SelectedWorks - Marco Duarte

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Selected Works of Marco Duarte

Assistant Professor, Department of Electrial & Computer Engineering, College of Engineering

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About Marco Duarte

Present	Assistant Professor, Department of Electrial & Computer Engineering, College of Engineering, University of Massachusetts Amherst
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Electrical and Computer Engineering

Signal and image processing, compressive sensing, dimensionality reduction, machine learning, computational imaging, distributed sensing, sensor networks.

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215I Marcus Hall
100 Natural Resources Road
University of Massachusetts Amherst
Amherst, MA 01003
Tel: 413-545-8583

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Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation

IEEE Transactions on Signal Processing (2015)

Karsten Fyhn, Marco Duarte and Søren Holdt Jensen

We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Our work ...

Compressive Parameter Estimation via Approximate Message Passing

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2015)

Conditioning of Random Block Subdictionaries with Applications to Block-Sparse Recovery ...

IEEE Transactions on Information Theory (2015)

Waheed U. Bajwa, Marco Duarte and Robert Calderbank

The linear model, in which a set of observations is assumed to be given by a linear combination of columns ...

Image Masking Schemes for Local Manifold Learning Methods

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2015)

Hamid Dadkhahi and Marco Duarte

We consider the problem of selecting a subset of the dimensions of an image manifold that best preserves the underlying ...

Performance Bounds for Grouped Incoherent Measurements in Compressive Sensing

IEEE Transactions on Signal Processing (2015)

Adam C. Polak, Marco Duarte and Dennis L. Goeckel

Compressive sensing (CS) allows for acquisition of sparse signals at sampling rates significantly lower than the Nyquist rate required for ...

Performance Guarantees for Sparse Regression-Based Unmixing

IEEE/ISPRS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) (2015)

Yuki Itoh, Marco Duarte and Mario Parente

Sparse regression-based unmixing has received much attention in recent years; however, its theoretical performance has not been explored in the ...

Recovery from Linear Measurements with Complexity-Matching Universal Signal Estimation

IEEE Transactions on Signal Processing (2015)

Junan Zhu, Dror Baron and Marco Duarte

We study the compressed sensing (CS) signal estimation problem where an input signal is measured via a linear matrix multiplication ...

Universality of Wavelet-Based Non-Homogeneous Hidden Markov Chain Model Features for ...

IEEE/ISPRS Workshop “Looking from Above: When Earth Observation Meets Vision” (EARTHVISION) (2015)

Siwei Feng, Marco Duarte and Mario Parente

Feature design is a crucial step in many hyperspectral signal processing applications like hyperspectral signature classification and unmixing, etc. In ...

Average Case Analysis of High-Dimensional Block-Sparse Recovery and Regression for ...

International Conference on Artificial Intelligence and Statistics (AISTATS) (2014)

Waheed U. Bajwa, Marco Duarte and Robert Calderbank

This paper studies conditions for highdimensional inference when the set of observations is given by a linear combination of a ...

Complexity-Adaptive Universal Signal Estimation for Compressed Sensing

IEEE Statistical Signal Processing Workshop (SSP) (2014)

Junan Zhu, Dror Baron and Marco Duarte

We study the compressed sensing (CS) signal estimation problem where a signal is measured via a linear matrix multiplication under ...

Hyperspectral Unmixing via Semantic Spectral Representations

IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) (2014)

Yuki Itoh, Siwei Feng, Marco Duarte and Mario Parente

We propose a new spectral unmixing method using a semantic spectral representation, which is produced via nonhomogeneous hidden Markov chain ...

Masking Schemes for Image Manifolds

IEEE Statistical Signal Processing Workshop (SSP) (2014)

Hamid Dadkhahi and Marco Duarte

We consider the problem of selecting an optimal mask for an image manifold, i.e., choosing a subset of the dimensions ...

Sparsity and Structure in Hyperspectral Imaging: Sensing, Reconstruction, and Target ...

IEEE Signal Processing Magazine (2014)

Rebecca M. Willett, Marco Duarte, Mark A. Davenport and Richard G. Baraniuk

Hyperspectral imaging is a powerful technology for remotely inferring the material properties of the objects in a scene of interest. ...

Tailoring Non-Homogeneous Markov Chain Models for Hyperspectral Signature Classification

IEEE International Conference on Image Processing (ICIP) (2014)

Siwei Feng, Yuki Itoh, Mario Parente and Marco Duarte

We consider the application of non-homogeneous hidden Markov chain (NHMC) models to the problem of hyperspectral signature classification. It has ...