12 papers · ranked by Valyu relevance
Shang Zhang, Yuhan Dong, Hongyan Fu, Shao-Lun Huang + 1 more
The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically…
Chunsheng Yan
Title: Summary Spectroscopic techniques are indispensable for material characterization, yet their weak signals remain highly prone to interference from environmental noise, instrumental artifacts, sample impurities, scattering effects, and radiation-based distortions (e.g., fluorescence and cosmic rays). These…
Nan Xia, Yunbao Huang, Haiyan Li, Pu Li + 2 more
In the experiment of inertial confinement fusion, soft X-ray spectrum unfolding can provide important information to optimize the design of the laser and target. As the laser beams increase, there are limited locations for installing detection channels to obtain measurements, and the soft X-ray spectrum can be…
Yarin Gal, Richard E. Turner
Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to over-fit. We suggest the use of variational inference for the sparse spectrum approximation to avoid both issues. We model the covariance…
Linda S. L. Tan, Victor M.-H. Ong, David J. Nott, Ajay Jasra
We develop a fast variational approximation scheme for Gaussian process (GP) regression, where the spectrum of the covariance function is subjected to a sparse approximation. Our approach enables uncertainty in covariance function hyperparameters to be treated without using Monte Carlo methods and is robust to…
Petre Stoica, Dave Zachariah, Jian Li
—In this paper we present the SPICE approach for sparse parameter estimation in a framework that unifies it with other hyperparameter-free methods, namely LIKES, SLI M and IAA. 1 Specifically, we show how the latter methods can be interpreted as variants of an adaptively reweighted SPIC E method. Furthermore, we…
Jose Velasco, Daniel Pizarro, Javier Macias-Guarasa
This paper presents a novel approach for indoor acoustic source localization using sensor arrays. The proposed solution starts by defining a generative model, designed to explain the acoustic power maps obtained by Steered Response Power (SRP) strategies. An optimization approach is then proposed to fit the model to…
Xiaonan Pan, Miao Yuan, Jianrui Zhang, Xiaojun Yu + 1 more
Optical coherence tomography (OCT) is an optical imaging modality that provides high-resolution cross-sectional imaging of biological tissues noninvasively. In Fourier-domain OCT, axial resolution is governed by both the center wavelength and the spectral bandwidth of the light source; therefore, limited or…
Kaan Gökcesu, Hakan Gökcesu
—We propose a decomposition method for the spectral peaks in an observed frequency spectrum, which is efficientl y acquired by utilizing the Fast Fourier Transform. In contrast to the traditional methods of waveform fitting on the spectrum, we optimize the problem from a more robust perspective. We mode l the peaks in…
Prashin Jethwa, Simon Hubmer, Ronny Ramlau, Glenn van de Ven
Context. Full spectrum fitting is the prevailing method for extracting stellar kinematic and population measurements from 1D galaxy spectra. 3D methods refer to analysis of Integral Field Spectroscopy (IFS) data where spatial and spectral dimensions are modelled simultaneously. While several 3D methods exist for…
Axel Boulais, Olivier Berné, G. Faury, Yannick Deville
An increasing number of astronomical instruments (on Earth and space-based) provide hyperspectral images, that is three-dimensional data cubes with two spatial dimensions and one spectral dimension. The intrinsic limitation in spatial resolution of these instruments implies that the spectra associated with pixels of…
Karsten Fyhn, Marco F. Duarte, Søren Holdt Jensen
—We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Our work extends previous approaches involving polar interpolation for compressive parameter estimation in two aspects: (i) we extend the formulation from real non-negative amplitude…