Events

Abstract: Microwave biomedical radar has achieved significant advancements, enabling transformative healthcare applications specifically for home-based care. Unlike conventional contact-based instruments that can cause discomfort, this non-invasive technology is ideal for continuous monitoring. This presentation will first detail key hardware innovations—including radar miniaturization, and enhanced accuracy and sensitivity—that are critical for deploying portable devices in residential settings. It will then explore specialized RF processing and AI techniques for extracting medical-grade physiological data from radar signals. Finally, the talk will highlight the technology’s breakthrough healthcare applications, from specific clinical applications like post-operative monitoring to broader home-based management. This includes revolutionizing remote patient monitoring, elderly fall detection, and chronic disease tracking for proactive, comprehensive home care. https://mtt.org/profile/changzhan-gu/ Speaker(s): Prof. Dr. Changzhan Gu, Agenda: Intro Talk Questions Virtual: https://events.vtools.ieee.org/m/580549

Leadership Dinner Celebration Room: Suite A, Puritan Back Room, 245 Hooksett Rd, Manchester, NH 03104, Manchester, New Hampshire, United States, 03106

Abstract: The binary reflected Gray code, patented by Frank Gray in 1953, is a permutation of the sequence ⟨0, 1, …, n-1⟩, where n is a power of 2, with the property that the binary representation of each number in the sequence differs from that of the preceding number in exactly one bit. The binary reflected Gray code is also cyclic, in that the last and first numbers also differ in only one bit. What if n is not a power of 2? How can we create a dense Gray code: a permutation of ⟨0, 1, …, n-1⟩ with the Gray-code property for any integer n > 1? The answer turns out to be surprisingly simple, but not obvious at first why it should work. When can we create a dense Gray code with the cyclic Gray-code property, and how do we do it? The answer here is a bit more obvious—once you have already seen it. Because Gray codes apply to non-binary integer representations as well, I will also touch on Gray codes for radices other than 2 and for mixed radices. All results are joint work with my senior thesis students, Jessica Fan ’17, D Speaker(s): Thoman Cormen, Agenda: 1 - Gather - 12 noon 2 - Introduction - 12:05 3 - Presentation -12:10 4 - Q&A 12:55 5 - Conversation over pizza 1 pm Room: PL128B, Bldg: School of Engineering, Technology and Aeronautics, 2500 North River Rd, Manchester, New Hampshire, United States, 03106

Abstract: As deep learning models scale into multi-billion parameter spaces, modern enterprise architectures face an immediate barrier: the memory bandwidth wall. Standard operator execution patterns bot- tleneck high-performance systems by forcing intermediate tensor states to repeatedly cycle through high-bandwidth memory (HBM). This lecture addresses the mathematical and hardware codesign patterns required to achieve sublinear search mandates and drive maximum runtime execution throughput across real-time, multi-modal environments. Through a deep dive into PyTorch implementation mechanics, we examine the dual paradigms of data vs. compute vectorization. We analyze how transforming unstructured real-world entities into continuous, high-dimensional dense numeric representations provides critical topological con- text for downstream similarity checks. Moving past standard eager-mode execution patterns, we demonstrate how custom OpenAI Triton and CUDA fused memory kernels mathematically com- bine scale, mask, and softmax routines into localized execution blocks inside fast SRAM registers, bypassing HBM (High Bandwidth Memory) latency loops completely. Furthermore, this session explores advanced index topologies|speci cally Hierarchical Navi- gable Small Worlds (HNSW) coupled with Vector Quantization (VQ) boundaries. By applying Vector Quantization to compress vast oating-point vector spaces into low-bit indices, we illustrate how to bridge the gap between continuous neural state spaces and discrete tool selection loops. Finally, we synthesize these optimization mechanics within the framework of Agentic AI control loops (ReAct- Reasoning and Acting), providing a uni ed approach to cognitive scaling, multi-agent synchronization, and real-world hallucination mitigation Talk will be at SNHU Speaker(s): Dr. Baris Kazar, Agenda: SNHU Talk Room: PL128B, Bldg: School of engineering, Technologyand Aeronautics Room Number: PL128B, 2500 North River Road, Manchester, New Hampshire, United States

As deep learning models scale into multi-billion parameter spaces, modern enterprise architectures face an immediate barrier: the memory bandwidth wall. Standard operator execution patterns bottleneck high-performance systems by forcing intermediate tensor states to repeatedly cycle through high-bandwidth memory (HBM). This lecture addresses the mathematical and hardware codesign patterns required to achieve sublinear search mandates and drive maximum runtime execution throughput across real-time, multi-modal environments. Through a deep dive into PyTorch implementation mechanics, we examine the dual paradigms of data vs. compute vectorization. We analyze how transforming unstructured real-world entities into continuous, high-dimensional dense numeric representations provides critical topological context for downstream similarity checks. Moving past standard eager-mode execution patterns, we demonstrate how custom OpenAI Triton and CUDA fused memory kernels mathematically combine scale, mask, and softmax routines into localized execution blocks inside fast SRAM registers, bypassing HBM (High Bandwidth Memory) latency loops completely. Furthermore, this session explores advanced index topologies—specifically Hierarchical Navigable Small Worlds (HNSW) coupled with Vector Quantization (VQ) boundaries. By applying Vector Quantization to compress vast floating-point vector spaces into low-bit indices, we illustrate how to bridge the gap between continuous neural state spaces and discrete tool selection loops. Finally, we synthesize these optimization mechanics within the framework of Agentic AI control loops (ReAct- Reasoning and Acting), providing a unified approach to cognitive scaling, multi-agent synchronization, and real-world hallucination mitigation. Speaker(s): Baris Kazar, Room: PL128B, Bldg: School of engineering, Technologyand Aeronautics, 2500 North River Rd, Manchester, New Hampshire, United States, 03106