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  • Electromagnetics Alumni Event

    Virtual - Zoom

    We are inviting several alumni members from the electromagnetics group, University of Toronto, Canada who are working in industry at senior positions and in academia as Professors to provide an insight on career choices after graduation. We are planning it as a semi-formal event where the speakers would share their experiences and the attendees could ask them questions. Zoom link will be provided to the registered participants. Contact: IEEE UofT AP-S Student Chapter Panelists: Dr. Michael Selvanayagam, IBM T.J. Watson Research Center, NY Dr. Rubaiyat Islam, AMD, Canada Dr. Marco Antoniades, Ryerson University, Canada Dr. Loic Markley, University of British Columbia, Canada Dr. Utkarsh Patel, AMD Canada

  • ComSoc Distinguished Lecture: AI to Enable Digital Medicine and Detect COVID-19

    Virtual - Zoom

    The IEEE Ottawa Joint Chapter of Communications Society, Consumer Electronics Society, and Broadcast Technology Society (ComSoc/CESoc/BTS), IEEE Toronto Chapter (ComSoc/BTS), IEEE ComSoc Montreal Chapter (ComSoc), IEEE Ottawa Educational Activities (EA), IEEE Ottawa Women In Engineering (WIE), IEEE Ottawa Young Professionals (YP), and Algonquin College Student Branch (ACSB) in conjunction with School of Advanced Technology, Algonquin College are inviting all interested IEEE members and other engineers, technologists, and students to ComSoc Distinguished Lecture (webinar) on AI to Enable Digital Medicine and Detect COVID-19. For any additional information please contact: Wahab Almuhtadi or Eman Hammad Abstract: Digitalize human beings using biosensors to track our complex physiologic system, process the large amount of data generated with artificial intelligence (AI) and change clinical practice towards individualized medicine: these are the goals of digital medicine. In this talk, we discuss how to design AI solutions in the clinical space and what are the key aspects to make a difference. We focus on two critical clinical topics that need AI: 1) atrial fibrillation (AF), and 2) viral illnesses (COVID-19). AF is the most common sustained cardiac arrhythmia, associated with stroke, heart failure and coronary artery disease. AF detection from single-lead electrocardiography (ECG) recordings is still an open problem, as AF events may be episodic and the signal noisy. We conduct a thoughtful analysis of recent convolutional neural network architectures developed in the computer vision field, redesigned to be suitable for a one-dimensional signal, and we evaluate their performance in the detection of AF using 200 thousand seconds of ECG, highlighting the potential and pitfall of this technology. We also discuss how to explain (global and local post hoc explanations) this AI model for AF detection using features that are commonly used by a cardiologist. To tackle the problem of COVID-19, we start with an overview of continuous, passively monitored vital signs from 200,000 individuals wearing a Fitbit wearable device for 2 years. This large study provides the baseline for DETECT, our app-based, nationwide clinical study enrolling individuals who routinely use a smartwatch or other wireless devices to determine if individualized tracking of changes in heart rate, activity and sleep can provide early diagnosis and self-monitoring for COVID-19. We analyze data from more than 36,000 individuals, showing how we can discriminate (on an individual level) between COVID-19 and other types of infections. We discuss how this can impact both the individual and public health, and how the use of AI can be a game changer in this fight against the virus. Speaker: Giorgio Quer Giorgio Quer is the Director of Artificial Intelligence at the Scripps Research Translational Institute, where he is leading the Data Science and Analytics team within the All of Us Research Program’s Participant Center (NIH). His research focuses on artificial intelligence and probabilistic modeling applied to heterogeneous data signals, in order to extract key information and make predictions on future occurrences based on past data. He is involved in several digital medicine initiatives within the Scripps Research Digital Trials Center. For the DETECT study, he is developing algorithms to predict COVID-19 and other viral infections from wearable sensor data. He is responsible for collaborations with several industry partners, studying changes in heart rate and sleep data monitored by commercial wearable devices. He is also interested in the detection and modeling of atrial fibrillation from single-lead ECG signals. He is leading the collaboration with the Halicioglu Data Science Institute at UC San Diego towards the development of new AI models for health data. He received his Ph.D. degree in Information Engineering from the University of Padova, Italy, and he continued his studies as a Postdoctoral researcher with the Qualcomm Institute at the University of California San Diego. He is a Senior Member of the IEEE and a Distinguished Lecturer for the IEEE Communications society.

  • Reconfigurable Intelligent Surfaces: A Signal Processing Perspective

    Montreal, Quebec Canada

    Wireless connectivity is becoming as essential as electricity in our modern world. Although we would like to deliver wireless broadband services everywhere, the underlying physics makes it inherently complicated: the signal power vanishes very quickly with the propagation distance and is absorbed or scattered when interacting with objects in the way. Even when we have a “strong" signal, only one in a million parts of the signal energy is being received, thus, there is a huge room for improvements! What if we could tune the propagation environment to our needs? This is the main goal of reconfigurable intelligent surfaces, which is an emerging concept for beyond-5G communications. The idea is to support the transmission from a source to a destination by deploying so-called metasurfaces that can reconfigure how incident signal waves are scattered. These surfaces can be electronically configured to interact with the wireless signals as if they had different shapes. For example, it can be configured to behave as a parabolic reflector that is rotated to gather signal energy and re-radiates it as a beam focused on the receiver. This feature makes use of a new design dimension: we can not only optimize the transmitter and receiver but also control the channel. This might be a game-changer when communicating at mmWave and THz frequencies, where the traditional propagation conditions are particularly cumbersome. This might sound like science fiction but is theoretically possible. In this talk, Dr. Emil will explain the fundamentals of this new technology from a signal processing perspective. By deriving a signals-and-systems description, we can look beyond the initial hype and understand what is actually happening when using reconfigurable intelligent surfaces. Dr. Emil will also describe recent experimental validations of the fundamentals. The talk will culminate in a description of the main research challenges that need to be tackled in the coming years. The virtual platform information will be sent to registrants a couple of hours ahead of starting the event. Contact: IEEE Young Professionals Montreal Speaker: Emil Björnson Biography: Emil Björnson received the M.S. degree in engineering mathematics from Lund University, Sweden, in 2007, and the Ph.D. degree in telecommunications from the KTH Royal Institute of Technology, Sweden, in 2011. From 2012 to 2014, he held a joint post-doctoral position at the Alcatel-Lucent Chair on Flexible Radio, SUPELEC, France, and the KTH Royal Institute of Technology. He joined Linköping University, Sweden, in 2014, where he is currently an Associate Professor. In September 2020, he became a part-time Visiting Full Professor at the KTH Royal Institute of Technology. He has authored the textbooks Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013), Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency (2017), and Foundations of User-Centric Cell-Free Massive MIMO (2021). He is dedicated to reproducible research and has made a large amount of simulation code publicly available. He performs research on MIMO communications, radio resource allocation, machine learning for communications, and energy efficiency. He has been on the Editorial Board of the IEEE Transactions on Communications since 2017. He has been a member of the Online Editorial Team of the IEEE Transactions on Wireless Communications since 2020. He has been an Area Editor in IEEE Signal Processing Magazine since 2021. He has performed MIMO research for over 14 years, his papers have received more than 12000 citations, and he has filed more than twenty patent applications. He is a host of the podcast Wireless Future and has a popular YouTube channel. He has received the 2014 Outstanding Young Researcher Award from IEEE ComSoc EMEA, the 2015 Ingvar Carlsson Award, the 2016 Best Ph.D. Award from EURASIP, the 2018 IEEE Marconi Prize Paper Award in Wireless Communications, the 2019 EURASIP Early Career Award, the 2019 IEEE Communications Society Fred W. Ellersick Prize, the 2019 IEEE Signal Processing Magazine Best Column Award, the 2020 Pierre-Simon Laplace Early Career Technical Achievement Award, and the 2020 CTTC Early Achievement Award. He also co-authored papers that received Best Paper Awards at the conferences, including WCSP 2009, the IEEE CAMSAP 2011, the IEEE SAM 2014, the IEEE WCNC 2014, the IEEE ICC 2015, and WCSP 2017.

  • IEEE VDL: Deep Learning for Physical Layer Communications: An Attempt towards 6G

    Kingston, Ontario, Canada, Virtual: https://events.vtools.ieee.org Kingston, Ontario, Canada, Virtual: https://events.vtools.ieee.org

    Join us for the IEEE Virtual Distinguished Lecture "Deep Learning for Physical Layer Communications: An Attempt towards 6G" presented by Prof. Feifei Gao of Tsinghua University, China. Contact: IEEE Kingston ComSoc Chapter Abstract: Merging artificial intelligence into the system design has appeared as a new trend in wireless communications areas and has been deemed as one of the 6G technologies. In this talk, we will present how to apply the deep neural network (DNN) for various aspects of physical layer communications design, including the channel estimation, channel prediction, channel feedback, data detection, and beamforming, etc. We will also present a promising new approach that is driven by both the communications data and the communication models. It will be seen that the DNN can be used to enhance the performance of the existing technologies once there is model mismatch. More interestingly, we will show that applying DNN can deal with the conventionally unsolvable problems, thanks to the universal approximation capability of DNN. With the well-defined propagation model in communication areas, we also attempt to explain the DNN under the scenario of channel estimation and reach a strong conclusion that DNN can always provide the asymptotically optimal channel estimations. We have also build test-bed to show the effectiveness of the AI aided wireless communications. In all, DNN is shown to be a very powerful tool for communications and would make the communications protocols more intelligently. Nevertheless, as a new born stuff, one should carefully select suitable scenarios for applying DNN rather than simply spreading it everywhere. Biography: Prof. Gao's research interest include signal processing for communications, array signal processing, convex optimizations, and artificial intelligence assisted communications. He has authored/ coauthored more than 150 refereed IEEE journal papers and more than 150 IEEE conference proceeding papers that are cited more than 10000 times in Google Scholar. Prof. Gao has served as an Editor of IEEE Transactions on Wireless Communications, IEEE Journal of Selected Topics in Signal Processing (Lead Guest Editor), IEEE Transactions on Cognitive Communications and Networking, IEEE Signal Processing Letters, IEEE Communications Letters, IEEE Wireless Communications Letters, and China Communications. He has also serves as the symposium co-chair for 2019 IEEE Conference on Communications (ICC), 2018 IEEE Vehicular Technology Conference Spring (VTC), 2015 IEEE Conference on Communications (ICC), 2014 IEEE Global Communications Conference (GLOBECOM), 2014 IEEE Vehicular Technology Conference Fall (VTC), as well as Technical Committee Members for more than 50 IEEE conferences.

  • The Analog Designer’s Toolbox (ADT): Towards A New Paradigm for Analog IC Design

    Virtual - Zoom

    The Circuits & Devices Chapter of IEEE Toronto is pleased to invite you to join us for a virtual talk by Dr. Hesham Omran of Ain Shams University. This event will be a virtual talk held on Zoom. The invitation will be sent to registerants. Topic: The Analog Designer's Toolbox (ADT): Towards A New Paradigm for Analog IC Design Abstract: The integrated circuit (IC) technology has witnessed an exponential advancement in the last decades and has changed every aspect in our life. On the other hand, the analog IC design flow did not experience any major change since the introduction of Berkeley SPICE in the 1970s, posing significant challenges to the design of complex systems and to the transfer of analog design expertise and knowledge. The Analog Designer’s Toolbox (ADT) is an analog EDA tool that addresses this problem by defining a new paradigm in analog IC design. ADT provides a turnkey solution that enables everyone to reap the benefits of the gm/ID design methodology powered by precomputed lookup tables (LUTs). At the device level, ADT Device Xplore gives an easy interface to plot arbitrary design charts involving complex expressions. The designer can explore devices from different technologies at different corners and temperatures, and extract simulator-accurate design points while taking second-order effects into consideration. At the block level, ADT Design Xplore gives the designer the power of design space exploration, constraints management, live tuning, and optimization, all in a single cockpit without invoking the simulator. Moreover, with a single click, ADT can build the testbenches in the background and report the results from your favorite simulator. The aim of ADT is to boost productivity, restore designer’s intuition, and make the design process systematic, optimized, and fun! Speaker: Hesham Omran Biography: Dr. Hesham Omran received the B.Sc. (with honors) and M.Sc. degrees from Ain Shams University, Cairo, Egypt, in 2007 and 2010, respectively, and the Ph.D. degree from King Abdullah University of Science and Technology (KAUST), Saudi Arabia, in 2015, all in Electrical Engineering. From 2008 to 2011, he was a Design Engineer with Si-Ware Systems (SWS), Cairo, Egypt, where he worked on the circuit and system design of the first miniaturized FT-IR MEMS spectrometer (NeoSpectra), and a Research and Teaching Assistant with the Integrated Circuits Lab (ICL), Ain Shams University. From 2011 to 2016 he was a Researcher with the Sensors Lab, KAUST. He held internships with Bosch Research and Technology Center, CA, USA, and with Mentor Graphics, Cairo, Egypt. In 2016, he rejoined the ICL, Ain Shams University, as an Assistant Professor. He developed and taught several advanced courses on different topics in the field of IC Design. Most of these courses are available on the Mastering Microelectronics YouTube channel with 4k+ subscribers. He co-founded Master Micro in 2020 to develop the Analog Designer’s Toolbox (ADT), a winner of the Egyptian ITIDA-TIEC startup incubation program. Dr. Hesham has received several awards including the Egyptian State Encouragement Award for Engineering Sciences in 2019, best paper award from the IEEE International Design and Test Conference in 2009, and Academic Excellence Awards from KAUST and Ain Shams University in 2011 and 2002, respectively. He has published 40+ papers in international journals and conferences. He serves as a reviewer for several international journals and conferences including IEEE Transactions on Circuits and Systems (TCAS) I & II, IEEE Transactions on Instrumentation and Measurement, and IEEE Transactions on Very Large Scale Integration Systems (TVLSI). His research interests are in the design of analog and mixed-signal integrated circuits, and especially in analog and mixed-signal CAD tools and design automation. Email: hesham.omran@master-micro.com

  • PD Course – e-lesson #1 – Basics of partial discharges

    The course is intended for utility engineers, both young and senior, especially those responsible for condition assessment and maintenance of power transformers, manufacturers of transformers, transformer components, monitoring systems, sensors, etc., students and anyone wishing to understand the scientific foundation of PD measurement, anyone interested in a deeper awareness of partial discharge measurement and interpretation and staff who are responsible for transformers and want them to be more operational and efficient. Contact: Ali Naderian Register: Please Register Directly Using Link: https://lnkd.in/dnDybDc Speaker: Stefan Tenbohlen of Stuttgart University Topic: Partial Discharge Measurement for Power Transformers- Basics Biography: Stefan Tenbohlen (M'04, S'14) received his Diploma and Dr.-Ing. degrees from the Technical University of Aachen, Germany, in 1992 and 1997, respectively. 1997 he joined ALSTOM Schorch Transformatoren GmbH, Mönchengladbach, Germany, where he was responsible for basic research and product development. From 2002 to 2004, he was the head of the electrical and mechanical design department. 2004 he was appointed to a professorship and head of the institute of Power Transmission and High Voltage Technology of the University of Stuttgart, Germany. In this position his main research fields are high voltage technique, power transmission and electromagnetic compatibility (EMC). Prof. Tenbohlen holds several patents and published more than 400 papers.

  • Enriching Public Speaking and Networking

    Virtual - Zoom

    Having good communication and networking skills are essential to succeed in any industry, especially for engineering students. Join our workshop to improve public speaking and gain networking skills from IEEE, our special guest, Ana Acioli. Ana has great experiences networking with other engineers in her field while being a student. She will share how she overcame the fear of public speaking, her techniques, and the resources she has been using to improve her communication skills. Furthermore, take the chance to join our community IEEE, an engineering student organization where we share our passion as potential engineering students. Speakers: Ana Acioli, Adi Malihi

  • Integrated Terrestrial-Aerial-Satellite Networks: Key Enabler for the Super Smart Cities of the Future

    Virtual - Zoom

    There have been rapid and exciting developments in recent years in satellite networks, in particular, in LEO mega-constellations such as SpaceX's Starlink. Although less visible, exciting developments have also been taking place in a certain type of aerial networks known as the high-altitude platform station (HAPS) systems, such as the formation of HAPS Alliance which brings together the connectivity and aerospace industries. It is worth noting that the satellite and aerial networks discussions have been occurring exclusively in the context of remote and rural connectivity. A major concern in this context is the rather questionable business case; there is limited revenue in rural and remote regions. In this talk, a novel vision will be presented for an integrated terrestrial-aerial-satellite networks architecture as a key enabler for the super smart cities of 2030s and beyond Speaker: Dr. Halim Yanikomeroglu Biography: Dr. Halim Yanikomeroglu is a Professor at Carleton University, Canada. He received his Ph.D. from the University of Toronto in 1998. He contributed to 4G/5G technologies and standards; his research focus in recent years has been on 6G and non-terrestrial networks (NTN). His extensive collaboration with industry resulted in 37 granted patents. He supervised or hosted in his lab around 140 postgraduate researchers. He co-authored IEEE papers with faculty members in 80+ universities in 25 countries. He is a Fellow of IEEE, Engineering Institute of Canada, and Canadian Academy of Engineering, and an IEEE Distinguished Speaker for Communications Society (ComSoc) and Vehicular Technology Society (VTS). He is currently chairing the IEEE WCNC (Wireless Communications and Networking Conference) Steering Committee; he is also a member of PIMRC Steering Committee and ComSoc Emerging Technologies Committee. He served as the General Chair of two VTCs and Technical Program Chair/Co-Chair of three WCNCs. He chaired ComSoc Technical Committee on Personal Communications. He received several awards for his research, teaching, and service including IEEE ComSoc Fred W. Ellersick Prize (2021), IEEE VTS Stuart Meyer Memorial Award (2020), and IEEE ComSoc Wireless Communications Technical Committee Recognition Award (2018).

  • AI against COVID-19: Screening X-ray Images for COVID-19 Infections

    Virtual

    Join the virtual competition on AI for COVID diagnosis, thanks to Microsoft Canada, the exclusive technology and cloud platform sponsor! The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has generated an unprecedented global health crisis, with more than 2.7 million deaths worldwide. Do you want to contribute to the fight against this pandemic? IEEE SIGHT (Special Interest Group on Humanitarian Technology) of Montreal Section, Vision and Image Processing Research Group of the University of Waterloo and DarwinAI Corp. invite data scientists, students and professionals working on Artificial Intelligence (AI) to participate in a virtual competition to help medical researchers diagnose COVID-19 with chest X-ray (CXR) images. The ultimate goal is to contribute to the development of highly accurate yet practical AI solutions for detecting COVID-19 cases and, hopefully, accelerating the treatment of those who need it the most. Moreover, this AI for Good initiative will also allow us to take action on at least one of the United Nations Sustainable Development Goals (SDGs), Good Health and Well-being. In the First Phase of the competition, the challenge consists of designing robust machine learning algorithms to predict if the subjects of study are either COVID-19 positive or COVID-19 negative. The dataset for this competition is the dataset curated by COVID-Net, a global open-source initiative launched by DarwinAI Corp., Canada, and Vision and Image Processing Research Group, University of Waterloo, Canada, for accelerating advancements in machine learning to aid healthcare workers around the world in the fight against the COVID-19 pandemic. More about the COVID-Net initiative and available open-source resources are available here. In the Second Phase, the 10 top teams of the first phase will have the opportunity to refine their solution and submit a proposal for a follow-up project to positively impact society or the academic community. This competition is organized in collaboration with the National Research Council Canada and is co-hosted by the IEEE Young Professionals Affinity Groups of Montreal, Ottawa, Toronto and Vancouver Sections, Vancouver Circuit and Systems (CAS) Technical Chapter, the Student Branches of INRS (Institut National de la Recherche Scientifique), University of Toronto and Vancouver Simon Fraser University, WIE (Women In Engineering) Ottawa. It is largely sponsored by Microsoft, and partially by the IEEE Canada Humanitarian Initiatives Committee and the IEEE Montreal Section. How to participate Note: This competition only accepts participants living in Canada, due to restrictions on funds transfer. NO PURCHASE NECESSARY TO ENTER OR WIN. The competition is hosted on the Eval.ai online platform. To participate, you or your team will need to perform the following steps: Register individually at the link provided below in the current webpage (vTools). Register yourself or your team at the link on Eval.ai: https://eval.ai/web/challenges/challenge-page/925/participate. Follow the instructions here: https://evalai.readthedocs.io/en/latest/participate.html#. Download the dataset from https://www.kaggle.com/andyczhao/covidx-cxr2. Design an AI algorithm that gets CXR images as inputs and predicts the labels of the images in the output (COVID or non-COVID). Train your AI algorithm using the training dataset. Submit your AI algorithm through Eval.ai for evaluation against the test dataset for the competition. Prizes For the First Phase, the first five best solutions will be awarded monetary prizes and Azure credits: First place: 1,000 CAD + 500 CAD in Azure. Second place: 800 CAD + 300 CAD in Azure. Third place: 600 CAD + 300 CAD in Azure. Fourth place: 400 CAD + 300 CAD in Azure. Fifth place: 300 CAD + 300 CAD in Azure. The top 10 teams on the leaderboard will also have the following opportunities: Participate in the 2nd phase to refine their solution and receive funding for a project. Write a scientific paper with the Vision and Image Processing Research Group, from the University of Waterloo, to explain their approach. For the Second Phase, the best three projects can receive funds up to the following amounts: Project 1: 5,000 CAD. Project 2: 5,000 CAD. Project 3: 4,000 CAD. Term of funding: Up to 4 months following the announcement of the selected teams. The deadline is December 31st, 2021. For more information, visit IEEE SIGHT Montreal website.  

  • Ubiquitous Machines Learning for Design and Implementation of Energy-Efficient Electrical Systems: A Wide Range of Uses and Applications

    Virtual

    IEEE Industry Relations Committee (on behalf of IEEE Canada) would like to invite you to attend the Webinar "Ubiquitous Machines Learning for Design and Implementation of Energy-Efficient Electrical Systems: A Wide Range of Uses and Applications" The objective of this webinar is to discuss how Artificial Intelligence (AI) can play a significant role in several applications in electrical engineering. The webinar intends to provide an update/overview of the recent trends and advances of AI research and to open a dialog on how to address the challenges faced in the design of energy-efficient electrical systems. Several pathways to innovate through electronic systems design will be discussed through a series of ongoing projects. Speakers: Prof. Yvon Savaria Yvon Savaria FIEEE (S' 77, M' 86, SM' 97, F’08) received the B.Ing. and M.Sc.A in electrical engineering from Polytechnique Montreal in 1980 and 1982 respectively. He also received the Ph.D. in electrical engineering in 1985 from McGill University. Since 1985, he has been with Polytechnique Montreal, where he is currently professor in the department of electrical engineering. He is also affiliated with Hangzhou Innovation Institute of Beihang University. He has carried work in several areas related to microelectronic circuits and microsystems such as testing, verification, validation, clocking methods, defect and fault tolerance, effects of radiation on electronics, high-speed interconnects and circuit design techniques, CAD methods, reconfigurable computing and applications of microelectronics to telecommunications, networking, aerospace, image processing, video processing, radar signal processing, and digital signal processing acceleration. He is currently involved in several projects that relate to aircraft embedded systems, asynchronous circuits design and test, virtual networks, software defined networks, machine learning, computational efficiency and application specific architecture design. He holds 16 patents, has published 180 journal papers and 470 conference papers, and he was the thesis advisor of 175 graduate students who completed their studies. Dr. Ahmed Ragab Ahmed Ragab is an AI research scientist working for CanmetENERGY, an energy innovation center of Natural Resources Canada (NRCan). He received a Ph.D. degree in Industrial Engineering from Polytechnique Montréal in 2014. His research interests include AI, Image Processing, Data Fusion, Causality Analysis, Operations Research, Discrete Event Systems, and Process Mining. He has a bunch of experience in developing advanced algorithms and tools in the manufacturing industry, aiming at reducing energy consumption, Greenhouse gas (GHG) emissions, and operational and maintenance costs while improving operations’ performance. His main thematic activities focus on the practical challenges of Big Data and AI in a number of applications including Abnormal Events Diagnosis & Prognosis, Predictive Maintenance, Supervisory Control, Real-Time Optimization and Systems Design.

  • [AP-S Seminar Series] Low Profile Antennas for Chip-to-Chip Data Communications: A Research Story, Prof. Kathleen Melde

    Virtual - Zoom

    Abstract: In this talk, we present our recent research involving the development of low profile antennas that are used to replace wired interconnects in multi-chip modules in electronic packaging. This presentation will discuss the evolution of chip-compatible pattern adaptable mm-wave antenna modules to be used in massively multicore computers. The result is an enabling technology that overcomes technology bottlenecks that are prevalent when wired lines are used in interconnect busses. While device technologies have scaled, the interconnection layers have not. The limits are in the pitch of the input and output (I/O) for chip-to-chip communications and losses due to physical transmission lines. This is a unique type of pattern adaptable antenna array in that the antenna patterns are in the same plane as the antenna elements. This is quite a departure from many other types of reconfigurable antennas where the patterns are broadside (90 degree angle) to the antennas. The approach is new in that it leverages mm-wave technology (60GHz) so that the antenna size is small. 60GHz allows the work to leverage the already-developed transceiver work done for WPAN technologies. 60GHz also has a natural attenuation at large transmission distances, which means sufficient isolation and elimination of interference outside of the MCMC system. The research impacts antenna technology, packaging technology (circuit stacking and advanced packaging), and wireless systems testing on an experimental testbed. The talk will focus on the story behind how the technology progresses and how the research unfolded along the way. Contact: UofT AP-S Student Chapter

  • Basic OrCad Workshop

    Virtual - Zoom

    IEEE Seneca is offering a basic OrCad Workshop. We will be reinforcing ETY155 concepts and learn about following topics: - Simple resistor circuits - Voltage divider - Current divider concepts - Parallel circuits vs series circuits To amplify the experience, please have OrCad installed or use virual commons by Seneca to follow through the instruction. Contact: IEEE Seneca Speakers: Gabriel Chen, Adi Malihi