Otto Debals

615 total citations
21 papers, 403 citations indexed

About

Otto Debals is a scholar working on Computational Mathematics, Signal Processing and Computational Mechanics. According to data from OpenAlex, Otto Debals has authored 21 papers receiving a total of 403 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computational Mathematics, 12 papers in Signal Processing and 7 papers in Computational Mechanics. Recurrent topics in Otto Debals's work include Tensor decomposition and applications (15 papers), Blind Source Separation Techniques (12 papers) and Advanced Adaptive Filtering Techniques (4 papers). Otto Debals is often cited by papers focused on Tensor decomposition and applications (15 papers), Blind Source Separation Techniques (12 papers) and Advanced Adaptive Filtering Techniques (4 papers). Otto Debals collaborates with scholars based in Belgium, China and United States. Otto Debals's co-authors include Lieven De Lathauwer, Nico Vervliet, Laurent Sorber, Marc Van Barel, Ignat Domanov, Sabine Van Huffel, Qiu‐Hua Lin, Ivan Markovsky, Xiao‐Feng Gong and Diana M. Sima and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Biomedical Engineering and IEEE Signal Processing Magazine.

In The Last Decade

Otto Debals

19 papers receiving 395 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Otto Debals Belgium 9 268 185 157 67 60 21 403
Nico Vervliet Belgium 9 278 1.0× 100 0.5× 125 0.8× 74 1.1× 68 1.1× 27 372
Dimitri Nion Belgium 11 328 1.2× 467 2.5× 215 1.4× 49 0.7× 72 1.2× 16 734
Kim Batselier Hong Kong 14 212 0.8× 59 0.3× 153 1.0× 38 0.6× 87 1.4× 51 457
Mariya Ishteva Belgium 9 175 0.7× 51 0.3× 98 0.6× 20 0.3× 52 0.9× 26 301
Zhen Long China 10 213 0.8× 52 0.3× 207 1.3× 65 1.0× 97 1.6× 20 436
Rémy Boyer France 10 119 0.4× 77 0.4× 57 0.4× 25 0.4× 29 0.5× 22 415
Zhening Li United Kingdom 12 255 1.0× 54 0.3× 127 0.8× 16 0.2× 42 0.7× 28 444
Bijan Afsari United States 10 27 0.1× 75 0.4× 60 0.4× 46 0.7× 65 1.1× 20 443
Luc Deneire France 15 55 0.2× 105 0.6× 63 0.4× 7 0.1× 59 1.0× 72 883
Yohann de Castro France 8 6 0.0× 68 0.4× 220 1.4× 46 0.7× 52 0.9× 24 347

Countries citing papers authored by Otto Debals

Since Specialization
Citations

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

Fields of papers citing papers by Otto Debals

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Otto Debals

This figure shows the co-authorship network connecting the top 25 collaborators of Otto Debals. A scholar is included among the top collaborators of Otto Debals 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 Otto Debals. Otto Debals 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.
Vervliet, Nico, Otto Debals, & Lieven De Lathauwer. (2019). Exploiting Efficient Representations in Large-Scale Tensor Decompositions. SIAM Journal on Scientific Computing. 41(2). A789–A815. 19 indexed citations
2.
Vervliet, Nico, et al.. (2018). Linear systems with a canonical polyadic decomposition constrained solution: Algorithms and applications. Numerical Linear Algebra with Applications. 25(6). 17 indexed citations
3.
Debals, Otto, et al.. (2018). Tensor-Based Method for Residual Water Suppression in $^1$H Magnetic Resonance Spectroscopic Imaging. IEEE Transactions on Biomedical Engineering. 66(2). 584–594. 5 indexed citations
4.
Debals, Otto, et al.. (2018). Coupled and Incomplete Tensors in Blind System Identification. IEEE Transactions on Signal Processing. 66(23). 6137–6147. 1 indexed citations
5.
Vervliet, Nico, et al.. (2017). Face recognition as a kronecker product equation. Lirias (KU Leuven). 1–5. 6 indexed citations
6.
Debals, Otto. (2017). Tensorization and Applications in Blind Source Separation.
7.
Debals, Otto, et al.. (2017). Tensor-Based Large-Scale Blind System Identification Using Segmentation. IEEE Transactions on Signal Processing. 65(21). 5770–5784. 17 indexed citations
8.
Debals, Otto, et al.. (2017). Tensor Similarity in Two Modes. IEEE Transactions on Signal Processing. 66(5). 1273–1285.
9.
Debals, Otto, Marc Van Barel, & Lieven De Lathauwer. (2017). Nonnegative Matrix Factorization Using Nonnegative Polynomial Approximations. IEEE Signal Processing Letters. 24(7). 948–952. 8 indexed citations
10.
Vervliet, Nico, et al.. (2017). Irregular heartbeat classification using Kronecker Product Equations. PubMed. 2017. 438–441. 8 indexed citations
11.
Gong, Xiao‐Feng, Qiu‐Hua Lin, Otto Debals, Nico Vervliet, & Lieven De Lathauwer. (2016). Coupled rank-(Lm, Ln,) block term decomposition by coupled block simultaneous generalized Schur decomposition. Lirias (KU Leuven). 2554–2558. 4 indexed citations
12.
Debals, Otto, et al.. (2016). A Tensor-Based Method for Large-Scale Blind Source Separation Using Segmentation. IEEE Transactions on Signal Processing. 65(2). 346–358. 86 indexed citations
13.
Vervliet, Nico, Otto Debals, & Lieven De Lathauwer. (2016). Tensorlab 3.0 — Numerical optimization strategies for large-scale constrained and coupled matrix/tensor factorization. Lirias (KU Leuven). 1733–1738. 60 indexed citations
14.
Debals, Otto, et al.. (2016). A tensor-based method for large-scale blind system identification using segmentation. Lirias (KU Leuven). 2015–2019. 3 indexed citations
15.
Debals, Otto, Marc Van Barel, & Lieven De Lathauwer. (2015). Blind signal separation of rational functions using Löwner-based tensorization. 4145–4149. 4 indexed citations
16.
Debals, Otto, et al.. (2015). A novel deterministic method for large-scale blind source separation. Lirias (KU Leuven). 1. 1890–1894. 3 indexed citations
17.
Debals, Otto, Marc Van Barel, & Lieven De Lathauwer. (2015). Löwner-Based Blind Signal Separation of Rational Functions With Applications. IEEE Transactions on Signal Processing. 64(8). 1909–1918. 36 indexed citations
18.
Vervliet, Nico, Otto Debals, Laurent Sorber, Marc Van Barel, & Lieven De Lathauwer. (2014). Structured data fusion using Tensorlab : a demonstration. 3 indexed citations
19.
Vervliet, Nico, Otto Debals, Laurent Sorber, & Lieven De Lathauwer. (2014). Breaking the Curse of Dimensionality using Decompositions of Incomplete Tensors. 4 indexed citations
20.
Vervliet, Nico, Otto Debals, Laurent Sorber, & Lieven De Lathauwer. (2014). Breaking the Curse of Dimensionality Using Decompositions of Incomplete Tensors: Tensor-based scientific computing in big data analysis. IEEE Signal Processing Magazine. 31(5). 71–79. 117 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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