François Panneton
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Computational Theory and Mathematics top 5%
- Computer Networks and Communications top 10%
- Management Science and Operations Research
- Topics
- Chaos-based Image/Signal Encryption (9 papers)Algorithms and Data Compression (6 papers)Cryptography and Residue Arithmetic (4 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputational Theory and MathematicsArtificial Intelligence
In The Last Decade
François Panneton
11 papers receiving 296 citations
Peers
Comparison fields: 5 of 66
- Computer Vision and Pattern Recognition 176
- Artificial Intelligence 175
- Computational Theory and Mathematics 123
- Computer Networks and Communications 60
- Management Science and Operations Research 30
Countries citing papers authored by François Panneton
This map shows the geographic impact of François Panneton'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 François Panneton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites François Panneton more than expected).
Fields of papers citing papers by François Panneton
This network shows the impact of papers produced by François Panneton. 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 François Panneton. The network helps show where François Panneton may publish in the future.
Co-authorship network of co-authors of François Panneton
This figure shows the co-authorship network connecting the top 25 collaborators of François Panneton. A scholar is included among the top collaborators of François Panneton 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 François Panneton. François Panneton is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 44 | |
| 2 | 3 | |
| 3 | Efficient Jump Ahead for 2 -Linear Random Number Generators | 5 |
| 4 | 141 | |
| 5 | 14 | |
| 6 | 23 | |
| 7 | 59 | |
| 8 | Improved Long-Period Generators Based on Linear Recurrences Modulo 2 | 1 |
| 9 | Random Number Generators Based on Linear Recurrences in F 2 w | 9 |
| 10 | 9 | |
| 11 | 13 |
About François Panneton
François Panneton is a scholar working on Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Artificial Intelligence, having authored 11 papers that have together received 321 indexed citations. Recurring topics across this work include Chaos-based Image/Signal Encryption (9 papers), Algorithms and Data Compression (6 papers) and Cryptography and Residue Arithmetic (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (176 citations), Computational Theory and Mathematics (123 citations) and Artificial Intelligence (175 citations). François Panneton has collaborated with scholars based in Canada and Japan. Frequent co-authors include Pierre L’Ecuyer, Makoto Matsumoto and Takuji Nishimura. Their work appears in journals such as ACM Transactions on Mathematical Software, Mathematics and Computers in Simulation and INFORMS journal on computing.
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.