Victor DeBrunner

1.8k total citations
160 papers, 1.3k citations indexed

About

Victor DeBrunner is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Victor DeBrunner has authored 160 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Signal Processing, 71 papers in Computer Vision and Pattern Recognition and 64 papers in Computational Mechanics. Recurrent topics in Victor DeBrunner's work include Image and Signal Denoising Methods (54 papers), Advanced Adaptive Filtering Techniques (53 papers) and Digital Filter Design and Implementation (44 papers). Victor DeBrunner is often cited by papers focused on Image and Signal Denoising Methods (54 papers), Advanced Adaptive Filtering Techniques (53 papers) and Digital Filter Design and Implementation (44 papers). Victor DeBrunner collaborates with scholars based in United States, China and Sweden. Victor DeBrunner's co-authors include Dayong Zhou, Linda S. DeBrunner, Tomasz Przebinda, Murad Özaydın, Valliappa Lakshmanan, Robert M. Rabin, A.A. Beex, Joseph Havlicek, Sebastián M. Torres and Minh C. Ta and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Information Theory and IEEE Transactions on Image Processing.

In The Last Decade

Victor DeBrunner

141 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Victor DeBrunner United States 17 547 533 370 309 177 160 1.3k
Rui Seara Brazil 17 613 1.1× 687 1.3× 336 0.9× 220 0.7× 90 0.5× 134 1.3k
C.W. Therrien United States 14 370 0.7× 198 0.4× 343 0.9× 223 0.7× 102 0.6× 57 1.1k
J.C.M. Bermudez Brazil 25 1.5k 2.7× 1.7k 3.2× 503 1.4× 230 0.7× 180 1.0× 160 2.4k
Siliang Wu China 25 907 1.7× 272 0.5× 147 0.4× 635 2.1× 204 1.2× 156 1.9k
Shuxue Ding Japan 17 351 0.6× 306 0.6× 325 0.9× 282 0.9× 166 0.9× 127 1.1k
Javier Vía Spain 20 738 1.3× 376 0.7× 225 0.6× 492 1.6× 56 0.3× 106 1.6k
Fuliang Yin China 18 552 1.0× 254 0.5× 288 0.8× 339 1.1× 90 0.5× 141 1.2k
J.H. Husøy Norway 13 445 0.8× 703 1.3× 1.5k 4.0× 90 0.3× 278 1.6× 39 2.4k
T.I. Laakso Finland 20 1.6k 2.9× 919 1.7× 631 1.7× 764 2.5× 482 2.7× 89 2.5k
Shane F. Cotter United States 14 1.0k 1.9× 1.3k 2.5× 429 1.2× 514 1.7× 443 2.5× 24 2.1k

Countries citing papers authored by Victor DeBrunner

Since Specialization
Citations

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

Fields of papers citing papers by Victor DeBrunner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Victor DeBrunner

This figure shows the co-authorship network connecting the top 25 collaborators of Victor DeBrunner. A scholar is included among the top collaborators of Victor DeBrunner based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Victor DeBrunner. Victor DeBrunner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
DeBrunner, Linda S. & Victor DeBrunner. (2023). Engaging Students in an Introductory Circuits Course. 36. 1–5. 2 indexed citations
3.
DeBrunner, Victor, et al.. (2021). A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors. SHILAP Revista de lepidopterología. 2(4). 688–705. 4 indexed citations
4.
DeBrunner, Victor, et al.. (2019). Hardware Implementation of Discrete Hirschman Transform Convolution Using Distributed Arithmetic. 1587–1590. 3 indexed citations
5.
6.
DeBrunner, Linda S., et al.. (2015). A low power radix-2 FFT accelerator for FPGA. 447–451. 11 indexed citations
7.
DeBrunner, Linda S., et al.. (2012). Field testing of indirect displacement estimation using accelerometers. 1868–1872. 7 indexed citations
8.
DeBrunner, Linda S., et al.. (2010). Indirect Displacements Measurement Using Accelerometers and High-Resolution Signal Modeling. 2 indexed citations
9.
DeBrunner, Victor, et al.. (2008). Hirschman optimal transform DFT block LMS algorithm. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. 3805–3808.
10.
Chicken, Eric, et al.. (2007). Sigma-Sampling Wavelet Denoising for Structural Health Monitoring. 3 indexed citations
11.
Zhou, Dayong & Victor DeBrunner. (2007). Efficient Adaptive Nonlinear Filters for Nonlinear Active Noise Control. IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications. 54(3). 669–681. 138 indexed citations
12.
Zhou, Dayong & Victor DeBrunner. (2006). A New Rapid Frequency-Domain Adaptation of Causal FIR Filters. 332–335. 4 indexed citations
13.
Zhou, Dayong & Victor DeBrunner. (2004). A novel adaptive nonlinear predistorter based on the direct learning algorithm. 2362–2366 Vol.4. 13 indexed citations
14.
Zhou, Dongfang & Victor DeBrunner. (2004). A simplified adaptive nonlinear predistorter for high power amplifiers based on the direct learning algorithm. 4. iv–1037. 5 indexed citations
15.
DeBrunner, Victor, et al.. (2003). Combining FIR filters and artificial neural networks to model stochastic processes. 1. 333–336. 1 indexed citations
16.
17.
DeBrunner, Victor, et al.. (2002). Lapped multiple bases realizations for the transform coding of still images. 2. 943–947. 3 indexed citations
18.
DeBrunner, Victor, et al.. (2002). Sensitivity and learning of two digital artificial neural network structures. 3. 445–448. 3 indexed citations
19.
Lakshmanan, Valliappa & Victor DeBrunner. (2001). A hierarchical, multiscale texture segmentation algorithm for real-world scenes. SHAREOK (University of Oklahoma). 7 indexed citations
20.
DeBrunner, Victor. (1992). The design of low sensitivity digital filters using multi-criterion optimization strategies. 317–320 vol.4. 5 indexed citations

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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