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Compressive sampling
Compressive sampling sparsity uniform uncertainty principle underdertermined systems of linear equations
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2015/6/17
Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to...
Sparsity and Incoherence in Compressive Sampling
`1-minimization basis pursuit restricted orthonormality sparsity singular values of random matrices wavelets discrete Fourier transform
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2015/6/17
We consider the problem of reconstructing a sparse signal x0 ∈ Rn from a limited number of linear measurements. Given m randomly selected samples of Ux0, where U is an orthonormal matrix, we show that...
“People Hearing Without Listening”:An Introduction To Compressive Sampling
People Hearing Without Listening Compressive Sampling
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2015/6/17
The conventional approach to sampling signals or images follows the celebrated Shannon sampling theorem: the sampling rate must be at least twice the maximum frequency present in the signal (the so-ca...
Reconstruction of Fractional Brownian Motion Signals From Its Sparse Samples Based on Compressive Sampling
Compressive Sampling fractional Brownian motion interpolation financial time-series fractal
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2011/6/21
This paper proposes a new fBm (fractional Brownian
motion) interpolation/reconstruction method from partially
known samples based on CS (Compressive Sampling). Since 1/f
property implies power law ...