7,073 research outputs found
Recommended from our members
A sparse semi-blind source identification method and its application to Raman spectroscopy for explosives detection
Rapid and reliable detection and identification of unknown chemical substances are critical to homeland security. It is challenging to identify chemical components from a wide range of explosives. There are two key steps involved. One is a non-destructive and informative spectroscopic technique for data acquisition. The other is an associated library of reference features along with a computational method for feature matching and meaningful detection within or beyond the library. In this paper, we develop a new iterative method to identify unknown substances from mixture samples of Raman spectroscopy. In the first step, a constrained least squares method decomposes the data into a sum of linear combination of the known components and a non-negative residual. In the second step, a sparse and convex blind source separation method extracts components geometrically from the residuals. Verification based on the library templates or expert knowledge helps to confirm these components. If necessary, the confirmed meaningful components are fed back into step one to refine the residual and then step two extracts possibly more hidden components. The two steps may be iterated until no more components can be identified. We illustrate the proposed method in processing a set of the so called swept wavelength optical resonant Raman spectroscopy experimental data by a satisfactory blind extraction of a priori unknown chemical explosives from mixture samples. We also test the method on nuclear magnetic resonance (NMR) spectra for chemical compounds identification. © 2013 Published by Elsevier B.V
Compressive Sensing for Spectroscopy and Polarimetry
We demonstrate through numerical simulations with real data the feasibility
of using compressive sensing techniques for the acquisition of
spectro-polarimetric data. This allows us to combine the measurement and the
compression process into one consistent framework. Signals are recovered thanks
to a sparse reconstruction scheme from projections of the signal of interest
onto appropriately chosen vectors, typically noise-like vectors. The
compressibility properties of spectral lines are analyzed in detail. The
results shown in this paper demonstrate that, thanks to the compressibility
properties of spectral lines, it is feasible to reconstruct the signals using
only a small fraction of the information that is measured nowadays. We
investigate in depth the quality of the reconstruction as a function of the
amount of data measured and the influence of noise. This change of paradigm
also allows us to define new instrumental strategies and to propose
modifications to existing instruments in order to take advantage of compressive
sensing techniques.Comment: 11 pages, 9 figures, accepted for publication in A&
Bayesian separation of spectral sources under non-negativity and full additivity constraints
This paper addresses the problem of separating spectral sources which are
linearly mixed with unknown proportions. The main difficulty of the problem is
to ensure the full additivity (sum-to-one) of the mixing coefficients and
non-negativity of sources and mixing coefficients. A Bayesian estimation
approach based on Gamma priors was recently proposed to handle the
non-negativity constraints in a linear mixture model. However, incorporating
the full additivity constraint requires further developments. This paper
studies a new hierarchical Bayesian model appropriate to the non-negativity and
sum-to-one constraints associated to the regressors and regression coefficients
of linear mixtures. The estimation of the unknown parameters of this model is
performed using samples generated using an appropriate Gibbs sampler. The
performance of the proposed algorithm is evaluated through simulation results
conducted on synthetic mixture models. The proposed approach is also applied to
the processing of multicomponent chemical mixtures resulting from Raman
spectroscopy.Comment: v4: minor grammatical changes; Signal Processing, 200
Signal detection for spectroscopy and polarimetry
The analysis of high spectral resolution spectroscopic and
spectropolarimetric observations constitute a very powerful way of inferring
the dynamical, thermodynamical, and magnetic properties of distant objects.
However, these techniques are photon-starving, making it difficult to use them
for all purposes. One of the problems commonly found is just detecting the
presence of a signal that is buried on the noise at the wavelength of some
interesting spectral feature. This is specially relevant for
spectropolarimetric observations because typically, only a small fraction of
the received light is polarized. We present in this note a Bayesian technique
for the detection of spectropolarimetric signals. The technique is based on the
application of the non-parametric relevance vector machine to the observations,
which allows us to compute the evidence for the presence of the signal and
compute the more probable signal. The method would be suited for analyzing data
from experimental instruments onboard space missions and rockets aiming at
detecting spectropolarimetric signals in unexplored regions of the spectrum
such as the Chromospheric Lyman-Alpha Spectro-Polarimeter (CLASP) sounding
rocket experiment.Comment: 10 pages, 5 figures, accepted for publication in A&
Estimating Spectroscopic Redshifts by Using k Nearest Neighbors Regression I. Description of Method and Analysis
Context: In astronomy, new approaches to process and analyze the
exponentially increasing amount of data are inevitable. While classical
approaches (e.g. template fitting) are fine for objects of well-known classes,
alternative techniques have to be developed to determine those that do not fit.
Therefore a classification scheme should be based on individual properties
instead of fitting to a global model and therefore loose valuable information.
An important issue when dealing with large data sets is the outlier detection
which at the moment is often treated problem-orientated. Aims: In this paper we
present a method to statistically estimate the redshift z based on a similarity
approach. This allows us to determine redshifts in spectra in emission as well
as in absorption without using any predefined model. Additionally we show how
an estimate of the redshift based on single features is possible. As a
consequence we are e.g. able to filter objects which show multiple redshift
components. We propose to apply this general method to all similar problems in
order to identify objects where traditional approaches fail. Methods: The
redshift estimation is performed by comparing predefined regions in the spectra
and applying a k nearest neighbor regression model for every predefined
emission and absorption region, individually. Results: We estimated a redshift
for more than 50% of the analyzed 16,000 spectra of our reference and test
sample. The redshift estimate yields a precision for every individually tested
feature that is comparable with the overall precision of the redshifts of SDSS.
In 14 spectra we find a significant shift between emission and absorption or
emission and emission lines. The results show already the immense power of this
simple machine learning approach for investigating huge databases such as the
SDSS.Comment: accepted for publication in A&
Sparsity and adaptivity for the blind separation of partially correlated sources
Blind source separation (BSS) is a very popular technique to analyze
multichannel data. In this context, the data are modeled as the linear
combination of sources to be retrieved. For that purpose, standard BSS methods
all rely on some discrimination principle, whether it is statistical
independence or morphological diversity, to distinguish between the sources.
However, dealing with real-world data reveals that such assumptions are rarely
valid in practice: the signals of interest are more likely partially
correlated, which generally hampers the performances of standard BSS methods.
In this article, we introduce a novel sparsity-enforcing BSS method coined
Adaptive Morphological Component Analysis (AMCA), which is designed to retrieve
sparse and partially correlated sources. More precisely, it makes profit of an
adaptive re-weighting scheme to favor/penalize samples based on their level of
correlation. Extensive numerical experiments have been carried out which show
that the proposed method is robust to the partial correlation of sources while
standard BSS techniques fail. The AMCA algorithm is evaluated in the field of
astrophysics for the separation of physical components from microwave data.Comment: submitted to IEEE Transactions on signal processin
- …