Consider a coherent system S hat has n nodes and a structure function /spl phi/. The state of S is determined by the "states of the components allocated at the n nodes" and /spl phi/. If the reliabilities of these it components are expressed by an n-dimensional vector p, then the reliability of S is a function of p. Using the concepts of node-criticality introduced by Boland et al. (1989), the main theorem in this paper proposes a unified approach by which the reliabilities of S corresponding to 2 different values of p can be compared. The conditions in the main theorem are both necessary and sufficient for k-out-of-n systems. Application of this theorem to various situations yields a unified approach for obtaining previous results in the literature. Also, the application of this theorem immediately extends the results established for k-out-of-n systems to coherent systems.

Checkpointing is an effective fault-tolerant technique for improving system availability and reliability. However, a blind checkpointing placement can result in either performance degradation or expensive recovery cost. By means of the calculus of variations, we derive an explicit formula that links the optimal checkpointing frequency with a general failure rate, with the objective of globally minimizing the total expected cost of checkpointing and recovery. Theoretical result shows that the optimal checkpointing frequency is proportional to the square root of the failure rate and can be uniquely determined by the failure rate (time-varying or constant) if the recovery function is strictly increasing and the failure rate is /spl lambda/(/spl infin/)>0. J.L. Bruno and E.G. Coffman (1997) suggest that optimal checkpointing by its nature is a function of system failure rate, i.e., the time-varying failure rate demands time-varying checkpointing in order to meet the criteria of certain optimality. The results obtained in this paper agree with their viewpoint.

When the available data from an exponential distribution are grouped, the maximum likelihood estimator (MLE) for the mean and several modified MLE have been discussed in literature. However, little work has been done on interval estimators based on such grouped data. This paper derives the asymptotic property of a statistic which is used to construct an approximate confidence interval for the mean. The width of this approximate confidence interval, based on grouped data, is compared with those based on complete samples, and samples with type-I and type-II censoring. The limits of the ratios of these widths are derived when the sample size approaches infinity. The approximate confidence interval from grouped data is wider than those from complete and censored samples. However, Monte Carlo simulation indicates that the proposed method based on grouped data is adequate, considering the restricted information in this case.

A synchronously averaged signal, acquired in real time using an A/D converter, a package of service routines and a computer, can be corrupted if precautions are not taken to minimize imprecise timing (jitter). The effects of jitter on the recovery of a signal from noise by averaging is discussed. Two cases are examined. In the first case, the jitter is the result of frequency mismatch between the acquisition repetition frequency and the signal frequency. In the second case, the jitter is due to a random phase shift present in each of the acquired waveforms. Both cases result in a reduction in magnitude and a variation in latency. A detailed analysis is given deriving several statistical descriptors. It is established that these descriptors are closely related to the characteristic function of jitter. Special cases where jitter obeys the Gaussian and uniform distributions are examined. A simple technique for determining the mean and standard deviation of the jitter is also given by example.>