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We begin with a basic overview of the principles and terminology involved, and then give a concise summary on the principles of the methods used.
Successful application of these advanced methods requires that persons taking the data and analyzing the data have a good understanding of the principles of the methods, how to set up data acquisition parameters for optimum results, and how to interpret recorded data.
The principles of the methods were first conceived by Gibbs as early as 1933 [ 1] and many variations and modifications have been proposed since.
The basic principles of the methods described below are the same, while modifications thereof will be presented in the sections dedicated to specific sample types.
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This paper first introduces the principles of the method, and then conducts tests on synthetic data.
A case study is used to show the principles of the method.
The principles of the method are illustrated with a case example typical of ion beam accelerators devoted to materials analysis.
Here the basic principles of the method and some of the current limitations when applied to composite materials and components are discussed.
Having outlined the general principles of the method, we now describe its application to detect selection in a simulated population.
In doing so he reiterated the key principles of the method [ 6]: Entities are the items that evolve through the simulation.
Briefly, the principles of the method were as follows: By CDS, the anatomical route of the temporal artery could be presented in longitudinal and transverse view.
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