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Full-Text Articles in Engineering
Masking Schemes For Image Manifolds, Hamid Dadkhahi, Marco Duarte
Masking Schemes For Image Manifolds, Hamid Dadkhahi, Marco Duarte
Marco Duarte
We consider the problem of selecting an optimal mask for an image manifold, i.e., choosing a subset of the dimensions of the image space that preserves the manifold structure present in the original data. Such masking implements a form of compressed sensing that reduces power consumption in emerging imaging sensor platforms. Our goal is for the manifold learned from masked images to resemble the manifold learned from full images as closely as possible. We show that the process of finding the optimal masking pattern can be cast as a binary integer program, which is computationally expensive but can be approximated …
Complexity-Adaptive Universal Signal Estimation For Compressed Sensing, Junan Zhu, Dror Baron, Marco Duarte
Complexity-Adaptive Universal Signal Estimation For Compressed Sensing, Junan Zhu, Dror Baron, Marco Duarte
Marco Duarte
We study the compressed sensing (CS) signal estimation problem where a signal is measured via a linear matrix multiplication under additive noise. While this setup usually assumes sparsity or compressibility in the signal during estimation, additional signal structure that can be leveraged is often not known a priori. For signals with independent and identically distributed (i.i.d.) entries, existing CS algorithms achieve optimal or near optimal estimation error without knowing the statistics of the signal. This paper addresses estimating stationary ergodic non-i.i.d. signals with unknown statistics. We have previously proposed a universal CS approach to simultaneously estimate the statistics of a …
Average Case Analysis Of High-Dimensional Block-Sparse Recovery And Regression For Arbitrary Designs, Waheed U. Bajwa, Marco Duarte, Robert Calderbank
Average Case Analysis Of High-Dimensional Block-Sparse Recovery And Regression For Arbitrary Designs, Waheed U. Bajwa, Marco Duarte, Robert Calderbank
Marco Duarte
This paper studies conditions for highdimensional inference when the set of observations is given by a linear combination of a small number of groups of columns of a design matrix, termed the \block-sparse" case. In this regard, it rst speci es conditions on the design matrix under which most of its block submatrices are well conditioned. It then leverages this result for average-case analysis of high-dimensional block-sparse recovery and regression. In contrast to earlier works: (i) this paper provides conditions on arbitrary designs that can be explicitly computed in polynomial time, (ii) the provided conditions translate into near-optimal scaling of …
Sparsity And Structure In Hyperspectral Imaging: Sensing, Reconstruction, And Target Detection, Rebecca M. Willett, Marco Duarte, Mark A. Davenport, Richard G. Baraniuk
Sparsity And Structure In Hyperspectral Imaging: Sensing, Reconstruction, And Target Detection, Rebecca M. Willett, Marco Duarte, Mark A. Davenport, Richard G. Baraniuk
Marco Duarte
Hyperspectral imaging is a powerful technology for remotely inferring the material properties of the objects in a scene of interest. Hyperspectral images consist of spatial maps of light intensity variation across a large number of spectral bands or wavelengths; alternatively, they can be thought of as a measurement of the spectrum of light transmitted or reflected from each spatial location in a scene. Because chemical elements have unique spectral signatures, observing the spectra at a high spatial and spectral resolution provides information about the material properties of the scene with much more accuracy than is possible with conventional three-color images. …
Tailoring Non-Homogeneous Markov Chain Models For Hyperspectral Signature Classification, Siwei Feng, Yuki Itoh, Mario Parente, Marco Duarte
Tailoring Non-Homogeneous Markov Chain Models For Hyperspectral Signature Classification, Siwei Feng, Yuki Itoh, Mario Parente, Marco Duarte
Marco Duarte
We consider the application of non-homogeneous hidden Markov chain (NHMC) models to the problem of hyperspectral signature classification. It has been previously shown that the NHMC model enables the detection of several semantic structural features of hyperspectral signatures. However, there are some aspects of the spectral data that are not fully captured by the proposed NHMC models such as the relatively smooth but fluctuating regions and the fluctuation orientations. In order to address these limitations, we propose an improved NHMC model based on Daubechies-1 wavelets in conjunction with an increased the model complexity. Experimental results show that the revised approach …