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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

As the demand for high-performance computing continues to surge, driven largely by advances in artificial intelligence and other data-intensive applications, the semiconductor industry faces growing challenges in meeting these requirements. Traditional approaches, constrained by the slowing of Moore’s Law and the end of Dennard Scaling, are proving inadequate in addressing the exponential increase in compute and bandwidth needs. Silicon Photonics is emerging as a key technology to overcome these limitations. By leveraging the unique properties of silicon for optical communication, Silicon Photonics can offer significant improvements in data transmission speeds and energy efficiency compared to conventional electrical interconnects. This technology has the potential to reduce latency and power consumption, making it a compelling solution for next-generation data centers and high-performance computing systems. Advanced Packaging Technologies are equally critical in this evolving landscape. As chips become more complex and power-hungry, innovative packaging solutions such as chiplets and advanced integration methods are essential to manage power consumption and thermal issues while enhancing performance. These technologies enable more efficient use of silicon area, improved thermal management, and higher bandwidth connections between components. In this talk, we will explore how Silicon Photonics and advanced packaging technologies are not just complementary but essential to addressing the challenges of future compute demands. We will discuss the current state of these technologies, their potential to transform the semiconductor industry, and the ongoing efforts to integrate them into scalable, high-performance systems Speaker(s): Sandeep Sane, Virtual: https://events.vtools.ieee.org/m/433612

Guest Speaker Dr. Ahmed El-Sayed is an associate professor of Electrical and Computer Engineering at the University of Bridgeport and also serves as instructor at Clark University teaching Machine Learning and Deep Learning. With phenomenal expertise in artificial intelligence, he has authored over 40 research papers in various fields. Dr. Ahmed is Director of the Laboratory for Advanced Control, Autonomous Systems, and Automation (LACASA). Dr. Ahmed will be discussing "Autonomous Vehicles: The Revolution of Artificial Intelligence" during the session. Room: S120, Bldg: Sackler Science Building, Clark University, 950 Main Street, Worcester, Massachusetts, United States, 01603

This seminar will discuss recent efforts to develop a global research, industry, and workforce ecosystem for sustainable microchip manufacturing. Speaker(s): Dr. Anu Agarwal Agenda: 6:00 pm Networking starts 6:15 pm Light meals served 7:00 pm Seminar starts Bldg: Forbes Rd. Cafeteria, MIT Lincoln Laboratory, 3 Forbes Rd, Lexington, Massachusetts, United States, 02421

Monthly ExCom Meeting Agenda: Agenda IEEE Buffalo Section Executive Committee Meeting Agenda 6:30 PM, Thursday, October 10, 2024 In Person (SUNY Buffalo State University, Technology Building 148) Virtual on Zoom 1) Call to Order 2) Review minutes from September 12, 2024 meeting (Vasili) 3) Review treasurer report (Mike W) 4) Review Membership Report (Mike W) a. Upcoming Events 1. Presentations for October – Sam, Steve 2. BNP Young Professionals – Ilya and Huamin b) Report of activities by Buffalo State Student branch 5) Action Items from September Meeting 1. Discussion on elections – Judy, John, Padma, Sam 2. Revision of Section Operations Manual – Kyle, Vasili 6) Membership/Society a. EDS b. Computer c. Control d. Communication e. TEMS f. AP/MTT g. PES h. Women in Engineering i. Young Professionals j. Life Members k. NTC l. Photonics 6) Adjourn Next ExCom Meetings / Locations: TBD Room: 148, Bldg: Technology, 1300 Elmwood Ave., Buffalo, New York, United States, 14222, Virtual: https://events.vtools.ieee.org/m/437723

IEEE NJ Coast Section - Executive Committee Meeting (October) (In-Person) Address: 415 NJ-18 East Brunswick, NJ 08816 https://g.co/kgs/JEYs9v3 Agenda: 1. Vote / Accept Meeting Minutes (Tima) 2. Treasurer's Report (Mike) 3. Chair's Report(s) (Filomena) 4. Old Business (Each Chapter Chair) - Status of Each Chapter - Status of Committee's and Affinity Groups 5. New Business (Each Chapter Chair) - Each Chapters’ Upcoming Plans - Each Committee and Affinity Group Upcoming Plans - Any New Business not already covered - Move To Close Bldg: Village Green, Sofra Turkish & Mediterranean Cuisine, 415 NJ-18, East Brunswick, New Jersey, United States, 08816

[] Ionization profile monitors (IPMs) are widely used in accelerators for non-destructive and fast diagnostics of high energy particle beams. These monitors provide information of particle beam shape and dynamics. Profile monitors extract from the passing beam the vertical and horizontal distributions of the beam particles, be they electrons, protons or heavier ions. Attendees will learn: - The deposition of energy in matter, in this case the ionization of a background gas - How shaped electrodes direct the ion pairs - How an amplification of 10^7 converts single ions to a detectable current I will cover basic principles of energy deposition in matter, applicable to particle detection in solid-state detectors as well as single-event upsets in CMOS, electrode design for shaped fields, and ultra-high vacuum considerations for the use of materials. Speaker(s): Arnold Stillman Agenda: 7:00 PM Networking 7:20 PM Presentation Virtual: https://events.vtools.ieee.org/m/433986