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To detect these attacks we propose an architecture based on robust nonlinear systematic error-detecting codes.
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A general overview of CS is introduced in Section 2, and a collection of robust estimators, known as M-estimators, are discussed in Section 3. We then present a review of nonlinear methods based on robust estimation in Section 4. Section 5 is devoted to illustrate the performance of the reviewed methods in the reconstruction of sparse signals from compressive contaminated samples.
Then the design is based on robust backstepping which leads to a memory-less nonlinear feedback of attitude, attitude-rate, and of the engine deflection angle.
We approach the problem first from a statistical point of view and then review nonlinear methods that have been proposed in the literature that are based on robust statistics, specifically methods that are based on M-estimators.
C.I. based on robust standard errors.
RDBC criterion is based on robust estimator which is biased.
The study was therefore based on robust data base.
All hazard ratios were modified based on robust variance estimates.
Standardized results based on robust maximum likelihood estimation are presented.
CIs based on robust standard errors, clustered by GP practice.
95% confidence intervals based on robust standard errors.
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