Search results for "Convolution"
showing 10 items of 334 documents
IsoSpec2: ultrafast fine structure calculator
2020
Abstract: High-resolution mass spectrometry becomes increasingly available with its ability to resolve the fine isotopic structure of measured analytes. It allows for high-sensitivity spectral deconvolution, leading to less false-positive identifications. Analytes can be identified by comparing their theoretical isotopic signal with the observed peaks. Necessary calculations are, however, computationally demanding and lead to long processing times. For wheat (trictum oestivum) alone, Uniprot holds more than 142 000 candidate protein sequences. This is doubled upon sequence reversal for identification FDR estimation and further multiplied by performing in silico digestion into peptides. The …
Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion
2020
Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning val…
Parabolic-Lorentzian modified Gaussian model for describing and deconvolving chromatographic peaks.
2002
Abstract A new mathematical model for characterising skewed chromatographic peaks, which improves the previously reported polynomially modified Gaussian (PMG) model, is proposed. The model is a Gaussian based equation whose variance is a combined parabolic-Lorentzian function. The parabola accounts for the non-Gaussian shaped peak, whereas the Lorentzian function cancels the variance growth out of the elution region, which gives rise to a problematic baseline increase in the PMG model. The proposed parabolic-Lorentzian modified Gaussian (PLMG) model makes a correct description of peaks showing a wide range of asymmetry with positive and/or negative skewness. The new model is shown to give b…
Peak deconvolution in one-dimensional chromatography using a two-way data approach.
2002
A deconvolution methodology for overlapped chromatographic signals is proposed. Several single-wavelength chromatograms of binary mixtures, obtained in different runs at diverse concentration ratios of the individual components, were simultaneously processed (multi-batch approach), after being arranged as two-way data. The chromatograms were modelled as linear combinations of forced peak profiles according to a polynomially modified Gaussian equation. The fitting was performed with a previously reported hybrid genetic algorithm with local search, leaving all model parameters free. The approach yielded more accurate solutions than those found when each experimental chromatogram was fitted in…
Levels in the interpretive optimisation of selectivity in high-performance liquid chromatography: a magical mystery tour.
2006
Interpretive approaches for selectivity optimisation, which are those supported by retention models, are able to exploit efficiently the capabilities of the chromatographic system. The resolution of a mixture is usually faced in a first trial by looking for a unique experimental condition, able to resolve all compounds in the sample. If this is not possible, the problem can be outlined with less ambitious aims, focusing on only some compounds. In an extreme case, a single analyte can be individually optimised. Current strategies that give answer to the different goals pursued in the analysis, which are classified as total, partial and specific, are reviewed. Optimisation oriented to deconvo…
Net analyte signal as a deconvolution-oriented resolution criterion in the optimisation of chromatographic techniques
2003
The performance of two multivariate calibration measurements, multivariate selectivity (SEL(s)) and scalar net analyte signal (scalar NAS), as chromatographic objective functions (COFs), was investigated. Since both assessments are straightforwardly related to the quantification of analytes in the presence of interferents, they were expected to confer new features in the optimisation of compound resolution, not present in conventional assessments. These capabilities are especially interesting in situations of low resolution, where peak deconvolution becomes an attractive alternative. For comparison purposes, chromatographic resolution (R(s)) and peak purity (p(s)) were used as reference COF…
Global treatment of chromatographic data with MICHROM
1997
Abstract The program MICHROM for the general treatment of chromatographic data is presented. MICHROM takes part in all the stages of the analytical process. It allows determination of dead time, smoothing of chromatograms, measurement of peak parameters, fitting of skewed peaks, and deconvolution of overlapped peaks. Tools for the experimental design, optimization of the mobile phase composition to resolve a mixture of analytes, and simulation of chromatograms in several experimental conditions, are implemented. Routines for the graphical representation of chromatograms, resolution surfaces, contour maps, management of data series, optimization and regression analysis, are also included. Th…
Development of the H-point standard additions method for coupled liquid chromatography and UV-visible spectrophotometry
1992
Abstract This work establishes the fundamentals of the H-point standard additions method for liquid chromatography for the simultaneous analysis of binary mixtures with overlapped chromatographic peaks. The method was compared with the deconvolution method of peak suppression and the second derivative of elution profiles. Different mixtures of diuretics were satisfactorily resolved.
Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks
2020
In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. While these planes are used for transportation they could similarly be used for remote sensing applications by adding sensors to the planes. Hyperspectral imagers are one this kind of sensor types. There is need for the efficient methods to interpret hyperspectral data to the wanted water quality parameters. In this work we survey the performance of neural networks in the prediction of water quality parameters from remotely sensed hyperspectral data in freshwater basin…
Estimating norms inC*-algebras of discrete groups
1976
LetG be a discrete group, letK be a finite subset ofG and let χ K be the characteristic function ofK. Then χ K acts by convolution as a bounded operator onL2(G). We will prove that the norm |||χ K ||| of this operator always satisfies the following estimate: $$|||\chi _{\rm K} |||^2 \leqq k + 2\sqrt {w\left( {k - 1} \right)\left( {k - w} \right)} + \left( {k - 2} \right)\left( {k - w} \right)$$ . Here .