Search Results for author: Yasamin Mostofi

Found 5 papers, 1 papers with code

Emergent Cooperation for Energy-efficient Connectivity via Wireless Power Transfer

no code implementations23 Apr 2024 Winston Hurst, Anurag Pallaprolu, Yasamin Mostofi

Other UEs in the vicinity are viewed as relay candidates, and our aim is to enable energy-efficient connectivity for the source, while accounting for the self-interested behavior and private channel state information of these candidates, by allowing the source to "pay" the candidates via wireless power transfer (WPT).

Unmanned Vehicles in 6G Networks: A Unifying Treatment of Problems, Formulations, and Tools

no code implementations23 Apr 2024 Winston Hurst, Spilios Evmorfos, Athina Petropulu, Yasamin Mostofi

Unmanned Vehicles (UVs) functioning as autonomous agents are anticipated to play a crucial role in the 6th Generation of wireless networks.

A Foundation for Wireless Channel Prediction and Full Ray Makeup Estimation Using an Unmanned Vehicle

no code implementations20 Feb 2022 Chitra R. Karanam, Yasamin Mostofi

We experimentally validate our proposed framework through extensive real-world experiments in three different areas, and show that our approach can accurately predict the received channel power and the detailed makeup of the rays at unvisited locations in an area, considerably outperforming the state-of-the-art in wireless channel prediction.

Nocturnal Seizure Detection Using Off-the-Shelf WiFi

no code implementations25 Mar 2021 Belal Korany, Yasamin Mostofi

Detection of nocturnal seizures in epilepsy patients is essential, both for the quick management of the seizure complications, and for the assessment of the ongoing seizure treatment.

Management Seizure Detection

PieAPP: Perceptual Image-Error Assessment through Pairwise Preference

1 code implementation CVPR 2018 Ekta Prashnani, Hong Cai, Yasamin Mostofi, Pradeep Sen

Our key observation is that our trained network can then be used separately with only one distorted image and a reference to predict its perceptual error, without ever being trained on explicit human perceptual-error labels.

Video Quality Assessment

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