Danilo P. Mandic

24.7k citations
548 papers · 17.2k indexed · 8 hit papers · h-index 65

Impact in

Papers in

Danilo P. Mandic

529 papers receiving 16.7k citations

Hit Papers

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction 2024 · 43 citations
432001202620092017250500750

Peers

Danilo P. Mandic
Comparison fields: 5 of 193
  • Computational Mathematics 1.3k
  • Signal Processing 4.8k
  • Cognitive Neuroscience 3.6k
  • Computational Mechanics 3.6k
  • Control and Systems Engineering 2.6k
Replace Sabine Van Huffel with:
Sabine Van Huffel Belgium
Andrzej Cichocki Japan
Pierre Comon France
Yi Ma United States
J.-F. Cardoso France
John Wright United States
Pierre Vandergheynst Switzerland
Michael Elad Israel
Lieven De Lathauwer Belgium
Erkki Oja Finland
Danilo P. Mandic relative to Sabine Van Huffel Belgium Sabine Van Huffel's profile →
Citations per field
00.5×3.2×
Sabine Van Huffel · 1×
Citations per year

Countries citing papers authored by Danilo P. Mandic

Since Specialization
Citations

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

Fields of papers citing papers by Danilo P. Mandic

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Danilo P. Mandic. 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 Danilo P. Mandic. The network helps show where Danilo P. Mandic may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
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12 202116
13 20195
14 201811
15 201738
16 20172
17 201682
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19 201114
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A class of adaptively regularised PNLMS algorithms
20070

About Danilo P. Mandic

Danilo P. Mandic is a scholar working on Computational Mathematics, Signal Processing, Computational Mechanics, Artificial Intelligence and Cognitive Neuroscience, having authored 548 papers that have together received 17.2k indexed citations. Recurring topics across this work include Blind Source Separation Techniques (172 papers), Advanced Adaptive Filtering Techniques (137 papers), Neural Networks and Applications (101 papers), EEG and Brain-Computer Interfaces (83 papers), Image and Signal Denoising Methods (57 papers), Speech and Audio Processing (53 papers), Target Tracking and Data Fusion in Sensor Networks (37 papers) and Neural dynamics and brain function (37 papers). The work is most often cited by research in Computational Mathematics (1.3k citations), Signal Processing (4.8k citations), Cognitive Neuroscience (3.6k citations), Computational Mechanics (3.6k citations) and Control and Systems Engineering (2.6k citations). Danilo P. Mandic has collaborated with scholars based in United Kingdom, United States and China. Frequent co-authors include Clive Cheong Took, Jonathon A. Chambers, Naveed ur Rehman, David Looney, Yili Xia, Vanessa Su Lee Goh, Andrzej Cichocki, Mosabber Uddin Ahmed, Preben Kidmose and Anh Huy Phan. Their work appears in journals such as IEEE Transactions on Signal Processing, Signal Processing, IEEE Signal Processing Letters, IEEE Signal Processing Magazine and IEEE Transactions on Neural Networks and Learning Systems.

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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