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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Dubey, S., Meena, T., Bozorgtabar, B., Mahapatra, D. & Roy, S. (2026). MBCE: Multi-Band Contrastive Encoding for Frequency-Aware Ultrasound Representation Learning. IEEE Access, 14, 122536-122547. https://doi.org/10.1109/ACCESS.2026.3721651
Hafeez, S., Mustafa Abro, G. E., Mackay, M. & Telmoudi, A. J. (2026). Robust Indoor Localisation for Swarm UAVs: An Adaptive Exponential Weighted Centroid Approach. I 12th 2026 International Conference on Control, Decision and Information Technologies, CoDIT 2026 (s. 215-220). IEEE. https://doi.org/10.1109/CoDIT70676.2026.11630846
Ponti, L., Madsen, M. S. N., Skovsen, S. K., Gentili, M. & Jørgensen, R. N. (2026). DecimeterWeedMan: An open-source platform for sub-decimeter spot-spraying integrating AI-vision and spray dynamics modeling. Smart Agricultural Technology, 15, Artikel 102436. https://doi.org/10.1016/j.atech.2026.102436
Bari, A., Fatima & Abro, G. E. M. (2026). Improving Pneumonia Segmentation via a Two-Stage Data-Centric Learning Framework. I 2026 IEEE 16th Symposium on Computer Applications & Industrial Electronics (ISCAIE) IEEE. https://doi.org/10.1109/ISCAIE68866.2026.11576321
Bjerge, K., Wogram, S. F. A., Serra-Marin, P. E., Sakhiashvili, O. & Høye, T. T. (2026). InsectDCT: A generalized pipeline for detection, taxonomic classification, and tracking of insects in camera-trap recordings. bioRxiv. https://doi.org/10.64898/2026.07.07.736939
Lord, W., Teagle, S., Alton, J., Bannister, G., Beuchert, J., Bjerge, K., Carbone, D., Segura, A. G., Howson, T., Høye, T. T., Lawson, J., Ravivarma, A., Roy, D. B., Rylett, D., Skinner, G., Warwick, A. & August, T. (2026). Automated monitoring of insects system: building a non-lethal and scalable solution for monitoring of nocturnal insects. HardwareX, 27, Artikel e00822. https://doi.org/10.1016/j.ohx.2026.e00822
Naveed, K., de Freitas, B. N. & Pauwels, R. (2026). NAADA: A noise-aware attention denoising autoencoder for dental panoramic radiographs. Biomedical Signal Processing and Control, 127, Artikel 111126. https://doi.org/10.1016/j.bspc.2026.111126
Vahedifar, M. A. & Zhang, Q. (2026). Discrete Mode Decomposition Meets Shapley Value: Robust Signal Prediction in Tactile Internet. I INFOCOM 2026 - IEEE Conference on Computer Communications IEEE. https://doi.org/10.1109/INFOCOM59046.2026.11571681
Asif, M. R., Rafiei, M., Jørgensen, R. N., Nørremark, M. & Teimouri, N. (2026). Impact of inter-seasonal climate variation on crop classification with deep learning and Sentinel-1 data in Denmark. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 19, 21880-21893. https://doi.org/10.1109/JSTARS.2026.3704500, https://doi.org/10.1109/JSTARS.2026.3704500
Hardenberg, M. & Larsen, J. J. (2026). Enhanced removal of very low frequency and low frequency radio noise from transient electromagnetic data with modeling and adaptive filtering. Geoscientific Instrumentation, Methods and Data Systems, 15(1), 183-193. https://doi.org/10.5194/gi-15-183-2026
Ong, S. Q., Lim, M. H., Bjerge, K., Peris-Felipo, F. J., Lind, R. & Høye, T. T. (2026). InsectRoleVision: A computer vision system to study arthropod diversity and functional roles. Ecological Informatics, 95, Artikel 103701. https://doi.org/10.1016/j.ecoinf.2026.103701
Djanian, S., Nielsen, T. D., Nielsen, S. H. & Bruun, A. (2026). Towards real-time sleep stage classification: a deep learning approach leveraging PPG and ECG. Physiological Measurement, 47(4), Artikel 045007. https://doi.org/10.1088/1361-6579/ae5458
Almpanis, A., Griffiths, M., Pedersen, J., Grombacher, D., Larsen, M., LaBianca, A. & Hag, M. (2025). In-situ bNMR testing for PFAS contamination: A promising approach. I 31st Meeting of Environmental and Engineering Geophysics 2025, Held at Near Surface Geoscience Conference and Exhibition, NSG 2025 (s. 1-5). European Association of Geoscientists and Engineers, EAGE. https://doi.org/10.3997/2214-4609.202520127
Almpanis, A., McLachlan, P., Kraghede, R. E., Griffiths, M. P., Zamora-Luria, J. C., Thangavelu, M., Pedersen, J. B. & Kazmierczak, J. (2026). Extent of the saltwater intrusion in coastal Minjur, India and implications for groundwater quality. Environmental Earth Sciences, 85(7), Artikel 180. https://doi.org/10.1007/s12665-026-12900-w
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
Hafeez, S., Abro, G. E. M. & Marimuthu, M. (2026). Quantum-Secured AI-Driven Drone Logistics for Real-Time Healthcare Delivery. Arabian Journal for Science and Engineering, 51(13), 16155-16175. https://doi.org/10.1007/s13369-026-11104-5
Hafeez, S., Abro, G. E. M., Memon, S. A., Khan, T. A., Memon, I. & Nasir, H. (2026). Quantum-secured routing in drone communication for 6G-enabled smart mobility. Scientific Reports, 16(1), Artikel 8626. https://doi.org/10.1038/s41598-026-36297-5
Rafiei, M., Asif, M. R., Nørremark, M. & Sørensen, C. A. G. (2026). Towards high-spatial-resolution, multi-depth soil water content estimation via SAR data and multimodal deep learning. Precision Agriculture, 27(2), Artikel 33. https://doi.org/10.1007/s11119-026-10322-z
Sarabakha, A. (2025). Data-Driven Identification of Observed Relative Degrees for Nonlinear Systems. I 2025 European Control Conference, ECC 2025 (2025 udg., s. 813-818). IEEE. https://doi.org/10.23919/ECC65951.2025.11187272
Naveed, K. & Pauwels, R. (2026). HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography.
Vahedifar, M. A. & Zhang, Q. (2025). Signal Prediction for Loss Mitigation in Tactile Internet: A Leader-Follower Game-Theoretic Approach. I 35th IEEE International Workshop on Machine Learning for Signal Processing: Signal Processing in the Age of Lorge Language Models, MLSP 2025 IEEE Computer Society Press. https://doi.org/10.1109/MLSP62443.2025.11204284
Amer, A., Felsager, D., Brodskiy, Y. & Sarabakha, A. (2025). Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control. I International Joint Conference on Neural Networks, IJCNN 2025 - Proceedings IEEE. https://doi.org/10.1109/IJCNN64981.2025.11228873
Carreto Picón, G., Oleksiienko, I., Hedegaard, L., Bakhtiarnia, A. & Iosifidis, A. (2026). Continual low-rank scaled dot-product attention. Neural Networks, 197, Artikel 108517. https://doi.org/10.1016/j.neunet.2025.108517
Dang, V. H., Redder, A., Pham, H. X., Sarabakha, A. & Kayacan, E. (2025). VDS-Nav: Volumetric Depth-Based Safe Navigation for Aerial Robots–Bridging the Sim-to-Real Gap. IEEE Robotics and Automation Letters, 10(10), 11038-11045. https://doi.org/10.1109/LRA.2025.3606806
Mikutavicius, R., Álvarez-Tuñón, O., Brodskiy, Y. & Sarabakha, A. (2025). Towards End-to-End Underwater vSLAM Using Neural Radiance Fields. I OCEANS 2025 Brest, OCEANS 2025 IEEE. https://doi.org/10.1109/OCEANS58557.2025.11104336
Liu, S., Lang, X., Wu, J. & Rehman, N. U. (2025). Selective Noise Empirical Mode Decomposition. IEEE Signal Processing Letters, 32, 2823-2827. https://doi.org/10.1109/LSP.2025.3588082
Vahedifar, M. A., Akhtarshenas, A., Rafatpanah, M. M. & Sabbaghian, M. (2025). Shapley-Based Data Valuation with Mutual Information: A Key to Modified K-Nearest Neighbors. I 2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP) https://doi.org/10.1109/MLSP62443.2025.11204262
Svenning, A., Mougeot, G., Alison, J., Chevalier, D., Molina, N. C., Ong, S. Q., Bjerge, K., Carrillo, J., Høye, T. T. & Geissmann, Q. (2026). A general method for detection and segmentation of terrestrial arthropods in images. Methods in Ecology and Evolution, 17(3), 727-739. https://doi.org/10.1111/2041-210x.70249
Akbar, H., Abro, G. E. M., Baloch, S. K., Khan, T. A., Memon, I., Nasir, H. & Memon, S. A. (2026). Exploring carbon nanotube-copper composites for enhanced induction motor design in electrical vehicles. Scientific Reports, 16(1), Artikel 3505. https://doi.org/10.1038/s41598-025-32761-w
Tang, F., An, X., Yang, H., Xie, Y., Yang, K., Hu, M., Cheng, Z., Zhou, X., Ran, Z., Razzak, I., Feng, Z., Bozorgtabar, B., Deng, J. & Ge, Z. (2025). Unifying Image and Video Understanding in One Vision Encoder. Afhandling præsenteret på The Thirty-ninth Annual Conference on Neural Information Processing Systems, San Diego, California, USA. https://openreview.net/pdf/46f20263cc2abbd139b0f9be3ada52e0fd7427d5.pdf
Bozorgtabar, B., Mahapatra, D., von Teng, H., Pollinger, A., Ebner, L., Thiran, J.-P. & Reyes, M. (2019). Informative sample generation using class aware generative adversarial networks for classification of chest Xrays.
Lebailly, T., Stegmüller, T., Bozorgtabar, B., Thiran, J.-P. & Tuytelaars, T. (2023). Adaptive Similarity Bootstrapping for Self-Distillation based Representation Learning. (s. 16459-16468). https://doi.org/10.1109/ICCV51070.2023.01513
Rad, M. S., Bozorgtabar, B., Musat, C., Marti, U.-V., Basler, M., Ekenel, H. K. & Thiran, J.-P. (2019). Benefiting from Multitask Learning to Improve Single Image Super-Resolution.
Abbet, C., Studer, L., Fischer, A., Dawson, H., Zlobec, I., Bozorgtabar, B. & Thiran, J.-P. (2021). Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection.
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. https://doi.org/10.1016/j.media.2023.102915
Rad, M. S., Bozorgtabar, B., Marti, U.-V., Basler, M., Ekenel, H. K. & Thiran, J.-P. (2019). Srobb: Targeted Perceptual Loss for Single Image Super-Resolution.
Rad, M. S., Yu, T., Musat, C., Ekenel, H. K., Bozorgtabar, B. & Thiran, J.-P. (2020). Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution.
Anklin, V., Pati, P., Jaume, G., Bozorgtabar, B., Foncubierta-Rodríguez, A., Thiran, J.-P., Sibony, M., Gabrani, M. & Goksel, O. (2021). Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs.
Jaume, G., Pati, P., Bozorgtabar, B., Foncubierta-Rodríguez, A., Feroce, F., Anniciello, A. M., Rau, T., Thiran, J.-P., Gabrani, M. & Goksel, O. (2020). Quantifying Explainers of Graph Neural Networks in Computational Pathology.
Rad, M. S., Yu, T., Bozorgtabar, B. & Thiran, J.-P. (2021). Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images.
Stegmüller, T., Lebailly, T., Bozorgtabar, B., Tuytelaars, T. & Thiran, J.-P. (2023). CrOC: Cross-View Online Clustering for Dense Visual Representation Learning.
Jaume, G., Bozorgtabar, B., Ekenel, H. K., Thiran, J.-P. & Gabrani, M. (2018). Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks.
Li, C., Bozorgtabar, B., Ping, Y., Huang, P. & Qin, J. (2025). Positive Semi-definite Latent Factor Grouping-Boosted Cluster-reasoning Instance Disentangled Learning for WSI Representation.
Abbet, C., Zlobec, I., Bozorgtabar, B. & Thiran, J.-P. (2020). Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer.
Mahapatra, D., Bozorgtabar, B., Thiran, J.-P. & Shao, L. (2020). Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation.
Mahapatra, D., Bozorgtabar, B., Thiran, J.-P. & Reyes, M. (2018). Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network.
Mahapatra, D., Bozorgtabar, B., Thiran, J.-P. & Shao, L. (2020). Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance.
Mahapatra, D. & Bozorgtabar, B. (2019). Progressive Generative Adversarial Networks for Medical Image Super resolution.
Mahapatra, D. & Bozorgtabar, B. (2017). Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution.
Tomar, D., Lortkipanidze, M., Vray, G., Bozorgtabar, B. & Thiran, J.-P. (2021). Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation. https://doi.org/10.1109/TMI.2021.3059265

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Revideret 07.07.2026
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AU Engineering