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By using Relevance Vector Machine (RVM) to solve the problem of sparse signal recovery, Bayesian Compressive Sensing (BCS) can obtain good performance in spectral discrete spike signal detection. However, in cognitive radio (CR) system, the spectrum of primary user's signal, which is continuous in narrowband and is block sparse in wideband, cannot be exactly recovered by BCS. In this paper, a Bayesian...
Reconstruction of the sparse signals, performed by two algorithms which belongs to the class of convex optimization algorithms, is considered in this paper. Widely used algorithm implemented by the l1-magic code packet is used as the first algorithm. Its realization is based on the primal dual interior point algorithm for convex optimization. The second considered algorithm also belongs to the convex...
Time domain synchronous OFDM (TDS-OFDM) has higher spectrum efficiency than standard cyclic prefix OFDM (OFDM) by replacing CP with a known training sequence as the guard interval of OFDM data block, but severe mutual interferences will be caused in multipath channels. Recent studies have shown that the theory of compressive sensing (CS) can be efficiently applied to achieve reliable channel estimation...
The algorithm for separation of signals from two different wireless standards (Bluetooth and IEEE 802.11b standard), operating within the same frequency band is proposed in this paper. The components separation is performed using the time-frequency representation and the concept of Compressive Sensing. Knowing the signals nature, it is possible to select just a small set of time-frequency points that...
The analysis of logo embedding based watermarking procedure in the presence of compressive sensing scenario is examined in this paper. The compressive sensed image is represented by a set of available coefficients, which are used for logo embedding using the image bit-planes modification. After the logo embedding and detection procedure are defined for the available CS measurements, the new type of...
An adaptive gradient based algorithm for signal reconstruction from a reduced set of samples is considered in the paper. An extension to complex-valued signals is proposed. It has been assumed that the signals are sparse in a transformation domain. The proposed algorithm is based on the previously published algorithm suitable for real-valued signals only. The algorithm is based on the steepest descent...
In large scale wireless sensor network (WSN) energy reservation is crucial, as in such an environment sensors cannot be periodically maintain. Therefore we investigate the opportunity to reduce the power consumption by reducing the data rate traffic of the network. This is done utilizing either data correlation and sparsity in one dimension or the spatial sparsity among clustered sensor nodes. We...
Compressive Sensing (CS) is a newly introduced signal processing technique that enables to recover sparse signals from fewer samples than the Shannon sampling theorem would typically require. It is based on the assumption that, for a sparse signal, a small collection of linear measurements contains enough information to allow its reconstruction. Combining the acquisition and compression stages, CS...
This contribution deals with the direction-of-arrival estimation of narrowband signals in near-field scenarios using compressed sensing strategies. The considerations relate to a single snapshot of the signal impinging on a sensor array. For the estimation a near-field formulation of the array manifold vector is used. This approach also allows to draw conclusion on the distance between the array and...
Surface ElectroMyoGraphy (sEMG) is a fundamental tool in medicine, rehabilitation, and prostethics but also made appearance on the consumer world with devices such as the Thalmic lab's MYO. Current state of the art transfers the whole sEMG signal but encounter problems when this signal has to be transferred wirelessly in real-time. To overcome limitations of the current state of the art we propose...
As Compressed Sensing (CS) emerges as an innovative approach for analog-to-information conversion, more realistic models for studying and coping with the non-idealities of real circuits are required. In this paper we consider the effect of the voltage drop due to leakage currents in the random modulation pre-integration approach, which is the most common CS architecture. In particular we focus on...
In the compressive sampling theory, a small number of random linear projections of a sparse or compressible signal have contained sufficient information and the original signal can be accurately reconstructed by taking advantage of modern optimization algorithms. We proposed an approximate l0 norm based signal reconstruction algorithm in this paper. It not only can convert the classical constrained...
The paper studies radar waveform and receiver filter design for the detection of multiple extended targets using a compressed sensing approach. A multiple-input multiple output (MIMO) radar system is considered and threshold based detection is used to indicate the presence or absence of each target sought in the presence of noise. The detection performance is illustrated with numerical results obtained...
Motion prediction algorithms are often used in dynamic magnetic resonance imaging to improve the compressed sensing based reconstruction. Previously, the difference calculation (DC) between the current frame (to be reconstructed) and the estimated frame was used as sparse residual signals. In order to obtain sparser signal, an improved Motion Estimation (ME) and Motion Compensation (MC) method was...
In recent years, with the theory of compressed sensing being proposed and applied widely, the sparse representation method has become one of the hotspots to handle the superresolution problem. Usually, this kind of algorithms use only one dictionary pair for all low-resolution patches, which makes the recovered results less satisfied due to its bad adaptability. To overcome such problem, in this paper,...
Compressed sensing (CS) explores sparsity nature of a given signal in a given domain and allows the entire signal to be determined from only a few measurements than that required by the Nyquist sampling rate. This advantage of CS framework is utilized in the design of sparse signal detectors. Specifically compressive and sub-space compressive detectors are the various CS based sparse signal detectors...
The area of compressed sensing has developed a lot and is of high interest in the last few years because it provides a solid and promising method to exactly recover signals by sampling at very low rate compared to traditional rates. It has been proved that the topic can be applied to almost all signal processing area which deals with sparse signals. But most of the works done in compressed sensing...
Bistatic/passive radars offer several advantages over monostatic radars. One of them is that the separation of transmitter and receiver opens the possibility to illuminate the targets from different aspect angels compared to the receiver. This spatial diversity can enhance the capability of target detection, parameter estimation, and identification, especially for stealth objects with low monostatic...
Channel estimation provides channel state information (CSI) for equalizing channel distortion and demodulating received signals. As a consequence, this technique takes an important part in modern wireless communications systems. Comparing with conventional pilot-aided channel estimators, compressive sensing based channel estimation methods can exploit the sparse property of the wireless channel and...
According to the characteristics of aerial image sequence shot by UAV, a novel compression and reconstruction algorithm based on compressed sensing is proposed. The random measurements of each image and the flight parameters are produced at the encoder, and then transmitted to the decoder. At the decoder, a motion estimation model is built based on analyzing the geometric relationship between camera...
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