Elmar W. Lang

5.4k total citations
205 papers, 3.6k citations indexed

About

Elmar W. Lang is a scholar working on Signal Processing, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Elmar W. Lang has authored 205 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Signal Processing, 39 papers in Artificial Intelligence and 37 papers in Cognitive Neuroscience. Recurrent topics in Elmar W. Lang's work include Blind Source Separation Techniques (63 papers), Spectroscopy and Chemometric Analyses (28 papers) and Neural dynamics and brain function (23 papers). Elmar W. Lang is often cited by papers focused on Blind Source Separation Techniques (63 papers), Spectroscopy and Chemometric Analyses (28 papers) and Neural dynamics and brain function (23 papers). Elmar W. Lang collaborates with scholars based in Germany, Portugal and Spain. Elmar W. Lang's co-authors include H.‐D. Lüdemann, Ana Maria Tomé, Carlos G. Puntonet, F. X. Prielmeier, Robin J. Speedy, K. Heinz, Klaus Müller, J. M. Górriz, Fabian J. Theis and Hans‐Dietrich Lüdemann and has published in prestigious journals such as Physical Review Letters, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.

In The Last Decade

Elmar W. Lang

202 papers receiving 3.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Elmar W. Lang Germany 30 863 855 804 570 461 205 3.6k
H. C. Longuet–Higgins United Kingdom 33 2.6k 3.0× 956 1.1× 564 0.7× 435 0.8× 1.3k 2.8× 98 8.6k
Peter Sollich United Kingdom 34 547 0.6× 2.6k 3.1× 829 1.0× 159 0.3× 163 0.4× 182 5.4k
J. F. J. van den Brand Belgium 49 2.3k 2.7× 1.1k 1.3× 773 1.0× 89 0.2× 629 1.4× 316 9.4k
Awadhesh Prasad India 38 612 0.7× 292 0.3× 239 0.3× 229 0.4× 1.0k 2.3× 173 5.4k
Elisha Moses Israel 37 598 0.7× 484 0.6× 900 1.1× 38 0.1× 698 1.5× 91 4.7k
P. V. E. McClintock United Kingdom 56 3.5k 4.1× 401 0.5× 1.2k 1.5× 160 0.3× 1.7k 3.6× 501 11.6k
Masaki Sano Japan 39 1.1k 1.3× 1.0k 1.2× 1.5k 1.9× 54 0.1× 340 0.7× 162 8.2k
P. Mansfield United Kingdom 41 1.5k 1.7× 636 0.7× 366 0.5× 161 0.3× 363 0.8× 125 6.4k
Rainer Hegger Germany 23 329 0.4× 399 0.5× 182 0.2× 282 0.5× 326 0.7× 43 3.1k
Pierre A. Deymier United States 31 759 0.9× 911 1.1× 2.5k 3.1× 158 0.3× 417 0.9× 214 4.6k

Countries citing papers authored by Elmar W. Lang

Since Specialization
Citations

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

Fields of papers citing papers by Elmar W. Lang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Elmar W. Lang. 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 Elmar W. Lang. The network helps show where Elmar W. Lang may publish in the future.

Co-authorship network of co-authors of Elmar W. Lang

This figure shows the co-authorship network connecting the top 25 collaborators of Elmar W. Lang. A scholar is included among the top collaborators of Elmar W. Lang 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 Elmar W. Lang. Elmar W. Lang 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.
Stroszczynski, Christian, et al.. (2025). Automatic segmentation of liver structures in multi-phase MRI using variants of nnU-Net and Swin UNETR. Scientific Reports. 15(1). 25740–25740.
2.
Sawant, Shrutika S., et al.. (2023). An adaptive binary particle swarm optimization for solving multi-objective convolutional filter pruning problem. The Journal of Supercomputing. 79(12). 13287–13306. 1 indexed citations
3.
Schmidkonz, Christian, et al.. (2019). A deep learning approach to radiation dose estimation. Physics in Medicine and Biology. 65(3). 35007–35007. 50 indexed citations
4.
Salas-González, D., J. M. Górriz, Javier Ramı́rez, I. Álvarez, & Elmar W. Lang. (2012). Linear intensity normalization of FP-CIT SPECT brain images using the α-stable distribution. NeuroImage. 65. 449–455. 29 indexed citations
5.
Álvarez, I., J. M. Górriz, Javier Ramı́rez, et al.. (2012). Bilateral symmetry aspects in computer-aided Alzheimer's disease diagnosis by single-photon emission-computed tomography imaging. Artificial Intelligence in Medicine. 56(3). 191–198. 7 indexed citations
6.
Tomé, Ana Maria, et al.. (2011). Linear invariant systems theory for signal enhancement. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 5(3). 290–294. 1 indexed citations
7.
Lang, Elmar W., et al.. (2011). Projective segmentation of metal implants in Cone Beam computed tomographic images. 507–512. 3 indexed citations
8.
Tomé, Ana Maria, et al.. (2010). Clustering evoked potential signals using subspace methods. PubMed. 118. 3986–3989. 1 indexed citations
9.
Lutter, Dominik, Péter Ugocsai, Christoph Moehle, et al.. (2009). Analyzing time-dependent microarray data using independent component analysis derived expression modes from human macrophages infected with F. tularensis holartica. Journal of Biomedical Informatics. 42(4). 605–611. 9 indexed citations
10.
Lutter, Dominik, et al.. (2008). Comparison of unsupervised and supervised gene selection methods. PubMed. 286. 5212–5215. 3 indexed citations
11.
Ritter, Daniel, et al.. (2007). Calibration model of a dual gain flat panel detector for 2D and 3D x‐ray imaging. Medical Physics. 34(9). 3649–3664. 33 indexed citations
12.
Teixeira, Ana Rita, Ana Maria Tomé, K. Stadlthanner, & Elmar W. Lang. (2006). Nonlinear projective techniques to extract artifacts in biomedical signals. Repositório Comum (Repositório Científico de Acesso Aberto de Portugal). 1–5. 10 indexed citations
13.
Lutter, Dominik, K. Stadlthanner, Fabian J. Theis, et al.. (2006). Analyzing gene expression profiles with ICA. University of Regensburg Publication Server (University of Regensburg). 25–30. 3 indexed citations
14.
Böhm, Matthias, K. Stadlthanner, Peter J. Gruber, et al.. (2006). On the use of simulated annealing to automatically assign decorrelated components in second-order blind source separation. IEEE Transactions on Biomedical Engineering. 53(5). 810–820. 2 indexed citations
15.
Tomé, Ana Maria, Ana Rita Teixeira, Elmar W. Lang, et al.. (2005). dAMUSE - A new tool for denoising and BSS. University of Regensburg Publication Server (University of Regensburg). 1 indexed citations
16.
Górriz, J. M., Carlos G. Puntonet, & Elmar W. Lang. (2004). Hybrid ICA - ANN model applied to volatile time series forecasting. University of Regensburg Publication Server (University of Regensburg). 1 indexed citations
17.
Theis, Fabian J. & Elmar W. Lang. (2004). Linearization identification and an application to BSS using a SOM. University of Regensburg Publication Server (University of Regensburg). 205–210. 1 indexed citations
18.
Álvarez, M., et al.. (2004). Lattice ICA for the separation of speech signals. University of Regensburg Publication Server (University of Regensburg). 337–342.
19.
Theis, Fabian J. & Elmar W. Lang. (2002). Geometric Overcomplete ICA. University of Regensburg Publication Server (University of Regensburg). 217–222. 9 indexed citations
20.
Lang, Elmar W.. (1986). Physical-chemical limits for the stability of biomolecules. Advances in Space Research. 6(12). 251–255. 8 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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