Diego F. Larios

457 total citations
40 papers, 314 citations indexed

About

Diego F. Larios is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Control and Systems Engineering. According to data from OpenAlex, Diego F. Larios has authored 40 papers receiving a total of 314 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Electrical and Electronic Engineering, 14 papers in Computer Networks and Communications and 8 papers in Control and Systems Engineering. Recurrent topics in Diego F. Larios's work include Energy Efficient Wireless Sensor Networks (11 papers), Indoor and Outdoor Localization Technologies (6 papers) and Experimental Learning in Engineering (4 papers). Diego F. Larios is often cited by papers focused on Energy Efficient Wireless Sensor Networks (11 papers), Indoor and Outdoor Localization Technologies (6 papers) and Experimental Learning in Engineering (4 papers). Diego F. Larios collaborates with scholars based in Spain, Colombia and Norway. Diego F. Larios's co-authors include Carlos León, Julio Barbancho, Francisco Javier Molina, Enrique Personal, José Luis Sevillano, Sebastián García, Félix Biscarri, Guillermo Rodríguez‐Gutiérrez, J. Luque and Juan Ignacio Guerrero and has published in prestigious journals such as Expert Systems with Applications, Sensors and Energies.

In The Last Decade

Diego F. Larios

38 papers receiving 298 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Diego F. Larios Spain	12	150	104	63	42	32	40	314
Marcin Bernaś Poland	11	86 0.6×	57 0.5×	61 1.0×	18 0.4×	34 1.1×	38	327
Liang Wen China	12	126 0.8×	59 0.6×	43 0.7×	17 0.4×	62 1.9×	49	454
Vladimir Dimčev North Macedonia	10	305 2.0×	245 2.4×	40 0.6×	125 3.0×	22 0.7×	33	633
Tuhina Samanta India	10	194 1.3×	168 1.6×	20 0.3×	79 1.9×	50 1.6×	55	329
Reino Virrankoski Finland	9	250 1.7×	224 2.2×	47 0.7×	31 0.7×	37 1.2×	32	434
Srikanth Namuduri United States	7	62 0.4×	37 0.4×	39 0.6×	39 0.9×	32 1.0×	9	338
Mobasshir Mahbub Bangladesh	9	127 0.8×	110 1.1×	12 0.2×	18 0.4×	31 1.0×	41	347
Vadim Zhmud Russia	9	94 0.6×	72 0.7×	147 2.3×	53 1.3×	33 1.0×	80	395
Andrew Sharp United Kingdom	5	104 0.7×	59 0.6×	28 0.4×	25 0.6×	9 0.3×	10	248
R. Mahalakshmi India	13	316 2.1×	58 0.6×	134 2.1×	18 0.4×	77 2.4×	85	480

Countries citing papers authored by Diego F. Larios

Since Specialization

Citations

This map shows the geographic impact of Diego F. Larios'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 Diego F. Larios with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego F. Larios more than expected).

Fields of papers citing papers by Diego F. Larios

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Diego F. Larios. 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 Diego F. Larios. The network helps show where Diego F. Larios may publish in the future.

Co-authorship network of co-authors of Diego F. Larios

This figure shows the co-authorship network connecting the top 25 collaborators of Diego F. Larios. A scholar is included among the top collaborators of Diego F. Larios 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 Diego F. Larios. Diego F. Larios is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Larios, Diego F., et al.. (2024). Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models. Sensors. 24(5). 1370–1370. 4 indexed citations

Luque, J., Benedikt Tepe, Diego F. Larios, Carlos León, & Holger C. Hesse. (2023). Machine Learning Estimation of Battery Efficiency and Related Key Performance Indicators in Smart Energy Systems. Energies. 16(14). 5548–5548. 2 indexed citations

Larios, Diego F., et al.. (2023). TEFNEN: Transformer for Energy Forecasting in Natural Environment. 2 indexed citations

Guerrero, Juan Ignacio, et al.. (2023). A General-Purpose Distributed Analytic Platform Based on Edge Computing and Computational Intelligence Applied on Smart Grids. Sensors. 23(8). 3845–3845. 4 indexed citations

Guerrero, Juan Ignacio, et al.. (2022). Design and Evaluation of a Heterogeneous Lightweight Blockchain-Based Marketplace. Sensors. 22(3). 1131–1131. 4 indexed citations

Barbancho, Julio, et al.. (2022). In-field hyperspectral imaging dataset of Manzanilla and Gordal olive varieties throughout the season. Data in Brief. 46. 108812–108812. 2 indexed citations

Guerrero, Juan Ignacio, et al.. (2022). Performance of Raspberry Pi during Blockchain Execution using Proof of Authority Consensus. idUS (Universidad de Sevilla). 287–292.

Barbancho, Julio, et al.. (2022). Full-Scale Digesters: An Online Model Parameter Identification Strategy. Energies. 15(20). 7685–7685. 2 indexed citations

Bracco, Stefano, et al.. (2021). Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models. Applied Sciences. 11(14). 6420–6420. 4 indexed citations

10.

Belloch, Jose A., José M. Badía, Diego F. Larios, et al.. (2021). On the performance of a GPU-based SoC in a distributed spatial audio system. The Journal of Supercomputing. 77(7). 6920–6935. 1 indexed citations

11.

García, Sebastián, et al.. (2020). Training Equipment for Automatic Control Systems and Industrial Automation subjects in Engineering Degrees. 1–6. 1 indexed citations

12.

Trueba, Paloma, Ana M. Beltrán, José Antonio Rodríguez-Ortiz, et al.. (2020). Porous Titanium Cylinders Obtained by the Freeze-Casting Technique: Influence of Process Parameters on Porosity and Mechanical Behavior. Metals. 10(2). 188–188. 27 indexed citations

13.

Monedero, Íñigo, Félix Biscarri, Diego F. Larios, & Julio Barbancho. (2019). A Framework to Predict Failures for Ground Tests on Aircrafts. IEEE Aerospace and Electronic Systems Magazine. 34(5). 30–40.

14.

Larios, Diego F., Julio Barbancho, Félix Biscarri, & Íñigo Monedero. (2019). A Research Study for the Design of a Portable and Configurable Ground Test System for the A400M Aircraft. International Journal of Aerospace Engineering. 2019. 1–16. 5 indexed citations

15.

Personal, Enrique, et al.. (2019). Monitoring and Fault Location Sensor Network for Underground Distribution Lines. Sensors. 19(3). 576–576. 11 indexed citations

16.

García, Sebastián, et al.. (2019). Heterogeneous LoRa-Based Wireless Multimedia Sensor Network Multiprocessor Platform for Environmental Monitoring. Sensors. 19(16). 3446–3446. 13 indexed citations

17.

Larios, Diego F., Carlos Rodríguez, Julio Barbancho, et al.. (2013). An Automatic Weighting System for Wild Animals Based in an Artificial Neural Network: How to Weigh Wild Animals without Causing Stress. Sensors. 13(3). 2862–2883. 12 indexed citations

18.

Larios, Diego F., et al.. (2013). Five Years of Designing Wireless Sensor Networks in the Doñana Biological Reserve (Spain): An Applications Approach. Sensors. 13(9). 12044–12069. 11 indexed citations

19.

Larios, Diego F., Julio Barbancho, Francisco Javier Molina, & Carlos León. (2013). Localization Method for Low-power Wireless Sensor Networks. Journal of Networks. 8(1). 3 indexed citations

20.

Larios, Diego F., et al.. (2012). mTOSSIM: A simulator that estimates battery lifetime in wireless sensor networks. Simulation Modelling Practice and Theory. 31. 39–51. 15 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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