• Publications
  • Influence
Multivariate empirical mode decomposition
  • N. Rehman, D. Mandic
  • Mathematics
  • Proceedings of the Royal Society A: Mathematical…
  • 8 May 2010
Despite empirical mode decomposition (EMD) becoming a de facto standard for time-frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are only emerging; yet, theyExpand
  • 582
  • 69
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Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis
TLDR
We show that higher-order tensors (i.e., multiway arrays) enable such a fundamental paradigm shift toward models that are essentially polynomial, the uniqueness of which, unlike the matrix methods, is guaranteed under very mild and natural conditions. Expand
  • 791
  • 45
  • PDF
Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability
TLDR
This book shows researchers how recurrent neural networks can be implemented to expand the range of traditional signal processing techniques. Expand
  • 593
  • 44
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Multivariate multiscale entropy: a tool for complexity analysis of multichannel data.
  • M. U. Ahmed, D. Mandic
  • Computer Science, Medicine
  • Physical review. E, Statistical, nonlinear, and…
  • 27 December 2011
TLDR
This work generalizes the recently introduced univariate multiscale entropy (MSE) analysis to the multivariate case, to suit real-world biological and physical systems. Expand
  • 221
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Filter Bank Property of Multivariate Empirical Mode Decomposition
TLDR
The multivariate empirical mode decomposition (MEMD) algorithm has been recently proposed in order to make multivariate EMD suitable for processing of multichannel signals. Expand
  • 290
  • 33
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A generalized normalized gradient descent algorithm
  • D. Mandic
  • Mathematics, Computer Science
  • IEEE Signal Processing Letters
  • 30 January 2004
TLDR
A generalized normalized gradient descent (GNGD) algorithm for linear finite-impulse response (FIR) adaptive filters. Expand
  • 209
  • 24
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Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models
This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of generalExpand
  • 443
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Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis
TLDR
This article addresses data-driven time-frequency (T-F) analysis of multivariate signals, which is achieved through the empirical mode decomposition (EMD) algorithm and its noise assisted and multivariate extensions, the ensemble EEMD (EEMD), multivariate EMD (MEMD). Expand
  • 252
  • 17
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
TLDR
We provide innovativesolutions to low-rank tensor network decompositions and easy to interpretgraphical representations of the mathematical operations ontensor networks. Expand
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  • 17
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Multivariate Multiscale Entropy Analysis
TLDR
We introduce multivariate sample entropy (MSampEn) and evaluate it over multiple time scales to perform the multivariate multiscale entropy (MMSE) analysis. Expand
  • 143
  • 17
  • PDF