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., 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. (2018). 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

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

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

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

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

Duan, R., Fu, C., Kayacan, E. & Pani Paudel, D. (2016). Recommended Keypoint-Aware Tracker: Adaptive Real-time Visual Tracking Using Consensus Feature Prior Ranking. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (pp. 449-453). Article 7532397 IEEE. https://doi.org/10.1109/ICIP.2016.7532397

Rafiei Foroushani, M., Tran, D. T. & Iosifidis, A. (2023). Recognition of Defective Mineral Wool Using Pruned ResNet Models. In H. Dorksen, S. Scanzio, J. Jasperneite, L. Wisniewski, K. F. Man, T. Sauter, L. Seno, H. Trsek & V. Vyatkin (Eds.), 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) IEEE. https://doi.org/10.1109/INDIN51400.2023.10217993

Iqbal, S., Khan, T. M., Naveed, K., Naqvi, S. S. & Nawaz, S. J. (2022). Recent trends and advances in fundus image analysis: A review. Computers in Biology and Medicine, 151, Article 106277. https://doi.org/10.1016/j.compbiomed.2022.106277

Kayacan, E. & Mehndiratta, M. (2017). Receding Horizon Control of 3 DOF Helicopter Using Online Estimation of Aerodynamic Parameters. Proceedings of the Institution of Mechanical Engineers Part G-Journal of Aerospace Engineering, 1-12.

Mehndiratta, M., Singh, K., Kayacan, E. & Feroskhan, M. (2021). Receding Horizon-based Fault-tolerant Control of QuadPlus: An Over-actuated Quadrotor. In 2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021 (pp. 853-859). IEEE. https://doi.org/10.1109/CASE49439.2021.9551527

Figueiredo, R. P. D., Sejersen, J. L. F., Hansen, J. G., Brandão, M. & Kayacan, E. (2021). Real-Time Volumetric-Semantic Exploration and Mapping: An Uncertainty-Aware Approach. https://doi.org/10.48550/arXiv.2109.01474

Jensen, L. A., Sørensen, C. G. & Jørgensen, R. N. (2008). Real-time Internet-Based Traceability Unit for Mobile Payload Vehicles. Agricultural Engineering International: CIGR Journal, 9. http://cigr-ejournal.tamu.edu/submissions/volume9/CIOSTA%2007%20003%20Jensen%204Jan2008.pdf

Jensen, L. A., Sørensen, C. A. G. & Jørgensen, R. (2008). Real-time internet-based traceability unit for mobile payload vehicles. In Proceedings of the XXXII CIOSTA-CIGR Section V Conference "Advances in Labour and Machinery Management for a Profitable Agriculture and Forestry" (pp. 368-374). <Forlag uden navn>.

Bjerge, K., Mann, H. M. R. & Høye, T. T. (2022). Real-time insect tracking and monitoring with computer vision and deep learning. Remote Sensing in Ecology and Conservation, 8(3), 315-327. https://doi.org/10.1002/rse2.245

Laursen, M. S., Larsen, R., Jensen, K. & Nyholm Jørgensen, R. (2016). Real time back projection onto prerecorded DHM surface of UAV recorded images with RTK GPS and IMU. Paper presented at International Conference on Agricultural Engineering 2016, Aarhus, Denmark. http://conferences.au.dk/uploads/tx_powermail/2016cigr_real__time__back__projection__onto_prerecorded_dhm_surface_of_uav_recorded_images_with_rtk_gps_and_imu.pdf

Jørgensen, R., Sørensen, C. A. G., Bak, T. & Moore, K. (2004). Rational agents for agricultural crop surveying: Adaptive task and motion planning. In Proc. 5th int. workshop on Artificial Intelligence in Agriculture (AIA'2004), (Rafea, M., (ed.)), Cairo, Egypt (pp. 11-16) https://doi.org/10.1016/S1474-6670(17)38682-2

Miranda, J., Cabral, J., Ravelo, B., Wagner, S. R., Pedersen, C. F., Memon, M. & Mathiesen, M. (2015). Radiated EMC immunity investigation of common recognition identification platform for medical applications. Journal of Physics D: Applied Physics, 69(1). https://doi.org/10.1051/epjap/2014140230

Ravelo, B., Miranda, J., Cabral, J., Wagner, S. R., Pedersen, C. F., Memon, M. & Mathiesen, M. (2016, Mar 24). Radiated EMC Characterization of Common Recognition and Identification Platform for E-Healthcare.

Yan, R. J., Kayacan, E., Chen, I. M., Tiong, L. K. & Wu, J. (2019). QuicaBot: Quality Inspection and Assessment Robot. IEEE Transactions on Automation Science and Engineering, 16(2), 506-517. https://doi.org/10.1109/TASE.2018.2829927

Madsen, S. L., Karstoft, H., Nyholm Jørgensen, R., Nørremark, M., Khokhar, Y., Gomez, J. S., pier van Gosliga, S. & Jaakkola, K. (2017). Quantifying behaviour of dairy cows via multi-stage Support Vector Machines. In D. Berckmans & A. Keita (Eds.), Book of proceedings: 8th European Conference on Precision Livestock Farming: ECPLF 2017 (pp. 90-100)

Rehman, N. U. & Mandic, D. P. (2009). Qualitative analysis of rotational modes within three dimensional empirical mode decomposition. In 2009 IEEE International Conference on Acoustics, Speech and Signal Processing IEEE. https://doi.org/10.1109/icassp.2009.4960367

Rehman, N. U. & Mandic, D. P. (2010). Quadrivariate Empirical Mode Decomposition. In The 2010 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/ijcnn.2010.5596768

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 from 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

Madsen, S. L., Karstoft, H. & Nyholm Jørgensen, R. (2018). Producing Artificial Data Samples Using Generative Adversarial Networks.. Poster session presented at 2018 summer school on Optimization in Image Analysis, Sønderborg, Denmark. http://optimization-image-analysis.compute.dtu.dk/posters/simon_madsen.pdf

Naveed, K., Madsen, L. M., Grombacher, D. & Larsen, J. J. (2024). Processing Time-domain Induced Polarization Data Using Variational Mode Decomposition. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-13. Article 5905713. https://doi.org/10.1109/TGRS.2024.3362290

Larsen, J. J. & Behroozmand, A. A. (2016). Processing of surface-nuclear magnetic resonance data from sites with high noise levels. Geophysics, 81(4), WB75-WB83. https://doi.org/10.1190/geo2015-0441.1

Larsen, J. J. & Behroozmand, A. A. (2015). Processing of Surface-NMR Data From Sites With High Noise. Abstract from AGU Fall Meeting 2015, San Fransisco, United States. https://agu.confex.com/agu/fm15/meetingapp.cgi/Paper/69401

Kremer, T., Juul Larsen, J. & Nguyen, F. (2019). Processing harmonic EM noise with multiple or unstable frequency content in surface NMR surveys. Geophysical Journal International, 219(2), 753-775. https://doi.org/10.1093/gji/ggz307

Jørgensen, R., Nørremark, M., Sørensen, C. A. G. & Andersen, N. A. (2004). Process and behaviour based scripting language for weeding agents. In CIGR2004, Beijing, China. Paper on CD: ISBN 7-80167-707-2/ (pp. 537) https://www.researchgate.net/publication/280315713_Process_and_behaviour_based_scripting_language_for_weeding_agents

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

Rafiei Foroushani, M., Niknam, T., Aghaei, J., Shafie-khah, M. & P.S.Catalão, J. (2018). Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine. IEEE Transactions on Smart Grid, 9(6), 6961 - 6971. Article 8298533. https://doi.org/10.1109/TSG.2018.2807845

Christiansen, M. P., Teimouri, N., Laursen, M. S., Mikkelsen, B. F., Jorgensen, R. N. & Sorensen, C. A. G. (2019). Preprocessed sentinel-1 data via a web service focused on agricultural field monitoring. IEEE Access, 7(1), 65139-65149. Article 8715769. https://doi.org/10.1109/ACCESS.2019.2917063

Mortensen, A. K., Karstoft, H., Søegaard, K., Gislum, R. & Nyholm Jørgensen, R. (2017). Preliminary Results of Clover and Grass Coverage and Total Dry Matter Estimation in Clover-Grass Crops Using Image Analysis. Journal of Imaging, 3(4), Article 59. https://doi.org/10.3390/jimaging3040059

Markopoulos, A., Dalsgaard, P., Gkanas, I., Jensen, J. J., Jiang, B., Kaldanis, V., Larsen, L. B., Pedersen, C. F., Christensen, D. S., Schultz, N., Sørensen, L. T. & Peréz Vila, J. (2004). Preliminary report: Draft user centric scenarios for PNs of a valid architecture. Information Society Technologies, My Personal Adaptive Global NET (MAGNET), IST 507102.

Nawoya, S., Geissmann, Q., Karstoft, H., Bjerge, K., Akol, R., Katumba, A., Mwikirize, C. & Gebreyesus, G. (2025). Prediction of black soldier fly larval sex and morphological traits using computer vision and deep learning. Smart Agricultural Technology, 11, Article 100953. https://doi.org/10.1016/j.atech.2025.100953

Lillelund, C. M., Pannullo, F., Jakobsen, M. O. & Pedersen, C. F. (2023). Predicting Survival Time of Ball Bearings in the Presence of Censoring. In AAAI Symposium on Survival Prediction: Algorithms, Challenges, and Applications (SPACA) AAAI Press.

Kragh, M. F., Rimestad, J., Lassen, J. T., Berntsen, J. & Karstoft, H. (2022). Predicting embryo viability based on self-supervised alignment of time-lapse videos. IEEE Transactions on Medical Imaging, 41(2), 465-475. https://doi.org/10.1109/TMI.2021.3116986

Skovsen, S., Dyrmann, M., Eriksen, J., Gislum, R., Karstoft, H. & Nyholm Jørgensen, R. (2018). Predicting Dry Matter Composition of Grass Clover Leys Using Data Simulation and Camera-based Segmentation of Field Canopies into White Clover, Red Clover, Grass and Weeds. In Proceedings of the 14th International Conference on Precision Engineering Article 5079 International Society of Precision Agriculture. https://ispag.org/proceedings/?action=abstract&id=5079

Skovsen, S., Dyrmann, M., Eriksen, J., Gislum, R., Karstoft, H. & Nyholm Jørgensen, R. (2018). Predicting Dry Matter Composition of Grass Clover Leys Using Data Simulation and Camera-based Segmentation of Field Canopies into White Clover, Red Clover, Grass and Weeds. Abstract from International Conference on Precision Agriculture, Montréal, Quebec, Canada. https://ispag.org/proceedings/?action=abstract&id=5079&search=authors

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Revised 10.08.2023

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