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A recent report by the US Department of Energy defines the area of scientific machine learning as “a core component of artificial intelligence (AI) and a computational technology that can be trained, with scientific data, to augment or automate human skills”, which has “the potential to transform science and energy research”. We explore the potential of scientific machine learning methods to problems in computational electromagnetics starting from standard microwave structure design and multiphysics modeling, employing an unsupervised learning strategy based on Physics-Informed Neural Networks (PINN). PINNs directly integrate physical laws into their loss function, so that the training process does not rely on the generation of ground truth data from a large number of simulations (as in typical neural networks). Moreover, we demonstrate the impact of machine learning on the computational modeling of radiowave propagation scenarios. We build convolutional neural network models that can process the geometry of indoor environments, along with physics-inspired parameters, to rapidly estimate received signal strength (RSS) maps. We show the *generalizability* of these models, which is their ability to "learn" the physics of radiowave propagation and produce accurate modeling predictions in new geometries well beyond those included in their training set. These models can be used to rapidly optimize the position of transmitters in wireless area networks, to maximize coverage or other relevant metrics. Co-sponsored by: STARaCom Speaker(s): Costas Sarris McGill Unversity, Room TBD, Montreal, Quebec, Canada

This is a weekly session of the CIT Summer Series, with Dr. Rami Abielmona presenting Distilling AI: The Hitchhiker's Guide : In this talk, I will present real-world AI/ML Big Data solutions involving unique learning algorithms that allow one to process vast amounts of critical information combined with knowledge acquired from specific domains. These unique models and architectures continually deliver the most accurate information possible in order to constantly optimize the decision maker’s domain awareness. Attendees will learn innovative concepts such as the five levels of Big Data Analytics, the various variations of AI including Machine Learning, Deep Learning and Machine Intelligence, the transformational changes that AI can bring about in the near future as well as the challenges and opportunities to deploy AI-ready applications in mission-critical tactical environments. Speaker(s): Dr. Rami Abielmona, Virtual: https://events.vtools.ieee.org/m/363999

Microwave photonics (MWP) is a typical optical signal processing application for optical communications, antenna systems, and 5G/6G networks. At the same time, optical frequency combs (OFC) and programmable optical filters enable this system to be reconfigurable. There are several approaches to creating OFC lines, such as the micro-ring resonator, cascaded electro-optic modulator, and mode-locked laser (MLL), in which the quantum dash (QDash) MLL is an ideal on-chip OFC source to provide low relative intensity noise (RIN), narrow linewidth, and flat comb spectrum. In this seminar, we will present three typical applications of MWP systems using QDash MLL as the OFC source. The photonic beamforming illustrates a phased antenna array system that can do directional radiation and scanning. The MWP filter is a reconfigurable finite impulse response (FIR) filter, and a specially designed MWP filter can also be used for instantaneous frequency measurement. In partnership with the (https://nrc.canada.ca/en/research-development/research-collaboration/programs/high-throughput-secure-networks-challenge-program)Challenge program at National Research Council (NRC), we invite you to join this virtual seminar series to promote scientific information sharing, discussions, and interactions between researchers. Co-sponsored by: National Research Council, Canada Speaker(s): Yuxuan Xie , Virtual: https://events.vtools.ieee.org/m/366426

This is a weekly session of the CIT Summer Series, with Nael Abu-Ghazaleh presenting Security challenges and opportunities at the Intersection of Architecture and ML/AI : Machine learning is an increasingly important computational workload as data-driven deep learning models are becoming increasingly important in a wide range of application spaces. Computer systems, from the architecture up, have been impacted by ML in two primary directions: (1) ML is an increasingly important computing workload, with new accelerators and systems targeted to support both training and inference at scale; and (2) ML supporting architecture decisions, with new machine learning based algorithms controlling systems to optimize their performance, reliability and robustness. In this talk, I will explore the intersection of security, ML and architecture, identifying both security challenges and opportunities. Machine learning systems are vulnerable to new attacks including adversarial attacks crafted to fool a classifier to the attacker’s advantage, membership inference attacks attempting to compromise the privacy of the training data, and model extraction attacks seeking to recover the hyperparameters of a (secret) model. Architecture can be a target of these attacks when supporting ML, but also provides an opportunity to develop defenses against them, which I will illustrate with three examples from our recent work. First, I show how ML based hardware malware detectors can be attacked with adversarial perturbations to the Malware and how we can develop detectors that resist these attacks. Second, I will also show an example of a microarchitectural side channel attacks that can be used to extract the secret parameters of a neural network and potential defenses against it. Finally, I will also discuss how architecture can be used to make ML more robust against adversarial and membership inference attacks using the idea of approximate computing. I will conclude with describing some other potential open problems. Speaker(s): Nael Abu-Ghazaleh, Virtual: https://events.vtools.ieee.org/m/364001

Abstract: Renewable energy systems are gaining increasing importance throughout the world. Proliferation of renewable energy sources in power systems has led to new opportunities and challenges in control, stability, protection, power quality and operation of power systems. Power electronics is an enabling technology for effective integration of renewables into the electrical grid. The aim of this talk is to provide an overview of a range of important power electronic applications in microgrids and smart grids with a high share of renewables. Participants will mainly gain insight into the control of power electronic converters in such applications. The main topics are as follows: -Distributed generation and microgrids -Photovoltaic systems -Wind energy systems -Grid codes for renewable energy systems Speaker(s): Prof. Mehdi Savaghebi Virtual: https://events.vtools.ieee.org/m/365502

Title: Optical Wireless Communications for Low Power Internet of Things Context: Optical wireless communication (OWC) is touted as a complementary technology to mitigate the scarcity issue of the RF spectrum. OWC relies on massively deployed low power consumption light emitted diodes (LED) to realise secure wireless communications in the optical domain. However, to implement low power OWC systems for IoT applications, it is necessary to investigate on energy efficient modulation schemes. For a typical OWC system, using intensity modulation and direct detection (IM-DD), the modulation signal should be unipolar and positive. Extensive research has been carried out on high data rate IoT applications, which are based on spectrally efficient linear modulation schemes, such as pulse-amplitude modulation (PAM), optical-orthogonal frequency-division multiplexing (O-OFDM), etc. Only very few studies have been carried on low power OWC technologies dedicated to low power/low data-rate IoT. To address this challenge, non-linear modulations such as frequency shift keying (FSK) have raised a substantial interest for OWC applications. As original FSK modulation is not compatible with the IM-DD OWC system due to its bipolar nature, two variants of FSK-based modulations (i.e., direct current (DC)-FSK and unipolar (U)-FSK) have been introduced recently which are compatible with IM-DD OWC systems. Abstract: In this presentation, a new modulation technique M-ary Asymmetrically Clipped (AC)-FSK which is compatible with IM-DD OWC systems to address the challenge of energy efficient modulation scheme for low data-rate OWC is proposed. The spectral analysis of M-ary AC-FSK waveforms allows us to build a low complexity frequency-domain (FD) harmonic receiver which has almost the same bit error rate (BER) performance as the optimal receiver but with a drastic reduction of receiver complexity. Moreover, a new modulation approach (called AC-FPSK) is proposed for OWC systems that is based on the amalgamation of the proposed M-ary AC-FSK and phase-shift keying (PSK). AC-FPSK further improves the energy efficiency versus spectral efficiency trade-off as compared to M-ary AC-FSK. Finally, an experimental demonstration, based on the software defined radio (SDR) test bench with OWC prototype is presented for the proposed M-ary AC-FSK. Experimental results are compliant with the simulation results and highlight the interest of the proposed modulation schemes for optical wireless communications. Speaker: Yannis Le Guennec Organizer: IEEE Student Branch Of Polytechnique Montréal Thomas Micallef, Poly-Grames Research Center, Polytechnique Montréal Speaker(s): Yannis Le Guennec Virtual: https://events.vtools.ieee.org/m/366315

This is a weekly session of the CIT Summer Series, with David A. Bader presenting Solving Global Grand Challenges with High Performance Data Analytics : Data science aims to solve grand global challenges such as: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats, and improving the resilience of the electric power grid. Unlike traditional applications in computational science and engineering, solving these social problems at scale often raises new challenges because of the sparsity and lack of locality in the data, the need for research on scalable algorithms and architectures, and development of frameworks for solving these real-world problems on high performance computers, and for improved models that capture the noise and bias inherent in the torrential data streams. In this talk, Bader will discuss the opportunities and challenges in massive data science for applications in social sciences, physical sciences, and engineering. Speaker(s): David A Bader, Virtual: https://events.vtools.ieee.org/m/364003

This is a weekly session of the CIT Summer Series, with David A Fisher presenting Why Software Fails and Why AI cannot Help : It was once widely believed that computers would enhance the speed, reliability, and applicability of human deductive reasoning in the physical and social sciences, much as motorized vehicles (e.g., cars, trains, airplanes) have enhanced the speed, reliability, and applicability of human manual abilities in transportation. Yet, 60 years later, computers can be used confidently only for paperwork tasks, analysis of regularly structured data, and simple process control applications. Complex software rarely satisfies user needs, is untrustworthy and difficult to maintain, and largely opaque to its users. Artificial intelligence (AI) methods including heuristics, machine learning, and statistical methods are in opposition to sound deductive reasoning. This presentation explains certain practical and logical impediments to computer enhancement of human deductive reasoning, the deductive limitations of modern programming languages, the role of AI, and provides some promising alternatives. Speaker(s): David A Fisher, Virtual: https://events.vtools.ieee.org/m/364005

IEEE ETS is hosting a women in engineering panel at the IEEE PEDS conference 2023, giving you a chance to get a sneak peak into the lives of successful women engineers. Date: August 09th, 2023 Time: 11:00 AM - 12:30 PM 📍 ÉTS: Block A, 1st Floor, Room A1600, 1100 Notre-Dame St W, Montreal, Quebec H3C 1K3, Canada Join us in celebrating the success of these inspiring Women Engineers who have triumphed over social and cultural barriers and achieved remarkable success in their careers. Our panelists will share their journey of overcoming the challenges in their professional and personal lives, and talk about the role family plays in their journey as women engineers. You'll have a chance to connect with women engineers who have demonstrated their success in both academia and industry. The panel discussion will be followed by a Q/A session where you get to ask them for career advice, and any questions you might have about how they "made it". Secure your seat now! Limited spots are available. Register to reserve your place at this enriching panel discussion. Join us on August 09th, 2023, and embark on a transformative journey as you delve into the professional lives of our distinguished panelists. Speaker(s): Sophie Larivière-Mantha, Chunyan Lai, Marie-José Nollet, Hakimeh Purmehdi, Danielle Sami Nasrallah Room: 1600, Bldg: A, École de technologie supérieure , 1100 Notre-Dame Ouest, Montreal, Quebec, Canada, H3C 1K3

The IEEE Young Professionals Montreal Panel Discussion at the esteemed IEEE PEDS 2023 Conference is your exclusive opportunity to gain valuable insights into the professional journeys of seven accomplished young professionals. Date: August 10th, 2023 Time: 10:30 AM - 12:30 PM 📍 ÉTS, Université du Quebec: Auditorium A-1600, Block A, 1st Floor, 1100 Notre-Dame St W, Montreal, Quebec H3C 1K3, Canada Immerse yourself in an inspiring discussion as these remarkable panelists share their personal experiences, triumphs, and challenges encountered on their path to success. Explore the multifaceted dimensions of their professional lives, including the role that family plays in shaping their careers. Uncover the secrets to achieving professional excellence and discover how these outstanding individuals have excelled in their respective fields. From academia to industry, our panelists represent a diverse range of expertise and can provide valuable insights to empower your own professional growth. Gain unparalleled guidance and actionable strategies as our panelists reveal the pivotal moments, career choices, and skills that have propelled them forward. Learn from their experiences and discover new perspectives to enhance your own professional journey. Connect with fellow young professionals and expand your network during this engaging event. Forge new connections, exchange ideas, and leverage the collective wisdom of the IEEE community. Secure your seat now! Limited spots available. Register to reserve your place at this enriching panel discussion. Join us on August 10th, 2023, and embark on a transformative journey as you delve into the professional lives of our distinguished panelists. Speaker(s): Dr. Matthew Posner, Dr. Seyed Masoud Mohseni-Bonab, Dr. Qingsong Wang, Dr. Carla Mouradian, Dr. Jasmine Boparai, Dr. Miloud Rezkallah, Dr. Ali Moeini Room: Auditorium A-1600, Bldg: Block A, 1st Floor, ÉTS, Université du Quebec, 1100 Notre-Dame St W, Montreal, Quebec, Canada, H3C 1K3