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Uriel Martinez-Hernandez

Researcher at University of Bath

Publications -  73
Citations -  1213

Uriel Martinez-Hernandez is an academic researcher from University of Bath. The author has contributed to research in topics: Robot & Gait (human). The author has an hindex of 17, co-authored 73 publications receiving 905 citations. Previous affiliations of Uriel Martinez-Hernandez include Imperial College London & University of Leeds.

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Journal ArticleDOI

Two-dimensional simulation of grain structure growth within selective laser melted AA-2024

TL;DR: In this paper, a two-dimensional cellular automata coupled model was developed to predict the microstructures formed during the laser melting of a powdered AA-2024 feedstock using the additive manufacturing (AM) process Selective Laser Melting (SLM).
Proceedings ArticleDOI

Active contour following to explore object shape with robot touch

TL;DR: This work proposes a control architecture that implements a perception-action cycle for the exploratory procedure, which allows the fingertip to react to tactile contact whilst regulating the applied contact force.
Journal ArticleDOI

Tactile Superresolution and Biomimetic Hyperacuity

TL;DR: Three key factors underlying superresolution that enable the perceptual acuity to surpass the sensor resolution are identified and envisage that these principles will enable cheap high-acuity tactile sensors that are highly customizable to suit their robotic use.
Proceedings ArticleDOI

Active Bayesian perception for angle and position discrimination with a biomimetic fingertip

TL;DR: This work applies active Bayesian perception to angle and position discrimination and extends the method to perform actions in a sensorimotor task using a biomimetic fingertip, observing a significant improvement over passive methods that lack a sensorsimotor loop for actively repositioning the sensor.
Journal ArticleDOI

Adaptive Bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors.

TL;DR: The proposed adaptive recognition system is accurate, fast and robust to sensor noise, but also capable to adapt its own performance over time, and demonstrates to be a robust and suitable computational approach for the intelligent recognition of activities of daily living using wearable sensors.