| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2012: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Outline of Final Research Achievements |
In the problem of compressed sensing, a significant algorithm called Approximate Message Passing (AMP) is widely-known, whose computational cost is small and convergence condition has been revealed theoretically. In deriving AMP, an assumption is made on the measurement matrix, which represents the process of signal measurement. In this project, we have attempted to construct generalized algorithm of AMP without such assumption, and have shown that such generalization can be achieved with the aid of formulation for the performance analysis of telecommunication system. We also investigated several topics on data sparsity: For example, we developed a sparse signal recovery algorithm of compressed sensing with less computational cost for spare measurement matrix. We also studied the sparsity-based learning algorithm for deep-learning.
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