Common use of Fault Tolerance Clause in Contracts

Fault Tolerance. ε2 Due to the presence of noises and manufacture variations, there may be a difference of CSI measurements hi in the ith sample, denoted as δi. When δ is larger than ε, Δσˆ begins to incur mismatched bits, which leads to a wrong information delivery. Using multiple samples in a block can reduce the variance of the represented features. According to Xxxxxxxxx inequality, we have P{|δ −E(δ)| ≥ ε} ≤ D(δ) . Block-based information delivery can efficiently reduce the variance of average δ, and then reduce the secret bit error rate. × Σ i TDS extracts the feature of block based on SVD. As afore- mentioned in Section 3.2, the block size is 10 n (typi- cally n = 6). SVD can be expressed as G = U ΣˆV T = Table 1: Experiments scenarios Index State Environment A Static Indoor C Mobile Indoor D Mobile Outdoor Test A B C D Monobit Frequency 0.611 0.757 0.900 0.784 Longest Run of Ones 0.724 0.660 0.861 0.883 FFT 0.553 0.848 0.757 0.752 Approximate Entropy 0.708 0.897 0.899 0.719 Cumulative Sums (Fwd) 0.530 0.776 0.905 0.681 Cumulative Sums (Rev) 0.787 0.749 0.955 0.919 Block Frequency 0.725 0.819 0.874 0.977 Runs 0.734 0.723 0.883 0.846 Serial 0.421 0.590 0.401 0.530 0.841 0.913 0.885 0.642 Table 2: NIST statistical test results. To pass this test, p-value must be greater than 0.01. β

Fault Tolerance. ε2 Due to the presence of noises and manufacture variations, there may be a difference of CSI measurements hi in the ith sample, denoted as δi. When δ is larger than ε, Δσˆ ∆σˆ begins to incur mismatched bits, which leads to a wrong information delivery. Using multiple samples in a block can ε2 reduce the variance of the represented features. According to Xxxxxxxxx Chebyshev inequality, we have P{|δ −E(δ)| − E(δ)| ≥ ε} ≤ D(δ) . Block-based information delivery can efficiently reduce the variance of average δ, and then reduce the secret bit error rate. × Σ i TDS extracts the feature of block based on SVD. As afore- mentioned in Section 3.2, the block size is 10 n (typi- cally n = 6). SVD can be expressed as G = U ΣˆV T = Table 1: Experiments scenarios Index State Environment A Static Indoor C Mobile Indoor D Mobile Outdoor Test A B C D Monobit Frequency 0.611 0.757 0.900 0.784 Longest Run of Ones 0.724 0.660 0.861 0.883 FFT 0.553 0.848 0.757 0.752 Approximate Entropy 0.708 0.897 0.899 0.719 Cumulative Sums (Fwd) 0.530 0.776 0.905 0.681 Cumulative Sums (Rev) 0.787 0.749 0.955 0.919 Block Frequency 0.725 0.819 0.874 0.977 Runs 0.734 0.723 0.883 0.846 Serial 0.421 0.590 0.401 0.530 0.841 0.913 0.885 0.642 Table 2: NIST statistical test results. To pass this test, p-value must be greater than 0.01. β

Fault Tolerance. ε2 Due to the presence of noises and manufacture variations, there may be a difference difference of CSI measurements hi in the ith sample, denoted as δi. When δ is larger than ε, Δσˆ begins to incur mismatched bits, which leads to a wrong information delivery. Using multiple samples in a block can reduce the variance of the represented features. According to Xxxxxxxxx Chebyshev inequality, we have P{|δ −E(δ)| − E(δ)| ≥ ε} ≤ D(δ) . Block-based information delivery can efficiently efficiently reduce the variance of average δ, and then reduce the secret bit error rate. × Σ i TDS extracts the feature of block based on SVD. As afore- mentioned in Section 3.2, the block size is 10 n (typi- cally n = 6). SVD can be expressed as G = U ΣˆV T = Table 1: Experiments scenarios Index State Environment A Static Indoor C Mobile Indoor D Mobile Outdoor Test A B C D Monobit Frequency 0.611 0.757 0.900 0.784 Longest Run of Ones 0.724 0.660 0.861 0.883 FFT 0.553 0.848 0.757 0.752 Approximate Entropy 0.708 0.897 0.899 0.719 Cumulative Sums (Fwd) 0.530 0.776 0.905 0.681 Cumulative Sums (Rev) 0.787 0.749 0.955 0.919 Block Frequency 0.725 0.819 0.874 0.977 Runs 0.734 0.723 0.883 0.846 Serial 0.421 0.590 0.401 0.530 0.841 0.913 0.885 0.642 Table 2: NIST statistical test results. To pass this test, p-value must be greater than 0.01. β

Fault Tolerance. ε2 Due to the presence of noises and manufacture variations, there may be a difference of CSI measurements hi in the ith sample, denoted as δi. When δ is larger than ε, Δσˆ ∆σˆ begins to incur mismatched bits, which leads to a wrong information delivery. Using multiple samples in a block can ε2 reduce the variance of the represented features. According to Xxxxxxxxx inequality, we have P{|δ −E(δ)| ≥ ε} ≤ D(δ) . Block-based information delivery can efficiently reduce the variance of average δ, and then reduce the secret bit error rate. × Σ i TDS extracts the feature of block based on SVD. As afore- mentioned in Section 3.2, the block size is 10 n (typi- cally n = 6). SVD can be expressed as G = U ΣˆV T = Table 1: Experiments scenarios Index State Environment A Static Indoor C Mobile Indoor D Mobile Outdoor Test A B C D Monobit Frequency 0.611 0.757 0.900 0.784 Longest Run of Ones 0.724 0.660 0.861 0.883 FFT 0.553 0.848 0.757 0.752 Approximate Entropy 0.708 0.897 0.899 0.719 Cumulative Sums (Fwd) 0.530 0.776 0.905 0.681 Cumulative Sums (Rev) 0.787 0.749 0.955 0.919 Block Frequency 0.725 0.819 0.874 0.977 Runs 0.734 0.723 0.883 0.846 Serial 0.421 0.590 0.401 0.530 0.841 0.913 0.885 0.642 Table 2: NIST statistical test results. To pass this test, p-value must be greater than 0.01. β