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Section 3 presents the proposed CFA pattern identification method.
In this paper, we introduce an efficient Bayer CFA pattern identification method based on singular value decomposition.
Using in silico primers and probes designed by a pattern identification method [14], our method utilized highly sensitive and specific tests for Bacteroidetes and Firmicutes.
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The average test accuracies of different pattern identification methods are presented in Table 1.
Targeting such limitation, a new pattern recognition method – variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification.
In addition, in the design of the classifier, targeting the limitation of existing pattern recognition method, a new pattern recognition method-variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification.
Artificial neural networks (ANNs) and a support vector machine (SVM) are used as pattern recognition methods for the identification tests.
Nineteen bacteria isolates recovered from shellfish samples (mussels and oysters) showed a new and specific 16S rDNA-RFLP pattern with an Arcobacter identification method designed to recognize all species described up to 2008.
Table 5 displays the Bayer CFA pattern identification performance for the proposed method and conventional algorithm.
For a 256 × 256 image block, the average CFA pattern identification time of the proposed method was approximately 0.035 s.
Two novel optical interferometry diagnostics methods, wave pattern identification and correlation detection, were developed for measuring the adhesion strength between a film or coatings and substrate.
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