Institut for Elektro- og Computerteknologi

Aarhus Universitets segl

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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Nielsen, L., Olesen, C. R., Nyholm Jørgensen, R. & Fris, H. J. (2015). Sikring af levesteder på engarealer med biomassehøst i kombination med vildthensyn. Abstract fra Biodiversitetssymposiet 2015, Aarhus , Danmark.
Nielsen, P. R., Jensen, N.-P., Dyrmann, M., Nielsen, P.-H. & Nyholm Jørgensen, R. (2017). RoboWeedSupport - Presentation of a cloud based system bridging the gap between in-field weed inspections and decision support systems. Advances in Animal Biosciences, 8(2), 860-864. https://doi.org/10.1017/S2040470017001054
Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2020). CloudCast: A Satellite-Based Dataset and Baseline for Forecasting Clouds. Abstract fra European Conference on Computer Vision Workshop . https://drive.google.com/file/d/1_TV20JCIyBaetN6Y1mt7K5FeMx431Rr3/view
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
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. Artikel 9366908. https://doi.org/10.1109/JSTARS.2021.3062936
Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2021). IrradianceNet: Spatiotemporal Deep Learning Model for Satellite-Derived Solar Irradiance Nowcasting. Solar Energy, 228, 659-669. https://doi.org/10.1016/j.solener.2021.09.073
Nielsen, A. H., Iosifidis, A. & Karstoft, H. (2022). Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data. Scientific Reports, 12(1), Artikel 8395. https://doi.org/10.1038/s41598-022-12167-8
Nielsen, J. B., Pham, X. H., Kayacan, E. & Sarabakha, A. (2026). 3D Gaussian Splatting for Reconstructing Large Sparse Environments (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 40(48), 41134-41136. https://doi.org/10.1609/aaai.v40i48.42189
Nazari, M., Korshoej, A. R. & Rehman, N. U. (2024). Multiscale Dynamic Graph Signal Analysis. Social Science Research Network (SSRN). https://doi.org/10.2139/ssrn.4713829
Nazari, M., Korshøj, A. R. & ur Rehman, N. (2024). Multiscale dynamic graph signal analysis. Signal Processing, 222, Artikel 109519. https://doi.org/10.1016/j.sigpro.2024.109519
Nazari, M., Korshøj, A. R. & Rehman, N. U. (2024). Jump Plus AM-FM Mode Decomposition. Cornell University. https://doi.org/10.48550/arXiv.2407.07800
Nazari, M., Korshoej, A. R. & Rehman, N. U. (2024). Graph-Aided Multivariate Signal Decomposition. I International Conference on Frontiers of Signal Processing (ICFSP) (s. 169-173) https://doi.org/10.1109/ICFSP62546.2024.10785500
Nazari, M., Korshj, A. R. & Rehman, N. U. (2025). Jump Plus AM-FM Mode Decomposition. IEEE Transactions on Signal Processing, 73, 1081-1093. https://doi.org/10.1109/TSP.2025.3535822
Nazari, M. (2025). Modeling Temporal Characteristics and Topological Structures in Data-driven Signal. [Ph.d.-afhandling, Aarhus Universitet].
Nazari, M., Korshoej, A. R. & Rehman, N. U. (2025). Successive Jump and Mode Decomposition. (s. 2622-2626). https://doi.org/10.23919/EUSIPCO63237.2025.11226714
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, Artikel 108503. https://doi.org/10.1016/j.compag.2023.108503
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 fra 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., 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, Artikel 100953. https://doi.org/10.1016/j.atech.2025.100953
Naveed, K., Akhtar, M. T., Siddiqui, M. F. & Rehman, N. U. (2021). A statistical approach to signal denoising based on data-driven multiscale representation. Digital Signal Processing, 108, Artikel 102896. https://doi.org/10.1016/j.dsp.2020.102896
Naveed, K. & Rehman, N. U. (2020). Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics. IEEE Transactions on Signal Processing, 68, 5997-6010. Artikel 9234624. https://doi.org/10.1109/TSP.2020.3029659
Naveed, K., Mukhtar, S. & Ur Rehman, N. (2021). Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis. I 2021 IEEE Statistical Signal Processing Workshop, SSP 2021 (s. 441-445). IEEE. https://doi.org/10.1109/SSP49050.2021.9513823
Naveed, K. & Rehman, N. U. (2021). A Novel Multivariate Goodness-of-Fit Test based on Mahalanobis Distance and Its Application in Denoising. I 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings (s. 2050-2054). IEEE. https://doi.org/10.23919/eusipco54536.2021.9616193
Naveed, K., Shaukat, B., Ehsan, S., Mcdonald-Maier, K. D., Rehman, N. U. & Baghaie, A. (red.) (2019). Multiscale image denoising using goodness-of-fit test based on EDF statistics. PLoS One, 14(5), e0216197. https://doi.org/10.1371/journal.pone.0216197
Naveed, K., Shaukat, B. & Rehman, N. U. (2018). Dual tree complex wavelet transform-based signal denoising method exploiting neighbourhood dependencies and goodness-of-fit test. Royal Society Open Science, 5(9), 180436. https://doi.org/10.1098/rsos.180436
Naveed, K., Shaukat, B. & Rehman, N. U. (2017). Signal denoising based on dual tree complex wavelet transform and goodness of fit test. I 2017 22nd International Conference on Digital Signal Processing (DSP) IEEE. https://doi.org/10.1109/icdsp.2017.8096067
Naveed, K., Ehsan, S., McDonald-Maier, K. D. & Ur Rehman, N. (2019). A multiscale denoising framework using detection theory with application to images from CMOS/CCD sensors. Sensors (Switzerland), 19(1), Artikel 206. https://doi.org/10.3390/s19010206
Naveed, K., Abdullah, F., Madni, H. A., Khan, M. A. U., Khan, T. M. & Naqvi, S. S. (2021). Towards automated eye diagnosis: An improved retinal vessel segmentation framework using ensemble block matching 3d filter. Diagnostics, 11(1), Artikel 114. https://doi.org/10.3390/diagnostics11010114
Naveed, K., Ali, B. & Akhtar, M. T. (2016). A variable step size affine projection algorithm based on the principle of orthogonality. Midwest Symposium on Circuits and Systems. https://doi.org/10.1109/MWSCAS.2016.7870057
Naveed, K., Iqbal, J. & Ur Rehman, H. (2012). Brain controlled human robot interface. Proceedings of 2012 International Conference on Robotics and Artificial Intelligence, 55-60. https://doi.org/10.1109/ICRAI.2012.6413410
Naveed, K., Meldgaard Madsen, L., Grombacher, D. & Larsen, J. J. (2023). Variational Mode Decomposition-Based Processing of Time-Domain Induced Polarization Data. Abstract fra NSG2023 29th European Meeting of Environmental and Engineering Geophysics, Edinburgh , Storbritannien. https://doi.org/10.3997/2214-4609.202320133
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. Artikel 5905713. https://doi.org/10.1109/TGRS.2024.3362290
Naveed, K. & Rehman, N. U. (2025). Fully multivariate detrended fluctuation analysis using Mahalanobis norm with application to multivariate signal denoising. Measurement: Journal of the International Measurement Confederation, 242(Part E), Artikel 116142. https://doi.org/10.1016/j.measurement.2024.116142
Naveed, K. & Larsen, J. J. (2024). Variational Mode Decomposition Based Processing of Surface NMR Data. I NSG2024, 30th European Meeting of Environmental and Engineering Geophysics European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202420100
Naveed, K. & Ur Rehman, N. (2024). Variational Mode Decomposition Denoising Using Anderson-Darling Statistics. I 2024 9th International Conference on Frontiers of Signal Processing, ICFSP 2024 (s. 189-193). IEEE. https://doi.org/10.1109/ICFSP62546.2024.10785266
Naveed, K., de Freitas, B. N. & Pauwels, R. (2025). Naada: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs.
Naveed, K. & Rehman, N. U. (2025). A Fully Multivariate Multifractal Detrended Fluctuation Analysis Method for Fault Diagnosis.
Naveed, K. & Pauwels, R. (2026). HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography.
Mortensen, A. K., Karstoft, H., Søegaard, K. & Nyholm Jørgensen, R. (2014). Estimating the content of clover and grass in the sward using a consumer camera and image processing. Abstract fra Agromek and NJF joint seminar, Herning, Danmark. http://www.njf.nu/477.htm
Mortensen, A. K., Søegaard, K. & Nyholm Jørgensen, R. (2014). Bestemmelse af kløver- og græsandele i kløvergræsmarker ved hjælp af billedanalyse. Oversigt over Landsforsøgene, 2014, 357-358. https://www.landbrugsinfo.dk/Planteavl/Landsforsoeg-og-resultater/Oversigten-og-tabelbilaget/Sider/pl_oversigt_over_landsforsoegene_2014.aspx
Mortensen, A. K., Gislum, R., Larsen, R. & Nyholm Jørgensen, R. (2015). Estimation of above-ground dry matter and nitrogen uptake in catch crops using images acquired from an octocopter. I J. V. Stafford (red.), Precision agriculture '15: Papers presented at the 10th European Conference on Precision Agriculture (Bind 2015, s. 127-134). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-814-8_15
Mortensen, A. K., Karstoft, H., Nyholm Jørgensen, R., Larsen, R., Jørgensen, J. R. & Gislum, R. (2015). BioMap - Mapping of Biomass using UAV. http://dce-conference.au.dk/fileadmin/dce-conference.au.dk/Abstracts15/28_Mortensen.pdf
Mortensen, A. K., Dyrmann, M., Karstoft, H., Nyholm Jørgensen, R. & Gislum, R. (2016). Semantic Segmentation of Mixed Crops using Deep Convolutional Neural Network. Afhandling præsenteret på CIGR 2016, World Congress. http://conferences.au.dk/uploads/tx_powermail/biomap_-_cigr_2016_-_paper_-_final.pdf
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), Artikel 59. https://doi.org/10.3390/jimaging3040059
Mortensen, A. K., Laursen, M. S. & Nyholm Jørgensen, R. (2018). Estimation of Above-Ground Biomass and Nitrogen-Content of Agricultural Field Crops using Computer Vision. Billeder, Video- og Lydoptagelser (digital)
Mortensen, A. K., Bender, A., Whelan, B., Barbour, M. M., Sukkarieh, S., Karstoft, H. & Gislum, R. (2018). Segmentation of lettuce in coloured 3D point clouds for fresh weight estimation. Computers and Electronics in Agriculture, 154, 373-381. https://doi.org/10.1016/j.compag.2018.09.010
Mortensen, A. K., Skovsen, S., Karstoft, H. & Gislum, R. (2019). The Oil Radish Growth Dataset for Semantic Segmentation and Yield Estimation. Poster-session præsenteret på IEEE Conference on Computer Vision and Pattern Recognition 2019, Long Beach, California, USA.
Mortensen, A. K., Skovsen, S., Karstoft, H. & Gislum, R. (2019). The Oil Radish Growth Dataset. Datasæt
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. I J. V. Stafford (red.), Precision Agriculture '19 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019 (s. 959-965). Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-888-9_118
Mortensen, A. K., Skovsen, S., Karstoft, H. & Gislum, R. (2019). The Oil Radish Growth Dataset for Semantic Segmentation and Yield Estimation. I Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 (s. 2703-2710). Artikel 9025602 IEEE. https://doi.org/10.1109/CVPRW.2019.00328
Morante Moreno, M., Frølich, K., Shahzaib, M., shakil, S. & Rehman, N. U. (2024). Multiscale Functional Connectivity analysis of episodic memory reconstruction. Frontiers in Cognition, 3, Artikel 1433234. https://doi.org/10.3389/fcogn.2024.1433234

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Revideret 13.11.2025
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AU Engineering