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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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? I 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

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

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

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

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

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

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

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

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

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

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

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

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., 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., 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

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

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), Artikel e0261698. https://doi.org/10.1371/journal.pone.0261698

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

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

ur Rehman, N., Ehsan, S., Abdullah, S. M. U., Akhtar, M. J., Mandic, D. P. & McDonald-Maier, K. D. (2015). Multi-Scale pixel-based image fusion using multivariate empirical mode decomposition. Sensors (Switzerland), 15(5), 10923-10947. https://doi.org/10.3390/s150510923

Rehman, N. U. & Mandic, D. P. (2009). Qualitative analysis of rotational modes within three dimensional empirical mode decomposition. I 2009 IEEE International Conference on Acoustics, Speech and Signal Processing IEEE. https://doi.org/10.1109/icassp.2009.4960367

Looney, D., Rehman, N. U., Mandic, D., Rutkowski, T. M., Heidenreich, A. & Beyer, D. (2009). Conditioning multimodal information for smart environments. I 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) IEEE. https://doi.org/10.1109/icdsc.2009.5289373

Rehman, N., Looney, D., Rutkowski, T. M. & Mandic, D. P. (2009). Bivariate EMD-based image fusion. I 2009 IEEE/SP 15th Workshop on Statistical Signal Processing IEEE. https://doi.org/10.1109/ssp.2009.5278639

Rehman, N. U. & Mandic, D. P. (2010). Quadrivariate Empirical Mode Decomposition. I The 2010 International Joint Conference on Neural Networks (IJCNN) IEEE. https://doi.org/10.1109/ijcnn.2010.5596768

Rehman, N. U. & Mandic, D. P. (2011). Filter Bank Property of Multivariate Empirical Mode Decomposition. IEEE Transactions on Signal Processing, 59(5), 2421-2426. https://doi.org/10.1109/tsp.2011.2106779

Park, C., Looney, D., Rehman, N. U., Ahrabian, A. & Mandic, D. P. (2013). Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(1), 10-22. https://doi.org/10.1109/tnsre.2012.2229296

Ahrabian, A., Rehman, N. U. & Mandic, D. (2013). Bivariate Empirical Mode Decomposition for Unbalanced Real-World Signals. IEEE Signal Processing Letters, 20(3), 245-248. https://doi.org/10.1109/lsp.2013.2242062

Rehman, N. U., Park, C., Huang, N. E. & Mandic, D. P. (2013). Emd via memd: Multivariate noise-aided computation of standard emd. Advances in Adaptive Data Analysis, 05(02), 1350007. https://doi.org/10.1142/s1793536913500076

Mandic, D. P., Rehman, N. U., Wu, Z. & Huang, N. E. (2013). Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis. IEEE Signal Processing Magazine, 30(6), 74-86. https://doi.org/10.1109/msp.2013.2267931

Rehman, N. U. & Mandic, D. P. (2014). Dynamically-Sampled Bivariate Empirical Mode Decomposition. IEEE Signal Processing Letters, 21(7), 857-861. https://doi.org/10.1109/lsp.2014.2317773

Li, L., Looney, D., Park, C., Rehman, N. U. & Mandic, D. P. (2011). Power independent EMG based gesture recognition for robotics. I 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE. https://doi.org/10.1109/iembs.2011.6090036

Rehman, N. U., Xia, Y. & Mandic, D. P. (2010). Application of multivariate empirical mode decomposition for seizure detection in EEG signals. I 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology IEEE. https://doi.org/10.1109/iembs.2010.5626665

Hemakom, A., Ahrabian, A., Looney, D., Rehman, N. U. & Mandic, D. P. (2015). Nonuniformly sampled trivariate empirical mode decomposition. I 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IEEE. https://doi.org/10.1109/icassp.2015.7178660

Rehman, N., Ehsan, S., Naveed, K., McDonald-Maier, K. D. & Safdar, M. W. (2015). Dynamically sampled multivariate empirical mode decomposition. Electronics Letters, 51(24), 2049-2051. https://doi.org/10.1049/el.2015.1176

Rehman, N., Khan, M. M., Sohaib, M. I., Jehanzaib, M., Ehsan, S. & McDonald-Maier, K. (2014). Image fusion using multivariate and multidimensional EMD. I 2014 IEEE International Conference on Image Processing (ICIP) IEEE. https://doi.org/10.1109/icip.2014.7026035

HAO, H., WANG, H., REHMAN, N. UR. & TIAN, H. (2016). A Study of the Characteristics of MEMD for Fractional Gaussian Noise. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E99.A(6), 1228-1232. https://doi.org/10.1587/transfun.e99.a.1228

Abdullah, S. M. U., Rehman, N. U., Khan, M. M. & Mandic, D. P. (2015). A Multivariate Empirical Mode DecompositionBased Approach to Pansharpening. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3974-3984. https://doi.org/10.1109/tgrs.2015.2388497

Ehsan, S., Clark, A., Rehman, N. & McDonald-Maier, K. (2015). Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems. Sensors, 15(7), 16804-16830. https://doi.org/10.3390/s150716804

Ferrarini, B., Ehsan, S., Rehman, N. U., Leonardis, A. & McDonald-Maier, K. D. (2016). Automatic Selection of the Optimal Local Feature Detector. I Lecture Notes in Computer Science (s. 284-289). Springer International Publishing. https://doi.org/10.1007/978-3-319-41501-7_32

Rehman, N. U., Naveed, K., Ehsan, S. & McDonald-Maier, K. (2016). Multi-scale image denoising based on goodness of fit (GOF) tests. I 2016 24th European Signal Processing Conference (EUSIPCO) IEEE. https://doi.org/10.1109/eusipco.2016.7760508

Khan, N., Baig, F., Nawaz, S., Rehman, N. U. & Sharma, S. (2016). Analysis of Power Quality Signals Using an Adaptive Time-Frequency Distribution. Energies, 9(11), 933. https://doi.org/10.3390/en9110933

Ehsan, S., Clark, A., Leonardis, A., Rehman, N. U., Khaliq, A., Fasli, M. & McDonald-Maier, K. (2016). A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors. Remote Sensing, 8(11), 928. https://doi.org/10.3390/rs8110928

Rehman, N. U., Abbas, S. Z., Asif, A., Javed, A., Naveed, K. & Mandic, D. P. (2017). Translation invariant multi-scale signal denoising based on goodness-of-fit tests. Signal Processing, 131, 220-234. https://doi.org/10.1016/j.sigpro.2016.08.019

Hao, H., Wang, H. L. & Rehman, N. U. (2017). A joint framework for multivariate signal denoising using multivariate empirical mode decomposition. Signal Processing, 135, 263-273. https://doi.org/10.1016/j.sigpro.2017.01.022

Zahra, A., Kanwal, N., Rehman, N. U., Ehsan, S. & McDonald-Maier, K. D. (2017). Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition. Computers in Biology and Medicine, 88, 132-141. https://doi.org/10.1016/j.compbiomed.2017.07.010

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

Khalid, S. S., Rehman, N. U. & Abrar, S. (2017). Robust stochastic integration filtering for nonlinear systems under multivariate t -distributed uncertainties. Signal Processing, 140, 53-59. https://doi.org/10.1016/j.sigpro.2017.05.007

Ali, M., Khan, A. & Rehman, N. U. (2017). Hybrid multiscale wind speed forecasting based on variational mode decomposition. International Transactions on Electrical Energy Systems, 28(1), e2466. https://doi.org/10.1002/etep.2466

Khalid, S. S., Rehman, N. U., Abrar, S. & Mihaylova, L. (2018). Robust Bayesian Filtering Using Bayesian Model Averaging and Restricted Variational Bayes. I 2018 21st International Conference on Information Fusion (FUSION) IEEE. https://doi.org/10.23919/icif.2018.8455608

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Revideret 13.11.2025

AU Engineering