Roland Priemer
- Control and Systems Engineering top 10%
- Artificial Intelligence top 10%
- Pulmonary and Respiratory Medicine
- Signal Processing top 10%
- Cardiology and Cardiovascular Medicine
- Co-authors
- Vivek NigamP. KalataChunshien LiM. FarooqΑ.Κ. MahalanabisRoberto M. LangClaudia E. KorcarzShin‐Min Song
- Topics
- Blind Source Separation Techniques (11 papers)Neural Networks and Applications (8 papers)Target Tracking and Data Fusion in Sensor Networks (8 papers)
- Journals
- IEEE Transactions on Automatic ControlProceedings of the IEEEIEEE Transactions on Communications
- Partner nations
- United StatesCzechiaCanada
In The Last Decade
Roland Priemer
40 papers receiving 330 citations
Peers
Comparison fields: 5 of 64
- Control and Systems Engineering 144
- Artificial Intelligence 143
- Pulmonary and Respiratory Medicine 111
- Signal Processing 64
- Cardiology and Cardiovascular Medicine 57
Countries citing papers authored by Roland Priemer
This map shows the geographic impact of Roland Priemer'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 Roland Priemer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roland Priemer more than expected).
Fields of papers citing papers by Roland Priemer
This network shows the impact of papers produced by Roland Priemer. 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 Roland Priemer. The network helps show where Roland Priemer may publish in the future.
Co-authorship network of co-authors of Roland Priemer
This figure shows the co-authorship network connecting the top 25 collaborators of Roland Priemer. A scholar is included among the top collaborators of Roland Priemer 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 Roland Priemer. Roland Priemer 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 | 11 | |
| 3 | 22 | |
| 4 | 4 | |
| 5 | 15 | |
| 6 | 52 | |
| 7 | 7 | |
| 8 | 1 | |
| 9 | 0 | |
| 10 | 1 | |
| 11 | 1 | |
| 12 | 3 | |
| 13 | 8 | |
| 14 | 37 | |
| 15 | 3 | |
| 16 | 22 | |
| 17 | 1 | |
| 18 | 1 | |
| 19 | 1 | |
| 20 | 18 |
About Roland Priemer
Roland Priemer is a scholar working on Signal Processing, Control and Systems Engineering and Artificial Intelligence, having authored 43 papers that have together received 369 indexed citations. Recurring topics across this work include Blind Source Separation Techniques (11 papers), Neural Networks and Applications (8 papers) and Target Tracking and Data Fusion in Sensor Networks (8 papers). The work is most often cited by research in Control and Systems Engineering (144 citations), Signal Processing (64 citations) and Artificial Intelligence (143 citations). Roland Priemer has collaborated with scholars based in United States, Czechia and Canada. Frequent co-authors include Vivek Nigam, P. Kalata, Chunshien Li, M. Farooq, Α.Κ. Mahalanabis, Roberto M. Lang, Claudia E. Korcarz, Shin‐Min Song, Jiangnan Chen and Michael C. Hughes. Their work appears in journals such as IEEE Transactions on Automatic Control, Proceedings of the IEEE and IEEE Transactions on Communications.
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.