Gauges used for dimension inspection of critical components in various assemblies need to be precise and accurate. At the same time, the rate of inspection should be as high as possible for increased production. This is more so with regard to steering assemblies of automobiles. In automotive lines, the dimension inspection of the steering assemblies (constant velocity assembly) by Coordinate Measuring Machine would not be feasible as the process is slow and productivity would be affected. The proposed constant velocity gauging system can be considered as an alternative. The constant velocity assembly has six opening windows whose height is extremely critical. The accuracy of the component and the criticality demand complete inspection of the window height and identification of oversized window parts. The proposed cost-effective gauging system has a separate replaceable probe for each window to check the window height. All six windows are checked simultaneously to save production time. The qualified components and rejected components have been checked by first principles and on Coordinate Measuring Machine. Both have been found to be in good agreement.
Pervasive mobile devices have enabled countless context-and location-based applications that facilitate navigation, life-logging, and more. As we build the next generation of smart cities, it is important to leverage the rich sensing modalities that these numerous devices have to offer. This work demonstrates how mobile devices can be used to accurately track driving patterns based solely on pressure data collected from the device’s barometer. Specifically, by correlating pressure time-series data against topographic elevation data and road maps for a given region, a centralized computer can estimate the likely paths through which individual users have driven, providing an exceptionally low-power method for measuring driving patterns of a given individual or for analyzing group behavior across multiple users. This work also brings to bear a more nefarious side effect of pressure-based path estimation: a mobile application can, without consent and without notifying the user, use pressure data to accurately detect an individual’s driving behavior, compromising both user privacy and security. We further analyze the ability to predict driving trajectories in terms of the variance in barometer pressure and geographical elevation, demonstrating cases in which more than 80% of paths can be accurately predicted.
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