Moshe Mishali
- Computational Mechanics top 0.1%
- Signal Processing top 0.2%
- Biomedical Engineering top 2%
- Electrical and Electronic Engineering top 5%
- Computer Vision and Pattern Recognition top 1%
- Co-authors
- Yonina C. EldarArvind GaneshJarvis HauptJosé Antonio UrigüenAndrea MontanariWeiyu XuRobert CalderbankThomas Blumensath
- Topics
- Sparse and Compressive Sensing Techniques (18 papers)Blind Source Separation Techniques (10 papers)Image and Signal Denoising Methods (8 papers)
- Journals
- IEEE Transactions on Information TheoryIEEE Transactions on Signal ProcessingComputers in Human Behavior
- Partner nations
- IsraelUnited StatesGermany
In The Last Decade
Moshe Mishali
38 papers receiving 4.5k citations
Hit Papers
Peers
Comparison fields: 5 of 129
- Computational Mechanics 3.3k
- Signal Processing 1.6k
- Biomedical Engineering 1.5k
- Electrical and Electronic Engineering 1.1k
- Computer Vision and Pattern Recognition 893
Countries citing papers authored by Moshe Mishali
This map shows the geographic impact of Moshe Mishali'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 Moshe Mishali with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Moshe Mishali more than expected).
Fields of papers citing papers by Moshe Mishali
This network shows the impact of papers produced by Moshe Mishali. 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 Moshe Mishali. The network helps show where Moshe Mishali may publish in the future.
Co-authorship network of co-authors of Moshe Mishali
This figure shows the co-authorship network connecting the top 25 collaborators of Moshe Mishali. A scholar is included among the top collaborators of Moshe Mishali 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 Moshe Mishali. Moshe Mishali is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 6 | |
| 4 | 8 | |
| 5 | 1 | |
| 6 | 121 | |
| 7 | 3 | |
| 8 | 6 | |
| 9 | Compressed Sensingbreakdown → | 1142 |
| 10 | 150 | |
| 11 | 179 | |
| 12 | 18 | |
| 13 | 103 | |
| 14 | From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signalsbreakdown → | 802 |
| 15 | 28 | |
| 16 | Xampling--Part I: Practice | 11 |
| 17 | 4 | |
| 18 | The ReMBo algorithm: Accelerated recovery of jointly sparse vectors | 2 |
| 19 | 46 | |
| 20 | 12 |
About Moshe Mishali
Moshe Mishali is a scholar working on Chemical Health and Safety, Signal Processing and Computational Mechanics, having authored 39 papers that have together received 4.7k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (18 papers), Blind Source Separation Techniques (10 papers) and Image and Signal Denoising Methods (8 papers). The work is most often cited by research in Computational Mechanics (3.3k citations), Signal Processing (1.6k citations) and Acoustics and Ultrasonics (73 citations). Moshe Mishali has collaborated with scholars based in Israel, United States and Germany. Frequent co-authors include Yonina C. Eldar, Arvind Ganesh, Jarvis Haupt, José Antonio Urigüen, Andrea Montanari, Weiyu Xu, Robert Calderbank, Thomas Blumensath, Gitta Kutyniok and Alexey Castrodad. Their work appears in journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and Computers in Human Behavior.
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.