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Stephen McLaughlin

Researcher at Heriot-Watt University

Publications -  469
Citations -  12016

Stephen McLaughlin is an academic researcher from Heriot-Watt University. The author has contributed to research in topics: Turbo code & Lidar. The author has an hindex of 51, co-authored 449 publications receiving 10648 citations. Previous affiliations of Stephen McLaughlin include University of Edinburgh & University of Toulouse.

Papers
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Automated Detection of Uninformative Frames in Pulmonary Optical Endomicroscopy

TL;DR: The proposed approach identifies and removes uninformative frames with a sensitivity of 93% and a specificity of 92.6% in pulmonary OEM frame sequences and can become applicable in other organs.
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Fast tracking of hidden objects with single-pixel detectors

TL;DR: In this paper, a fast tracking system based on single laser illumination and a few single-pixel single photon avalanche diode (SPAD) detectors was demonstrated that improves on the previous tracking of non-line-of-sight motion by a factor of 300 in laser power.
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Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans.

TL;DR: This study assesses the efficacy of incorporating additional information from label-free fibre-based optical endomicrosopy of the nodule on assessing risk of malignancy and finds that this information does not yield any gain in predictive performance in a cohort of patients.
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Improving Supply Chain Performance through the Implementation of Process Related Knowledge Transfer Mechanisms

TL;DR: The study of how employees work with information and knowledge around a core business function, in this case a supply chain process, found that when the organization considered the employees’ preferred transfer mechanisms as part of an overall process improvement, the E2E supply chain performance was seen to improve significantly.
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Fast online 3D reconstruction of dynamic scenes from individual single-photon detection events

TL;DR: In this article, a Bayesian model is constructed to capture the dynamics of the 3D profile and an approximate inference scheme based on assumed density filtering is proposed, yielding a fast and robust reconstruction algorithm able to process efficiently thousands to millions of frames.