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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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Proceedings ArticleDOI
Fast Multiscale 3D Reconstruction Using Single-Photon Lidar Data
Sandor Plosz,Istvan Gyongy,Jonathan Leach,Stephen McLaughlin,Gerald S. Buller,Abderrahim Halimi +5 more
TL;DR: In this article , the authors present a reconstruction algorithm that exploits data statistics and multi-scale information to deliver clean depth and reflectivity images together with associated uncertainty maps, and demonstrate the robust and efficient performance of the proposed method.
Proceedings ArticleDOI
Color Image Restoration in the Low Photon-Count Regime Using Expectation Propagation
TL;DR: In this article , a new Expectation Propagation (EP) algorithm using ℓ1-norm total variation (ℓ 1-TV) prior is proposed for color image restoration in the low photon-count regime.
MA Parameter Estimation
TL;DR: In this article, a batch least squares method is used to estimate the parameters of a moving average model from either only third- or fourth-order cumulants of the noisy observations of the system output.
Proceedings ArticleDOI
UMTS FDD frequency domain equalization based on slot segmentation
TL;DR: In this article, a chip-level frequency domain equalizer (FDE) was proposed for a universal mobile telephone system (UMTS) downlink, where one slot signal is split into multiple segments for the sake of combating channel variance within one slot.
Posted Content
Patch-Based Image Restoration using Expectation Propagation.
TL;DR: In this article, patch-based prior distributions are used to approximate the posterior distributions using products of multivariate Gaussian densities, imposing structural constraints on the covariance matrices of these densities allows for greater scalability and distributed computation.