Diego Carrera

577 citations
38 papers · 400 · h-index 12

Impact in

Papers in

Diego Carrera

32 papers receiving 380 citations

Peers

Diego Carrera
Comparison fields: 5 of 81
  • Industrial and Manufacturing Engineering 95
  • Artificial Intelligence 190
  • Computer Vision and Pattern Recognition 94
  • Signal Processing 44
  • Media Technology 34
Replace Hehua Zhang with:
Hehua Zhang China
Yihao Xue China
Xizhou Pan China
Baidya Nath Saha Canada
Juan-Carlos Pérez-Cortés Spain
Hang Zhang United States
Seyyed Mohammad Razavi Iran
A. Vasuki India
Ue-Hwan Kim South Korea
Qi Dong China
Diego Carrera relative to Hehua Zhang China Hehua Zhang's profile →
Citations per field
00.5×3.4×
Hehua Zhang · 1×
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 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201699
2 201839
3 201536
4 201428
5
QuantTree: Histograms for change detection in multivariate data streams
201820
6 202120
7 202018
8 201616
9 201513
10 201913
11 197812
12 201611
13 20179
14 20178
15 20227
16 20216
17 20185
18 20175
19 20225
20 20164

About Diego Carrera

Diego Carrera is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Signal Processing, Computational Mechanics and Cardiology and Cardiovascular Medicine, having authored 38 papers that have together received 400 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (11 papers), Data Stream Mining Techniques (7 papers), Sparse and Compressive Sensing Techniques (5 papers), Industrial Vision Systems and Defect Detection (5 papers), Time Series Analysis and Forecasting (5 papers), ECG Monitoring and Analysis (5 papers), Image Processing Techniques and Applications (4 papers) and Integrated Circuits and Semiconductor Failure Analysis (4 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (95 citations), Artificial Intelligence (190 citations), Computer Vision and Pattern Recognition (94 citations), Signal Processing (44 citations) and Media Technology (34 citations). Diego Carrera has collaborated with scholars based in Italy, United States and Switzerland. Frequent co-authors include Giacomo Boracchi, Pasqualina Fragneto, Beatrice Rossi, Ettore Lanzarone, Brendt Wohlberg, Alessandro Foi, Danilo Macciò, Cristiano Cervellera, P.M. Mannucci and L. Mannucci. Their work appears in journals such as Pattern Recognition, Thrombosis and Haemostasis, Big Data Research, IEEE Transactions on Knowledge and Data Engineering and IEEE Signal Processing Letters.

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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