D. Hush

36 papers receiving 1.7k citations

Hit Papers

Progress in supervised neural networks 1993 · 942 citations
9421993202620042015250500750

Peers

D. Hush
Comparison fields: 5 of 134
  • Media Technology 281
  • Signal Processing 312
  • Artificial Intelligence 664
  • Computer Vision and Pattern Recognition 417
  • Control and Systems Engineering 279
Replace M. N. S. Swamy with:
M. N. S. Swamy Canada
Mark E. Oxley United States
Jian Lü China
Wenli Xu China
Filip Mulier United States
Paul Honeiné France
Erkan Beşdok Türkiye
Min Gan China
Erçan E. Kuruoğlu Italy
Hamid Hassanpour Iran
D. Hush relative to M. N. S. Swamy Canada M. N. S. Swamy's profile →
Citations per field
00.5×1.5×1.8×
M. N. S. Swamy · 1×
Citations per year

Countries citing papers authored by D. Hush

Since Specialization
Citations

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

Fields of papers citing papers by D. Hush

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Progress in supervised neural networks
Hit paper breakdown →
1993942
2 2002234
3 2006147
4 1986101
5
Query by image example: The CANDID approach
199597
6 199595
7 199242
8 199838
9 199314
10 200313
11 198712
12 199410
13 200510
14 199710
15
Statistical modeling of targets and clutter in single-look non-polarimetric SAR imagery
19989
16 20027
17 20026
18 20075
19 20035
20 20035

About D. Hush

D. Hush is a scholar working on Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics and Aerospace Engineering, having authored 42 papers that have together received 1.8k indexed citations. Recurring topics across this work include Neural Networks and Applications (16 papers), Blind Source Separation Techniques (6 papers), Advanced SAR Imaging Techniques (6 papers), Fuzzy Logic and Control Systems (5 papers), Structural Health Monitoring Techniques (5 papers), Advanced Adaptive Filtering Techniques (5 papers), Fault Detection and Control Systems (4 papers) and Speech and Audio Processing (4 papers). The work is most often cited by research in Media Technology (281 citations), Signal Processing (312 citations), Artificial Intelligence (664 citations), Computer Vision and Pattern Recognition (417 citations) and Control and Systems Engineering (279 citations). D. Hush has collaborated with scholars based in United States, South Korea and Spain. Frequent co-authors include B.G. Horne, Carol Wood, Patrick Kelly, Clint Scovel, Ingo Steinwart, Michael Cannon, T. M. Cannon, N. Ahmed, S.D. Stearns and Radu‐Codruţ David. Their work appears in journals such as IEEE Signal Processing Magazine, Journal of Optimization Theory and Applications, IEEE Transactions on Aerospace and Electronic Systems, The Journal of the Acoustical Society of America and IEEE Transactions on Information Theory.

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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