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  • Influence
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis
TLDR
Benefiting from the power of multilinear algebra as their mathematical backbone, data analysis techniques using tensor decompositions are shown to have great flexibility in the choice of constraints which match data properties and extract more general latent components in the data than matrix-based methods.
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.
Filter Bank Property of Multivariate Empirical Mode Decomposition
  • N. Rehman, D. Mandic
  • Computer Science, Engineering
    IEEE Transactions on Signal Processing
  • 1 May 2011
TLDR
It is found that, similarly to EMD, MEMD also essentially acts as a dyadic filter bank on each channel of the multivariate input signal, but better aligns the corresponding intrinsic mode functions from different channels across the same frequency range which is crucial for real world applications.
Multivariate multiscale entropy: a tool for complexity analysis of multichannel data.
TLDR
The multivariate MSE (MMSE) method is shown to provide an assessment of the underlying dynamical richness of multichannel observations, and more degrees of freedom in the analysis than standard MSE.
A generalized normalized gradient descent algorithm
  • D. Mandic
  • Computer Science
    IEEE Signal Processing Letters
  • 30 January 2004
TLDR
A generalized normalized gradient descent algorithm for linear finite-impulse response (FIR) adaptive filters is introduced that adapts its learning rate according to the dynamics of the input signal, with the additional adaptive term compensating for the simplifications in the derivation of NLMS.
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 general
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
TLDR
A focus is on the Tucker and tensor train TT decompositions and their extensions, and on demonstrating the ability of tensor network to provide linearly or even super-linearly e.g., logarithmically scalablesolutions, as illustrated in detail in Part 2 of this monograph.
Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis
TLDR
Simulations using real-world case studies illuminate several practical aspects, such as the role of noise in T-F localization, dealing with unbalanced multichannel data, and nonuniform sampling for computational efficiency.
Empirical Mode Decomposition for Trivariate Signals
TLDR
An extension of empirical mode decomposition (EMD) is proposed, which extracts rotating components embedded within the signal and performs accurate time-frequency analysis, via the Hilbert-Huang transform.
Multivariate Multiscale Entropy Analysis
TLDR
This work first introduces multivariate sample entropy (MSampEn) and evaluates it over multiple time scales to perform the multivariate multiscale entropy (MMSE) analysis, which makes it possible to assess structural complexity of multivariate physical or physiological systems, together with more degrees of freedom and enhanced rigor in the analysis.
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