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Found 8 papers, 0 papers with code
Sensing in Bi-Static ISAC Systems with Clock Asynchronism: A Signal Processing Perspective
Integrated Sensing and Communications (ISAC) has been identified as a pillar usage scenario for the impending 6G era.
Natural Language based Context Modeling and Reasoning for Ubiquitous Computing with Large Language Models: A Tutorial
Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4.
Integration of Radar Sensing into Communications with Asynchronous Transceivers
Clock asynchronism is a critical issue in integrating radar sensing into communication networks.
Integrated Human Activity Sensing and Communications
In this article, we provide a review of human related sensing in the context of ISAC.
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares.
Inverse Reinforcement Learning with Multiple Ranked Experts
We consider the problem of learning to behave optimally in a Markov Decision Process when a reward function is not specified, but instead we have access to a set of demonstrators of varying performance.
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing
Since the sensed data from different cells (sub-areas) of the target sensing area will probably lead to diverse levels of inference data quality, cell selection (i. e., choose which cells of the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i. e., data collection costs) for ensuring a certain level of quality.
Ridesourcing Car Detection by Transfer Learning
Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool.
