Mohammad Shekaramiz
- Computer Vision and Pattern Recognition top 10%
- Aerospace Engineering
- Computational Mechanics top 10%
- Mechanical Engineering
- Control and Systems Engineering
- Co-authors
- Todd K. MoonMohammad A. S. MasoumJacob H. GuntherAbdennour SeibiM. R. DavisMostafa HassanalianZachary J. WardÁngel Gaspar González Rodríguez
- Topics
- Sparse and Compressive Sensing Techniques (12 papers)Blind Source Separation Techniques (9 papers)Structural Health Monitoring Techniques (7 papers)
- Cited by
- Industrial and Manufacturing EngineeringSignal ProcessingComputer Vision and Pattern Recognition
- Journals
- IEEE AccessEnergiesApplied Sciences
- Partner nations
- United StatesIran
In The Last Decade
Mohammad Shekaramiz
42 papers receiving 285 citations
Hit Papers
Peers
Comparison fields: 5 of 49
- Computer Vision and Pattern Recognition 76
- Aerospace Engineering 70
- Computational Mechanics 65
- Mechanical Engineering 57
- Control and Systems Engineering 51
Countries citing papers authored by Mohammad Shekaramiz
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 22 | |
| 3 | Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detectionbreakdown → | 50 |
| 4 | 5 | |
| 5 | 26 | |
| 6 | 2 | |
| 7 | 4 | |
| 8 | 1 | |
| 9 | 6 | |
| 10 | 5 | |
| 11 | 2 | |
| 12 | 3 | |
| 13 | 1 | |
| 14 | 2 | |
| 15 | 6 | |
| 16 | 2 | |
| 17 | 4 | |
| 18 | 3 | |
| 19 | 7 | |
| 20 | 14 |
About Mohammad Shekaramiz
Mohammad Shekaramiz is a scholar working on Signal Processing, Computational Mechanics and Environmental Engineering, having authored 47 papers that have together received 296 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (12 papers), Blind Source Separation Techniques (9 papers) and Structural Health Monitoring Techniques (7 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (48 citations), Signal Processing (41 citations) and Computer Vision and Pattern Recognition (76 citations). Mohammad Shekaramiz has collaborated with scholars based in United States and Iran. Frequent 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. Their work appears in journals such as IEEE Access, Energies and Applied Sciences.
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.