نتایج جستجو برای: حسگری فشرده compressed sensing
تعداد نتایج: 148250 فیلتر نتایج به سال:
The advent of compressed sensing provides a new way to sample and compress signals. In this thesis, a parallel compressed sensing architecture is proposed, which samples a twodimensional reshaped multidimensional signal column by column using the same sensing matrix. Compared to architectures that sample a vector-reshaped multidimensional signal, the sampling device in the parallel compressed s...
Continuous fine-grain status monitoring of a cloud data center enables rapid response to anomalies, but handling the resulting torrent of data poses a significant challenge. As a solution, we propose CloudSense, a new switch design that performs in-network compression of status streams via compressive sensing. Using MapReduce straggler detection as an example of cloud monitoring, we give eviden...
SAR Tomography has proven to be a unique tool for the retrieval of 3D structure information from forest scenarios: it can reveal different scattering mechanisms at different heights. However, the translation of these measurements into relevant forest structure information is not straightforward and research is still ongoing. In this direction, this paper suggest a framework for the estimation o...
We introduce q-ary compressive sensing, an extension of 1-bit compressive sensing. We propose a novel sensing mechanism and a corresponding recovery procedure. The recovery properties of the proposed approach are analyzed both theoretically and empirically. Results in 1-bit compressive sensing are recovered as a special case. Our theoretical results suggest a tradeoff between the quantization p...
Compressive sensing (CS) has been widely used for the data gathering in wireless sensor networks for the purpose of reducing the communication overhead recent years. In this paper, we first show that with simple modification, 1-bit compressive sensing can also been used for the data gathering in wireless sensor networks to further reduce the communication overhead. We also propose a novel blind...
In this paper, we investigate compressed sensing principles to devise an in-situ data reduction framework for visualization of volumetric datasets. We exploit the universality of the compressed sensing framework and show that the proposed method offers a refinable data reduction approach for volumetric datasets. The accurate reconstruction is obtained from partial Fourier measurements of the or...
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