Abstract: Reinforcement learning with multiple, potentially conflicting objectives is pervasive in real-world applications, while this problem remains theoretically under-explored. This paper tackles the multi-objective reinforcement learning (MORL) problem and introduces an innovative actor-critic algorithm named MOAC which finds a policy by iteratively making trade-offs among conflicting reward signals. Notably, we provide the first analysis of finite-time Pareto-stationary convergence and corresponding sample complexity in both discounted and average reward settings. Our approach has two salient features: (a) MOAC mitigates the cumulative estimation bias resulting from finding an optimal common gradient descent direction out of stochastic samples. This enables provable convergence rate and sample complexity guarantees independent of the number of objectives; (b) With proper momentum coefficient, MOAC initializes the weights of individual policy gradients using samples from the environment, instead of manual initialization. This enhances the practicality and robustness of our algorithm. Finally, experiments conducted on a real-world dataset validate the effectiveness of our proposed method. Room: 430, Bldg: EOW, University of Victoria, Victoria, British Columbia, Canada

There is a concern about the adverse health effects of exposure to electromagnetic fields (EMF) radiated from the numerous wireless devices and base stations. This becomes more critical as wireless technologies have rapidly evolved, implementing the mm-wave frequency range to fulfill massive communication demands. EMF exposure can be categorized into two parts: at lower frequencies (below 6 GHz) and high frequencies (above 6 GHz). For lower frequencies, the EMF exposure is quantified by a specific absorption rate (SAR), while for high frequencies, the EMF exposure is quantified by power density (PD). Compliance with EMF exposure limits is necessary for designing wireless devices and networks. Furthermore, the introduction of millimeter-wave (mm-wave) frequencies in cellular networks addresses the need for high-speed wireless communication. However, mm-wave signals experience high attenuation predominantly due to their susceptibility to blockage and high directivity. This consequently causes non-line-of-sight (NLOS) conditions and signal attenuations. A reconfigurable intelligent surface (RIS) is one of the possible methods that can solve blockage issues by passively reflecting and rerouting mm-wave signals in desired directions. RIS can enhance network coverage and decrease the effects of blockages compared to networks without RISs. Speaker(s): Norhuda, Dr. Nor Room: 212, Bldg: E, 1 Georgian Drive, Barrie, Ontario, Canada, L4M 3X9

Abstract: It is well known that a beamformer aims to receive the signal-of-interest at a possibly known direction-of-arrival while suppressing the surrounding interferences and noise. In this talk, the beamforming concept is exploited to the application of video background and object extraction. The formulated problems are solved by alternating direction method of multipliers, fixed point iterations, and Lagrange programming neural network. Experimental results on real video sequences with different complex backgrounds demonstrate the excellent performance of the proposed approach. Possible research directions in this area will also be discussed. Biography: Hing Cheung So was born in Hong Kong. He received the B.Eng. degree from the City University of Hong Kong and the Ph.D. degree from The Chinese University of Hong Kong, both in electronic engineering, in 1990 and 1995, respectively. From 1990 to 1991, he was an Electronic Engineer with the Research and Development Division, Everex Systems Engineering Ltd., Hong Kong. During 1995–1996, he was a Postdoctoral 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 currently a Professor. His research interests include detection and estimation, fast and adaptive algorithms, multidimensional harmonic retrieval, robust signal processing, source localization, and sparse approximation. He has been on the editorial boards of IEEE Signal Processing Magazine (2014–2017), IEEE Transactions on Signal Processing (2010–2014), Signal Processing (2010–), and Digital Signal Processing (2011–). He was also Lead Guest Editor for IEEE Journal of Selected Topics in Signal Processing, special issue on “Advances in Time/Frequency Modulated Array Signal Processing” in 2017. In addition, he was an elected member in Signal Processing Theory and Methods Technical Committee (2011–2016) of the IEEE Signal Processing Society where he was chair in the awards subcommittee (2015–2016). He has been named a 2015 IEEE Fellow in recognition of his contributions to spectral analysis and source localization. Agenda: 12:30 PM: Pizza lunch 1:00 PM: IEEE Talk Room: WLH 314, Bldg: Walter Light Hall, Queen's University, 19 Union St, Kingston, Ontario, Canada

[] Join the IEEE Toronto Instrumentation & Measurement – Robotics & Automation Joint Chapter for a technical talk presented by Dr. Shideh Kabiri Ameri from Queen's University. Monday, July 21, 2025 @ 2:00 – 3:00 PM (EST) Abstract: Wearable devices for health monitoring are conveniently being miniaturized, their functionalities have been increased, and they are rapidly being integrated into our daily life. However, the current commercialized wearables are not mechanically compatible with soft, stretchable and dynamic skin which is normally the first point of contact to the body in wearables. This results not only in discomfort but also causes low fidelity and reliability during long term sensing. In this talk, Dr. Kabiri will discuss various novel approaches they have taken to address these issues. Their developed unconventional wearable devices for health monitoring have high sensing performance and low motion artifacts, and in some cases offer visual imperceptibility and non-intrusive sensing that satisfy the user’s privacy and mental comfort. Speaker(s): Shideh Kabiri Ameri, Ph.D., Virtual: https://events.vtools.ieee.org/m/486792

Please join us for an upcoming seminar by Dr. Zhi Ning Chen, Professor at the National University of Singapore. Date: Monday, 21 July 2025 Time: 3 – 4 pm (ET) Location: University of Toronto, Room BA 2135, Bahen Centre for Information Technology, 40 St George St, Toronto, M5S 2E4 Abstract: The rapid evolution of wireless technologies continues to drive the demand for increasingly advanced antenna solutions. Simultaneously, breakthroughs in artificial intelligence (AI)—particularly in generative neural network methodologies—are opening new frontiers in antenna design innovation. This talk begins with a brief overview of the challenges involved in high-performance antenna design, emphasizing the need to explore broader design spaces to increase degrees of freedom and uncover novel solutions. To address the resulting complexity, transformative deep learning (DL) models—a subset of machine learning (ML) within AI—are introduced as tools for generative synthesis in antenna design. Three design exemplars utilizing generative adversarial networks (GANs) are presented, demonstrating innovative approaches to metacell design within metasurfaces. These are achieved through pixelization strategies and DL-driven algorithms. A particular focus is placed on the integration of prior knowledge (PK) into the DL-based synthesis process, illustrating its effectiveness in generating metacells for metalens applications. The presentation further highlights how metalenses synthesized using PK-guided DL methods exhibit breakthrough performance and offer enhanced functionalities in metalens antenna design. The talk concludes with a forward-looking perspective on the integration of AI and antenna engineering, outlining its transformative potential and the emerging challenges and opportunities in this evolving interdisciplinary domain. Biography: Zhi Ning Chen received his two Ph.D. degrees in 1993 and 2003 from institutions in China and Japan, respectively. He currently serves as a Provost’s Chair Professor in the Department of Electrical and Computer Engineering and as Director of the Advanced Research and Technology Innovation Centre at the National University of Singapore. Professor Chen has authored or co-authored 722 journal and conference papers, along with seven books. His current research interests include electromagnetic materials and metasurfaces, antenna engineering and its applications, and algorithms for generative antenna design. He was elevated to IEEE Fellow in 2007, elected Fellow of the Academy of Engineering, Singapore in 2019, and is a Fellow and Vice President of the Asia-Pacific Artificial Intelligence Association (2021). Among numerous academic and technical honors, he received the IEEE AP-S John Kraus Antenna Award in 2021 and the EurAAP Antenna Award in 2025. In addition to his research achievements, Professor Chen has played leading roles in international conferences. He served as the General Chair of the 2021 IEEE AP-S Symposium (Singapore) and is the Founding General Chair of several key conferences, including the IEEE International Workshop on Antenna Technology (2005), the Asia-Pacific Conference on Antennas and Propagation (2012), and the Marina Forum on Metantennas+X (2021). [] Speaker(s): Dr. Zhi Ning Chen, Room: BA 2135, Bldg: Bahen Centre for Information Technology, 40 St George Street, Toronto, Ontario, Canada, M5S 2E4

Dear all, The IEEE Women in Engineering (WIE) Victoria Chapter warmly invites you to an exciting evening of Lightning Talks, taking place on July 21, 2025, from 5:00 PM to 7:00 PM at ECS 660, University of Victoria. This event is designed to showcase the dynamic research and innovation in our local engineering community. Event Highlights include: - Faculty & Industry Lightning Talks: 5-minute presentations from speakers sharing cutting-edge work and technical interests - Student Pitch Competition: Undergraduate and graduate students will present a past/current project, interesting topics, or co-op experience (max. 3 slides). Top presenters will receive gift cards ($100 CAD, $75 CAD, and $50 CAD for 1st, 2nd, and 3rd places, respectively) - Networking: Connect with faculty, students, and industry guests over light refreshments 📅 Register by July 14: 👉 (https://can01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.google.com%2Fforms%2Fd%2Fe%2F1FAIpQLScD8bmj0HhSvy3ZAsn6ugv2m2uG25Qba-19Z6NSCQV-DjZhKw%2Fviewform&data=05%7C02%7Ckvalente%40uvic.mail.onmicrosoft.com%7Cd22776544fc84cfce3ae08ddbe3e611b%7C9c61d3779894427cb13b1d6a51662b4e%7C0%7C0%7C638875897337430689%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=qvK3XV%2F1OBgeDx5JjeYP36282li7ALxR3zxiQh%2Blww8%3D&reserved=0) We look forward to celebrating the incredible talent and innovation in our community. Warm regards, Dr. Karolina Valente Chair, IEEE Women in Engineering – Victoria Chapter Co-sponsored by: VoxCell BioInnovation Room: 660, Bldg: ECS, 3800 Finnerty Road, Engineering Office Wing Room 548, Victoria, British Columbia, Canada, V8P 5C2

Please join us for an upcoming seminar by Dr. Zhi Ning Chen, Professor at the National University of Singapore. Date: Monday, 21 July 2025 Time: 3 – 4 pm (ET) Location: University of Toronto, Room BA 2135, Bahen Centre for Information Technology, 40 St George St, Toronto, M5S 2E4 Abstract: The rapid evolution of wireless technologies continues to drive the demand for increasingly advanced antenna solutions. Simultaneously, breakthroughs in artificial intelligence (AI)—particularly in generative neural network methodologies—are opening new frontiers in antenna design innovation. This talk begins with a brief overview of the challenges involved in high-performance antenna design, emphasizing the need to explore broader design spaces to increase degrees of freedom and uncover novel solutions. To address the resulting complexity, transformative deep learning (DL) models—a subset of machine learning (ML) within AI—are introduced as tools for generative synthesis in antenna design. Three design exemplars utilizing generative adversarial networks (GANs) are presented, demonstrating innovative approaches to metacell design within metasurfaces. These are achieved through pixelization strategies and DL-driven algorithms. A particular focus is placed on the integration of prior knowledge (PK) into the DL-based synthesis process, illustrating its effectiveness in generating metacells for metalens applications. The presentation further highlights how metalenses synthesized using PK-guided DL methods exhibit breakthrough performance and offer enhanced functionalities in metalens antenna design. The talk concludes with a forward-looking perspective on the integration of AI and antenna engineering, outlining its transformative potential and the emerging challenges and opportunities in this evolving interdisciplinary domain. Biography: Zhi Ning Chen received his two Ph.D. degrees in 1993 and 2003 from institutions in China and Japan, respectively. He currently serves as a Provost’s Chair Professor in the Department of Electrical and Computer Engineering and as Director of the Advanced Research and Technology Innovation Centre at the National University of Singapore. Professor Chen has authored or co-authored 722 journal and conference papers, along with seven books. His current research interests include electromagnetic materials and metasurfaces, antenna engineering and its applications, and algorithms for generative antenna design. He was elevated to IEEE Fellow in 2007, elected Fellow of the Academy of Engineering, Singapore in 2019, and is a Fellow and Vice President of the Asia-Pacific Artificial Intelligence Association (2021). Among numerous academic and technical honors, he received the IEEE AP-S John Kraus Antenna Award in 2021 and the EurAAP Antenna Award in 2025. In addition to his research achievements, Professor Chen has played leading roles in international conferences. He served as the General Chair of the 2021 IEEE AP-S Symposium (Singapore) and is the Founding General Chair of several key conferences, including the IEEE International Workshop on Antenna Technology (2005), the Asia-Pacific Conference on Antennas and Propagation (2012), and the Marina Forum on Metantennas+X (2021). Speaker(s): Dr. Zhi Ning Chen, Room: BA 2135, Bldg: Bahen Centre for Information Technology, 40 St George Street, Toronto, Ontario, Canada, M5S 2E4

Please join us for an upcoming seminar by Dr. Alex Wong, Associate Professor at City University of Hong Kong. Date: Tuesday, 22 July 2025 Time: 3 – 4 pm (ET) Location: University of Toronto, Room BA 2135, Bahen Centre for Information Technology, 40 St George St, Toronto, M5S 2E4 Abstract: Electromagnetic meta-devices of various kinds have emerged in the last 20 years to manipulate electromagnetic waves with unprecedented freedom, implicating microwave to optical frequencies, enhancing our understanding on fundamental physical phenomena and finding applications in microscopy, biomedicine and wireless communication and power transfer, to name a few. In this talk, I will review recent progress in my research group into main electromagnetic meta-devices: the discrete metasurface and the directional source. The discrete metasurface is an approach to treat the metasurface as an inherently pixelated surface with spatially discrete electromagnetic properties. Through taking this approach, we understand how discretization changes the metasurface performance and to what degree the metasurface can tolerate discretization. In some cases, we can also achieve functionalities which are impossible or unobvious to the continuous metasurface. Aggressive discretization can help to enlarge the size of the meta-atom, enabling enhanced bandwidth, reduction of fabrication tolerance, and the design of multifunction and intelligent metasurfaces for sensing, communication and imaging. Directional electromagnetic sources have attracted much recent attention as they form building blocks to meta-devices that manipulate the travel direction of electromagnetic waves. We juxtapose the near- and far-field properties of the circular, Huygens, and Janus dipoles, and show that the Huygens and Janus dipoles both exhibit directional near-field coupling behavior, but possess very different far-field radiation behaviors. This gives them complementary application potentials. While existing Janus dipoles are essentially sub-wavelength structures that scatter a small part of an incident wave, we introduce the Janus antenna – an active Janus dipole fed by a transmission line, which dramatically increases the power throughput and the bandwidth over which the near-field directional behavior can be achieved. The Janus dipole can be used as either an antenna a meta-device or a meta-atom, with promising potentials in directional switching, MIMO antenna and compact WPT systems. Biography: Alex M. H. Wong (M’ 2014, SM’2019) received the B.A.Sc. degree in engineering science (electrical option) and the M.A.Sc. and Ph.D. degrees in electrical and computer engineering from the University of Toronto, Toronto, ON, Canada, in 2006, 2009, and 2014, respectively. He was a Post-Doctoral Fellow with the University of Toronto. He is currently an Associate Professor with the Department of Electrical Engineering, City University of Hong Kong, Hong Kong, where he is also a Member of the State Key Laboratory of Terahertz and Millimeter Waves. He has advanced multiple projects in applied electromagnetics on next-generation RF, infrared, and optical metasurfaces, super-resolution imaging and radar systems. Particularly, he has made academic contributions to research on wave shaping using discrete Huygens’ metasurfaces and far-field imaging based on super-oscillation waves. His current research interests include metasurfaces, metamaterials, superresolution systems, bioelectromagnetics, and wireless power transfer. Prof. Wong is a member of IEEE Antennas and Propagation Society, Microwave Theory and Technology Society, and Photonics Society. He received accolades include an IEEE RWP King Award (for the best annual publication by a young author in IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION), the URSI Young Scientist Award, the Raj Mittra Grant, the IEEE Doctoral Research Awards from the AP and MTT societies, and the Canada Graduate Scholarship (doctoral level). He has served as the General Co-Chair for the 2025 International Workshop in Electromagnetics: Applications and Student Innovation Competition (iWEM 2025), the General Co-Chair for the 2022 IEEE HK AP-MTT Postgraduate Student Conference, and the TPC Vice-Chair for the 2020 Asia-Pacific Microwave Conference (APMC 2020). He has taken on various program committee, session chair, technical judge, and reviewer duties for IEEE conferences and journal publications in the AP and MTT societies, as well as relevant venues in applied physics. Speaker(s): Dr. Alex Wong, Room: BA 2135, Bldg: Bahen Centre for Information Technology, 40 St George Street, Toronto, Ontario, Canada, M5S 2E4

Please join us for an upcoming seminar by Dr. Zhi Ning Chen, Professor at the National University of Singapore. Date: Monday, 21 July 2025 Time: 3 – 4 pm (ET) Location: University of Toronto, Room BA 2135, Bahen Centre for Information Technology, 40 St George St, Toronto, M5S 2E4 Abstract: The rapid evolution of wireless technologies continues to drive the demand for increasingly advanced antenna solutions. Simultaneously, breakthroughs in artificial intelligence (AI)—particularly in generative neural network methodologies—are opening new frontiers in antenna design innovation. This talk begins with a brief overview of the challenges involved in high-performance antenna design, emphasizing the need to explore broader design spaces to increase degrees of freedom and uncover novel solutions. To address the resulting complexity, transformative deep learning (DL) models—a subset of machine learning (ML) within AI—are introduced as tools for generative synthesis in antenna design. Three design exemplars utilizing generative adversarial networks (GANs) are presented, demonstrating innovative approaches to metacell design within metasurfaces. These are achieved through pixelization strategies and DL-driven algorithms. A particular focus is placed on the integration of prior knowledge (PK) into the DL-based synthesis process, illustrating its effectiveness in generating metacells for metalens applications. The presentation further highlights how metalenses synthesized using PK-guided DL methods exhibit breakthrough performance and offer enhanced functionalities in metalens antenna design. The talk concludes with a forward-looking perspective on the integration of AI and antenna engineering, outlining its transformative potential and the emerging challenges and opportunities in this evolving interdisciplinary domain. Biography: Zhi Ning Chen received his two Ph.D. degrees in 1993 and 2003 from institutions in China and Japan, respectively. He currently serves as a Provost’s Chair Professor in the Department of Electrical and Computer Engineering and as Director of the Advanced Research and Technology Innovation Centre at the National University of Singapore. Professor Chen has authored or co-authored 722 journal and conference papers, along with seven books. His current research interests include electromagnetic materials and metasurfaces, antenna engineering and its applications, and algorithms for generative antenna design. He was elevated to IEEE Fellow in 2007, elected Fellow of the Academy of Engineering, Singapore in 2019, and is a Fellow and Vice President of the Asia-Pacific Artificial Intelligence Association (2021). Among numerous academic and technical honors, he received the IEEE AP-S John Kraus Antenna Award in 2021 and the EurAAP Antenna Award in 2025. In addition to his research achievements, Professor Chen has played leading roles in international conferences. He served as the General Chair of the 2021 IEEE AP-S Symposium (Singapore) and is the Founding General Chair of several key conferences, including the IEEE International Workshop on Antenna Technology (2005), the Asia-Pacific Conference on Antennas and Propagation (2012), and the Marina Forum on Metantennas+X (2021). Speaker(s): Dr. Zhi Ning Chen, Room: BA 2135, Bldg: Bahen Centre for Information Technology, 40 St George Street, Toronto, Ontario, Canada, M5S 2E4

Please join us for an upcoming seminar by Dr. Zhi Ning Chen, Professor at the National University of Singapore. Date: Monday, 21 July 2025 Time: 3 – 4 pm (ET) Location: University of Toronto, Room BA 2135, Bahen Centre for Information Technology, 40 St George St, Toronto, M5S 2E4 Abstract: The rapid evolution of wireless technologies continues to drive the demand for increasingly advanced antenna solutions. Simultaneously, breakthroughs in artificial intelligence (AI)—particularly in generative neural network methodologies—are opening new frontiers in antenna design innovation. This talk begins with a brief overview of the challenges involved in high-performance antenna design, emphasizing the need to explore broader design spaces to increase degrees of freedom and uncover novel solutions. To address the resulting complexity, transformative deep learning (DL) models—a subset of machine learning (ML) within AI—are introduced as tools for generative synthesis in antenna design. Three design exemplars utilizing generative adversarial networks (GANs) are presented, demonstrating innovative approaches to metacell design within metasurfaces. These are achieved through pixelization strategies and DL-driven algorithms. A particular focus is placed on the integration of prior knowledge (PK) into the DL-based synthesis process, illustrating its effectiveness in generating metacells for metalens applications. The presentation further highlights how metalenses synthesized using PK-guided DL methods exhibit breakthrough performance and offer enhanced functionalities in metalens antenna design. The talk concludes with a forward-looking perspective on the integration of AI and antenna engineering, outlining its transformative potential and the emerging challenges and opportunities in this evolving interdisciplinary domain. Biography: Zhi Ning Chen received his two Ph.D. degrees in 1993 and 2003 from institutions in China and Japan, respectively. He currently serves as a Provost’s Chair Professor in the Department of Electrical and Computer Engineering and as Director of the Advanced Research and Technology Innovation Centre at the National University of Singapore. Professor Chen has authored or co-authored 722 journal and conference papers, along with seven books. His current research interests include electromagnetic materials and metasurfaces, antenna engineering and its applications, and algorithms for generative antenna design. He was elevated to IEEE Fellow in 2007, elected Fellow of the Academy of Engineering, Singapore in 2019, and is a Fellow and Vice President of the Asia-Pacific Artificial Intelligence Association (2021). Among numerous academic and technical honors, he received the IEEE AP-S John Kraus Antenna Award in 2021 and the EurAAP Antenna Award in 2025. In addition to his research achievements, Professor Chen has played leading roles in international conferences. He served as the General Chair of the 2021 IEEE AP-S Symposium (Singapore) and is the Founding General Chair of several key conferences, including the IEEE International Workshop on Antenna Technology (2005), the Asia-Pacific Conference on Antennas and Propagation (2012), and the Marina Forum on Metantennas+X (2021). Speaker(s): Dr. Zhi Ning Chen, Room: BA 2135, Bldg: Bahen Centre for Information Technology, 40 St George Street, Toronto, Ontario, Canada, M5S 2E4