IEEE North Jersey Section

The 2025-Spring IEEE Metropolitan AI Applications Symposium (IEEE METAI-2025-Spring) will be held online (Zoom) on Wednesday, May 7, 2025, from 12:00 pm until 2:30 pm. The symposium consists of one keynote presentation and several invited presentations, covering various topics in AI methods, systems, and applications. This symposium is organized in collaboration with Canadian-American Research Forum on AI Technologies. Organizers: IEEE North Jersey Engineering in Medicine and Biology Chapter (EMB) IEEE North Jersey Communications Chapter (COM) IEEE North Jersey Computer Chapter (C) IEEE North Jersey Section Canadian-American Research Forum on AI Technologies Registration Required: https://events.vtools.ieee.org/m/483589 Join Zoom Meeting: Meeting ID: https://zoom.us/j/5068754099 2025-Spring IEEE Metropolitan AI Applications Symposium (METAI-2025-Spring) Symposium Program 12:00PM: Welcome Messages Prof. Yu-Dong Yao (IEEE North Jersey Section) 12:01PM-12:35PM: Keynote Speaker: Prof. Shirantha Welikala, Stevens Institute of Technology Decentralization and Dissipativity: A Framework for Control and Topology Co-Design in Networked Cyber-Physical Systems 12:35PM-12:45PM: Melanie Montanez (Stevens), "ChatGPT and Generative AI Use in the Construction Industry", Presentation at 2025-Spring IEEE Metropolitan AI Applications Symposium (IEEE METAI-2025-Spring), May 7, 2025. 12:45PM-1:00PM: Kedarnath Umanath Naik (Stevens), "Audio Genre Categorisation Using CNN Model", Presentation at 2025-Spring IEEE Metropolitan AI Applications Symposium (IEEE METAI-2025-Spring), May 7, 2025. 1:00PM-1:15PM: Mohammad Rostami (Rowan), "Plug-and-Play AMC: Context Is King in Training-Free, Open-Set Modulation with LLMs", Presentation at 2025-Spring IEEE Metropolitan AI Applications Symposium (IEEE METAI-2025-Spring), May 7, 2025. 1:15PM-1:30PM: Prathyusha Sukumar (FDU), Othoniel Joseph, Rayner Ulloa, Avimanyou Vatsa, "Stock Sentiment Forecasting Using Sequential Models", Presentation at 2025-Spring IEEE Metropolitan AI Applications Symposium (IEEE METAI-2025-Spring), May 7, 2025. 1:30PM-1:45PM: Anandha Ragaven Ravi (Stevens), "Comparison of CNN Models in Brain Tumor Classification Using MRI Images", Presentation at 2025-Spring IEEE Metropolitan AI Applications Symposium (IEEE METAI-2025-Spring), May 7, 2025. 1:45PM-2:00PM: Dinithi Samarakoon (Stevens), "Optimizing Alzheimer’s Clinical Trials with Predictive Modeling Using ADNI Clinical and BioBERT-Enhanced Data", Presentation at 2025-Spring IEEE Metropolitan AI Applications Symposium (IEEE METAI-2025-Spring), May 7, 2025. Conclusion Remark Registration IEEE member $ 0.00 Non-member $ 0.00 IEEE Student/Graduate Student/Life Member $ 0.00 Non-IEEE Student/Graduate Student $ 0.00 Organizing Committee Prof. Yu-Dong Yao, IEEE North Jersey Section Prof. Elisa Kallioniemi, NJIT, Newark Prof. Huaxia Wang, Rowan University Prof. Hong Zhao, FDU Prof. Wei-Ping Zhu, Concordia University, Montreal Virtual: https://events.vtools.ieee.org/m/483589

Deep neural networks make it possible to learn key characteristics of data without having to assume mathematically tractable models. This in turn results in an ability to compress data in a model-free way. One of the promising areas for the application of deep learning to the physical layer of communication networks is compression. Hundred-fold compression with a small loss of the CSI in massive MIMO systems has been shown to be both feasible and necessary. However, model-free and data driven compression comes with a downside: the encoding and decoding models need to be trained on a large set of CSI arrays indicative of a wide spectrum of propagation and environmental conditions. As a result, in the early stages of the deployment of deep CSI compression models, it would be necessary to detect if and when users’ channels have drifted significantly away from the distribution of the CSI data on which the deep compression model was trained. In this paper, we present both 1) a technique for detecting harmful channel drift and 2) a lightweight scheme for fine-tuning the deep compression models to adjust to such shifts. Using public-domain synthetic channel data as well as 3GPP-compliant simulated data, we demonstrate the practicality of our proposed deep compression and detection framework. We close with recommendations for a viable implementation of the proposed drift detection by the standards bodies. Co-sponsored by: New Jersey Coast Section Chapters, ComSoc Chapter, COM19, Jt Chp,ED15/MTT17/PHO36, Jt. Chapter,SP01/CAS04 and Jt. Chapter,IM09/C16, and North Jersey Section Chapter,COM 19 Speaker(s): Dr. Kursat Metsav, Agenda: 06:30 p.m. - Introduction of Dr. Kursat Metsav 06:35 p.m. - Presentation by Dr. Kursat Metsav 07:10 p.m. - Question & Answer session Virtual: https://events.vtools.ieee.org/m/476641

This webinar will provide a comprehensive examination of the role of Digital Pre-Distortion (DPD) in mitigating distortion effects, enhancing linearity, and improving overall system efficiency. Participants will acquire insights into advanced DPD techniques, practical implementation strategies, and the latest tools for effective analysis of Error Vector Magnitude (EVM) and DPD performance. A thorough understanding of EVM is essential for assessing the quality of transmitted signals. The discussion will include an in-depth analysis of amplifier testing and its critical importance in evaluating system performance. Through the presentation of real-world examples and case studies, this session aims to equip engineers and industry professionals with the knowledge necessary to optimize their designs. Co-sponsored by: IEEE North Jersey Section Speaker(s): Martin Lim Virtual: https://events.vtools.ieee.org/m/481323

Robotics and AI Trip at Montclair Enjoy the robotics and AI project demonstrations and hands-on interactions with robots and AI systems at Montclair. [] Bldg: CCIS, 1 Normal Ave, Montclair, New Jersey, United States, 07043