scholarly journals Optical Interferometry in the Multi-Speckle Mode

1994 ◽  
Vol 158 ◽  
pp. 373-375
Author(s):  
T. Reinheimer ◽  
K.-H. Hofmann ◽  
G. Weigelt
Keyword(s):  
Computer Simulations ◽  
Building Block ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Optical Interferometry ◽  
Data Sets ◽  
Interferometric Imaging ◽  
Signal To Noise ◽  
Block Method ◽  
Simulated Data Sets

We have studied interferometric imaging in the multi-speckle mode by computer simulations. From various simulated data sets diffraction-limited images were reconstructed by the speckle masking method and the iterative building block method. The reconstructed images show the dependence of the signal-to-noise ratio on photon noise.

scholarly journals linestacker: a spectral line stacking tool for interferometric data

2020 ◽  
Vol 499 (3) ◽  
pp. 3992-4010
Author(s):  
Jean-Baptiste Jolly ◽  
Kirsten K Knudsen ◽  
Flora Stanley
Keyword(s):  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Spectral Lines ◽  
Emission Lines ◽  
Data Sets ◽  
Large Array ◽  
Line Profiles ◽  
Signal To Noise ◽  
Very Large Array ◽  
Simulated Data Sets

ABSTRACT linestacker is a new open access and open source tool for stacking of spectral lines in interferometric data. linestacker is an ensemble of casa tasks, and can stack both 3D cubes or already extracted spectra. The algorithm is tested on increasingly complex simulated data sets, mimicking Atacama Large Millimeter/submillimeter Array, and Karl G. Jansky Very Large Array observations of [C ii] and CO(3–2) emission lines, from z ∼ 7 and z ∼ 4 galaxies, respectively. We find that the algorithm is very robust, successfully retrieving the input parameters of the stacked lines in all cases with an accuracy ≳90 per cent. However, we distinguish some specific situations showcasing the intrinsic limitations of the method. Mainly that high uncertainties on the redshifts (Δz > 0.01) can lead to poor signal-to-noise ratio improvement, due to lines being stacked on shifted central frequencies. Additionally, we give an extensive description of the embedded statistical tools included in linestacker: mainly bootstrapping, rebinning, and subsampling. Velocity rebinning is applied on the data before stacking and proves necessary when studying line profiles, in order to avoid artificial spectral features in the stack. Subsampling is useful to sort the stacked sources, allowing to find a subsample maximizing the searched parameters, while bootstrapping allows to detect inhomogeneities in the stacked sample. linestacker is a useful tool for extracting the most from spectral observations of various types.

scholarly journals A posterioridetermination of the useful data range for small-angle scattering experiments on dilute monodisperse systems

IUCrJ ◽  
2015 ◽  
Vol 2 (3) ◽  
pp. 352-360 ◽  
Author(s):  
Petr V. Konarev ◽  
Dmitri I. Svergun
Keyword(s):  
Experimental Data ◽  
Small Angle ◽  
Model Building ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Data Sets ◽  
Data Set ◽  
Angle Scattering ◽  
Angular Increment ◽  
Simulated Data Sets

Small-angle X-ray and neutron scattering (SAXS and SANS) experiments on solutions provide rapidly decaying scattering curves, often with a poor signal-to-noise ratio, especially at higher angles. On modern instruments, the noise is partially compensated for by oversampling, thanks to the fact that the angular increment in the data is small compared with that needed to describe adequately the local behaviour and features of the scattering curve. Given a (noisy) experimental data set, an important question arises as to which part of the data still contains useful information and should be taken into account for the interpretation and model building. Here, it is demonstrated that, for monodisperse systems, the useful experimental data range is defined by the number of meaningful Shannon channels that can be determined from the data set. An algorithm to determine this number and thus the data range is developed, and it is tested on a number of simulated data sets with various noise levels and with different degrees of oversampling, corresponding to typical SAXS/SANS experiments. The method is implemented in a computer program and examples of its application to analyse the experimental data recorded under various conditions are presented. The program can be employed to discard experimental data containing no useful information in automated pipelines, in modelling procedures, and for data deposition or publication. The software is freely accessible to academic users.

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

Multilag Correlation Estimators for Polarimetric Radar Measurements in the Presence of Noise

2012 ◽  
Vol 29 (6) ◽  
pp. 772-795 ◽  
Author(s):  
Lei Lei ◽  
Guifu Zhang ◽  
Richard J. Doviak ◽  
Robert Palmer ◽  
Boon Leng Cheong ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Data Quality ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Signal To Noise ◽  
Spectral Moments ◽  
Radar Measurements

Abstract The quality of polarimetric radar data degrades as the signal-to-noise ratio (SNR) decreases. This substantially limits the usage of collected polarimetric radar data to high SNR regions. To improve data quality at low SNRs, multilag correlation estimators are introduced. The performance of the multilag estimators for spectral moments and polarimetric parameters is examined through a theoretical analysis and by the use of simulated data. The biases and standard deviations of the estimates are calculated and compared with those estimates obtained using the conventional method.

scholarly journals Benchmarking Statistical Multiple Sequence Alignment

2018 ◽  
Author(s):  
Michael Nute ◽  
Ehsan Saleh ◽  
Tandy Warnow
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Structural Alignment ◽  
Estimation Method ◽  
Simulated Data ◽  
Protein Sequences ◽  
Data Sets ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Simulated Data Sets

AbstractThe estimation of multiple sequence alignments of protein sequences is a basic step in many bioinformatics pipelines, including protein structure prediction, protein family identification, and phylogeny estimation. Statistical co-estimation of alignments and trees under stochastic models of sequence evolution has long been considered the most rigorous technique for estimating alignments and trees, but little is known about the accuracy of such methods on biological benchmarks. We report the results of an extensive study evaluating the most popular protein alignment methods as well as the statistical co-estimation method BAli-Phy on 1192 protein data sets from established benchmarks as well as on 120 simulated data sets. Our study (which used more than 230 CPU years for the BAli-Phy analyses alone) shows that BAli-Phy is dramatically more accurate than the other alignment methods on the simulated data sets, but is among the least accurate on the biological benchmarks. There are several potential causes for this discordance, including model misspecification, errors in the reference alignments, and conflicts between structural alignment and evolutionary alignments; future research is needed to understand the most likely explanation for our observations. multiple sequence alignment, BAli-Phy, protein sequences, structural alignment, homology

scholarly journals New processing tools for weak and/or spatially overlapped macromolecular diffraction patterns

1999 ◽  
Vol 55 (10) ◽  
pp. 1733-1741 ◽  
Author(s):  
Dominique Bourgeois
Keyword(s):  
Signal To Noise Ratio ◽  
Data Sets ◽  
Signal To Noise ◽  
Weighting Method ◽  
Time Resolved ◽  
Accurate Evaluation ◽  
Profile Fitting ◽  
Integration Program ◽  
Diffraction Patterns ◽  
New Processing

Tools originally developed for the treatment of weak and/or spatially overlapped time-resolved Laue patterns were extended to improve the processing of difficult monochromatic data sets. The integration programPrOWallows deconvolution of spatially overlapped spots which are usually rejected by standard packages. By using dynamically adjusted profile-fitting areas, a carefully built library of reference spots and interpolation of reference profiles, this program also provides a more accurate evaluation of weak spots. In addition, by using Wilson statistics, it allows rejection of non-redundant strong outliers such as zingers, which otherwise may badly corrupt the data. A weighting method for optimizing structure-factor amplitude differences, based on Bayesian statistics and originally applied to low signal-to-noise ratio time-resolved Laue data, is also shown to significantly improve other types of subtle amplitude differences, such as anomalous differences.

scholarly journals Bayesian Planet Searches for the 10 cm/s Radial Velocity Era

2015 ◽  
Vol 11 (A29A) ◽  
pp. 205-207
Author(s):  
Philip C. Gregory
Keyword(s):  
Radial Velocity ◽  
State Of The Art ◽  
Simulated Data ◽  
Model Parameters ◽  
Data Sets ◽  
Stellar Activity ◽  
Bayesian Fusion ◽  
Multiple State ◽  
Simulated Data Sets ◽  
Apodization Function

AbstractA new apodized Keplerian model is proposed for the analysis of precision radial velocity (RV) data to model both planetary and stellar activity (SA) induced RV signals. A symmetrical Gaussian apodization function with unknown width and center can distinguish planetary signals from SA signals on the basis of the width of the apodization function. The general model for m apodized Keplerian signals also includes a linear regression term between RV and the stellar activity diagnostic In (R'hk), as well as an extra Gaussian noise term with unknown standard deviation. The model parameters are explored using a Bayesian fusion MCMC code. A differential version of the Generalized Lomb-Scargle periodogram provides an additional way of distinguishing SA signals and helps guide the choice of new periods. Sample results are reported for a recent international RV blind challenge which included multiple state of the art simulated data sets supported by a variety of stellar activity diagnostics.

An evaluation of HapMap sample size and tagging SNP performance in large-scale empirical and simulated data sets

2005 ◽  
Vol 37 (12) ◽  
pp. 1320-1322 ◽  
Author(s):  
Eleftheria Zeggini ◽  
William Rayner ◽  
Andrew P Morris ◽  
Andrew T Hattersley ◽  
Mark Walker ◽  
...  
Keyword(s):  
Sample Size ◽  
Large Scale ◽  
Simulated Data ◽  
Data Sets ◽  
Hapmap Sample ◽  
Tagging Snp ◽  
Simulated Data Sets
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