24 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…
Karl Irikura
When using ab initio calculations to predict the spectra of diatomic molecules, discrete ab initio data must be converted to continuous form. As in other applications of interpolation throughout science and engineering, high-resolution (i.e., fine-mesh) data are desirable so that the method of interpolation does not…
Anselm W. Hahn, Joseph Zsombor-Pindera, Pierre Kennepohl, Serena DeBeer
In chemistry, analyzing spectra through peak fitting is a crucial task that helps scientists extract useful quantitative information about a sample's chemical composition or electronic structure. To make this process more efficient, we have developed a new open-source software tool called SpectraFit. This tool allows…
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…
Shiang Hu, Pedro A. Valdes-Sosa
Neural oscillations excitability shape sensory, motor, and cognitive processes of the brain operation. To quantify the spectrum of brain magnetic and electrical recordings has become the main methodology to study neural oscillations. However, there is still lacking a valid approach, although the literatures on the…
Gabriel Barello, Adam S. Charles, Jonathan W. Pillow
The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete dictionary. The classic sparse coding model suffers from two key limitations, however: (1) computing the neural response to an image patch requires minimizing a…
Authors not listed
Soot, coal, graphite and similar black-carbon materials contain layered sheets of sp2-bonded carbon in regions larger than about 4-nm. Such materials typically exhibit Raman spectra (RS) with a peak near 1580 cm^-1 (G band) and, except in the case of graphene or highly ordered graphite, a peak in the range 1300 cm^-1 -…
Martin Wilson
Accurate analysis of metabolite levels from ^1^H MRS data is a significant challenge, typically requiring the estimation of approximately 100 parameters from a single spectrum. Signal overlap, spectral noise and common artefacts further complicate analysis, leading to instability and reports of poor agreement between…
Kevin Robben, Christopher Cheatum
We report a comprehensive study of the efficacy of least-squares fitting of multidimensional spectra to generalized Kubo lineshape models and introduce a novel least-squares fitting metric, termed the Scale Invariant Gradient Norm (SIGN), that enables a highly reliable and versatile algorithm. The precision of…
Amir M Shamaei, Jana Starcukova, Zenon Starcuk
While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning…
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…
Sebastian Reinhard, Dominic A. Helmerich, Dominik Boras, Markus Sauer + 1 more
Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a…
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…
Sanjar Adilov
Machine learning models for molecular-property prediction typically work with molecular representations in the form of fingerprints, descriptors, or graphs. In case of fingerprints and descriptors, molecular representations usually comprise thousands of features, which causes the curse of dimensionality for many…
Amelia Carolina Sparavigna
Tsallis q-Gaussian functions are probability distributions possessing a shape which can be stretched from a Lorentzian to a Gaussian profile, according to the q-index parameter of their generalized exponentials. As we have previously shown, the q-Gaussian functions can be successfully applied to the deconvolution of…
Eleni Litsa, Vijil Chenthamarakshan, Payel Das, Lydia Kavraki
Elucidating the structure of a chemical compound is a fundamental task in chemistry with application in multiple domains including the emerging field of metabolomics, with promising applications in drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a…