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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Grombacher, D., Liu, L., Kass, M. A., Osterman, G., Fiandaca, G., Auken, E. & Larsen, J. J. (2020). Inverting surface NMR free induction decay data in a voltage-time data space. Journal of Applied Geophysics, 172, Article 103869. https://doi.org/10.1016/j.jappgeo.2019.103869

Asif, M. R., Qi, C., Wang, T., Fareed, M. S. & Khan, S. (2019). License plate detection for multi-national vehicles – a generalized approach. Multimedia Tools and Applications, 78(24), 35585-35606. https://doi.org/10.1007/s11042-019-08199-4

Madsen, S. L., Mortensen, A. K., Nyholm Jørgensen, R. & Karstoft, H. (2019). Disentangling Information in Artificial Images of Plant Seedlings Using Semi-Supervised GAN. Remote Sensing, 11(22), Article 2671. https://doi.org/10.3390/rs11222671

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

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.

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

Kragh, M. F., Rimestad, J., Berntsen, J. & Karstoft, H. (2019). Automatic grading of human blastocysts from time-lapse imaging. Computers in Biology and Medicine, 115, Article 103494. https://doi.org/10.1016/j.compbiomed.2019.103494

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

Sarabakha, A. & Kayacan, E. (2019). Online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge. In 2019 International Conference on Robotics and Automation, ICRA 2019 (pp. 7727-7733). Article 8794314 IEEE. https://doi.org/10.1109/ICRA.2019.8794314

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. In 2019 18th European Control Conference, ECC 2019 Article 8796140 IEEE. https://doi.org/10.23919/ECC.2019.8796140

Madsen, S. L., Dyrmann, M., Nyholm Jørgensen, R. & Karstoft, H. (2019). Generating artificial images of plant seedlings using generative adversarial networks. Biosystems Engineering, 187, 147-159. https://doi.org/10.1016/j.biosystemseng.2019.09.005

Liu, L., Grombacher, D., Auken, E. & Juul Larsen, J. (2019). Apsu: A wireless multichannel receiver system for surface nuclear magnetic resonance groundwater investigations. Geoscientific Instrumentation, Methods and Data Systems, 8(1), 1-11. https://doi.org/10.5194/gi-8-1-2019

Somerville, G. J., Nyholm Jørgensen, R., Bojer, O. M., Rydahl, P., Dyrmann, M., Andersen, P., Jensen, N.-P. & Green, O. (2019). Marrying futuristic weed mapping with current herbicide sprayer capacities. In J. V. Stafford (Ed.), Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (pp. 231-237). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_28

Hussain, S., Qi, C., Asif, M. R. & Sohrab Khan, M. (2016). Active Contours for image segmentation using complex domain-based approach. IET Image Processing, 10(2), 121-129. https://doi.org/10.1049/iet-ipr.2014.0730

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

Asif, M. R., Qi, C., Hussain, S. & Sadiq Fareed, M. (2016). Multiple License Plate Detection for Chinese Vehicles in Dense Traffic Scenarios. IET Intelligent Transport Systems, 10(8). https://doi.org/10.1049/iet-its

Mortensen, A. K., Laursen, M. S., Nyholm Jørgensen, R. & Gislum, R. (2019). Drone dataflow - a MATLAB toolbox for extracting plots from images captured by a UAV. In J. V. Stafford (Ed.), Precision Agriculture '19 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (pp. 959-965). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_118

Asif, M. R., Qi, C., Hussain, S., Sadiq Fareed, M. & Khan, S. (2017). Multinational vehicle license plate detection in complex backgrounds. Journal of Visual Communication and Image Representation, 46, 176-186. https://doi.org/10.1016/j.jvcir.2017.03.020

Asif, M. R., Qi, C., Wang, T., Sadiq Fareed, M. & Ali Reza, S. (2019). License Plate Detection for Multi-national Vehicles: An Illumination Invariant Approach in Multi-lane Environment. Computers & Electrical Engineering, 78, 132-147. https://doi.org/10.1016/j.compeleceng.2019.07.012

Ma, X., Hegarty, P. J., Jørgensen, K. F. & Larsen, J. J. (2019). Nonlinear distortion reduction in sound zones by constraining individual loudspeaker control effort. Journal of the Audio Engineering Society, 67(9), 641-654. https://doi.org/10.17743/jaes.2019.0015

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

Bjerge, K., Frigaard, C. E., Mikkelsen, P. H., Nielsen, T. H., Misbih, M. & Kryger, P. (2019). A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony. Computers and Electronics in Agriculture, 164(September), Article 104898. https://doi.org/10.1016/j.compag.2019.104898

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.

Skovsen, S., Dyrmann, M., Mortensen, A. K., Laursen, M. S., Gislum, R., Eriksen, J., Farkhani, S., Karstoft, H. & Nyholm Jørgensen, R. (2019). The GrassClover Image Dataset for Semantic and Hierarchical Species Understanding in Agriculture. Poster session presented at IEEE Conference on Computer Vision and Pattern Recognition 2019, Long Beach, California, United States.

Skovsen, S., Dyrmann, M., Mortensen, A. K., Laursen, M. S., Gislum, R., Eriksen, J., Farkhani, S., Karstoft, H. & Nyholm Jørgensen, R. (2019). The GrassClover Image Dataset for Semantic and Hierarchical Species Understanding in Agriculture. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops IEEE. http://openaccess.thecvf.com/content_CVPRW_2019/html/CVPPP/Skovsen_The_GrassClover_Image_Dataset_for_Semantic_and_Hierarchical_Species_Understanding_CVPRW_2019_paper.html

Skovsen, S., Laursen, M. S., Gislum, R., Eriksen, J., Dyrmann, M., Mortensen, A. K., Farkhani, S., Karstoft, H., Jensen, N.-P. & Nyholm Jørgensen, R. (2019). Species distribution mapping of grass clover leys using images for targeted nitrogen fertilization. In J. V. Stafford (Ed.), Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (pp. 639-645). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_79

Mortensen, A. K., Skovsen, S., Karstoft, H. & Gislum, R. (2019). The Oil Radish Growth Dataset. Dataset

Mortensen, A. K., Skovsen, S., Karstoft, H. & Gislum, R. (2019). The Oil Radish Growth Dataset for Semantic Segmentation and Yield Estimation. Poster session presented at IEEE Conference on Computer Vision and Pattern Recognition 2019, Long Beach, California, United States.

Mortensen, A. K., Skovsen, S., Karstoft, H. & Gislum, R. (2019). The Oil Radish Growth Dataset for Semantic Segmentation and Yield Estimation. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 (pp. 2703-2710). Article 9025602 IEEE. https://doi.org/10.1109/CVPRW.2019.00328

Gislum, R., Nyholm Jørgensen, R., Thomsen, I. K., Hansen, E. M. & Olesen, J. E., (2019). Beskrivelse af setup for vidensindsamling og måling af NDVI-værdi i pilotprojekt om biomasse, No. 2019-760-001283, 3 p., Jul 03, 2019.

Kragh, M. F., Rimestad, J., Berntsen, J. & Karstoft, H. (2019). Automatic morphological grading of human blastocysts with time-lapse imaging and artificial intelligence. Abstract from ESHRE, Vienna, Austria. https://www.eshre.eu/ESHRE2019/Programme/Searchable#!abstractdetails/0000573820

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

Sarabakha, A., Fu, C. & Kayacan, E. (2019). Intuit before tuning: Type-1 and type-2 fuzzy logic controllers. Applied Soft Computing Journal, 81, Article 105495. https://doi.org/10.1016/j.asoc.2019.105495

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.

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), Article 990. https://doi.org/10.3390/rs11080990

Rafiei Foroushani, M., Khooban, M. H., Afshari-Igder, M. & Boudjadar, J. (2020). A Novel Approach to Overcome the Limitations of Reliability Centered Maintenance Implementation on the Smart Grid Distance Protection System. IEEE Transactions on Circuits and Systems II: Express Briefs, 67(2), 320-324. Article 8668570. https://doi.org/10.1109/TCSII.2019.2905639

Kryger, P. & Bjerge, K. (2019). Tælling af varroamider. In Varroatolerance i Danmark : de første skridt februar 2019 (pp. 28-31). Danmarks Biavlerforening.

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

Larsen, D., Skovsen, S., Steen, K. A., Grooters, K., Eriksen, J., Green, O. & Nyholm Jørgensen, R. (2018). Autonomous Mapping of Grass-Clover Ratio Based on Unmanned Aerial Vehicles and Convolutional Neural Networks. In Proceedings of the 14th International Conference on Precision Agriculture Article 5107 International Society of Precision Agriculture. https://www.ispag.org/proceedings/?action=download&item=5107

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

Klausen, K. C., Pagels, K. Ø., Bjerre, J. (Ed.) & Pedersen, C. F. (Ed.) (2019). LoRa and LoRaWAN Case Studies. European Regional Development Fund and Central Denmark Region.

Bjerge, K., Fynbo, H. O. U. & Johansen, J. G. (2019). A system on programmable chip design of a digitizer with improved trapezoidal filter validation. Microprocessors and Microsystems, 65, 7-13. https://doi.org/10.1016/j.micpro.2018.12.004

Liu, L., Grombacher, D. J., Auken, E. & Larsen, J. J. (2018). Low noise, multichannel surface NMR receiver system with wireless connections to receiver coils. Abstract from First Australian Exploration Geoscience Conference, Sydney, Australia. http://www.publish.csiro.au/ex/pdf/ASEG2018abT7_1H

Osterman, G. K., Haldrup, J. N. R., Liu, L., Larsen, J. J., Auken, E. & Grombacher, D. J. (2018). Accounting for MRS signal phases imparted by measurement parameters. Paper presented at 7th International Workshop on Magnetic Resonance, Jilin, China.

Liu, L., Grombacher, D. J., Auken, E. & Larsen, J. J. (2018). Improved parameters estimates for surface-NMR data using spectral analysis. Paper presented at 7th International Workshop on Magnetic Resonance, Jilin, China.

Larsen, J. J., Liu, L., Grombacher, D. J., Osterman, G. K. & Auken, E. (2018). Apsu - a new surface NMR instrument.. C01. Paper presented at 7th International Workshop on Magnetic Resonance, Jilin, China.

Rasmussen, S., Nyboe, N. S., Mai, S. & Larsen, J. J. (2018). Cancellation of minimum shift keying encoded radio signals from airborne TEM data. In 7th International Workshop on Airborne Electromagnetics: AEM2018 Article 21 https://rushfiles.one/client/publiclink.aspx?id=Gy1X0iDziy#

Kremer, T., Larsen, J. J. & Nguyen, F. (2018). New Processing Schemes for the Removal of Harmonic Noise from SNMR Signals in Complex Multi-Frequency Noise Environments. In 24th European Meeting of Environmental and Engineering Geophysics European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.201802552

Bækkegaard, S., Blixenkrone-Møller, J., Larsen, J. J. & Jochumsen, L. (2018). Target Classiﬁcation Using Kinematic Data and a Recurrent Neural Network. In H. Rohling (Ed.), 2018 19th International Radar Symposium, IRS 2018: Proceedings (Vol. 2018). Article 8448118 IEEE. https://doi.org/10.23919/IRS.2018.8448118

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Revised 13.11.2025

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