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Gary Ushaw

Researcher at Newcastle University

Publications -  48
Citations -  335

Gary Ushaw is an academic researcher from Newcastle University. The author has contributed to research in topics: Video game & Lyapunov exponent. The author has an hindex of 10, co-authored 47 publications receiving 313 citations. Previous affiliations of Gary Ushaw include University of Edinburgh & Swinburne University of Technology.

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Journal ArticleDOI

Validity of the Microsoft Kinect sensor for assessment of normal walking patterns in pigs

TL;DR: It was concluded that the Kinect device could distinguish sound from lame pigs by tracking neck region elevation during walking; however, markerfree tracking algorithms need refinement and further development to become sensitive and reliable.
Proceedings ArticleDOI

Smart Routing: A Novel Application of Collaborative Path-Finding to Smart Parking Systems

TL;DR: In this article, the authors introduce the concept of collaborative path-finding to the problem of traffic congestion in parking garages and show that a novel approach to collaboratively planning paths for multiple agents can lead to reduced traffic congestion on routes toward busy parking areas, while reducing the amount of time when parking spaces are vacant.
Journal ArticleDOI

How to extract Lyapunov exponents from short and noisy time series

TL;DR: An algorithm for the extraction of Lyapunov spectra from short and noisy time series is discussed and a novel concatenation technique is presented and combined with the extraction algorithm previously utilized.

Smart Routing: A Novel Application of Collaborative Path-finding to Smart Parking Systems

TL;DR: This work simulates a smart parking system for an urban environment, and shows that a novel approach to collaboratively planning paths for multiple agents can lead to reduced traffic congestion on routes toward busy parking areas, while reducing the amount of time when parking spaces are vacant, thereby increasing the revenue earned.
Journal ArticleDOI

Lyapunov exponents from a time series: a noise-robust extraction algorithm

TL;DR: A novel technique for improving the performance of Lyapunov exponent calculations from a time series and results for the algorithm's performance in the presence of noise corruption are shown.