T. Kozek

738 total citations
24 papers, 500 citations indexed

About

T. Kozek is a scholar working on Computer Networks and Communications, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, T. Kozek has authored 24 papers receiving a total of 500 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Networks and Communications, 17 papers in Artificial Intelligence and 13 papers in Electrical and Electronic Engineering. Recurrent topics in T. Kozek's work include Neural Networks Stability and Synchronization (18 papers), Neural Networks and Applications (17 papers) and Advanced Memory and Neural Computing (13 papers). T. Kozek is often cited by papers focused on Neural Networks Stability and Synchronization (18 papers), Neural Networks and Applications (17 papers) and Advanced Memory and Neural Computing (13 papers). T. Kozek collaborates with scholars based in Hungary, United States and Germany. T. Kozek's co-authors include T. Roska, Leon O. Chua, Dietrich E. Wolf, Ronald Tetzlaff, Frank Puffer, Tamás Roska, Ákos Zarándy, D.L. Vilariño, Ricardo Carmona‐Galán and Tamás Szirányi and has published in prestigious journals such as IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Multimedia and International Journal of Circuit Theory and Applications.

In The Last Decade

T. Kozek

24 papers receiving 469 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
T. Kozek Hungary 12 355 290 175 114 74 24 500
P. Szolgay Hungary 10 296 0.8× 217 0.7× 216 1.2× 88 0.8× 39 0.5× 36 390
K.R. Crounse United States 11 468 1.3× 233 0.8× 156 0.9× 157 1.4× 167 2.3× 21 572
H. Harrer Germany 11 327 0.9× 206 0.7× 292 1.7× 137 1.2× 44 0.6× 28 487
Csaba Rekeczky Hungary 13 274 0.8× 196 0.7× 216 1.2× 99 0.9× 26 0.4× 54 519
D.E. Van den Bout United States 9 98 0.3× 315 1.1× 164 0.9× 37 0.3× 32 0.4× 23 552
A. Lozowski United States 8 230 0.6× 253 0.9× 182 1.0× 16 0.1× 104 1.4× 22 478
Jun Muramatsu Japan 13 313 0.9× 205 0.7× 346 2.0× 98 0.9× 180 2.4× 72 663
Miguel Atencia Spain 12 160 0.5× 267 0.9× 106 0.6× 28 0.2× 85 1.1× 32 484
Evangelos Pikasis Greece 11 195 0.5× 171 0.6× 504 2.9× 24 0.2× 125 1.7× 33 742
Recai Kılıç Türkiye 15 243 0.7× 158 0.5× 132 0.8× 87 0.8× 446 6.0× 58 632

Countries citing papers authored by T. Kozek

Since Specialization
Citations

This map shows the geographic impact of T. Kozek'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 T. Kozek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Kozek more than expected).

Fields of papers citing papers by T. Kozek

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of T. Kozek

This figure shows the co-authorship network connecting the top 25 collaborators of T. Kozek. A scholar is included among the top collaborators of T. Kozek 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 T. Kozek. T. Kozek 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.
Szolgay, P., Ákos Zarándy, T. Roska, et al.. (2003). The computational infrastructure for cellular visual microprocessors. iii. 54–60. 1 indexed citations
2.
Mizutani, Eiji, T. Kozek, & Leon O. Chua. (2002). Road lane marker extraction by motion-detector CNNs. 1. 503–508. 3 indexed citations
3.
Kozek, T., et al.. (2002). Multi-scale image analysis on the CNN Universal Machine. 2. 69–74. 2 indexed citations
4.
Carmona‐Galán, Ricardo, S. Espejo, R. Domínguez‐Castro, et al.. (2002). A 0.5 μm CMOS CNN analog random access memory chip for massive image processing. idUS (Universidad de Sevilla). 271–276. 14 indexed citations
5.
Chua, Leon O., et al.. (2000). Morphology and autowave metric on CNN applied to bubble-debris classification. IEEE Transactions on Neural Networks. 11(6). 1385–1393. 16 indexed citations
6.
Kozek, T., et al.. (1999). An Evaluation of a Sensor Fusion System to Improve Drivers' Nighttime Detection of Road Hazards. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 43(23). 1333–1337. 11 indexed citations
7.
Carmona‐Galán, Ricardo, Á. Rodríguez‐Vázquez, S. Espejo, et al.. (1999). An 0.5-μm CMOS analog random access memory chip for TeraOPS speed multimedia video processing. IEEE Transactions on Multimedia. 1(2). 121–135. 13 indexed citations
8.
Kozek, T. & D.L. Vilariño. (1999). An Active Contour Algorithm for Continuous-Time Cellular Neural Networks. The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology. 23(2-3). 403–414. 8 indexed citations
9.
Kozek, T., et al.. (1998). Analogic Macro Code (AMC) : extended Assembly language for CNN computers : version 1.1. 1 indexed citations
10.
Kozek, T., Chai Wah Wu, Ákos Zarándy, et al.. (1997). New results and measurements related to some tasks in object-oriented dynamic image coding using CNN universal chips. IEEE Transactions on Circuits and Systems for Video Technology. 7(4). 606–614. 3 indexed citations
11.
Chua, Leon O., T. Roska, T. Kozek, & Ákos Zarándy. (1996). CNN universal chips crank up the computing power. IEEE Circuits and Devices Magazine. 12(4). 18–28. 21 indexed citations
12.
Kozek, T. & T. Roska. (1996). A DOUBLE TIME—SCALE CNN FOR SOLVING TWO-DIMENSIONAL NAVIER—STOKES EQUATIONS. International Journal of Circuit Theory and Applications. 24(1). 49–55. 10 indexed citations
13.
Kozek, T.. (1996). Constructive use of spatiotemporal dynamics in cellular neural networks. SZTAKI Publication Repository (Hungarian Academy of Sciences). 2 indexed citations
14.
Kozek, T., et al.. (1995). Smart image scanning algorithms for the CNN universal machine. SZTAKI Publication Repository (Hungarian Academy of Sciences). 17 indexed citations
15.
Kozek, T., Leon O. Chua, T. Roska, et al.. (1995). Simulating nonlinear waves and partial differential equations via CNN. II. Typical examples. IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications. 42(10). 816–820. 65 indexed citations
16.
Roska, T., Leon O. Chua, Dietrich E. Wolf, et al.. (1995). Simulating nonlinear waves and partial differential equations via CNN. I. Basic techniques. IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications. 42(10). 807–815. 122 indexed citations
17.
Roska, Tamás, et al.. (1993). Solving partial differential equations by CNN. SZTAKI Publication Repository (Hungarian Academy of Sciences). 17 indexed citations
18.
Kozek, T., T. Roska, & Leon O. Chua. (1993). Genetic algorithm for CNN template learning. IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications. 40(6). 392–402. 130 indexed citations
19.
Kozek, T., et al.. (1992). Genetic algorithm for CNN template learning. (Memo UCB/ERL No. M92/82.). SZTAKI Publication Repository (Hungarian Academy of Sciences). 1 indexed citations
20.
Roska, T., et al.. (1992). A digital multiprocessor hardware accelerator board for cellular neural networks: CNN‐HAC. International Journal of Circuit Theory and Applications. 20(5). 589–599. 23 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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