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.
Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing (CS) in many applications such as Radar imaging and sparse channel estimation. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted...
Compressive sensing (CS) has been viewed as a promising technology to greatly improve the communication efficiency of data gathering in wireless sensor networks. However, this new data collection paradigm may bring in new threats but few study has paid attention to prevent information leakage during compressive data gathering. In this paper, we identify two statistical inference attacks and demonstrate...
Recent results in compressed sensing have shown that a wide variety of structured signals can be recovered from undersampled and noisy linear observations. In this paper, we show that many of these signal structures can be modeled using an union of affine subspaces, and that the fundamental number of observations needed for stable recovery is given by the number of “free” values, i.e. the dimension...
There is a recent interest in developing algorithms for the reconstruction of jointly sparse signals, which arises in a large number of applications. In this work, we study the problem of wide-band spectrum sensing for cognitive radio networks using compressed sensing to exploit the underlying joint sparsity structure in a distributed setting. In particular, we use the recently proposed Approximate...
In this paper, we introduce a 1-bit compressive sensing reconstruction algorithm that is not only robust against bit flips in the binary measurement vector, but also does not require a priori knowledge of the sparsity level of the signal to be reconstructed. Through numerical experiments, we show that our algorithm outperforms state-of-the-art reconstruction algorithms for the 1-bit compressive sensing...
Minimum mean square error (MMSE) estimation of block sparse signals from noisy linear measurements is considered. Unlike in the standard compressive sensing setup where the non-zero entries of the signal are independently and uniformly distributed across the vector of interest, the information bearing components appear here in large mutually dependent clusters. Using the replica method from statistical...
A multiplicative Gaussian wire-tap channel inspired by compressed sensing is studied. Lower and upper bounds on the secrecy capacity are derived, and shown to be relatively tight in the large system limit for a large class of compressed sensing matrices. Surprisingly, it is shown that the secrecy capacity of this channel is nearly equal to the capacity without any secrecy constraint provided that...
The utility of Compressed Sensing (CS) for demosaicing of images captured using random panchromatic color filter arrays (CFA) has been investigated in [1]. Meanwhile, most camera manufacturers employ periodic CFAs such as the popular Bayer CFA. In this paper, we derive a CS-based solution to demosaicing images captured using the general class of periodic CFAs. It is well known that periodic CFAs can...
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.