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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Pedersen, C. F., Andersen, O. & Dalsgaard, P. (2009). ZZT-domain Immiscibility of the Opening and Closing Phases of the LF GFM under Frame Length Variations. Proceedings of the International Conference on Spoken Language Processing, 1099-1102.

Pedersen, C. F., Andersen, O. & Dalsgaard, P. (2009). ZZT-domain Immiscibility of the Opening and Closing Phases of the LF GFM under Frame Length Variations. Poster session presented at International Conference on Spoken Language Processing, Interspeech, Brighton, United Kingdom.

Pedersen, C. F. (2012). Z-transform Zeros in Mixed Phase Deconvolution of Speech. Aalborg University, Faculty of Engineering and Science, Dept. of Electronic Systems, Niels Jernes Vej 10, 9220 Aalborg East, Denmark.

Sarabakha, A. & Kayacan, E. (2016). Y6 tricopter autonomous evacuation in an Indoor Environment using Q-learning algorithm. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 5992-5997). Article 7799189 IEEE. https://doi.org/10.1109/CDC.2016.7799189

Jensen, K., Jørgensen, R. N., Bøgild, A., Jørgensen, O. J., Nielsen, S. H. & Persson, R. B. (2011). Work in progress: Robotics mapping of landmine and UXO contaminated areas.

Miranda, J., Cabral, J., Ravelo, B., Wagner, S. R., Pedersen, C. F., Memon, M. & Mathiesen, M. (2015). Wireless Authentication Platform for Healthcare Applications. In 2015 IEEE International Wireless Symposium (IWS) (pp. 1-4). IEEE. https://doi.org/10.1109/IEEE-IWS.2015.7164620

Miranda, J., Cabral, J., Ravelo, B., Wagner, S. R., Pedersen, C. F., Memon, M. & Mathiesen, M. (2015). Wireless Authentication Platform for Healthcare Applications. Poster session presented at IEEE MTT-S International Wireless Symposium, Shenzhen, China.

Steen, K. A., Therkildsen, O. R., Karstoft, H. & Green, O. (2012). Wildlife Communication: Electrical and Computer Engineering. https://pure.au.dk/portal/files/45572923/samlet_TCR_7_ver2.pdf

Khan, T. M., Khan, M. A. U., Rehman, N. U., Naveed, K., Afridi, I. U., Naqvi, S. S. & Raazak, I. (2022). Width-wise vessel bifurcation for improved retinal vessel segmentation. Biomedical Signal Processing and Control, 71(Part A), Article 103169. https://doi.org/10.1016/j.bspc.2021.103169

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), Article 1580. https://doi.org/10.3390/s18051580

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

Pati, P., Jaume, G., Ayadi, Z., Thandiackal, K., Bozorgtabar, B., Gabrani, M. & Goksel, O. (2023). Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer. https://doi.org/10.1016/j.media.2023.102915

Naveed, K. & Rehman, N. U. (2020). Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics. IEEE Transactions on Signal Processing, 68, 5997-6010. Article 9234624. https://doi.org/10.1109/TSP.2020.3029659

Liu, L., Grombacher, D., Osterman, G., Auken, E. & Larsen, J. J. (2019). Vs data space retrieval and inversion for surface NMR. Paper presented at 25th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience Conference and Exhibition 2019, NSG 2019, The Hague, Netherlands.

Oleksiienko, I., Nousi, P., Passalis, N., Tefas, A. & Iosifidis, A. (2024). Vpit: real-time embedded single object 3D tracking using voxel pseudo images. Neural Computing and Applications, 36(32), 20341-20354. https://doi.org/10.1007/s00521-024-10259-2

Amer, A. H. K. S. A., Mehndiratta, M., Sejersen, J. L. F., Pham, X. H. & Kayacan, E. (2024). Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection. In 2023 21st International Conference on Advanced Robotics (ICAR) (pp. 431-438). IEEE. https://doi.org/10.1109/ICAR58858.2023.10406329

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

Olesen, C. R., Nielsen, L., Hald, A. B., Fris, H. J., Madsen, C. F., Nyholm Jørgensen, R., Larsen, R. & Laursen, M. S. (2015). Vildtforvaltning i forbindelse med biomassehøst i ådale: Bedre forhold for vildtet og færre påkørsler. http://www.natlan.dk/Baggrund%20projekter/Vildtforvaltning_biomasse/Slutrapport_Vildtforvaltning%20i%20forbindelse%20med%20biomasseh%C3%B8st%20i%20%C3%A5dal_31102015.pdf

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

Jakobsen, M. O., Herskind, E. S., Bjerge, K., Ahrendt, P., Pedersen, C. F. & Knudsen, M. B. (2021). Vibration signatures in ball bearings as a function of lubricant viscosity ratio κ, under alternating lubrication conditions. Tribology International, 156, Article 106840. https://doi.org/10.1016/j.triboint.2020.106840

Lillelund, C. M., Pedersen, C. F., Harbo, M. & Fibiger Smed, C. (2021). Vejen til genoptræning går gennem data. Medicoteknik, 8(3), 18-19. https://ipaper.ipapercms.dk/TechMedia/Medicoteknik/2021/3/?page=18

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

Oleksiienko, I., Nousi, P., Passalis, N., Tefas, A. & Iosifidis, A. (2023). Variational Voxel Pseudo Image Tracking. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 323-328). IEEE. https://doi.org/10.1109/SSCI52147.2023.10371810

Oleksiienko, I., Tran, D. T. & Iosifidis, A. (2022). Variational Neural Networks implementation in Pytorch and JAX[Formula presented]. Software Impacts, 14, Article 100431. https://doi.org/10.1016/j.simpa.2022.100431

Oleksiienko, I., Tran, D. T. & Iosifidis, A. (2023). Variational Neural Networks. Procedia Computer Science, 222, 104-113. https://doi.org/10.1016/j.procs.2023.08.148

Naveed, K. & Ur Rehman, N. (2024). Variational Mode Decomposition Denoising Using Anderson-Darling Statistics. In 2024 9th International Conference on Frontiers of Signal Processing, ICFSP 2024 (pp. 189-193). IEEE. https://doi.org/10.1109/ICFSP62546.2024.10785266

Naveed, K., Meldgaard Madsen, L., Grombacher, D. & Larsen, J. J. (2023). Variational Mode Decomposition-Based Processing of Time-Domain Induced Polarization Data. Abstract from NSG2023 29th European Meeting of Environmental and Engineering Geophysics, Edinburgh , United Kingdom. https://doi.org/10.3997/2214-4609.202320133

Naveed, K. & Larsen, J. J. (2024). Variational Mode Decomposition Based Processing of Surface NMR Data. In NSG2024, 30th European Meeting of Environmental and Engineering Geophysics European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202420100

Laursen, M. S., Midtiby, H., Jørgensen, R. N. & Krüger, N. (2012). Validation of Modicovi - Monocot and Dicot Coverage Ration Vision based method for real time estimation of Canopy Coverage Ration between Cereal Crops and Dicotyledon Weeds.

Gislum, R. & Nyholm Jørgensen, R., (2018). Valg af biomasseindeks i pilotprojekt om efterafgrøder/biomasse, No. 2018-760-000909, 4 p., Nov 09, 2018.

Jørgensen, R. N., Nørremark, M., Sørensen, C. G. & Andersen, N. A. (2008). Utilising scripting language for unmanned and automated guided vehicles operating within row crops. Computers and Electronics in Agriculture, 62(2), 190-203. https://doi.org/10.1016/j.compag.2008.01.009

Dalsgaard, P., Pedersen, C. F., Andersen, O. & Bayya, Y. (2008). Using Zeros of the z-transform in the Analysis of Speech Signals. In Proceedings of 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. Poster session presented at Speech Analysis and Processing for Knowledge Discovery, Aalborg, Denmark.

Bjerge, K. & Larsen, P. G. (2013). Using VDM in a Co-Simulation Setting for an Industrial Conveyor System. In The Modelling, Identification and Control - MIC 2013 conference ACTA Press. https://doi.org/10.2316/P.2013.794-005

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

Siboska, D. & Karstoft, H. (2015). Using illumination changes to synchronize eye tracking in visual paradigms. In G. Plantier, T. Schlutz, A. Fred & H. Gamboa (Eds.), Biomedical Engineering Systems and Technologies: 7th International Joint Conference, BIOSTEC 2014, Angers, France, March 3-6, 2014, Revised Selected Papers (Vol. 511, pp. 289-298) https://doi.org/10.1007/978-3-319-26129-4_19

Steen, K. A., Christiansen, P., Karstoft, H. & Nyholm Jørgensen, R. (2016). Using Deep Learning to Challenge Safety Standard for Highly Autonomous Machines in Agriculture. Journal of Imaging, 2(1), Article 6. https://doi.org/10.3390/jimaging2010006

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 from 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. In Proceedings of the 14th International Conference on Precision Agriculture International Society of Precision Agriculture. https://www.ispag.org/proceedings/?action=download&item=5081

Olesen, H., Jiang, B., Butkus, A., Schultz, N., Saugstrup, D., Sørensen, L., Pedersen, C. F., Jensen, J. J., Larsen, L. B., Björksten, M., Markopoulos, A., Gkanas, I., Kaldanis, V., Prasad, R., Alm, K., Vila, J. P. & Köstering, N. (2004). User Requirements for PN to Drive the Definition of a Valid Architecture. (1. udgave ed.).

Sørensen, L., Saugstrup, D., Schultz, N., Larsen, J., Jensen, K. L., Larsen, L. B., Pedersen, C. F., Kaldanis, V. & Vila, J. (2005). User centric scenarios for PNs of a valid architecture. Information Society Technologies, My Personal Adaptive Global NET (MAGNET), IST 507102.

Pedersen, J. S., Ahola, T., Alutoin, M., Karamolegkos, P., Larsen, L. B., Eg Larsen, J., G. Nikolakopoulos, I., Z. Patrikakis, C., Pedersen, C. F., Roswall, R., Schultz, N., Tolstrup Sørensen, L. & Tan, S.-E. (2007). Usability of PN services (Low-Fi prototyping): D1.4.1. Information Society Technologies, IST-FP6-IP-027396, My Personal Adaptive Global NET (MAGNET) Beyond, EU.

Midtiby, H. S., Åstrand, B., Jørgensen, O. & Jørgensen, R. N. (2016). Upper limit for context-based crop classification in robotic weeding applications. Biosystems Engineering, 146, 183-192. https://doi.org/10.1016/j.biosystemseng.2016.01.012

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

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

Tang, F., An, X., Yang, H., Xie, Y., Yang, K., Hu, M., Cheng, Z., Zhou, X., Ran, Z., Razzak, I., Feng, Z., Bozorgtabar, B., Deng, J. & Ge, Z. (2025). Unifying Image and Video Understanding in One Vision Encoder. Paper presented at The Thirty-ninth Annual Conference on Neural Information Processing Systems, San Diego, California, United States. https://openreview.net/pdf/46f20263cc2abbd139b0f9be3ada52e0fd7427d5.pdf

Jørgensen, J. R. & Jørgensen, R. N. (2007). Uniformity of wheat yield and quality using sensor assisted application of nitrogen. Precision Agriculture, 8(1-2), 63-73. https://doi.org/10.1007/s11119-006-9029-2

Lillelund, C. M., Magris, M. & Pedersen, C. F. (2023). Uncertainty Estimation in Deep Bayesian Survival Models. In IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI’23) IEEE. https://doi.org/10.1109/BHI58575.2023.10313466

Oleksiienko, I. (2023). Uncertainty Estimation for 3D Object Detection and Tracking. [PhD dissertation, Aarhus University]. Aarhus University.

Amer, A. H. K. S. A., Alvarez Tunon, O., Ugurlu, H. I., Sejersen, J. L. F., Brodskiy, Y. & Kayacan, E. (2024). UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-Generation Framework. In 2023 21st International Conference on Advanced Robotics (ICAR) https://ieeexplore.ieee.org/abstract/document/10406819

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Revised 13.11.2025

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