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Institut for Elektro- og Computerteknologi

Publications

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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), Artikel e0297131. https://doi.org/10.1371/journal.pone.0297131

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

Eriksen, T. & Rehman, N. U. (2023). Data-driven nonstationary signal decomposition approaches: a comparative analysis. Scientific Reports, 13(1), Artikel 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. Artikel 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

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, Artikel 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), Artikel 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, Artikel 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., 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, Artikel 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. I 2021 IEEE Statistical Signal Processing Workshop, SSP 2021 (s. 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. Artikel 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, Artikel 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. Artikel 9234624. https://doi.org/10.1109/TSP.2020.3029659

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

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. Artikel 8890883. https://doi.org/10.1109/TSP.2019.2951223

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

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

Ahmed, M. U., Rehman, N., Looney, D., Rutkowski, T. M., Kidmose, P. & Mandic, D. P. (2012). Multivariate entropy analysis with data-driven scales. I 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (s. 3901-3904). IEEE. https://doi.org/10.1109/icassp.2012.6288770, https://doi.org/10.1109/ICASSP.2012.6288770

Revideret 10.08.2023

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Naveed Ur Rehman