Burn-In: an Engineering Approach to the Design and Analysis of Burn-In Procedures

We describe a digital signature scheme in which the public key is fixed but the secret signing key is updated at regular intervals so as to provide a forward security property: compromise of the current secret key does not enable an adversary to forge signatures pertaining to the past. This can be useful to mitigate the damage caused by key exposure without requiring distribution of keys. Our construction uses ideas from the Fiat-Shamir and Ong-Schnorr identification and signature schemes, and is proven to be forward secure based on the hardness of factoring, in the random oracle model. The construction is also quite efficient.

A forward-secure digital signature scheme.

For resisting the adaptive chosen message attack and improving the sign generation efficiency, a signature scheme based on the strong RSA assumption is described in this paper. The scheme uses a fixed base rather than by raising them to a fixed power, which is different from the RSA algorithm. Moreover, one can use pre-computation techniques in order to get a better efficiency. In addition, a hash function can be incorporated into the scheme in such a way that it offers a trapdoor to the sign algorithm. The merits of this amendatory scheme are that if one makes a distinction between the 搊ff line?and the 搊n line?cost of signing, the signer can reduce 搊n line?cost significantly by using hash trapdoor. It is proved that the scheme is secure against the adaptive chosen message attack under the strong RSA assumption. The experimental results show that the scheme has high efficiency.

A Signature Scheme Based on the Strong RSA Assumption

We get the maximum likelihood estimator and inverse moment estimator of parameters of Gompertz distribution based on tampered failure rate model under simple step stress accelerated life testing.Afterwards we show the feasibility of our method through simulation examples.Finally,the interval estimators of parameters are obtained as well in the present paper.

The statistical analysis of Gompertz distribution based on tampered failure rate model under simple step stress accelerated life testing

Previous studies showed that two components of survival mixture model performed better than pure classical parametric survival model. However there are crucial needs for three components of survival mixture model due to the behaviour of heterogeneous survival data which commonly comprises of more than two distributions. Therefore in this study two models of three components of survival mixture model were developed. Model 1 is three components of parametric survival mixture model of Gamma distributions and Model 2 is three components of
parametric survival mixture model of Exponential, Gamma and Weibull distributions. Both models were estimated using the Expectation Maximization (EM)
and validated via simulation and empirical studies. The simulation was repeated 300 times by incorporating three different sample sizes: 100, 200, 500; three different censoring percentages: 10%, 20%, 40%; and two different sets of mixing
probabilities: ascending (10%, 40%, 50%) and descending (50%, 30%, 20%). Several sets of real data were used in the empirical study and models comparisons were implemented. Model 1 was compared with pure classical parametric survival model, two and four components parametric survival mixture models of Gamma distribution, respectively. Model 2 was compared with pure classical parametric
survival models and three components parametric survival mixture models of the same distribution. Graphical presentations, log likelihood (LL), Akaike Information Criterion (AIC), Mean Square Error (MSE) and Root Mean Square Error (RMSE)
were used to evaluate the performance. Simulation findings revealed that both models performed well at large sample size, small percentage of censoring and ascending mixing probabilities. Both models also produced smaller errors compared to other type of survival models in the empirical study. These indicate that both of the developed models are more accurate and provide better option to analyse
heterogeneous survival data.

Parametric mixture model of three components for modelling heterogeneos survival data

Multivariate Mixed Weibull Distributions play an important role in life time data analysis. In this paper, by using the new algorithm, the maximize likelihood estimation is obtained for the multivariate mixed Weibull model under the normal stress life time test with complete sample data or censored samples. The iteration formulae are given in the paper. Simulation results show the performance of estimation.

A new algorithm for parameters estimations of Multivariate Mixed Weibull Distributions with censoring data

In this paper, A new threshold secret sharing scheme is proposed. Based on one-dimensional memory cellular automata, a big secret is decomposed to some sub-secrets using the Chinese remainder theorem. These sub-secrets in the form of binary texts are considered as one of the k initial configurations of the memory cellular automata of k order and then secret shares are evolved. The function of backward iteration of the cellular automata is used to recover these sub-secrets. Moreover, we can reconstruct the shared secret. Analyses show that the proposed scheme can be constructed quickly. It can be applied easily and computed securely.

A Threshold Secret Sharing Scheme Based on LMCA and Chinese Remainder Theorem

The burn-in process is operated under severe (stress) conditions involving high temperature, voltage, etc. and the product's residual life depends on the burn-in stress level and the length of burn-in period. Accelerated burn-in before shipment will reject poor-quality products and improve product reliability within a warranty period. Accelerated burn-in saves time but may cost more. In this paper, our goal is to find the appropriate testing parameters to minimize the total of testing, manufacturing, quality and reliability costs. The upper and lower bounds for the optimal burn-in time are derived. It is assumed in our work that the failure pattern follows Gamma distribution and the burn-in process is operated under approximately the same environment as that of the early operating life of the product.

Cost optimization of Gamma distribution accelerated burn-in

In this paper, we described assumptions, the standardization, and other generalizations that we used to develop SDS-ALT plans with bivariates. And a method is provided for finding a non-degenerate optimal bivariate SDS-AL T scheme. The important technical steps for finding optimum (or compromise) bivariate SDS-ALT scheme are described. We obtained a two-factor step-down-stress accelerated life test of a two-factor non-interactive model and can decompose the optimal solution of degradation into a non-degraded optimal (or compromise) test scheme.

Planning Step-down-Stress Accelerated Life Tests with Two Experimental Variables

Mixed exponential Weibull distribution is an important statistical model in life data analysis. It is difficult to estimate the parameters of this model due to its large number of parameters using traditional moment estimation and maximum likelihood estimation. Using EM algorithm and ECM algorithm to estimate model parameters and convergence is better in large sample. In this paper, a mixed exponential Weibull distribution parameter estimation model is established under the condition of small samples. The probability graph estimation method and L-M algorithm are used to solve the optimization problem. The convergence is analyzed theoretically. Finally, the validity and reliability of the algorithm are verified by data simulation.

Parameter Optimization Estimation Based on Mixed Exponential Weibull Distribution

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