7 events found.
Ongoing
Week of Events
WIND INSTITUTE TECHNICAL BOOTCAMP @ NJIT
(https://njitcl.catalog.instructure.com/) Bootcamp Objective: This 2-day offshore wind training bootcamp is for students and professionals with engineering or physical science backgrounds who are interested in starting or upskilling their career in the offshore wind industry. Through intensive training, the bootcamp aims to cover the technical aspects of offshore wind farm development, including environmental impact assessments, energy generation, transmission, grid integration, energy storage systems, and more! This bootcamp training will issue a certificate of completion from the New Jersey Institute of Technology (NJIT) and the opportunity to claim 14 Professional Development Hours (PDH) after successful completion of the online examination (multiple-choice questions) at the end of the bootcamp. Bootcamp Overview: Day 1: - Opening Keynote from NJ Economic Development Authority - Course 1: Offshore Wind Farm Overview - Course 2: Generation - Course 3: Offshore Transmission - Course 4: Onshore Transmission and Construction Day 2: - Opening Keynote from NJ Board of Public Utilities - Presentation on the Participation of Minority and Underrepresented Engineers in Wind Energy - Course 5: Community and Environmental Management - Course 6: Offshore Wind Farm Project Management - Course 7: Energy Storage System for Renewable - Course 8: Grid Interconnection and Integration - Take-Home Online Examination (Multiple-Choice Questions) Target Audience: There are no formal prerequisite courses, but participants are encouraged to have a foundational understanding of engineering or physical sciences, preferably at a college or advanced-placement level. The bootcamp aims to provide a short-term, rigorous, fast-paced, and focused fundamental training to help practicing engineers, researchers, and graduates to be well-prepared for the new field of offshore wind energy. Co-sponsored by: New Jersey Institute of Technology (NJIT) Agenda: [] Bldg: Agile Strategy Lab, New Jersey Institute of Technology, 355 Dr Martin Luther King Jr Blvd, Newark, New Jersey, United States, 07102
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Brain-Inspired Computing Using Magnetic Domain Wall Devices
Brain-Inspired Computing Using Magnetic Domain Wall Devices
Neuromorphic computing or brain-inspired computing is considered as a potential solution to overcome the energy inefficiency of the von Neumann architecture for artificial intelligence applications -. In order to realize spin-based neuromorphic computing practically, it is essential to design and fabricate electronic analogues of neurons and synapses. An electronic analogue of a synaptic device should provide multiple resistance states. A neuron device should receive multiple inputs and should provide a pulse output when the summation of the multiple inputs exceeds a threshold. We have been carrying out investigations on the design and development of various synaptic and neuron devices in our laboratory. Domain wall (DW) devices based on magnetic tunnel junctions (MTJs), where the DW can be moved by spin-orbit torque, are suitable candidates for the fabrication of synaptic and neuron devices . Spin-orbit torque helps in achieving DW motion at low energies whereas the use of MTJs helps in translating DW position information into resistance levels (or voltage pulses) . This talk will summarize various designs of synthetic neurons synaptic elements and materials . The first half of the talk will be at an introductory level, aimed at first-year graduate students. The second half will provide details of the latest research. K. Roy, A Jaiswal, and P Panda, “Towards Spike-Based Machine Intelligence With Neuromorphic Computing,” Nature 575, 607-617 (2019). W. L. W. Mah, J. P. Chan, K. R. Ganesh, V. B. Naik, S. N. Piramanayagam, “Leakage Function in Magnetic Domain Wall Based Artificial Neuron Using Stray Field,” Appl. Phys. Lett. 123, 092401 (2023). D. Kumar, H. J. Chung, J. P. Chan, T. L. Jin, S. T. Lim, S. S. P. Parkin, R. Sbiaa, S. N. Piramanayagam, “Ultralow Energy Domain Wall Device for Spin-Based Neuromorphic Computing,” ACS Nano 17, 6261-6274 (2023). R. Maddu, D. Kumar, S. Bhatti, S. N. Piramanayagam, “Spintronic Heterostructures for Artificial Intelligence: A Materials Perspective,” Phys. Stat. Sol. RRL 17, 2200493 (2023). Speaker(s): Prem Bldg: ECEC Building, ECEC 327, New Jersey Institute of Technology, Newark, NJ 07102, USA, Newark, New Jersey, United States, 07102, Virtual: https://events.vtools.ieee.org/m/451248
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2024 IEEE Metropolitan AI Applications Symposium (Saturday 12/14 @10AM Zoom 506 875 4099)
2024 IEEE Metropolitan AI Applications Symposium (Saturday 12/14 @10AM Zoom 506 875 4099)
The 2024 IEEE Metropolitan AI Applications Symposium (IEEE MAIAS-2024) will be held online (Zoom) on Saturday, December 14, 2024, from 10 am until 12:30 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 Section Canadian-American Research Forum on AI Technologies Registration Required: https://events.vtools.ieee.org/m/447282 Conference Room: Join Zoom Meeting Meeting ID: 506 875 4099 https://zoom.us/j/5068754099 2024 IEEE Metropolitan AI Applications Symposium Symposium Program 10:00AM: Welcome Messages Prof. Yu-Dong Yao (IEEE North Jersey Section) 10:05AM: Keynote Speaker: Prof. Zhifeng Kou, NJIT AI in Medical Imaging: An Initial Investigation 10:45AM: Jake Ciocca, Stevens Institute of Technology Time Series Forecasting for Stock Prices 11:00AM: Pranay Reddy Baireddy, Stevens Institute of Technology Implementation of Diabetic Retinopathy Detection Using Deep Learning 11:15AM: Pratik Jain, NJIT Enhancing Functional Connectivity with Dictionary Learning for Brain Fingerprints 11:30AM: Marco Marena, NJIT Generative AI for Solar Physics 11:45AM: Cleopatra Mozolewski, Stevens Institute of Technology Bond Pricing Using Deep Learning 12:00PM: Rose Friedma, Stevens Institute of Technology AI and Gardening 12:15PM: Claudio Obregon, Stevens Institute of Technology ML Applications on IT Incidents Management 12:30PM: Yian Chen, Stevens Institute of Technology A Survey of Breast Cancer Detection Techniques based on CNN Techniques 12:45PM: Conclusion Remark Prof. Elisa Kallioniemi (NJIT) 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. Wei-Ping Zhu, Concordia University, Montreal Virtual: https://events.vtools.ieee.org/m/447282