Browse Topic: Cloud computing
A hybrid navigation system [1] that performs route calculations and highly flexible natural speech location searches in the cloud using dynamic databases that combine probe data collected from the vehicle and external data, and transmits to on-board devices has been developed. The system automatically switches to the on-board device when the vehicle is out of mobile network communication range or when faster processing is required for tasks such as re-routing. The transition between the on-board devices and the cloud provide a seamless user experience adapted to use conditions and other factors. In addition, representing the route downloaded from the cloud by the on-board device requires synchronizing the map with the cloud, and a map caching function has been used to reduce the volume of data that needs to be synchronized. The cloud-based route calculation is based not only on average travel time, but on dispersion as well. Moreover, integrating entry and exit link direction data
An integrated ground support equipment (GSE) tracking and management tool is designed for tracking and managing GSE data used in support of KSC/Ground Systems Development and Operations (GSDO) planning and launch campaigns. This software (the Ground Hardware Management Tool, GHMT) will be fully integrated with the Ground Operations Planning Database (GOPDb) to provide a complete ground operations planning solution.
With the standardization of 4G wireless, the increase in cloud storage and computing, and the push for faster network data rates, the highest quality passive interconnect systems must be used. While the robustness and size of these interconnections, fiber types, and cable management all play major roles in the backbone, what happens at the tip of the connector also greatly affects the optical performance of the system.
In a crisis, up-to-date information is one of the most important commodities for decision-makers. Remote sensing data have been instrumental in regional scale damage detection and recovery progress monitoring after significant disasters. However, using remotely sensed data to support an emergency response requires not only the availability of hardware, software, and manpower to process and analyze the data, but also the time to stage the datasets that are required for analyses. Additionally, the volume of remote sensing data that needs to be processed to detect temporal changes accurately in a terrestrial or oceanic ecosystem can easily exceed several terabytes, even for a small region. This is because emergency response requires the use of well-calibrated remotely sensed data products, like those that are generated by the MODIS (Moderate Resolution Imaging Spectroradiometer) Adaptive Processing System (MODAPS). These data sets are stored and distributed by the Level 1 and Atmosphere
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