GaAs MMICs (Monolithic Microwave Integrated Circuits) reliability is a critical part of the overall reliability of the thermal solution in semiconductor devices. With MMICs reliability improved, GaAs MMICs failure rates will reach levels which are impractical to measure with conventional methods in the near future. This letter proposes a methodology to predict the GaAs MMICs reliability by combining empirical and statistical methods based on zero-failure GaAs MMICs life testing data. Besides, we investigate the effect of accelerated factors on MMICs degradation and make a comparison between the Weibull and lognormal distributions. The method has been used in the reliability evaluation of GaAs MMICs successfully.

Cloud computing is a unlimited computing resource and storing resource, which provides a lot of convenient services, for example, Internet and education, intelligent transportation system. With the rapid development of cloud computing, more and more people pay attention to reducing the cost of data management. Data sharing is a effective model to decrease the cost of individuals or companies in dealing with data. However, the existing data sharing scheme cannot reduce communication cost under ensuring the security of users. In this paper, an anonymous and traceable data sharing scheme is presented. The proposed scheme can protect the privacy of the user. In addition, the proposed scheme also can trace the user uploading irrelevant information. Security and performance analyses show that the data sharing scheme is secure and effective.

The dynamic time warping (DTW) algorithm is widely used to determine time series similarity search. As DTW has quadratic time complexity, the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. In this paper, we present a parallel approach for DTW on a heterogeneous platform with a graphics processing unit (GPU). In order to exploit fine-grained data-level parallelism, we propose a specific parallel decomposition in DTW. Furthermore, we introduce an optimization technique called diamond tiling to improve the utilization of threads. Results show that our approach substantially reduces computational time.