Abstract: A context aware mobile personalization system is disclosed for a software development environment with plug-in capabilities for providing personalized phone capabilities based on the automated detection of user context.

Abstract: The Multi-Device Continuum and Seamless Sensing Platform for Context Aware Analytics provide a platform for continuous sensing across multiple devices towards a unified target. The Multi-Device Continuum and Seamless Sensing Platform provides a platform for extracting, loading, integrating, and tracking related data across multiple smart devices capable of integrating with internal and external sensors, such as wearable devices. The Multi-Device Continuum and Seamless Sensing Platform develop context aware solutions, which are targeted at automated recognition of context extracted from users' devices, as people are often always interacting with a digital device such as phone, tablet, or desktop.

Abstract: The Multi-Device Continuum and Seamless Sensing Platform for Context Aware Analytics provide a platform for continuous sensing across multiple devices towards a unified target. The Multi-Device Continuum and Seamless Sensing Platform provides a platform for extracting, loading, integrating, and tracking related data across multiple smart devices capable of integrating with internal and external sensors, such as wearable devices. The Multi-Device Continuum and Seamless Sensing Platform develop context aware solutions, which are targeted at automated recognition of context extracted from users' devices, as people are often always interacting with a digital device such as phone, tablet, or desktop.

Abstract: A context aware mobile personalization system is disclosed for a software development environment with plug-in capabilities for providing personalized phone capabilities based on the automated detection of user context.

Abstract: In one embodiment, a method to analyze a semiconductor wafer comprises extracting inline defect data from a data source, counting a total number of inline defects and end-of-line defects, terminating the analysis when the total number of inline defects and end-of-line defects exceeds a threshold, and mapping the inline defects onto the end-of-line defects when the total number of inline defects and end-of-line defects is less than a threshold.

Abstract: Systems and methods associated with automated semiconductor fabrication device yield analysis are described. One embodiment includes a computing system that includes a working file storage system that stores files corresponding to semiconductor fabrication test devices. The working file storage system may include working directories and executable flows corresponding to working directories. The computing system also includes a data controller that may select a working directory, transfer an input file to the selected working directory, and execute a flow to process an input file and to produce an output file. The output file may include a yield analysis based on data provided by a semiconductor fabrication test device.

Abstract: In one embodiment, a method to analyze a semiconductor wafer comprises extracting inline defect data from a data source, counting a total number of inline defects and end-of-line defects, terminating the analysis when the total number of inline defects and end-of-line defects exceeds a threshold, and mapping the inline defects onto the end-of-line defects when the total number of inline defects and end-of-line defects is less than a threshold.

Abstract: An engineering analysis system may provide an engineering analysis framework that operates both with an engineering analysis application and the databases that include the data to be analyzed. The framework includes a plurality of components which may be used in different combinations in different situations.