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Institut for Elektro- og Computerteknologi

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

Sortér efter: Dato | Forfatter | Titel

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

Gul, S., Siddiqui, M. F. & Rehman, N. U. (2019). FPGA Based Real-Time Implementation of Online EMD With Fixed Point Architecture. IEEE Access, 7, 176565-176577. https://doi.org/10.1109/access.2019.2957819

Khawaja, A., Khan, T. M., Naveed, K., Naqvi, S. S., Rehman, N. U. & Nawaz, S. J. (2019). An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser. IEEE Access, 7, 164344-164361. https://doi.org/10.1109/access.2019.2953259

Saleem, S., Naqvi, S. S., Manzoor, T., Saeed, A., Rehman, N. U. & Mirza, J. (2019). A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings. Frontiers in Physiology, 10. https://doi.org/10.3389/fphys.2019.00246

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. & 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

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

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..

Liu, Z., Liang, J., Zhang, S. & Kayacan, E. (2022). ANN-Based vibration control of an aerial refueling hose system with input nonlinearity and prescribed output constraint. Journal of the Franklin Institute, 359(6), 2627-2645. https://doi.org/10.1016/j.jfranklin.2022.01.006

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

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

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

Imanberdiyev, N., Sood, S., Kircali, D. & Kayacan, E. (2022). Design, development and experimental validation of a lightweight dual-arm aerial manipulator with a COG balancing mechanism. Mechatronics, 82, Artikel 102719. https://doi.org/10.1016/j.mechatronics.2021.102719

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

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

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

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 fra AGU 2021, USA.

Frederiksen, T. & Larsen, J. J. (2019). Detection of Capacitive Couplings in Ground-Based TEM Data with a 1D Convolutional Neural Network. Afhandling præsenteret på 25th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience Conference and Exhibition 2019, NSG 2019, The Hague, Holland.

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

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

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

Grombacher, D., Liu, L., Griffiths, M. P., Vang, M. & Larsen, J. J. (2021). Steady-State Surface NMR for Mapping of Groundwater. Geophysical Research Letters, 48(23), Artikel e2021GL095381. https://doi.org/10.1029/2021GL095381

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

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., 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

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

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

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

Mehndiratta, M., Singh, K., Kayacan, E. & Feroskhan, M. (2021). Receding Horizon-based Fault-tolerant Control of QuadPlus: An Over-actuated Quadrotor. I 2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021 (s. 853-859). IEEE. https://doi.org/10.1109/CASE49439.2021.9551527

Figueiredo, R. P. D., Hansen, J. G., Le Fevre Sejersen, J., Brandão, M. & Kayacan, E. (2021). On the Advantages of Multiple Stereo Vision Camera Designs for Autonomous Drone Navigation.

le Fevre Sejersen, J., Pimentel De Figueiredo, R. & Kayacan, E. (2021). Safe Vessel Navigation Visually Aided by Autonomous Unmanned Aerial Vehicles in Congested Harbors and Waterways. I 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) (s. 1901-1907) https://doi.org/10.1109/CASE49439.2021.9551637

Figueiredo, R. P. D., Sejersen, J. L. F., Hansen, J. G., Brandão, M. & Kayacan, E. (2021). Real-Time Volumetric-Semantic Exploration and Mapping: An Uncertainty-Aware Approach. (s. 9064-9070). https://doi.org/10.48550/arXiv.2109.01474

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

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

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

Farkhani, S., Skovsen, S. K., Dyrmann, M., Nyholm Jørgensen, R. & Karstoft, H. (2021). Weed classification using explainable multi-resolution slot attention. Sensors, 21(20), Artikel 6705. https://doi.org/10.3390/s21206705

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

Bozcan, I. & Kayacan, E. (2020). Contextual Anomaly Detection for Low Altitude Aerial Surveillance.

Pham, X. H., Bozcan, I., Sarabakha, A., Haddadin, S. & Kayacan, E. (2021). GateNet: An Efficient Deep Neural Network Architecture for Gate Perception Using Fish-Eye Camera in Autonomous Drone Racing. 4176-4183. Afhandling præsenteret på 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, Prag, Tjekkiet. https://sarabakha.info/files/papers/conference/IROS_2021.pdf

Bozcan, I., Korndörfer, C., Madsen, M. W. & Kayacan, E. (2022). Score-based Anomaly Detection for Smart Manufacturing Systems. IEEE - ASME Transactions on Mechatronics, 27(6), 5233-5242. https://doi.org/10.1109/TMECH.2022.3169724

Mehndiratta, M., Camci, E. & Kayacan, E. (2021). Can deep models help a robot to tune its controller? A step closer to self-tuning model predictive controllers. Electronics, 10(18), Artikel 2187. https://doi.org/10.3390/electronics10182187

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), Artikel 6126. https://doi.org/10.3390/s21186126

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. I 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021 IEEE. https://doi.org/10.1109/MLSP52302.2021.9596307

Lillelund, C. M., Pedersen, C. F., Harbo, M. & Fibiger Smed, C. (2021). Vejen til genoptræning går gennem data. Medicoteknik, 8(3), 18-19. https://ipaper.ipapercms.dk/TechMedia/Medicoteknik/2021/3/?page=18

Lati, R. N., Rasmussen, J., Andujar, D., Dorado, J., Berge, T. W., Wellhausen, C., Pflanz, M., Nordmeyer, H., Schirrmann, M., Eizenberg, H., Neve, P., Jørgensen, R. N. & Christensen, S. (2021). Site-specific weed management—constraints and opportunities for the weed research community: Insights from a workshop. Weed Research, 61(3), 147-153. https://doi.org/10.1111/wre.12469

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.

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

Imanberdiyev, N. & Kayacan, E. (2020). Redundancy Resolution based Trajectory Generation for Dual-Arm Aerial Manipulators via Online Model Predictive Control. I Proceedings - IECON 2020: 46th Annual Conference of the IEEE Industrial Electronics Society (s. 674-681). Artikel 9254702 IEEE. https://doi.org/10.1109/IECON43393.2020.9254702

Camci, E., Campolo, D. & Kayacan, E. (2020). Deep Reinforcement Learning for Motion Planning of Quadrotors Using Raw Depth Images. I 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings Artikel 9207490 IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207490

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

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Revideret 13.11.2025

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AU Engineering