Week of Events
The Metaverse & Extended Reality – A First Test & Measurement Perspective
The Metaverse & Extended Reality – A First Test & Measurement Perspective
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 Metaverse & Extended Reality – A First Test & Measurement Perspective
The Metaverse & Extended Reality – A First Test & Measurement Perspective
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 Metaverse & Extended Reality – A First Test & Measurement Perspective
The Metaverse & Extended Reality – A First Test & Measurement Perspective
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
Security Considerations for Mobile Edge Computing webinar
Security Considerations for Mobile Edge Computing webinar
Mobile Edge Computing (MEC) is a technology framework that brings computational resources, including processing power, storage, and network connectivity, closer to mobile devices and users. In MEC, these resources are deployed at the edge of the cellular network, typically in close proximity to cell towers and base stations. This contrasts with traditional cloud computing, where data processing and storage occur in centralized data centers that can be distant from the end-user. Key aspects and features of MEC include Low Latency, Proximity, Resource Optimization, Scalability, Context Awareness and Security. Security measures are implemented at the edge to protect sensitive data and ensure the integrity of the network. This can include encryption, access control, and authentication mechanisms. Applications and use cases of MEC are diverse and include AR/VR, IoT, Content Delivery, Connected Vehicles, Smart Grids. MEC is also a critical component in the evolution of 5G networks. The webinar will provide a forum for researchers and engineers from academia and industry to develop a deep understanding of MEC Security. The participants would be exposed to wide and diverse topics in MEC Security, *This event is being recorded Co-sponsored by: IEEE Future Networks Speaker(s): Rajeev Shorey, Giridhar (Giri) D. Mandyam Virtual: https://events.vtools.ieee.org/m/403457
Security Considerations for Mobile Edge Computing webinar
Security Considerations for Mobile Edge Computing webinar
Mobile Edge Computing (MEC) is a technology framework that brings computational resources, including processing power, storage, and network connectivity, closer to mobile devices and users. In MEC, these resources are deployed at the edge of the cellular network, typically in close proximity to cell towers and base stations. This contrasts with traditional cloud computing, where data processing and storage occur in centralized data centers that can be distant from the end-user.Key aspects and features of MEC include Low Latency, Proximity, Resource Optimization, Scalability, Context Awareness and Security. Security measures are implemented at the edge to protect sensitive data and ensure the integrity of the network. This can include encryption, access control, and authentication mechanisms.Applications and use cases of MEC are diverse and include AR/VR, IoT, Content Delivery, Connected Vehicles, Smart Grids. MEC is also a critical component in the evolution of 5G networks.The webinar will provide a forum for researchers and engineers from academia and industry to develop a deep understanding of MEC Security. The participants would be exposed to wide and diverse topics in MEC Security,*This event is being recordedCo-sponsored by: IEEE Future NetworksSpeaker(s): Rajeev Shorey, Giridhar (Giri) D. MandyamVirtual: https://events.vtools.ieee.org/m/403457
IEEE Montclair State University STEM Fair
IEEE Montclair State University STEM Fair
The first 2024 IEEE Montclair State University STEM Fair will be held on Feb. 21, 2024 (please see the flyer beside for detailed time and location). This new event will be an introduction to promote various of the university’s research laboratories and clubs, meet colleagues in STEM fields, and check out cool equipment the university has here on campus. Each lab and club are dedicated to developing STEM not only within their own discipline but also within other majors across campus. Attendees will have the ability to network with their peers and demonstrate their projects to university students and faculty, local schools, and the public.This event will be co-organized by the IEEE Montclair Student Branch, IEEE STEM Champion Montclair Site, IEEE North Jersey Section SMC Chapter, IEEE North Jersey Section RAS Chapter, Montclair CRoSS Lab, and Montclair Robotics Club. Plenty of other clubs and labs will also be in attendance. If you are looking for a new way to get involved on campus and to network within the IEEE and STEM atmosphere, we hope to see you there!Room: 120, Bldg: CELS, 1 Normal Ave, Montclair, New Jersey, United States, 07043
IEEE Montclair State University STEM Fair
IEEE Montclair State University STEM Fair
The first 2024 IEEE Montclair State University STEM Fair will be held on Feb. 21, 2024 (please see the flyer beside for detailed time and location). This new event will be an introduction to promote various of the university’s research laboratories and clubs, meet colleagues in STEM fields, and check out cool equipment the university has here on campus. Each lab and club are dedicated to developing STEM not only within their own discipline but also within other majors across campus. Attendees will have the ability to network with their peers and demonstrate their projects to university students and faculty, local schools, and the public. This event will be co-organized by the IEEE Montclair Student Branch, IEEE STEM Champion Montclair Site, IEEE North Jersey Section SMC Chapter, IEEE North Jersey Section RAS Chapter, Montclair CRoSS Lab, and Montclair Robotics Club. Plenty of other clubs and labs will also be in attendance. If you are looking for a new way to get involved on campus and to network within the IEEE and STEM atmosphere, we hope to see you there! Attention all students! We highly encourage you to become a member of IEEE. The IEEE North Jersey Section will cover 50% of your membership fee, while the IEEE Antenna & Propagation Society will cover the full student membership fee. To avail of this benefit, please send your membership fee invoice via email to Dr. Ajay Poddar at [email protected]. Room: 120, Bldg: CELS, 1 Normal Ave, Montclair, New Jersey, United States, 07043
Battery Monitoring and Maintenance for NERC Compliance – BatteryDAQ
Battery Monitoring and Maintenance for NERC Compliance – BatteryDAQ
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
Battery Monitoring and Maintenance for NERC Compliance – BatteryDAQ
Battery Monitoring and Maintenance for NERC Compliance – BatteryDAQ
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
I Did Not Sign Up for This: Limited Sharing in Privacy-Aware Smart Environments
I Did Not Sign Up for This: Limited Sharing in Privacy-Aware Smart Environments
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
I Did Not Sign Up for This: Limited Sharing in Privacy-Aware Smart Environments
I Did Not Sign Up for This: Limited Sharing in Privacy-Aware Smart Environments
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