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Department of Electrical and Computer Engineering

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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Rubak, J. A. B., Naveed, K., Jain, S., Esterle, L., Iosifidis, A. & Pauwels, R. (2025). Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning. ArXiv. https://doi.org/10.48550/arXiv.2509.06553

Jaume, G., Bozorgtabar, B., Ekenel, H. K., Thiran, J.-P. & Gabrani, M. (2018). Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks.

Morales, T., Sarabakha, A. & Kayacan, E. (2020). Image Generation for Efficient Neural Network Training in Autonomous Drone Racing. In 2020 International Joint Conference on Neural Networks (IJCNN) Article 9206943 IEEE. https://doi.org/10.1109/IJCNN48605.2020.9206943

Rehman, N., Khan, M. M., Sohaib, M. I., Jehanzaib, M., Ehsan, S. & McDonald-Maier, K. (2014). Image fusion using multivariate and multidimensional EMD. In 2014 IEEE International Conference on Image Processing (ICIP) IEEE. https://doi.org/10.1109/icip.2014.7026035

Tu, G. J., Karstoft, H., Pedersen, L. J. & Jørgensen, E. (2015). Illumination and Reflectance Estimation with its Application in Foreground Detection. Sensors, 15(9), 21407-21426. https://doi.org/10.3390/s150921407

Asif, M. R., Kass, A., Larsen, J. J. & Christiansen, A. V. (2025). Identifying Induced Polarization Effects in TEM Data by Deep Learning. In 7th Asia Pacific Meeting on Near Surface Geoscience and Engineering, NSGE 2025 (pp. 1-5). European Association of Geoscientists and Engineers, EAGE. https://doi.org/10.3997/2214-4609.202572056

Asif, M. R., Kass, A., Larsen, J. J. & Christiansen, A. V. (2024). Identification of Induced Polarization affected Transient Electromagnetic Data by Deep Learning. Poster session presented at AGU Fall meeting 2024, Washington DC, United States. https://ui.adsabs.harvard.edu/abs/2024AGUFMNS23B1231A/abstract

Jeppesen, J. H., Jacobsen, R. H., Nyholm Jørgensen, R., Halberg, A. & Toftegaard, T. S. (2017). Identification of High-Variation Fields based on Open Satellite Imagery. Advances in Animal Biosciences, 8(2), 388-393. https://doi.org/10.1017/S2040470017000693

Cielecki, P. P., Hardenberg, M., Amariei, G., Henriksen, M. L., Hinge, M. & Klarskov, P. (2023). Identification of black plastics with terahertz time-domain spectroscopy and machine learning. Scientific Reports, 13(1), Article 22399. https://doi.org/10.1038/s41598-023-49765-z

Gheisarnejad Chirani, M., Mosayebi, M., Rafiei Foroushani, M., Boudjadar, J. & Khooban, M. H. (2023). Hybrid Power Systems for Smart Marine Power Grids: Demonstration and Case Study. In Electric Transportation Systems in Smart Power Grids (pp. 473-492). CRC Press. https://doi.org/10.1201/9781003293989

Ali, M., Khan, A. & Rehman, N. U. (2017). Hybrid multiscale wind speed forecasting based on variational mode decomposition. International Transactions on Electrical Energy Systems, 28(1), e2466. https://doi.org/10.1002/etep.2466

Sridharan, K. S., Højlund, A., Sunde, N., Johnsen, E. L., Johansen, L. G., Beniczky, S. & Østergaard, K. (2016). How do treatments affect somatosensory processing in Parkinson's disease - A MEG study. Poster session presented at PhD Day 2016, Aarhus, Denmark.

Olesen, C. R., Nyholm Jørgensen, R., Laursen, M. S., Larsen, R. & Rosendahl Bjorholm, S. (2017). Høstdrab af rådyr: analyse af høstdrabsproblemets omfang i Danmark samt test af dronebåret losurin som afværgemetode. Danmarks Jægerforbund. http://udgivelser.jaegerforbundet.dk/2017/foldere-og-tryksager/hoestdrab-af-raadyr-analyse-af-hoestdrabsproblemets-omfang-i-danmark-samt-test-af-dronebaaret-losurin-som-afvaergemiddel/?page=2

Sørensen, C. A. G., Nørremark, M., Jørgensen, R., Jensen, K., Jensen, L. A. & Maagaard, J. (2007). Hortibot: Feasibility study of a plant nursing robot performing weeding operations - part IV: Paper No. 077019. In Ikke angivet ASABE - American Society of Agricultural and Biological Engineers. http://asae.frymulti.com/techpapers.asp?confid=min2007

Nørremark, M., Sørensen, C. A. G. & Jørgensen, R. N. (2006). HortiBot: Comparison of present and future phytotechnologies for weed control - part III: Paper No. 067023. In Proc. ASABE Annual Meeting http://asae.frymulti.com/request.asp?JID=5&AID=21017&CID=por2006&T=1

Jørgensen, R., Sørensen, C. A. G., Maagaard, J., Havn, I., Jensen, K., Søgaard, H. T. & Sørensen, L. B. (2007). HortiBot: A System Design of a Robotic Tool Carrier for High-tech PlantNursing. Agricultural Engineering International: CIGR Journal, IX. http://cigr-ejournal.tamu.edu/submissions/volume9/ATOE%2007%20006%20Jorgensen%2011July2007.pdf

Jørgensen, R., Sørensen, C. G., Pedersen, J. M., Havn, I., Jensen, K., Søgaard, H. T. & Sørensen, L. B. (2006). Hortibot: A system design of a robotic tool carrier for high-tech plant nursing. In Proc. Automation Technology for Off-Road Equipment (pp. 13-22) https://cigrjournal.org/index.php/Ejounral/article/view/937/931

Sørensen, C. G., Jørgensen, R. N., Pedersen, J. M. & Nørremark, M. (2006). HortiBot: Application of Quality Function Deployment (QFD) Method for Horticultural Robotic Tool Carrier Design Planning - Part II: Paper No. 067021. In Proc. ASABE Annual Meeting http://asae.frymulti.com/request.asp?JID=5&AID=21015&CID=por2006&T=1

Jørgensen, R. N. & Vinstrup, A. B. (2005). Hortibot - A plant nursing robot. DaNet NewsLetter. https://drive.google.com/file/d/0B7n4HMXh8Cx3MXhxVWFyeTZYdjQ/view?resourcekey=0-qVqhxrsxiKtf5MQNz4aykQ

Jørgensen, R., Sørensen, C. G., Jensen, K., Søgaard, H. T., Pedersen, J. M., Sørensen, L. B., Havn, I., Sibbesen, S. & Nielsen, J. (2006). Hortibot: An Accessory Kit Transforming a Slope Mower into a Robotic Tool Carrier for High-Tech Plant Nursing - Part I: Paper No. 063082. In ASABE Annual Meeting ASABE - American Society of Agricultural and Biological Engineers. http://asae.frymulti.com/request.asp?JID=5&AID=20886&CID=por2006&T=1

Jørgensen, R. N. (2009). Horsens-robot sparer penge og hjælper miljøet: HORSENS - Forskere fra det tidligere Bygholm Forskningscenter repræsenterer i disse dage Danmark ved den store hollandske udstilling for mark-robotter, Field Robot Event. Horsens Folkeblad. http://www.horsens-folkeblad.dk/?artikel=2892

Green, O., Jørgensen, R. N., Sørensen, C. A. G., Kristensen, K., Bochtis, D. & Sørensen, C. A. G. (2010). Hjulbelastning og dæktryks påvirkning af græsudbyttet. In Indvielse af nye high-tech forskningsfaciliteter, Foulum, Danmark https://www.researchgate.net/publication/233725865_Hjulbelastning_og_daektryks_pavirkning_af_graesudbyttet

Rafiei, M., Asif, MR., Nørremark, M. & Sørensen, CAG. (2024). High-Resolution Near-Surface Soil Water Content Estimation, Using Remote Sensing Data and Deep Computer Vision Methods. In NSG 2024 30th European Meeting of Environmental and Engineering Geophysics (pp. 1-5). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202420111

Jørgensen, R., Nørremark, M., Sørensen, C. A. G. & Andersen, N. A. (2005). High-level control of robots for row crops. In Proc. XXXI CIOSTA-CIGR V cong. on Increasing Work Efficiency in Agriculture, Horticulture and Forestry, University of Hohenheim, Stuttgart, Germany. ISBN 3-00-016346-8 (pp. 123-132) https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=a43aba0d78f0e4c0c89d8e4fd1df2dc2612e6985

Bjerge, K., Geissmann, Q., Alison, J., Mann, H. M. R., Høye, T. T., Dyrmann, M. & Karstoft, H. (2023). Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection. bioRxiv. https://doi.org/10.1101/2023.06.29.546989

Bjerge, K., Geissmann, Q., Alison, J., Mann, H. M. R., Høye, T. T., Dyrmann, M. & Karstoft, H. (2023). Hierarchical classification of insects with multitask learning and anomaly detection. Ecological Informatics, 77, Article 102278. https://doi.org/10.1016/j.ecoinf.2023.102278

Laursen, S. R. L., Holm, H. A. & Nielsen, S. H. (2022). Heart rate-based dynamic sound intervention - a pilot study. In Proceedings of the EUROREGIO/BNAM2022 Conference: Joimt Acoustics Conference (pp. 473-481). https://www.conforg.fr/erbnam2022/output_directory/data/articles/000032.pdf

Malekian, R., Curran, K., Pedersen, C. F., Cao, B. & Qi, X. (2018). Guest Editorial Special Issue on Sensor Technologies for Connected Cars: Devices, Systems and Modeling. IEEE Sensors Journal, 18(12), 4775-4776. https://doi.org/10.1109/JSEN.2018.2830478

Skov, K., Mandrup, L., Johannsen, L. & Lysgaard, P. (2001). Grundlæggende EMC I: Fænomener og konstruktionsprincipper. In Grundlæggende EMC I: Fænomener og konstruktionsproncipper (1.3 ed., Vol. T-306, pp. 164).

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. In Proceedings of the 14th International Conference on Precision Agriculture Article 5145 International Society of Precision Agriculture. https://ispag.org/proceedings/?action=abstract&id=5145

Griffiths, M. P., Grombacher, D., Mashhadi, S. R. & Larsen, J. J. (2024). Ground-Truth Validation of T₂ Estimates From Steady-State Surface NMR. Geophysical Research Letters, 51(24), Article e2024GL112094. https://doi.org/10.1029/2024GL112094

Griffiths, M. P., Grombacher, D., Mashhadi, S. R. & Larsen, J. J. (2024). Ground-truth Validation of T₂ Estimates from Steady-state Surface NMR. Abstract from AGU Fall meeting 2024, Washington DC, United States.

Wang, Z., Chen, L., Chen, H., Yang, J. & Rehman, N. U. (2025). Graph signal processing meets machine learning: Multi-scale spatial-temporal ensemble learning methodology for air quality forecasting. Expert Systems with Applications, 291, Article 128538. https://doi.org/10.1016/j.eswa.2025.128538

Nazari, M., Korshoej, A. R. & Rehman, N. U. (2024). Graph-Aided Multivariate Signal Decomposition. In International Conference on Frontiers of Signal Processing (ICFSP) (pp. 169-173) https://doi.org/10.1109/ICFSP62546.2024.10785500

Madsen, S. L., Dyrmann, M., Nyholm Jørgensen, R. & Karstoft, H. (2019). Generating artificial images of plant seedlings using generative adversarial networks. Biosystems Engineering, 187, 147-159. https://doi.org/10.1016/j.biosystemseng.2019.09.005

Mahapatra, D., Bozorgtabar, B., Ge, Z. & Reyes, M. (2024). GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification. Medical Image Analysis, 93, Article 103075. https://doi.org/10.1016/j.media.2023.103075

Nielsen, M. R., Laursen, M. S., Jonassen, M. S., Nielsen, K. & Nyholm Jørgensen, R. (2013). Gall mite inspection on dormant black currant buds using machine vision. In Precision Agriculture 2013 - Papers Presented at the 9th European Conference on Precision Agriculture, ECPA 2013 (pp. 517-524) http://www.scopus.com/inward/record.url?scp=84893421680&partnerID=8YFLogxK

Nyholm Jørgensen, R., Sørensen, R. A., Laursen, M. S., Rasmussen, J. & Nielsen, J. (2017). FutureCropping Generering af tidselkort med Pix4D, DeepLearning og QGIS 18AUG2017. Pictures, Video and sound recordings (digital), YouTube. https://www.youtube.com/watch?v=3X8h4U4o2-o

Naveed, K. & Rehman, N. U. (2025). Fully multivariate detrended fluctuation analysis using Mahalanobis norm with application to multivariate signal denoising. Measurement: Journal of the International Measurement Confederation, 242(Part E), Article 116142. https://doi.org/10.1016/j.measurement.2024.116142

Bertelsen, K. K., Duzel, J., Nielsen, S. H. & Jørgensen, R. N. (2011). FruitGrowth - Gasburning in Orchards - Environment friendly weed control. http://www.horteam-expo.dk/

Wu, S., Deng, S., Wei, X., Asif, M. R., Vignoli, G. & Farquharson, C. G. (2025). Frontiers in electromagnetic geophysics - Introduction. Geophysics, 90(3), WAi-WAii. https://doi.org/10.1190/geo2025-0312-spseintro.1

Jensen, K., Bøgild, A., Nielsen, S. H., Christiansen, M. P. & Jørgensen, R. N. (2011). FroboMind, proposing a conceptual architecture for agricultural field robot navigation. http://www.njf.nu/site/seminarRedirect.asp?intSeminarID=441&p=1004

Grombacher, D., Griffiths, M. P., Liu, L., Vang, M. & Larsen, J. J. (2022). Frequency Shifting Steady-State Surface NMR Signals to Avoid Problematic Narrowband-Noise Sources. Geophysical Research Letters, 49(7), Article e2021GL097402. https://doi.org/10.1029/2021GL097402

Sveistrup, D., Jørgensen, R. N., Green, O., Nørremark, M. & Sørensen, C. A. G. (2011). Fremtidens markforsøg. http://pure.agrsci.dk:8080/research/fremtidens_markforsoeg(4162764)/

Bach, P. D., Jørgensen, R. N. & Jensen, K. (2012). Fremtidens landbrugsmaskiner kører selv. Dagbladet Holstebro-Struer, 14-15. https://docs.google.com/open?id=0B7n4HMXh8Cx3Qk1DTHRfMHo2ZEU

Jørgensen, R. (2007). Fremtiden for Forskningscenter Bygholm. Horsens Folkeblad, 2.

Gul, S., Siddiqui, M. F. & Rehman, N. U. (2019). FPGA Based Real-Time Implementation of Online EMD With Fixed Point Architecture. IEEE Access, 7, 176565-176577. https://doi.org/10.1109/access.2019.2957819

Malik, Q. W., Rehman, N. U., Gull, S., Ehsan, S. & McDonald-Maier, K. D. (2019). FPGA-Based Real-Time Implementation of Bivariate Empirical Mode Decomposition. Circuits, Systems, and Signal Processing, 38(1), 118-137. https://doi.org/10.1007/s00034-018-0844-2

Gul, S., Siddiqui, M. F. & Rehman, N. U. (2020). FPGA-Based Design for Online Computation of Multivariate Empirical Mode Decomposition. IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 67(12), 5040-5050. Article 9159690. https://doi.org/10.1109/TCSI.2020.3012351

Jensen, T., Green, O., Munkholm, L. J. & Karstoft, H. (2016). Fourier and granulometry methods on 3D images of soil surfaces for evaluating soil aggregate size distribution. American Society of Agricultural and Biological Engineers. Transactions, 32(5), 609-615. https://doi.org/10.13031/aea.32.10938

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