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

Abstract Machine learning (ML) has recently been applied for the classification of radio frequency (RF) signals. One use case of interest relates to the discernment between different wireless protocols that operate over a shared and potentially contested spectrum. Although highly accurate classifiers have been developed for various wireless scenarios, research points to the vulnerability of such classifiers to adversarial machine learning (AML) attacks. In one such attack, a surrogate deep neural network (DNN) model is trained by the attacker to produce intelligently crafted low power “perturbations” that degrade the classification accuracy of the legitimate classifier. In this talk, I will first present several novel DNN protocol classifiers that we designed for a shared spectrum environment. These classifiers performed quite well in both simulations and OTA experimentation, considering benign (non-adversarial) noise. I will then present several AML techniques that an attacker may use to generate low power perturbations. When combined with a legitimate signal, these perturbations are shown to uniformly degrade the classification accuracy, even in the very high SNR regime. Different attack models are studied, depending on how much information the attacker has about the defender’s classifier. Finally, I will discuss possible defense mechanisms as well as other research efforts related to detection of adversarial transmissions. Co-sponsored by: Dr. Jun Yan Room: EV001.162, Bldg: EV001.162, 1515 St. Catherine St. West, Montreal, Quebec H3G 2W1, Montreal, Quebec, Canada, H3G 2W1

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