IEEE North Jersey Section

SEMINAR OUTLINE: • Fundamentals of dry type transformers • Using transformers for current limitation • Live demonstration of Power Quality solution • Linear vs. non-linear loads • Harmonics • VFD operation • Single phase harmonic solutions • Three phase harmonic solutions • VFD load side filtering • Emerging markets and applications Hammond Power Solutions c/o the IEEE is proud to offer the informative presentation and live demonstration of power quality solutions for variable frequency drives (VFDs). VFDs provide fine motor control and energy savings but they also produce harmonic content on the system. High harmonic content can lead to issues such as: equipment overheating, communication issues, nuisance OCPD trips and possible utility fines. We will present the various transformer filtering technologies and demonstrate how they work. In addition, participants will learn more about dry type transformer ratings, such as K factor, temperature degree rise, etc. for the proper development of engineer’s specifications, as well as transformer applications in commercial/industrial/datacenter projects. Speaker(s): Dave, Chris, Dr. Razak Agenda: PLEASE NOTE THE CHANGE IN TIME FOR THIS PARTICULAR SEMINAR. WE WILL BEGIN AT 8AM AND WRAP UP BY NOON. The seminar fee includes lunch, refreshments and handouts. Non-members joining IEEE within 30 days of the seminar will be rebated 50% of the IEEE registration charge. Four hours of instruction will be provided. If desired, IEEE Continuing Education Units (0.4 CEUs) will be offered for this course - a small fee of $55 will be required for processing. Please pay attention to the “Registration Fee” and choose the appropriate choice either with or without CEUs. CEU Evaluation Form can be found at: (https://innovationatwork.ieee.org/ieee-pes-northjersey-certificates/) Room: Auditorium, Bldg: PSE&G - Hadley Road Facility, 4000 Hadley Road, South Plainfield, New Jersey, United States, 07080

Special Presentation by Dr. Mahdi Boloursaz Mashhadi (U. of Surrey, UK) Hosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Date/Time: Thursday, 20 February 2025 @ 12:00 UTC Topic: Goal-Oriented Generative Semantic Communications with Multimodal LLMs Abstract: The integration of Generative Artificial Intelligence (GenAI) models with wireless networks provides ample opportunities to develop innovative technologies with transformative potential. One such technologies is Generative Semantic Communications (Gen SemCom), which leverages the capabilities of state-of-the-art GenAI models to develop ultra-low bitrate semantic communication systems aiming to transmit only the semantic message of interest with high fidelity. GenAI models such as Diffusion, Flow-based, and GAN models, can learn the general distribution of natural signals through training and generate new samples at the inference time. This generative process can be guided or conditioned to synthesize outputs with a desired semantic content. In Gen SemCom, the semantics of interest are extracted at the transmitter, communicated over the channel, and then used at the receiver to guide a generative model to locally synthesize a semantically consistent signal. The emerging generative foundation AI models and Multi-modal Large Language Models (MLLMs) can be leveraged in the SemCom framework to convey the most important semantics of the source signal to the receiver through textual prompts in a super compact form. These models possess a vast general knowledge through intensive pre-training on huge amount of data. This alleviates the need for a shared knowledge base/graph between the semantic transmitter and receiver, obviating the need for corresponding knowledge sharing overheads imposed in current SemCom frameworks. Despite the above benefits, deployment of such large models in the SemCom framework is challenging due to their high computational complexity, energy consumption, and latency. This talk focuses on novel generative approaches to semantic communications, the fundamental bounds on Gen SemCom, and its emerging applications in wireless networks. It investigates the challenges and opportunities of deploying Gen SemCom at various layers in future wireless networks and provides the corresponding future research directions. Speaker: [] Dr. Mahdi Boloursaz Mashhadi (Senior Member, IEEE) is a Lecturer at the 5G/6G Innovation Centre (5G/6GIC) at the Institute for Communication Systems (ICS), University of Surrey (UoS), and a Surrey AI fellow. His research is focused at the intersection of AI/ML with wireless communication, learning and communication co-design, generative AI for telecommunications, and collaborative machine learning. He received B.S., M.S., and Ph.D. degrees in mobile telecommunications from the Sharif University of Technology (SUT), Tehran, Iran. He has more than 40 peer reviewed publications and patents in the areas of wireless communications, machine learning, and signal processing. He is a PI/Co-PI for various government and industry funded projects including the UKTIN/DSIT 12M£ national project TUDOR. He received the Best Paper Award from the IEEE EWDTS conference, and the Exemplary Reviewer Award from the IEEE ComSoc in 2021 and 2022. He served as a panel judge for the International Telecommunication Union (ITU) on the “AI/ML in 5G” challenge 2021- 2022. He is an editor for the Springer Nature Wireless Personal Communications Journal. Co-sponsored by: Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Virtual: https://events.vtools.ieee.org/m/463737