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- BY N. REHMAN, Danilo P. Mandic, Danilo P. Mandic
- 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, they are a prerequisite for direct multichannel data analysis. An important step in this direction is the computation of the local mean, as the concept of local… (More)

- Mosabber Uddin Ahmed, Danilo P. Mandic
- Physical review. E, Statistical, nonlinear, and…
- 2011

This work generalizes the recently introduced univariate multiscale entropy (MSE) analysis to the multivariate case. This is achieved by introducing multivariate sample entropy (MSampEn) in a rigorous way, in order to account for both within- and cross-channel dependencies in multiple data channels, and by evaluating it over multiple temporal scales. The… (More)

- Danilo P. Mandic
- IEEE Signal Processing Letters
- 2004

A generalized normalized gradient descent (GNGD) algorithm for linear finite-impulse response (FIR) adaptive filters is introduced. The GNGD represents an extension of the normalized least mean square (NLMS) algorithm by means of an additional gradient adaptive term in the denominator of the learning rate of NLMS. This way, GNGD adapts its learning rate… (More)

- Book Reviews, In Foreword, +7 authors G F Hudiakov
- IEEE Transactions on Neural Networks
- 2006

The reviewed book is devoted to the neural networks that are based on the neurons with the complex-valued weights and complex-valued activation functions. In recent years, these neural networks have becomemore andmore popular. A number of the original solutions in pattern recognition and classification, in artificial neural information processing, in image… (More)

- Andrzej Cichocki, Danilo P. Mandic, +4 authors Anh Huy Phan
- IEEE Signal Processing Magazine
- 2015

The widespread use of multisensor technology and the emergence of big data sets have highlighted the limitations of standard flat-view matrix models and the necessity to move toward more versatile data analysis tools. We show that higher-order tensors (i.e., multiway arrays) enable such a fundamental paradigm shift toward models that are essentially… (More)

- Clive Cheong Took, Danilo P. Mandic
- Signal Processing
- 2011

Second order statistics of quaternion random variables and signals are revisited in order to exploit the complete second order statistical information available. The conditions for Q-proper (second order circular) random processes are presented, and to cater for the non-vanishing pseudocovariance of such processes, the use of ı-E-k-covariances is… (More)

- Mosabber Uddin Ahmed, Danilo P. Mandic
- IEEE Signal Processing Letters
- 2012

Multivariate physical and biological recordings are common and their simultaneous analysis is a prerequisite for the understanding of the complexity of underlying signal generating mechanisms. Traditional entropy measures are maximized for random processes and fail to quantify inherent long-range dependencies in real world data, a key feature of complex… (More)

- Ramaswamy Palaniappan, Danilo P. Mandic
- IEEE Transactions on Pattern Analysis and Machine…
- 2007

The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the visual evoked potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis… (More)

- Danilo P. Mandic, Cyrus Jahanchahi, Clive Cheong Took
- IEEE Signal Processing Letters
- 2011

Real functions of quaternion variables are typical cost functions in quaternion valued statistical signal processing, however, standard differentiability conditions in the quaternion domain do not permit direct calculation of their gradients. To this end, based on the isomorphism with real vectors and the use of quaternion involutions, we introduce the HR… (More)

- Temujin Gautama, Danilo P. Mandic, Marc M. Van Hulle
- Physical review. E, Statistical, nonlinear, and…
- 2003

The dynamical properties of electroencephalogram (EEG) segments have recently been analyzed by Andrzejak and co-workers for different recording regions and for different brain states, using the nonlinear prediction error and an estimate of the correlation dimension. In this paper, we further investigate the nonlinear properties of the EEG signals using two… (More)