Cybersecurity attacks on critical infrastructure are increasing in frequency and sophistication, causing significant disruptions and highlighting vulnerabilities in essential systems. This paper focuses on the impact of such attacks on electrical power systems, particularly on three-phase induction motors. Analyzing historical cyberattacks, we identify potential vulnerabilities and simulate unbalanced voltage conditions to observe their effects on motor performance and lifespan. Our simulations reveal that these conditions lead to increased mechanical and thermal stresses, resulting in potential physical damage and reduced operational lifespan of the motors. Through this approach, we aim to enhance the resilience of cybersecurity measures and safeguard critical industrial assets from future cyber threats. Speaker(s): Gaving McCormick, Virtual: https://events.vtools.ieee.org/m/460945

Virtual: https://events.vtools.ieee.org/m/460410

IEEE Future Networks World Forum: How are we moving towards 6G? We are hosting a special event to discuss how IEEE Future Networks held a very successful event , 15-17 October 2024 with focus on "Advancing 5G Towards 6G". A panel of organizers and presenters will cover aspects of 2024 event, including: - Keynotes - Technical Program - Special sessions (WIE, YP, Student Leadership, etc) Join us as we recap our flagship event! *This event is being recorded Thank you to our (https://fnwf2024.ieee.org/) sponsoring Societies Virtual: https://events.vtools.ieee.org/m/460718

January 2025 NH Section ExCom meeting Virtual: https://events.vtools.ieee.org/m/461761

Special Presentation by Dr. Mérouane Debbah (Khalifa U., UAE) Hosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Date/Time: Thursday, January 16th, 2025 @ 12:00 UTC Topic: Generative Diffusion Models for Network Optimization Abstract: Network optimization is a fundamental challenge in Internet-of-Things (IoT) networks, often characterized by complex features that make it difficult to solve these problems. Recently, generative diffusion models (GDMs) have emerged as a promising new approach to network optimization, with the potential to directly address these optimization problems. However, the application of GDMs in this field is still in its early stages, and there is a noticeable lack of theoretical research and empirical findings. In this study, we first explore the intrinsic characteristics of generative models. Next, we provide a concise theoretical proof and intuitive demonstration of the advantages of generative models over discriminative models in network optimization. Based on this exploration, we implement GDMs as optimizers aimed at learning high-quality solution distributions for given inputs, sampling from these distributions during inference to approximate or achieve optimal solutions. Specifically, we utilize denoising diffusion probabilistic models (DDPMs) and employ a classifier-free guidance mechanism to manage conditional guidance based on input parameters. We conduct extensive experiments across three challenging network optimization problems. By investigating various model configurations and the principles of GDMs as optimizers, we demonstrate the ability to overcome prediction errors and validate the convergence of generated solutions to optimal solutions. Speaker: Dr. Mérouane Debbah is a Professor at the Khalifa University of Science and Technology in Abu Dhabi and founding Director of the KU 6G Research Center. He is a frequent keynote speaker at international events in the field of telecommunication and AI. His research has been lying at the interface of fundamental mathematics, algorithms, statistics, information and communication sciences with a special focus on random matrix theory and learning algorithms. In the Communication field, he has been at the heart of the development of small cells (4G), Massive MIMO (5G) and Large Intelligent Surfaces (6G) technologies. In the AI field, he is known for his work on Large Language Models, distributed AI systems for networks and semantic communications. He received multiple prestigious distinctions, prizes and best paper awards (more than 40 IEEE best paper awards) for his contributions to both fields and according to research.com he is ranked as the best scientist in France in the field of Electronics and Electrical Engineering. He is an IEEE Fellow, a WWRF Fellow, a Eurasip Fellow, an AAIA Fellow, an Institut Louis Bachelier Fellow, an AIIA Fellow, and a Membre émérite SEE. He is chair of the IEEE Large Generative AI Models in Telecom (GenAINet) Emerging Technology Initiative and a member of the Marconi Prize Selection Advisory Committee. Co-sponsored by: Artificial Intelligence & Machine Learning (AIML) Working Group Virtual: https://events.vtools.ieee.org/m/453702

IEEE NJ Coast Section - Executive Committee Meeting (January) (Virtual) Co-sponsored by: [email protected] Agenda: 1. Vote / Accept Meeting Minutes (Laura) 2. Treasurer's Report (Mike) 3. Chair's Report(s) (Filomena) 4. Chapter Reports (Each Chapter Chair) - Old Business - Committee and Affinity Group Status - New Business 5. Move To Close Virtual: https://events.vtools.ieee.org/m/461708

This month's meeting is a panel discussion. Our panelists will address the overall technology outlook - with a special emphasis on how it impacts job seekers, career choices, hiring/layoff dynamics, and the general shape of future of technology that we should expect. Our speakers have consulting experience throughout the business community - including startups, business strategy, and customer experience - and they will share their advice about how that might affect how we position ourselves in the job market. Speaker(s): Jeffery Lee Funk, Leonard Lee, Debbie Levitt Room: CS 105, Bldg: Princeton University Computer Science Building, 35 Olden Street, Princeton, New Jersey, United States, 08544, Virtual: https://events.vtools.ieee.org/m/460650