Signal structure: from manifolds to molecules and structured sparsity
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We consider the problem of classification of a pattern from multiple compressed observations that are collected in a sensor network. In particular, we exploit the properties of random projections in generic sensor devices and we take some first steps in in ...
We compare and contrast from a geometric perspective a number of low-dimensional signal models that support stable information-preserving dimensionality reduction. We consider sparse and compressible signal models for deterministic and random signals, stru ...
This paper presents a compact model for LINC (linear amplification with non linear components) transmitters an their power combiners. The study focuses on the detrimental effect of the transmission line nonidealities. A mathematical description of the syst ...
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011
Demand has emerged for next generation visual technologies that go beyond conventional 2D imaging. Such technologies should capture and communicate all perceptually relevant three-dimensional information about an environment to a distant observer, providin ...
The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement le ...
We have recently quantified and validated the potential of the emerging compressed sensing (CS) paradigm for real-time energy-efficient electrocardiogram (ECG) compression on resource-constrained sensors. In the present work, we investigate applying sparsi ...
This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via ℓ1-minimisation. The problem can also be seen as factorising a \ddim×\nsig matrix $Y=(y_1 \ldots y_\nsig), , y_n\in \R^\ ...
Institute of Electrical and Electronics Engineers2010
Sparse Component Analysis is a relatively young technique that relies upon a representation of signal occupying only a small part of a larger space. Mixtures of sparse components are disjoint in that space. As a particular application of sparsity of speech ...
An equivalence relation on the tangent bundle of a manifold is defined in order to extend a structure (modulated or not) onto it. This extension affords a representation of a structure in any tangent space and that in another tangent space can easily be de ...
A great deal of theoretic and algorithmic research has revolved around sparsity view of signals over the last decade to characterize new, sub-Nyquist sampling limits as well as tractable algorithms for signal recovery from dimensionality reduced measuremen ...
Institute of Electrical and Electronics Engineers2010
