Andrew M. Fraser

6.4k total citations · 1 hit paper
22 papers, 4.6k citations indexed

About

Andrew M. Fraser is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Aerospace Engineering. According to data from OpenAlex, Andrew M. Fraser has authored 22 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Statistical and Nonlinear Physics and 6 papers in Aerospace Engineering. Recurrent topics in Andrew M. Fraser's work include Chaos control and synchronization (6 papers), Neural Networks and Applications (5 papers) and Infrared Target Detection Methodologies (4 papers). Andrew M. Fraser is often cited by papers focused on Chaos control and synchronization (6 papers), Neural Networks and Applications (5 papers) and Infrared Target Detection Methodologies (4 papers). Andrew M. Fraser collaborates with scholars based in United States. Andrew M. Fraser's co-authors include Harry L. Swinney, Hermann Haken, James McNames, Bernard R. Foy, James Theiler, Reid Porter, Don Hush, Nicolas Hengartner, Larry J. Schultz and A. Klimenko and has published in prestigious journals such as IEEE Transactions on Information Theory, IEEE Transactions on Image Processing and Optics Express.

In The Last Decade

Andrew M. Fraser

21 papers receiving 4.3k citations

Hit Papers

Independent coordinates for strange attractors from mutua... 1986 2026 1999 2012 1986 1000 2.0k 3.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Independent coordinates for strange attractors from mutual information
    1986 3.4k citations Andrew M. Fraser, Harry L. Swinney Physical review. A, General physics profile →

Peers

Andrew M. Fraser
Matthew B. Kennel United States
Martin Casdagli United States
Bernd Pompe Germany
Christoph Bandt Germany
Sylvie Oliffson Kamphorst Brazil
Reggie Brown United States
Jianbo Gao United States
M. Carmen Romano United Kingdom
Marko Thiel Germany
Joseph P. Zbilut United States
Matthew B. Kennel United States
Andrew M. Fraser
Citations per year, relative to Andrew M. Fraser Andrew M. Fraser (= 1×) peers Matthew B. Kennel

Countries citing papers authored by Andrew M. Fraser

Since Specialization
Citations

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

Fields of papers citing papers by Andrew M. Fraser

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew M. Fraser. 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 Andrew M. Fraser. The network helps show where Andrew M. Fraser may publish in the future.

Co-authorship network of co-authors of Andrew M. Fraser

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew M. Fraser. A scholar is included among the top collaborators of Andrew M. Fraser 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 Andrew M. Fraser. Andrew M. Fraser 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.
Fraser, Andrew M., et al.. (2019). Estimating Physics Models and Quantifying Their Uncertainty Using Optimization With a Bayesian Objective Function. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 4(1). 12 indexed citations
3.
Porter, Reid, Andrew M. Fraser, & Don Hush. (2010). Wide-Area Motion Imagery. IEEE Signal Processing Magazine. 27(5). 56–65. 49 indexed citations
4.
Foy, Bernard R., James Theiler, & Andrew M. Fraser. (2009). Decision boundaries in two dimensions for target detection in hyperspectral imagery. Optics Express. 17(20). 17391–17391. 16 indexed citations
5.
Porter, Reid, et al.. (2008). A recurrent velocity filter for detecting large numbers of moving objects. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6969. 69690C–69690C. 1 indexed citations
6.
Fraser, Andrew M.. (2008). Hidden Markov Models and Dynamical Systems. Society for Industrial and Applied Mathematics eBooks. 57 indexed citations
7.
Schultz, Larry J., K. Borozdin, Andrew M. Fraser, et al.. (2007). Statistical Reconstruction for Cosmic Ray Muon Tomography. IEEE Transactions on Image Processing. 16(8). 1985–1993. 107 indexed citations
8.
Theiler, James, Bernard R. Foy, & Andrew M. Fraser. (2007). Beyond the adaptive matched filter: nonlinear detectors for weak signals in high-dimensional clutter. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6565. 656503–656503. 20 indexed citations
9.
Theiler, James, Bernard R. Foy, & Andrew M. Fraser. (2006). Nonlinear signal contamination effects for gaseous plume detection in hyperspectral imagery. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6233. 62331U–62331U. 17 indexed citations
10.
Schultz, Larry J., K. Borozdin, Andrew M. Fraser, et al.. (2006). ML/EM Reconstruction Algorithm for Cosmic Ray Muon Tomography. 2006 IEEE Nuclear Science Symposium Conference Record. 2574–2577. 9 indexed citations
11.
Fraser, Andrew M., et al.. (2004). Incorporating invariants in mahalanobis distance based classifiers: application to face recognition. 4. 3118–3123. 10 indexed citations
12.
Fraser, Andrew M., et al.. (2003). Detecting chaotic signals with nonlinear models. 213–216.
13.
Fraser, Andrew M., et al.. (2003). Classification Modulo Invariance, With Application to Face Recognition. Journal of Computational and Graphical Statistics. 12(4). 829–852. 1 indexed citations
14.
Fraser, Andrew M.. (1996). Chaos and detection. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 53(5). 4514–4523. 2 indexed citations
15.
Dimitriadis, Alexis & Andrew M. Fraser. (1993). Modeling double scroll time series. IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing. 40(10). 683–687. 3 indexed citations
16.
Fraser, Andrew M.. (1992). Modeling nonlinear time series. 313–316 vol.5. 5 indexed citations
17.
Fraser, Andrew M.. (1989). Reconstructing attractors from scalar time series: A comparison of singular system and redundancy criteria. Physica D Nonlinear Phenomena. 34(3). 391–404. 160 indexed citations
18.
Fraser, Andrew M.. (1989). Information and entropy in strange attractors. IEEE Transactions on Information Theory. 35(2). 245–262. 253 indexed citations
19.
Haken, Hermann & Andrew M. Fraser. (1989). Information and Self-Organization: A Macroscopic Approach to Complex Systems. American Journal of Physics. 57(10). 958–959. 368 indexed citations
20.
Fraser, Andrew M. & Harry L. Swinney. (1986). Independent coordinates for strange attractors from mutual information. Physical review. A, General physics. 33(2). 1134–1140. 3366 indexed citations breakdown →

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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