Region 1

The monthly Rochester IEEE Executive Committee meeting brings together all of the leaders of the Section, Chapters, and Groups. ExCom members: Please send your updates on past and upcoming events to the (mailto:[email protected]) to be included on the agenda prior to the meeting. We review plans for upcoming Rochester meetings within our Section, Chapters, and groups at this meeting. If you are looking to become more engaged in IEEE in the Rochester Section, please plan on attending an Excom meeting! Agenda: - Section Officer Reports - Section Chair Report: (mailto:[email protected]) - Section Vice-Chair Report: Emmett Ientilucci - Section Treasurer Report: Lyle Tague - Section Secretary Report: Eric Zeise - Old Business - New Business - Chapter Society and Group Reports - Aerospace and Electronic Systems Society and Communications Society (AES10/COMM19); Nirmala Shenoy - Computer Society and Computational Intelligence Society (C16/CIS11); Bo Yuan - Electron Devices and Circuits and Systems: (mailto:[email protected]) - Engineering in Medicine and Biology Society (EMB18): (mailto:[email protected]) - Rochester/Binghamton/Buffalo/Ithaca/Syracuse Geoscience and Remote Sensing Society (GRS29): (mailto:[email protected]) - Life Members Group: - Microwave Theory and Techniques Society / Antennas and Propagation Society (MTT17/AP03): (mailto:[email protected]), (mailto:[email protected]) - Photonics Society (PHO36): (mailto:[email protected]) and (mailto:[email protected]) - Power and Energy Society / Industry Applications Society (PE31/IA34): (mailto:[email protected]); (mailto:[email protected]) - Signal Processing Society (SP01): Eric Zeise - Technology Management Council (TM14): (mailto:[email protected]) - Women In Engineering (WIE): (mailto:[email protected]) - Young Professionals: (mailto:[email protected]) - Student Chapter Reports: (mailto:[email protected]) - Rochester Institute of Technology: (mailto:[email protected]) - University of Rochester: (mailto:[email protected]) - Committee Reports - Membership Report: (mailto:[email protected]%20) - Awards Report: (mailto:[email protected]) - Electronic Communications Coordinator: (mailto:[email protected]), (mailto:[email protected]) - Newsletter Report: (mailto:[email protected]) - PACE Report: (mailto:[email protected]) - E. Liaison Reports - R1 Western Area Chair: Emmett Ientilucci - Rochester Engineering Society (RES) Report: (mailto:[email protected]) - Rochester Council of Scientific Societies (RCSS) Report: (mailto:[email protected]) - Open Discussion - Adjournment Bldg: Tandoor of India, 376 Jefferson Rd, Rochester, New York, United States, 14623, Virtual: https://events.vtools.ieee.org/m/399006

Monthly Meeting Virtual: https://events.vtools.ieee.org/m/401130

CANCELLED - TBD New Date / Time / Virtual NJ Coast Section - Executive Committee Meeting (March) (In-Person) 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 TBD, New Jersey, United States

Course Format: Live Webinar, 3.5 hours of instruction! Series Overview: From the book introduction: “Neural networks and deep learning currently provides the best solutions to many problems in image recognition, speech recognition, and natural language processing.” This Part 1 and the planned Part 2, (to be confirmed) series of courses will teach many of the core concepts behind neural networks and deep learning. This is a live instructor-led introductory course on Neural Networks and Deep Learning. It is planned to be a two-part series of courses. The first course is complete by itself and covers a feedforward neural network (but not convolutional neural network in Part 1). It will be a pre-requisite for the planned Part 2 second course. The class material is mostly from the highly-regarded and free online book “Neural Networks and Deep Learning” by Michael Nielsen, plus additional material such as some proofs of fundamental equations not provided in the book. More from the book introduction: Reference book: “Neural Networks and Deep Learning” by Michael Nielsen, http://neuralnetworksanddeeplearning.com/ “We’ll learn the core principles behind neural networks and deep learning by attacking a concrete problem: the problem of teaching a computer to recognize handwritten digits. …it can be solved pretty well using a simple neural network, with just a few tens of lines of code, and no special libraries.” “But you don’t need to be a professional programmer.” The code provided is in Python, which even if you don’t program in Python, should be easy to understand with just a little effort. Benefits of attending the series: * Learn the core principles behind neural networks and deep learning. * See a simple Python program that solves a concrete problem: teaching a computer to recognize a handwritten digit. * Improve the result through incorporating more and more core ideas about neural networks and deep learning. * Understand the theory, with worked-out proofs of fundamental The demo Python program (updated from version provided in the book) can be downloaded from the speaker’s GitHub account. The demo program is run in a Docker container that runs on your Mac, Windows, or Linux personal computer; we plan to provide instructions on doing that in advance of the class. (That would be one good reason to register early if you plan to attend, in order that you can receive the straightforward instructions and leave yourself with plenty of time to prepare the Git and Docker software that are widely used among software professionals.) Course Background and Content: This is a live instructor-led introductory course on Neural Networks and Deep Learning. It is planned to be a two-part series of courses. The first course is complete by itself and covers a feedforward neural network (but not convolutional neural network in Part 1). It will be a pre-requisite for the planned Part 2 second course. The class material is mostly from the highly-regarded and free online book “Neural Networks and Deep Learning” by Michael Nielsen, plus additional material such as some proofs of fundamental equations not provided in the book. Outline: - Feedforward Neural Networks - Simple (Python) Network to classify a handwritten digit - Learning with Stochastic Gradient Descent - How the backpropagation algorithm work - Improving the way neural networks learn: - - Cross-entropy cost function - SoftMax activation function and log-likelihood cost function - Rectified Linear Unit - Overfitting and Regularization: - - L2 regularization - Dropout - Artificially expanding data set Pre-requisites: There is some heavier mathematics in learning the four fundamental equations behind backpropagation, so a basic familiarity with multivariable calculus and matrix algebra is expected, but nothing advanced is required. (The backpropagation equations can be also just accepted without bothering with the proofs since the provided Python code for the simple network just make use of the equations.) Basic familiarity with Python or similar computer language. Speaker(s): CL Kim, Boston, Massachusetts, United States, Virtual: https://events.vtools.ieee.org/m/401136

(https://ieeeboston.org/event/pythonapplications/?instance_id=3232) Course Kick-off / Orientation 6:00PM - 6:30PM EDT; Thursday, February 29, 2024 First Video Release, Thursday, February 29, 2024. Additional videos released weekly in advance of that week’s live session! Live Workshops: 6:00PM – 7:30PM EDT; Thursdays, March 7, 14, 21, 28, 2024 Registration is open through the last live workshop date. Live workshops are recorded for later use. Registration Fees: IEEE Member Early Rate (by February 15): $190.00 IEEE Member Rate (after February 15th): $285.00 IEEE Non-Member Early Rate (by February 15): $210.00 IEEE Non-Member Rate (after February 15): $315.00 Decision to run/cancel course: Thursday, February 22, 2024 Course Information will be distributed on Thursday, February 29 in advance of and in preparation for the first live workshop session. A live orientation session will be held on February 29, 2024. Attendees will have access to the recorded session and exercises for two months (until May 28, 2024) after the last live session ends! This is a hands-on course combining pre-recorded lectures with live Q&A and workshop sessions in the popular and powerful open-source Python programming language. Pre-Recorded Videos: The course format has been updated to release pre-recorded video lectures that students can watch on their own schedule, and an unlimited number of times, prior to live Q&A workshop sessions on Zoom with the instructor. The videos will also be available to the students for viewing for up to two months after the conclusion of the course. Overview: Dan provides simple, straight-forward navigation through the multiple configurations and options, providing a best-practices approach for quickly getting up to speed using Python for modelling and analysis for applications in signal processing and digital design verification. Students will be using the Anaconda distribution, which combines Python with the most popular data science applications, and Jupyter Notebooks for a rich, interactive experience. The course begins with basic Python data structures and constructs, including key “Pythonic” concepts, followed by an overview and use of popular packages for scientific computing enabling rapid prototyping for system design. During the course students will create example designs including a sigma delta converter and direct digital synthesizer both in floating point and fixed point. This will include considerations for cycle and bit accurate models useful for digital design verification (FPGA/ASIC), while bringing forward the signal processing tools for frequency and time domain analysis. Jupyter Notebooks: This course makes extensive use of Jupyter Notebooks which combines running Python code with interactive plots and graphics for a rich user experience. Jupyter Notebooks is an open-source web-based application (that can be run locally) that allows users to create and share visually appealing documents containing code, graphics, visualizations and interactive plots. Students will be able to interact with the notebook contents and use “take-it-with-you” results for future applications in signal processing. Target Audience: This course is targeted toward users with little to no prior experience in Python, however familiarity with other modern programming languages and an exposure to object-oriented constructs is very helpful. Students should be comfortable with basic signal processing concepts in the frequency and time domain. Familiarity with Matlab or Octave is not required, but the equivalent operations in Python using the NumPy package will be provided for those students that do currently use Matlab and/or Octave for signal processing applications. Benefits of Attending / Goals of Course: Attendees will gain an overall appreciation of using Python and quickly get up to speed in best practice use of Python. All set-up information for the installation of all tools will be provided before the start of class. Speaker(s): Dan Boschen , Agenda: Topics / Schedule: Pre-recorded lectures (3 hours each) will be distributed Friday prior to all Workshop dates. Workshop/ Q&A Sessions are 6pm-7:30pm on the dates listed below: Kick-off / Orientation: February 29, 2024 Thursday, March 7, 2024 Topic 1: Intro to Jupyter Notebooks, the Spyder IDE and the course design examples. Core Python constructs. Thursday,March 14, 2024 Topic 2: Core Python constructs; iterators, functions, reading writing data files. Thursday, March 21, 2024 Topic 3: Signal processing simulation with popular packages including NumPy, SciPy, and Matplotlib. Thursday, March 28, 2024 Topic 4: Bit/cycle accurate modelling and analysis using the design examples and simulation packages Virtual: https://events.vtools.ieee.org/m/398498