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Compressive Sampling: The Future of Efficiency and Signal Processing

Mueller, Peter
2009-04-08
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Compressive Sampling: The Future of Efficiency and Signal Processing

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2009-04-08

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The majority of data stored in a computer or in a signal are place-holding zeroes. Compressive sampling enables us to reduce the memory a set amount of data takes up until we need to use it again. Additionally, the limiting factor most people are worried about is time. Compressive sampling allows the sending of signals and data with less time and effort. The process is based on probability and the principles of random number generators which is built in to most computers. This makes the process of compressive sampling widely available.

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Additional contributors: Emmanuel Candès; Stephen Boyd; Michael Grant; Yinyu Ye; Ronald DeVore; Terrence Tao; Willard Miller (faculty mentor)

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UMTC Undergraduate Research Presentations and Papers (UROP)

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Mueller, Peter. (2009). Compressive Sampling: The Future of Efficiency and Signal Processing. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/59951.

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