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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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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

Tøttrup, D., Skovgaard, S. L., Sejersen, J. L. F. & de Figueiredo, R. P. (2022). A Real-Time Method for Time-to-Collision Estimation from Aerial Images. Journal of Imaging, 8(3), Article 62. https://doi.org/10.3390/jimaging8030062

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

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. In 2018 21st International Conference on Information Fusion (FUSION) IEEE. https://doi.org/10.23919/icif.2018.8455608

Ferrarini, B., Ehsan, S., Leonardis, A., Rehman, N. U. & McDonald-Maier, K. D. (2018). Performance Characterization of Image Feature Detectors in Relation to the Scene Content Utilizing a Large Image Database. IEEE Access, 6, 8564-8573. https://doi.org/10.1109/access.2018.2795460

Malik, Q. W., Rehman, N. U., Gull, S., Ehsan, S. & McDonald-Maier, K. D. (2019). FPGA-Based Real-Time Implementation of Bivariate Empirical Mode Decomposition. Circuits, Systems, and Signal Processing, 38(1), 118-137. https://doi.org/10.1007/s00034-018-0844-2

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. (Ed.) (2019). Multiscale image denoising using goodness-of-fit test based on EDF statistics. P L o S 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. In 29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings (pp. 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. In 34th Symposium on the Application of Geophysics to Engineering and Environmental Problems, SAGEEP 2022 (pp. 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), Article 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), Article 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, Article 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), Article 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, Article 151. https://doi.org/10.1007/s42979-022-01042-y

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