• MakeUofT 2022

    Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/302181

    Apply to Canada's Largest Makeathon by February 5th @11:59pm EST! Take part in Canada's Largest Makeathon and transform your ideas into reality! Happening online February 19th-20th, and open to all university students! Your team of 2-4 will have 24h to design and build a project from scratch that integrates hardware and software. We are providing $150 hardware reimbursements to all teams that demo! This year, the themes for MakeUofT are: wearables, transport & travel, and useless inventions! Our online makeathon is designed for everyone to participate, from beginners to experts. To help facilitate your learning experience, we will have mentors available throughout the event to assist you! You will also be able to interact with some of the top companies in the industry through our networking booths. Apply at makeuoft.ca by February 5th @11:59pm EST!

  • Fast Solvers for Electromagnetics-Based Analysis and Design of Integrated Circuits and Systems

    Virtual: https://events.vtools.ieee.org/m/303190

    The design of advanced integrated circuits and microsystems from zero to terahertz frequencies calls for fast and accurate electromagnetics-based modeling and simulation. The sheer complexity and high design cost associated with the integrated circuits and microsystems prevent one from designing them based on hand calculation, approximation, intuition, or trial and error. The move towards higher frequencies and heterogeneous technologies stresses the need even more. However, the analysis and design of integrated circuits (ICs) and microsystems impose many unique challenges on electromagnetic analysis such as exponentially increased problem size and extremely multiscaled system spanning from nano- to centi-meter scales. These challenges become new driving forces of the advancement of Computational Electromagnetics (CEM) in recent years, since past techniques do not address them well. In this talk, recent advances in fast direct solvers of O(N) (optimal) complexity will be presented, including both direct PDE and IE solvers, for addressing the ultra large problem size encountered in the IC design problems. In these solvers, the underlying dense or sparse system matrix is directly inverted or factorized in O(N) complexity. To show how these solvers work, a series of new accuracy controlled fast matrix arithmetic will be elaborated including the representation of a dense matrix of O(N2) elements using O(N) parameters with controlled accuracy, subsequent matrix-matrix multiplication, matrix factorization, and inversion performed in O(N) complexity with directly controlled accuracy. The application of these fast algorithms to the design and analysis of industry product-level integrated circuits and systems will be presented. Comparisons with direct and iterative solvers in the past will be made, which demonstrate the clear advantages of the new O(N) direct solvers. Co-sponsored by: Center for Computational Science and Engineering (CCSE), Faculty of Applied Science and Engineering, University of Toronto Speaker(s): Dan Jiao Biography: Dan Jiao received the Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign, Urbana, IL, USA, in 2001. She then joined the Technology Computer-Aided Design (CAD) Division, Intel Corporation, until September 2005, where she was a Senior CAD Engineer, Staff Engineer, and Senior Staff Engineer. In September 2005, she joined Purdue University, West Lafayette, IN, USA, as an Assistant Professor with the School of Electrical and Computer Engineering. She is currently a Professor with Purdue University. She has authored 3 book chapters and over 300 papers in refereed journals and international conferences. Her current research interests include computational electromagnetics; high-frequency digital, analog, mixed-signal, and RF integrated circuit (IC) design and analysis; high-performance very large scale integration (VLSI) CAD; modeling of microscale and nanoscale circuits; applied electromagnetics; fast and high-capacity numerical methods; fast time-domain analysis, scattering and antenna analysis; RF, microwave, and millimeter-wave circuits; wireless communication; and bioelectromagnetics. Dr. Jiao has served as a reviewer for many IEEE publications and conferences. She is an associate editor for the IEEE Transactions on Components, Packaging, and Manufacturing Technology. She was the recipient of the 2013 S. A. Schelkunoff Prize Paper Award of the IEEE Antennas and Propagation Society, which recognizes the Best Paper published in the IEEE Transactions on Antennas and Propagation during the previous year. She has been named a University Faculty Scholar by Purdue University since 2013. She was among the 85 engineers selected throughout the nation for the National Academy of Engineerings 2011 U.S. Frontiers of Engineering Symposium. She was the recipient of the 2010 Ruth and Joel Spira Outstanding Teaching Award, the 2008 National Science Foundation (NSF) CAREER Award, the 2006 Jack and Cathie Kozik Faculty Start Up Award (which recognizes an outstanding new faculty member of the School of Electrical and Computer Engineering, Purdue University), a 2006 Office of Naval Research (ONR) Award under the Young Investigator Program, the 2004 Best Paper Award presented at the Intel Corporation’s annual corporate-wide technology conference (Design and Test Technology Conference) for her work on generic broadband model of high-speed circuits, the 2003 Intel Corporation Logic Technology Development (LTD) Divisional Achievement Award, the Intel Corporation Technology CAD Divisional Achievement Award, the 2002 Intel Corporation Components Research Award, the Intel Hero Award (Intel-wide she was the tenth recipient), the Intel Corporation LTD Team Quality Award, and the 2000 Raj Mittra Outstanding Research Award presented by the University of Illinois at Urbana–Champaign. Register: https://events.vtools.ieee.org/m/303190

  • Computation Offloading and Task Scheduling at Network Edge

    Virtual: https://events.vtools.ieee.org/m/305769

    In the 5G era, wireless networks are anticipated to provide connectivity for massive mobile devices and to enable a variety of innovative applications, which generate enormous computing service demands with diverse and stringent Quality of Service requirements. To support the emerging computing service demands, Mobile Edge Computing (MEC), as a cutting-edge technology in 5G, utilizes computing resources on the network edge to provide computing services for mobile devices within a radio access network. In this talk, we will investigate computing resource management for MEC to satisfy diverse computing requirements in wireless networks. We will introduce three computation offloading and task scheduling schemes tailored for supporting representative use cases and network scenarios in 5G, including autonomous driving, Unmanned Aerial Vehicle (UAV) assisted networks, and highly dense vehicular networks. Machine learning algorithms are applied to facilitate low-latency and reliable computing services in complex and dynamic network environments. Speaker(s): Mushu Li Biography: Dr. Mushu Li received the Ph.D. degree from the University of Waterloo, ON, Canada, in 2021, and the M.A.Sc. degree from Ryerson University, Toronto, ON, Canada, in 2017. She is currently a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, University of Waterloo.  Dr. Li was a recipient of the NSERC Canada Graduate Scholarship (2018-2021) and Ontario Graduate Scholarship in 2015 and 2016, respectively. Her research interests include Internet of vehicles, resource management, multi-access edge computing, and reinforcement learning. She has authored/co-authored over 20 technical papers. She serves/served as a reviewer for IEEE Journals on Selected Areas in Communications (JSAC), IEEE Transactions on Communications, IEEE Transactions on Vehicular Technologies, etc. Virtual: https://events.vtools.ieee.org/m/305769

  • Machine Learning and TensorFlow: An Introduction and Overview

    Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/306166

    The workshop host will present an overview of the essential structure of machine learning along with an example using TensorFlow Hub. We will aim to take a practical approach to this complex subject for the purposes of gaining insight into some of the theory behind the code. Over the course of this workshop we will aim to cover: - A general overview of machine learning (ML) and its current context in artificial intelligence. - Useful mathematics for gaining an intuitive understanding of some basic processes in ML. - A discussion of possible projects that could involve ML along with example code in Python programming language. Prior exposure to Python/C programming and calculus will be helpful but isn’t required. Speaker(s): John Hooper Virtual: https://events.vtools.ieee.org/m/306166

  • Women in Leadership

    Virtual: https://events.vtools.ieee.org/m/306228

    "Women in Leadership", a collaboration between IEEE Toronto Section, Gybo Robotics, and Humber College. Co-sponsored by: Humber College Speaker(s): Dr. Azadeh Yadollahi Virtual: https://events.vtools.ieee.org/m/306228

  • Integration of Terrestrial Networks and Extreme Environments: Challenges and Capabilities

    Virtual: https://events.vtools.ieee.org/m/308601

    The IEEE ComSoc New York Chapter a long with the IEEE ComSoc Toronto chapter are organizing a series of technical seminars. We invite researchers and professionals to share their latest work on a variety of topics in communications and related areas. This time, we have the great pleasure to invite Prof. Mehdi Rahmati from Cleveland State University to talk about the integration of terrestrial networks and extreme environments. Agenda: 06:45 PM - 07:00 PM Connecting to the ZOOM meeting 07:00 PM - 07:05 PM Welcoming & IEEE ComSoc Membership Promotion 07:05 PM - 07:10 PM Speaker Introduction 07:10 PM - 07:55 PM Presentation 07:55 PM - 08:10 PM Questions and Answers 08:10 PM - 08:15 PM Closing Remarks Virtual: https://events.vtools.ieee.org/m/308601

  • Overview of Factory Automation Market in Canada

    Virtual: https://events.vtools.ieee.org/m/309678

    The goal of the session is to present a summary of the automation industry in Canada with a focus on factory automation. The following topics would be covered: - Active Industries - Market Structure - Key Players - Future Trends - Automation Jobs - Required Skills - Challenges Speaker(s): Shahram Fahimi Virtual: https://events.vtools.ieee.org/m/309678 Biography: Shahram Fahimi is the Automation Manager of the Life Science group in SNC-Lavalin. He has over 20 years of experience in the automation industry with a focus on Factory and Process Automation. Shahram has contributed to many exciting automation projects such as the first battery line for Tesla in California using Trak technology. He has been graduated from Sharif University of Technology in 1998 with a Bachelor of Computer Engineering.

  • Integrated Solar-Pannel Antennas by Prof. Reyhan Baktur

    Toronto, Ontario, Canada

    Please join us for an upcoming talk on Apr 13, 3-4 pm (Eastern Time) by Prof. Reyhan Baktur titled "Integrated Solar-Pannel Antennas," as part of the 2021-2022 IEEE AP-S seminar series. Abstract: Conformal Integration of antennas with solar panels has wide applications, from small spacecraft, Mars rovers, to self-powered wireless sensors. It is particularly beneficial when the surface real estate is a major challenge, such as a CubeSat. A strategic integration not only reduces the development cost, promotes a robust communication link, but also increases the mission capacity by allowing more science instruments to be mounted on the CubeSat. This lecture covers different conformal antenna designs for solar panel integration, from UHF to Ka band. It includes antennas integrated under solar cells, around solar cells, and optically transparent antennas integrated on top of solar cells. It also covers low gain and high gain design. The high gain design mainly focuses on reflectarray antenna, which may be beneficial to those who wishes to study the subject. As these antennas are integrated with solar panel, a unique and complex subsystem, effects of solar cells on the antenna and vice versa need to be analyzed and quantified. The lecture presents analysis of a typical space-certified solar cell, extracted model, experimental set-up to quantify the interaction between solar cells and the integrated antennas. About Speaker: Dr. Reyhan Baktur is an associate professor at the department of Electrical and Computer Engineering (ECE), Utah State University (USU). Her research interests include antennas and microwave engineering with a focus on antenna design for CubeSats; optically transparent antennas; multifunctional integrated antennas, sensors, and microwave circuits. She is affiliated with the Center for Space Engineering at USU, the Space Dynamics Laboratory (the university affiliated research center), and collaborates with NASA Goddard Space Flight Center. Dr. Baktur is an AdCom member of IEEE Antennas and Propagation Society, and is active in US National Committee of the International Union of Radio Science, serving as the vice chair for commission B, and the inaugural chair for the Women in Radio Science. She is passionate and committed to electromagnetic education and student recruiting by introducing CubeSat projects in undergraduate classrooms. She is the recipient of the IEEE Antennas and Propagation Society’s (APS) the Donald G. Dudley Jr. Undergraduate Teaching Award in 2013 and has been actively serving IEEE APS student paper competition and student design contest. Dr. Baktur’s lectures will focus on CubeSat Development Basics, Link Budget Analysis and Development, Antenna Designs for CubeSats and Small Satellites, Transparent Antennas, and Class Projects for Electromagnetic Courses

  • Intelligent and Secure Integration of Electric Vehicles into the Smart Grid

    Virtual: https://events.vtools.ieee.org/m/309875

    The transition to electric vehicles (EVs) is gaining momentum around the world and the major drivers for this acceleration are the rising awareness by the public for maintaining a clean environment, reducing pollutant emissions, breaking dependencies on oil, as well as tapping into cleaner sources of energies. EVs acceptance however is hindered by several challenges; among them is their shorter driving range, slower charging rates, and the ubiquitous availability of charging locations, collectively contributing to higher anxieties for EVs drivers. To mitigate this anxiety, a naïve approach is to expand the charging network, while an unplanned expansion may challenge the generation, transmission and distribution sector of the grid along with being a potential cyber-physical attack platform. As a consequence, to attain a graceful EV penetration for curtailing GHG emission, along with the socioeconomic initiatives, an extensive research is required, especially to mitigate the range anxiety and ameliorate the load congestion on the grid. Fortunately, the IoT enabled charging ecosystem (i.e., EVs, charging stations, the grid etc.) enables smart and informed charging schemes to exploit the benefit of different distributed energy sources (e.g., renewable energy based standalone chargers, vehicle to grid or vehicle to vehicle energy transfer technology, etc.) to minimize the load burden of the grid. But, on the other hand, this IoT enabled charging ecosystem unveils a new cyber-physical attack surface and hence, new challenges also need to be addressed to make this charging ecosystem secure as well. Virtual: https://events.vtools.ieee.org/m/309875 Speaker: Dr. Mohammad Ekramul Kabir Biography: Dr. Mohammad Ekramul Kabir is currently working as a Horizon postdoctoral research fellow in CIISE at Concordia University, Montreal, Canada. He obtained his PhD on Information and Systems Engineering from Concordia University in May 2021. He has received the B.Sc. and M.S. degree in Applied Physics, Electronics and Communication engineering from University of Dhaka, Bangladesh. His research interests include green, smart, and secure charging of electric vehicle, cloud/edge computing security and applications of artificial intelligence. He is a coauthor of a number of peer-reviewed journal and conference papers. He also serves/served as a reviewer for IEEE Transactions on Transportation Electrification, IEEE Transactions on Vehicular Technology, IEEE Transactions on Mobile Computing, IEEE Transactions on Network and Service Management, IEEE Intelligent Transportation Systems Magazine, IEEE PES General Meeting, etc.

  • Distributed Phased Arrays: Challenges and Recent Progress

    Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/311733

    There has been significant research devoted to the development of distributed microwave wireless systems in recent years. The progression from large, single-platform wireless systems to collections of smaller, coordinated systems on separate platforms enables significant benefits for radar, remote sensing, communications, and other applications. The ultimate level of coordination between platforms is at the wavelength level, where separate platforms operate as a coherent distributed system. Wireless coherent distributed systems operate in essence as distributed phased arrays, and the signal gains that can be achieved scale proportionally to the number of transmitters squared multiplied by the number of receivers, providing potentially dramatic increases in wireless system capabilities. Distributed array coordination requires accurate control of the relative electrical states of the nodes. Generally, such control entails wireless frequency synchronization, phase calibration, and time alignment, but for remote sensing operations, phase control also requires high-accuracy knowledge of the relative positions of the nodes in the array to support beamforming. This lecture presents an overview of the challenges involved in distributed phased array coordination, and describes recent progress on microwave technologies that address these challenges. Requirements for achieving distributed phase coherence at microwave frequencies are discussed, including the impact of component non-idealities such as oscillator drift on beamforming performance. Architectures for enabling distributed beamforming are reviewed, along with the relative challenges between transmit and receive beamforming. Microwave and millimeter-wave technologies enabling wireless phase-coherent synchronization are discussed, focusing on technologies for high-accuracy internode ranging, wireless frequency transfer, and high-accuracy time alignment. The lecture concludes with a discussion of open challenges in distributed phased arrays, and where microwave technologies may play a role. Speaker(s): Prof. Jeffrey Nanzer Register: https://events.vtools.ieee.org/m/311733 Biography: Jeffrey Nanzer (S’02-M’08-SM’14) received the B.S. degree in electrical engineering and computer engineering from Michigan State University, East Lansing, MI, USA, in 2003, and the M.S. and Ph.D. degrees in electrical engineering from The University of Texas at Austin, Austin, TX, USA, in 2005 and 2008, respectively. From 2008 to 2009, he was a Postdoctoral Fellow with Applied Research Laboratories, The University of Texas at Austin, where he was involved in designing electrically small HF antennas and communication systems. From 2009 to 2016, he was with The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA, where he created and led the Advanced Microwave and Millimeter-Wave Technology Section. In 2016, he joined the Department of Electrical and Computer Engineering, Michigan State University, where he is currently the Dennis P. Nyquist Associate Professor. He has authored or co-authored more than 150 refereed journal and conference papers, authored the book Microwave and Millimeter-Wave Remote Sensing for Security Applications (Artech House, 2012), and co-authored chapters in the books Wireless Transceiver Circuits (Taylor and Francis, 2015) and Short-Range Micro-Motion Sensing: Hardware, signal processing and machine learning (IET, 2019). His current research interests include distributed arrays, radar and remote sensing, antennas, electromagnetics, and microwave photonics. Dr. Nanzer was a founding member and the First Treasurer of the IEEE APS/MTT-S Central Texas Chapter. He is also a member of the IEEE Antennas and Propagation Society Education Committee and the USNC/URSI Commission B. He was a recipient of the Outstanding Young Engineer Award from the IEEE Microwave Theory and Techniques Society in 2019, the DARPA Director’s Fellowship in 2019, the National Science Foundation (NSF) CAREER Award in 2018, the DARPA Young Faculty Award in 2017, and the JHU/APL Outstanding Professional Book Award in 2012. He has served as the Vice-Chair for the IEEE Antenna Standards Committee from 2013 to 2015 and the Chair of the Microwave Systems Technical Committee (MTT-16) of the IEEE Microwave Theory and Techniques Society from 2016 to 2018. He is also an Associate Editor of the IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION.

  • Wideband Digital-to-Analog Converters for mmWave Transmitter

    Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/309574

    Over the past ten years, the data rate of cellular communication networks has increased by 100x. The next-generation software-defined-radio based wireless transmission in mmWave bands demands multi-GHz bandwidth digital-to-analog conversion with medium to high resolution (e.g., 14-16 bit) and sampling rates beyond 10GS/s. The rate of bandwidth increase and the required improvements in energy efficiency have exceeded the benefits of CMOS process scaling alone. There are compelling needs for novel architecture and circuit design techniques. In this talk, I will review recent development and present emerging parallel-path DAC architectures for extending the bandwidth with higher power and area efficiency than conventional interleaving designs. I will discuss the practical challenges along with several key analog design techniques. I will conclude with some future directions. At the end of the talk, I will briefly introduce some other research activities in my group, such as low power bioelectronics, neural interfacing and modulation circuits, and machine-learning accelerators. Speaker(s): Dr. Xilin Liu Virtual: https://events.vtools.ieee.org/m/309574 Biography: Dr. Xilin Liu (Senior Member, IEEE) is currently an Assistant Professor at the University of Toronto. He obtained his Ph.D. degree from the University of Pennsylvania. Before joining the University of Toronto in 2021, he held industrial positions at Qualcomm Inc., where he conducted R&D of high-performance mixed-signal circuits for cellular communication. He led and contributed to the IPs that have been integrated into products in high-volume production, including the industry’s first 5G chipset. He was a visiting scholar at Princeton University in 2014. He has co-authored two books along with over 30 peer-reviewed articles. He was the first author of the papers that have received the Best Student Paper Award at the 2017 ISCAS, the Best Paper Award at the 2015 BioCAS, the Best Track Award at the 2014 ISCAS, and the student research preview (SRP) award at 2014 ISSCC. He also received the SSCS predoctoral achievement award at the 2016 ISSCC.

  • Fake News Detection – Students Research in ML and DL at Durham College

    Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312334

    The term "fake news" was pretty much unknown and unpopular a few decades ago, but it has emerged as a massive monster in the digital era of social media. Fake news is spreading like wildfire these days, and people share it without confirming it. Often, it is to promote or enforce specific views, and it is carried out through political agendas. Fake news refers to news that may or may not be correct and is widely disseminated via social media and other internet platforms. In this digital age, it is not easy to tackle the spread of fake news, where thousands of information-sharing sites via fake news or misinformation can be shared. It has become a greater issue as AI advances, bringing with it artificial bots that may be used to create and propagate fake news. The problem is critical because many individuals believe anything they read on the internet, and those who are inexperienced or new to digital technologies are vulnerable to being misled. Fraud is another issue that can arise as a result of spam or harmful emails and communications. Fake news has grown in popularity and spread as a result of recent political events. Humans are inconsistent, if not outright terrible detectors of fake news, as evidenced by the pervasive effects of the widespread onset of fake news. As a result, efforts have been made to automate detecting fake news. The most prominent of these attempts are "blacklists" of unreliable sources and authors. While these technologies are useful we need to account for more complex instances when trusted sources and authors leak fake news in order to provide a complete end-to-end solution. As a result, the goal of this project was to develop a tool that used machine learning and natural language processing techniques to recognize the language patterns that distinguish fake and true news. The outcomes of this project show that machine learning can be effective in this situation. We developed a model that detects a variety of intuitive indicators of real and fake news and an application to aid in the visual representation of the classification decision. We aim to give users the ability to classify news as fake or real and verify the website legitimacy that published it. Speaker(s): Roshna Babu, Abraham Mathew, Neha Joseph Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312334