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Department of Electrical and Computer Engineering

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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

Wang, Z., Chen, L., Chen, H. & Rehman, N. U. (2023). Monthly ship price forecasting based on multivariate variational mode decomposition. Engineering Applications of Artificial Intelligence, 125, Article 106698. https://doi.org/10.1016/j.engappai.2023.106698

Wang, Z., Chen, L., Chen, H., Yang, J. & Rehman, N. U. (2025). Graph signal processing meets machine learning: Multi-scale spatial-temporal ensemble learning methodology for air quality forecasting. Expert Systems with Applications, 291, Article 128538. https://doi.org/10.1016/j.eswa.2025.128538

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

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

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

Rehman, N. U. & Aftab, H. (2019). Multivariate Variational Mode Decomposition. IEEE Transactions on Signal Processing, 67(23), 6039-6052. Article 8890883. https://doi.org/10.1109/TSP.2019.2951223

Rehman, N. U., Naveed, K. & Khan, B. (2019). Data-Driven Multivariate Signal Denoising Using Mahalanobis Distance. IEEE Signal Processing Letters, 26(9), 1408-1412. https://doi.org/10.1109/LSP.2019.2932715

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

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

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

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. 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

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

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

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

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., 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. (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

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

Nazari, M., Korshoej, A. R. & Rehman, N. U. (2024). Multiscale Dynamic Graph Signal Analysis. Social Science Research Network (SSRN). https://doi.org/10.2139/ssrn.4713829

Nazari, M., Korshøj, A. R. & ur Rehman, N. (2024). Multiscale dynamic graph signal analysis. Signal Processing, 222, Article 109519. https://doi.org/10.1016/j.sigpro.2024.109519

Nazari, M., Korshøj, A. R. & Rehman, N. U. (2024). Jump Plus AM-FM Mode Decomposition. Cornell University. https://doi.org/10.48550/arXiv.2407.07800

Nazari, M., Korshoej, A. R. & Rehman, N. U. (2024). Graph-Aided Multivariate Signal Decomposition. In International Conference on Frontiers of Signal Processing (ICFSP) (pp. 169-173) https://doi.org/10.1109/ICFSP62546.2024.10785500

Nazari, M., Korshj, A. R. & Rehman, N. U. (2025). Jump Plus AM-FM Mode Decomposition. IEEE Transactions on Signal Processing, 73, 1081-1093. https://doi.org/10.1109/TSP.2025.3535822

Nazari, M., Korshoej, A. R. & Rehman, N. U. (2025). Successive Jump and Mode Decomposition. (pp. 2622-2626). https://doi.org/10.23919/EUSIPCO63237.2025.11226714

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

Naveed, K. & Rehman, N. U. (2020). Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics. IEEE Transactions on Signal Processing, 68, 5997-6010. Article 9234624. https://doi.org/10.1109/TSP.2020.3029659

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

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

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. PLoS One, 14(5), e0216197. https://doi.org/10.1371/journal.pone.0216197

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

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

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

Naveed, K. & Rehman, N. U. (2025). Fully multivariate detrended fluctuation analysis using Mahalanobis norm with application to multivariate signal denoising. Measurement: Journal of the International Measurement Confederation, 242(Part E), Article 116142. https://doi.org/10.1016/j.measurement.2024.116142

Naveed, K. & Ur Rehman, N. (2024). Variational Mode Decomposition Denoising Using Anderson-Darling Statistics. In 2024 9th International Conference on Frontiers of Signal Processing, ICFSP 2024 (pp. 189-193). IEEE. https://doi.org/10.1109/ICFSP62546.2024.10785266

Naveed, K. & Rehman, N. U. (2025). A Fully Multivariate Multifractal Detrended Fluctuation Analysis Method for Fault Diagnosis.

Morante Moreno, M., Frølich, K., Shahzaib, M., shakil, S. & Rehman, N. U. (2024). Multiscale Functional Connectivity analysis of episodic memory reconstruction. Frontiers in Cognition, 3, Article 1433234. https://doi.org/10.3389/fcogn.2024.1433234

Morante, M., Frølich, K. & Rehman, N. U. (2025). Exploring functional connectivity at different timescales with multivariate mode decomposition. Frontiers in Neuroscience, 19, Article 1653007. https://doi.org/10.3389/fnins.2025.1653007

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

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

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

Liu, S., Lang, X., Wu, J. & Rehman, N. U. (2025). Selective Noise Empirical Mode Decomposition. IEEE Signal Processing Letters, 32, 2823-2827. https://doi.org/10.1109/LSP.2025.3588082

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

Lang, X., Rehman, N. U., Zhang, Y., Xie, L. & Su, H. (2020). Median ensemble empirical mode decomposition. Signal Processing, 176, Article 107686. https://doi.org/10.1016/j.sigpro.2020.107686

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

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

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

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

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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Naveed Ur Rehman