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In stationary signal case, we investigate the parameter estimation performance of the proposed method by performing variance and mean bias simulations for SNRs ranging from −20 to 20 dB with 1 dB increments.
In addition, we also confirmed the effectiveness of the proposed method by performing an experiment with a DC motor.
This method overcomes the difficulties arising in the project gradient method by performing an additional forward step and a projection at each iteration according to double projection.
Finally, we can obtain higher quality reconstructed images than the traditional LUT method by performing the proposed hybrid method with the extracted templates and the halftone image.
Monfared and Yang (2004) further developed the finite version of SES method by performing a sensitivity analysis on the smoothing parameters.
We demonstrate the effectiveness of the method by performing uncertainty analysis of the frequency response of a nonproportionally damped plate made of a carbon fibre/epoxy composite material.
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We compared the ability of our method to reconstruct hand movements from neural recordings with several commonly used, real-time methods by performing 10-fold cross-validation on 16 previously recorded sessions.
In contrast with conventional gene-based methods, by performing the differential network analysis, more characteristic genes or subnetworks known to be related to disease development are identified.
We next investigated the consistency of gene expression measurements of leukemia samples when using different total RNA extraction methods by performing an unsupervised hierarchical clustering analysis.
Finally, in addition to assessing ranges of N e values, we examined if ranking was similar with the different methods by performing for each species, a Principal Component Analysis (PCA), considering breeds as observations and the six methods as variables.
Results: The present method begins by performing the kernel density estimation in order to convert each 3D image into a smooth, continuous function.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com