial Intelligence, potential applications of DAS are only limited by imagination. In this talk, we shall propose an overview on Machine Learning algorithms for DAS data processing with applications to smart cities supervision, in particular real-time traffic monitoring.
Bio: Cédric Richard received the Dipl.-Ing. and the M.S. degrees in 1994, and the Ph.D. degree in 1998, all from Compiègne University of Technology, France, in Electrical and Computer Engineering. He is a Full Professor at Université Côte d'Azur, France. His current research interests include statistical signal processing and machine learning. He is the author of over 350 journal and conference papers.Prof. Richard is the Editor-in-Chief of ELSEVIER Signal Processing. He is also the Chair of the Signal Processing Theory and Methods Technical Committee of the IEEE-SPS. In 2010-2015, Prof. Richard was distinguished as junior Member of the Institut Universitaire de France (IUF). The IUF is a French service of the Ministry of Higher Education that distinguishes a small number of Professors for their research excellence, as evidenced by their international recognition. Only 2% of French Professors have been distinguished by the IUF. Prof. Richard has been the Director-at-Large of the Region 8 (Europe, Middle East, Africa) of the IEEE Signal Processing Society (IEEE-SPS) in 2019-2020, and a Member of the Board of Governors of the IEEE-SPS. He has been also the Director of the French federative CNRS research association ISIS (Information, Signal, Image, Vision) in 2019-2023. This academic task force is committed to animating, inspiring and encouraging research in the signal and image processing disciplines. It has a driving role to amplify ongoing research actions in France, and to facilitate the emergence of promising research topics. The French federative CNRS research association ISIS has more than 4600 members (faculty members, doctoral students, and researchers), in 205 academic research labs, and is supported by 20 big companies. In 2012-2019, Prof. Richard has also been an elected member of the National Council of French Universities in charge of Electrical Engineering. Prof. Richard served as a Senior Area Chair of the IEEE Signal Processing Letters (2020-22) and of the IEEE Transactions on Signal Processing (2015-18). He also served as an Associate Editor of the IEEE Open Journal on Signal Processing (2019-22), of Elsevier Signal Processing (2009-19), of the IEEE Transactions on Signal Processing (2006-10), and of the IEEE Transactions on Signal and Information Processing over Networks (2015-2018). He is an elected member of the IEEE Signal Processing Theory and Methods Technical Committee of the IEEE-SPS (2009-2014, and 2018-…), and was an elected member of the IEEE Machine Learning for Signal Processing Technical Committee (2012-2018). Prof. Richard was the General Co-Chair of the IEEE SSP’11 Workshop that was held in Nice, France. He was the Technical Co-Chair of EUSIPCO’15 that was held in Nice, France, and of the IEEE CAMSAP’15 Workshop that was held in Cancun, Mexico. He was also the Special Session Chair of the IEEE SAM’16 Workshop that was held in Rio de Janeiro, Brazil, and the Local Co-Chair of the IEEE CAMSAP’19 Workshop. He was the General Co-Chair of EUSIPCO’20 that took place in Amsterdam, Netherlands.
Source separation: from early history to recent advances
Abstract: This talk, focused on source separation, is divided into two parts. The first part will recall the early years of source separation, with the scientific context of 1980’s and the incredible impact of the meetings of the GDR (national group of research) Isis in interaction and promotion of source separation both for academics, industrial people and PhD students in France. The second part will present results on source separation in nonlinear mixtures, including recent and novel methodological ideas and illustrations with various applications.
Bio: Christian Jutten received Ph.D. (1981) and Doctor ès Sciences (1987) degrees from Grenoble Inst. of Technology, France. He was Associate Professor (1982-1989), Professor (1989-2019) at Univ. Grenoble Alpes, where he is now Emeritus Professor since Sept. 2019. Since 1980’s, his research interests are in machine learning and source separation, including theory and applications (biomedical engineering, hyperspectral imaging, chemical sensing, speech). He is author/co-author of five books, 125+ papers in international journals and 250+ publications in international conferences. He was a visiting professor at EPFL, RIKEN labs and University of Campinas. He served as director or deputy director of the signal/image processing lab in Grenoble (1993-2010), as scientific advisor for signal/image processing at the French Ministry of Research (1996–98) and at CNRS (2003–06 and 2012-19). He was organizer or program chair of many international conferences, e.g., the first Independent Component Analysis conference in 1999 (ICA’99) and IEEE MLSP 2009. He was the technical program co-chair of ICASSP 2020. He was a member of the IEEE MLSP and SPTM Technical Committees. He was associate editor for Signal Processing and IEEE Trans. on Circuits and Systems, and guest co-editor for IEEE Signal Processing Magazine (2014) and the Proceedings of the IEEE (2015). From 2021 to 2023, he was editor-in-chief of IEEE Signal Processing Magazine. He received many awards, e.g., best paper awards of EURASIP (1992) and IEEE GRSS (2012), Medal Blondel (1997) from the French Electrical Engineering society, one Grand Prix of the French Académie des Sciences (2016), and the Claude Shannon-Harry Nyquist Technical Achievement Award of the IEEE Signal Processing Society (2023). He was elevated as IEEE fellow (2008), EURASIP fellow (2013) and as a Senior Member of Institut Universitaire de France during 10 years since 2008. He was the recipient of a 2012 ERC Advanced Grant for the project Challenges in Extraction and Separation of Sources (CHESS).
Wireless Communications for Distributed Computations
Abstract: In the recent years, the need of running machine learning (ML) services over wireless communication networks has promoted the design of new wireless communication protocols capable to efficiently support such ML services. In fact, in wireless networks, ML services face major challenges in terms of computation, bandwidth, scalability, privacy, and security. One proposal to overcome such challenges is Over-the-air computation (OAC), which is a known technique where wireless devices transmit values by analog amplitude modulation so that a function of these values (e.g., Federated Learning gradient aggregations) is computed over the communication channel at a common receiver. OAC dramatically reduces communication energy use, amplify spectrum efficiency of several order of magnitudes, and achieve privacy protections. The physical reason is the superposition properties of the electromagnetic waves, which naturally return sums of analog values. Consequently, the applications of OAC are almost entirely restricted to analog communication systems. However, the use of digital communications for OAC would have several benefits, such as error correction, synchronization, acquisition of channel state information, and easier adoption by current digital communication systems. Nevertheless, a common belief is that digital modulations are generally unfeasible for computation tasks because the overlapping of digitally modulated signals returns, in general, meaningless values. In this talk, we will break through such belief and present a fundamentally new computing method, named ChannelComp, for performing OAC by any digital modulation. We will show how digital modulation formats allow us to compute a wider class of functions than OAC can compute, and we propose a feasibility optimization problem that ascertains the optimal digital modulation for computing functions over the-air. We show by simulation the superior performance of ChannelComp in comparison to OAC.
Bio: Dr. Carlo Fischione is full Professor at KTH Royal Institute of Technology, Electrical Engineering and Computer Science, Division of Network and Systems Engineering (NSE), Stockholm, Sweden. He is Director of the KTH-Ericsson Data Science Micro Degree Program directed to Ericsson globally, Director of the undergraduate education at NSE, Chair of the IEEE Machine Learning for Communications Emerging Technologies Initiative, and founding General Chair of the IEEE International Conference on Machine Learning for Communications and Networking – IEEE ICMLCN 2024. He is distinguished lecturer of the IEEE Communication Society. He received the Ph.D. degree in Electrical and Information Engineering (3/3 years) in May 2005 and the Laurea degree in Electronic Engineering (Laurea, Summa cum Laude, 5/5 years) in April 2001, both from University of L’Aquila, Italy. He received the Starting Grant of the Swedish Research Council in 2008. Prof. Fischione has held research positions at Massachusetts Institute of Technology, Cambridge, MA (2015, Visiting Professor); Harvard University, Cambridge, MA (2015, Associate); and University of California at Berkeley, CA (2004-2005, Visiting Scholar, and 2007-2008, Research Associate). He is Honorary Professor at University of L’Aquila, Italy, Department of Mathematics, Information Engineering, and Computer Science. His research interests include applied optimization, wireless Internet of Things, and machine learning. He received a number of awards, such as the “IEEE Communication Society S. O. Rice” award for the best IEEE Transactions on Communications paper of 2018, the best paper award of IEEE Transactions on Industrial Informatics (2007). He is Editor of IEEE Transactions on Communications (Machine Learning for Communications area) and IEEE Transactions on Machine Learning for Communication and Networking, and has served as Associated Editor of IFAC Automatica (2014-2019). He is co-founder and scientific advisor of ELK.Audio. Prof. Fischione is Member of IEEE (the Institute of Electrical and Electronic Engineers), and Ordinary Member of DASP (the Italian academy of history Deputazione Abruzzese di Storia Patria).
Goal-oriented Semantic Communication for Decentralized Intelligence
Abstract: Future communication systems are evolving to cater to cyber-physical and decentralized interactive systems, such as swarm robotics, self-driving cars, and smart Internet of Things (IoT). The interconnection of myriad sensing- and learning-empowered devices will underpin the global functioning of our societies, enabling formidable progress in various sectors. The realization of this euphoric dream however hinges upon networks’ ability to deliver on an unprecedented number of highly demanding requirements. As we are entering the era of networked intelligence, fundamental advances are necessary to satisfy the pressing requirements for real-time communication, timely autonomous decision-making, and effective distributed processing. A promising step forward toward effective communication is goal-oriented semantic communication; a paradigm shift that aims at redefining data importance and timing, looking through the prism of the semantics of information. We highlight fundamental concepts, essential principles, and key functionalities required for effectively conveying only information representations and features, which are timely, relevant, and valuable for achieving end users’ goals. We discuss several recent theoretical results in the realm of rate-distortion-perception theory, timely source coding, pull-based communication, and relativistic information transfer. We conclude this talk by discussing the potential and the technical challenges associated with this promising avenue of research.
Bio: Marios Kountouris is a Professor at the Communication Systems Department, EURECOM, France, and a Distinguished Researcher at the Department of Computer Science and Artificial Intelligence, University of Granada, Spain. Before his current appointment, he has held positions at CentraleSupélec, France, Huawei Paris Research Center, France, the University of Texas at Austin, USA, and Yonsei University, S. Korea. He received a diploma degree in electrical and computer engineering from the National Technical University of Athens, Greece in 2002, and the M.S. and Ph.D. degrees in electrical engineering from Télécom Paris, France in 2004 and 2008, respectively. He is the recipient of a Consolidator Grant from the European Research Council (ERC) in 2020 on goal-oriented semantic communications. He has served as Editor for the IEEE Transactions on Wireless Communications, the IEEE Transactions on Signal Processing, and the IEEE Wireless Communication Letters. He has received several awards and distinctions, including the 2022 Blondel Medal, the 2020 IEEE ComSoc Young Author Best Paper Award, the 2016 IEEE ComSoc CTTC Early Achievement Award, the 2013 IEEE ComSoc Outstanding Young Researcher Award for the EMEA Region, the 2012 IEEE SPS Signal Processing Magazine Award, the IEEE SPAWC 2013 Best Paper Award, and the IEEE Globecom 2009 Communication Theory Best Paper Award. He is an IEEE Fellow, an AAIA Fellow, and a Professional Engineer of the Technical Chamber of Greece.
Organizers
Prof. Marcello Luiz Rodrigues de Campos (COPPE/UFRJ)
Prof. Charles Casimiro Cavalcante (DETI/UFC)
Sponsors
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