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.
Predictive Control in Power Electronics and Drives
20081.4k citationsDaniel E. Quevedo et al.profile →
Jamming Attacks on Remote State Estimation in Cyber-Physical Systems: A Game-Theoretic Approach
2015350 citationsLing Shi, Peng Cheng et al.IEEE Transactions on Automatic Controlprofile →
Predictive Optimal Switching Sequence Direct Power Control for Grid-Connected Power Converters
2014313 citationsDaniel E. Quevedo et al.profile →
Multistep Finite Control Set Model Predictive Control for Power Electronics
2014310 citationsDaniel E. Quevedo et al.profile →
SINR-Based DoS Attack on Remote State Estimation: A Game-Theoretic Approach
2016255 citationsDaniel E. Quevedo, Ling Shi et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Daniel E. Quevedo
Since
Specialization
Citations
This map shows the geographic impact of Daniel E. Quevedo'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 Daniel E. Quevedo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel E. Quevedo more than expected).
Fields of papers citing papers by Daniel E. Quevedo
This network shows the impact of papers produced by Daniel E. Quevedo. 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 Daniel E. Quevedo. The network helps show where Daniel E. Quevedo may publish in the future.
Co-authorship network of co-authors of Daniel E. Quevedo
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel E. Quevedo.
A scholar is included among the top collaborators of Daniel E. Quevedo 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 Daniel E. Quevedo. Daniel E. Quevedo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Quevedo, Daniel E. & Vijay Gupta. (2011). Stability of sequence-based anytime control with Markovian processor availability. QUT ePrints (Queensland University of Technology). 56–61.1 indexed citations
Quevedo, Daniel E., Anders Åhlén, & Graham C. Goodwin. (2009). Predictive power control of wireless sensor networks for closed loop control. Lecture notes in control and information sciences. 215–224.3 indexed citations
17.
Silva, Eduardo I., Milan S. Derpich, Jan Østergaard, & Daniel E. Quevedo. (2008). Proceedings of the IEEE Conference on Decision and Control. NOVA (University of Newcastle, Australia).56 indexed citations
18.
Quevedo, Daniel E. & Graham C. Goodwin. (2004). When is the naive quantized control law globally optimal. QUT ePrints (Queensland University of Technology). 3. 1468–1476.2 indexed citations
19.
Goodwin, Graham C., et al.. (2003). Moving horizon optimal quantizer for audio signals. Journal of the Audio Engineering Society. 51(3). 138–149.18 indexed citations
20.
Goodwin, Graham C. & Daniel E. Quevedo. (2003). Finite Alphabet Control and Estimation. International Journal of Control Automation and Systems. 1(4). 412–430.21 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.