Sentence examples for a set of distortion from inspiring English sources

(2016), have been verified with the calculation detailed in Section 2 of the Additional file 1. Fig. 11 a Det and b ssq impedances from the 3D example (as shown in Fig. 9a, b, respectively) distorted with a set of distortion parameters with an SD of 0.3 Fig. 12 Average a det and b ssq impedances from the 3D datasets distorted with different galvanic distortion strengths.

For the cache replacement strategy, we exploit that access patterns remain deterministic for a set of distortion parameters and a given homography.

Fig. 22 a Comparison of synthetic site gains (crosses) with the mean apparent det (diamonds) and ssq (squares) gains from the 1D example, where a set of distortion parameters with an SD of 0.3 was applied.

Fig. 20 a Comparison of synthetic LDIs (black crosses) with the mean LDIs (red circles) from the 3D example, where a set of distortion parameters with an SD of 0.3 was applied.

Fig. 18 LDIs from the distorted a 1D and b 3D data (in '1D example'3D'3D example' sections, respectively), where a set of distortion parameters with an SD of 0.3 was applied.

Fig. 4 Example of det (diamonds) and ssq (squares) impedances from the synthetic 1D impedance distorted with ((g,t,e,s)=(1.20,0.11,-0.37,0.49)) Fig. 5 Distorted a det and b ssq impedances from the 1D example, where a set of distortion parameters with an SD of 0.3 was applied.

Also, it was observed that the most ambitious and active workers tend to display a set of cognitive distortions related to perfectionism, omnipotence and the belief that other people should respond to their needs, data that confirm the relation between type A behavior pattern and some type of cognitive distortion.

However, most of the conventional NR-IQA methods are designed only for one or a set of predefined specific image distortion types, which are unlikely to generalize for evaluating image/video distorted with other types of distortions.

Towards solving this problem, we describe a new framework for repairing an image that has undergone an unknown set of distortions, based on identifying the distortion(s) present in the image (if any) and applying possibly multiple distortion-specific image repair algorithms.

More explicitly, we assumed that each set of distortion parameters has a mean of zero and is bounded by ((-1,+1)).

A set of parameters describing the possible distortion in a video has been defined and classified in 'ITU-T SG9 for RRNR project' [4].