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Full-Text Articles in Engineering
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 …