Javier Echanobe

822 total citations
48 papers, 552 citations indexed

About

Javier Echanobe is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Javier Echanobe has authored 48 papers receiving a total of 552 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 12 papers in Electrical and Electronic Engineering. Recurrent topics in Javier Echanobe's work include Neural Networks and Applications (14 papers), Machine Learning and ELM (11 papers) and Fuzzy Logic and Control Systems (11 papers). Javier Echanobe is often cited by papers focused on Neural Networks and Applications (14 papers), Machine Learning and ELM (11 papers) and Fuzzy Logic and Control Systems (11 papers). Javier Echanobe collaborates with scholars based in Spain, United Kingdom and Germany. Javier Echanobe's co-authors include I. del Campo, Koldo Basterretxea, Faiyaz Doctor, V. Sanchez Martinez, J. G. Muga, Adolfo del Campo, Estibaliz Asua, José Ramón González de Mendívil, José Javier Astráin and Pradyumn Kumar Shukla and has published in prestigious journals such as Applied Energy, Physical Review A and Information Sciences.

In The Last Decade

Javier Echanobe

47 papers receiving 532 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Javier Echanobe Spain 15 244 117 117 113 110 48 552
Ye Shi China 14 168 0.7× 115 1.0× 154 1.3× 153 1.4× 280 2.5× 42 731
Chao Jiang United States 11 80 0.3× 76 0.6× 212 1.8× 252 2.2× 53 0.5× 53 615
Sergio García-Nieto Spain 16 51 0.2× 66 0.6× 289 2.5× 93 0.8× 57 0.5× 49 606
Neng‐Sheng Pai Taiwan 12 91 0.4× 40 0.3× 145 1.2× 160 1.4× 47 0.4× 64 527
Koldo Basterretxea Spain 12 217 0.9× 41 0.4× 88 0.8× 97 0.9× 121 1.1× 39 440
J. Darby Smith United States 13 115 0.5× 261 2.2× 139 1.2× 50 0.4× 190 1.7× 40 650
Gwi-Tae Park South Korea 13 229 0.9× 33 0.3× 532 4.5× 95 0.8× 305 2.8× 99 895
Fanny Spagnolo Italy 14 51 0.2× 48 0.4× 156 1.3× 158 1.4× 338 3.1× 51 608
Damian Grzechca Poland 12 79 0.3× 73 0.6× 129 1.1× 52 0.5× 262 2.4× 76 494
Pengfei Li China 16 91 0.4× 22 0.2× 320 2.7× 45 0.4× 145 1.3× 87 700

Countries citing papers authored by Javier Echanobe

Since Specialization
Citations

This map shows the geographic impact of Javier Echanobe's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Javier Echanobe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Javier Echanobe more than expected).

Fields of papers citing papers by Javier Echanobe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Javier Echanobe. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Javier Echanobe. The network helps show where Javier Echanobe may publish in the future.

Co-authorship network of co-authors of Javier Echanobe

This figure shows the co-authorship network connecting the top 25 collaborators of Javier Echanobe. A scholar is included among the top collaborators of Javier Echanobe based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Javier Echanobe. Javier Echanobe is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Basterretxea, Koldo, et al.. (2025). Balancing Robustness and Efficiency in Embedded DNNs Through Activation Function Selection. Electronics Letters. 61(1).
2.
Basterretxea, Koldo, et al.. (2024). Evaluating single event upsets in deep neural networks for semantic segmentation: An embedded system perspective. Journal of Systems Architecture. 154. 103242–103242. 5 indexed citations
3.
Basterretxea, Koldo, et al.. (2023). Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving*. arXiv (Cornell University). 1–6. 2 indexed citations
4.
Basterretxea, Koldo, et al.. (2023). On-chip hyperspectral image segmentation with fully convolutional networks for scene understanding in autonomous driving. Journal of Systems Architecture. 139. 102878–102878. 15 indexed citations
5.
Echanobe, Javier, et al.. (2022). Fast ion shuttling which is robust versus oscillatory perturbations. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 380(2239). 20210269–20210269. 1 indexed citations
6.
Basterretxea, Koldo, et al.. (2021). HSI-Drive: A Dataset for the Research of Hyperspectral Image Processing Applied to Autonomous Driving Systems. Communities in ADDI (University of the Basque Country). 866–873. 14 indexed citations
7.
Campo, I. del, et al.. (2019). A Hardware/Software Extreme Learning Machine Solution for Improved Ride Comfort in Automobiles. Zenodo (CERN European Organization for Nuclear Research). 20. 1–8. 3 indexed citations
8.
Echanobe, Javier, et al.. (2016). A neuro-genetic approach for modeling and optimizing a complex cogeneration process. Applied Soft Computing. 48. 347–358. 13 indexed citations
9.
Campo, I. del, et al.. (2015). Driving Behavior Signals and Machine Learning: A Personalized Driver Assistance System. 2933–2940. 36 indexed citations
10.
Echanobe, Javier, et al.. (2015). A divide-and-conquer strategie for FPGA implementations of large MLP-based classifiers. 4. 1–7. 1 indexed citations
11.
Campo, I. del, et al.. (2015). Electric Efficiency Modelling of a Complex Cogeneration Process Using Extreme Learning Machines. International Journal of Machine Learning and Computing. 5(5). 399–403. 1 indexed citations
12.
Campo, I. del, et al.. (2014). A real-time driver identification system based on artificial neural networks and cepstral analysis. Pure (Coventry University). 29 indexed citations
13.
Campo, I. del, et al.. (2012). A hardware/software embedded agent for real-time control of ambient-intelligence environments. Zenodo (CERN European Organization for Nuclear Research). 43. 1–8. 6 indexed citations
14.
Echanobe, Javier, et al.. (2012). Dynamic Partial Reconfiguration in Embedded Systems for Intelligent Environments. 178. 109–113. 3 indexed citations
15.
Campo, I. del, et al.. (2011). A System-on-Chip Development of a Neuro–Fuzzy Embedded Agent for Ambient-Intelligence Environments. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 42(2). 501–512. 20 indexed citations
16.
Lizuain, I., Javier Echanobe, A. Ruschhaupt, J. G. Muga, & D. Steck. (2010). Structural and dynamical aspects of avoided-crossing resonances in a three-levelΛsystem. Physical Review A. 82(6). 2 indexed citations
17.
Basterretxea, Koldo, I. del Campo, & Javier Echanobe. (2010). A semi-active suspension embedded controller in a FPGA. 69–78. 2 indexed citations
18.
Echanobe, Javier, et al.. (2010). Hardware Implementation of a Neural-Network Recognition Module for Visual Servoing in a Mobile Robot. 2 1. 226–232. 3 indexed citations
19.
Echanobe, Javier, et al.. (2009). A NEURO-FUZZY EMBEDDED SYSTEM FOR INTELLIGENT ENVIRONMENTS. 559–564. 1 indexed citations
20.
Campo, I. del, et al.. (2008). Efficient Hardware/Software Implementation of an Adaptive Neuro-Fuzzy System. IEEE Transactions on Fuzzy Systems. 16(3). 761–778. 53 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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