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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Asif, M. R., Foged, N., Bording, T. S., Larsen, J. J. & Christiansen, A. V. (2023). DL-RMD: a geophysically constrained electromagnetic resistivity model database for deep learning applications. Earth System Science Data, 15(3), 1389-1401. https://doi.org/10.5194/essd-2022-345, https://doi.org/10.5194/essd-15-1389-2023

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

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

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. In Proceedings of the Symposium on the Application of Geophyics to Engineering and Environmental Problems, SAGEEP (pp. 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. In Proceedings of the EUROREGIO/BNAM2022 Conference: Joimt Acoustics Conference (pp. 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, Article 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, Article 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, Article 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, Article 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, Article 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, Article 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), Article 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), Article 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), Article 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. In 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. In 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. In 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. In 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. In 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. In 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. In 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. In 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. In Lecture Notes in Computer Science (pp. 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. In 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. In 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

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