Marco Duarte

Marco Duarte
University of Massachusetts Amherst | UMass Amherst · Department of Electrical and Computer Engineering

Ph.D. Electrical Engineering

About

150
Publications
30,422
Reads
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15,538
Citations
Citations since 2016
22 Research Items
7614 Citations
201620172018201920202021202202004006008001,0001,200
201620172018201920202021202202004006008001,0001,200
201620172018201920202021202202004006008001,0001,200
201620172018201920202021202202004006008001,0001,200
Additional affiliations
August 2011 - present
University of Massachusetts Amherst
September 2010 - August 2011
Duke University
September 2009 - August 2010
Princeton University

Publications

Publications (150)
Article
Full-text available
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational o...
Article
Full-text available
Self-taught learning is a technique that uses a large number of unlabeled data as source samples to improve the task performance on target samples. Compared with other transfer learning techniques, self-taught learning can be applied to a broader set of scenarios due to the loose restrictions on the source data. However, knowledge transferred from...
Article
Full-text available
Few-shot learning is a technique to learn a model with a very small amount of labeled training data by transferring knowledge from relevant tasks. In this paper, a few-shot learning method for wearable sensor based human activity recognition, which is a technique that seeks high-level human activity knowledge from low-level sensor inputs, is propos...
Preprint
Full-text available
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational o...
Preprint
Full-text available
Few-shot learning is a technique to learn a model with a very small amount of labeled training data by transferring knowledge from relevant tasks. In this paper, we propose a few-shot learning method for wearable sensor based human activity recognition, a technique that seeks high-level human activity knowledge from low-level sensor inputs. Due to...
Article
In this letter, Laplace distribution is used to model the prior for the direction of arrival (DoA) of sources. In order to incorporate the real and imaginary part of the received signal, we propose a method that pairwise estimates the hyperparameters for parts of the signal coefficients. In addition, we propose a multi-task algorithm to extend the...
Preprint
Wideband communication is often expected to deal with a very wide spectrum, which in many environments of interest includes strong interferers. Thus receivers for the wideband communication systems often need to mitigate interferers to reduce the distortion caused by the amplifier nonlinearity and noise. Recently, a new architecture for communicati...
Preprint
Wideband communication receivers often deal with the problems of detecting weak signals from distant sources received together with strong nearby interferers. When the techniques of random modulation are used in communication system receivers, one can design a spectrally shaped sequence that mitigates interferer bands while preserving message bands...
Preprint
Full-text available
Self-taught learning is a technique that uses a large number of unlabeled data as source samples to improve the task performance on target samples. Compared with other transfer learning techniques, self-taught learning can be applied to a broader set of scenarios due to the loose restrictions on source data. However, knowledge transferred from sour...
Article
Full-text available
Feature selection is a dimensionality reduction technique that selects a subset of representative features from high-dimensional data by eliminating irrelevant and redundant features. Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compared with traditional feature sel...
Article
Full-text available
Cognitive radio (CR) is a multiuser, wireless communication concept that allows for a dynamic and adaptive assignment of spectral resources. The coexistence of multiple users, often transmitting at significantly different power levels, makes CR receivers vulnerable to spectral leakage caused by components’ nonlinearities and time-truncation of the...
Article
Full-text available
High spectral resolution brings hyperspectral images with large amounts of information, which makes these images more useful in many applications than images obtained from traditional multispectral scanners with low spectral resolution. However, the high data dimensionality of hyperspectral images increases the burden on data computation, storage,...
Article
This paper proposes a new hyperspectral unmixing method for nonlinearly mixed hyperspectral data using a semantic representation in a semi-supervised fashion, assuming the availability of a spectral reference library. Existing semi-supervised unmixing algorithms select members from an endmember library that are present at each of the pixels; most s...
Conference Paper
Full-text available
The high data dimensionality of hyperspectral images increases the burden on data computation, storage, and transmission ; fortunately, the high redundancy in the spectral domain allows for significant dimensionality reduction. Band selection provides a simple dimensionality reduction scheme by discarding bands that are highly redundant, therefore...
Article
This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts. Given a set of noise-free training data and its embedding, the proposed framework extends the embedding for a noisy time...
Article
We consider the problem of selecting an optimal mask for an image manifold, i.e., choosing a subset of the pixels of the image that preserves the manifold's geometric structure present in the original data. Such masking implements a form of compressive sensing through emerging imaging sensor platforms for which the power expense grows with the numb...
Article
Full-text available
Hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from the correspondin...
Article
Full-text available
Sparse modeling has been widely and successfully used in many applications such as computer vision, machine learning, and pattern recognition and, accompanied with those applications, significant research has studied the theoretical limits and algorithm design for convex relaxations in sparse modeling. However, only little has been done for theoret...
Conference Paper
In wideband communication one often aims to detect a weak signal received together with a strong interferer. One can design a spectrally shaped sequence to be convolved with the received signal featuring a bandpass for the message and a notch for the interferer. Unfortunately, when the sequence must be quantized, commonly used constrained optimizat...
Conference Paper
This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts. Given a set of noise-free training data and its embedding, the proposed framework extends the embedding for a noisy time...
Conference Paper
Full-text available
Feature design is a crucial step in many hyperspectral signal processing applications like hyperspectral signature classification and unmixing, etc. In this paper, we describe a technique for automatically designing universal features of hyperspectral signatures. Universality is considered both in terms of the application to a multitude of classifi...
Article
Full-text available
We study the compressed sensing (CS) signal estimation problem where an input signal is measured via a linear matrix multiplication under additive noise. While this setup usually assumes sparsity or compressibility in the input signal during recovery, the signal structure that can be leveraged is often not known a priori. In this paper, we consider...
Article
Full-text available
Cross-correlation is a popular signal processing technique used in numerous localization and tracking systems for obtaining reliable range information. However, a practical efficient implementation has not yet been achieved on resource constrained wireless sensor network platforms. In this paper, we propose: SparseXcorr: cross-correlation via spars...
Article
In recent years, compressive sensing (CS) has attracted significant attention in parameter estimation tasks, including frequency estimation, time delay estimation, and localization. In order to use CS in parameter estimation, parametric dictionaries (PDs) collect observations for a sampling of the parameter space and yield sparse representations fo...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
We propose a new spectral unmixing method using a semantic spectral representation, which is produced via non-homogeneous hidden Markov chain (NHMC) models applied to wavelet transforms of the spectra. Previous studies have shown that the representation is robust to spectral variability in the same materials because it can automatically detect the...
Conference Paper
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 a...
Conference Paper
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 man...
Article
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 transmi...
Conference Paper
While manifolds have attracted significant attention from the image processing and computer vision communities, we are not aware of openly available tools for visualization of manifold-modeled data that allows for interactive navigation over the embedded dataset. We introduce the Manifold Analysis GUI (MAGI), a freely available toolbox for Matlab t...
Conference Paper
Motivated by the compelling application of interference mitigation at wideband receivers in wireless communication and sensing systems, we consider the recovery of a frequency-sparse signal from samples of small magnitude. The standard ℓ1-norm minimization results in an inadequate signal-dependent recovery performance, and hence we introduce three...
Conference Paper
We consider the use of non-homogeneous Markov chain (NHMC) models for wavelet transformations of hyperspectral signatures to generate features for signal processing purposes. Inspired by the use of hidden Markov trees for natural images, the NHMC model enables the characterization of absorption bands and other structural features of mineral spectra...
Article
Full-text available
This paper makes several contributions toward the "underdetermined" setting in linear models when the set of observations is given by a linear combination of a small number of groups of columns of a dictionary, termed the "block-sparse" case. First, it specifies conditions on the dictionary under which most block submatrices of the dictionary are w...
Article
In recent years, sparsity and compressive sensing have attracted significant attention in parameter estimation tasks, including frequency estimation, delay estimation, and localization. Parametric dictionaries collect observations for a sampling of the parameter space and can yield sparse representations for the signals of interest when the samplin...
Patent
Full-text available
The recently introduced theory of Compressive Sensing (CS) enables a new method for signal recovery from incomplete information (a reduced set of “compressive” linear measurements), based on the assumption that the signal is sparse in some dictionary. Such compressive measurement schemes are desirable in practice for reducing the costs of signal ac...
Article
Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many app...
Article
In compressive sensing, a small collection of linear projections of a sparse signal contains enough information to permit signal recovery. Distributed compressive sensing extends this framework by defining ensemble sparsity models, allowing a correlated ensemble of sparse signals to be jointly recovered from a collection of separately acquired comp...
Article
Full-text available
Time delay estimation has long been an active area of research. In this work, we show that compressive sensing with interpolation may be used to achieve good estimation precision while lowering the sampling frequency. We propose an Interpolating Band-Excluded Orthogonal Matching Pursuit algorithm that uses one of two interpolation functions to esti...
Article
Full-text available
We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Moreover, we evaluate six algorithms for estimation of parameters in sparse translation-invariant signals, exemplified with the time delay estimation problem. The evaluation is based on three performance metrics: estim...
Article
The Delsarte-Goethals frame (DGF) has been proposed for deterministic compressive sensing of sparse and compressible signals. Results in compressive sensing theory show that the DGF enables successful recovery of an overwhelming majority of sufficiently sparse signals. However, these results do not give a characterization of the sparse vectors for...
Article
Existing approaches to compressive sensing of frequency-sparse signals focuses on signal recovery rather than spectral estimation. Furthermore, the recovery performance is limited by the coherence of the required sparsity dictionaries and by the discretization of the frequency parameter space. In this paper, we introduce a greedy recovery algorithm...
Article
This paper describes our methods for repairing and restoring images of hidden paintings (paintings that have been painted over and are now covered by a new surface painting) that have been obtained via noninvasive X-ray fluorescence imaging of their canvases. This recently developed imaging technique measures the concentrations of various chemical...
Article
Full-text available
Recent work has leveraged sparse signal models for parame-ter estimation purposes in applications including localization and bearing estimation. A dictionary whose elements corre-spond to observations for a sampling of the parameter space is used for sparse approximation of the received signals; the resulting sparse coefficient vector's support ide...
Conference Paper
Compressive sensing (CS) allows for acquisition of sparse signals at sampling rates significantly lower than the Nyquist rate required for bandlimited signals. Recovery guarantees for CS are generally derived based on the assumption that measurement projections are selected independently at random. However, for many practical signal acquisition app...
Article
Full-text available
Compressive sensing (CS) allows for acquisition of sparse signals at sampling rates significantly lower than the Nyquist rate required for bandlimited signals. Recovery guarantees for CS are generally derived based on the assumption that measurement projections are selected independently at random. However, for many practical signal acquisition app...
Article
Full-text available
This paper is dedicated to the memory of Hyeokho Choi, our colleague, mentor, and friend. Compressive sensing is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery. In this paper we introduce a new theory for distributed compressive...
Conference Paper
Full-text available
The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that acquire large amounts of very high-dimensional data. To cope with such a data deluge, manifold models are often developed that provide a powerful theoretical and algorithmic framework for capturing the intrinsic structure of da...
Article
Full-text available
We study the compressed sensing (CS) signal estimation problem where an input is measured via a linear matrix multiplication under additive noise. While this setup usually assumes sparsity or compressibility in the observed signal during recovery, the signal structure that can be leveraged is often not known a priori. In this paper, we consider uni...
Article
Full-text available
Signal compression is an important tool for reducing communication costs and increasing the lifetime of wireless sensor network deployments. In this paper, we overview and classify an array of proposed compression methods, with an emphasis on illustrating the differences between the various approaches.
Article
Full-text available
Compressed sensing (CS) is an exciting, rapidly growing, field that has attracted considerable attention in signal processing, statistics, and computer science, as well as the broader scientific community. Since its initial development only a few years ago, thousands of papers have appeared in this area, and hundreds of conferences, workshops, and...
Conference Paper
We report on a number of extensions of our previously published pump-probe technique that resolves the two kinds of melanin in human skin including new flexibility, three-dimensional sectioning capabilities, and improved image processing results.
Article
Full-text available
We study the compressed sensing (CS) estimation problem where an input is measured via a linear matrix multiplication under additive noise. While this setup usually assumes sparsity or compressibility in the observed signal during recovery, the signal structure that can be leveraged is often not known a priori. In this paper, we consider universal...
Article
Full-text available
Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the construction of sparsifying bases and measurement...
Article
Full-text available
Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way i...
Article
The Delsarte-Goethals frame has been proposed for deterministic compressive sensing of sparse and compressible signals. Its performance in compressive imaging applications falls short of that obtained for arbitrary sparse vectors. Prior work has proposed specially tailored signal recovery algorithms that partition the recovery of the input vector i...
Conference Paper
Full-text available
The aim of compressed sensing is to recover attributes of sparse signals using very few measurements. Given an overall bit budget for quantization, this paper demonstrates that there is value to redundant measurement. The measurement matrices considered here are required to have the property that signal recovery is still possible even after droppin...
Article
Full-text available
In compressive sensing, a small collection of linear projections of a sparse signal contains enough information to permit signal recovery. Distributed compressive sensing (DCS) extends this framework, allowing a correlated ensemble of sparse signals to be jointly recovered from a collection of separately acquired compressive measurements. In this p...
Article
In compressive sensing, a small collection of linear projections of a sparse signal contains enough information to permit signal recovery. Distributed compressive sensing (DCS) extends this framework by defining ensemble sparsity models, allowing a correlated ensemble of sparse signals to be jointly recovered from a collection of separately acquire...
Article
Many applications in cellular systems and sensor networks involve a random subset of a large number of users asynchronously reporting activity to a base station. This paper examines the problem of multiuser detection (MUD) in random access channels for such applications. Traditional orthogonal signaling ignores the random nature of user activity in...
Article
This paper describes our work on the problem of reconstructing the original visual appearance of underpaintings (paintings that have been painted over and are now covered by a new surface painting) from noninvasive X-ray fluorescence imaging data of their canvases. This recently-developed imaging technique yields data revealing the concentrations o...
Conference Paper
This paper considers on-off random access channels where users transmit either a one or a zero to a base station. Such channels represent an abstraction of control channels used for scheduling requests in third-generation cellular systems and uplinks in wireless sensor networks deployed for target detection. This paper introduces a novel convex-opt...
Conference Paper
Full-text available
Compressive sensing (CS) is a new approach to simultaneous sensing and compression for sparse and compressible signals. While the discrete Fourier transform has been widely used for CS of frequency-sparse signals, it provides optimal sparse representations only for signals with components at integral frequencies. There exist redundant frames that p...
Article
Full-text available
The emergence of low-cost sensing architectures for diverse modalities has made it possible to deploy sensor networks that capture a single event from a large number of vantage points and using multiple modalities. In many scenarios, these networks acquire large amounts of very high-dimensional data. For example, even a relatively small network of...
Conference Paper
Full-text available
Two-photon calcium imaging is an emerging experimental technique that enables the study of information processing within neural circuits in vivo. While the spatial resolution of this technique permits the calcium activity of individual cells within the field of view to be monitored, inferring the precise times at which a neuron emits a spike is ch...
Article
Full-text available
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition of sparse or compressible signals that can be well approximated by just K ¿ N elements from an N -dimensional basis. Instead of taking periodic samples, CS measures inner products with M < N random vectors and then recovers the signal via a sparsity-seeking...
Conference Paper
Full-text available
Compressive sensing (CS) is an emerging approach for acquisition of signals having a sparse or compressible representation in some basis. While CS literature has mostly focused on problems involving 1-D and 2-D signals, many important applications involve signals that are multidimensional. We propose the use of Kronecker product matrices in CS for...
Conference Paper
Full-text available
This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity to further reduce the number of measurements required for recovery. DCS is well-suited for sensor network applications due to its...
Article
A recent revisionist theory claims that as early as 1430 European artists secretly invented optical projectors and used them as aids during the execution of their paintings. Key artworks adduced in support of this theory are a pair of portraits of Cardinal Niccolo Albergati by Jan van Eyck: a silverpoint study (1431) and a formal oil work (1432). W...