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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Gentili, M., Tanaka, T., Nichols, V. A., Jørgensen, R. N., Jørgensen, J. R. & Gislum, R. (2025). Investigating weed and companion plant ecosystem services and disservices distribution maps using machine learning: Joint approaches for sustainable weed management. I 20TH EUROPEAN WEED RESEARCH SOCIETY SYMPOSIUM: Joint Approaches for Sustainable Weed Management (s. 340-340). European Weed Research Society. https://doi.org/10.21001/20.weed.research.society.symposium.2025

Gebreyesus, G., Cheruiyot Bett , R., Nakimbugwe, D., Hansen, L. S., Nielsen, H. M., Karstoft, H., Bjerge, K., Nkirote Kunyanga , C., MBI Tanga, C., Mwikirize, C., Akol, R., Katumba, A., Khamis, F., Kinyua, J., Walusimbi, S., Geoffrey, S., Roos, N. & Sahana, G. (2024). Prospects of implementing black soldier fly (BSF) selective breeding in Kenya and Uganda: Status from the FlyGene Project. Abstract fra Insects for the Green Economy: Sustainable Food
Systems and Livelihoods in Africa, Nairobi, Kenya. https://qgg.au.dk/fileadmin/site_files/mb/QGG/Billeder/FLYgene/book-of-abstracts-insects-for-the-green-economy-conference-feb2024.pdf

Gebreyesus, G., Nawoya, S., Ssemakula, F., Karstoft, H., Bjerge, K., Kunyanga, C. N., Mwikirize, C., Akol, R., Katumba, A., Nakimbugwe, D. & Geissmann, Q. (2024). Innovative phenotyping systems to advance selective breeding in black soldier fly: status from the FlyGene project. Journal of Insects as Food and Feed, 10(Supplement 1 (2024) S1–S353), S250. Artikel Volume 10, Supplement 1 (2024). https://doi.org/10.1163/23524588-20241013

Gaikwad, N., Liu, L., Griffiths, M. P., Vang, M. Ø., Grombacher, D. & Larsen, J. J. (2022). Thermal Model of the Apsu Transmitter for Lightweight and Compact Heat Sink Design. 11-13. Abstract fra The 8th International Workshop on Magnetic Resonance Sounding, Strasbourg, Frankrig. https://mrs2021.sciencesconf.org/data/pages/proceedings_MRS2021_distrib_v2.pdf

Gaikwad, N., Liu, L., Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2023). A High Duty Cycle Transmitter Unit for Steady-State Surface NMR Instruments. (s. 1-22). Geoscientific Instrumentation, Methods and Data Systems https://doi.org/10.5194/gi-2023-5

Gaikwad, N. B., Liu, L., Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2025). A high-duty-cycle transmitter unit for steady-state surface NMR instruments. Geoscientific Instrumentation, Methods and Data Systems, 14(2), 139-151. https://doi.org/10.5194/gi-14-139-2025

Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R. & Garibaldi, J. M. (2018). Input Uncertainty Sensitivity Enhanced Non-Singleton Fuzzy Logic Controllers for Long-Term Navigation of Quadrotor VTOL UAVs. IEEE - ASME Transactions on Mechatronics, 23(2), 725-734. https://doi.org/10.1109/TMECH.2018.2810947

Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R. & Garibaldi, J. M. (2017). Similarity-based Non-Singleton Fuzzy Logic Control for Improved Uncertainty Handling in Quadrotor UAVs. I Fuzzy Systems (FUZZ-IEEE), 2017 IEEE International Conference on (s. 1-6). Artikel 8015440 IEEE. https://doi.org/10.1109/FUZZ-IEEE.2017.8015440

Fu, C., Sarabakha, A. & Kayacan, E. (2016). A Comparative Study on the Control of Quadcopter UAVs by using Singleton and Non-Singleton Fuzzy Logic Controllers. I 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 (s. 1023-1030). Artikel 7737800 IEEE. https://doi.org/10.1109/FUZZ-IEEE.2016.7737800

Frisk, O., Dormann, F., Lillelund, C. M. & Pedersen, C. F. (2021). Super-convergence and Differential Privacy: Training faster with better privacy guarantees. I 55th Annual Conference on Information Sciences and Systems (s. 1-6). Artikel 9400274 IEEE. https://doi.org/10.1109/CISS50987.2021.9400274

Frederiksen, T. & Larsen, J. J. (2019). Detection of Capacitive Couplings in Ground-Based TEM Data with a 1D Convolutional Neural Network. Afhandling præsenteret på 25th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience Conference and Exhibition 2019, NSG 2019, The Hague, Holland.

Fountas, S., Blackmore, B. S., Vougioiukas, S., Tang, L., Sørensen, C. A. G. & Jørgensen, R. (2007). Decomposition of Agricultural tasks into Robotic Behaviours. Agricultural Engineering International: CIGR Journal, IX. http://cigr-ejournal.tamu.edu/submissions/volume9/PM%2007%20006%20Blackmore%20final%202Oct2007.pdf

Fiandaca, G., Olsson, P.-I., Auken, E., Larsen, J. J., Maurya, P. K. & Dahlin, T. (2015). Doubling the Spectrum of Time-Domain Induced Polarization: Removal of Harmonic Noise and Self-Potential Drift. Abstract fra AGU Fall Meeting 2015, San Fransisco, USA. https://agu.confex.com/agu/fm15/meetingapp.cgi/Paper/75469

Fiandaca, G., Olsson, P. I., Larsen, J. J., Dahlin, T. & Auken, E. (2016). Doubling the spectrum of time-domain induced polarization by harmonic de-noising, drift/spike removal and tapered gating. I 22nd European Meeting of Environmental and Engineering Geophysics, Near Surface Geoscience 2016 European Association of Geoscientists and Engineers, EAGE. https://doi.org/10.3997/2214-4609.201602021

Ferrarini, B., Ehsan, S., Leonardis, A., Rehman, N. U. & McDonald-Maier, K. D. (2018). Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database. IEEE Access, 6, 8564-8573. https://doi.org/10.1109/access.2018.2795460

Ferrarini, B., Ehsan, S., Rehman, N. U., Leonardis, A. & McDonald-Maier, K. D. (2016). Automatic Selection of the Optimal Local Feature Detector. I Lecture Notes in Computer Science (s. 284-289). Springer International Publishing. https://doi.org/10.1007/978-3-319-41501-7_32

Farsizadeh, H., Gheisarnejad, M., Mosayebi, M., Rafiei Foroushani, M. & Khooban, M. H. (2020). An Intelligent and Fast Controller for DC/DC Converter Feeding CPL in a DC Microgrid. IEEE Transactions on Circuits and Systems II: Express Briefs, 67(6), 1104-1108. Artikel 8764402. https://doi.org/10.1109/TCSII.2019.2928814

Farkhani, S., Kragh, M. F., Christiansen, P. H., Nyholm Jørgensen, R. & Karstoft, H. (2019). Sparse-to-Dense Depth Completion in Precision Farming. I Proceedings of the 3rd International Conference on Vision, Image and Signal Processing, ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing Artikel 35 Association for Computing Machinery. https://doi.org/10.1145/3387168.3387230

Farkhani, S., Skovsen, S. K., Mortensen, A. K., Laursen, M. S., Nyholm Jørgensen, R. & Karstoft, H. (2020). Initial evaluation of enriching satellite imagery using sparse proximal sensing in precision farming. I C. M. U. Neale & A. Maltese (red.), Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII (Bind 11528). Artikel 115280A SPIE - International Society for Optical Engineering. https://doi.org/10.1117/12.2573626

Farkhani, S., Skovsen, S. K., Dyrmann, M., Nyholm Jørgensen, R. & Karstoft, H. (2021). Weed classification using explainable multi-resolution slot attention. Sensors, 21(20), Artikel 6705. https://doi.org/10.3390/s21206705

Eriksen, J., Frandsen, T. S., Knudsen, L., Skovsen, S., Nyholm Jørgensen, R., Steen, K. A., Green, O. & Rasmussen, J. (2019). Nitrogen fertilization of grass-clover leys. I O. Huguenin-Elie, B. Studer, R. Kölliker, D. Reheul, M. Probo, P. Barre, U. Feuerstein, I. Roldan-Ruiz, P. Mariotte & A. Hopkins (red.), Improving sown grasslands through breeding and management: Proceedings of the Joint 20th Symposium of the European Grassland Federation and the 33rd Meeting of the EUCARPIA Section "Fodder Crops and Amenity Grasses", Zürich, Switzerland, 24-27 June 2019 (s. 103-109). Wageningen Academic Publishers.

Eriksen, T. & Rehman, N. U. (2023). Data-driven nonstationary signal decomposition approaches: a comparative analysis. Scientific Reports, 13(1), Artikel 1798. https://doi.org/10.1038/s41598-023-28390-w

Engholm, R., Karstoft, H. & Jensen, E. B. V. (2009). Adaptive kernel filtering used in video processing. I Visual Communications and Image Processing 2009 (s. 72571E). SPIE - International Society for Optical Engineering. https://doi.org/10.1117/12.808155

Ehsan, S., Clark, A., Leonardis, A., Rehman, N. U., Khaliq, A., Fasli, M. & McDonald-Maier, K. (2016). A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors. Remote Sensing, 8(11), 928. https://doi.org/10.3390/rs8110928

Ehsan, S., Clark, A., Rehman, N. & McDonald-Maier, K. (2015). Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems. Sensors, 15(7), 16804-16830. https://doi.org/10.3390/s150716804

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

Dyrmann, M., Mortensen, A. K., Midtiby, H. S. & Nyholm Jørgensen, R. (2016). Pixel-wise classification of weeds and crops in images by using a Fully Convolutional neural network. Afhandling præsenteret på International Conference on Agricultural Engineering 2016, Aarhus, Danmark. http://conferences.au.dk/uploads/tx_powermail/cigr2016paper_semanticsegmentation.pdf

Dyrmann, M., Midtiby, H. S. & Nyholm Jørgensen, R. (2016). Evaluation of intra variability between annotators of weed species in color images. Afhandling præsenteret på International Conference on Agricultural Engineering 2016, Aarhus, Danmark. http://conferences.au.dk/uploads/tx_powermail/cigr2016paper_annotation.pdf

Dyrmann, M., Karstoft, H. & Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151(November), 72-80. https://doi.org/10.1016/j.biosystemseng.2016.08.024

Dyrmann, M., Nyholm Jørgensen, R. & Midtiby, H. S. (2017). Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network. Advances in Animal Biosciences, 8(2), 842-847. https://doi.org/10.1017/S2040470017000206

Dyrmann, M., Skovsen, S., Sørensen, R. A., Nielsen, P. R. & Nyholm Jørgensen, R. (2018). Using a fully convolutional neural network for detecting locations of weeds in images from cereal fields. Abstract fra International Conference on Precision Agriculture, Montréal, Quebec, Canada.

Dyrmann, M., Skovsen, S., Laursen, M. S. & Nyholm Jørgensen, R. (2018). Using a fully convolutional neural network for detecting locations of weeds in images from cereal fields. I Proceedings of the 14th International Conference on Precision Agriculture International Society of Precision Agriculture. https://www.ispag.org/proceedings/?action=download&item=5081

Dyrmann, M., Mortensen, A. K., Linneberg, L., Høye, T. T. & Bjerge, K. (2021). Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning. Sensors (Switzerland), 21(18), Artikel 6126. https://doi.org/10.3390/s21186126

Dormann, F., Frisk, O., Andersen, L. N. & Pedersen, C. F. (2021). Not All Noise Is Accounted Equally: How Differentially Private Learning Benefits From Large Sampling Rates. I 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021 IEEE. https://doi.org/10.1109/MLSP52302.2021.9596307

D'Inverno, G. A., Moradizadeh, S., Salavatidezfouli, S., Africa, P. C. & Rozza, G. (2025). Mesh-informed Reduced Order Models for aneurysm rupture risk prediction. Journal of Computational and Applied Mathematics, 470, Artikel 116727. https://doi.org/10.1016/j.cam.2025.116727

Deria, A., Mahapatra, D., Bozorgtabar, B., Chakraborty, M., Chakraborty, S. & Roy, S. (2025). MuGa-VTON: Multi-Garment Virtual Try-On via Diffusion Transformers with Prompt Customization.

Dang, V. H., Redder, A., Pham, H. X., Sarabakha, A. & Kayacan, E. (2025). VDS-Nav: Volumetric Depth-Based Safe Navigation for Aerial Robots–Bridging the Sim-to-Real Gap. IEEE Robotics and Automation Letters, 10(10), 11038-11045. https://doi.org/10.1109/LRA.2025.3606806

D’Ambrosio, F., Harbo, M., Contiero, D., Bonfigli, A. R., Cicconi, D., Heuer, N., Roos, A., Pedersen, C. F., Fabbietti, P. & Gagliardi, C. (2024). Preact to lower the risk of falling by customized rehabilitation across Europe: the feasibility study protocol of the PRECISE project in Italy. Frontiers in Public Health, 12, Artikel 1293621. https://doi.org/10.3389/fpubh.2024.1293621

Dalsgaard, P., Pedersen, C. F., Andersen, O. & Bayya, Y. (2008). Using zeros of the z-transform in the Analysis of Speech Signals. Poster-session præsenteret på Speech Analysis and Processing for Knowledge Discovery, Aalborg, Danmark.

Dalsgaard, P., Pedersen, C. F. & Andersen, O. (red.) (2008). Proceedings for ISCA ITRW Speech Analysis and Processing for Knowledge Discovery.

Dalsgaard, P., Pedersen, C. F., Andersen, O. & Bayya, Y. (2008). Using Zeros of the z-transform in the Analysis of Speech Signals. I Proceedings of ISCA ITRW - Speech Analysis and Processing for Knowledge Discovery

Dalgaard, E., Auken, E. & Larsen, J. J. (2012). Adaptive noise cancelling of multichannel magnetic resonance sounding signals. Geophysical Journal International, 191(1), 88-100. https://doi.org/10.1111/j.1365-246X.2012.05618.x

Dalgaard, E., Auken, E. & Larsen, J. J. (2011). Noise Cancellation of Multichannel Magnetic Resonance Sounding Measurements with Wiener and Adaptive Filters. Abstract fra EAGE Near Surface 2011, Leiceister, Storbritannien. http://www.earthdoc.org/publication/publicationdetails/?publication=53995

Dalgaard, E. B., Auken, E. & Larsen, J. J. (2012). Optimized Wiener filtering with the Numis Poly system. Abstract fra Magnetic Resonance in the Subsurface, Hannover, Tyskland.

Dalgaard, E., Auken, E. & Larsen, J. J. (2012). Model-based noise cancelling of multichannel magnetic resonance sounding measurements. I Near Surface Geoscience 2012 European Association of Geoscientists and Engineers, EAGE. http://www.scopus.com/inward/record.url?scp=84898482260&partnerID=8YFLogxK

Dalgaard, E. B., Christiansen, P., Larsen, J. J. & Auken, E. (2014). A temporal and spatial analysis of anthropogenic noise sources affecting SNMR. Journal of Applied Geophysics, 110, 34-42. https://doi.org/10.1016/j.jappgeo.2014.08.009

Cros, O., Volden, M., Flaaris, J. J., Brix, L. C., Pedersen, C. F., Hansen, K. V., Larsen, O. V., Østergaard, L. R. & Haase, J. (2002). Simulating the puncture of the human ventricle. I 10th Annual Medicine Meets Virtual Reality (MMVR) Conference, Digital upgrades: Applying Moore's Law to health

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), Artikel 22399. https://doi.org/10.1038/s41598-023-49765-z

Christiansen, M. P., Jensen, K., Ellekilde, L.-P. & Jørgensen, R. N. (2011). Localization in orchards using Extended Kalman Filter for sensor-fusion - A FroboMind component.. Abstract fra NJF seminar 441 Automation and System Technology in Plant Production, Herning, Danmark. http://www.njf.nu/site/seminarRedirect.asp?intSeminarID=441&p=1004

Christiansen, M. P., Larsen, M. & Nyholm Jørgensen, R. (2013). Collaborative Model Based Development of Adaptive Controller Settings for a Load-carrying Vehicle with Changing Loads. Afhandling præsenteret på CIOSTA XXXV Conference, Billund, Danmark. https://www.researchgate.net/publication/333658745_Collaborative_Model_Based_Development_of_Adaptive_Controller_Settings_for_a_Load-carrying_Vehicle_with_Changing_Loads

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Revideret 13.11.2025

AU Engineering