Danilo P. Mandic

Imperial College London

659Publications

48H-index

11kCitations

Publications 663

Newest

#1Ljubisa Stankovic (University of Montenegro)H-Index: 39

#2Milos Brajovic (University of Montenegro)H-Index: 5

Last.Danilo P. Mandic (Imperial College London)H-Index: 48

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Abstract With their ability to cater for simultaneously for multifaceted information, multichannel (multivariate) signals have been used to solve problems that are normally not solvable with signals obtained from a single source. One such problem is the decomposition of signals which comprise several components for which the domains of support significantly overlap in both the time, frequency and the joint time-frequency domain. Earlier, we proposed a solution to this problem based on the Wigner...

In this article we demonstrate how graph theory can be used to identify those stations in the London underground network which have the greatest influence on the functionality of the traffic, and proceed, in an innovative way, to assess the impact of a station closure on service levels across the city. Such underground network vulnerability analysis offers the opportunity to analyse, optimize and enhance the connectivity of the London underground network in a mathematically tractable and physica...

The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions. Decision-makers would rather not ignore the impact of other participants on these dynamics and should employ tools and models that take this into account. To this end, we demonstrate the efficacy of applying opponent-modeling in a number of simulated market settings. While our simulations are simplified representations ...

Understanding the Basis of Graph Signal Processing via an Intuitive Example-Driven Approach [Lecture Notes]

#1Ljubisa Stankovic (University of Montenegro)H-Index: 39

#2Danilo P. Mandic (Imperial College London)H-Index: 48

Last.Anthony G. Constantinides (Imperial College London)H-Index: 26

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Graphs are irregular structures that naturally represent the multifaceted data attributes; however, traditional approaches have been established outside signal processing and largely focus on analyzing the underlying graphs rather than signals on graphs. Given the rapidly increasing availability of multisensor and multinode measurements, likely recorded on irregular or ad hoc grids, it would be extremely advantageous to analyze such structured data as "signals on graphs" and thus benefit from th...

#1Bruno Scalzo DeesH-Index: 1

#2Ljubisa StankovicH-Index: 39

Last.Danilo P. MandicH-Index: 48

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A boundedness analysis is performed on a class of doubly stochastic graph shift operators (GSOs), which is shown to exhibit: (i) L_{1}isometry over graph shifts; (ii) lower and upper L_{2}norm boundedness with the asymptotic increase in the incoming neighbourhood size of vertices; and (iii) L_{2}isometry for \textit{i.i.d.} graph signals. These properties are derived by employing the dual role of doubly stochastic GSOs as the graph expectation (diffusion) operator. In this way, the cond...

#1Giuseppe G. CalviH-Index: 2

#2Bruno Scalzo DeesH-Index: 1

Last.Danilo P. MandicH-Index: 48

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Tensors and tensor decompositions are natural tools to analyse datasets of high dimensionality and variety, with a pillar of tensor decompositions being the Canonical Polyadic Decomposition (CPD). While the notion of CPD is closely intertwined with that of tensor rank, R unlike the matrix rank, the computation of tensor rank is as NP-hard problem, with an associated computational burden on the CPD. To address this issue, we derive a lower bound on Rto reduce its search space, hence reducin...

#1Victor Solo (UNSW: University of New South Wales)H-Index: 18

#2Maria Greco (UniPi: University of Pisa)H-Index: 28

Last.Monica F. Bugallo (SBU: Stony Brook University)H-Index: 20

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The anniversary of a number of significant signal processing algorithms from the 1960s, including the least mean square algorithm and the Kalman filter, provided an opportunity at ICASSP 2019 to reflect on the links between education and innovation. This led ultimately to the proposal of some special sessions as well a panel session that would provide some insight, via a historical perspective, consideration of the current status, and an assessment of the emerging educational future.

#1Sayed Pouria Talebi (Aalto University)H-Index: 3

#2Stefan Werner (NTNU: Norwegian University of Science and Technology)H-Index: 27

Last.Danilo P. Mandic (Imperial College London)H-Index: 48

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A nonlinear adaptive filtering framework for processing complex-valued signals is derived. The introduced adaptive filter extends the fractional-order framework of the authors for dealing with real-valued signals to the complex domain via the augmented statistical approach to complex-valued signal processing. This results in a versatile class of adaptive filtering techniques, which allows the classical Gaussian assumption to be extended to the generalized context of \alpha-stables. For rigor,...

#1Min Xiang (Imperial College London)H-Index: 4

#2Yili Xia (SEU: Southeast University)H-Index: 15

Last.Danilo P. Mandic (Imperial College London)H-Index: 48

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Widely linear (WL) models have been demonstrated to be superior to conventional strictly linear models for the estimation of noncircular complex and quaternion signals. Existing studies on their performance bounds focus on the analysis of mean square error (MSE). However, the single degree of freedom within standard MSE allows for only the minimization of error power, with no means to understand how the error contribution is distributed across the data channels. To this end, we introduce novel c...

Quo Vadis ICASSP: Echoes of 2019 ICASSP in Brighton, United Kingdom: Signal Processing Meets the Needs of Modern Humankind [Conference Highlights]

#1Danilo P. MandicH-Index: 48

#2Petar M. DjuricH-Index: 39

Last.Lajos HanzoH-Index: 75

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