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Device-Free Human State Estimation using UWB Multi-Static Radios

Autor: Laham, Saria Al, Baghi, Bobak H., Lajoie, Pierre-Yves, Feriani, Amal, Herath, Sachini, Liu, Steve, Dudek, Gregory

We present a human state estimation framework that allows us to estimate the location, and even the activities, of people in an indoor environment without the requirement that they carry a specific devices with them. To achieve this "device free" loc

Externí odkaz: http://arxiv.org/abs/2401.05410

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SAGE: Smart home Agent with Grounded Execution

Autor: Rivkin, Dmitriy, Hogan, Francois, Feriani, Amal, Konar, Abhisek, Sigal, Adam, Liu, Steve, Dudek, Greg

The common sense reasoning abilities and vast general knowledge of Large Language Models (LLMs) make them a natural fit for interpreting user requests in a Smart Home assistant context. LLMs, however, lack specific knowledge about the user and their

Externí odkaz: http://arxiv.org/abs/2311.00772

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CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation

Autor: Feriani, Amal, Wu, Di, Liu, Steve, Dudek, Greg

Deep learning has been extensively used in wireless communication problems, including channel estimation. Although several data-driven approaches exist, a fair and realistic comparison between them is difficult due to inconsistencies in the experimen

Externí odkaz: http://arxiv.org/abs/2306.13761

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AI Clinics on Mobile (AICOM): Universal AI Doctors for the Underserved and Hard-to-Reach

Autor: Yang, Tim Tianyi, Yang, Tom Tianze, An, Na, Kong, Ao, Liu, Shaoshan, Liu, Steve Xue

This paper introduces Artificial Intelligence Clinics on Mobile (AICOM), an open-source project devoted to answering the United Nations Sustainable Development Goal 3 (SDG3) on health, which represents a universal recognition that health is fundament

Externí odkaz: http://arxiv.org/abs/2306.10324

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Communication Load Balancing via Efficient Inverse Reinforcement Learning

Autor: Konar, Abhisek, Wu, Di, Xu, Yi Tian, Jang, Seowoo, Liu, Steve, Dudek, Gregory

Communication load balancing aims to balance the load between different available resources, and thus improve the quality of service for network systems. After formulating the load balancing (LB) as a Markov decision process problem, reinforcement le

Externí odkaz: http://arxiv.org/abs/2303.16686

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