Daniel Mozos

1.3k total citations
66 papers, 951 citations indexed

About

Daniel Mozos is a scholar working on Hardware and Architecture, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Daniel Mozos has authored 66 papers receiving a total of 951 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Hardware and Architecture, 26 papers in Computer Networks and Communications and 21 papers in Electrical and Electronic Engineering. Recurrent topics in Daniel Mozos's work include Embedded Systems Design Techniques (34 papers), Interconnection Networks and Systems (19 papers) and Parallel Computing and Optimization Techniques (19 papers). Daniel Mozos is often cited by papers focused on Embedded Systems Design Techniques (34 papers), Interconnection Networks and Systems (19 papers) and Parallel Computing and Optimization Techniques (19 papers). Daniel Mozos collaborates with scholars based in Spain, Belgium and United States. Daniel Mozos's co-authors include Carlos González, Javier Resano, Antonio Plaza, Sebastián López, Francky Catthoor, Hortensia Mecha, Juan Antonio Clemente, Tanya Vladimirova, D.M. Fernandez and Jesús Tabero and has published in prestigious journals such as SHILAP Revista de lepidopterología, Proceedings of the IEEE and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Daniel Mozos

63 papers receiving 901 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Mozos Spain 17 370 319 294 253 191 66 951
Javier Resano Spain 15 289 0.8× 215 0.7× 152 0.5× 179 0.7× 187 1.0× 42 721
Carlos González Spain 15 379 1.0× 84 0.3× 304 1.0× 79 0.3× 194 1.0× 39 741
Francisco Argüello Spain 16 437 1.2× 48 0.2× 285 1.0× 83 0.3× 248 1.3× 80 858
Christian Tenllado Spain 10 110 0.3× 246 0.8× 186 0.6× 170 0.7× 61 0.3× 34 566
Shaoteng Liu China 10 425 1.1× 57 0.2× 436 1.5× 117 0.5× 214 1.1× 34 779
Weidong Yan China 12 143 0.4× 18 0.1× 230 0.8× 232 0.9× 72 0.4× 70 645
Jarno Mielikäinen United States 18 175 0.5× 27 0.1× 1.2k 3.9× 74 0.3× 298 1.6× 56 1.6k
Syed Gilani United States 9 48 0.1× 501 1.6× 104 0.4× 447 1.8× 14 0.1× 12 837
Gianluca Furano Netherlands 14 61 0.2× 159 0.5× 113 0.4× 154 0.6× 14 0.1× 62 821
Raúl Guerra Spain 16 335 0.9× 15 0.0× 229 0.8× 25 0.1× 111 0.6× 43 644

Countries citing papers authored by Daniel Mozos

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Mozos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Mozos. 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 Daniel Mozos. The network helps show where Daniel Mozos may publish in the future.

Co-authorship network of co-authors of Daniel Mozos

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Mozos. A scholar is included among the top collaborators of Daniel Mozos 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 Daniel Mozos. Daniel Mozos 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.
González, Carlos, et al.. (2024). A Real-Time FPGA Implementation of the LCMV Algorithm for Target Classification in Hyperspectral Images Using LDL Decomposition. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–14. 2 indexed citations
2.
González, Carlos, et al.. (2024). Parametric Pipelined k-Means Implementation for Hyperspectral Processing on Spacecraft Embedded FPGA. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17. 15927–15941. 1 indexed citations
3.
González, Carlos, et al.. (2022). A Real-Time FPGA Implementation of the CCSDS 123.0-B-2 Standard. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–13. 9 indexed citations
4.
González, Carlos, et al.. (2020). An FPGA Accelerator for Real-Time Lossy Compression of Hyperspectral Images. Remote Sensing. 12(16). 2563–2563. 16 indexed citations
5.
González, Carlos, et al.. (2020). An Extremely Pipelined FPGA Implementation of a Lossy Hyperspectral Image Compression Algorithm. IEEE Transactions on Geoscience and Remote Sensing. 58(10). 7435–7447. 12 indexed citations
6.
González, Carlos, et al.. (2018). Hyperspectral Image Compression Using Vector Quantization, PCA and JPEG2000. Remote Sensing. 10(6). 907–907. 57 indexed citations
7.
González, Carlos, et al.. (2017). FPGA Implementation of the CCSDS 1.2.3 Standard for Real-Time Hyperspectral Lossless Compression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11(4). 1158–1165. 31 indexed citations
8.
González, Carlos, Sergio Bernabé, Daniel Mozos, & Antonio Plaza. (2016). FPGA Implementation of an Algorithm for Automatically Detecting Targets in Remotely Sensed Hyperspectral Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(9). 4334–4343. 32 indexed citations
9.
González, Carlos, Sebastián López, Daniel Mozos, & Roberto Sarmiento. (2015). FPGA Implementation of the HySime Algorithm for the Determination of the Number of Endmembers in Hyperspectral Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(6). 2870–2883. 19 indexed citations
10.
Mozos, Daniel, et al.. (2012). Martian dust devils detector over FPGA. SHILAP Revista de lepidopterología. 1(1). 23–31. 3 indexed citations
11.
González, Carlos, Daniel Mozos, Javier Resano, & Antonio Plaza. (2010). FPGA for Computing the Pixel Purity Index Algorithm on Hyperspectral Images.. 293. 6 indexed citations
12.
Fernández-Conde, Jesús & Daniel Mozos. (2008). Pull vs. Hybrid: Comparing Scheduling Algorithms for Asymmetric Time-Constrained Environments.. International Conference on Wireless Networks. 102(9). 222–228. 1 indexed citations
13.
Resano, Javier, et al.. (2007). Reducing the reconfiguration overhead: a survey of techniques.. 191–194. 5 indexed citations
14.
Mecha, Hortensia, et al.. (2006). 2D defragmentation heuristics for hardware multitasking on reconfigurable devices. International Parallel and Distributed Processing Symposium. 188–188. 5 indexed citations
15.
Resano, Javier, et al.. (2006). A configuration memory hierarchy for fast reconfiguration with reduced energy consumption overhead. 8 pp.–8 pp.. 2 indexed citations
16.
Resano, Javier, Daniel Mozos, & Francky Catthoor. (2005). A Hybrid Prefetch Scheduling Heuristic to Minimize at Run-Time the Reconfiguration Overhead of Dynamically Reconfigurable Hardware. Design, Automation, and Test in Europe. 106–111. 42 indexed citations
17.
Resano, Javier, et al.. (2003). Analyzing communication overheads during hardware/software partitioning. Microelectronics Journal. 34(11). 1001–1007. 2 indexed citations
18.
Mozos, Daniel, et al.. (2002). Bounding the design space in hardware allocation. 913–916. 1 indexed citations
19.
Maestro, Juan Antonio, Daniel Mozos, & Hortensia Mecha. (1998). A macroscopic time and cost estimation model allowing task parallelism and hardware sharing for the codesign partitioning process. Design, Automation, and Test in Europe. 218–225. 3 indexed citations
20.
Mozos, Daniel, et al.. (1992). Heuristics for branch-and-bound global allocation. European Design Automation Conference. 334–340. 10 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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