World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
4891
World Ranking
10895
National Ranking
176

Eduardo Ros publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Eduardo Ros sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 176 publications — 37th percentile

37% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Eduardo Ros D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Eduardo Ros sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Eduardo Ros is affiliated with the University of Granada in Spain and has published extensively across multiple domains in science and engineering. Their work spans notable fields such as Computer Science, Neuroscience, and Engineering, demonstrating a multidisciplinary approach to research. The subfields of their expertise include Cognitive Neuroscience, Computer Vision and Pattern Recognition, Computer Networks and Communications, Neurology, and Electrical and Electronic Engineering.

Their research explores a variety of main topics including Vestibular and auditory disorders, Network Time Synchronization Technologies, Neural dynamics and brain function, Distributed systems and fault tolerance, Advanced Vision and Imaging, Real-Time Systems Scheduling, and Motor Control and Adaptation.

Frequent collaborators in their research include Niceto R. Luque, Javier Díaz, Ignacio Abadía, Francisco Naveros, and Jesús A. Garrido, highlighting a collaborative network in their scientific community.

Eduardo Ros has published articles in several well-known venues, with multiple publications in IEEE Access and Scientific Reports, as well as contributions to Neural Networks, bioRxiv (Cold Spring Harbor Laboratory), and Science Robotics.

Representative recent papers include:

  • IEEE 1588 High Accuracy Default Profile: Applications and Challenges, 2020, IEEE Access
  • A cerebellar-based solution to the nondeterministic time delay problem in robotic control, 2021, Science Robotics
  • Implementation of a Time-Sensitive Networking (TSN) Ethernet Bus for Microlaunchers, 2021, IEEE Transactions on Aerospace and Electronic Systems
  • Digital Electrical Substation Communications Based on Deterministic Time-Sensitive Networking Over Ethernet, 2020, IEEE Access
  • Enhancing White Rabbit Synchronization Stability and Scalability Using P2P Transparent and Hybrid Clocks, 2021, IEEE Transactions on Industrial Informatics

The scientific output reflects a focus on network synchronization and communication technologies alongside neural and cognitive system modeling, connecting engineering principles with neuroscience applications.

Best Publications

  • FPGA-based real-time optical-flow system

    J. Diaz;E. Ros;F. Pelayo;E.M. Ortigosa

  • Neural networks

    Alberto Prieto;Beatriz Prieto;Eva Martinez Ortigosa;Eduardo Ros

  • A Comparison of FPGA and GPU for Real-Time Phase-Based Optical Flow, Stereo, and Local Image Features

    Karl Pauwels;M. Tomasi;Javier Diaz Alonso;E. Ros

  • Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics

    Eduardo Ros;Richard Carrillo;Eva M. Ortigosa;Boris Barbour

  • Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform.

    Egidio Falotico;Lorenzo Vannucci;Alessandro Ambrosano;Ugo Albanese

  • A real-time spiking cerebellum model for learning robot control.

    Richard R. Carrillo;Eduardo Ros;Christian Boucheny;Olivier J.-M.D. Coenen

  • Background Subtraction Based on Color and Depth Using Active Sensors

    Enrique J. Fernandez-Sanchez;Javier Diaz;Eduardo Ros

  • Real-time computing platform for spiking neurons (RT-spike)

    E. Ros;E. M. Ortigosa;R. Agis;R. Carrillo

  • Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning

    Egidio D’Angelo;Lisa Mapelli;Claudia Casellato;Jesus A. Garrido;Jesus A. Garrido

  • Robust Bioinspired Architecture for Optical-Flow Computation

    G. Botella;A. Garcia;M. Rodriguez-Alvarez;E. Ros

  • Hardware description of multi-layer perceptrons with different abstraction levels

    Eva M. Ortigosa;Antonio Cañas;Eduardo Ros;Pilar Martínez Ortigosa

  • Real-Time Model-Based Rigid Object Pose Estimation and Tracking Combining Dense and Sparse Visual Cues

    Karl Pauwels;Leonardo Rubio;Javier Diaz;Eduardo Ros

  • Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation

    Jesús A. Garrido;Niceto R. Luque;Egidio D'Angelo;Eduardo Ros

  • PSFGA: parallel processing and evolutionary computation for multiobjective optimisation

    F. de Toro Negro;J. Ortega;E. Ros;S. Mota

  • Parallel Architecture for Hierarchical Optical Flow Estimation Based on FPGA

    F. Barranco;M. Tomasi;J. Diaz;M. Vanegas

  • Adaptive robotic control driven by a versatile spiking cerebellar network.

    Claudia Casellato;Alberto Antonietti;Jesus A. Garrido;Richard R. Carrillo

  • Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue.

    Egidio Ugo D'Angelo;Alberto Antonietti;Stefano Casali;Claudia Casellato

  • A Quantitative Study of Fault Tolerance, Noise Immunity, and Generalization Ability of MLPs

    J. L. Bernier;J. Ortega;E. Ros;I. Rojas

  • Adaptive cerebellar spiking model embedded in the control loop: context switching and robustness against noise.

    Niceto R. Luque;Jesús Alberto Garrido;Richard R. Carrillo;Silvia Tolu

  • Real-Time Clustering and Multi-Target Tracking Using Event-Based Sensors

    Francisco Barranco;Cornelia Fermuller;Eduardo Ros

  • Superpipelined high-performance optical-flow computation architecture

    Javier Díaz;Eduardo Ros;Rodrigo Agís;Jose Luis Bernier

Frequent Co-Authors

Egidio D'Angelo
Egidio D'Angelo University of Pavia
Alberto Prieto
Alberto Prieto University of Granada
Ignacio Rojas
Ignacio Rojas University of Granada
Cornelia Fermüller
Cornelia Fermüller University of Maryland, College Park
Alois Knoll
Alois Knoll Technical University of Munich
Patrick van der Smagt
Patrick van der Smagt Volkswagen Group (United States)
Norbert Krüger
Norbert Krüger University of Southern Denmark
Alwyn J. Seeds
Alwyn J. Seeds University College London
Ronald Holzwarth
Ronald Holzwarth Max Planck Society
Rafael Rodríguez
Rafael Rodríguez University of Wisconsin–Milwaukee

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