Tohid Ardeshiri
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- Control Systems and Identification 4
- Fault Detection and Control Systems 3
- Advanced Control Systems Optimization 2
- Artificial Intelligence top 5%
- Target Tracking and Data Fusion in Sensor Networks 14
- Gaussian Processes and Bayesian Inference 9
- Bayesian Methods and Mixture Models 3
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- Distributed Sensor Networks and Detection Algorithms 3
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- Structural Health Monitoring Techniques 2
Tohid Ardeshiri
21 papers receiving 405 citations
Peers
Comparison fields: 5 of 48
- Control and Systems Engineering 196
- Artificial Intelligence 268
- Aerospace Engineering 85
- Statistics, Probability and Uncertainty 18
- Signal Processing 26
Countries citing papers authored by Tohid Ardeshiri
This map shows the geographic impact of Tohid Ardeshiri'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 Tohid Ardeshiri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tohid Ardeshiri more than expected).
Fields of papers citing papers by Tohid Ardeshiri
This network shows the impact of papers produced by Tohid Ardeshiri. 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 Tohid Ardeshiri. The network helps show where Tohid Ardeshiri may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Tohid Ardeshiri, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 45 | |
| 2 | 2017 | 3 | |
| 3 | 2017 | 0 | |
| 4 | 2017 | 2 | |
| 5 | {A central limit theorem with application to inference in $\alpha$-stable regression models} | 2016 | 2 |
| 6 | Mean and covariance matrix of a multivariate normal distribution with one doubly-truncated component | 2016 | 2 |
| 7 | 2016 | 37 | |
| 8 | 2015 | 21 | |
| 9 | Variational Iterations for Filtering and Smoothing with skew-t measurement noise | 2015 | 3 |
| 10 | 2015 | 4 | |
| 11 | 2015 | 88 | |
| 12 | Variational Iterations for Smoothing with Unknown Process and Measurement Noise Covariances | 2015 | 4 |
| 13 | 2015 | 98 | |
| 14 | 2014 | 15 | |
| 15 | An adaptive PHD filter for tracking with unknown sensor characteristics | 2013 | 4 |
| 16 | On Reduction of Mixtures of the Exponential Family Distributions | 2013 | 2 |
| 17 | On mixture reduction for multiple target tracking | 2012 | 10 |
| 18 | Bicycle tracking using ellipse extraction | 2011 | 5 |
| 19 | 2011 | 34 | |
| 20 | 2006 | 1 |
About Tohid Ardeshiri
Tohid Ardeshiri is a scholar working on Artificial Intelligence, Control and Systems Engineering and Statistics and Probability, having authored 22 papers that have together received 413 indexed citations. Recurring topics across this work include Target Tracking and Data Fusion in Sensor Networks (14 papers), Gaussian Processes and Bayesian Inference (9 papers), Control Systems and Identification (4 papers), Distributed Sensor Networks and Detection Algorithms (3 papers), Bayesian Methods and Mixture Models (3 papers), Fault Detection and Control Systems (3 papers), Advanced Control Systems Optimization (2 papers) and Structural Health Monitoring Techniques (2 papers). The work is most often cited by research in Control and Systems Engineering (196 citations), Artificial Intelligence (268 citations) and Aerospace Engineering (85 citations). Tohid Ardeshiri has collaborated with scholars based in Sweden, United Kingdom and Türkiye. Frequent co-authors include Fredrik Gustafsson, Henri Nurminen, Robert Piché, Umut Orguner, Emre Özkan, Mikael Norrlöf, Anders Hansson, Johan Löfberg, Tianshi Chen and Lennart Ljung. Their work appears in journals such as Automatica, IEEE Transactions on Signal Processing and IEEE Signal Processing Letters.
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.