Daniel M. Muñoz

57 papers receiving 432 citations

Peers

Daniel M. Muñoz
Comparison fields: 5 of 58
  • Control and Systems Engineering 148
  • Hardware and Architecture 39
  • Computational Theory and Mathematics 88
  • Artificial Intelligence 153
  • Computer Vision and Pattern Recognition 93
Replace Daniele Giardino with:
Daniele Giardino Italy
Chung-Hao Huang Taiwan
Theodore W. Manikas United States
Tan Zhang China
Jonathan DeCastro United States
Leena Vachhani India
Saleh Zein-Sabatto United States
Humberto Martínez Barberá Spain
Xingfa Shen China
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Citations per year

Countries citing papers authored by Daniel M. Muñoz

Since Specialization
Citations

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

Fields of papers citing papers by Daniel M. Muñoz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Daniel M. Muñoz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel M. Muñoz Line = papers co-authored together Daniel M. Muñoz links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 70 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201041
2 201030
3 200826
4 201325
5 201025
6 200922
7 200917
8 200914
9 201314
10 201014
11 201211
12 201710
13 20189
14 20139
15 20229
16 20219
17 20169
18 20159
19 20068
20 20118

About Daniel M. Muñoz

Daniel M. Muñoz is a scholar working on Control and Systems Engineering, Electrical and Electronic Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 70 papers that have together received 454 indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (8 papers), Metaheuristic Optimization Algorithms Research (8 papers), Robot Manipulation and Learning (7 papers), Numerical Methods and Algorithms (6 papers), CCD and CMOS Imaging Sensors (6 papers), Robotic Path Planning Algorithms (5 papers), Advanced Multi-Objective Optimization Algorithms (5 papers) and Elevator Systems and Control (5 papers). The work is most often cited by research in Control and Systems Engineering (148 citations), Hardware and Architecture (39 citations), Computational Theory and Mathematics (88 citations), Artificial Intelligence (153 citations) and Computer Vision and Pattern Recognition (93 citations). Daniel M. Muñoz has collaborated with scholars based in Brazil, Colombia and Germany. Frequent co-authors include Carlos H. Llanos, Maurício Ayala-Rincón, Leandro dos Santos Coelho, Janier Arias-García, Helon Vicente Hultmann Ayala, José Maurício Santos Torres da Motta, Geovany A. Borges, Ricardo Pezzuol Jacobi, Larissa H. Oliveira and Diana Mendes. Their work appears in journals such as Engineering Applications of Artificial Intelligence, Mechanical Systems and Signal Processing, Journal of Energy Storage, Analog Integrated Circuits and Signal Processing and Evolutionary Computation.

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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