IEEE North Jersey Section

The following will be discussed in this lecture: - Spectrum utilization for XR: Understand the significance of the FR3 spectrum and THz frequencies in 5G-Advanced and 6G networks for XR applications - 3GPP standards and XR enhancements: Gain insights into how 3GPP Release 18 addresses XR requirements - Challenges in XR traffic management: Learn about the complexities of managing XR traffic and how various factors influence the design and functionality of XR services Co-sponsored by: IEEE North Jersey Section Speaker(s): Andreas Roessler Virtual: https://events.vtools.ieee.org/m/405367

The following will be discussed in this lecture:- Spectrum utilization for XR: Understand the significance of the FR3 spectrum and THz frequencies in 5G-Advanced and 6G networks for XR applications- 3GPP standards and XR enhancements: Gain insights into how 3GPP Release 18 addresses XR requirements- Challenges in XR traffic management: Learn about the complexities of managing XR traffic and how various factors influence the design and functionality of XR servicesCo-sponsored by: IEEE North Jersey SectionSpeaker(s): Andreas RoesslerVirtual: https://events.vtools.ieee.org/m/405367

AbstractNERC PRC-005-2 compliance drives utility operators to seek a reliable, validated, and advanced Battery Monitoring System (BMS) for their power plants and substations. This seminar is intended to present and discuss the available technologies and implementation challenges. Experiences with field installations and real-time data/charts will be shared in the meeting.Contents- Battery condition and maintenance basics- Battery monitoring technologies- IEEE standards for battery maintenance and monitoring- NERC PRC-005-2 standard- Communication and IT security- Data driven battery maintenance- Planning for implementation- Product and software demoSpeaker(s): Alan LongAgenda: 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/)At this time, our attendance is being limited to fifty (50). Please only register if you know you are going to attend, and you must be registered to participate.Room: Aruba Room, Bldg: PSE&G - Hadley Road Facility, 4000 Hadley Road, South Plainfield, New Jersey, United States, 07080

Abstract NERC PRC-005-2 compliance drives utility operators to seek a reliable, validated, and advanced Battery Monitoring System (BMS) for their power plants and substations. This seminar is intended to present and discuss the available technologies and implementation challenges. Experiences with field installations and real-time data/charts will be shared in the meeting. Contents - Battery condition and maintenance basics - Battery monitoring technologies - IEEE standards for battery maintenance and monitoring - NERC PRC-005-2 standard - Communication and IT security - Data driven battery maintenance - Planning for implementation - Product and software demo Speaker(s): Alan Long Agenda: 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/) At this time, our attendance is being limited to fifty (50). Please only register if you know you are going to attend, and you must be registered to participate. Room: Aruba Room, Bldg: PSE&G - Hadley Road Facility, 4000 Hadley Road, South Plainfield, New Jersey, United States, 07080

IEEE Communications Society Distinguished Lecture, sponsored by IEEE North Jersey ComSoc Chapter, Systems Council and NJIT (host). Smart assistive environments adapt to the needs and preferences of disabled or elderly users who need help with the activities of daily living. However, the needs and requests of users vary greatly, both due to personal preferences and type of disability. As handcrafting an environment is prohibitively expensive, in recent years significant research was done in systems that use machine learning to create a predictive model of the user. Machine learning, however, typically requires large amounts of data. A stand-alone smart environment, however, only has access to the data collected from its user since it was deployed. A possible solution is to perform centralized, cloud-based learning by pooling the training data collected from multiple users. However, uploading data collected from the personal habits of elderly and disabled users create significant security and privacy concerns. In this talk, we investigate the type of data sharing necessary for learning user models in smart environments and propose several novel considerations. We point out that data sharing is only ethical if the user derives a benefit from it. This implies that the decision to share data must be periodically revisited, it is not a commitment extending indefinitely in the future. We study the data sharing decisions made by users under several machine learning frameworks: local, cloud, and federated learning. We show that most users only benefit from data sharing for a limited interval after the deployment of the system. We also investigate machine learning techniques that predict whether the user will benefit from sharing the data before the data is shared. Co-sponsored by: Systems Counil North Jersey Speaker(s): , Damla Turgut Agenda: Seminar from 11 am to noon. On-campus ECE 202 and zoom. Room: 202, Bldg: ECEC, New Jersey Institute of Technology, Newark, New Jersey, United States, Virtual: https://events.vtools.ieee.org/m/405574

IEEE Communications Society Distinguished Lecture, sponsored by IEEE North Jersey ComSoc Chapter, Systems Council and NJIT (host).Smart assistive environments adapt to the needs and preferences of disabled or elderly users who need help with the activities of daily living. However, the needs and requests of users vary greatly, both due to personal preferences and type of disability. As handcrafting an environment is prohibitively expensive, in recent years significant research was done in systems that use machine learning to create a predictive model of the user. Machine learning, however, typically requires large amounts of data. A stand-alone smart environment, however, only has access to the data collected from its user since it was deployed. A possible solution is to perform centralized, cloud-based learning by pooling the training data collected from multiple users. However, uploading data collected from the personal habits of elderly and disabled users create significant security and privacy concerns.In this talk, we investigate the type of data sharing necessary for learning user models in smart environments and propose several novel considerations. We point out that data sharing is only ethical if the user derives a benefit from it. This implies that the decision to share data must be periodically revisited, it is not a commitment extending indefinitely in the future. We study the data sharing decisions made by users under several machine learning frameworks: local, cloud, and federated learning. We show that most users only benefit from data sharing for a limited interval after the deployment of the system. We also investigate machine learning techniques that predict whether the user will benefit from sharing the data before the data is shared.Co-sponsored by: Systems Counil North JerseySpeaker(s): , Damla TurgutAgenda: Seminar from 11 am to noon. On-campus ECE 202 and zoom.Room: 202, Bldg: ECEC, New Jersey Institute of Technology, Newark, New Jersey, United States, Virtual: https://events.vtools.ieee.org/m/405574

For nearly 50 years, chemical engineer and inventor Mária Telkes applied her prodigious intellect to harnessing the power of the sun. She designed and built the world’s first successfully solar-heated modern residence and identified a promising new chemical that, for the first time, could store solar heat like a battery. And yet, along the way, she was undercut and thwarted by her boss and colleagues — all men — at MIT. Despite these obstacles, Telkes persevered and, upon her death in 1995, held more than 20 patents. She is now recognized as a visionary pioneer in the field of sustainable energy. An unexpected and largely forgotten heroine, Telkes was remarkable in her vision and tenacity — a scientist and a woman in every way ahead of her time. Her research and innovations from the 1930s through the ‘70s continue to shape how we power our lives today AFTER the documentary - we can have a brief discussion session. NOTE: You must supply your own soda pop and popcorn! :-) Trivia may also follow, so bring your Jeopardy hats too! Agenda: 6:00 PM - Welcome and Introductions, Chapter business update; (on your own) Pizza, Popcorn and Soda Pop6:05 PM - Documentary7:05 PM - End of Documentary; Start of Q & A; Group Discussion7:15 PM - Wrap Up ALL times are in EST/EDT (UTC-4 or UTC-5) depending upon local day light savings times in when effect Virtual: https://events.vtools.ieee.org/m/401391