| Tensor decompositions for signal processing applications: From two-way to multiway component analysis A Cichocki, D Mandic, L De Lathauwer, G Zhou, Q Zhao, C Caiafa, ... IEEE signal processing magazine 32 (2), 145-163, 2015 | 1613 | 2015 |
| Recurrent neural networks for prediction: Architectures, learning algorithms and stability DP Mandic, JA Chambers Wiley, 2001 | 1603* | 2001 |
| Multivariate empirical mode decomposition N Rehman, DP Mandic Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2010 | 1185 | 2010 |
| Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models DP Mandic, VSL Goh Wiley, 2009 | 737 | 2009 |
| Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions A Cichocki, N Lee, I Oseledets, AH Phan, Q Zhao, DP Mandic Foundations and Trends® in Machine Learning 9 (4-5), 249-429, 2016 | 660* | 2016 |
| Audioldm: Text-to-audio generation with latent diffusion models H Liu, Z Chen, Y Yuan, X Mei, X Liu, D Mandic, W Wang, MD Plumbley arXiv preprint arXiv:2301.12503, 2023 | 574 | 2023 |
| Filter bank property of multivariate empirical mode decomposition N Ur Rehman, DP Mandic IEEE transactions on signal processing 59 (5), 2421-2426, 2011 | 499 | 2011 |
| Empirical mode decomposition-based time-frequency analysis of multivariate signals: The power of adaptive data analysis DP Mandic, N Ur Rehman, Z Wu, NE Huang IEEE signal processing magazine 30 (6), 74-86, 2013 | 488 | 2013 |
| Multivariate multiscale entropy: A tool for complexity analysis of multichannel data MU Ahmed, DP Mandic Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 84 (6 …, 2011 | 394 | 2011 |
| The quaternion LMS algorithm for adaptive filtering of hypercomplex processes CC Took, DP Mandic IEEE Transactions on Signal Processing 57 (4), 1316-1327, 2008 | 368 | 2008 |
| Complex empirical mode decomposition T Tanaka, DP Mandic IEEE Signal Processing Letters 14 (2), 101-104, 2007 | 368 | 2007 |
| A generalized normalized gradient descent algorithm DP Mandic IEEE signal processing letters 11 (2), 115-118, 2004 | 364 | 2004 |
| Biometrics from brain electrical activity: A machine learning approach R Palaniappan, DP Mandic IEEE transactions on pattern analysis and machine intelligence 29 (4), 738-742, 2007 | 353 | 2007 |
| Tensor networks for dimensionality reduction and large-scale optimization. Part 2: Applications and future perspectives DPM A. Cichocki, A.-H. Phan, Q. Zhao, N. Lee, I. Oseledets, M. Sugiyama Foundations & Trends in Machine Learning 9 (6), 431-673, 2017 | 311 | 2017 |
| The in-the-ear recording concept: User-centered and wearable brain monitoring D Looney, P Kidmose, C Park, M Ungstrup, ML Rank, K Rosenkranz, ... IEEE pulse 3 (6), 32-42, 2012 | 307 | 2012 |
| Classification of motor imagery BCI using multivariate empirical mode decomposition C Park, D Looney, N ur Rehman, A Ahrabian, DP Mandic IEEE Transactions on neural systems and rehabilitation engineering 21 (1), 10-22, 2012 | 303 | 2012 |
| Empirical mode decomposition for trivariate signals N ur Rehman, DP Mandic IEEE Transactions on signal processing 58 (3), 1059-1068, 2009 | 290 | 2009 |
| Augmented second-order statistics of quaternion random signals CC Took, DP Mandic Signal Processing 91 (2), 214-224, 2011 | 289 | 2011 |
| Resolving ambiguities in the LF/HF ratio: LF-HF scatter plots for the categorization of mental and physical stress from HRV W Von Rosenberg, T Chanwimalueang, T Adjei, U Jaffer, V Goverdovsky, ... Frontiers in physiology 8, 360, 2017 | 277 | 2017 |
| Multivariate multiscale entropy analysis MU Ahmed, DP Mandic IEEE signal processing letters 19 (2), 91-94, 2011 | 260 | 2011 |
Yili XiaProfessor in Signal Processing, School of Information Science & Engineering, Southeast UniversityPatvirtintas el. paštas seu.edu.cn