Mario Biey

529 citations
63 papers · 418 · h-index 10

Impact in

Papers in

Mario Biey

57 papers receiving 393 citations

Peers

Mario Biey
Comparison fields: 5 of 70
  • Statistical and Nonlinear Physics 187
  • Computer Networks and Communications 196
  • Cognitive Neuroscience 81
  • Signal Processing 47
  • Computational Theory and Mathematics 41
Replace Recai Kılıç with:
Recai Kılıç Türkiye
Yibo Zhao China
Kais Bouallegue Tunisia
Longkun Tang China
T.G. Clarkson United Kingdom
Giacomo Baggio Italy
Yuankun Xue United States
Masaharu Adachi Japan
J.S. Armand Eyebe Fouda Cameroon
Ronny Meir Israel
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Citations per field
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Citations per year

Countries citing papers authored by Mario Biey

Since Specialization
Citations

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

Fields of papers citing papers by Mario Biey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 63 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201090
2 200829
3 198527
4 200626
5 200422
6 200617
7 200216
8 200312
9 197810
10 19979
11
Analysis of Chaotic Signals in the Time-Frequency Plane
19999
12 20118
13 19947
14 20097
15 20037
16 19776
17 19776
18
Information Processing in Networks of Coupled Hindmarsh-Rose Neurons
20066
19 20176
20 19796

About Mario Biey

Mario Biey is a scholar working on Computer Networks and Communications, Statistical and Nonlinear Physics, Signal Processing, Electrical and Electronic Engineering and Biomedical Engineering, having authored 63 papers that have together received 418 indexed citations. Recurring topics across this work include Nonlinear Dynamics and Pattern Formation (22 papers), Neural Networks Stability and Synchronization (19 papers), stochastic dynamics and bifurcation (16 papers), Digital Filter Design and Implementation (15 papers), Neural dynamics and brain function (11 papers), Advanced Adaptive Filtering Techniques (9 papers), Chaos control and synchronization (8 papers) and Analog and Mixed-Signal Circuit Design (7 papers). The work is most often cited by research in Statistical and Nonlinear Physics (187 citations), Computer Networks and Communications (196 citations), Cognitive Neuroscience (81 citations), Signal Processing (47 citations) and Computational Theory and Mathematics (41 citations). Mario Biey has collaborated with scholars based in Italy, United States and Switzerland. Frequent co-authors include Ljupčo Kocarev, Igor Mishkovski, A. Premoli, Marco Gilli, Marco Righero, Fernando Corinto, Roberto Merletti, Giuseppe Da Prato, Alon Ascoli and Ronald Tetzlaff. Their work appears in journals such as International Journal of Circuit Theory and Applications, Electronics Letters, Chaos Solitons & Fractals, IEEE Transactions on Biomedical Engineering and Communications in Nonlinear Science and Numerical Simulation.

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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