Mohammad Shekaramiz

481 total citations · 1 hit paper
47 papers, 296 citations indexed

About

Mohammad Shekaramiz is a scholar working on Computational Mechanics, Aerospace Engineering and Control and Systems Engineering. According to data from OpenAlex, Mohammad Shekaramiz has authored 47 papers receiving a total of 296 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Mechanics, 13 papers in Aerospace Engineering and 11 papers in Control and Systems Engineering. Recurrent topics in Mohammad Shekaramiz's work include Sparse and Compressive Sensing Techniques (12 papers), Blind Source Separation Techniques (9 papers) and Structural Health Monitoring Techniques (7 papers). Mohammad Shekaramiz is often cited by papers focused on Sparse and Compressive Sensing Techniques (12 papers), Blind Source Separation Techniques (9 papers) and Structural Health Monitoring Techniques (7 papers). Mohammad Shekaramiz collaborates with scholars based in United States and Iran. Mohammad Shekaramiz's co-authors include Todd K. Moon, Mohammad A. S. Masoum, Jacob H. Gunther, Abdennour Seibi, M. R. Davis, Mostafa Hassanalian, Zachary J. Ward, Ángel Gaspar González Rodríguez, D. Arias and Farid Sheikholeslam and has published in prestigious journals such as IEEE Access, Energies and Applied Sciences.

In The Last Decade

Mohammad Shekaramiz

42 papers receiving 285 citations

Hit Papers

Review on the Advancements in Wind Turbine Blade Inspecti... 2024 2026 2025 2024 10 20 30 40 50
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. Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection
    2024 50 citations Mohammad Shekaramiz, Abdennour Seibi et al. IEEE Access profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mohammad Shekaramiz United States 10 76 70 65 57 51 47 296
Yongchun Miao China 10 135 1.8× 71 1.0× 29 0.4× 33 0.6× 90 1.8× 25 353
Jinlong Chen China 12 96 1.3× 117 1.7× 25 0.4× 132 2.3× 43 0.8× 38 365
Xiaoqiang Guo China 11 112 1.5× 14 0.2× 29 0.4× 100 1.8× 43 0.8× 60 391
Abderrezak Guessoum Algeria 12 101 1.3× 31 0.4× 62 1.0× 87 1.5× 80 1.6× 56 535
Sedat Dogru Portugal 10 170 2.2× 139 2.0× 22 0.3× 99 1.7× 79 1.5× 32 430
Quan Shi United States 11 150 2.0× 132 1.9× 39 0.6× 72 1.3× 17 0.3× 28 323
Jiawei Gao China 8 77 1.0× 15 0.2× 55 0.8× 79 1.4× 60 1.2× 24 361
Tae-Hyoung Park South Korea 13 233 3.1× 58 0.8× 25 0.4× 37 0.6× 56 1.1× 83 469
Kazuo Yonekura Japan 13 41 0.5× 67 1.0× 93 1.4× 101 1.8× 31 0.6× 35 399
Guoxiong Hu China 8 198 2.6× 60 0.9× 10 0.2× 56 1.0× 17 0.3× 16 289

Countries citing papers authored by Mohammad Shekaramiz

Since Specialization
Citations

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

Fields of papers citing papers by Mohammad Shekaramiz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mohammad Shekaramiz

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammad Shekaramiz. A scholar is included among the top collaborators of Mohammad Shekaramiz 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 Mohammad Shekaramiz. Mohammad Shekaramiz 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.
Shekaramiz, Mohammad, et al.. (2025). Enhanced Non-Destructive Testing of Small Wind Turbine Blades Using Infrared Thermography. Machines. 13(2). 108–108.
2.
Davis, M. R., et al.. (2024). Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms. Energies. 17(5). 982–982. 22 indexed citations
3.
Shekaramiz, Mohammad, et al.. (2024). Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection. IEEE Access. 12. 33236–33282. 50 indexed citations breakdown →
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Moon, Todd K., et al.. (2024). Velocity-Based Wind Turbine Blade Deblurring Using Richardson-Lucy Algorithm. 215–220. 1 indexed citations
9.
Shekaramiz, Mohammad, et al.. (2024). Wind Turbine Blade Fault Detection via Thermal Imaging Using Deep Learning. 23–28. 6 indexed citations
10.
Davis, M. R. & Mohammad Shekaramiz. (2023). Desert/Forest Fire Detection Using Machine/Deep Learning Techniques. Fire. 6(11). 418–418. 5 indexed citations
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Masoum, Mohammad A. S., et al.. (2023). Hyperparameter Tuning of Support Vector Machines for Wind Turbine Detection Using Drones. 55–60. 3 indexed citations
13.
Shekaramiz, Mohammad, et al.. (2020). Withdrawn: Placement of UAV-Mounted Mobile Base Station through User Load-Feature K-means Clustering. AIAA AVIATION 2020 FORUM. 1 indexed citations
14.
Shekaramiz, Mohammad, Todd K. Moon, & Jacob H. Gunther. (2019). A Note on Kriging and Gaussian Processes A NOTE ON KRIGING AND GAUSSIAN PROCESSES. Digital Commons - USU (Utah State University). 1. 2 indexed citations
15.
Shekaramiz, Mohammad, Todd K. Moon, & Jacob H. Gunther. (2017). Sparse Bayesian learning using variational Bayes inference based on a greedy criterion. PubMed. 51. 858–862. 6 indexed citations
16.
Shekaramiz, Mohammad, Todd K. Moon, & Jacob H. Gunther. (2017). Exploration and data refinement via multiple mobile sensors based on Gaussian processes. PubMed. 51. 885–889. 2 indexed citations
17.
Shekaramiz, Mohammad, Todd K. Moon, & Jacob H. Gunther. (2016). Sparse Bayesian learning boosted by partial erroneous support knowledge. PubMed. 2016. 389–393. 4 indexed citations
18.
Shekaramiz, Mohammad, Todd K. Moon, & Jacob H. Gunther. (2015). On the block-sparse solution of single measurement vectors. PubMed. 2015. 508–512. 3 indexed citations
19.
Shekaramiz, Mohammad, Todd K. Moon, & Jacob H. Gunther. (2015). On the block-sparsity of multiple-measurement vectors. PubMed. 2015. 220–225. 7 indexed citations
20.
Shekaramiz, Mohammad, Todd K. Moon, & Jacob H. Gunther. (2014). Hierarchical Bayesian approach for jointly-sparse solution of multiple-measurement vectors. 2014 48th Asilomar Conference on Signals, Systems and Computers. 20. 1962–1966. 14 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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