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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

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

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

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

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. https://arxiv.org/abs/2504.08453

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

Haldrup, M., Nazari, M., Gu, C., Rasmussen, M., Dyrskog, S., Ziegler Simonsen, C., Grønhøj, M., Poulsen, F. R., Ur Rehman, N. & Rosendal Korshoej, A. (2024). Reliability and performance of the IRRAflow® system for intracranial lavage and evacuation of hematomas-A technical note. PLoS One, 19(4 April), Article e0297131. https://doi.org/10.1371/journal.pone.0297131

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

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

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

Eriksen, T. & Rehman, N. U. (2023). Data-driven nonstationary signal decomposition approaches: a comparative analysis. Scientific Reports, 13(1), Article 1798. https://doi.org/10.1038/s41598-023-28390-w

Haldrup, M., Rasmussen, M., Mohamad, N., Dyrskog, S., Thorup, L., Mikic, N., Wismann, J., Grønhøj, M., Poulsen, F. R., Nazari, M., Rehman, N. U., Simonsen, C. Z. & Korshøj, A. R. (2023). Intraventricular Lavage vs External Ventricular Drainage for Intraventricular Hemorrhage: A Randomized Clinical Trial. JAMA network open, 6(10), e2335247. Article e2335247. https://doi.org/10.1001/jamanetworkopen.2023.35247

Jensen, M. H., Nazari, M., Gu, C., Rasmussen, M., Dyrskog, S. E., Simonsen, C. Z., Grønhøj, M. H., Rom Poulsen, F., Rehman, N. U. & Korshoej, A. R. (2023). Reliability and Performance of the IRRAflow® System for Intracranial Lavage and Evacuation of Hematomas - A Technical Note. https://doi.org/10.1101/2023.07.07.23292372

Jensen, M. H., Rasmussen, M., Mohamad, N., Dyrskog, S. E., Thorup, L., Mikic, N., Wismann, J., Grønhøj, M. H., Rom Poulsen, F., Nazari, M., Rehman, N. U., Simonsen, C. Z. & Korshoej, A. R. (2023). Safety, harm, and efficacy of IRRAflow® versus external ventricular drainage for intraventricular hemorrhage: A randomized clinical trial. medRxiv. https://doi.org/10.1101/2023.07.08.23292286

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

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

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

Chen, Q., Lang, X., Lu, S., Rehman, N. U., Xie, L. & Su, H. (2021). Detection and root cause analysis of multiple plant-wide oscillations using multivariate nonlinear chirp mode decomposition and multivariate Granger causality. Computers and Chemical Engineering, 147, Article 107231. https://doi.org/10.1016/j.compchemeng.2021.107231

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

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., 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., 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

Gul, S., Siddiqui, M. F. & Rehman, N. U. (2020). FPGA-Based Design for Online Computation of Multivariate Empirical Mode Decomposition. IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 67(12), 5040-5050. Article 9159690. https://doi.org/10.1109/TCSI.2020.3012351

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

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

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

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., 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., 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Naveed Ur Rehman