scholarly journals A gain series method for accurate EMCCD calibration

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Duncan P. Ryan ◽  
Megan K. Dunlap ◽  
Martin P. Gelfand ◽  
James H. Werner ◽  
Alan K. Van Orden ◽  
...  
Keyword(s):  
Conversion Factor ◽  
Photon Counting ◽  
New Method ◽  
Noise Model ◽  
Calibration Data ◽  
Series Method ◽  
Digital Conversion ◽  
Consistency Test ◽  
Self Consistency ◽  
Emccd Camera

AbstractCalibration of the gain and digital conversion factor of an EMCCD is necessary for accurate photon counting. We present a new method to quickly calibrate multiple gain settings of an EMCCD camera. Acquiring gain-series calibration data and analyzing the resulting images with the EMCCD noise model more accurately estimates the gain response of the camera. Furthermore, we develop a method to compare the results from different calibration approaches. Gain-series calibration outperforms all other methods in this self-consistency test.

scholarly journals A Gain-series Method for Accurate EMCCD Calibration

2021 ◽  
Author(s):  
Duncan P. Ryan ◽  
Megan K. Dunlap ◽  
Martin P. Gelfand ◽  
James H. Werner ◽  
Alan Orden ◽  
...  
Keyword(s):  
Conversion Factor ◽  
Photon Counting ◽  
New Method ◽  
Noise Model ◽  
Calibration Data ◽  
Series Method ◽  
Digital Conversion ◽  
Consistency Test ◽  
Self Consistency ◽  
Emccd Camera

Abstract Calibration of the gain and digital conversion factor of an EMCCD is necessary for accurate photon counting. We present a new method to quickly calibrate multiple gain settings of an EMCCD camera. Acquiring gain-series calibration data and analyzing the resulting images with the EMCCD noise model more accurately estimates the gain response of the camera. Furthermore, we develop a method to compare the results from different calibration approaches. Gain-series calibration outperforms all other methods in this self-consistency test.

STATISTICAL INFERENCE FOR MEASUREMENT EQUATION SELECTION IN THE LOG-REALGARCH MODEL

2018 ◽  
Vol 35 (05) ◽  
pp. 943-977 ◽  
Author(s):  
Yu-Ning Li ◽  
Yi Zhang ◽  
Caiya Zhang
Keyword(s):  
Statistical Inference ◽  
Asymptotic Results ◽  
Measurement Equation ◽  
Inference Problem ◽  
Consistency Test ◽  
Sufficient And Necessary Conditions ◽  
Quasi Maximum Likelihood ◽  
Strict Stationarity ◽  
Self Consistency ◽  
Theoretical Results

This article investigates the statistical inference problem of whether a measurement equation is self-consistent in the logarithmic realized GARCH model (log-RealGARCH). First, we provide the sufficient and necessary conditions for the strict stationarity of both the log-RealGARCH model and the log-GARCH-X model. Under these conditions, strong consistency and asymptotic normality of the quasi-maximum likelihood estimators of these two models are obtained. Then, based on the asymptotic results, we propose a Hausman-type self-consistency test for diagnosing the suitability of the measurement equation in the log-RealGARCH model. Finally, the results of simulations and an empirical study are found to accord with the theoretical results.

scholarly journals Improved Single-Molecule Imaging Based on Photon Counting with an EMCCD Camera

2012 ◽  
Vol 102 (3) ◽  
pp. 480a
Author(s):  
Abbas Padeganeh ◽  
Etienne Lareau ◽  
Olivier Daigle ◽  
Anne-Marie Ladouceur ◽  
Paul Maddox
Keyword(s):  
Single Molecule ◽  
Photon Counting ◽  
Single Molecule Imaging ◽  
Emccd Camera

scholarly journals An Algorithm for the Numerical Integration of Perturbed and Damped Second-Order ODE Systems

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2028
Author(s):  
Fernando García-Alonso ◽  
José Antonio Reyes ◽  
Mónica Cortés-Molina
Keyword(s):  
Numerical Integration ◽  
Structural Dynamics ◽  
Truncation Error ◽  
First Integral ◽  
Second Order ◽  
New Method ◽  
Series Method ◽  
Initial Value ◽  
Satellite Problem ◽  
Damped Systems

A new method of numerical integration for a perturbed and damped systems of linear second-order differential equations is presented. This new method, under certain conditions, integrates, without truncation error, the IVPs (initial value problems) of the type: x″(t)+Ax′(t)+Cx(t)=εF(x(t),t), x(0)=x0, x′(0)=x0′, t∈[a,b]=I, which appear in structural dynamics, astrodynamics, and other fields of physics and engineering. In this article, a succession of real functions is constructed with values in the algebra of m×m matrices. Their properties are studied and we express the solution of the proposed IVP through a serial expansion of the same, whose coefficients are calculated by means of recurrences involving the perturbation function. This expression of the solution is used for the construction of the new numerical method. Three problems are solved by means of the new series method; we contrast the results obtained with the exact solution of the problem and with its first integral. In the first problem, a quasi-periodic orbit is integrated; in the second, a problem of structural dynamics associated with an earthquake is studied; in the third, an equatorial satellite problem when the perturbation comes from zonal harmonics J2 is solved. The good behavior of the series method is shown by comparing the results obtained against other integrators.

Identification of Lysine Carboxylation Sites in Proteins by Integrating Statistical Moments and Position Relative Features via General PseAAC

2020 ◽  
Vol 15 (5) ◽  
pp. 396-407 ◽  
Author(s):  
Saba Amanat ◽  
Adeel Ashraf ◽  
Waqar Hussain ◽  
Nouman Rasool ◽  
Yaser D. Khan
Keyword(s):  
Prediction Model ◽  
Model Validation ◽  
Cross Validation ◽  
Covalent Attachment ◽  
Side Chain ◽  
Consistency Test ◽  
Post Translational Modifications ◽  
Independent Dataset ◽  
Proposed Model ◽  
Self Consistency

Background: Carboxylation is one of the most biologically important post-translational modifications and occurs on lysine, arginine, and glutamine residues of a protein. Among all these three, the covalent attachment of the carboxyl group with the lysine side chain is the most frequent and biologically important type of carboxylation. For studying such biological functions, it is essential to correctly determine the lysine sites sensitive to carboxylation. Objective: Herein, we present a computational model for the prediction of the carboxylysine site which is based on machine learning. Methods: Various position and composition relative features have been incorporated into the Pse- AAC for construction of feature vectors and a neural network is employed as a classifier. The model is validated by jackknife, cross-validation, self-consistency, and independent testing. Results: The results of the self-consistency test elaborated that model has 99.76% Acc, 99.76% Sp, 99.76% Sp, and 0.99 MCC..Using the jackknife method, prediction model validation gave 97.07% Acc, while for 10-fold cross-validation, prediction model validation gave 95.16% Acc. Conclusion: The results of independent dataset testing were 94.3% which illustrated that the proposed model has better performance as compared to the existing model PreLysCar; however, the accuracy can be improved further, in the future, due to the increasing number of carboxylysine sites in proteins.

scholarly journals A New Basis for Understory Stocking Standards for Partially Harvested Stands in the British Columbia Interior

2005 ◽  
Vol 20 (1) ◽  
pp. 5-12
Author(s):  
Patrick J. Martin ◽  
Bryce Bancroft ◽  
Ken Day ◽  
Kim Peel
Keyword(s):  
British Columbia ◽  
Basal Area ◽  
Tree Density ◽  
New Method ◽  
Mean Deviation ◽  
Calibration Data ◽  
Potential Yield ◽  
Stand Management ◽  
Management Context ◽  
Understory Tree

Abstract For partially harvested stands in the British Columbia interior, we present a new method for specifying regeneration stocking standards and a procedure for comparing achieved regeneration to the standard. Understory stocking is assessed in terms of the deviation from potential yield that the observed understory tree density represents. For a harvested area, the minimum stocking standard is stated as the maximum allowable mean deviation from potential. Deviation from potential is expressed on a relative (0–1) scale and predicted from overstory basal area and understory tree density. Because of a scarcity of calibration data, we undertook a crude, preliminary calibration of this relationship. As overstory basal area increases, the understory density required to achieve a given degree of understory stocking decreases. Issues pertaining to the stand management context and other approaches to regulating regeneration also are discussed. West. J. Appl. For. 20(1):5–12.

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