Johan Swärd

458 total citations
38 papers, 337 citations indexed

About

Johan Swärd is a scholar working on Computational Mechanics, Signal Processing and Civil and Structural Engineering. According to data from OpenAlex, Johan Swärd has authored 38 papers receiving a total of 337 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computational Mechanics, 14 papers in Signal Processing and 9 papers in Civil and Structural Engineering. Recurrent topics in Johan Swärd's work include Sparse and Compressive Sensing Techniques (15 papers), Blind Source Separation Techniques (9 papers) and Structural Health Monitoring Techniques (9 papers). Johan Swärd is often cited by papers focused on Sparse and Compressive Sensing Techniques (15 papers), Blind Source Separation Techniques (9 papers) and Structural Health Monitoring Techniques (9 papers). Johan Swärd collaborates with scholars based in Sweden, United States and Estonia. Johan Swärd's co-authors include Andreas Jakobsson, Bo Ranneby, Maria Hansson-Sandsten, Hongbin Li, Xiaotong Tu, Fucai Li, Christian Jamtheim Gustafsson, Xin Zhang, Mikael Akke and Braham Himed and has published in prestigious journals such as IEEE Transactions on Signal Processing, The Journal of Physical Chemistry A and Physics in Medicine and Biology.

In The Last Decade

Johan Swärd

36 papers receiving 308 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Johan Swärd Sweden 10 87 74 64 60 50 38 337
Bing Ouyang United States 13 66 0.8× 35 0.5× 24 0.4× 14 0.2× 35 0.7× 91 585
T. C. A. Molteno New Zealand 10 165 1.9× 13 0.2× 93 1.5× 26 0.4× 7 0.1× 22 535
Anders Gustavsson Sweden 22 56 0.6× 18 0.2× 39 0.6× 25 0.4× 272 5.4× 88 1.3k
Jingsheng Zhai China 16 37 0.4× 26 0.4× 10 0.2× 16 0.3× 46 0.9× 68 620
Max Deffenbaugh United States 15 42 0.5× 15 0.2× 30 0.5× 16 0.3× 4 0.1× 61 607
Martin Servin Sweden 17 178 2.0× 3 0.0× 16 0.3× 110 1.8× 9 0.2× 51 685
Özlem Kıłıc United States 13 26 0.3× 30 0.4× 19 0.3× 5 0.1× 69 1.4× 83 741
Xu Yang China 16 71 0.8× 10 0.1× 10 0.2× 19 0.3× 40 0.8× 65 655
Mario Zampolli United States 13 27 0.3× 32 0.4× 5 0.1× 30 0.5× 16 0.3× 64 613

Countries citing papers authored by Johan Swärd

Since Specialization
Citations

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

Fields of papers citing papers by Johan Swärd

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Johan Swärd

This figure shows the co-authorship network connecting the top 25 collaborators of Johan Swärd. A scholar is included among the top collaborators of Johan Swärd 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 Johan Swärd. Johan Swärd 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.
Gustafsson, Christian Jamtheim, Michael Lempart, Johan Swärd, et al.. (2021). Deep learning‐based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy. Journal of Applied Clinical Medical Physics. 22(12). 51–63. 7 indexed citations
2.
Gustafsson, Christian Jamtheim, et al.. (2020). Development and evaluation of a deep learning based artificial intelligence for automatic identification of gold fiducial markers in an MRI-only prostate radiotherapy workflow. Physics in Medicine and Biology. 65(22). 225011–225011. 12 indexed citations
3.
Zhang, Xin, Johan Swärd, Hongbin Li, Andreas Jakobsson, & Braham Himed. (2019). A Sparsity-Based Passive Multistatic Detector. IEEE Transactions on Aerospace and Electronic Systems. 55(6). 3658–3666. 11 indexed citations
4.
Carlström, Göran, et al.. (2019). Rapid NMR Relaxation Measurements Using Optimal Nonuniform Sampling of Multidimensional Accordion Data Analyzed by a Sparse Reconstruction Method. The Journal of Physical Chemistry A. 123(27). 5718–5723. 8 indexed citations
5.
Swärd, Johan, et al.. (2018). Computationally Efficient Estimation of Multi-dimensional Damped Modes using Sparse Wideband Dictionaries. Lund University Publications (Lund University). 1745–1749. 2 indexed citations
6.
Swärd, Johan, et al.. (2018). Estimating Faults Modes in Ball Bearing Machinery using a Sparse Reconstruction Framework. Lund University Publications (Lund University). 2330–2334. 1 indexed citations
7.
Swärd, Johan, et al.. (2018). Estimating Sparse Signals Using Integrated Wideband Dictionaries. IEEE Transactions on Signal Processing. 66(16). 4170–4181. 14 indexed citations
8.
Swärd, Johan, Hongbin Li, & Andreas Jakobsson. (2017). Off-Grid Fundamental Frequency Estimation. IEEE/ACM Transactions on Audio Speech and Language Processing. 26(2). 296–303. 9 indexed citations
9.
Swärd, Johan, et al.. (2017). Designing optimal sampling schemes. Lund University Publications (Lund University). 912–916. 3 indexed citations
10.
Swärd, Johan, et al.. (2017). Grid-less estimation of saturated signals. Lund University Publications (Lund University). 12. 372–376. 2 indexed citations
11.
Swärd, Johan, et al.. (2017). Multi-dimensional grid-less estimation of saturated signals. Signal Processing. 145. 37–47. 2 indexed citations
12.
Swärd, Johan, et al.. (2017). Estimating sparse signals using integrated wide-band dictionaries. Lund University Publications (Lund University). 58. 4426–4430. 4 indexed citations
13.
Swärd, Johan, et al.. (2016). High resolution sparse estimation of exponentially decaying N-dimensional signals. Signal Processing. 128. 309–317. 18 indexed citations
14.
Swärd, Johan, et al.. (2016). Conjugate priors for Gaussian emission plsa recommender systems. Lund University Publications (Lund University). 3. 2096–2100. 1 indexed citations
15.
Swärd, Johan, et al.. (2016). Enhancing smoothness in amplitude modulated sparse signals. Lund University Publications (Lund University). 4 indexed citations
16.
Swärd, Johan & Andreas Jakobsson. (2014). Canceling stationary interference signals exploiting secondary data. Lund University Publications (Lund University). 1014–1018. 3 indexed citations
17.
Swärd, Johan, et al.. (2014). A Sparse Approach for Estimation of Amplitude Modulated Sinusoids. Lund University Publications (Lund University). 2 indexed citations
18.
Swärd, Johan, et al.. (2014). Sparse semi-parametric chirp estimation. 2014 48th Asilomar Conference on Signals, Systems and Computers. 48. 1236–1240. 3 indexed citations
19.
Swärd, Johan, et al.. (2012). Detection of illegal narcotics using NQR. Lund University Publications Student Papers (Lund University). 2 indexed citations
20.
Gobakken, Terje, et al.. (2004). Evaluering av Levende Skog - Tilstand og utvikling i norsk skog vurdert i forhold til enkelte standarder. Duo Research Archive (University of Oslo). 1 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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