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

Kaushik Rajashekara University of Houston, Texas USA The transportation industry is facing challenges in terms of improving emissions and fuel economy, and at the same time reducing the overall cost. The trend is towards replacing mechanical and pneumatic systems with electrical systems, thus transitioning toward “more electric” architectures and electric/hybrid propulsion systems. To meet these challenges in the automotive industry, significant work has been done in the areas of electric, hybrid, and fuel cell vehicles. In the case of airplanes, more electric architecture , fuel cell, and hybrid propulsion strategies are the emerging trends. Recently, there is also an increasing interest in flying cars, and Electrical vertical take-off and landing vehicles (eVTOL) to be used as air taxis. Similar strategies have been adopted in Marine propulsion systems also. This presentation examines present trends and advancements in electric/hybrid vehicles, electric and hybrid aircrafts, hydrogen-based systems, and flying cars/VTOL vehicles. In addition, recent trends in the enabling technologies, power electronics and electric motors, for the advancement of the electrified transportation will be briefly presented. Kaushik Rajashekara (Fellow, IEEE) received the Ph.D. degree in electrical engineering from the Indian Institute of Science, Bangalore, India. In 1989, he joined the Delphi division of General Motors Corporation in Indianapolis, USA, as a Staff Project Engineer. In Delphi and General Motors, he held various lead technical and managerial positions, and was a Technical Fellow and the Chief Scientist for developing propulsion and power electronics systems for electric, hybrid, and fuel cell vehicle systems. In 2006, he joined Rolls-Royce Corporation, as a Chief Technologist for electric systems for electric and hybrid aircraft systems. In August 2012, he joined as a Distinguished Professor of Engineering with the University of Texas at Dallas, TX, USA. Since September 2016, he has been a Distinguished Professor of engineering in University of Houston, Houston, TX, USA. He has authored or coauthored over 300 papers in international journals and conferences, has 37 US and 15 foreign patents, and has written one book. He has given over 200 invited presentations in international conferences and universities. He has received a number of awards including the 2022 Global Energy Prize, 2021 IEEE Medal on Environment & Safety Technologies, and 2013 IEEE Richard Harold Kaufmann Award for his contributions to electrification of transportation and renewable energy. He was elected as a member of the U.S. National Academy of Engineering in 2012, , a Fellow of the National Academy of Inventors in 2015, and an International Fellow of Indian (2013), Chinese (2021), and Japanese (2024) Academies of Engineering. His research interests include power/energy conversion, transportation electrification, renewable energy, and microgrid systems. Virtual: https://events.vtools.ieee.org/m/435753

IEEE PCJS Broadcast Technology Society is excited to announce the following talk: Digital Television Standards and Their Worldwide Impact Guest Speaker: Glenn Reitmeier Location: Computer Science Building (CS 105) Princeton University, Princeton, NJ Event: October 28, 2024, starting at 7:00pm to 9:00pm Brief abstract of talk: The development of digital standards for broadcast television was a seminal event - it was a pivot from many decades of analog video technology to the world of digital media and video streaming that today's audiences enjoy all over the world. This talk will discuss the technical roots of analog video standards, how the quest for a high-definition broadcast television standard in the 1990s was met by a radical leap to digital technology, the world's first digital TV standard (ATSC), and the recent development of the ATSC 3.0 standard for internet based broadcasting and streaming. Also discussed will be the adoption of digital television standards throughout the world, how government regulators have managed to transition from analog to digital broadcasting and the commercial impact of standards on consumer electronics products from TVs to smart phones. Speaker Bio: Glenn Reitmeier is widely recognized as a technology visionary and pioneer in the television industry. Throughout his career, he has been a leader in establishing revolutionary new digital standards that are now widely used in video production and in content delivery by over-the-air broadcasting, satellite, cable and video streaming over the internet. Now an independent consultant, Glenn is retired from 17 years at NBC Universal as Senior Vice President, Technology Standards and Policy, where he contributed to industry technical standards and to the technical aspects of the company’s government policy positions and commercial agreements. Previously, Glenn spent 25 years in digital video research at RCA/Sarnoff Laboratories. In addition to leading Sarnoff’s work on digital HDTV, his laboratory also spun out six technology startup companies in digital television and media. Glenn has served the industry as a Board member of the Advanced Television Systems Committee (ATSC), the North American Broadcasters Associations (NABA) and the Open Authentication Technical Committee (OATC), and he has been Chairman of the Board of both ATSC and OATC. He is a SMPTE Fellow and a recipient of the Progress Medal and the Signal Processing Medal. He is also an inaugural member of the Consumer Technology Association’s (CTA) Academy of Digital Television Pioneers, a recipient of the National Association of Broadcasters (NAB) Television Engineering Award for lifetime achievement and a recipient of the ATSC’s Bernard J. Lechner Award for outstanding technical contributions. Glenn holds over 60 patents, has contributed to many Emmy award winning technologies and is recognized in the New Jersey Inventors Hall of Fame. He received his B.E.E from Villanova University and an M.S.E in Systems Engineering from the University of Pennsylvania. Bldg: Department of Computer Science (CS 105), Princeton University, 35 Olden Street, Princeton, New Jersey, United States, 08540