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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Oleksiienko, I. (2023). Uncertainty Estimation for 3D Object Detection and Tracking. [Ph.d.-afhandling, Aarhus Universitet]. Aarhus University.

Lillelund, C. M., Magris, M. & Pedersen, C. F. (2023). Uncertainty Estimation in Deep Bayesian Survival Models. I IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI’23) IEEE. https://doi.org/10.1109/BHI58575.2023.10313466

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

Oleksiienko, I., Tran, D. T. & Iosifidis, A. (2023). Variational Neural Networks. Procedia Computer Science, 222, 104-113. https://doi.org/10.1016/j.procs.2023.08.148

Oleksiienko, I., Nousi, P., Passalis, N., Tefas, A. & Iosifidis, A. (2023). Variational Voxel Pseudo Image Tracking. I 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (s. 323-328). IEEE. https://doi.org/10.1109/SSCI52147.2023.10371810

Pati, P., Jaume, G., Ayadi, Z., Thandiackal, K., Bozorgtabar, B., Gabrani, M. & Goksel, O. (2023). Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer.

Oleksiienko, I. & Iosifidis, A. (2022). 3D object detection and tracking. I A. Iosifidis & A. Tefa (red.), Deep Learning for Robot Perception and Cognition (s. 313-340). Elsevier. https://doi.org/10.1016/B978-0-32-385787-1.00018-X

Bjerge, K., Alison, J., Dyrmann, M., Frigaard, C. E., Mann, H. M. R. & Høye, T. T. (2022). Accurate detection and identification of insects from camera trap images with deep learning. bioRxiv. https://doi.org/10.1101/2022.10.25.513484

Høye, T. T., Dyrmann, M., Kjær, C., Nielsen, J., Bruus, M., Mielec, C. L., Vesterdal, M. S., Bjerge, K., Madsen, S. A., Jeppesen, M. R. & Melvad, C. (2022). Accurate image-based identification of macroinvertebrate specimens using deep learning — How much training data is needed? PeerJ, 10, Artikel e13837. https://doi.org/10.7717/peerj.13837

Rysgaard, S., Bjerge, K., Boone, W., Frandsen, E., Graversen, M., Thomas Høye, T., Jensen, B., Johnen, G., Antoni Jackowicz-Korczynski, M., Taylor Kerby, J., Kortegaard, S., Mastepanov, M., Melvad, C., Schmidt Mikkelsen, P., Mortensen, K., Nørgaard, C., Poulsen, E., Riis, T., Sørensen, L. & Røjle Christensen, T. (2022). A mobile observatory powered by sun and wind for near real time measurements of atmospheric, glacial, terrestrial, limnic and coastal oceanic conditions in remote off-grid areas. HardwareX, 12, Artikel e00331. https://doi.org/10.1016/j.ohx.2022.e00331

Asif, M. R., Bording, T. S., Maurya, P. K., Zhang, B., Fiandaca, G., Grombacher, D. J., Christiansen, A. V., Auken, E. & Larsen, J. J. (2022). A Neural Network-Based Hybrid Framework for Least-Squares Inversion of Transient Electromagnetic Data. IEEE Transactions on Geoscience and Remote Sensing, 60, Artikel 4503610. https://doi.org/10.1109/TGRS.2021.3076121

Griffiths, M., Grombacher, D., Kass, M. A., Liu, L., Vang, M. & Larsen, J. J. (2022). A Reformulated Surface Nmr Forward For Multi-Sequence Acquisitions. I 28th European Meeting of Environmental and Engineering Geophysics, Held at the Near Surface Geoscience Conference and Exhibition 2022, NSG 2022 European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202220109

Kass, M. A., Grombacher, D., Griffiths, M., Vang, M. Ø., Liu, L. & Larsen, J. J. (2022). A steady-state approach to surface nuclear magnetic resonance. I Proceedings of the Symposium on the Application of Geophyics to Engineering and Environmental Problems, SAGEEP (s. 54). J and N Group, Ltd.. https://www.eegs.org/assets/docs/Symposium/2022SAGEEP/AbstractsbyAuthor/Kass%2C%20A%20STEADY-STATE%20APPROACH%20TO%20SURFACE%20NUCLEAR%20MAGNETIC%20RESONANCE%20-%20SAGEEP%202022%20-%20205367449.pdf

Asif, M. R., Maurya, P. K., Foged, N., Larsen, J. J., Auken, E. & Christiansen, A. V. (2022). Automated transient electromagnetic data processing for ground-based and airborne systems by a deep learning expert system. IEEE Transactions on Geoscience and Remote Sensing, 60, Artikel 5919814. https://doi.org/10.1109/TGRS.2022.3202304

Lillelund, C. M., Jensen, H. B. & Pedersen, C. F. (2022). Cloud K-SVD for Image Denoising. SN Computer Science, 3(2), Artikel 151. https://doi.org/10.1007/s42979-022-01042-y

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. Afhandling præsenteret på NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, Beograd, Serbien. https://doi.org/10.3997/2214-4609.202220038

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. I 34th Symposium on the Application of Geophysics to Engineering and Environmental Problems, SAGEEP 2022 (s. 6). J and N Group, Ltd..

Larsen, J. J., Langhof, R. B., Kjær, M. W., Vang, M., Liu, L., Griffiths, M. & Grombacher, D. (2022). Efficient processing of surface NMR data with spectral analysis. Geophysical Journal International, 229(1), 286-298. https://doi.org/10.1093/gji/ggab472

Kjaer-Rasmussen, A., Griffiths, M. P., Grombacher, D. & Larsen, J. J. (2022). Fast removal of powerline harmonic noise from surface NMR datasets using a projection-based approach on graphical processing units. IEEE Geoscience and Remote Sensing Letters, 19, Artikel 8024005. https://doi.org/10.1109/LGRS.2021.3118064

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

Griffiths, M. P., Grombacher, D., Liu, L., Vang, M. Ø. & Larsen, J. J. (2022). Forward Modeling Steady-State Free Precession in Surface NMR. IEEE Geoscience and Remote Sensing Letters, 60, Artikel 4513510. https://doi.org/10.1109/TGRS.2022.3221624

Grombacher, D., Griffiths, M. P., Liu, L., Vang, M. & Larsen, J. J. (2022). Frequency Shifting Steady-State Surface NMR Signals to Avoid Problematic Narrowband-Noise Sources. Geophysical Research Letters, 49(7), Artikel e2021GL097402. https://doi.org/10.1029/2021GL097402

Laursen, S. R. L., Holm, H. A. & Nielsen, S. H. (2022). Heart rate-based dynamic sound intervention - a pilot study. I Proceedings of the EUROREGIO/BNAM2022 Conference: Joimt Acoustics Conference (s. 473-481). https://www.conforg.fr/erbnam2022/output_directory/data/articles/000032.pdf

Asif, M. R., Foged, N., Maurya, P. K., Grombacher, D. J., Christiansen, A. V., Auken, E. & Larsen, J. J. (2022). Integrating neural networks in least-squares inversion of airborne time-domain electromagnetic data. Geophysics, 87(4), E177-E187. https://doi.org/10.1190/geo2021-0335.1

Asif, M. R., Maurya, P. K., Larsen, J. J. & Christiansen, A. V. (2022). Investigating the Integration of Neural Networks in Least-Squares Method for Airborne Electromagnetic Data Inversion. 1-5. Afhandling præsenteret på NSG2022 3rd Conference on Airborne, Drone and Robotic Geophysics, Beograd, Serbien. https://doi.org/10.3997/2214-4609.202220034

Bjerge, K., Frigaard, C. E. & Karstoft, H. (2022). Motion Informed Object Detection of Small Insects in Time-lapse Camera Recordings. arxiv.org. https://arxiv.org/abs/2212.00423

Sadiq, M. T., Yu, X., Yuan, Z., Aziz, M. Z., Rehman, N. U., Ding, W. & Xiao, G. (2022). Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(5), 1177-1189. https://doi.org/10.1109/tetci.2022.3147030

McLachlan, P. J., Khare, S. K., Grombacher, D., Larsen, J. J., A. Christiensen, A. & Luria, J. C. Z. (2022). On The Presence of Correlated Noise in Transient Electromagnetic (Tem) Monitoring Data. 1-5. Abstract fra NSG2022 28th European Meeting of Environmental and Engineering Geophysics, Belgrade , Serbien. https://doi.org/10.3997/2214-4609.202220119

McLachlan, P. J., Khare, S. K., Grombacher, D., Larsen, J. J., Christensen, A. & Zamora Luria, J. C. (2022). On The Presence of Correlated Noise in Transient Electromagnetic (Tem) Monitoring Data. I NSG2022 28th European Meeting of Environmental and Engineering Geophysics (s. 1-5). European Association of Geoscientists and Engineers. https://doi.org/10.3997/2214-4609.202220119

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

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

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, Artikel 106277. https://doi.org/10.1016/j.compbiomed.2022.106277

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, Artikel 5923410. https://doi.org/10.1109/TGRS.2022.3223737

Kremer, T., Irons, T., Müller-Petke, M. & Juul Larsen, J. (2022). Review of Acquisition and Signal Processing Methods for Electromagnetic Noise Reduction and Retrieval of Surface Nuclear Magnetic Resonance Parameters. Surveys in Geophysics, 43(4), 999-1053. https://doi.org/10.1007/s10712-022-09695-3

Stegmüller, T., Bozorgtabar, B., Spahr, A. & Thiran, J.-P. (2022). ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification.

Gaikwad, N., Liu, L., Griffiths, M. P., Vang, M. Ø., Grombacher, D. & Larsen, J. J. (2022). Thermal Model of the Apsu Transmitter for Lightweight and Compact Heat Sink Design. 11-13. Abstract fra The 8th International Workshop on Magnetic Resonance Sounding, Strasbourg, Frankrig. https://mrs2021.sciencesconf.org/data/pages/proceedings_MRS2021_distrib_v2.pdf

Besson, M., Alison, J., Bjerge, K., Gorochowski, T. E., Høye, T. T., Jucker, T., Mann, H. M. R. & Clements, C. F. (2022). Towards the fully automated monitoring of ecological communities. Ecology Letters, 25(12), 2753-2775. https://doi.org/10.1111/ele.14123

Oleksiienko, I., Tran, D. T. & Iosifidis, A. (2022). Variational Neural Networks implementation in Pytorch and JAX[Formula presented]. Software Impacts, 14, Artikel 100431. https://doi.org/10.1016/j.simpa.2022.100431

Khan, T. M., Khan, M. A. U., Rehman, N. U., Naveed, K., Afridi, I. U., Naqvi, S. S. & Raazak, I. (2022). Width-wise vessel bifurcation for improved retinal vessel segmentation. Biomedical Signal Processing and Control, 71(Part A), Artikel 103169. https://doi.org/10.1016/j.bspc.2021.103169

Lindahl Petersen , K., Ladegaard Jensen, K., Back Nielsen, M., Pas, L.-C., Jensen, N.-P., Nielsen, P. R., Bøjer, O. M., Nyholm Jørgensen, R., Laursen, M. S., Teimouri, N. & Hartmann, B. (2021). Analyse af mulige herbicidbesparelser ved brug af erfaringer og data fra RoboWeedMaPS. Datalogisk Institut. https://datalogisk.dk/wp-content/uploads/2021/03/MST_20rapport_version_2_2002092021.pdf

Somerville, G. J., Mathiassen, S. K., Melander, B., Bøjer, O. M. & Nyholm Jørgensen, R. (2021). Analysing the number of images needed to create robust variable spray maps. Precision Agriculture, 22(5), 1377-1396. https://doi.org/10.1007/s11119-021-09800-3

Oleksiienko, I. & Iosifidis, A. (2021). Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems. I N. Mallenahalli, A. Bhattacharya, S. Senatore, A. Negi & A. Hirose (red.), 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) (s. 59-64). IEEE. https://doi.org/10.1109/ICETCI51973.2021.9574075

Bjerge, K., Nielsen, J. B., Sepstrup, M. V., Helsing-Nielsen, F. & Høye, T. T. (2021). An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning. Sensors (Switzerland), 21(2), Artikel 343. https://doi.org/10.3390/s21020343

Ahmadi, H., Rafiei Foroushani, M., Afshari-Igder, M., Gheisarnejad Chirani, M. & Khooban, M. H. (2021). An Energy Efficient Solution for Fuel Cell Heat Recovery in Zero-Emission Ferry Boats: Deep Deterministic Policy Gradient. I E E E Transactions on Vehicular Technology, 70(8), 7571-7581. Artikel 9477203. https://doi.org/10.1109/TVT.2021.3094899.

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

Larsen, J. J., Griffiths, M., Vang, M., Liu, L. & Grombacher, D. (2021). Apsu - A compact surface NMR instrument for groundwater investigations. I SEG Technical Program Expanded Abstracts 2021 (s. 3135-3139). Society of Exploration Geophysicists. https://doi.org/10.1190/segam2021-3582046.1

Bjerge, K., Schougaard, J. H. & Larsen, D. E. (2021). A scalable and efficient convolutional neural network accelerator using HLS for a system-on-chip design. Microprocessors and Microsystems, 87, Artikel 104363. https://doi.org/10.1016/j.micpro.2021.104363

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

Kass, M. A., Auken, E., Larsen, J. J. & Christiansen, A. V. (2021). A towed magnetic gradiometer array for rapid, detailed imaging of utility, geological, and archaeological targets. Geoscientific Instrumentation, Methods and Data Systems, 10(2), 313-323. https://doi.org/10.5194/gi-10-313-2021

Shah, A., Naqvi, S. S., Naveed, K., Salem, N., Khan, M. A. U. & Alimgeer, K. S. (2021). Automated Diagnosis of Leukemia: A Comprehensive Review. IEEE Access, 9, 132097-132124. https://doi.org/10.1109/ACCESS.2021.3114059

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Revideret 13.11.2025

AU Engineering