Diego Carrera

23 papers receiving 294 citations

Peers

Diego Carrera
Comparison fields: 5 of 80
  • Industrial and Manufacturing Engineering 71
  • Artificial Intelligence 141
  • Computer Vision and Pattern Recognition 71
  • Media Technology 28
  • Signal Processing 31
Replace Xizhou Pan with:
Xizhou Pan China
L. Jani Anbarasi India
Xiaofeng Zhang China
Chandranath Adak India
Yiqi Zhong China
Gouranga Charan United States
Shichao Kan China
Baidya Nath Saha Canada
Ue-Hwan Kim South Korea
Zunkai Huang China
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Citations per year

Countries citing papers authored by Diego Carrera

Since Specialization
Citations

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

Fields of papers citing papers by Diego Carrera

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Diego Carrera, 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 Diego Carrera Line = papers co-authored together Diego Carrera links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201685
2 201838
3 201535
4 201421
5
QuantTree: Histograms for change detection in multivariate data streams
201817
6 202116
7 201614
8 202014
9 201513
10 197811
11 20178
12 20227
13 20177
14 20184
15 20133
16 20232
17 20172
18 20222
19 20192
20 20222

About Diego Carrera

Diego Carrera is a scholar working on Artificial Intelligence, Computational Mechanics, Electrical and Electronic Engineering, Biomedical Engineering and Cardiology and Cardiovascular Medicine, having authored 30 papers that have together received 309 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (9 papers), Sparse and Compressive Sensing Techniques (5 papers), Industrial Vision Systems and Defect Detection (4 papers), Data Stream Mining Techniques (4 papers), ECG Monitoring and Analysis (4 papers), Image Processing Techniques and Applications (3 papers), Integrated Circuits and Semiconductor Failure Analysis (3 papers) and Time Series Analysis and Forecasting (3 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (71 citations), Artificial Intelligence (141 citations), Computer Vision and Pattern Recognition (71 citations), Media Technology (28 citations) and Signal Processing (31 citations). Diego Carrera has collaborated with scholars based in Italy, United States and Finland. Frequent co-authors include Giacomo Boracchi, Pasqualina Fragneto, Brendt Wohlberg, Ettore Lanzarone, Beatrice Rossi, Alessandro Foi, Cristiano Cervellera, Danilo Macciò, P.M. Mannucci and L. Mannucci. Their work appears in journals such as Pattern Recognition, Thrombosis and Haemostasis, IEEE Transactions on Industrial Informatics, IEEE Transactions on Neural Networks and Learning Systems and EURASIP Journal on Wireless Communications and Networking.

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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