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Nondestructive evaluation methods are widely used in quality control processes for critical components in systems such as aircraft engines and nuclear power plants. These same methods are also used in periodic field inspection to assure that the system continues to be reliable while it is in service. This paper describes a detection algorithm to automatically analyze vibrothermography sequence-of-image inspection data used to detect cracks in jet engine fan blades. Principal components analysis is used for dimension reduction. Then the fitted coefficients of the first principal component are processed by using robust regression to produce studentized residuals that are used in the crack-detection rules. We also show how to quantify the probability of detection for the algorithm. The detection algorithm is both computationally efficient and accurate. It correctly identified several cracks in our experimental data that were not detected by the standard human visual inspection method. AbstractNondestructive evaluation methods are widely used in quality control processes for critical components in systems such as aircraft engines and nuclear power plants. These same methods are also used in periodic field inspection to assure that the system continues to be reliable while it is in service. This paper describes a detection algorithm to automatically analyze vibrothermography sequence-of-image inspection data used to detect cracks in jet engine fan blades. Principal components analysis is used for dimension reduction. Then the fitted coefficients of the first principal component are processed by using robust regression to produce studentized residuals that are used in the crack-detection rules. We also show how to quantify the probability of detection for the algorithm. The detection algorithm is both computationally efficient and accurate. It correctly identified several cracks in 1 2 our experimental data that were not detected by the standard human visual inspection method.

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Location awareness is critical for supporting location-based access control (LBAC). The challenge is how to determine locations accurately and efficiently in indoor environments. Existing solutions based on WLAN signal strength either cannot provide high accuracy, or are too complicated in general indoor environments. In this paper, we propose a statistical indoor localization method for supporting locationbased access control. In an offline phase, we fit a LOESS [3, 4, 16] local regression model on a training set to build a radio map containing the distribution of signal strength. In an online phase, we estimate locations using Maximum Likelihood Estimation (MLE) [7, 8, 9] based on the measured signal strength and the stored distribution. A Bootstrapping method [11] is further exploited to give a confidence interval of estimation. Compared with others, our method is simpler, more systematic and more accurate. Experimental results show that the average error of our method is less than 2m. Hence, it can better support LBAC applications.

The Nee1 temperatures, TN, of Tb,Er,.,NizBzC samples have been determined from the temperature dependence of magnetization measurements. A breakdown of the de Gennes scaling of TN with a clear turning point around x=0.45 has been observed. The TN values of TbXErl.,Ni2B2C do not change much within the range of O

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