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In the following sub-sections, this fault missing rate for L-tap FIR filters is analyzed.
Figure 8 shows the fault missing rate for different modulus and different number of samples for checking in the MSC-DM-CRC based FIR design.
As a result, the fault missing rate for a modulus m = 2 n ± 1 is P m _ MIF 2 n + 1 = 1 m = 1 2 n ± 1. (13).
For example, the fault missing rate for n = 2 and N = 3 can be read from Figure 8 as about 5.25%.
The fault missing problem, which is common for residue code based checking schemes, is firstly revealed in this article, and the fault missing rate for SSC is analyzed in detail based on the DM-CRC structure.
so the fault missing rate for the SSC-DM-CRC with module m, which is the probability of the happening of Equation (10), can be expressed as P m _ MIF = Prob ( ( x l * 2 q ) m = 0 ).
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The autocorrelation of missing rates, for the first time, is introduced to reflect the interaction of network bandwidth at adjacent sampling times.
The questionnaire was long, which may have increased missing rates for later questions.
The missing rates for each phenotype were calculated (see Table S10) and were usually very small.
The final dataset used in our analysis contained 96,448 records of medical services, with a missing rate of 10% (n=11,264 missing values), a quite low missing rate for a 24-month prospective cohort study.
For example, the missing rate of MIF is 33.3% for m = 3.
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