Publications by Signal Processing & Machine Learning

Are you looking for publications by Section of Signal Processing & Machine Learning? On this page you can find all the publications made by the Section of Signal Processing & Machine Learning - Department of Electrical and Computer Engineering, Aarhus University.

Below you can find a list of all the publications, their publishing date, their author(s), and titles. The list can be sorted by date, author, and title:

List of Publications

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Skjødt, P. & Jørgensen, R. (2003). Sensor based nitrogen fertilization increasing grain protein yield in winter wheat. Risø Report Bind Risø-R-1389(EN)

Streibig, J. C., Rasmussen, J., Andujar, D., Andreasen, C., Berge, T. W., Chachalis, D., Dittmann, T., Gerhards, R., Giselsson, T. M., Hamouz, P., Jaeger-Hansen, C., Jensen, K., Nyholm Jørgensen, R., Keller, M., Laursen, M., Midtiby, H. S., Nielsen, J., Müller, S., Nordmeyer, H. ... Christensen, S. (2014). Sensor-based assessment of herbicide effects. Weed Research, 54(3), 223–233. https://doi.org/10.1111/wre.12079

Christensen, S., Dyrmann, M., Nyholm Jørgensen, R. & Laursen, M. S. (2021). Sensing for Weed Detection. I Sensing Approaches for Precision Agriculture (s. 275-300). Springer. https://doi.org/10.1007/978-3-030-78431-7, https://doi.org/10.1007/978-3-030-78431-7_10

Mortensen, A. K., Dyrmann, M., Karstoft, H., Nyholm Jørgensen, R. & Gislum, R. (2016). Semantic Segmentation of Mixed Crops using Deep Convolutional Neural Network. Afhandling præsenteret på CIGR 2016, World Congress. http://conferences.au.dk/uploads/tx_powermail/biomap_-_cigr_2016_-_paper_-_final.pdf

Larsen, D., Steen, K. A., Skovsen, S., Grooters, K., Eriksen, J., Nyholm Jørgensen, R., Dyrmann, M. & Green, O. (2018). Semantic Segmentation of Clover-Grass Images using Images from Commercially Available Drones. I P. W. G. Groot Koerkamp, C. Lokhorst , A. H. Ipema, C. Kempenaar, C. M. Groenestein, C. G. van Oostrum & N. J. Ros (red.), Book of Abstracts of the European Conference on Agricultural Engineering: AgEng2018 (s. 110). Wageningen University. https://doi.org/10.18174/471678

Chen, Q., Chen, J., Lang, X., Xie, L., Rehman, N. U. & Su, H. (2021). Self-tuning variational mode decomposition. Journal of the Franklin Institute, 358(15), 7825-7862. https://doi.org/10.1016/j.jfranklin.2021.07.021

Spahr, A., Bozorgtabar, B. & Thiran, J.-P. (2021). Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays.

Hansen, M. K., Underwood, J. & Karstoft, H. (2016). Self-supervised Traversability Assessment in Field Environments with Lidar and Camera. Poster-session præsenteret på International Conference on Agricultural Engineering 2016, Aarhus, Danmark.

Stegmüller, T., Abbet, C., Bozorgtabar, B., Clarke, H., Petignat, P., Vassilakos, P. & Thiran, J. P. (2024). Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime. Computers in Biology and Medicine, 169, Artikel 107809. https://doi.org/10.1016/j.compbiomed.2023.107809

Tomar, D., Bozorgtabar, B., Lortkipanidze, M., Vray, G., Rad, M. S. & Thiran, J.-P. (2021). Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation.

Kumar, K., Chakraborty, S., Mahapatra, D., Bozorgtabar, B. & Roy, S. (2025). Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet. I Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025 (s. 7928-7938). IEEE. https://doi.org/10.1109/WACV61041.2025.00770

Abbet, C., Studer, L., Fischer, A., Dawson, H., Zlobec, I., Bozorgtabar, B. & Thiran, J.-P. (2021). Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection.

Tomar, D., Lortkipanidze, M., Vray, G., Bozorgtabar, B. & Thiran, J.-P. (2021). Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation. https://doi.org/10.1109/TMI.2021.3059265

Liu, S., Lang, X., Wu, J. & Rehman, N. U. (2025). Selective Noise Empirical Mode Decomposition. IEEE Signal Processing Letters, 32, 2823-2827. https://doi.org/10.1109/LSP.2025.3588082

Zahra, A., Kanwal, N., Rehman, N. U., Ehsan, S. & McDonald-Maier, K. D. (2017). Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition. Computers in Biology and Medicine, 88, 132-141. https://doi.org/10.1016/j.compbiomed.2017.07.010

Tu, G. J., Karstoft, H., Pedersen, L. J. & Jørgensen, E. (2014). Segmentation of sows in farrowing pens. IET Image Processing, 8(1), 56-68. https://doi.org/10.1049/iet-ipr.2012.0734

Mortensen, A. K., Bender, A., Whelan, B., Barbour, M. M., Sukkarieh, S., Karstoft, H. & Gislum, R. (2018). Segmentation of lettuce in coloured 3D point clouds for fresh weight estimation. Computers and Electronics in Agriculture, 154, 373-381. https://doi.org/10.1016/j.compag.2018.09.010

Giselsson, T. M., Midtiby, H. & Jørgensen, R. N. (2013). Seedling Discrimination with Shape Features Derived from a Distance Transform. Sensors, 13(5), 5585-5602. https://doi.org/10.3390/s130505585

Stegmüller, T., Bozorgtabar, B., Spahr, A. & Thiran, J.-P. (2022). ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification.

Ong, S.-Q., Pinoy, N., Hui Lin, M., Bjerge, K., Peris-Felipo, F. J., Lind, R., P. Cuff, J., M. Cook, S. & Høye, T. T. (2025). ScannerVision: Scanner-based image acquisition of medically important arthropods for the development of computer vision and deep learning models. Current Research in Parasitology & Vector-Borne Diseases, 7, Artikel 100268. https://doi.org/10.1016/j.crpvbd.2025.100268

Sveistrup, D., Jørgensen, R. N., Green, O., Nørremark, M. & Sørensen, C. A. G. (2010). Satellite, Internet & Computer Aided Trails.

Jensen, M. H., Rasmussen, M., Mohamad, N., Dyrskog, S. E., Thorup, L., Mikic, N., Wismann, J., Grønhøj, M. H., Rom Poulsen, F., Nazari, M., Rehman, N. U., Simonsen, C. Z. & Korshoej, A. R. (2023). Safety, harm, and efficacy of IRRAflow® versus external ventricular drainage for intraventricular hemorrhage: A randomized clinical trial. medRxiv. https://doi.org/10.1101/2023.07.08.23292286

Sørensen, R. A., Nielsen, M. & Karstoft, H. (2020). Routing in congested baggage handling systems using deep reinforcement learning. Integrated Computer-Aided Engineering, 27(2), 139-152. https://doi.org/10.3233/ICA-190613

Sveistrup, D., Jørgensen, R. N., Nørremark, M., Green, O., Sørensen, C. A. G. & S. Nadimi, E. (2010). RoPDeF - semi-automated and web-based Route Plan Designer of full-scale Field trials for tractor guidance systems. I Conference on Precision Crop Protection (Bind 3). http://www.precision-crop-protection.uni-bonn.de/gk_conference/abstracts_2010.pdf

Khalid, S. S., Rehman, N. U. & Abrar, S. (2017). Robust stochastic integration filtering for nonlinear systems under multivariate t -distributed uncertainties. Signal Processing, 140, 53-59. https://doi.org/10.1016/j.sigpro.2017.05.007

Skovsen, S. K., Laursen, M. S., Kristensen, R. K., Rasmussen, J., Dyrmann, M., Eriksen, J., Gislum, R., Nyholm Jørgensen, R. & Karstoft, H. (2021). Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks. Sensors, 21( 1), Artikel 175. https://doi.org/10.3390/s21010175

Chakraborty, S., Kumar, K., Deria, A., Mahapatra, D., Bozorgtabar, B. & Roy, S. (2025). Robust semantic learning for precise medical image segmentation. Biomedical Signal Processing and Control, 110, Artikel 108251. https://doi.org/10.1016/j.bspc.2025.108251

Iqbal, S., Naveed, K., Naqvi, S. S., Naveed, A. & Khan, T. M. (2023). Robust retinal blood vessel segmentation using a patch-based statistical adaptive multi-scale line detector. Digital Signal Processing: A Review Journal, 139, Artikel 104075. https://doi.org/10.1016/j.dsp.2023.104075

Rasmussen, S., Nyboe, N. S., Mai, S. & Larsen, J. J. (2018). Robust cancellation of minimum shift keying-encoded radio interference in data from the transient electromagnetic method. Geophysics, 83(2), E87-E94. https://doi.org/10.1190/GEO2017-0611.1

Khalid, S. S., Rehman, N. U., Abrar, S. & Mihaylova, L. (2018). Robust Bayesian Filtering Using Bayesian Model Averaging and Restricted Variational Bayes. I 2018 21st International Conference on Information Fusion (FUSION) IEEE. https://doi.org/10.23919/icif.2018.8455608

Nyholm Jørgensen, R., Madsen, S. L. & Laursen, M. S. (2016). RoboWeedSupport - Weed Mapping with drones using a DJI Matrice 100 with a Velodyne Lidar.. Billeder, Video- og Lydoptagelser (digital), YouTube. https://www.youtube.com/watch?v=91LaKHiHiDw

Laursen, M. S., Nyholm Jørgensen, R., Dyrmann, M. & Poulsen, R. (2017). RoboWeedSupport: Sub Millimeter Weed Image Acquisition in Cereal Crops with Speeds up till 50 Km/h. World Academy of Science, Engineering and Technology. Proceedings, 4(4), 1391. https://www.waset.org/abstracts?q=Mads%20Dyrmann

Laursen, M. S., Nyholm Jørgensen, R., Dyrmann, M. & Poulsen, R. (2017). RoboWeedSupport - Sub Millimeter Weed Image Acquisition in Cereal Crops with Speeds up till 50 Km/h. International Journal of Agricultural and Biosystems Engineering, 11(4), 311-315. http://waset.org/publications/10007027/roboweedsupport-sub-millimeter-weed-image-acquisition-in-cereal-crops-with-speeds-up-till-50-km-h

Madsen, S. L., Laursen, M. S., Poulsen, R. N. & Nyholm Jørgensen, R. (2017). RoboWeedSupport: Semi-automated Unmanned Aerial System for Cost Efficient High Resolution in Sub-millimeter Scale Acquisition of Weed Images. I 19th International Conference on Precision Agriculture: ICPA 2017

Nielsen, P. R., Jensen, N.-P., Dyrmann, M., Nielsen, P.-H. & Nyholm Jørgensen, R. (2017). RoboWeedSupport - Presentation of a cloud based system bridging the gap between in-field weed inspections and decision support systems. Advances in Animal Biosciences, 8(2), 860-864. https://doi.org/10.1017/S2040470017001054

Nyholm Jørgensen, R. (2016). RoboWeedSupport Præsentation og status ano 02SEP2016. Billeder, Video- og Lydoptagelser (digital) https://youtu.be/tiGtGArXOHw

Dyrmann, M. & Nyholm Jørgensen, R. (2015). RoboWeedSupport: Weed recognition for reduction of herbicide consumption. I J. V. Stafford (red.), Precision agriculture '15: Papers presented at the 10th European Conference on Precision Agriculture Volcani Center, Israel 12-16 July 2015 (s. 571-578). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-814-8_71, https://doi.org/10.3920/978-90-8686-814-8

Nyholm Jørgensen, R. (2019). RoboWeedMaps - Walking the weed maps with your mobile phone. Billeder, Video- og Lydoptagelser (digital), YouTube. https://www.youtube.com/watch?v=9rKahKe_a5c&t=108s

Nyholm Jørgensen, R. (2019). RoboWeedMaps præsenterer pletsprøjtning på FutureCropping demodag den 9 oktober 2019. Billeder, Video- og Lydoptagelser (digital), YouTube. https://www.youtube.com/watch?v=YKCR-hARLS8&feature=youtu.be

Nyholm Jørgensen, R. & Laursen, M. S. (2017). RoboWeedMaPS - First Grass-weed field inspection in Winter Wheat using DJI Goggles. Billeder, Video- og Lydoptagelser (digital), YouTube. https://www.youtube.com/watch?v=Yma1Psi9OEw

Nyholm Jørgensen, R. & Madsen, S. L. (2017). RoboWeedMaPS - First Automated Weed Mapping test flight with DJI Phantom 4 Pro. Billeder, Video- og Lydoptagelser (digital), YouTube. https://www.youtube.com/watch?v=-dHMbt9rwak

Nyholm Jørgensen, R., Nielsen, E. D. & Madsen, S. L. (2017). RoboWeedMaPS - Demo DroneApp beta med DJI Phantom 4. Billeder, Video- og Lydoptagelser (digital), YouTube. https://www.youtube.com/watch?v=PQ6sSIk7-0s

Nyholm Jørgensen, R., Laursen, M. S., Teimouri, N., Madsen, S. L., Dyrmann, M., Somerville, G. J. & Mathiassen, S. K. (2019). RoboWeedMaps - Automated weed detection and mapping - Invited talk at SSWM 2019, SDU, Odense Denmark. Billeder, Video- og Lydoptagelser (digital), YouTube.

Bochtis, D., Sørensen, C. A. G., Jørgensen, R. N., Nørremark, M., Hameed, I. A. & Swain, K. C. (2011). Robotic weed monitoring. Acta Agriculturae Scandinavica, Section B - Soil & Plant Science, 61(3), 202-208. https://doi.org/10.1080/09064711003796428

Christensen, S., Søgaard, H. T., Jørgensen, R. & Bak, T. (2003). Robotic weeding and automated weed measurements. I DJF rapport (Bind 88, s. 161-172). Aarhus University.

Christiansen, M. P., Larsen, P. G. & Nyholm Jørgensen, R. (2015). Robotic Design Choice Overview using Co-simulation and Design Space Exploration. Robotics, 4, 398-421. https://doi.org/10.3390/robotics4040398

Christiansen, M. P., Larsen, P. G. & Nyholm Jørgensen, R. (2014). Robotic design choice overview using co-simulation. Abstract fra Agromek and NJF joint seminar, Herning, Danmark.

Ravelo, B., Miranda, J., Cabral, J., Wagner, S. R., Pedersen, C. F., Memon, M., Mathiesen, M. & Lalléchère, S. (2015). RF and EMC Investigation on CRIP System for the E-Healthcare CareStore Platform. I 9th European Conference on Antennas and Propagation (EuCAP) 2015 IEEE. http://ieeexplore.ieee.org.ez.statsbiblioteket.dk:2048/xpl/articleDetails.jsp?arnumber=7228157&newsearch=true&queryText=ravelo%20emc%20rf

Kremer, T., Irons, T., Müller-Petke, M. & Juul Larsen, J. (2022). Review of Acquisition and Signal Processing Methods for Electromagnetic Noise Reduction and Retrieval of Surface Nuclear Magnetic Resonance Parameters. Surveys in Geophysics, 43(4), 999-1053. https://doi.org/10.1007/s10712-022-09695-3

Mahapatra, D. & Bozorgtabar, B. (2017). Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution.

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Revideret 13.11.2025

AU Engineering