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researcher

T. Mcginnity

4 papers hereh-index 212.1k citations78 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.RO3
  • q-bio.QM1
same name
  • T. McGinnity — 5 papers, h 30
  • T. McGinnity — 2 papers, h 16
  • T. McGinnity — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202023
most citedDeep Learning in Mining Biological Data

17 citations · 30 across the 4 of their papers we have counts for

collaborators

4 papers

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…

cs.RO2022

Exploiting High Quality Tactile Sensors for Simplified Grasping

Pedro Machado, T. M. McGinnity

Robots are expected to grasp a wide range of objects varying in shape, weight or material type. Providing robots with tactile capabilities similar to humans is thus essential for a…

cs.RO2021

Object recognition for robotics from tactile time series data utilising different neural network architectures

Wolfgang Bottcher, Pedro Machado, Nikesh Lama +1

Robots need to exploit high-quality information on grasped objects to interact with the physical environment. Haptic data can therefore be used for supplementing the visual modalit…

q-bio.QM2020★ 17 cited

Deep Learning in Mining Biological Data

Mufti Mahmud, M Shamim Kaiser, Amir Hussain

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Broadly categorized in…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.