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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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. In Conference on Precision Crop Protection (Vol. 3). http://www.precision-crop-protection.uni-bonn.de/gk_conference/abstracts_2010.pdf

Kayacan, E., Kayacan, E., Ramon, H. & Saeys, W. (2014). Robust Tube-based Decentralized Nonlinear Model Predictive Control of an Autonomous Tractor-Trailer System. IEEE - ASME Transactions on Mechatronics, 20(1), 447-456. https://doi.org/10.1109/TMECH.2014.2334612

Mehndiratta, M., Kayacan, E., Reyhanoglu, M. & Kayacan, E. (2020). Robust Tracking Control of Aerial Robots Via a Simple Learning Strategy-Based Feedback Linearization. IEEE Access, 8, 1653-1669. https://doi.org/10.1109/ACCESS.2019.2962512

Iqbal, S., Naveed, K., Naqvi, S. S., Naveed, A. & Khan, T. M. (2023). Robust retinal blood vessel segmentation using a patch-based statistical adaptive multi-scale line detector. Digital Signal Processing: A Review Journal, 139, Article 104075. https://doi.org/10.1016/j.dsp.2023.104075

Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2021). Robust hypersphere-based weight imprinting for few-shot learning. In 2020 28th European Signal Processing Conference (EUSIPCO (pp. 1392-1396). Article 9287340 IEEE. https://doi.org/10.23919/Eusipco47968.2020.9287340

Chumachenko, K., Iosifidis, A. & Gabbouj, M. (2021). Robust fast subclass discriminant analysis. In 2020 28th European Signal Processing Conference (EUSIPCO) (pp. 1397-1401). Article 9287557 IEEE. https://doi.org/10.23919/Eusipco47968.2020.9287557

Laakom, F., Raitoharju, J., Nikkanen, J., Iosifidis, A. & Gabbouj, M. (2021). Robust channel-wise illumination estimation. In 32rd British Machine Vision Conference https://arxiv.org/pdf/2111.05681.pdf

Rasmussen, S., Nyboe, N. S., Mai, S. & Larsen, J. J. (2018). Robust cancellation of minimum shift keying-encoded radio interference in data from the transient electromagnetic method. Geophysics, 83(2), E87-E94. https://doi.org/10.1190/GEO2017-0611.1

Nyholm Jørgensen, R., Madsen, S. L. & Laursen, M. S. (2016). RoboWeedSupport - Weed Mapping with drones using a DJI Matrice 100 with a Velodyne Lidar.. Pictures, Video and sound recordings (digital), YouTube. https://www.youtube.com/watch?v=91LaKHiHiDw

Laursen, M. S., Nyholm Jørgensen, R., Dyrmann, M. & Poulsen, R. (2017). RoboWeedSupport: Sub Millimeter Weed Image Acquisition in Cereal Crops with Speeds up till 50 Km/h. World Academy of Science, Engineering and Technology. Proceedings, 4(4), 1391. https://www.waset.org/abstracts?q=Mads%20Dyrmann

Laursen, M. S., Nyholm Jørgensen, R., Dyrmann, M. & Poulsen, R. (2017). RoboWeedSupport - Sub Millimeter Weed Image Acquisition in Cereal Crops with Speeds up till 50 Km/h. International Journal of Agricultural and Biosystems Engineering, 11(4), 311-315. http://waset.org/publications/10007027/roboweedsupport-sub-millimeter-weed-image-acquisition-in-cereal-crops-with-speeds-up-till-50-km-h

Madsen, S. L., Laursen, M. S., Poulsen, R. N. & Nyholm Jørgensen, R. (2017). RoboWeedSupport: Semi-automated Unmanned Aerial System for Cost Efficient High Resolution in Sub-millimeter Scale Acquisition of Weed Images. In 19th International Conference on Precision Agriculture: ICPA 2017

Nyholm Jørgensen, R. (2016). RoboWeedSupport Præsentation og status ano 02SEP2016. Pictures, Video and sound recordings (digital) https://youtu.be/tiGtGArXOHw

Dyrmann, M. & Nyholm Jørgensen, R. (2015). RoboWeedSupport: Weed recognition for reduction of herbicide consumption. In J. V. Stafford (Ed.), Precision agriculture '15: Papers presented at the 10th European Conference on Precision Agriculture Volcani Center, Israel 12-16 July 2015 (pp. 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

Nyholm Jørgensen, R. (2019). RoboWeedMaps - Walking the weed maps with your mobile phone. Pictures, Video and sound recordings (digital), YouTube. https://www.youtube.com/watch?v=9rKahKe_a5c&t=108s

Nyholm Jørgensen, R. (2019). RoboWeedMaps præsenterer pletsprøjtning på FutureCropping demodag den 9 oktober 2019. Pictures, Video and sound recordings (digital), YouTube. https://www.youtube.com/watch?v=YKCR-hARLS8&feature=youtu.be

Nyholm Jørgensen, R. & Laursen, M. S. (2017). RoboWeedMaPS - First Grass-weed field inspection in Winter Wheat using DJI Goggles. Pictures, Video and sound recordings (digital), YouTube. https://www.youtube.com/watch?v=Yma1Psi9OEw

Nyholm Jørgensen, R. & Madsen, S. L. (2017). RoboWeedMaPS - First Automated Weed Mapping test flight with DJI Phantom 4 Pro. Pictures, Video and sound recordings (digital), YouTube. https://www.youtube.com/watch?v=-dHMbt9rwak

Nyholm Jørgensen, R., Nielsen, E. D. & Madsen, S. L. (2017). RoboWeedMaPS - Demo DroneApp beta med DJI Phantom 4. Pictures, Video and sound recordings (digital), YouTube. https://www.youtube.com/watch?v=PQ6sSIk7-0s

Nyholm Jørgensen, R., Laursen, M. S., Teimouri, N., Madsen, S. L., Dyrmann, M., Somerville, G. J. & Mathiassen, S. K. (2019). RoboWeedMaps - Automated weed detection and mapping - Invited talk at SSWM 2019, SDU, Odense Denmark. Pictures, Video and sound recordings (digital), YouTube.

Christensen, S., Søgaard, H. T., Jørgensen, R. & Bak, T. (2003). Robotic weeding and automated weed measurements. In DJF rapport (Vol. 88, pp. 161-172). Aarhus Universitet, Det Jordbrugsvidenskabelige Fakultet.

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, M. P., Larsen, P. G. & Nyholm Jørgensen, R. (2014). Robotic design choice overview using co-simulation. Abstract from Agromek and NJF joint seminar, Herning, Denmark.

Ravelo, B., Miranda, J., Cabral, J., Wagner, S. R., Pedersen, C. F., Memon, M., Mathiesen, M. & Lalléchère, S. (2015). RF and EMC Investigation on CRIP System for the E-Healthcare CareStore Platform. In 9th European Conference on Antennas and Propagation (EuCAP) 2015 IEEE. http://ieeexplore.ieee.org.ez.statsbiblioteket.dk:2048/xpl/articleDetails.jsp?arnumber=7228157&newsearch=true&queryText=ravelo%20emc%20rf

Kremer, T., Irons, T., Müller-Petke, M. & Juul Larsen, J. (2022). Review of Acquisition and Signal Processing Methods for Electromagnetic Noise Reduction and Retrieval of Surface Nuclear Magnetic Resonance Parameters. Surveys in Geophysics, 43(4), 999-1053. https://doi.org/10.1007/s10712-022-09695-3

Chumachenko, K., Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2021). Rethinking compactness in deep neural networks. Paper presented at International Conference on Machine Learning Workshop on Overparameterization. https://drive.google.com/file/d/1pu5xJMPvKuGFeVCcylEvLz00uLK3UN9I/view

Iosifidis, A., Tefas, A. & Pitas, I. (2013). Representative Class Vector Clustering based Discriminant Analysis. In Intelligent Information Hiding and Multimedia Signal Processing, 2013 Ninth International Conference on (pp. 526-529). IEEE. https://doi.org/10.1109/IIH-MSP.2013.136

Pedersen, C. F., Jensen, J. J., Dalsgaard, P., Larsen, L. B., Saugstrup, D. & Kaldanis, V. (2004). Report on set-up of field-trial. (1. udgave ed.). Information Society Technologies, 507102, My Personal Adaptive Global NET (MAGNET)

Pedersen, C. F., Jensen, K. L., Larsen, S. & Larsen, L. B. (2005). Report on field trial results. Information Society Technologies, My Personal Adaptive Global NET (MAGNET) No. 507102

Larsen, J. J., Lévy, L. & Asif, M. R. (2022). Removal of powerline noise in geophysical data sets with a scientific machine-learning based approach. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5923410. https://doi.org/10.1109/TGRS.2022.3223737

Hein, A., Larsen, J. J. & D. Parsekian, A. (2017). Removal of Harmonic Noise from Surface Nuclear Magnetic Resonance Measurements in the Frequency Domain. Poster session presented at SAGEEP 2017, Denver, United States.

Liu, L., Grombacher, D. J., Auken, E. & Larsen, J. J. (2018). Removal of Co-Frequency Powerline Harmonics From Multichannel Surface NMR Data. IEEE Geoscience and Remote Sensing Letters, 15(1), 53-57. https://doi.org/10.1109/LGRS.2017.2772790

Larsen, J. J., Liu, L., Grombacher, D. & Auken, E. (2017). Removal of Co-Frequency powerline harmonics from multichannel Surface NMR Data. In 23rd European Meeting of Environmental and Engineering Geophysics European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.201702040

Larsen, J. J., Liu, L., Grombacher, D. J. & Auken, E. (2017). Removal of Co-frequency Powerline Harmonics from Multichannel Surface NMR Data. In 23rd European Meeting of Environmental and Engineering Geophysics 2017 (Vol. 15, pp. 53-57). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.201702040

Tran, D. T., Gabbouj, M. & Iosifidis, A. (2022). Remote Multilinear Compressive Learning with Adaptive Compression. IEEE Internet of Things Journal, 9(9), 6905-6913. https://doi.org/10.1109/jiot.2021.3114743

Jørvang, J., Kusk, K., Nielsen, D. B., Thylstrup, T., Pedersen, C. F. & Wagner, S. R. (2013). Reliable Unsupervised Home Blood Pressure Self-Measurement with a Focus on Time-to-Rest using Sensor Fusion. The Journal of Pervasive Systems Engineering, 1. http://pervasivesystems.org/Papers/2013-1/Reliable%20Unsupervised%20Home%20Blood%20Pressure.pdf

Hasanvand , S., Rafiei Foroushani, M., Gheisarnejad, M. & Khooban, M. H. (2020). Reliable Power Scheduling of an Emission-Free Ship: Multi-Objective Deep Reinforcement Learning. IEEE Transactions on Transportation Electrification, 6(2), 832-843. Article 9046850. https://doi.org/10.1109/TTE.2020.2983247

Afshari-Igder, M., Rafiei Foroushani, M., Boudjadar, J. & Khooban, M. H. (2021). Reliability and Safety Improvement of Emission-Free Ships: Systemic Reliability Centered Maintenance. IEEE Transactions on Transportation Electrification, 7(1), 256-266. Article 9220168. https://doi.org/10.1109/TTE.2020.3030082

Jensen, M. H., Nazari, M., Gu, C., Rasmussen, M., Dyrskog, S. E., Simonsen, C. Z., Grønhøj, M. H., Rom Poulsen, F., Rehman, N. U. & Korshoej, A. R. (2023). Reliability and Performance of the IRRAflow® System for Intracranial Lavage and Evacuation of Hematomas - A Technical Note. https://doi.org/10.1101/2023.07.07.23292372

Haldrup, M., Nazari, M., Gu, C., Rasmussen, M., Dyrskog, S., Ziegler Simonsen, C., Grønhøj, M., Poulsen, F. R., Ur Rehman, N. & Rosendal Korshoej, A. (2024). Reliability and performance of the IRRAflow® system for intracranial lavage and evacuation of hematomas-A technical note. PLOS ONE, 19(4 April), Article e0297131. https://doi.org/10.1371/journal.pone.0297131

Ugurlu, H. I., Kalkan, S. & Saranli, A. (2021). Reinforcement Learning versus Conventional Control for Controlling a Planar Bi-rotor Platform with Tail Appendage. Journal of Intelligent and Robotic Systems: Theory and Applications, 102(4), Article 77. https://doi.org/10.1007/s10846-021-01412-3

Abkar, M., Zehtabiyan-Rezaie, N. & Iosifidis, A. (2023). Reinforcement learning for wind-farm flow control: Current state and future actions. Theoretical and Applied Mechanics Letters, 13(6), Article 100475. https://doi.org/10.1016/j.taml.2023.100475

Iosifidis, A., Tefas, A. & Pitas, I. (2014). Regularized Extreme Learning Machine for Multi-view Semi-supervised Action Recognition. Neurocomputing, 145, 250-262. https://doi.org/10.1016/j.neucom.2014.05.036

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Regularized Extreme Learning Machine for large-scale media content analysis. In Procedia Computer Science (pp. 420-427). ELSEVIER. https://doi.org/10.1016/j.procs.2015.07.319

Imanberdiyev, N. & Kayacan, E. (2020). Redundancy Resolution based Trajectory Generation for Dual-Arm Aerial Manipulators via Online Model Predictive Control. In Proceedings - IECON 2020: 46th Annual Conference of the IEEE Industrial Electronics Society (pp. 674-681). Article 9254702 IEEE. https://doi.org/10.1109/IECON43393.2020.9254702

Laakom, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2024). Reducing redundancy in the bottleneck representation of autoencoders. Pattern Recognition Letters, 178, 202-208. https://doi.org/10.1016/j.patrec.2024.01.013

Kayacan, E. & Ahmadieh Khanesar, M. (2016). Recurrent Interval Type-2 Fuzzy Control of 2-DOF Helicopter With Finite Time Training Algorithm. In IFAC-PapersOnLine (13 ed., Vol. 49, pp. 293-299). Elsevier. https://doi.org/10.1016/j.ifacol.2016.07.977

Krestenitis, M., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2020). Recurrent bag-of-features for visual information analysis. Pattern Recognition, 106, Article 107380. https://doi.org/10.1016/j.patcog.2020.107380

Duan, R., Fu, C. & Kayacan, E. (2016). Recoverable Recommended Keypoint-aware Visual Tracking Using Coupled-layer Appearance Modelling. In IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 4085-4091). Article 7759601 IEEE. https://doi.org/10.1109/IROS.2016.7759601

Mehndiratta, M. & Kayacan, E. (2018). Reconfigurable Fault-tolerant NMPC for Y6 Coaxial Tricopter with Complete Loss of One Rotor. In 2018 IEEE Conference on Control Technology and Applications, CCTA 2018: CCTA (pp. 774-780). Article 8511444 IEEE. https://doi.org/10.1109/CCTA.2018.8511444

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Revised 10.08.2023

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