Engineered microbiomes can hugely benefit human, plant, and animal health. However, the diversity and complexity of microbiomes hinder a full understanding, and hence, prediction, of community ...
Abstract: Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing ...
Abstract: This paper introduces a novel framework and corresponding methods for sampling and reconstruction of sparse signals in shift-invariant (SI) spaces. We reinterpret the random demodulator, a ...
Photoacoustic tomography (PAT) is a propitious imaging modality, which is helpful for biomedical study. However, fast PAT imaging and denoising is an exigent task in medical research. To address the ...
The past decade has seen the emergence of novel techniques for signal reconstruction from few measurements. This resurgence due in part to the development of compressed sensing is a fast-evolving ...
A large number of sparse signal reconstruction algorithms have been continuously proposed, but almost all greedy algorithms add a fixed number of indices to the support set in each iteration. Although ...
ABSTRACT: In digital signal processing (DSP), Nyquistrate sampling completely describes a signal by exploiting its bandlimitedness. Compressed Sensing (CS), also known as compressive sampling, is a ...
The Fast Fourier Transform (FFT) is a fundamental algorithm that computes the Discrete Fourier Transform of an n-dimensional signal in O(n log n) time. It is unknown whether the running time can be ...
Compressed sensing aims to undersample certain high-dimensional signals yet accurately reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible when the object to be ...
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