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.
An analysis of errors in the reconstruction of approximately sparse and nonsparse noisy signals in the discrete Fourier transform domain is considered in this letter. Signal reconstruction is performed from a reduced set of data, using compressive sensing methods and the sparsity assumption. Random sampling positions in time are considered. Reconstruction results are compared with those obtained with...
A new algorithm implemented as combination of gradient based and single iteration reconstruction algorithms for compressively sensed sparse signals is proposed in the paper. A good feature of the gradient algorithm to perform reconstruction for a wide range of applications is combined with the speed of single iteration algorithm in order to perform faster reconstruction in the cases where single iteration...
We address the problem of computing the level-crossings of an analog signal from samples measured on a uniform grid. Such a problem is important, for example, in multilevel analog-to-digital (A/D) converters. The first operation in such sampling modalities is a comparator, which gives rise to a bilevel waveform. Since bilevel signals are not bandlimited, measuring the level-crossing times exactly...
Recent results in compressed sensing show that a sparse or compressible signal can be reconstructed from a few incoherent measurements. Compressive sensing systems are not immune to noise, which is always present in practical acquisition systems. In this paper we propose robust methods for sampling and reconstructing sparse signals in the presence of impulsive noise. Analysis of the proposed methods...
The emerging theory of compressed sensing (CS) provides a universal signal detection approach for sparse signals at sub-Nyquist sampling rates. A small number of random projection measurements from the received analog signal would suffice to provide salient information for signal detection. However, the compressive measurements are not efficient at gathering signal energy. In this paper, a set of...
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.