Miguel Figueroa
- Electrical and Electronic Engineering top 10%
- Artificial Intelligence top 5%
- Biomedical Engineering
- Hardware and Architecture top 5%
- Computer Vision and Pattern Recognition top 5%
- Co-authors
- C. DiorioDaniel HsuJorge E. PezoaGonzalo CarvajalL. JóźwiakJosé EspinozaNadia NedjahJavier Muñoz
- Topics
- CCD and CMOS Imaging Sensors (21 papers)Analog and Mixed-Signal Circuit Design (16 papers)Infrared Target Detection Methodologies (15 papers)
- Partner nations
- ChileUnited StatesFrance
In The Last Decade
Miguel Figueroa
85 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 117
- Electrical and Electronic Engineering 468
- Artificial Intelligence 299
- Biomedical Engineering 169
- Hardware and Architecture 167
- Computer Vision and Pattern Recognition 164
Countries citing papers authored by Miguel Figueroa
This map shows the geographic impact of Miguel Figueroa'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 Miguel Figueroa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Miguel Figueroa more than expected).
Fields of papers citing papers by Miguel Figueroa
This network shows the impact of papers produced by Miguel Figueroa. 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 Miguel Figueroa. The network helps show where Miguel Figueroa may publish in the future.
Co-authorship network of co-authors of Miguel Figueroa
This figure shows the co-authorship network connecting the top 25 collaborators of Miguel Figueroa. A scholar is included among the top collaborators of Miguel Figueroa 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 Miguel Figueroa. Miguel Figueroa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 4 | |
| 5 | 1 | |
| 6 | 16 | |
| 7 | 20 | |
| 8 | 8 | |
| 9 | 11 | |
| 10 | 1 | |
| 11 | 3 | |
| 12 | 6 | |
| 13 | 17 | |
| 14 | 34 | |
| 15 | Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors | 18 |
| 16 | Subspace-Based Face Recognition in Analog VLSI | 5 |
| 17 | Segunda actualización del consenso de la Sociedad Española de Reumatología sobre la terapia biológica en la artritis reumatoide | 5 |
| 18 | Adaptive Quantization and Density Estimation in Silicon | 5 |
| 19 | Field-Programmable Learning Arrays | 4 |
| 20 | A Silicon Primitive for Competitive Learning | 10 |
About Miguel Figueroa
Miguel Figueroa is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Hardware and Architecture, having authored 92 papers that have together received 1.3k indexed citations. Recurring topics across this work include CCD and CMOS Imaging Sensors (21 papers), Analog and Mixed-Signal Circuit Design (16 papers) and Infrared Target Detection Methodologies (15 papers). The work is most often cited by research in Hardware and Architecture (167 citations), Computational Mathematics (10 citations) and Artificial Intelligence (299 citations). Miguel Figueroa has collaborated with scholars based in Chile, United States and France. Frequent co-authors include C. Diorio, Daniel Hsu, Jorge E. Pezoa, Gonzalo Carvajal, L. Jóźwiak, José Espinoza, Nadia Nedjah, Javier Muñoz, David Hsu and Chris Fisher. Their work appears in journals such as Physical Review Letters, Nature Communications and NeuroImage.
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.