Our speaker for this month is Mr. Jeffrey Mayer, whose initial degree was in Zoology followed by a 10-year career in the Navy, separating as a Lieutenant Commander. He returned to college earning a BS EE and later a Master's in Technical Management. After a brief stint in private industry, Mr. Mayer returned to the federal government to serve as the lead systems engineer and program manager for several small but challenging training and weapon system programs. He will discuss what it takes to be successful when dealing with others (management and engineers) and how to groom yourself for advancement…the plusses and minuses. One area, as an engineering manager, he found that young people entering the workforce have certain expectations and shortcomings. He always felt (since he was 22) that it was his job to make them successful. Agenda: 4:15 PM registration and refreshments 4:30 PM Presentation by Jeffrey Mayer followed by questions and answers. The presentation will be livestreamed. Room: Classroom, The Baldwin, 50 Woodmont Avenue, Londonderry, New Hampshire, United States, 03053, Virtual: https://events.vtools.ieee.org/m/439189

This meeting will focus on how Enterprise Architecture (EA) can be a strategic enabler for IT professionals and consultants. Attendees will learn how EA helps organizations streamline processes, reduce costs, and align IT with business objectives. We will explore key concepts, methodologies, and tools to enhance their consulting practices and IT strategies. Speaker(s): Dr. Raj Vayyavur Agenda: Welcome and Introductions Enterprise Architecture Overview Tools and Techniques for EA Implementation Challenges and Opportunities in EA Q&A and Networking Closing Remarks Virtual: https://events.vtools.ieee.org/m/440420

Abstract: There are several common misunderstandings and controversies that can have significant impact on costs, quality and reliability of ESD programs. These misunderstandings or "myths" result in costly unnecessary expenditures and/or a compromise of the program integrity. These same myths are often cited by skeptics who do not fully understand the physics involved. Consequently, it is important to identify and dispel these myths. Latency is a significant reliability consideration that is surrounded with controversy. Some experts will argue that latency is virtually non-existent while others claim that it is the dominant failure mode. Join us for this highly interactive discussion and learn about Latency as well as common myths such as: Myth: ANSI/ESD S20.20 Is Sufficient For Class 0 Technologies Myth: HBM Failures Dominate ESD Failures Myth: Circuit Boards are Always Less Sensitive to ESD than Devices Myth: Airflow Causes Charging Myth: Humidity Control is Essential for ESD Myth: Fieldmeters Make Accurate Measurements on Machines Myth: Latency Failures Comprise 90% of ESD Failures Co-sponsored by: ESDA NE Chapter Speaker(s): Ted Dangelmayer Agenda: 5:30 PM Pizza, salad, soda, and Networking 6:00 PM Technical Presentation 6:45 PM Questions and Answers 7:00 PM Adjournment Bldg: Main Cafeteria, Lincoln Laboratory, 244 Wood St, Lexington, Massachusetts, United States, 02421, Virtual: https://events.vtools.ieee.org/m/438543

Join us for a special fun event where SNHU Alumni and IEEE Young Professionals come back and give our students advice on how to navigate SNHU program specifically how to positioning yourself best for a productive career in general. Learn what worked for the alumni and what advice they can give on specific classes, activities and things to do to maximize your chance of getting a job. Learn how IEEE Young Professionals can help you advance your career. We invite all IEEE professionals who would like to join us for this event and share their knowledge with our students. We appreciate your mentorship. Please join us over food and fun while educating our students. Raffle with prizes to follow. Room: 128 A, Bldg: School of Engineering, Technology and Aeronatics, 2500 North River Rd., Manchester, New Hampshire, United States, 03106

Exploring the Potential of Deep-Learning and Machine-Learning in Dual-Band Antenna Design Keywords Antennas, Dual Band, Computational Modeling, Mathematical Models, Antenna Arrays, Training, Accuracy, Machine Learning, Deep Learning, Antenna Design, Dual Band Antennas, AI Antenna, Deep Learning, Antenna Design, Dual Band Antenna, Dual Band Antenna Design, Neural Network, Support Vector Machine, Design Process, Machine Learning Models, Machine Learning Techniques, Design Parameters, Internet Of Things, Reflection Coefficient, Operating Frequency, Regression Problem, Residual Network, Wireless Communication Systems, Internet Of Things Applications, Modern Communication, Antenna Performance, Residual Neural Network, Electromagnetic Simulation, Multiple Outputs, Neurons In Layer, Hidden Layer, Internet Of Things Devices, Res Net Model, Horizontal Stripes, Vertical Stripes, Simulation Technology, Traditional Antenna Abstract This article presents an in-depth exploration of machine learning (ML) and deep learning (DL) for the optimization and design of dual-band antennas in Internet of Things (IoT) applications. Dual-band antennas, which are essential for the functionality of current and forthcoming flexible wireless communication systems, face increasing complexity and design challenges as demands and requirements for IoT-connected devices become more challenging. The study demonstrates how artificial intelligence (AI) can streamline the antenna design process, enabling customization for specific frequency ranges or performance characteristics without exhaustive manual tuning. By utilizing ML and DL tools, this research not only enhances the efficiency of the design process but also achieves optimal antenna performance with significant time savings. The integration of AI in antenna design marks a notable advancement over traditional methods, offering a systematic approach to achieving dual-band functionality tailored to modern communication needs. We approached the antenna design as a regression problem, using the reflection coefficient, operating frequency, bandwidth, and voltage standing wave ratio as input parameters. The ML and DL models then are used to predict the corresponding design parameters for the antenna by using 1,000 samples, from which 700 are allocated for training and 300 for testing. This effectiveness of this approach is demonstrated through the successful application of various ML techniques, including Fine Gaussian Support Vector Machines (SVM), as well as Regressor and Residual Neural Networks (ResNet) with different activation functions, to optimize the design of a dual-band T-shaped monopole antenna, thereby substantiating AI's transformative potential in antenna design. Authors: RIDA GADHAFI 1 (Senior Member, IEEE), ABIGAIL COPIACO 1 (Member, IEEE), YASSINE HIMEUR 1 (Senior Member, IEEE), KIYAN AFSARI 2 (Member, IEEE), HUSAMELDIN MUKHTAR 1 (Senior Member, IEEE), KHALIDA GHANEM 3,4, AND WATHIQ MANSOOR 1 (Senior Member, IEEE) Agenda: 7:30 Tea and Coffee 7:45 Paper discussion 8:30 End 6 Flagstone Dr, Hudson, New Hampshire, United States, 03051, Virtual: https://events.vtools.ieee.org/m/442040

Leadership recognition dinner to honor our New Hampshire Volunteer Leaders. This includes our 2023 and 2024 ExCom members, Chapter and Affiliate Group teams, and functional group leaders (Student Activities, Educational Activities, History, Newsletter and professional activities) Also our NH Student Branch leaders - Faculty advisor, and chapter president/vice presidents or their designees. $5 Volunteer registration $20 Plus1/non-member registration Room: Suite C, Puritan Conference & Event Center , 245 Hooksett Road, Manchester, New Hampshire, United States, 03104