Invited talks

Prof. Alessandra Costanzo, University of Bologna, Italy

Title: Toward Self-Sustaining Sensing Systems: Advances in Passive and Self-Oscillating Microwave Architectures
Abstract: Microwave sensing has proven to be a powerful enabler for real-time, non-invasive monitoring in biomedical, environmental, and chemical domains. In this talk, an overview of the research developed at the University of Bologna, with a focus on energy-efficient and wearable microwave sensors for hydration and ethanol detection will be presented.
Our work spans multiple application domains—from human skin hydration sensing using compact resonant structures and machine learning, to ethanol detection via battery-less, wirelessly powered “filtennas” integrated with microfluidic channels. These solutions offer low-cost, scalable approaches for real-world diagnostics in healthcare and agricoltural applications. A key innovation I will highlight is the use of self-oscillating antennas (SOAs) for plant hydration monitoring. In this design a tree trunk becomes part of the oscillator’s load, allowing hydration levels to be inferred from changes in the system steady-state oscillation regime. This approach eliminates the need for external measurement circuitry, creating a truly autonomous sensing node ideal for smart agriculture and forestry.
Biography: Alessandra Costanzo is full Professor at the University of Bologna, Italy since 2018. She is IEEE Fellow, class 2022, for contribution to “nonlinear electromagnetic co-design of RF and microwave circuits”. Her current research activities are dedicated to the design of entire wireless power transmission systems, for several power levels and operating frequencies. She has developed efficient design procedures based on the combination of electromagnetic and nonlinear numerical techniques, adopting both far-field and near-field solutions, thus creating the bridge between system-level and circuit-level analysis techniques of RF/microwave wireless links. She has accomplished this goal by means of a general-purpose approach combining electromagnetic (EM) theory, EM simulation inside the nonlinear circuit analysis, based on the Harmonic Balance Technique. She is currently the principal investigator of many research and industrial international projects at microwave and millimeter wave dedicated to Industrial IoT, and smart mobility. She has authored more than 300 scientific publications on peer reviewed international journals and conferences and several chapter books. She owns four international patents. She is past associate editor of the IEEE Transaction on MTT, of the Cambridge Journals IJMWT and JWPT. She is past Vice-President for publication of the IEEE CRFID and conference chair of EuMC2022 and of IEEE WiSEE 2023. She has been TPC chair of IEEE WPTCE 2025. She is past chair of the Steering Committee of the IEEE WPTCE. She is independent member of the Board of Directors of Rai Way S.p.A. since 2023

Prof. Branislav M. Notaroš, Colorado State University, USA

Title: Recent Advances in Electromagnetic Computation, Design, and AI: From Multifaceted Methods to Interdisciplinary Applications
Abstract: Among most notable challenges in electromagnetic computation and design, a need for coordinated error estimation, adaptive refinement, and uncertainty quantification is nearly universal for realistic problems across application domains. This is essential for modern effective and reliable simulation-based design in mission-critical applications. On the other hand, advancements in the methods and applications of artificial intelligence (AI) and machine learning (ML) have recently made huge strides. This invited paper presents recent advances in electromagnetic computation, design, and AI, from multifaceted methods to interdisciplinary applications. We present a synergistic combination of error control, automatic adaptivity, hp-refinement, and sensitivity analysis to enhance the accuracy, efficiency, versatility, reliability, and practicality of a broad class of simulation and design approaches. The paper also presents utilization of AI/ML for a range of state-of-the-art topics and applications. As a result, we outline some emerging mission-critical interdisciplinary applications, from indoor and outdoor wireless propagation characterization and optical and radar observations and analyses of precipitation to medical imaging and clinical diagnostics using electromagnetic fields.
Biography: Branislav M. Notaroš is a Professor of Electrical and Computer Engineering, Director of Electromagnetics Laboratory, and University Distinguished Teaching Scholar at Colorado State University. Previously, he held assistant/associate-professor positions at the University of Massachusetts Dartmouth and University of Belgrade. His research contributions are in computational and applied electromagnetics. His publications include about 340 journal and conference papers, and textbooks “Electromagnetics” (2010) and “MATLAB-Based Electromagnetics” (2013) with Pearson Prentice Hall and “Conceptual Electromagnetics” (2017) with CRC Press. Prof. Notaroš serves as Immediate Past President of the IEEE Antennas and Propagation Society (AP-S) and the Applied Computational Electromagnetics Society (ACES), Immediate Past Chair of the USNC-URSI Commission B, and Track Editor of the IEEE Transactions on Antennas and Propagation. He served as General Chair of the IEEE APS/URSI 2022 Denver Conference, Chair of the IEEE AP-S Meetings Committee, Chair of the Joint Meetings Committee, and AP-S AdCom member. He was the recipient of the 1999 IEE Marconi Premium, 2005 IEEE MTT-S Microwave Prize, 2022 IEEE Antennas and Propagation Edward E. Altshuler Prize Paper Award, 2019 ACES Technical Achievement Award, 2014 Carnegie Foundation Colorado Professor of the Year Award, 2015 ASEE ECE Distinguished Educator Award, 2015 IEEE Undergraduate Teaching Award, and many other research and teaching awards. He is Fellow of IEEE and ACES.

Prof. Dejan Drajić, University of Belgrade, Serbia

Title: The influence of input buffer size on neural network performance in LEO satellite channel forecasting
Abstract:  Communication through LEO satellites creates an opportunity to cover remote areas and provide a reliable connection in various scenarios. Optimizing this communication through channel forecasting creates multiple benefits by increasing communication reliability and communication speed. This can be done by using artificial neural networks, and training them to forecast the future channel SNR values, based on  previously observed ones. These previous SNR values that are the inputs to the neural networks are pivotal for the success of the prediction, and the performance can vary greatly based on the number of samples that the prediction should be based on. In this paper, we investigate how this input sequence length affects the quality of predictions, as well as practical implications in terms of the computational resource usage.
Biography: Dejan Drajić is a full professor at the Department of Telecommunications at the School of Electrical Engineering of the University of Belgrade and a senior research associate at the Innovation center of the School of Electrical Engineering in Belgrade. He received his doctorate in 2004 at the School of Electrical Engineering of the University of Belgrade. He worked as a research scientist at the Center for Wireless Communications at the University of Oulu, Finland, from 2000 to 2002. His main research areas include M2M communications, IoT, wireless sensor networks and smart cities. He actively participates in numerous projects. He is a senior member of the IEEE organization, the Telecommunications Society and a reviewer in numerous journals and a participant in conferences. He is the author of four textbooks and co-author of one textbook in Serbian, as well as eight chapters in books in English. He has published 25 papers in journals from the SCI list. He is the winner of numerous awards.

Prof. Dragan Olćan, University of Belgrade, Serbia

Title: Recent advances in applications of machine learning in electromagnetics
Abstract: The overview of current research regarding applications of machine learning in electromagnetics is presented. The first line of the research focuses on regressions of electromagnetic fields and related parameters using feed-forward neural networks. Of interest is an exploration of the compromises between the accuracy of regressions, minimizing topological complexity of networks, and fast training. The second line of research explores the influence of the sizes of training electromagnetic datasets (up to 10 million), since their creation consumes significant computer resources. The third line of research investigates the accuracy of models and their improvements using ensembles of (weak) prediction models and involved algorithms.
Biography: Dragan I. Olćan (S’05-M’09) is a Full Professor at the School of Electrical Engineering, University of Belgrade, Serbia. At the same school he received B.Sc., M.Sc., and Ph.D. degrees in 2001, 2004, and 2008, respectively. He is a coauthor of three commercial electromagnetic software codes, 28 journal papers, 120 conference papers, and 7 textbooks. His main research interests are optimization algorithms and machine learning applied to electromagnetic design and numerical electromagnetic analysis, and electromagnetic compatibility.

Prof. Nebojsa Bacanin, Singidunum University, Serbia

Title: Intrusion Detection for Smart City Security by Boosting Algorithms Optimized by Metaheuristics Algorithm
Abstract: The flourishing of smart cities has introduced complex and mutually connected cyber-physical systems depending on IoT devices, making them extremely vulnerable to sophisticated cyber attacks. Efficient intrusion detection systems are essential to safeguard these environments, since traditional security solutions fall short in these circumstances. This study explores the integration of XGBoost, a powerful ensemble learning algorithm, with an adapted implementation of a well-known metaheuristic algorithm to improve the detection of network intrusions. Metaheuristics algorithm is employed to fine-tune XGBoost’s hyperparameters, improving classification acuracy and reducing computational overhead. Experimental assessment on benchmark intrusion detection dataset demonstrates that the metaheuristic-optimized XGBoost models significantly outperform baseline models in terms of accuracy, precision, recall, and F1-score. The introduced methodology presents a robust and scalable IDS framework appropriate for the complex and resource-constrained infrastructure of smart cities.
Biography: Nebojsa Bacanin received his Ph.D. degree from the Faculty of Mathematics, University of Belgrade, in 2015 (study program Computer Science, average grade 10,00). He started his university career in Serbia 18 years ago at the Graduate School of Computer Science in Belgrade. He currently works as Full Professor, Vice-Rector for Scientific Research and Head of Applied Artificial Intelligence study program at Singidunum University, Belgrade, Serbia. He is involved in scientific research in the field of computer science, and his specialty includes stochastic optimization algorithms, swarm intelligence, soft-computing, optimization, and modeling, as well as artificial intelligence algorithms, swarm intelligence, machine learning, image processing, and cloud and distributed computing. He has published more than 480 scientific papers (more than 220 SCIE papers) in high-quality journals and international conferences indexed in Clarivate Analytics JCR, Scopus, WoS, IEEExplore, and other scientific databases. As member of numerous Editorial boards in cutting-edge international journals and Committee boards of international conferences, he regularly edit and perform review activities. He has also been included in the prestigious Stanford University list of the 2% best world researchers, when the whole career is considered, in the field of Artificial Intelligence. Also, according to AD scientific index, he is currently listed as the best researcher from Computer Science area in Serbia.

Prof. Sara Goze, Cognitek Digital, Turkey

Title: From ENGINEERINGELECTROMAGNETICS to ELECTROMAGNETIC ENGINEERING: Teaching/Training Next Generations
Abstract: The role of Electromagnetic (EM) fields in our lives has been increasing. Communication, remote sensing, integrated command/ control/surveillance systems, intelligent transportation systems, medicine, environment, education, marketing, and defense are only a few areas where EM fields have critical importance. We have witnessed the transformation from Engineering Electromagnetics to Electromagnetic Engineering for the last few decades after being surrounded by EM waves everywhere. Among many others, EM engineering deals with broad range of problems from antenna design to EM scattering, indoor–outdoor radiowave propagation to wireless communication, radar systems to integrated surveillance, subsurface imaging to novel materials, EM compatibility to nano-systems, electroacoustic devices to electro-optical systems, etc. The range of the devices we use in our daily life has extended from DC up to Terahertz frequencies. We have had both large-scale (kilometers-wide) and small-scale (nanometers) EM systems. A large portion of these systems are broadband and digital and must operate in close proximity that results in severe EM interference problems. Engineers must take EM issues into account from the earliest possible design stages. This necessitates establishing an intelligent balance between strong mathematical background (theory), engineering experience (practice), and modeling and numerical computations (simulation). This Distinguished/keynote lecture aims at a broad-brush look at current complex EM problems as well as certain teaching / training challenges that confront wave-oriented EM engineering in the 21st century, in a complex computer and technology-driven world with rapidly shifting societal and technical priorities.
Biography: Prof. Dr. Levent Sevgi is a Fellow of the IEEE (since 2009) and the recipient of IEEE APS ChenTo Tai Distinguished Educator Award (2021). He was with Istanbul Technical University (1991– 1998), TUBITAK-MRC, Information Technologies Research Institute (1999–2000), Weber Research Institute / NY Polytechnic University (1988–1990), Scientific Research Group of Raytheon Systems Canada (1998 – 1999), Center for Defense Studies, ITUV-SAM (1993 –1998 and 2000– 2002) and with University of Massachusetts, Lowell (UML) MA/USA as a full-time faculty (2012 – 2013), DOGUS University (2001-2014), Istanbul OKAN (2014 – 2021), and ATLAS (2022-2024) Universities. He served four years (2020-2023) as an IEEE AP-S Distinguished Lecturer. Since Jan 2024 he has been the chair of the IEEE AP-S DL Committee. He served one-term in the IEEE AP-S AdCom (2013-2015) and one-term and as a member of IEEE AP-S Field Award Committee (2018-2019). He had been the writer/editor of the “Testing ourselves” Column in the IEEE AP Magazine (2007-2021), a member of the IEEE AP-S Education Committee (2006-2021), He also served in several editorial boards (EB) of other prestigious journals / magazines, such as the IEEE AP Magazine (2007-2021), Wiley’s International Journal of RFMiCAE (2002-2018), and the IEEE Access (2017-2019 and 2020 – 2022). He is the founding chair of the EMC TURKIYE International Conferences (www.emcturkiye.org). He has been involved with complex electromagnetic problems for nearly four decades. His research study has focused on electromagnetic radiation, propagation, scattering and diffraction; RCS prediction and reduction; EMC/EMI modelling, simulation, tests and measurements; multi-sensor integrated wide area surveillance systems; surface wave HF radars; analytical and numerical methods in electromagnetics; FDTD, TLM, FEM, SSPE, and MoM techniques and their applications; bio-electromagnetics. He is also interested in novel approaches in engineering education, teaching electromagnetics via virtual tools. He also teaches popular science lectures such as Science, Technology and Society. He has published many books / book chapters in English and Turkish, over 180 journal/magazine papers / tutorials and attended more than 100 international conferences / symposiums. His three books Complex Electromagnetic Problems and Numerical Simulation Approaches, Electromagnetic Modeling and Simulation and Radiowave Propagation and Parabolic Equation Modeling were published by the IEEE Press – WILEY in 2003, 2014, and 2017, respectively. His fourth and fifth books, A Practical Guide to EMC Engineering (Sep 2017) and Diffraction Modeling and Simulation with MATLAB (Feb 2021) were published by ARTECH HOUSE. His h-index is 38, with a record of 5150+ citations (source: Google Scholar, Dec 2024).

Prof. Levent Sevgi, Istanbul Technical University, Turkey

Title: Structuring Ethical and Scalable Data Systems: Operational Models from Telecom, Healthcare, and Supply Chain
Abstract: High-stakes domains such as telecommunications, healthcare, and global supply chains demand data systems that are both scalable and ethically robust. This paper introduces a six-pillar framework—human oversight, explainability, privacy-by-design, modularity, interoperability, and accessibility—developed to guide the design of reliable large-scale infrastructures. The framework is examined through operational case studies: the migration to paperless workflows and automated KPI pipelines in telecom networks, harmonized performance models in multinational supply chains, and a decision-support tool for Type 1 Diabetes management. A conceptual simulation of the latter demonstrated a 15–20% reduction in insulin dosing errors when operated with human validation. Results show that embedding ethical safeguards in system architecture strengthens transparency, reliability, and adaptability, while providing a transferable blueprint for responsible AI deployment across regulated and data-intensive sectors.
Biography: Sara Goze is an AI & Digital Health Inventor with over 17 years of international experience across healthcare, pharma, telecom, supply chain, and energy sectors. She is the founder of Cognitek Digital and the innovator behind a patent-pending AI assistant for Type 1 Diabetes. Her work focuses on ethical AI, human-in-the-loop systems, and data democratization, with notable contributions at Boston Scientific, IQVIA, Nokia, and BP. A 2025 TÜBİTAK BİGG nominee, she is also a Neurodiverse Women in STEM advocate and an active IEEE Senior Member, contributing to WIE, EMBS, TEMS, Computer Society, Communications Society, and IEEE Young Professionals.