An efficient LSP parameters quantization scheme is proposed using the compressed sensing (CS). The LSP parameters extracted from consecutive speech frames are compressed by CS on the approximate KLT domain to produce a measurement vector, which is quantized using the split vector quantizer. Then, from the quantized measurements, the original LSP parameters are reconstructed by the orthogonal ma...

In this paper, compressed sensing (CS) theory is utilized in a medium-access control (MAC) scheme for wireless sensor networks (WSNs). We propose a new, cross-layer compressed sensing medium-access control (CL CS-MAC) scheme, combining the physical layer and data link layer, where the wireless transmission in physical layer is considered as a compress process of requested packets in a data link...

As a newly proposed theory, compressive sensing (CS) is commonly used in signal processing area. This paper investigates the applications of compressed sensing (CS) in wireless sensor networks (WSNs). First, the development and research status of compressed sensing technology and wireless sensor networks are described, then a detailed investigation of WSNs research based on CS are conducted fro...

To estimate the wideband or multi-channel signals’ spectrum swiftly and exactly is a key technology to improve the performance of wideband spectrum sensing. The paper was proposed a novel spectrum estimation algorithm based on compressed sensing (CS) and multitaper method (MTM), which is called CS-MTM. The new algorithm was validated by single-tone, multi-tone and QPSK signals. Meanwhile, the p...

Compressive sensing (CS) based multi-input multi-output (MIMO) radar systems that explore the sparsity of targets in the target space enable either the same localization performance as traditional methods but with significantly fewer measurements, or significantly improved performance with the same number of measurements. However, the enabling assumption, i.e., the target sparsity, diminishes i...

Based on the sparsity of scene (moving target and few scatterers on the same resolution cell), MTD and 3D imaging are investigated by means of compressed sensing (CS) for airship sparse array radar and airborne three-aperture MMW SAR. Based on the sparsity of continuous scene sparse spectrum, sidelooking 3D imaging is investigated by means of CS for airborne cross-track sparse array SAR. Some s...

The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been devel...

Introduction Compressed sensing (CS) has been shown to provide accurate reconstructions from highly undersampled data for certain types of MR acquisitions [1, 2]. This offers the promise of faster MR acquisitions, and further speed gains are possible when CS is used in conjunction with parallel acquisition schemes such as SENSE [3]. Several approaches have been recently proposed to reconstruct ...

The compressed signal in compressed sensing (CS) may be corrupted by noise during transmission. The effect of Gaussian noise can be reduced by averaging, hence a robust reconstruction method using compressed signal ensemble from one compressed signal is proposed. The compressed signal is subsampled for L times to create the ensemble of L compressed signals. Orthogonal matching pursuit with part...

The broadband spectrum contains more information than what the human eye can detect. Spectral information from different wavelengths can provide unique information about the intrinsic properties of an object. Recently compressed sensing imaging systems with low acquisition time have been introduced. To utilize compressed sensing strategies, strong reconstruction algorithms that can reconstruct ...