IEEE North Jersey Section

September 21 through November 2, 2024. Six Saturdays 1:30-4:30pm (9/21, 9/28, 10/5, 10/19, 10/26, 11/2). The IEEE North Jersey Section Communications Society Chapter is offering a course entitled "DEEP LEARNING WITH APPLICATIONS". Deep learning is a transformative field within artificial intelligence and machine learning that has revolutionized our ability to solve complex problems in various domains, including computer vision, natural language processing, and reinforcement learning. This hands-on course on deep learning is designed to provide students with an understanding how these amazing successes are made possible by drawing inspiration from the way that brains, both human and otherwise, operate. Students will gain a comprehensive foundation in the principles, techniques, and applications of deep neural networks. Learning how to solve real data-set based applications will teach students how to really apply deep learning with Python programming software. Participants will be asked to design and train deep neural networks to perform tasks such as image classification using commonly available data sets. However, participants are encouraged to apply the techniques from this course to other data sets according to their interests. Discuss with the instructor in order to propose your own project. More importantly, this will set the foundations for understanding and developing Generative AI applications. The IEEE North Jersey Section's Communications Society Chapter can arrange for providing IEEE CEUs - Continuing Education Units (for a $5 charge) upon completion of the course. Course prices: $75 for Undergrad/Grad/Life/ComSoc members, $100 for IEEE members, $150 for non-IEEE members Co-sponsored by: Education Committee Speaker(s): Thomas Long, Agenda: 1. Introduction to Neural Networks: Explore the fundamental concepts of artificial neural networks, backpropagation, activation functions, and gradient descent, laying the groundwork for deep learning understanding. 2. Introduction to PyTorch: Learn how to implement and train neural networks using PyTorch one of the most popular deep learning frameworks. Understand tensors. 3. Computer Vision Applications: Apply deep learning to computer vision problems, including image classification and object detection using Convolutional Neural Networks (CNNs) 4. Training and Optimizing Deep Neural Networks: Study techniques for training deep neural networks effectively, including optimization algorithms, weight initialization, regularization, and dropout. 5. Sequential Data Analysis: Explore how deep learning is used to analyze sequential data using Recurrent Neural Networks (RNNs). In particular, explore how neural networks are used in Natural Language Processing (NLP) tasks such as sentiment analysis and machine translation. 6. Generative AI: Overview of generative ai techniques that leverage the patterns present in a dataset to generate new content. Applications of generative ai include large language models such as ChatGPT and image generation models such as Midjourney and Stable Diffusion. This course assumes a basic understanding of machine learning concepts and programming skills in Python. Familiarity with linear algebra and calculus will be beneficial, but not mandatory. Statistical software (Python, Scikit-learn) and Deep Learning Frameworks (Pytorch, TensorFlow) will be used throughout the course for the exploration of different learning algorithms and for the creation of appropriate graphics for analysis. Learning objectives: Subjects covered include these and other deep learning related materials: artificial neural networks, training deep neural networks, RNN, CNN, image recognition, natural language processing, GANs, data processing techniques, and NN architectures. The course is intended to be subdivided into 3-hour sessions. Each lecture is further subdivided into lecture, guided and independent project based exercises to build experience with hands-on techniques. This course will be held at FDU - Teaneck, NJ campus. Checks should NOT be mailed to this address. Can bring checks in person or use online payments at registration. Email the organizer for any questions about course, registration, or other issues. Technical Requirements: Students will need access to the Python programming language. In addition to a standard Python installation, most programming exercises will use the package Scikit-learn. Basic programming skills and some familiarity with the Python language are assummed. Students are expected to be able to bring a laptop onto which most of these libraries can be pre-installed using python's pip install. Most of the coding in this course will use the Python programming language. Coding examples and labs will be distributed in the form of Juypter notebooks. In addition to standard Python, most programming exercises will use either the PyTorch or TensorFlow libraries. Books and other resources will be referenced. Room: Room 306, Bldg: Becton Building , FDU Metropolitan Campus, 960 River Road, Teaneck, New Jersey, United States, 07666

October 1, Tuesday, 6~ 8:00 PM Virtual Seminar through IEEE WebEx hosting For program questions, Please email to [email protected] Co-sponsored by: New York Section and Region 1 & Region 2 Computer Society Speaker(s): Mrinal , Agenda: - Event Agenda - Event Agenda: 6:00 PM Opening Remark – IEEE NY Section Chair, Chamara Johnson Welcome - Introduction - (Prof. Ping-Tsai Chung, IEEE New York Section, Vice Chair of Section Activities & Prof. Xin-Zhou Wei, IEEE New York Section, Chair of Student Activities) 6:10 ~7:10 PM (Presentation- Mrinal Karvir, Manager, Intel - Generative AI) 7:10 PM Q/A Virtual: https://events.vtools.ieee.org/m/430850

This talk highlights the next generation of wireless power transfer (WPT) network concept that is scalable, safe, and efficient and can be deployed in a UAS by incorporating waveform engineering, electromechanical beamforming, integrated phased-array antenna, and transmitter (TX)/receiver (RX) co-design. Although interest in radiative (far-field) WPT using beamforming has been growing rapidly because of its capability to energize a large number of autonomous devices, most of these works are still in the theoretical phase without any practical implementation. This talk presents the implementation of a distributed beamforming network using a bottom-up approach (from the antenna to the inter-connected network) that is highly important for addressing the challenges associated with a dynamically changing environment. Practical system-level implementation strategies and multi-scale and multi-technique approaches to building a resilient WPT network for UAVs will be discussed. First, the challenges associated with the dual approach of electrical beamforming and the mechanical steering of the TX antennas to maximize the RF-RF link efficiency will be discussed. Secondly, an investigation of the efficient rectifier circuitry designed on-chip as well as commercial off-the-shelf components (COTS) to maximize the power conversion efficiency RF-DC efficiency will be presented. Finally, future research directions on increasing the power transfer distance to scale up the amount of power delivered to the load for the proposed wireless power beaming network system will be highlighted. Co-sponsored by: Ali Daneshmand Speaker(s): Ifana, Agenda: Virtual: https://events.vtools.ieee.org/m/428457

[] On this celebratory IEEE Day, let us rejoice in the combined IEEE worldwide activities of 39 technical societies and 7 interdisciplinary councils on one side of the IEEE coin, balanced with local sections and student chapters, bringing a physical presence of IEEE to local communities. By the way, one could argue that IEEE also has a 3rd leg into humanitarian activities. These S/C span a great swathe from hardware to software, including electronic devices, solar/wind energy production, power transmission, control theory, image compression, signal processing, and computer architecture. One could say this moves from applied physics to applied math. However, this session drills down to predominantly 1 of these societies, the electron device society, where the field of semiconductor materials and devices thrives. So, with the USA's Chips and Science Act to onshore semiconductor chip manufacturing, what does this mean to you and your communities. First, the Chips portion supports semiconductor companies to build or expand domestic chip production. The Science portion aims to perform workforce development to populate those factories. Where will this new workforce come from and what do you need to do to prepare for your future? This talk aims to take a critical look at the opportunities before you in the semiconductor industry. With time permitting and audience interest, some discussion in humanitarian roles IEEE plays will also be discussed. Speaker(s): Paul Berger, PhD Virtual: https://events.vtools.ieee.org/m/434305