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In Big Data Processing we typically face very large data sets that are highly structured. To save the computation and storage cost, it is desirable to extract the essence of the data from a reduced number of observations. One example of such a structural constraint is sparsity. If the data possesses a sparse representation in a suitable domain, it can be recovered from a small number of linear projections...
For low-cost sound monitoring of machineries, we propose a novel audio reconstruction method superior in terms of accuracy and processing time. A conventional method based on the Orthogonal Matching Pursuit (OMP) has been proposed for audio recovery. However, the conventional method has a low performance for sounds of machineries because, sounds of machineries tend to be not highly sparse, and the...
Distributed compressive sensing is a framework considering jointly sparsity within signal ensembles along with multiple measurement vectors (MMVs). The current theoretical bound of performance for MMVs, however, is derived to be the same with that for single MV (SMV) because the characteristics of signal ensembles are ignored. In this work, we introduce a new factor called "Euclidean distances...
In Compressed Sensing, a real-valued sparse vector has to be recovered from an underdetermined system of linear equations. In many applications, however, the elements of the sparse vector are drawn from a finite set. Adapted algorithms incorporating this additional knowledge are required for the discrete-valued setup. In this paper, turbo-based algorithms for both cases are elucidated and analyzed...
Compressed sensing is a signal acquisition scheme that measures signals at sub-Nyquist rate amenable to sparse recovery, with high probability, from a reduced set of measurements. One of the main requirements of compressive sensing is the sparsity of the class of signals of interest in some basis. A method to construct a sparsifying basis for a class of signals using information theoretic measures...
A new 3-D microwave imaging technique, based on compressive sensing, is proposed for use with sparse antenna arrays. It was designed to enable cost-effective 3-D imaging and tracking of people in an indoor environment. This algorithm is able to image both sparse and cluttered environments, through the use of wavelet transforms and compressive sensing techniques. The main advantage of the proposed...
We introduce a new signal sampling scheme which allows high quality signal conversion to overcome the constraint of effective number of bits in high speed signal acquisition. The proposed scheme is based on the popular successive approximation register (SAR) and employs compressive sensing technique to increase the resolution of a SAR analog-to-digital converter (ADC) architecture. We present signal...
For a wideband radar system adopting stepped frequency signal (SFS), the micro-motion parameter is usually obtained by time-frequency analysis of the echo HRRPs (high resolution profiles). The data to be collected mainly includes each sub-frequency echo in the slow-time domain, which brings a great burden to the signal generation equipment and data storage on the radar system. Because of the sparseness...
This paper deals with the sub-Nyquist sampling of analog multiband signals. The Modulated Wideband Converter (MWC) is a promising compressive sensing architecture, foreseen to be able to break the usual compromise between bandwidth, noise figure and energy consumption of Analog-to-Digital Converters. The pseudorandom code sequences yielding the sensing matrix are yet the bottleneck of it. Our contributions...
In this paper, we consider a practical signal transmission application with fixed power budget such as radar/sonar. The system is modeled by a linear equation with the assumption that the signal energy per measurement decreases linearly and the noise energy per measurement increases approximately linearly with the increasing of the number of measurements. Thus the SNR decreases quadratically with...
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