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One way to achieve linear amplification is by using a class A power amplifier working with a high backoff, which corresponds to moving the operating point of the amplifier to the linear region.
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Such wave-based architecture has been facilitated here by using a class of "isodiffracting pulsed beams" (PB) which not only provides simple wave-objects for local interrogation of a particular sub-environment in the global conglomerate, but also provides a complete basis for construction of arbitrary source-excited field in the global environment.
The equivalence between a named class A and a class description D can be asserted into the terminology by using a class definition (denoted by ≡).
Although not popular in feature selection literature, perhaps the simplest way to understand discriminatory nature of feature in a training set with two classes can be by using a search using naive bayes classifier.
This could for instance be achieved by using a multi class classifier [46], instead of the binary classifier used.
This is achieved by using a combination of class and individual characteristics.
The population balance equation describing this system is discretized by using a method of classes to generate a system of ordinary differential equations and the latter is solved by an adaptive step Runge-Kutta method.
The first is the probability that (varvec{o}) is generated by using a known model of the class behavior, which gives (P varvec{o} | s_j)).
Each TLV field and each packet exchanged have been modeled by using a different Java class, as shown in the UML class diagram shown in Figure 6.
The second part of the algorithm, evaluating the diffusion approximation, was done by using a modified octree class.
Sensitivity and specificity of serologic testing and herdsmen reporting were estimated by using a Bayesian latent class model (24, 26, 27 ) and the JAGS (Just Another Gibbs Sampler) (http://mcmc-jags.sourceforge.net/ ) software package in R.
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