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

This presentation shows radio frequency (RF) design solutions for wireless sensor nodes to address sustainability issues in the Internet of Things (IoT), which arise due to the massive deployment of wireless IoT nodes on environmental and economic levels. Engineers can apply these RF design solutions to improve the ultra-low-power operation of IoT nodes, avoid batteries’ eco-toxicity, and decrease maintenance costs due to battery replacement. The solutions offer high integration levels based on system-on-chip and system-in-package concepts in low-cost complementary metal-oxide-semiconductor technologies to limit these nodes’ costs and footprints. In particular, the presentation covers solutions for ultra-low-power wireless communication systems based on high-frequency (HF) and ultra-high frequency (UHF) radio frequency identification (RFID) technologies. The talk offers RF design solutions for HF and UHF RFID systems, revealing how to develop passive miniaturized IoT nodes that operate robustly in harsh application environments. Co-sponsored by: IEEE Worcester County Section Speaker(s): , Jasmin Grosinger Agenda: 10:00 - 10:15: Welcome 10:15 - 11:00: Presentation 11:00 - 11:30: Q &A 11:30 - 11:45: Wrap up Virtual: https://events.vtools.ieee.org/m/424006

IEEE MIT Undergraduate Research Technology Conference (URTC) will be hosting the 2024 MIT Micromouse Competition on Saturday, October 12, 2024 at 6:00pm to 8:30pm. Competition Guideline: (https://drive.google.com/file/d/1oug6NgCnbRZzPLArwnRas28Y0ZIDwEHq/view?usp=drive_link) A Micromouse is a small robot vehicle that is able to navigate its way through an unknown maze. It is autonomous, battery-operated and self-contained, encompassing computer technology, robotics and artificial intelligence. The main challenge for the Micromouse designers is to import the Micromouse with an adaptive intelligence which enables exploration of different maze configurations, and to work out the optimum route with the shortest run time from start to destination and back. In addition, the Micromouse must reliably negotiate the maze at a very high speed without crashing into the maze walls. The objective of the competition is to build a Micromouse that can negotiate a specified maze in the shortest time. All IEEE Student Members and Graduate Student Members are eligible to participate. High School Students (non-IEEE Member) are also welcome to participate, but they need to attend a 30 minutes introduction session about IEEE and how IEEE can help them when they are an Undergraduate Student at a college. Whether your Micromouse is not quite functioning, or you have no Micromouse yet, you are welcome to participate. Besides the usual awards towards the fastest maze solvers, there is an award for the best Micromouse design. This contest will focus on the overall design as demonstrated by an oral presentation. Although there is no specific format required for the presentation, the following guidelines should be used for preparing for the presentation. The team should plan to address the following points in the presentation: ❖ Overall design goals and strategies ❖ High level structure of the design (mechanical, control) ❖ Design choices for sub functions (locomotion, sensing, etc) ❖ Overview of software ❖ Implementation and testing process and results ❖ Conclusions We encourage groups to feature the most innovative part (coding, mechanical design, etc) during the design presentation. You can give the Oral Presentation while running your micromouse. Each presentation/micromouse run will be 5 minutes, with another up to 5 minutes for questions. Please help to promote the MIT Micromouse competition and encourage your students or friends to participate. If you, school robotics club or IEEE Student Branch have been working on designing and building a Micromouse project/program, this competition at MIT will be a great venue to celebrate your achievement and what you have learnt. If you have not worked on Micromouse, and you are interested to develop one, you should start now. It is only about 3 weeks left to the competition. But it is not too late to start the development. You are allowed to get a robot kit and do your best to build and program your Micromouse to solve the maze. You should still consider participating this year competition even your Micromouse is not fully function or is not moving. There is a judging award for Best Design Micromouse and Innovative Program, and the Micromouse does not have to perform well in the maze. Please contact Soon Wan, IEEE Region 1 Student Professional Awareness (SPAx) Chair, [email protected] for any question. Co-sponsored by: Soon Wan Agenda: MIT Stata Center - TSMC (near the buildlng main entrance) 3:00pm - 5:00pm Micromouse Prractice 6:00pm - 6:30pm Check-In 6:30pm - 8:30pm Micromouse Competition Room: TSMC (near the buildlng main entrance), Bldg: Building 32, MIT - Ray and Maria Stata Center , 32 Vassar Street, Cambridge, Massachusetts, United States

2024 IEEE MIT Undergraduate Research Technology Conference (URTC) is once again hosting the 2024 MIT Micromouse Competition on Saturday, October 12, 2024 at 6:00pm to 8:30pm. Please note that this registration is to participate the Micromouse Competition only. If you want to attend the URTC conference, you have to register for the conference at (https://url.us.m.mimecastprotect.com/s/ypcCCZ6o5kCoB7YPuzfjCBIfLR) A Micromouse is a small robot vehicle that is able to navigate its way through an unknown maze. It is autonomous, battery-operated and self-contained, encompassing computer technology, robotics and artificial intelligence. The main challenge for the Micromouse designers is to import the Micromouse with an adaptive intelligence which enables exploration of different maze configurations, and to work out the optimum route with the shortest run time from start to destination and back. In addition, the Micromouse must reliably negotiate the maze at a very high speed without crashing into the maze walls. The objective of the competition is to build a Micromouse that can negotiate a specified maze in the shortest time. Competition Guideline: (https://drive.google.com/file/d/1oug6NgCnbRZzPLArwnRas28Y0ZIDwEHq/view?usp=drive_link) All IEEE Student Members and Graduate Student Members are eligible to participate. High School Students (non-IEEE Member) are also welcome to participate, but they need to attend a 30 minutes introduction session about IEEE and how IEEE can help them when they are an Undergraduate Student at a college. Whether your Micromouse is not quite functioning, or you have no Micromouse yet, you are welcome to participate. Besides the usual awards towards the fastest maze solvers, there is an award for the best Micromouse design. This contest will focus on the overall design as demonstrated by an oral presentation. Although there is no specific format required for the presentation, the following guidelines should be used for preparing for the presentation. The team should plan to address the following points in the presentation: ❖ Overall design goals and strategies ❖ High level structure of the design (mechanical, control) ❖ Design choices for sub functions (locomotion, sensing, etc) ❖ Overview of software ❖ Implementation and testing process and results ❖ Conclusions We encourage groups to feature the most innovative part (coding, mechanical design, etc) during the design presentation. You can give the Oral Presentation while running your micromouse. Each presentation/micromouse run will be 5 minutes, with another up to 5 minutes for questions. If you have not worked on Micromouse, and you are interested to develop one, you should start now. It is only about 2 weeks left to the competition. But, it is not too late to start the development. You are allow to get a robot kit, and do your best to build and program your Micromouse to solve the maze. You should still consider to participate this year competition even your Micromouse is not fully function, or is not moving. There is a judging award for Best Design Micromouse and Innovative Program, and the Micromouse does not have to perform well in the maze. Please contact Soon Wan, IEEE Region 1 Student Professional Awareness Coordinator ([email protected]) for any question. Co-sponsored by: Soon Wan Agenda: MIT Stata Center - TSMC (near the buildlng main entrance) 3:00pm - 5:00pm Micromouse Prractice 6:00pm - 6:30pm Check-In 6:30pm - 8:30pm Micromouse Competition Room: TSMC (near the buildlng main entrance), Bldg: Building 32, MIT - Ray and Maria Stata Center , 32 Vassar Street, Cambridge, Massachusetts, United States