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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Skovsen, S. K., Kutugata, M., Jennewein, J., Reberg-Horton, C. S. & Mirsky, S. B. (2023). Detailed Species Competition Mapping of Mixed Cover Crop Species Using Tractor Mounted Color Camera and Computer Vision. Abstract from 2023 ASA/CSSA/SSSA International Annual Meeting, St. Louis, Missouri, United States.

Larsen, J. J. (2016). Despiking of Surface-NMR Data Using a Model-based Approach. In Near Surface Geoscience 2016 : 22nd European Meeting of Environmental and Engineering Geophysics (Vol. 2016). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.201602068

Larsen, J. J. & Auken, E. (2015). Despiking of magnetic resonance sounding signals. Paper presented at The 6th International Workshop on Magnetic Resonance, Aarhus , Denmark. http://hgg.au.dk/fileadmin/www.dna18.au.dk/MRS2015_program.pdf

Kayacan, E., Kaynak, O., H. Abiyev, R., Tørresen, J., Høvin, M. & Glette, K. (2010). Design of an Adaptive Interval Type-2 Fuzzy Logic Controller for the Position Control of a Servo System with an Intelligent Sensor. In Fuzzy Systems (FUZZ), 2010 IEEE International Conference on (pp. 1125-1132). IEEE . https://doi.org/10.1109/FUZZY.2010.5584629

Shahrak Nadimi, E., Andersson, K. J., Jørgensen, R. N., Maagaard, J., Mathiassen, S. K. & Christensen, S. (2009). Designing, modeling and controlling a novel autonomous laser weeding system. In Conference papers World Congress on Computers in Agriculture. https://elibrary.asabe.org/pdfviewer.asp?param1=s:/8y9u8/q8qu/tq9q/5tv/H/Cs43IGGP/KP_dqty2y.5tv&param2=HI/I/IGIK&param3=HJG.IIL.IH.IK&param4=29077

Christiansen, M. P., Laursen, M. S., Nyholm Jørgensen, R., Skovsen, S. & Gislum, R. (2017). Designing and Testing a UAV Mapping System for Agricultural Field Surveying. Sensors, 17(12), Article 2703. https://doi.org/10.3390/s17122703

Imanberdiyev, N., Sood, S., Kircali, D. & Kayacan, E. (2022). Design, development and experimental validation of a lightweight dual-arm aerial manipulator with a COG balancing mechanism. Mechatronics, 82, Article 102719. https://doi.org/10.1016/j.mechatronics.2021.102719

Mehndiratta, M., Kayacan, E. & Kumbasar, T. (2016). Design and Experimental Validation of Single Input Type-2 Fuzzy PID Controllers as Applied to 3 DOF Helicopter Testbed. In 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 (pp. 1584-1591). Article 07737879 IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7737879

Nyholm Jørgensen, R. & Christiansen, M. P. (2018). Demonstration of plant labs web explorer combined with data from Danish field boundaries. Pictures, Video and sound recordings (digital), YouTube. https://www.youtube.com/watch?v=ExlN0XrIx7E

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.

Sørensen, R. A., Nielsen, M. & Karstoft, H. (2019). Deep Reinforcement Learning for Route Optimization in Baggage Handling Systems. In S. Y. Yurish (Ed.), Advances in Signal Processing and Artificial Intelligence: Proceedings of the 1st International Conference on Advances in Signal Processing and Artifical Intelligence (pp. 29-33). Article 11 IFSA Publishing.

Camci, E., Campolo, D. & Kayacan, E. (2020). Deep Reinforcement Learning for Motion Planning of Quadrotors Using Raw Depth Images. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings Article 9207490 IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207490

Pham, H. X., Ugurlu, H. I., Fevre, J. L., Bardakci, D. & Kayacan, E. (2022). Deep learning for vision-based navigation in autonomous drone racing. In A. Iosifidis & A. Tefas (Eds.), Deep Learning for Robot Perception and Cognition (pp. 371-406). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00020-8

Millinge, O., Böttner, C. & Asif, MR. (2024). Deep Learning based Identification of Oil-Slick Emissions in the Arctic Using Satellite SAR Data. In NSG 2024 4th Conference on Airborne, Drone and Robotic Geophysics (pp. 1-5). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202420159

Asif, M. R., Maurya, P. K., Christiansen, A. V., Larsen, J. J. & Auken, E. (2022). Deep learning based expert system to automate time-domain electromagnetic data processing. In 34th Symposium on the Application of Geophysics to Engineering and Environmental Problems, SAGEEP 2022 (pp. 6). J and N Group, Ltd..

Rafiei Foroushani, M., Griffiths, M. P., Boudjadar, J. & Khooban, M. H. (2021). Deep Learning-Based Energy Management of an All-electric City Bus with Wireless Power Transfer. IEEE Access, 9, 43981-43990. Article 9380640. https://doi.org/10.1109/ACCESS.2021.3066300

Høye, T. T., Ärje, J., Bjerge, K., Hansen, O. L. P., Iosifidis, A., Leese, F., Mann, H. M. R., Meissner, K., Melvad, C. & Raitoharju, J. (2021). Deep learning and computer vision will transform entomology. Proceedings of the National Academy of Sciences (PNAS), 118(2), Article e2002545117. https://doi.org/10.1073/pnas.2002545117

Bjerge, K., Bodesheim, P. & Karstoft, H. (2025). Deep Image Clustering with Model-Agnostic Meta-Learning. In In Proceedings of the 20th International Joint Conference on Computer Vision (Vol. 2, pp. 286-297). Imaging and Computer Graphics Theory and Applications - VISAPP 2025. https://doi.org/10.5220/0013114600003912

Asif, M. R., Larsen, J. J., Auken, E. & Christiansen, A. V. (2022). Deep Convolutional Auto-Encoder for Automated Processing of Airborne Time-Domain Electromagnetic Data. 1-5. Paper presented at NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, Beograd, Serbia. https://doi.org/10.3997/2214-4609.202220038

Christiansen, P., N. Nielsen, L., A. Steen, K., Nyholm Jørgensen, R. & Karstoft, H. (2016). DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field. Sensors, 16(11), Article 1904. https://doi.org/10.3390/s16111904

Skovsen, S. K., Haraldsson, H., Davis, A., Karstoft, H. & Belongie, S. (2020). Decoupled Localization and Sensing with HMD-based AR for Interactive Scene Acquisition. In 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 167-171). Article 9288396 IEEE. https://doi.org/10.1109/ISMAR-Adjunct51615.2020.00053

Rasmussen, S., Nyboe, N. S., Mai, S. S. & Larsen, J. J. (2015). Deconvolution of AEM Data via Analytically Described System Responses. In First European Airborne Electromagnetics Conference (Vol. 2015). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.201413882

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

Raza, M., Naveed, K., Akram, A., Salem, N., Afaq, A., Madni, H. A., Khan, M. A. U. & Mui-Zzud-din (2021). DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images. PLOS ONE, 16(12), Article e0261698. https://doi.org/10.1371/journal.pone.0261698

Beevi, F. H. A., Wagner, S. R., Hallerstede, S. & Pedersen, C. F. (2016). Data Quality Oriented Taxonomy of Ambient Assisted Living Systems. In IET International Conference on Technologies for Active and Assisted Living: TechAAL, 2015 Institution of Engineering and Technology. https://doi.org/10.1049/ic.2015.0140

Beevi, F. H. A., Wagner, S. R., Pedersen, C. F. & Hallerstede, S. (2016). Data Quality Oriented Efficacy Evaluation Method for Ambient Assisted Living Technologies. In PervasiveHealth 2016 - 10th EAI International Conference on Pervasive Computing Technologies for Healthcare Association for Computing Machinery. https://doi.org/10.4108/eai.16-5-2016.2263753

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

Rehman, N. U., Naveed, K. & Khan, B. (2019). Data-Driven Multivariate Signal Denoising Using Mahalanobis Distance. IEEE Signal Processing Letters, 26(9), 1408-1412. https://doi.org/10.1109/LSP.2019.2932715

Madsen, S. L., Dyrmann, M., Laursen, M. S., Mathiassen, S. K. & Nyholm Jørgensen, R. (2018). Data Acquisition Platform for Collecting High-Quality Images of Cultivated Weed. In P. W. G. Groot Koerkamp, C. Lokhorst , A. H. Ipema, C. Kempenaar, C. M. Groenestein, C. van Oostrum & N. Ros (Eds.), Proceedings of the European Conference on Agricultural Engineering: AgEng2018 (pp. 360-369). Wageningen University. https://doi.org/10.18174/471679

Hansen, B., Christiansen, A. V., Dalgaard, T., Jørgensen, F., Iversen, B. V., Larsen, J. J., Kjærgaard, C., Jacobsen, B. H., Auken, E., Hojberg, A. L. & Schaper, S. (2020). Danish review on advances in assessing: N retention in the subsurface in relation to future targeted N-regulation of agriculture. GEUS, Geological Survey of Denmark and Greenland. GEUS Rapport Vol. 2020 No. 11 https://mapfield.dk/media/21858/d26_synthesis_report_mapfield.pdf

Christiansen, M. P., Laursen, M. S., Feld Mikkelsen, B., Nyholm Jørgensen, R., Teimouri, N. & Sørensen, C. A. G. (2018). Current potentials and challenges using Sentinel-1 for broadacre field remote sensing. In Book of Abstracts of the European Conference on Agricultural Engineering: AgEng2018 (pp. 56). Wageningen University. https://doi.org/10.18174/471678

Jeppesen, J. H., Jacobsen, R. H. & Jorgensen, R. N. (2020). Crop Type Classification based on Machine Learning with Multitemporal Sentinel-1 Data. In A. Trost, A. Zemva & A. Skavhaug (Eds.), 2020 23rd Euromicro Conference on Digital System Design (DSD) (pp. 557-564). IEEE. https://doi.org/10.1109/DSD51259.2020.00092

Ahmadieh Khanesar, M. & Kayacan, E. (2015). Controlling the Pitch and Yaw Angles of a 2-DOF Helicopter Using Interval Type-2 Fuzzy Neural Networks. In X. Yu & M. Önder Efe (Eds.), Recent Advances in Sliding Modes: From Control to Intelligent Mechatronics (pp. 349 - 370). Springer. https://doi.org/10.1007/978-3-319-18290-2

Qiao, Z., Pham, X. H., Ramasamy, S., Jiang, X., Kayacan, E. & Sarabakha, A. (2024). Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing. In 2024 International Joint Conference on Neural Networks, IJCNN 2024 - Proceedings IEEE. https://doi.org/10.1109/ijcnn60899.2024.10649903

Bozcan, I. & Kayacan, E. (2020). Contextual Anomaly Detection for Low Altitude Aerial Surveillance.

Pedersen, C. F., Dalsgaard, P. & Andersen, O. (2008). Conference web page for ISCA ITRW, Speech Analysis and Processing for Knowledge Discovery. Interactive production, ISCA/AAU.

Looney, D., Rehman, N. U., Mandic, D., Rutkowski, T. M., Heidenreich, A. & Beyer, D. (2009). Conditioning multimodal information for smart environments. In 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) IEEE. https://doi.org/10.1109/icdsc.2009.5289373

Bäckström, T., Pedersen, C. F., Fischer, J., Hüttenberger, M. & Alfonso, P. (2015). Concept for Encoding of Information. (Patent No. EP20140158396).

Rafiei Foroushani, M., Raitoharju, J. & Iosifidis, A. (2023). Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey. IEEE Access, 11, 2445 - 2477. https://doi.org/10.1109/ACCESS.2023.3234187

Nawoya, S., Geissmann, Q., Karstoft, H., Bjerge, K., Akol, R., Katumba, A., Mwikirize, C. & Gebreyesus, G. (2024). Computer-vision based prediction of body traits and larval sex in black soldier fly. 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

Nawoya, S., Ssemakula, F., Akol, R., Geissmann, Q., Karstoft, H., Bjerge, K., Mwikirize, C., Katumba, A. & Gebreyesus, G. (2024). Computer vision and deep learning in insects for food and feed production: A review. Computers and Electronics in Agriculture, 216, Article 108503. https://doi.org/10.1016/j.compag.2023.108503

Agerskov, J., Nielsen, K. & Pedersen, C. F. (2023). Computationally Efficient Labeling of Cancer-Related Forum Posts by Non-Clinical Text Information Retrieval. SN Computer Science, 4(6), Article 711. https://doi.org/10.1007/s42979-023-02244-8

D. Nona, K., Kayacan, E. & Saeys, W. (2013). Compression Modeling in the Large Square Baler. In 71st International Conference on Agricultural Engineering 2013 (pp. 1-6) https://lirias.kuleuven.be/bitstream/123456789/424705/2/AgEng2013_CompressionModeling.pdf

Liu, L., Grombacher, D. J., Auken, E. & Larsen, J. J. (2019). Complex envelope retrieval for surface nuclear magnetic resonance data using spectral analysis. Geophysical Journal International, 217(2), 894-905. https://doi.org/10.1093/gji/ggz068

Aslund, S., Jensen, K. & Jørgensen, R. N. (2011). Complete coverage path planning of a random polygon - A FroboMind component. http://www.njf.nu/site/seminarRedirect.asp?intSeminarID=441&p=1004

Asif, M. R., Larsen, J. J., Maurya, P. K., Christiansen, A. V. & Auken, E. (2021). Complete automation of time-domain transient electromagnetic data processing using deep convolutional autoencoder. Abstract from AGU 2021, United States.

Meldgaard Madsen, L., Asif, M. R., Maurya, P. K., Kühl, A. K., Domenzain, D., Jensen, C., Martin, T., Bastani, M. & Persson, L. (2023). Comparison of tTEM-IP and ERT-IP: Cases from Mine Tailing Sites in Sweden. Abstract from NSG2023 29th European Meeting of Environmental and Engineering Geophysics, Edinburgh , United Kingdom. https://doi.org/10.3997/2214-4609.202320114

Bravo, C., Huizinga, P., Jørgensen, R., Olsen, H. J., Søgaard, H. T., Moshou, D., Jørgensen, M. H., Christensen, S. & Ramon, H. (2004). Comparison of 2 different weed detection systems by use of the API. In AgEng 2004, Leuven, Belgium. Book of Abstracts, ISBN 90-76019-258, 310-311. Paper on CD, 8 pp. (pp. 8)

Christensen, S., Mouridsen, K., Wu, O., Hjort, N., Karstoft, H., Thomalla, G., Röther, J., Fiehler, J., Kucinski, T. & Østergaard, L. (2009). Comparison of 10 Perfusion MRI Parameters in 97 Sub 6 Hour Stroke Patients using Voxel based Receiver Operating Characteristics Analysis. Stroke, 40(6), 2055-61. https://doi.org/10.1161/STROKEAHA.108.546069

Karim, S., Zhang, Y., Asif, M. R. & Ali, S. (2017). Comparative analysis of feature extraction methods in satellite imagery. Journal of Applied Remote Sensing, 11(4), Article 042618. https://doi.org/10.1117/1.JRS.11.042618

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