Keynote and Plenary Sessions Speakers

FAKHRI KARRAY is the founding co-director of the University of Waterloo Artificial Intelligence Institute and the Loblaws Research Chair in Artificial Intelligence in the department of electrical and computer engineering at  the University of Waterloo, Canada. He is also Professor of Machine Learning and the founding Provost at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a  graduate level, research based artificial intelligence (AI) university, in Abu Dhabi, UAE. Fakhri’s research interests are in the areas of operational AI, cognitive machines, natural human-machine interaction, autonomous and intelligent systems. Applications of his research include virtual care systems, cognitive and self-aware machines/robots/vehicles, predictive analytics in supply chain management and intelligent transportation systems. Recent work of Fakhri and his research team on deep learning-based driver behavior recognition and prediction has been featured on The Washington Post, Wired Magazine, Globe and Mail, CBC radio and Canada's Discovery Channel. He serves as Associate Editor and member of editorial board of major publications in the field of intelligent systems and information fusion. His most recent textbook Elements of Dimensionality Reduction and Manifold Learning is published by Springer Nature in February 2023. He was honored in 2021 by the IEEE Vehicular Technology Society (VTS) with the IEE EVTS Best Land Transportation  Paper Award for his pioneering work on  improving traffic flow prediction with weather Information in connected cars using deep learning and AI. His recent work on federated learning in communication systems, earned him and his co-authors, the 2022 IEEE Communication Society’s MeditCom Conference Best Paper Award. Fakhri is the co-founder and Chief Scientist of, a provider of AI based online learning systems. He is a Fellow of the IEEE, a Fellow of the Canadian Academy of Engineering, a Fellow of the Engineering Institute of Canada and the President of the Association for Image and Machine Intelligence. He served as a Distinguished Lecturer for the IEEE and is a Fellow of the Kavli Frontiers of Science. Fakhri received the Ing. Dip degree in electrical engineering from the School of Engineering of the University of Tunis, Tunisia and the PhD degree from the University of Illinois Urbana-Champaign, USA.  
PROF. FAKHRI KARRAY (Workshop Speaker)
University of Waterloo (Canada)

Impact of Operational Artificial Intelligence on Education and Research

Prof. Abdulmotaleb El Saddik is acting chair of the department of computer vision in MBZUAI and a Distinguished University Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa, Canada. He is an internationally recognized scholar who has made strong contributions to the knowledge and understanding of intelligent multimedia computing, communications and applications.  His research focus is on the establishment of digital twins for the metaverse  using AI, IoT, SN, AR/VR, and haptics to allow people to interact in real-time with one another as well as with their smart digital representations in the metaverse in a secure manner. He is Editor-in-Chief of the ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMM).
Dr. El Saddik is a Fellow of the Royal Society of Canada, Fellow of IEEE, Fellow of the Canadian Academy of Engineering and Fellow of the Engineering Institute of Canada. He is an ACM Distinguished Scientist and has received several awards, including the Friedrich Wilhelm Bessel Award from the German Humboldt Foundation, the IEEE Instrumentation and Measurement Society Technical Achievement Award. He also received IEEE Canada C.C. Gotlieb (Computer) Medal and A.G.L. McNaughton Gold Medal for important contributions to the field of computer engineering and science and the IEEE TCSC Achievement Award for Excellence in Scalable Computing.
University of Ottawa, Canada

AI-Empowered Metaverse Streaming

Due to the advancements in emerging technologies such as extended reality, artificial intelligence, blockchain, 5G, cloud/edge computing, etc., the metaverse has attracted a great deal of attention from both industry and academia. Through the extended reality devices, people can take advantage of the virtual world provided by the metaverse to play, work, and socialize. Metaverse streaming is a promising solution that performs complex rendering tasks at the edge server and streams the resulting video sequence back to the users over the network. We will discuss metaverse streaming techniques and the potential usage of ML to dynamically choose the appropriate bit rates for multiple users with a shared bottleneck network. We will show preliminary results and discuss open questions and possible R&D venues.  
MONCEF GABBOUJ received his BS degree in 1985 from Oklahoma State University, and his MS and PhD degrees from Purdue University, in 1986 and 1989, respectively, all in electrical engineering. Dr. Gabbouj is a Professor of Information Technology at the Department of Computing Sciences, Tampere University, Tampere, Finland. He was Academy of Finland Professor during 2011-2015. His research interests include Big Data analytics, multimedia content-based analysis, indexing and retrieval, artificial intelligence, machine learning, pattern recognition, nonlinear signal and image processing and analysis, voice conversion, and video processing and coding. Dr. Gabbouj is a Fellow of the IEEE and member of the Academia Europaea and the Finnish Academy of Science and Letters. He is the past Chairman of the IEEE CAS TC on DSP and committee member of the IEEE Fourier Award for Signal Processing. He served as associate editor and guest editor of many IEEE, and international journals and Distinguished Lecturer for the IEEE CASS. Dr. Gabbouj served as General Co-Chair of IEEE ISCAS 2019, ICIP 2020, ICIP 2024 and ICME 2021. Gabbouj is Finland Site Director of the USA NSF IUCRC funded Center for Visual and Decision Informatics (CVDI) and led the Artificial Intelligence Research Task Force of Finland’s Ministry of Economic Affairs and Employment funded Research Alliance on Autonomous Systems (RAAS).
PROF. MONCEF GABBOUJ (Plenary Speaker)
Tampere, University, Tampere, Finland

The Super Neuron Model – A new generation of ANN-based Machine Learning and Applications

Operational Neural Networks (ONNs) are new generation network models targeting to address two major drawbacks of conventional Convolutional Neural Networks (CNNs): the homogenous network configuration and the “linear” neuron model that can only perform linear transformations over previous layer outputs. ONNs can perform any linear or non-linear transformation with a proper combination of “nodal” and “pool” operators. This is a great leap towards expanding the neuron’s learning capacity in CNNs, which thus far required the use of a single nodal operator for all synaptic connections for each neuron. This restriction has recently been lifted by introducing a superior neuron called the “generative neuron” where each nodal operator can be customized during the training in order to maximize learning. As a result, the network is able to self-organize the nodal operators of its neurons’ connections. Self-Organized ONNs (Self-ONNs) equipped with superior generative neurons can achieve diversity even with a compact configuration. We shall explore several signal processing applications of neural network models equipped with the superior neuron.
Ahmed Chemori received his M.Sc. and Ph.D. degrees, both in automatic control from the Polytechnic Institute of Grenoble, France, in 2001 and 2005 respectively. During the year 2004/2005 he has been a Research and Teaching Assistant at Laboratoire de Signaux et Systèmes (LSS - Centrale Supelec) and University Paris 11. Then he joined Gipsa-Lab (Former LAG) as a CNRS postdoctoral researcher. He is currently a senior researcher in Automatic Control and Robotics for the French National Center for Scientific Research (CNRS), at the Montpellier Laboratory of Computer Science, Robotics and Microelectronics (LIRMM). His research interests include nonlinear (robust, adaptive, and predictive) control and their real-time applications in different fields of robotics (underactuated robotics, parallel robotics, underwater robotics, humanoid robotics, and wearable robotics). He is the author of more than 150 scientific publications, including international journals, patents, books, book chapters, and international conferences. He served as Associate Editor for various journals. He has been a TPC/IPC member and associate editor for different international conferences. He organized different scientific events, including summer schools, workshops, and international conferences. He has also delivered various plenary/keynote lectures at international conferences.
PROF. AHMED CHEMORI (Plenary Speaker)
LIRMM, University of Montpellier, CNRS Montpellier, France

Recent Advances in Motion Control of Parallel Robots for High-Speed Industrial Applications

Serial robotic manipulators consist of a set of sequentially connected links, forming an open kinematic chain. These robots are mainly characterized by their large workspace and their high dexterity. However, despite these advantages, in order to perform tasks requiring high speeds/accelerations and/or high precision; such robots are not always recommended because of their lack of stiffness and accuracy. Indeed, parallel kinematic manipulators (PKMs) are more suitable for such tasks. The main idea of their mechanical structure consists in using at least two kinematic chains linking the fixed base to the travelling plate, where each of these chains contains at least one actuator. This may allow a good distribution of the load between the chains. PKMs have important advantages with respect to their serial counterparts in terms of stiffness, speed, accuracy and payload. However, these robots are characterized by their high nonlinear dynamics, kinematic redundancy, uncertainties, actuation redundancy, singularities, etc. Besides, when interested in high-speed robotized repetitive tasks, such as food packaging and waste sorting applications, the key idea lies in looking for short cycle times. This means obviously to look for short motion and short stabilization times while guaranteeing the robustness and performance with respect to disturbances and changes/uncertainties in the operational conditions. Consequently, if we are interested in control of such robots, all these issues should be taken into account, which makes it a bit challenging task. This talk will give an overview of some proposed advanced control solutions for high-speed industrial applications of PKMs in food packaging, waste sorting, and machining tasks. The proposed solutions are mainly borrowed from nonlinear robust and adaptive control techniques and have been validated through real-time experiments on different PKM prototypes.
Dr Ashammakhi is leading translational research in biomaterials and tissue engineering with focus on advancing frontiers that integrate fields of science to develop microphysiological systems (MPSs), three-dimensional bioprinting (3DBP), and smart implants for disease modeling, tissue repair and regenerative medicine, leveraging his long-standing experience with biodegradable biomaterials, regenerative therapeutics and drug releasing devices. Previously, he led the development of innovative multifunctional drug-releasing biodegradable implants for reparative and regenerative medicine. He assumed various leading positions in different Universities before he relocated to the University of California Los Angeles (UCLA), as a Full Professor of Biomaterials Technology and Vice Director of the Institute of Biomaterials at Tampere University of Technology, Finland, Chair (Full Professor) of Regenerative Medicine at Keele University, UK and Adjunct Professor at Oulu University, Finland. At UCLA Dr. Ashammakhi has been an Associate Director of the Center for Minimally Invasive Therapeutics (C-MIT), Associate Adjunct Professor, Department of Radiological Sciences, David Geffen School of Medicine, UCLA, and Affiliate Faculty at the Department of Biomedical Engineering, Henry Samueli School of Engineering, UCLA. In Michigan State University, he has been an Associate Adjunct Professor, Department of Biomedical Engineering and Institute for Quantitative Health Science and Engineering (IQ), where he is currently a Senior Specialist. He has been leading various externally funded collaborative multidisciplinary projects in multifunctional biomaterials, tissue engineering, 3DBP and MPS technology (Funds from the NIH, AHA, EU and Industry). He has published hundreds of papers, patent/patent applications, book chapters and conference abstracts (H Index = 54) and succeeded in cofounding a startup and translating technologies to the clinic. To advance the field, he has been leading special sessions and panels in major conferences on translational 3DBP and MPS technologies. For up-to-date and relevant publications, see: Mail: Institute for Quantitative Health Science and Engineering (IQ), Department of Biomedical Engineering, College of Engineering, Michigan State University, 775 Woodlot Dr., East Lansing, MI 48824. Email:,
Department of Biomedical Engineering and Institute for Quantitative Health Science and Engineering (IQ), Michigan State University, Michigan, USA

Integrating technologies to develop smarter implants and improved health care

Biomaterials have evolved from inert to biodegradable, bioactive and multifunctional. With advances in innovative processing techniques, it was possible to build strong yet biodegradable multifunctional implants that made an impact on clinical care of many patients worldwide. Advances in tissue engineering made it possible to accommodate cells in biodegradable scaffolds and develop living implants. To mimic tissue structure, nanofiber-based constructs were then developed. With the advent of three-dimensional (3D) bioprinting, cell containing bioinks were developed and control over cell distribution in engineered tissue constructs was achieved. To further leverage the advantages biodegradable materials offer, biodegradable sensors were developed to allow temporary monitoring of certain functions and parameters in the body. Further, it was possible to develop sensor-integrating implants that can sense changes in their microenvironment before these changes evolve into irreversible problems that lead to implant failure and necessitate surgical removal. There are already major developments that include developing electroconductive, self-healing and four-dimensional (4D) biomaterials. In future, combined approaches and technologies merge will enable the development of implants with self-awareness, actuation, self-correction/healing, and behavior mimicking that of native tissues.
Mohsen Guizani (Fellow, IEEE) received the BS (with distinction), MS and PhD degrees in Electrical and Computer engineering from Syracuse University, Syracuse, NY, USA in 1985, 1987 and 1990, respectively. He is currently a Professor of Machine Learning and the Associate Provost at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE. Previously, he worked in different institutions in the USA. His research interests include applied machine learning and artificial intelligence, Internet of Things (IoT), intelligent autonomous systems, smart city, and cybersecurity. He was elevated to the IEEE Fellow in 2009 and was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2019, 2020 and 2021. Dr. Guizani has won several research awards including the “2015 IEEE Communications Society Best Survey Paper Award”, the Best ComSoc Journal Paper Award in 2021 as well five Best Paper Awards from ICC and Globecom Conferences. He is the author of ten books and more than 800 publications. He is also the recipient of the 2017 IEEE Communications Society Wireless Technical Committee (WTC) Recognition Award, the 2018 AdHoc Technical Committee Recognition Award, and the 2019 IEEE Communications and Information Security Technical Recognition (CISTC) Award. He served as the Editor-in-Chief of IEEE Network and is currently serving on the Editorial Boards of many IEEE Transactions and Magazines. He was the Chair of the IEEE Communications Society Wireless Technical Committee and the Chair of the TAOS Technical Committee. He served as the IEEE Computer Society Distinguished Speaker and is currently the IEEE ComSoc Distinguished Lecturer.
PROF. MOHSEN GUIZANI (Plenary Speaker)
Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE.

Smart City Applications with Pervasive AI

Abstract: Internet of Things (IoT) systems have expanded the role of Artificial intelligence (AI) in many applications. On the other hand, AI has witnessed a substantial usage in different IoT applications and services, spanning the smart city systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensed data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams to predict future insights and revolutionize the decision-taking process, inaugurates pervasive AI systems as a worthy paradigm to achieve better predictions which can lead to a better quality-of-life. The confluence of pervasive computing and artificial intelligence (Pervasive AI) expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy concerns, scalability, and latency requirements. In this context, a wise cooperation and resource scheduling should be envisaged for a smart city using IoT devices (e.g., smartphones, smart healthcare, and smart vehicles) and infrastructure (e.g., edge nodes and base stations) to avoid communication and computation overheads and ensure maximum performance and efficient accuracy. In this talk, a quick review of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems will be given. Specifically, a description of pervasive computing, its architecture, and its intersection with artificial intelligence is presented. Then, we review the background, applications, and performance metrics of AI, particularly Federated Learning (FL), running in a ubiquitous system. Next, we present some communication-efficient techniques of distributed inference, training and learning tasks across a plethora of IoT devices, edge devices and cloud servers. Finally, we discuss future directions in this area and provide some research challenges.
Kaan Ozbay joined Civil and Urban Engineering at NYU Tandon School of Engineering and Center for Urban Science and Progress (CUSP) as a tenured full Professor at in 2013.  He is currently the founding Director of the C2SMART Center (Tier 1 UTC funded by USDOT).  Prior to that Professor Ozbay was a tenured full Professor at Rutgers University’s Department of Civil and Environmental Engineering where he joined as an Assistant Professor in July 1996. In 2008, he was a visiting scholar at the Operations Research and Financial Engineering (ORFE) Department at, Princeton University. Dr. Ozbay is the recipient of the prestigious National Science Foundation (NSF) CAREER award. His research interests in transportation cover a wide range of topics including data analytics for advanced technology and sensing applications in smart cities, development of simulation models of large scale complex transportation systems, modeling and evaluation of traffic incident and emergency management, feedback based on-line real-time traffic control techniques, traffic safety, application of operations research techniques in network optimization and humanitarian inventory control, and transportation economics. He has co-authored 4 books and published approximately 400 refereed papers in scholarly journals and conference proceedings. Prof. Ozbay is also an Associate Editor of the ITS journal. Dr. Ozbay is the co-editor of a book titled “Dynamic Traffic Control & Guidance” published by Springer Verlag’s "Complex Social, Economic and Engineered Networks" series in 2013. He also serves as the Associate Editor of Networks and Spatial Economic journal and Transportmetrica B: Transportation Dynamics journal. Since 1994, Dr. Ozbay, has been the Principal Investigator and Co-Principal Investigator of more than 100 projects funded at a level of more than $25,00,000 by National Science Foundation, NJDOT, NYMTC, NY State DOT, New Jersey Highway Authority, USDOT, FHWA, VDOT, CUNY University Transportation Research Center (UTRC), Department of Homeland Security, USDOT ITS Research Center of Excellence.
PROF. KAAN OZBAY (Plenary Speaker)
Department of Civil and Urban Engineering & C2SMART Center Tandon School of Engineering New York University, U.S.A.

Traffic Control for Efficient and Safe Transportation Systems in the Era of Connected and Automated Vehicles 

In the last several years,  there have been number of novel approaches to improve traffic operations and safety especially in highly congested urban areas.  Most of the innovation in these research and deployment efforts are fueled by the advances in connected & autonomous vehicles (CAV) as well as ubiquitous mobile devices, sensors and cameras deployed throughout these urban areas. In this talk, we will first provide a high-level state of the traffic control research and deployment as a result of advances in connected and automated vehicles as well as V2X communication technologies.  We will then discuss research on pro-active traffic management and control approaches with a focus on operations and safety in tandem.  A case study from the recently completed NY City connected vehicle pilot test where NY University C2SMART researchers participated as one of the University partners will be presented to describe the “soft control” approach in terms of in-vehicle warnings given to drivers. Second, the role and importance of a real-world cyber-physical test bed in these research efforts will be discussed using a learning-based headway control algorithm for transit buses tested in microscopic simulation. The talk will be concluded with a discussion of opportunities and challenges in traffic control research, development, and deployment in complex urban environments such as NYC and Washington D.C.
Mohamed Deriche  received his B.Sc. degree in electrical engineering from the National Polytechnic School, Algeria, and his Ph.D. degree in signal processing from the University of Minnesota in 1994. He worked at Queensland University of Technology, Australia, before joining King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran, Saudi Arabia, where he leads the signal processing group. He has published more than 300 papers in multimedia signal and image processing. He delivered numerous invited talks and chaired several conferences including GlobalSIP-MPSP, IEEE Gulf (GCC), Image Processing Tools and Applications, and TENCON (a Region 10 conference). He has supervised more than 35 M.Sc. and Ph.D. students and is the recipient of the IEEE Third Millennium Medal. He also received the Shauman Best Researcher Award, and both the Excellence in Research and Excellence in Teaching Awards at KFUPM.
Artificial Intelligence Research Centre, Ajman University, UAE

Multimodal Emotion Recognitions: Needs, challenges, and Opportunities

Emotion plays an important role in diverse real-life applications. Applications include: Video gaming, Medical diagnosis, Education, Employee safety, Patient care, Autonomous car, among others.  The emotion of a person can be identified by various sources of information like speech, transcript, facial expression, brain signal (EEG), and a combination of two or more of these signals. Among these sources, speech is seen at the most common attribute and the easiest to acquire, and use. Speech attributes are not substantially affected by side information such as physical movement, visual occlusion, beard, etc. Moreover, speech features for emotion recognition are quite invariant to language. However, recent research efforts have shown that the accuracy of speech based emotion recognition systems can still be enhanced using visual cues such as facial expressions. With the advances made in computing power and the availability of large amounts of data, it is becoming now possible to combine and analysis huge amounts of data using advanced neural networks such as deep networks. In this presentation, we will discuss the fundamental concept of emotion recognition. We will then analysis current research using speech and facial expression separately, then we move to more recent multimodal emotion recognition systems. Finally, we will provide a perspective on future research directions in this area with some major challenges and potential applications in this era of multimedia and smart living.  
Abdesselam Bouzerdoum received the M.Sc. and Ph.D. degrees in electrical engineering from the University of Washington, Seattle, USA. He has extensive experience in teaching, research, and academic leadership. He is currently serving as Associate Provost for Academic Affairs at Hamad Bin Khalifa University (HBKU), Doha, Qatar. Most recently, he served as Head of the ICT Division, College of Science and Engineering, HBKU. In 2004, he was appointed Professor and Head of School of Electrical, Computer and Telecommunications Engineering at the University of Wollongong (UOW), Wollongong, Australia, where he also served as Associate Dean (Research) from 2007 to 2013. In 2015, he was promoted to Senior Professor of computer engineering at UOW. From 2009 to 2011, he was a member of the Australian Research Council College of Experts and served as Deputy Chair of the EMI panel. He was a Distinguished Visiting Professor at several international institutions in France, USA, Germany, China, and New Zealand.  Dr. Bouzerdoum is the recipient of the Eureka Prize for Outstanding Science in Support of Defence or National Security (2011), the Chester Sall Award of IEEE Trans. Consumer Electronics (2005), and a Distinguished Researcher Award (Chercheur de Haut Niveau) from the French Ministry (2001). He served as Associate Editor for 5 International journals, including IEEE Transactions on Image Processing. His main research interests include signal and image processing, radar imaging, vision, machine learning, and pattern recognition  
Hamad Bin Khalifa University (HBKU), Doha, Qatar

Semantic Segmentation using Deep Learning and its Applications

Semantic scene segmentation is a challenging problem that has great importance in many applications, including assistive and autonomous navigation systems. Such vision systems must cope with image distortions, lighting variations, changing surfaces, and varying illumination conditions. In this talk, we will present deep learning-based vision systems for fast and accurate object segmentation and scene parsing. Furthermore, the talk will present a hybrid deep learning approach for semantic segmentation. The new architecture combines Bayesian learning with deep Gabor convolutional neural networks (GCNNs) to perform semantic segmentation of unstructured scenes. In this approach, the Gabor filter parameters are modeled as normal distributions with mean and variance that are learned using variational Bayesian inference. The resulting network has a compact architecture with smaller number of trainable parameters, which helps mitigate the overfitting problem.        
Pasquale Arpaia took Master Degree and PhD in Electrical Engineering at University of Napoli Federico II (Italy), where he is full professor of Instrumentation and Measurements. He is Director of the Interdepartmental Center for Research on Management and Innovation of Health (CIRMIS), Head of the Instrumentation and Measurement for Particle Accelerators Laboratory (IMPALab), the Augmented Reality for Health Monitoring Laboratory (ARHeMlab), the Hi-Tech Academic FabLab Unina, as well as Chairman of the Stage Project of the University Federico II. He is Team Leader at European Organization for Nuclear Research (CERN). He was also professor at University of Sannio, associate at Institutes of Engines and Biomedical Engineering of CNR, and now of INFN Section of Naples. He is Associate Editor of the Institute of Physics Journal of Instrumentation, Elsevier Journal Computer Standards & Interfaces, MDPI Instruments, and in the past also of IEEE Transactions on Electronics Packaging and Manufacturing. He was Editor at Momentum Press of the Book Collection “Emerging Technologies in Measurements, Instrumentation, and Sensors”. In last years, he was scientific responsible of more than 30 awarded research projects in cooperation with industry, with related patents and international licences, and funded 4 academic spin off companies. He acted as scientific evaluator in several international research call panels. He continuously serves as organizing and scientific committee member in IEEE and IMEKO Conferences. He is plenary speaker in several scientific conferences. He published 3 books, several book chapters, and about 300 scientific papers in journals and national and international conference proceedings. His PhD students were awarded in 2006, 2010, and 2020 at IEEE I2MTC, as well as in 2016 and 2012, 2018 at IMEKO TC-10 and World Conferences, respectively.  
University of Naples Federico II

Wearable Brain-Computer Interfaces for measuring mental states: After data, are we loosing also thoughts privacy?

In the last two decades, Brain-Computer Interface (BCI) has gained great interest in the technical-scientific community, and more and more effort has been done to overcome its limitations in daily use. In Industry 4.0 framework, human becomes part of a highly-composite automated system and new-generation user interfaces, integrating cognitive, sensorial, and motor skills, are designed. Humans can send messages or decisions to the automation system through BCI by intentional modulation of brain waves. However, through the same signal, the system (and, hence, also the human being part of it) acquires information on the user status. In this talk, most interesting results of this technological research effort, as well as its further most recent developments, are reviewed. In particular, after a short survey on research at University of Naples Federico II also in cooperation with CERN, the presentation focuses mainly on state-of-the-art research on wearable measurement systems for acting robots and monitoring mental states (emotions, engagement, distraction, stress and so on). Tens of disparate case studies, carried out by Federico II researchers, spacing from children autism rehabilitation to robotic inspection in hazardous sites, are reported. Special attention is given also to ethic and law issues arising from daily use, by leaving puzzling questions to attendees.    
Professor Dr.-Ing. habil. Thomas Fröhlich (born 1969) completed undergraduate and graduate studies at the Technical University of Ilmenau (TUI). From 1992 to 2000 he performed research at the Institute of process measurement and sensor technology (IPMS) at TUI in the areas of temperature measurement, humidity, high-precision force measurement as well as signal processing and disturbance compensation. His habilitation, which carried the title Temperature Compensation of Precision Measuring Devices, discusses the possibilities for modelling the static and dynamic thermal behaviour of measuring devices. Building upon regularly used methods for static temperature compensation and using control theory and system identification, model-generation methods were developed for use in measuring systems to reduce undesired temperature influence. During his time at the Institute of Process Measurement and Sensor Technology, he successfully completed a second course of studies at the Institute of Mathematics at the TUI, making him a "Diplom- Mathematikeräs well. He was employed as a researcher at Sartorius AG Göttingen from January 2001 to August 2009, his last position being that of Director of Development in the area of mass comparators. There he dealt with the high-precision determination of mass using comparator balances and with mass metrology and among other things he was the project leader responsible for the development of the 1 kg prototype comparator in cooperation with the Bureau International des Poids et Mesures (BIPM), Sartorius AG Göttingen and the Institute of Process Measurement and Sensor Technology at the Ilmenau University of Technology. This prototype com-parator makes it possible to perform high-precision, dependable measurement on 1 kg prototypes with a standard deviation of under 50 nano gramm in a vacuum and under 100 nano gramm under air-tight conditions (atmosphere). In 2009 Thomas Fröhlich was named professor of process measurement technology at the Ilmenau University of Technology, becoming the successor of Professor Gerd Jäger, who was the long-time chair of the Department of Process Measurement Technology and the spokesman of the Collaborative Research Centre Nanopositioning and Nanomeasuring Machines. The Institute of Process Measurement and Sensor Technology in the Faculty of Mechanical Engineering, which has been headed by Prof. Fröhlich since 2010, is a worldwide leader in the area of force and mass measurement. As part of the bachelor and master programmes Thomas Fröhlich holds lectures entitled Process Measurement and Sensor Technology, Digital Signal Processing with MATLAB, Computer-Aided Methods in Mechanical Engineering, Temperatur Measurement and Force and Mass Measurement Technology. Thomas Fröhlich was appointed as visiting professor of China Jiliang University at Hangzhou in 2013 and of Tianjin University in 2017 and act as editor in chief of the journal Technisches Messen since 2021. He had many short term visits to BIPM and national institutes of metrology: LNE/France, CEM/Spain, NIST/USA, Canada, Singapore, Thailand, NPL/India, PTB/Germany, NIM/China, SIMT/China, Algeria, Egypt, KRISS/South Korea and VNIIM Russia.  
Faculty of Mechanical Engineering, Technische Universität Ilmenau

One-Step Traceability

With progressive digitization, modern production and with ever more complex measurements, measurement uncertainties and digital twins are becoming increasingly important. Metrological traceability is an important property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement uncertainty. In every calibration, metrological traceability is an important consideration. Metrological traceability requires an established calibration hierarchy which could be shortened to a single step if the measurement is a direct fundamental physical realization of the measurement unit from its definition. With the SI unit definitions that have been valid since 2019, such measurements based on direct realizations are practically possible and are referred to as "one-step traceability" methods. After a short introduction into measurement uncertainties and metrological traceability we show different approaches for one-step traceability methods for the practical measurement of time, length, electrical units, mass, force and temperature.  
Joao Pinto was born in Valparaiso, Brazil. He received his B.S degree in Electrical Engineering from the Universidade Estadual Paulista, in Brazil, in 1990, his M.S. degree from the Universidade Federal de Uberlândia, in Brazil, in 1993, and his Ph.D. degree from The University of Tennessee, in Knoxville, in 2001. He was a Faculty Member at the Federal University of Mato Grosso do Sul (UFMS), Campo Grande, Brazil, from 1994 to 2021, where he also served as the Dean of the Engineering College from 2013 to 2017. He was the founder and director of BATLAB, Artificial Intelligence Applications, Power Electronics and Drives, and Energy Systems. He is a Faculty Member of the Federal University of Rio de Janeiro, Rio de Janeiro, Brazil, on leave of absence since 2021.  Currently, he is a Senior Researcher at Oak Ridge National Laboratory, Oak Ridge, U.S. He served for two years as Editor-in-Chief of the Brazilian Power Electronics Transactions, and for two years as the President of the Brazilian Power Electronics Society, and he is a Fellow Member of The International Society of Engineering Asset Management. From August 2008 to December 2011 he was the Mato Grosso do Sul State Undersecretary for Research and Development Issues. He is two-time first award winner of the IEEE – IFEC International Future Energy Challenge. He has over 200 published papers in journals and conferences proceedings. His research interests include power electronics, artificial intelligence applications, energy systems, electrical machine drives, among others.
PROF. JOAO O. P. PINTO (Keynote Speaker_ PSE)
Oak Ridge National Laboratory, Oak Ridge - TN - USA

Artificial Intelligence/Machine Learning Applications on Power Electronics and Motor Drives

Udaya K. Madawala graduated with a B.Sc. (Electrical Engineering) (Hons) degree from The University of Moratuwa, Sri Lanka in 1987, and received his PhD (Power Electronics) from The University of Auckland, New Zealand in 1993 as a Commonwealth Doctoral Scholar.  At the completion of his PhD, he was employed by Fisher & Paykel Ltd, New Zealand, as a Research and Development Engineer to develop new technologies for motor drives.  In 1997 he joined the Department of Electrical and Computer Engineering at The University of Auckland and, at present as a Full Professor, he leads a large group of researchers focusing on a number of power electronics projects that are related to energy and wireless EV charging for V2X applications. Udaya is a Fellow of the IEEE and was a Distinguished Lecturer of the IEEE Power Electronic Society (PELS), and has over 30 years of both industry and research experience in the fields of power electronics and energy. He has served both the IEEE Power Electronics and Industrial Electronics Societies in numerous roles, relating to editorial, advisory, conference, technical committees and chapter activities. Currently, Udaya is an Associate Editor for IEEE Transactions on Power Electronics, and a member of both the Administrative Committee and Membership Development Committee of the IEEE Power Electronics Society.  He was the General Chair of the 2nd IEEE Southern Power Electronics Conference (SPEC)- 2016, held in New Zealand, and is also the Chair of SPEC Steering Committee. Udaya, who has over 300 journal and conference publications, holds a number of patents related to wireless power transfer (WPT) and power converters, and is a consultant to industry.
University of Auckland New Zealand

Advances in Wireless Power Transfer Technology

Wireless Power Transfer (WPT) systems, based on inductive power transfer (IPT) technology, are becoming increasing popular in many applications.  As research in this area is progressing at a rapid rate, this seminar introduces some of the recent advances in IPT based WPT technology.  New circuit and magnetic modelling techniques that can be employed to investigate different types of compensation circuits and coil structures are discussed.  An optimal control strategy that allows for regulated power transfer with impedance matching for maximum efficiency, regardless of large variations in coupling and load, is also presented  highlighting the key advantages.  
Jorge Rodas (S'08--M'12--SM'19) was born in Asuncion, Paraguay, in 1984. He received his Engineer degree in electronic engineering from the Universidad Nacional de Asuncion (UNA), Paraguay, in 2009. He received his M.Sc. degrees from the Universidad de Vigo, Spain, in 2012 and from the Universidad de Sevilla, Spain, in 2013, and his joint-university Ph.D. degree between the Universidad Nacional de Asunción and the Universidad de Sevilla in 2016.   In 2011, he joined the Laboratory of Power and Control Systems, Faculty of Engineering, Universidad Nacional de Asuncion, where he currently serves as a Professor and Research Director. He was visiting professor at the Power Electronics and Industrial Control Research Group of École de Technologie Supérieure (Montreal, Canada) and the Center of Technological Innovation in Static Converters and Drives (CITCEA) of Polytechnic University of Catalonia, (Barcelona, Spain), in 2017 and 2022, respectively. He serves as an Associate Editor of Elsevier Alexandria Engineering Journal (AEJ) and Guest Editor in the IEEE Journal of Emerging and Selected Topics in Power Electronics, and MDPI World Electric Vehicle Journal. He served as Guest Editor of MDPI Energies and frontiers in Energy Research. In 2020, Prof. Rodas received the Paraguayan National Science Award. He has authored and co-authored two book chapters and more than 100 journal and conference papers. His research interest focuses on applications of advanced control to real-world problems. Current research activities include applying finite control set model predictive control and nonlinear control to power electronic converters, renewable energy conversion systems, electric motor drives, and robotic systems (especially unmanned aerial vehicles).
PROF. JORGE RODAS (Keynote Speaker_ SAC)
National University of Asunción, Paraguay

Finite-Control-Set Model Predictive Control Techniques of Multiphase Electric Drives

With more than three phases, multiphase machines recently captured high-power, high-reliability applications such as electric vehicles, ship propulsion and wind energy conversion systems. Its innate fault-tolerant ability without needing extra hardware is still considered its most practical benefit. Moreover, its additional degrees of freedom opened the window for miscellaneous nontraditional objectives at the expense of the need for more advanced control strategies. For that reason, numerous papers are now available regarding implementing control techniques for multiphase machines, moving towards classic control techniques: field-oriented control and direct torque control,  to more sophisticated ones: sliding mode control and finite-control-set model predictive control (FCS-MPC). Thus, this presentation discusses the latest developments in FCS-MPC of two of the most popular multiphase electric drive configurations, five-phase and six-phase.