output
19982025
most citedProbing the Thermal Deoxygenation of Graphene Oxide using High Resolution In Situ X-Ray based Spectroscopies

1.6k citations

Showing 2023Show all

6 papers · 1 filter

cs.RO2023★ 13 cited

A multi-modal approach to continuous material identification through tactile sensing

Augusto Gómez Eguíluz, Ignacio Rañó, Sonya A. Coleman +1

Tactile sensing has recently been used in robotics for object identification, grasping, and material recognition. Most material recognition approaches use vibration information fro…

eess.IV2023★ 8 cited

Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks

Jad F. Assaf, Anthony Abou Mrad, Dan Z. Reinstein +7

This paper presents the development and validation of a Generative Adversarial Network (GAN) purposed to create high-resolution, realistic Anterior Segment Optical Coherence Tomogr…

cs.CY2023★ 74 cited

IoT trust and reputation: a survey and taxonomy

Muhammad Aaqib, Aftab Ali, Liming Chen +1

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capabilit…

physics.med-ph2023★ 14 cited

Frequency content and filtering of head sensor kinematics: A method to enable field-based inter-study comparisons

Gregory Tierney, Steve Rowson, Ryan Gellner +5

Wearable head sensor systems use different kinematic signal processing approaches which limits field-based inter-study comparisons, especially when artefacts are present in the sig…

cs.CV2023

Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation

Asiri Lindamulage, Nuwan Kodagoda, Shyam Reyal +2

Magnitude-based pruning is a technique used to optimise deep learning models for edge inference. We have achieved over 75% model size reduction with a higher accuracy than the orig…

hep-ph2023★ 17 cited

Anomalies, Representations, and Self-Supervision

Barry M. Dillon, Luigi Favaro, Friedrich Feiden +2

We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The AnomalyCLR tech…