Room: EV003-309, Bldg: Electrical & Computer Engineering Department EV, Concordia University, 1515 Ste. Catherine West, MONTREAL, Quebec, Canada, H3G 1M8

In 1994, Dr. Amy Pinchuk founded InField Scientific Inc., a company specializing in electromagnetic simulation, analysis, and testing for Navy shipboard environments. She has served as the lead industrial Electromagnetic Environmental Effects (E3) subject matter expert for the design and refit of Royal Canadian Navy (RCN) ships and also provides E3 expertise to the Royal New Zealand Navy. InField’s work focuses on optimizing ship design for system performance and Electromagnetic Compatibility (EMC) across mission-critical communication, navigation, and electronic warfare systems. The safety of personnel, ordnance, and fuel is a critical concern in shipboard environments characterized by extremely high electromagnetic fields. This presentation explores the scientific, engineering, and management challenges encountered over three decades in this highly specialized <a href="http://domain.Speaker(s):" target="_blank" title="domain.Speaker(s):">domain.Speaker(s): Dr. Amy Pinchuk, Room: EV003-309, Bldg: Electrical & Computer Engineering Department EV, Concordia University, 1515 Ste. Catherine West, MONTREAL, Quebec, Canada, H3G 1M8

Abstract:This talk presents recent advances in WiFi sensing for human activity recognition (HAR), demonstrating how existing wireless communication infrastructure can be leveraged as a powerful sensing modality. By exploiting Channel State Information (CSI) readily available in commodity WiFi devices, it is possible to infer both large-scale and fine-grained human activities without requiring wearable sensors or dedicated hardware. The presentation begins with an overview of the emerging paradigm of Integrated Sensing and Communication (ISAC), including developments such as IEEE 802.11bf and future 6G systems, where communication signals are repurposed for environmental sensing. It then introduces signal processing techniques for extracting meaningful features from CSI, including time-frequency analysis and Doppler-based representations that capture motion dynamics. A key contribution discussed in the talk is the use of lightweight and scalable machine learning approaches, such as random convolutional kernels and deep neural networks, for efficient end-to-end activity <a href="http://recognition.Speaker(s):" target="_blank" title="recognition.Speaker(s):">recognition.Speaker(s): Professor Shahrokh Valaee, Room: EV003-309, Bldg: Electrical & Computer Engineering Department EV, Concordia University, 1515 Ste. Catherine West, MONTREAL, Quebec, Canada, H3G 1M8