Fault Tolerance. The Software Products are not fault-tolerant and are not designed, manufactured or intended for use with on-line control equipment in hazardous environments requiring fail-safe performance (e.g., the operation of nuclear facilities, aircraft navigation or communication systems, air traffic control, direct life support machines, or weapons systems environments), in which the failure of the Software Products could lead directly to death, personal injury, or severe physical, property or environmental damage ("High Risk Activities"). MICROSOFT AND ITS SUPPLIERS SPECIFICALLY DISCLAIM ANY EXPRESS OR IMPLIED WARRANTY OF FITNESS FOR HIGH RISK ACTIVITIES.
Fault Tolerance. In the course of group key agreement processes, malicious participants should be re- moved from the group without affecting the operations of remaining honest participants. In B-GKAP, detection and elimination of faulty participants occurs the during execution of veri f yPublicKeys(·), veri f ySecretKeys(·) and f aultCorrection(·) functions. If a malicious participant is detected in veri f yPublicKeys(·) or veri f ySecretKeys(·) functions, related key of the participant is not written to the ledger. Therefore, f aultCorrection(·) detects the miss- ing key, and removes related participant from the group.
Fault Tolerance. Following is a list of fault tolerant capabilities of the System. It is important to keep in mind that although this discussion treats a board as a single point failure, typically this is not accurate. Many of the boards contain identical functional blocks such as T1/E1 interfaces or voice transcoding circuits. In such instances, a board may be capable of functioning with some but not all of its capability. In other instances, performance of a board may degrade without an apparent failure. Critical circuitry is monitored as practical, and equipment alarms are generated so that a board may be replaced before an interruption of service occurs.
Fault Tolerance. The Software is not one hundred percent (100%) fault tolerant. Unless the Software’s Documentation expressly provides the contrary, the Software is not designed or intended for use in any situation where failure or fault of any kind of the Software could lead to death or serious bodily injury of any person, or to severe physical, property or environmental damage (“High-Risk Use”); and, User is not licensed to use the Software in, or in conjunction with, any High-Risk Use. High-Risk Use is STRICTLY PROHIBITED. High Risk Use includes, for example, the following: operation of aircraft or other modes of human mass transportation, nuclear or chemical facilities, and Class III medical devices. User hereby agrees not to use the Software in, or in connection with, any High-Risk Use. High Risk Use shall not mean use of the Software for purposes for which it is regularly marketed and sold (e.g., public safety and utility dispatch software may be used to dispatch police, fire, emergency medical services, and emergency utility services).
Fault Tolerance. Backup and replication strategies are designed to ensure redundancy and failover protections during a significant processing failure. Customer data is backed up to multiple durable data stores and replicated across multiple availability zones.
Fault Tolerance. In the course of group key agreement processes, malicious partici- pants should be immediately detected and removed from the group. In B-GKAP, detec- tion and elimination of faulty participants occurs during the execution of verifyPK( ), verifySK( ) and faultCorr( ) functions. If a malicious participant is detected in verifyPK( ) or verifySK( ) functions, the key of the malicious participant is not written to the ledger. Later, the faultCorr( ) function is used for removing this par- ticipant from the group. ·
Fault Tolerance. Cloud environment uses the various fault tolerance strategies and counter measures to deal with these issues. Fault tolerance is the property of a system which enables it to continue operating in proper manner in the occurrence of the failure of some of systems components. In a life-critical system, the existence of a fault-tolerant control system is really important. One of its important functions is to steer the procedure to a safe state whenever unwanted events like faults occur. To achieve this role availability, the reliably of the fault-tolerant control system has to be high. To attain a high degree of availability alongside random failures, one has to recourse to redundancy. Moreover, to avoid common failures, there are distinct necessities on the redundancy, such as independence, reliability, diversity and separation.
Fault Tolerance. 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, ∆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 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 = 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.
i=1 σˆiUiV T , where σˆ is the singular value of G, and Ui, Σ Vi are the ith column vectors of U and V , respectively. The power of noise is PN = β (σw)2, where σw is the ith sin- Σby gular value of noise matrix. TDS uses the second or third singular values σˆ2 and σˆ3 to represent the signal features and discards the singular value smaller than σˆ4 which are mainly relevant to noises. Therefore, the noise is decreased (σw)2 through SVD.
Fault Tolerance. At user level Mserve requires users confirmation for critical operations, e.g. deleting file.
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 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 = Index State Environment A Static Indoor C Mobile Indoor D Mobile Outdoor 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.
i=1 σˆiUiV T , where σˆ is the singular value of G, and Ui, Σ Vi are the ith column vectors of U and V , respectively. The power of noise is PN = β (σw)2, where σw is the ith sin- Σby gular value of noise matrix. TDS uses the second or third singular values σˆ2 and σˆ3 to represent the signal features and discards the singular value smaller than σˆ4 which are mainly relevant to noises. Therefore, the noise is decreased (σw)2 through SVD.
