The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we study a support set reconstruction problem for multiple measurement vectors (MMV) with different sensing matrices, where the signals of interest are assumed to be jointly sparse and each signal is sampled by its own sensing matrix in the presence of noise. Using mathematical tools, we develop upper and lower bounds of the failure probability of the support set reconstruction in terms...
In this paper, we study mixing sequences of modulated wideband converters (MWC). The MWC is a sub-Nyquist sampling system which mixes an input analog signal by multiple numbers of fast mixing sequences in parallel. When the mixing sequences are random cyclic shifts of a base sequence for a memory efficiency, the system turns into random partial Fourier structured MWC (RPFMWC). In RPFMWC, the spectrum...
Compressed sensing is a novel technique where one can recover sparse signals from the undersampled measurements. In this paper, a new class of partial Fourier matrices is studied for deterministic compressed sensing. A basic partial Fourier matrix is constructed by choosing the rows deterministically from the inverse discrete Fourier transform (DFT) matrix. By a column rearrangement, the matrix is...
Compressed sensing is a novel technique where one can recover sparse signals from the undersampled measurements. In this paper, a K × N measurement matrix for compressed sensing is deterministically constructed via additive character sequences. The Weil bound is then used to show that the matrix has asymptotically optimal coherence for N = K2, and that it is a tight frame. A sparse recovery guarantee...
Compressed sensing is a novel technique where one can recover sparse signals from the undersampled measurements. In this paper, a K×N measurement matrix for compressed sensing is deterministically constructed via multiplicative character sequences. Precisely, a constant multiple of a cyclic shift of an M-ary power residue or Sidelnikov sequence is arranged as a column vector of the matrix, through...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.