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Department of Electrical and Computer Engineering

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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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, Article 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, Article 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 from The 8th International Workshop on Magnetic Resonance Sounding, Strasbourg, France. 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, Article 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), Article 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. In N. Mallenahalli, A. Bhattacharya, S. Senatore, A. Negi & A. Hirose (Eds.), 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) (pp. 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), Article 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. Article 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. In 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings (pp. 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. In SEG Technical Program Expanded Abstracts 2021 (pp. 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, Article 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, Article 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

Barfod, A. A. S., Lévy, L. & Larsen, J. J. (2021). Automatic processing of time domain induced polarization data using supervised artificial neural networks. Geophysical Journal International, 224(1), 312-325. https://doi.org/10.1093/gji/ggaa460

Sørensen, R. A., Rosenberg, J. & Karstoft, H. (2021). Baggage Routing with Scheduled Departures using Deep Reinforcement Learning. In Proceedings - 2021 International Symposium on Computer Science and Intelligent Controls, ISCSIC 2021 (pp. 13-19). IEEE. https://doi.org/10.1109/ISCSIC54682.2021.00014

Dyrmann, M., Mortensen, A. K., Linneberg, L., Høye, T. T. & Bjerge, K. (2021). Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning. Sensors (Switzerland), 21(18), Article 6126. https://doi.org/10.3390/s21186126

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

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.

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

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

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

Chen, Q., Lang, X., Lu, S., Rehman, N. U., Xie, L. & Su, H. (2021). Detection and root cause analysis of multiple plant-wide oscillations using multivariate nonlinear chirp mode decomposition and multivariate Granger causality. Computers and Chemical Engineering, 147, Article 107231. https://doi.org/10.1016/j.compchemeng.2021.107231

Asif, M. R., Bording, T. S., Barfod, A. S., Grombacher, D. J., Maurya, P. K., Christiansen, A. V., Auken, E. & Larsen, J. J. (2021). Effect of Data Pre-Processing on the Performance of Neural Networks for 1-D Transient Electromagnetic Forward Modeling. IEEE Access, 9, 34635-34646. https://doi.org/10.1109/ACCESS.2021.3061761

Griffiths, M. P., Grombacher, D. J. & Larsen, J. J. (2021). Efficient numerical Bloch solutions for multipulse surface NMR. Geophysical Journal International, 227(3), 1905-1916. https://doi.org/10.1093/gji/ggab321

Kass, M. A., Christiansen, A. V., Auken, E. & Larsen, J. J. (2021). Efficient reduction of powerline signals in magnetic data acquired from a moving platform. IEEE Transactions on Geoscience and Remote Sensing, 59(8), 7137-7146. Article 9246269. https://doi.org/10.1109/TGRS.2020.3029658

Kragh, M. F. & Karstoft, H. (2021). Embryo selection with artificial intelligence: how to evaluate and compare methods? Journal of Assisted Reproduction and Genetics, 38(7), 1675-1689. https://doi.org/10.1007/s10815-021-02254-6

Rafiei Foroushani, M., Boudjadar, J. & Khooban, M. H. (2021). Energy Management of a Zero-Emission Ferry Boat with a Fuel Cell-Based Hybrid Energy System: Feasibility Assessment. IEEE Transactions on Industrial Electronics, 68(2), 1739-1748. https://doi.org/10.1109/TIE.2020.2992005

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

Liu, L., Griffiths, M. P., Vang, M., Grombacher, D. J. & Larsen, J. J. (2021). Is it redundant to use model-based subtraction together with the reference noise cancellation? In 27th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience Conference and Exhibition 2021, NSG 2021 EAGE Publishing BV. https://doi.org/10.3997/2214-4609.202120108

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.

Bording, T. S., Asif, M. R., Barfod, A. S., Larsen, J. J., Zhang, B., Grombacher, D. J., Christiansen, A. V., Engebretsen, K. W., Pedersen, J. B., Maurya, P. K. & Auken, E. (2021). Machine learning based fast forward modelling of ground-based time-domain electromagnetic data. Journal of Applied Geophysics, 187, Article 104290. https://doi.org/10.1016/j.jappgeo.2021.104290

Grombacher, D., Liu, L., Osterman, G. K. & Larsen, J. J. (2021). Mitigating Narrowband Noise Sources Close to the Larmor Frequency in Surface NMR. IEEE Geoscience and Remote Sensing Letters, 18(8), 1376-1380. https://doi.org/10.1109/LGRS.2020.3000639

Naveed, K., Mukhtar, S. & Ur Rehman, N. (2021). Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis. In 2021 IEEE Statistical Signal Processing Workshop, SSP 2021 (pp. 441-445). IEEE. https://doi.org/10.1109/SSP49050.2021.9513823

Dormann, F., Frisk, O., Andersen, L. N. & Pedersen, C. F. (2021). Not All Noise Is Accounted Equally: How Differentially Private Learning Benefits From Large Sampling Rates. In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021 IEEE. https://doi.org/10.1109/MLSP52302.2021.9596307

Afshari-Igder, M., Rafiei Foroushani, M., Boudjadar, J. & Khooban, M. H. (2021). Reliability and Safety Improvement of Emission-Free Ships: Systemic Reliability Centered Maintenance. IEEE Transactions on Transportation Electrification, 7(1), 256-266. Article 9220168. https://doi.org/10.1109/TTE.2020.3030082

Skovsen, S. K., Laursen, M. S., Kristensen, R. K., Rasmussen, J., Dyrmann, M., Eriksen, J., Gislum, R., Nyholm Jørgensen, R. & Karstoft, H. (2021). Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks. Sensors, 21( 1), Article 175. https://doi.org/10.3390/s21010175

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

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.

Tomar, D., Bozorgtabar, B., Lortkipanidze, M., Vray, G., Rad, M. S. & Thiran, J.-P. (2021). Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation.

Spahr, A., Bozorgtabar, B. & Thiran, J.-P. (2021). Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays.

Chen, Q., Chen, J., Lang, X., Xie, L., Rehman, N. U. & Su, H. (2021). Self-tuning variational mode decomposition. Journal of the Franklin Institute, 358(15), 7825-7862. https://doi.org/10.1016/j.jfranklin.2021.07.021

Christensen, S., Dyrmann, M., Nyholm Jørgensen, R. & Laursen, M. S. (2021). Sensing for Weed Detection. In Sensing Approaches for Precision Agriculture (pp. 275-300). Springer. https://doi.org/10.1007/978-3-030-78431-7, https://doi.org/10.1007/978-3-030-78431-7_10

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