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Public defence in Networking Technology, M.Sc. Dariush Salami

Public defence from the Aalto University School of Electrical Engineering, Department of Information and Communications Engineering
Doctoral hat floating above a speaker's podium with a microphone

The title of the thesis: Spectrum Aware Human Centric Sensing using mmWave FMCW Radars

Doctoral student: Dariush Salami
Opponent: Prof. Moustafa Youssef, The American University in Cairo, Egypt
Custos: Prof. Stephan Sigg, Aalto University School of Electrical Engineering, Department of Information and Communications Engineering 

I am pleased to announce my upcoming public defense about Human Centric Sensing (HCS) using mmWave radars, a study that dives deep into crucial aspects of utilizing Radio Frequency (RF) sensing technology in human-related applications using Artificial Intelligence (AI) and Machine Learning (ML). 

The purpose of this study is to investigate the potential of mmWave radar technology in HCS. The primary goal is to explore how RF sensing can enhance human-related applications, such as localization, gesture recognition, and water quality estimation. This thesis is highly relevant to the field of sensing technology and AI/ML. By focusing on mmWave radars, the research aims to contribute to the advancements of RF sensing in human-centric scenario since the mmWave radars are cheap (about 30 euros), small (about the size of a 1 euro coin), robust to lighting and weather conditions, reliable in cluttered environments, and privacy-preserving. The research has yielded promising results, showcasing the efficacy of mmWave radars in detecting human positions and gestures, and assessing quality of water. These findings open new possibilities for applications in healthcare, smart-homes, and human-machine interaction. The primary finding of this study is the successful integration of mmWave radars into embedded devices thanks to proposed novel AI methods that are up to 40 times computationally less expensive than the state of the art. The results are published in more than ten journal and conference papers. 

Based on the results, it can be concluded that mmWave radars offer a promising avenue for advancing HCS capabilities. The study emphasizes the potential impact of this technology on healthcare, smart environments, and other human-centric applications which in turn contributes directly to six Sustainable Development Goals (SDGs) outlined by the United Nations (UN). In the context of current technological trends, where the demand for non-intrusive, high-precision, affordable, and privacy-preserving sensing is on the rise, this research aligns with the broader goals of creating more efficient and user-friendly technologies. As we continue to strive for innovation in healthcare and smart living, the integration of mmWave radars in HCS represents a significant step forward. Please join me for my public defense as we explore the transformative potential of mmWave radars in enhancing our interaction with technology in a human-centric manner.

Keywords: Machine Learning, Artificial Intelligence, RF sensing, mmWave radar, Human-Centric

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/

Contact:

Email  [email protected]
Mobile  +358504115292


Doctoral theses in the School of Electrical Engineering: https://aaltodoc.aalto.fi/handle/123456789/53

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