Compressed sensing and low rank matrix recovery

Compressed sensing and low rank matrix recovery Master Degree

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Tel : +8615850513534

E-mail : apply@acasc.cn

  • Application Deadline:2018/06/12
  • Tuition:¥0.00
  • Application Fee:¥800.00
  • Service Fee:¥0.00
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1. Click “Apply Now” button at the top of the page.

2. Fill in online application form.

3. Upload required documents.

4. Pay the application fee and the ACASC service fee

5. Click “Submit” button.

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Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem. There are two conditions under which recovery is possible. The first one is sparsity which requires the signal to be sparse in some domain. The second one is incoherence which is applied through the isometric property which is sufficient for sparse signals.


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