Region 1

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

The topics to be covered are: Technical discussion on ACCC as an advanced conductor option replacing ACSR and ACSS. - Conductor design - Key Technical Benefits - Testing and Case Studies - Line Design with ACCC - Federal and State Outlook – FERC 1920 + NJ State proposed ruling - Commercial Outlook Speaker(s): Jack, Jaunice Agenda: There will be no charge for this seminar (other than fee for CEUs). We will provide breakfast (lunch will NOT be provided). Two hours of instruction will be provided. If desired, IEEE Continuing Education Units (0.2 CEUs) will be offered for this course - a small fee of $25 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/) Room: Aruba Room, Bldg: PSE&G - Hadley Road Facility, 4000 Hadley Road, South Plainfield, New Jersey, United States, 07080

CubeSat, a modular type of standardized modern small satellites, have been gaining steady popularity and attention from universities and space industries. In addition to education purpose, CubeSats have various promising applications as low-cost space exploration vehicles for technology demonstrations, multi-point observations of space environment, and monitoring/reporting proper deployment of expensive deep space instruments. Antennas are critical components for CubeSat missions. A CubeSat antenna may provide some or all of the following functions: telemetry, tracking, command (TT\&C), high speed downlink for payload data, receiving positioning data, and inter-satellite cross links. Most often, different antennas are required to keep the CubeSat assembly in modular fashion. On the other hand, antenna engineers strive to create solutions that could pack more functionality into one unit. This brings up a need to understand basics of CubeSat development cycle and link budget analysis, so that an electrical engineer would have sound knowledge of limiting factors (posed by the mechanical system and hardware of a CubeSat) to the antennas design. With rapid advancement of electronics, novel mechanical design, and aerospace technology, new progress in CubeSats is emerging every day. This calls for interests and early involvements of creative young minds. The objective of this presentation is to convey the basics of CubeSat development cycle, launch methods, typical CubeSat orbits, link budget analysis, various antenna solutions, and feasible classroom projects. This lecture is open to everyone and IEEE membership is not required. [] Speaker(s): Prof. Reyhan Baktur Agenda: 11:45am - WebEx Waiting Room 12:00pm-1:00pm - Presentation 1:00pm-1:15pm - Questions Virtual: https://events.vtools.ieee.org/m/438346

A major hurdle in developing next-generation systems for high-performance applications and industries that require handling large, secure data - such as System-in-Package (SiP) and System-on-Chip (SoC) - is the absence of low-latency, high-bandwidth, and high-density off-chip/chiplet/core interconnects. Achieving high-bandwidth chip-to-chip (or chiplet-to-chiplet) communication using electrical interconnects faces challenges like high substrate dielectric losses, reflections, impedance discontinuities, and susceptibility to crosstalk. This underscores the motivation to adopt photonics to address these challenges and enable low-latency, high-bandwidth communication. The objective is to develop a CMOS-compatible technology to support the next-generation photonic layer within 3D SiP/SoC, moving towards converged microsystems. Co-sponsored by: Habib Hichiri Speaker(s): Tolga Tekin, Virtual: https://events.vtools.ieee.org/m/433297

IEEE Region-1 Women in Engineering Affinity Group officers and volunteers - a Virtual meeting to share updates about the activities, information about upcoming events, opportunities, and discussions. [] Agenda: A brief discussion about past events and upcoming events by R1 WIE Co-Ordinator Dr. Anisha Apte A brief discussion about the R1R2 WIE Forum East by Dr. Charlotte Blair Discuss past and upcoming events, addressing queries of the officers attending the meeting. Virtual: https://events.vtools.ieee.org/m/441717