O. De Feo

1.4k citations
58 papers · 1.0k · h-index 16

Impact in

Papers in

O. De Feo

50 papers receiving 957 citations

Peers

O. De Feo
Comparison fields: 5 of 91
  • Statistical and Nonlinear Physics 365
  • Cognitive Neuroscience 290
  • Computer Networks and Communications 312
  • Public Health, Environmental and Occupational Health 125
  • Biophysics 21
Replace Yanmei Kang with:
Yanmei Kang China
Tanmoy Banerjee India
Jonathan Bell United States
Yuanhua Qiao China
Hil G. E. Meijer Netherlands
Allan R. Willms Canada
Chittaranjan Hens India
Khashayar Pakdaman France
Marco Thiel United Kingdom
Manish Dev Shrimali India
O. De Feo relative to Yanmei Kang China Yanmei Kang's profile →
Citations per field
00.5×5.3×
Yanmei Kang · 1×
Citations per year

Countries citing papers authored by O. De Feo

Since Specialization
Citations

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

Fields of papers citing papers by O. De Feo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by O. De Feo. 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 O. De Feo. The network helps show where O. De Feo may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1999206
2 2005110
3 2001100
4 200060
5 200757
6 201556
7 200450
8 200844
9 199838
10 200930
11 200329
12 200720
13 199919
14 201519
15 201117
16 201015
17 200013
18 200413
19 200413
20 200711

About O. De Feo

O. De Feo is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications, Artificial Intelligence, Cognitive Neuroscience and Control and Systems Engineering, having authored 58 papers that have together received 1.0k indexed citations. Recurring topics across this work include Chaos control and synchronization (18 papers), Nonlinear Dynamics and Pattern Formation (12 papers), Neural Networks and Applications (11 papers), Neural dynamics and brain function (10 papers), Control Systems and Identification (8 papers), stochastic dynamics and bifurcation (5 papers), Complex Systems and Time Series Analysis (5 papers) and Radio Frequency Integrated Circuit Design (4 papers). The work is most often cited by research in Statistical and Nonlinear Physics (365 citations), Cognitive Neuroscience (290 citations), Computer Networks and Communications (312 citations), Public Health, Environmental and Occupational Health (125 citations) and Biophysics (21 citations). O. De Feo has collaborated with scholars based in Switzerland, Ireland and Italy. Frequent co-authors include Michael Peter Kennedy, G.M. Maggio, S. Rinaldi, Maria G. Knyazeva, Cristian Carmeli, Yuri A. Kuznetsov, Giorgio M. Innocenti, Howard Takiff, Saeid Daneshgar and Valeri A. Makarov. Their work appears in journals such as International Journal of Bifurcation and Chaos, Journal of Diabetes Science and Technology, Chaos An Interdisciplinary Journal of Nonlinear Science, IEEE Transactions on Circuits and Systems I Regular Papers and PLoS ONE.

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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