Browsing by Subject "Map"

Now showing 1 - 5 of 5
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    A fever called living
    (2019-05-08) Ku, Ling-lin; Hauft, Amy, 1957-; McMaster, R. Eric
    A Fever Called Living is a floor-based installation that bridges fantasy and reality by mining digital features to create physical objects. The works hint at my past while reflecting my research interests in Animism and the uncanny, language play, fetishism, and display. I find that working back and forth between my mind and hands, the virtual and the physical, is like wandering without specific destination, like abandoning the sidewalk for the open field
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    Automatic semiconductor wafer map defect signature detection using a neural network classifier
    (2010-12) Radhamohan, Ranjan Subbaraya; Ghosh, Joydeep; El-Hamdi, Mohamed
    The application of popular image processing and classification algorithms, including agglomerative clustering and neural networks, is explored for the purpose of grouping semiconductor wafer defect map patterns. Challenges such as overlapping pattern separation, wafer rotation, and false data removal are examined and solutions proposed. After grouping, wafer processing history is used to automatically determine the most likely source of the issue. Results are provided that indicate these methods hold promise for wafer analysis applications.
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    Fusion-based Hadoop MapReduce job for fault tolerance in distributed systems
    (2013-05) Ho, Iat-Kei; Garg, Vijay K. (Vijay Kumar), 1963-
    Standard recovery solution on a failed task in Hadoop systems is to execute the task again. After retrying for a configured number of times, it is marked as failure. With significant amount of data, complicated Map and Reduce functions, recovering corrupted or unfinished data from a failed job can be more efficient than re-executing the same job. This paper is an extension of [1] by applying fusion-based technique [7][8] in Hadoop MapReduce tasks execution to enhance its fault tolerance. Multiple data sets are executed through Hadoop MapReduce with and without fusion in various pre-defined failure scenarios for comparison. As the complexity of the Map and Reduce function relative to the Recover function increases, it becomes more efficient to utilize fusion and users can tolerate faults by incurring less than ten percent of extra execution time.
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    StarMapper : an android-based application to map celestial objects
    (2013-12) O'Donnell, John Jason; Aziz, Adnan
    This report describes StarMapper, a mobile appliation designed for the Android platform that interactively maps the celestial sky and can provide information from Wikipedia about celestial objects to the user. The stars, constellations, planets, sun, and moon are all rendered in real-time and the user can navigate the celestial map simply by pointing the device around the sky to find and identify the different celestial objects. However, if the user prefers, a manual touch-based map navigation feature is also available in StarMapper. While other Android applications currently exist for mapping the sky, such as Google's Sky Map, StarMapper aims to enhance the experience by also providing additional information about celestial objects to the user by means of a simple click on the screen. For obtaining more information about a particular constellation or other celestial object, the user only needs to click on the object's name in the map, and the device's web browser opens to the Wikipedia page of the clicked object. Through this simple mechanism, the user can learn much more about astronomy than just locations of celestial objects.