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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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

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

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

Asif, M. R., Kass, M. A., Herpe, M., Rawlinson, Z., Westerhoff, R., Larsen, J. J. & Christiansen, A. V. (2025). Comparative analysis of deep learning and traditional airborne electromagnetic data processing: A case study. Geophysics, 90(3), WA103-WA112. https://doi.org/10.1190/geo2024-0282.1

Wagner, S. R., Stenner, R., Memon, M., Beevi, F. H. A. & Pedersen, C. F. (2014). Common Ambient Assisted Living Home Platform for Seamless Care. Abstract from 8th International Conference on Pervasive Computing Technologies for Healthcare, Oldenburg, Germany.

Green, O., Schmidt, T., Pietrzkowski, R. P., Jensen, K., Larsen, M. & Nyholm Jørgensen, R. (2014). Commercial Autonomous Agricultural Platform - Kongskilde Robotti. In Proceedings of the Second International Conference on Robotics, Associated High-Technologies and Equipment for Agriculture and Forestry - RHEA 2014: New trends in mobile robotics, perception and actuation for agriculture and forestry (pp. 351-356) https://www.semanticscholar.org/paper/Commercial-autonomous-agricultural-platform%3A-Green-Schmidt/c13e209be6e181138d081a5dc6751286cd67d2ad

Pedersen, C. F. & Brix, L. C. (2001). Collision detection and response: Generation and update methods for finite element meshes of deformable objects.

Christiansen, M. P., Larsen, M. & Nyholm Jørgensen, R. (2013). Collaborative Model Based Development of Adaptive Controller Settings for a Load-carrying Vehicle with Changing Loads. Paper presented at CIOSTA XXXV Conference, Billund, Denmark. https://www.researchgate.net/publication/333658745_Collaborative_Model_Based_Development_of_Adaptive_Controller_Settings_for_a_Load-carrying_Vehicle_with_Changing_Loads

Christiansen, M. P., Laursen, M. S., Nyholm Jørgensen, R. & Hameed, I. A. (2014). Collaborative model based design of automated and robotic agricultural vehicles in the Crescendo Tool. Abstract from Agromek and NJF joint seminar, Herning, Denmark. https://arxiv.org/pdf/1802.06296

Vang, M. Ø., Grombacher, D., Larsen, J. J. & Christiansen, A. V. (2024). Clustering on SNMR and TEM parameters over a saline-freshwater interface to resolve hydrogeological layers. In NSG2024, 30th European Meeting of Environmental and Engineering Geophysics (pp. 1-5). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202420102

Nielsen, A. H., Wagner, A., Iosifidis, A. & Karstoft, H. (2020). Cloud Nowcasting on Satellite Images: A Novel Dataset and Experimental Comparisons. https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/365696

Lillelund, C. M., Jensen, H. B. & Pedersen, C. F. (2022). Cloud K-SVD for Image Denoising. SN Computer Science, 3, Article 151. https://doi.org/10.1007/s42979-022-01042-y

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2020). CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds. Abstract from European Conference on Computer Vision Workshop . https://drive.google.com/file/d/1_TV20JCIyBaetN6Y1mt7K5FeMx431Rr3/view

Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2021). CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 3485-3494. Article 9366908. https://doi.org/10.1109/JSTARS.2021.3062936

Rafiei Foroushani, M. & Iosifidis, A. (2023). Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval. In 2023 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/IJCNN54540.2023.10191068

Park, C., Looney, D., Rehman, N. U., Ahrabian, A. & Mandic, D. P. (2013). Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(1), 10-22. https://doi.org/10.1109/tnsre.2012.2229296

Laursen, M. S., Larsen, R., Jensen, K. & Nyholm Jørgensen, R. (2016). CIGR2016 Talk - Real time back projection onto prerecorded DHM surface of UAV recorded images with RTK GPS and IMU. Pictures, Video and sound recordings (digital) https://www.youtube.com/watch?v=j34nctK7D60

Kayacan, E., M. Peschel, J. & Kayacan, E. (2016). Centralized, Decentralized and Distributed Nonlinear Model Predictive Control of a Tractor-Trailer System: A Comparative Study. In 2016 American Control Conference, ACC 2016 (pp. 4403-4408). Article 7525615 IEEE. https://doi.org/10.1109/ACC.2016.7525615

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