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Compressive Sensing (CS) is a new methodology to reconstruct sparse signals from a few number of measurements. These measurements are captured by a sensing matrix, which has a direct impact on the performance of the reconstruction algorithms. Among the sensing matrices proposed in the literature, Bernoulli and Gaussian random sensing matrices are the most prominent. In this work, we propose to improve...
Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we introduce a distributed compressive sensing approach, which utilizes spatial correlation among sensor nodes to group them into coalitions. The coalition formation method is represented by a block diagonal measurement matrix...
Nowadays, Compressive Sensing (CS), is one of the emerging fields of research in signal processing. It aims at reconstructing a signal from a significantly reduced number of measurements. This is done by exploiting the sparsity property of the original signal. Since its start in 2006, CS has been applied in several domains such as wireless channel estimation, cognitive radio, and other domains. In...
In this work we focus on the scheme where an ultradense heterogeneous networks acquires a signal of interest cancellation by an interfering signal. We propose a method of compressive sensing based interference Alignment algorithm for UltraDense Network. This paper is aimed at the complex interference of ultra-dense network. Firstly, we apply compressive sensing processing techniques for the signal,...
Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal. Block-based CS is a lightweight CS approach that is mostly suitable for processing very high-dimensional images and videos: it operates on local patches, employs a low-complexity reconstruction operator and requires significantly...
Reliable and efficient spectrum sensing through dynamic selection of a subset of spectrum sensors is studied. The problem of selecting K sensor measurements from a set of M potential sensors is considered where K
Considering an incomplete signals acquisition due to a sparse beacon deployment, this paper proposes a generalized similarity filter to improve the performance of proximity detection and thus guarantee the quality of proximity-based service (PBS). In particular, this paper leverages Bluetooth Low Energy (BLE) Beacons to realize a PBS system which comprises a number of Proximities of Interest (PoIs)...
Compressed sensing is a novel theory which combined signal sampling and compression together, and this paper propose an improved reconstruction algorithm. Firstly this paper analysis the sparsity of the power quality disturbance signal and the selection of the measurement matrix, then, this paper proposes an improved regularization sparsity adaptive matching pursuit algorithm (RCoSaMP) on the basis...
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...
Compressive sensing (CS) is a recent signal processing paradigm that exploits the inherent sparsity in input signal through data compression before wireless transmission. Recent CS implementations have shown impressive energy-efficiencies with good signal recovery but require apriori sparsity estimation and are thus not adaptable dynamic IoT environments resulting in loss of accuracy. This paper describes...
We present a hardware-friendly spatiotemporal compressed sensing framework for video compression. The spatiotemporal compressed sensing incorporates random sampling in both spatial and temporal domain to encode the video scene into a single coded image. During decoding, the video is reconstructed using dictionary learning and sparse recovery. The evaluation results demonstrate the proposed approach...
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...
This paper deals with the optimization of sensing matrices and sparsifying dictionaries for compressed sensing systems. A gradient-based method with a new measurement strategy denoted as real mutual coherence is proposed. Further more, the sensing matrix is optimized by minimizing an objective function in which the target Gram is selected as Ψ Ψ, this choice has advantages to reconstruct real images...
Compressed Sensing is for sparse and compressible signals, the data is compressed while the signal is sampled. This paper proposes the new deterministic measurement matrices that are studied: according to the compressible signal characteristics, we will use the unit matrix added with random orthogonal matrix and complementary sequences as the measurement matrix, and then using orthogonal matching...
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 order to obtain accurate single-phase-to-ground zero-sequence current signal to improve the accuracy of fault line selection, a new data acquisition method—compressed sensing theory, was applied to single-phase-to- ground fault line selection of distribution network. A new method of fault line selection based on Bayesian compressed sensing theory was proposed. The method used compressed sampling...
Single event transients (SETs) have seriously deteriorated the reliability Integrated circuits (ICs), especially for those in mission- or security-critical applications. Detecting and locating SETs can be useful for fault analysis and future enhancement. Traditional SET detecting methods usually require special sensors embedded into the circuits, or radiation scanning with fine resolutions over the...
In image reconstruction based on the compressed sensing (CS), linear measurement on the image is required, and the original signal is not only sampled and compressed by the measurement, but also the signal dimension is greatly reduced. Then the original signal is reconstructed from the measured value by the reconstruction algorithm, so the structure of the measurement matrix not only affects the results...
The Sparsity Adaptive Matching Pursuit (SAMP) algorithm needs not to know a priori information of sparsity, which makes it has unique advantages compared to other greedy algorithms. In the reconstruction process, the step size can be changed with stage, which increases its reconstruction accuracy. Aiming at the step size of SAMP, it is unreasonable, will only increase and it is not decrease properly...
Compressive Sensing is practical and implemented into many areas. For these applications the conventional sensing noise (e.g. AWGN) with low energy on measurements could be reduced by the robustness of Compressive Sensing. Parallel, there exist some errors, which would strongly noise or remove some parts of measurements. In this case, the recovered information would contain much noise, since the robustness...
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