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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Christiansen, P., Steen, K. A., Nyholm Jørgensen, R. & Karstoft, H. (2014). Automated Detection and Recognition of Wildlife Using Thermal Cameras. Sensors, 14(8), 13778-13793. https://doi.org/10.3390/s140813778

Christiansen, M. P., Laursen, M. S., Nyholm Jørgensen, R. & Hameed, I. A. (2014). Collaborative model based design of automated and robotic agricultural vehicles in the Crescendo Tool. Abstract fra Agromek and NJF joint seminar, Herning, Danmark. https://arxiv.org/pdf/1802.06296

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.

Christiansen, M. P. & Nyholm Jørgensen, R. (2015). Method for recording and predicting position data for a selfpropelled wheeled vehicle, and delivery or pick up system comprising a self-propelled, self-guided wheeled vehicle. (Patentnummer DK178276 (B1) ).

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, P., Hansen, M. K., Steen, K. A., Karstoft, H. & Nyholm Jørgensen, R. (2015). Advanced sensor platform for human detection and protection in autonomous farming. I J. V. Stafford (red.), Precision agriculture '15: Papers presented at the 10th European Conference on Precision Agriculture (Bind 2015, s. 291-298). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-814-8_35

Christiansen, P., Sørensen, R., Skovsen, S., Jæger, C. D., Nyholm Jørgensen, R., Karstoft, H. & Steen, K. A. (2016). Towards Autonomous Plant Production using Fully Convolutional Neural Networks. Afhandling præsenteret på International Conference on Agricultural Engineering 2016, Aarhus, Danmark. http://conferences.au.dk/uploads/tx_powermail/2016cigr-towardsautomousplantproductionusingfullyconvolutionalneuralnetworks.pdf

Christiansen, M. P., Larsen, P. G. & Nyholm Jørgensen, R. (2016). Agricultural robotic candidate overview using co-model driven development. I A. G. Bruzzone, S. Junco, G. Dauphin-Tanguy & Y. Merkuryev (red.), 9th International Conference on Integrated Modeling and Analysis in Applied Control and Automation, IMAACA 2016: IMAACA 2016 (s. 41-48). Artikel IMAACA_10 Dime University of Genoa. https://www.msc-les.org/proceedings/imaaca/2016/IMAACA2016_41.pdf

Christiansen, P., N. Nielsen, L., A. Steen, K., Nyholm Jørgensen, R. & Karstoft, H. (2016). DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field. Sensors, 16(11), Artikel 1904. https://doi.org/10.3390/s16111904

Christiansen, P., Kragh, M. F., Steen, K. A., Karstoft, H. & Nyholm Jørgensen, R. (2017). Platform for evaluating sensors and human detection in autonomous mowing operations. Precision Agriculture, 18(3), 350-365. https://doi.org/10.1007/s11119-017-9497-6

Christiansen, M. P., Nyholm Jørgensen, R., Laursen, M. S. & Gislum, R. (2017). UAV LIDAR biomass estimation/Using Sentinel-1 to estimate yield potential. Poster-session præsenteret på DIGIT, Aarhus, Danmark.

Christiansen, M. P., Laursen, M. S., Nyholm Jørgensen, R., Skovsen, S. & Gislum, R. (2017). Designing and Testing a UAV Mapping System for Agricultural Field Surveying. Sensors, 17(12), Artikel 2703. https://doi.org/10.3390/s17122703

Christiansen, M. P., Laursen, M. S., Nyholm Jørgensen, R., Skovsen, S. & Gislum, R. (2018). Ground Vehicle Mapping of Fields using LiDAR to Enable Prediction of Crop Biomass. I Proceedings of the 14th International Conference on Precision Agriculture Artikel 5145 International Society of Precision Agriculture. https://ispag.org/proceedings/?action=abstract&id=5145

Christiansen, M. P., Teimouri, N., Laursen, M. S., Mikkelsen, B. F., Jorgensen, R. N. & Sorensen, C. A. G. (2019). Preprocessed sentinel-1 data via a web service focused on agricultural field monitoring. IEEE Access, 7(1), 65139-65149. Artikel 8715769. https://doi.org/10.1109/ACCESS.2019.2917063

Christiansen, M. P., Laursen, M. S., Feld Mikkelsen, B., Nyholm Jørgensen, R., Teimouri, N. & Sørensen, C. A. G. (2018). Current potentials and challenges using Sentinel-1 for broadacre field remote sensing. I Book of Abstracts of the European Conference on Agricultural Engineering: AgEng2018 (s. 56). Wageningen University. https://doi.org/10.18174/471678

Christiansen, P. H., Kragh, M. F., Brodskiy, Y. & Karstoft, H. (2019). UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor. arxiv.org. https://arxiv.org/abs/1907.04011

Christensen, S., Jørgensen, R. & Egebirk, T. B. (2003). Trådløst netværk i landbruget. I Nyeste teknologi i markbruget. Danmarks JordbrugsForskning og Landbrugets Rådgivningscenter konference: Teknik i Landbruget 2003, Scandinavian Congress Center, Aarhus, 20.3.03. Bila (s. 48-48)

Christensen, L. K. & Jørgensen, R. (2003). Spatial reflectance at sub-leaf scale discriminating NPK stress in barley. I Proc. 4th ECPA and 1st ECPLF, Berlin, Germany (s. 622-622). Wageningen Academic Publishers.

Christensen, L. K., Bennedsen, B. S., Jørgensen, R. & Nielsen, H. (2004). Modelling nitrogen and phosphorus content at early growth stages in spring barley using spectral line scanning. Biosystems Engineering, 88(1), 19-24.

Christensen, L. K. & Jørgensen, R. (2004). Nutrient stress discrimination of N, P, and K deficiencies in barley utilising multi-band reflection at sub-leaf scale. I CIGR2004, Beijing, China. Collection of Extent Abstracts, Vol. 1, p. I-63. Paper on CD: ISBN 7-80167-707-2/ (s. 537) https://www.academia.edu/67063468/Nutrient_stress_discrimination_of_N_P_and_K_deficiencies_in_barley_utilising_multi_band_reflection_at_sub_leaf_scale?uc-g-sw=67063526

Christensen, L. K. & Jørgensen, R. (2004). Spectral NPK stress discrimination in barley plants by use of three wavebands. I Proc. 4th int. Crop Science Congress, Brisbane, Queensland, Australia. Handbook and Abstracts, Section 2.5.3., p. 195. http://www.regional.org.au/au/cs/2004/index.htm

Christensen, S., Jørgensen, R. & Christiansen, T. (2003). Trådløst netværk i landbruget: Nyeste teknologi i markbruget. I Ikke angivet (s. 48). Dansk Landbrugsrådgivning og Danmarks JordbrugsForskning.

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.

Christensen, S., Wu, O., Butcher, K., Hjort, N., Karstoft, H., Davis, S. & Østergaard, L. (2006). The Physiological Significance of the Tmax Parameter in Bolus Tracking MRI. Abstract fra ISMRM 14th Annual Meeting, Seattle, USA.

Christensen, S., Mouridsen, K., Wu, O., Hjort, N., Karstoft, H., Thomalla, G., Röther, J., Fiehler, J., Kucinski, T. & Østergaard, L. (2009). Comparison of 10 Perfusion MRI Parameters in 97 Sub 6 Hour Stroke Patients using Voxel based Receiver Operating Characteristics Analysis. Stroke, 40(6), 2055-61. https://doi.org/10.1161/STROKEAHA.108.546069

Christensen, L. K. & Jørgensen, R. (2003). Spatial Reflectance at Sub-Leaf Scale Discriminating NPK Stress Characteristics in Barley Using Multiway Partial Least Squares Regression. Afhandling præsenteret på ASAE Annual International Meeting, Las Vegas, USA. http://asae.frymulti.com/request.asp?JID=5&AID=13744&CID=lnv2003&T=2

Christensen, S., Søgaard, H. T., Kudsk, P., Nørremark, M., Lund, I., Shahrak Nadimi, E. & Jørgensen, R. N. (2009). Site Specific Weed Control Technologies. Weed Research, 49(3), 225-232. https://doi.org/10.1111/j.1365-3180.2009.00694.x

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

Christensen, L. T. B., Straadt, D., Vassis, S., Lillelund, C. M., Stoustrup, P. B., Pauwels, R., Pedersen, T. K. & Pedersen, C. F. (2024). An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis. (s. 1-4). https://arxiv.org/abs/2405.01617#

Chen, Q., Lang, X., Lu, S., Rehman, N. U., Xie, L. & Su, H. (2021). Detection and root cause analysis of multiple plant-wide oscillations using multivariate nonlinear chirp mode decomposition and multivariate Granger causality. Computers and Chemical Engineering, 147, Artikel 107231. https://doi.org/10.1016/j.compchemeng.2021.107231

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

Chavez Plascencia, A. & Karstoft, H. (2012). Improvement of KinectTM Sensor Capabilities by Fusion with Laser Sensing Data Using Octree. Sensors, 12, 3868-3878. https://doi.org/10.3390/s120403868

Chavez Plascencia, A. & Karstoft, H. (2014). Map Building based on a Xtion Pro Live RGBD and a Laser Sensors. Journal of Information Technology & Software Engineering, 4, Artikel 126. https://doi.org/10.4172/2165-7866.1000126

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

Carreto Picón, G., Oleksiienko, I., Morsing, L. H., Bakhtiarnia, A. & Iosifidis, A. (2024). Continual Low-Rank Scaled Dot-product Attention. https://doi.org/10.48550/arXiv.2412.03214

Carreto Picón, G., Oleksiienko, I., Hedegaard, L., Bakhtiarnia, A. & Iosifidis, A. (2026). Continual low-rank scaled dot-product attention. Neural Networks, 197, Artikel 108517. https://doi.org/10.1016/j.neunet.2025.108517

Brix, L. C., List Jensen, P., Pedersen, C. F., Hugo Pedersen, M. & Villadsen, K. (2000). AAUVIT, Aalborg University Virtual Interactive Tool: For conducting projects across the Internet. Afhandling præsenteret på 3rd Euro Education Conference, Aalborg, Danmark.

Brix, L., Pedersen, C. F., Hansen, K. V. & Larsen, O. V. (2001). Automatic mesh generation of the human brain for use in a neurosurgical simulator. I Magjarevic, R. : Tonkovic, S. : Bilas, V. : Lackovic, I. (eds.) (red.), Proceedings of the International Federation for Medical & Biological Engineering, IFMBE, IX Mediterranean Conference on Medical and Biological Engineering and Computing, Medicon 2001, part 2, June 12-15, Pula, Croatia (s. 978-982)

Bravo, C., Huizinga, P., Jørgensen, R., Olsen, H. J., Søgaard, H. T., Moshou, D., Jørgensen, M. H., Christensen, S. & Ramon, H. (2004). Comparison of 2 different weed detection systems by use of the API. I AgEng 2004, Leuven, Belgium. Book of Abstracts, ISBN 90-76019-258, 310-311. Paper on CD, 8 pp. (s. 8)

Bozorgtabar, B., Mahapatra, D. & Thiran, J.-P. (2023). Amae: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays.

Bozorgtabar, B., Mahapatra, D., Vray, G. & Thiran, J.-P. (2020). Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning.

Bozorgtabar, B., Rad, M. S., Ekenel, H. K. & Thiran, J.-P. (2019). Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis.

Bozorgtabar, B., Rad, M. S., Ekenel, H. K. & Thiran, J.-P. (2019). Learn to synthesize and synthesize to learn.

Bozorgtabar, B., Mahapatra, D., von Teng, H., Pollinger, A., Ebner, L., Thiran, J.-P. & Reyes, M. (2019). Informative sample generation using class aware generative adversarial networks for classification of chest Xrays.

Böttner, C., Jakobsen, F. W., Nielsen, T., Winsborrow, M., Polteau, S., Mazzini, A., Planke, S., Andresen, K. J., Millinge, O. J. S., Asif, M. R., Laberg, J. S., Hopper, J., Myklebust, R. & Seidenkrantz, M. S. (2025). Natural hydrocarbon seepage at the Northeast Greenland continental shelf. Communications Earth and Environment, 6(1), Artikel 879. https://doi.org/10.1038/s43247-025-02932-8

Bording, T. S., Asif, M. R., Barfod, A. S., Larsen, J. J., Zhang, B., Grombacher, D. J., Christiansen, A. V., Engebretsen, K. W., Pedersen, J. B., Maurya, P. K. & Auken, E. (2021). Machine learning based fast forward modelling of ground-based time-domain electromagnetic data. Journal of Applied Geophysics, 187, Artikel 104290. https://doi.org/10.1016/j.jappgeo.2021.104290

Bochtis, D., Sørensen, C. A. G., Jørgensen, R. N. & Green, O. (2009). Modelling of material handling operations using controlled traffic. Biosystems Engineering, 103(4), 397-408. https://doi.org/10.1016/j.biosystemseng.2009.02.006

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

Blanchet, V., Lochbrunner, S., Schmitt, M., P. Shaffer, J., Larsen, J. J., Z. Zgierski, M., Seideman, T. & Stolow, A. (2000). Towards disentangling coupled electronic-vibrational dynamics in ultrafast non-adiabatic processes. Faraday Discussions, 115, 33-48. https://doi.org/10.1039/b001138j

Blackmore, B. S., Fountas, S., Vougioukas, S., Tang, L., Sørensen, C. A. G. & Jørgensen, R. (2004). A method to define agricultural robot behaviours. I Proc. int. conf. on Mechatronics & Robotics (MECHROB) 2004, Aachen, Germany. Vol. III (s. 1197-1200)

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Revideret 13.11.2025

AU Engineering