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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Zullo, H., Cline, K., Parker, M., Buckmire, R., George, J., Gurski, K., Larsen, J. J., Mellor, B. & Oberweiser, J. (2011). Student Surveys: What Do They Think? I K. Cline & H. Zullo (red.), Teaching Mathematics with Classroom Voting, With and Without Clicker (s. 29-34). Mathematical Association of America. http://www.maa.org/publications/ebooks/teaching-mathematics-with-classroom-voting-with-and-without-clickers

Zhou, S., Helwa, M. K., Schoellig, A. P., Sarabakha, A. & Kayacan, E. (2019). Knowledge transfer between robots with similar dynamics for high-accuracy impromptu trajectory tracking. I 2019 18th European Control Conference, ECC 2019 Artikel 8796140 IEEE. https://doi.org/10.23919/ECC.2019.8796140

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

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

Westh Nicolajsen, H., Ahola, T., Fleury, A. M., Milkova Ilieva, T., Eg Larsen, J., Larsen, L. B., G. Nikolakopoulos, I., Z. Patrikakis, C., Pedersen, C. F., Roswall, R., Schultz, N. & Tolstrup Sørensen, L. (2007). Defining Usability of PN Services. Information Society Technologies, IST-FP6-IP-027396, My Personal Adaptive Global NET (MAGNET) Beyond, EU.

Wang, Z., Chen, L., Chen, H. & Rehman, N. U. (2023). Monthly ship price forecasting based on multivariate variational mode decomposition. Engineering Applications of Artificial Intelligence, 125, Artikel 106698. https://doi.org/10.1016/j.engappai.2023.106698

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, Artikel 128538. https://doi.org/10.1016/j.eswa.2025.128538

Wagner, S. R., Hansen, F. O., Pedersen, C. F., Memon, M., Beevi, F. H. A., Mathissen, M., Nielsen, C. & Wesby, O. L. (2013). CareStore Platform for Seamless Deployment of Ambient Assisted Living Applications and Devices. I Pervasive Computing Technologies for Healthcare (PervasiveHealth) 2013: 7th International Conference on (s. 240-243). IEEE. https://doi.org/10.4108/icst.pervasivehealth.2013.252110

Wagner, S. R., Stenner, R., Memon, M., Beevi, F. H. A. & Pedersen, C. F. (2014). Common Ambient Assisted Living Home Platform for Seamless Care. Abstract fra 8th International Conference on Pervasive Computing Technologies for Healthcare, Oldenburg, Tyskland.

Wagner, S. R., Beevi, F. H. A., Pedersen, C. F., Miranda, J., Cabral, J. & Ravelo, B. (2016). Evaluation of Caregivers' First Time Use of a Seamless Deployment Platform. I Proceedings of the International Conference on E-Health, EH 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016 (s. 59-66). IADIS.

Vray, G., Tomar, D., Bozorgtabar, B. & Thiran, J. P. (2024). Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology. IEEE Transactions on Medical Imaging, 43(5), 2021-2032. https://doi.org/10.1109/TMI.2024.3355645

Vray, G., Tomar, D., Gao, X., Thiran, J.-P., Shelhamer, E. & Bozorgtabar, B. (2025). ReservoirTTA: Prolonged Test-time Adaptation for Evolving and Recurring Domains. Curran Associates, Inc. Advances in Neural Information Processing Systems Bind 38

Vassis, S., Todnem, L., Straadt, D., Pedersen, C. F., Noeldeke, B., Resnick, C. M., Glerup, M., Pedersen, T. K., Pauwels, R. & Stoustrup, P. (2025). Improving early detection of temporomandibular joint involvement in juvenile idiopathic arthritis with a clinically interpretable machine learning model. Scientific Reports, 15(1), Artikel 39120. https://doi.org/10.1038/s41598-025-25988-0

Vang, M., Grombacher, D., Griffiths, M. P., Liu, L. & Larsen, J. J. (2023). Technical note: High-density mapping of regional groundwater tables with steady-state surface nuclear magnetic resonance - three Danish case studies. Hydrology and Earth System Sciences, 27(16), 3115-3124. https://doi.org/10.5194/hess-27-3115-2023

Vang, M., Grombacher, D., Larsen, J. J. & Wilson, S. (2023). Small coil surface nuclear magnetic resonance in dynamic river systems in New Zealand. I 2nd Conference on Hydrogeophysics 2023, Held at Near Surface Geoscience Conference and Exhibition 2023, NSG 2023 European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202320070

Vang, M., Grombacher, D., Larsen, J. J. & Wilson, S. (2023). SMALL COIL SURFACE NUCLEAR MAGNETIC RESONANCE FOR SHALLOW RESOLUTION OF RIVERS IN NEW ZEALAND. Proceedings of the Symposium on the Application of Geophyics to Engineering and Environmental Problems, SAGEEP, 2023-April.

Vang, M. Ø., Grombacher, D., Larsen, J. J. & Christiansen, A. V. (2024). Extrapolating sparse SNMR data sets onto dense TEM coverage using spatially variable correlations. Abstract fra AGU Fall meeting 2024, Washington DC, USA.

Vang, M. Ø., Grombacher, D., Larsen, J. J. & Christiansen, A. V. (2024). Clustering on SNMR and TEM parameters over a saline-freshwater interface to resolve hydrogeological layers. I NSG2024, 30th European Meeting of Environmental and Engineering Geophysics (s. 1-5). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202420102

Vang, M. Ø., Grombacher, D., Larsen, J. J. & Wilson, S. (2025). Efficient mapping of complex groundwater systems associated with braided rivers using small coil surface nuclear magnetic resonance. Geophysics, 90(3), 1-12. https://doi.org/10.1190/geo2023-0765.1

Vang, M., Larsen, J. J., Christiansen, A. V. & Grombacher, D. (2025). Alleviating interpretational ambiguity in hydrogeology through clustering-based analysis of transient electromagnetic and surface nuclear magnetic resonance data. Hydrology and Earth System Sciences, 29(20), 5315-5329. https://doi.org/10.5194/hess-29-5315-2025

ur Rehman, N., Ehsan, S., Abdullah, S. M. U., Akhtar, M. J., Mandic, D. P. & McDonald-Maier, K. D. (2015). Multi-Scale pixel-based image fusion using multivariate empirical mode decomposition. Sensors (Switzerland), 15(5), 10923-10947. https://doi.org/10.3390/s150510923

Tu, G. J., Karstoft, H., Pedersen, L. J. & Jørgensen, E. (2013). Foreground detection using loopy belief propagation. Biosystems Engineering, 116(1), 88-96. https://doi.org/10.1016/j.biosystemseng.2013.06.011

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

Tu, G. J. & Karstoft, H. (2015). Logarithmic dyadic wavelet transform with its applications in edge detection and reconstruction. Applied Soft Computing, 26, 193-201. https://doi.org/10.1016/j.asoc.2014.09.044

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

Tomar, D., Vray, G., Mahapatra, D., Roy, S., Thiran, J. P. & Bozorgtabar, B. (2025). Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology. I ISBI 2025 - 2025 IEEE 22nd International Symposium on Biomedical Imaging, Proceedings IEEE Computer Society. https://doi.org/10.1109/ISBI60581.2025.10980748

Tomar, D., Vray, G., Thiran, J. P. & Bozorgtabar, B. (2024). UN-MIXING TEST-TIME NORMALIZATION STATISTICS: COMBATTING LABEL TEMPORAL CORRELATION. Afhandling præsenteret på 12th International Conference on Learning Representations, ICLR 2024, Hybrid, Vienna, Østrig.

Tomar, D., Vray, G., Bozorgtabar, B. & Thiran, J.-P. (2023). TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation.

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.

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

Todnem Bach Christensen, L., 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. I 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA (s. 1-4). IEEE. https://doi.org/10.1109/EMBC53108.2024.10781771

Thorndal, A., Larsen, J. J. & Johansen, L. G. (2012). Active road noise reduction in Audi A8 W12 with adaptive suspension: A feasibility study. Afhandling præsenteret på AES Conference on Automotive Audio, München, Tyskland.

Therkildsen, O. R. & Nyholm Jørgensen, R. (2018). Vildtvenlige Og Simple Høststrategier kan Reducere Høstdrab. Danske Maelkeproducenter, (2), 38-39.

Teimouri, N., Omid, M., Mollazade, K., Mousazadeh, H., Alimardani, R. & Karstoft, H. (2018). On-line separation and sorting of chicken portions using a robust vision-based intelligent modelling approach. Biosystems Engineering, 167, 8-20. https://doi.org/10.1016/j.biosystemseng.2017.12.009

Teimouri, N., Dyrmann, M., Nielsen, P. R., Mathiassen, S. K., Somerville, G. J. & Jørgensen, R. N. (2018). Weed Growth Stage Estimator Using Deep Convolutional Neural Networks. Sensors, 18(5), Artikel 1580. https://doi.org/10.3390/s18051580

Teimouri, N., Dyrmann, M. & Nyholm Jørgensen, R. (2019). A novel spatio-temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images. Remote Sensing, 11(8), Artikel 990. https://doi.org/10.3390/rs11080990

Teimouri, N., Christiansen, M. P., Nyholm Jørgensen, R. & Grøn Sørensen, C. (2019). A deep learning-based approach for crop classification using dual-polarimetric C-band radar data.

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

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)/

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

Suvei, S.-D., Bodenhagen, L., Kiforenko, L., Christiansen, P., Nyholm Jørgensen, R., Buch, A. G. & Krüger, N. (2016). Stereo and active-sensor data fusion for improved stereo block matching. I A. Campilho, A. Campilho & F. Karray (red.), Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings: Proceedings of 13th International Conference ICIAR in Memory of Mohamed Kamel (Bind 9730, s. 451-461). Springer. https://doi.org/10.1007/978-3-319-41501-7_51

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

Stegmüller, T., Lebailly, T., Ðukić, N., Bozorgtabar, B., Tuytelaars, T. & Thiran, J. P. (2025). A SIMPLE FRAMEWORK FOR OPEN-VOCABULARY ZERO-SHOT SEGMENTATION. I 13th International Conference on Learning Representations, ICLR 2025 (s. 72821-72842)

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

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

Stegmüller, T., Lebailly, T., Bozorgtabar, B., Tuytelaars, T. & Thiran, J.-P. (2023). CrOC: Cross-View Online Clustering for Dense Visual Representation Learning.

Steen, K. A., Karstoft, H. & Green, O. (2011). A Multimedia Capture System for Wildlife Studies. Afhandling præsenteret på The Third International Conference on Emerging Network Intelligence, Lissabon, Portugal. http://www.thinkmind.org/index.php?view=article&articleid=emerging_2011_6_50_40032

Steen, K. A., Therkildsen, O. R., Karstoft, H. & Green, O. (2012). A Vocal-Based Analytical Method for Goose Behaviour Recognition. Sensors, 12, 3773-3788. https://doi.org/10.3390/s120303773

Steen, K. A., Therkildsen, O. R., Karstoft, H. & Green, O. (2012). Wildlife Communication: Electrical and Computer Engineering. Aarhus University, Department of Engineering. Technical report Nr. ECE-TR-7 https://pure.au.dk/portal/files/45572923/samlet_TCR_7_ver2.pdf

Steen, K. A., Therkildsen, O. R., Karstoft, H. & Green, O. (2012). Video-Based Detection of Goose Behaviours. Afhandling præsenteret på Agriculture & Engineering for a Healthier Life, Valencia, Spanien. http://cigr.ageng2012.org/images/fotosg/tabla_137_C0358.pdf

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Revideret 13.11.2025

AU Engineering