Biblio

Compressive radar receiver has attracted a lot of research interest due to its capability to keep balance between sub-Nyquist sampling and high resolution. In evaluating the performance of compressive time delay estimator, Cramer-Rao bound (CRB) has been commonly utilized for lower bounding the mean square error (MSE). However, behaving as a local bound, CRB is not tight in the a priori performance region. In this paper, we introduce the Ziv-Zakai bound (ZZB) methodology into compressive sensing framework, and derive a deterministic ZZB for compressive time delay estimators as a function of the compressive sensing kernel. By effectively incorporating the a priori information of the unknown time delay, the derived ZZB performs much tighter than CRB especially in the a priori performance region. Simulation results demonstrate that the derived ZZB outperforms the Bayesian CRB over a wide range of signal-to-noise ratio, where different types of a priori distribution of time delay are considered.

Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quantify the nanoscale atomic structure and composition of materials and biological specimens. In many cases, however, the resolution is limited by the electron beam damage, since in traditional STEM, a focused electron beam scans every location of the sample in a raster fashion. In this paper, we propose a scanning method based on the theory of Compressive Sensing (CS) and subsampling the electron probe locations using a line hop sampling scheme that significantly reduces the electron beam damage. We experimentally validate the feasibility of the proposed method by acquiring real CS-STEM data, and recovering images using a Bayesian dictionary learning approach. We support the proposed method by applying a series of masks to fully-sampled STEM data to simulate the expectation of real CS-STEM. Finally, we perform the real data experimental series using a constrained-dose budget to limit the impact of electron dose upon the results, by ensuring that the total electron count remains constant for each image.

The compressed sensing (CS) method can reconstruct images with a small amount of under-sampling data, which is an effective method for fast magnetic resonance imaging (MRI). As the traditional optimization-based models for MRI suffered from non-adaptive sampling and shallow” representation ability, they were unable to characterize the rich patterns in MRI data. In this paper, we propose a CS MRI method based on iterative shrinkage threshold algorithm (ISTA) and adaptive sparse sampling, called DSLS-ISTA-Net. Corresponding to the sampling and reconstruction of the CS method, the network framework includes two folders: the sampling sub-network and the improved ISTA reconstruction sub-network which are coordinated with each other through end-to-end training in an unsupervised way. The sampling sub-network and ISTA reconstruction sub-network are responsible for the implementation of adaptive sparse sampling and deep sparse representation respectively. In the testing phase, we investigate different modules and parameters in the network structure, and perform extensive experiments on MR images at different sampling rates to obtain the optimal network. Due to the combination of the advantages of the model-based method and the deep learning-based method in this method, and taking both adaptive sampling and deep sparse representation into account, the proposed networks significantly improve the reconstruction performance compared to the art-of-state CS-MRI approaches.

The camera constructed by a megahertz range intensity modulation active light source and a kilo-frame rate range fast camera based on compressive sensing (CS) technique for three-dimensional (3D) image acquisition was proposed in this research.

Communication systems across a variety of applications are increasingly using the angular domain to improve spectrum management. They require new sensing architectures to perform energy-efficient measurements of the electromagnetic environment that can be deployed in a variety of use cases. This paper presents the Directional Spectrum Sensor (DSS), a compressive sampling (CS) based analog-to-information converter (CS-AIC) that performs spectrum scanning in a focused beam. The DSS offers increased spectrum sensing sensitivity and interferer tolerance compared to omnidirectional sensors. The DSS implementation uses a multi-antenna beamforming architecture with local oscillators that are modulated with pseudo random waveforms to obtain CS measurements. The overall operation, limitations, and the influence of wideband angular effects on the spectrum scanning performance are discussed. Measurements on an experimental prototype are presented and highlight improvements over single antenna, omnidirectional sensing systems.

A power amplifier(PA) is inherently nonlinear device and is used in a communication system widely. Due to the nonlinearity of PA, the communication system is hard to work well. Digital predistortion (DPD) is the way to solve this problem. Using Volterra function to fit the PA is what most DPD solutions do. However, when it comes to wideband signal, there is a deduction on the performance of the Volterra function. In this paper, we replace the Volterra function with B-spline function which performs better on fitting PA at wideband signal. And the other benefit is that the orthogonality of coding matrix A could be improved, enhancing the stability of computation. Additionally, we use compressive sampling to reduce the complexity of the function model.

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qualitative, often making histopathologic examination subjective and difficult to quantify. MIRSI addresses these challenges through quantitative and repeatable imaging that leverages native molecular contrast. Fourier transform infrared (FTIR) imaging, the best-known MIRSI technology, has two challenges that have hindered its widespread adoption: data collection speed and spatial resolution. Recent technological breakthroughs, such as photothermal MIRSI, provide an order of magnitude improvement in spatial resolution. However, this comes at the cost of acquisition speed, which is impractical for clinical tissue samples. This paper introduces an adaptive compressive sampling technique to reduce hyperspectral data acquisition time by an order of magnitude by leveraging spectral and spatial sparsity. This method identifies the most informative spatial and spectral features, integrates a fast tensor completion algorithm to reconstruct megapixel-scale images, and demonstrates speed advantages over FTIR imaging while providing spatial resolutions comparable to new photothermal approaches.

Signals get sampled using Nyquist rate in conventional sampling method, but in compressive sensing the signals sampled below Nyquist rate by randomly taking the signal projections and reconstructing it out of very few estimations. But in case of recovering the image by utilizing compressive measurements with the help of multi-resolution grid where the image has certain region of interest (RoI) that is more important than the rest, it is not efficient. The conventional Cartesian sampling cannot give good result in motion image sensing recovery and is limited to stationary image sensing process. The proposed work gives improved results by using Radial sampling (a type of compression sensing). This paper discusses the approach of Radial sampling along with the application of Sparse Fourier Transform algorithms that helps in reducing acquisition cost and input/output overhead.

The Compressive Sensing (CS) has wide range of applications in various domains. The sampling of sparse signal, which is periodic or aperiodic in nature, is still an out of focus topic. This paper proposes novel Sparse Spasmodic Sampling (SSS) techniques for different sparse signal in original domain. The SSS techniques are proposed to overcome the drawback of the existing CS sampling techniques, which can sample any sparse signal efficiently and also find location of non-zero components in signals. First, Sparse Spasmodic Sampling model-1 (SSS-1) which samples random points and also include non-zero components is proposed. Another sampling technique, Sparse Spasmodic Sampling model-2 (SSS-2) has the same working principle as model-1 with some advancements in design. It samples equi-distance points unlike SSS-1. It is demonstrated that, using any sampling technique, the signal is able to reconstruct with a reconstruction algorithm with a smaller number of measurements. Simulation results are provided to demonstrate the effectiveness of the proposed sampling techniques.

A weather radar is expected to provide information about weather conditions in real time and valid. To obtain these results, weather radar takes a lot of data samples, so a large amount of data is obtained. Therefore, the weather radar equipment must provide bandwidth for a large capacity for transmission and storage media. To reduce the burden of data volume by performing compression techniques at the time of data acquisition. Compressive Sampling (CS) is a new data acquisition method that allows the sampling and compression processes to be carried out simultaneously to speed up computing time, reduce bandwidth when passed on transmission media, and save storage media. There are three stages in the CS method, namely: sparsity transformation using the Discrete Cosine Transform (DCT) algorithm, sampling using a measurement matrix, and reconstruction using the Orthogonal Matching Pursuit (OMP) algorithm. The sparsity transformation aims to convert the representation of the radar signal into a sparse form. Sampling is used to extract important information from the radar signal, and reconstruction is used to get the radar signal back. The data used in this study is the real data of the IDRA beat signal. Based on the CS simulation that has been done, the best PSNR and RMSE values are obtained when using a CR value of two times, while the shortest computation time is obtained when using a CR value of 32 times. CS simulation in a sector via DCT using the CR value two times produces a PSNR value of 20.838 dB and an RMSE value of 0.091. CS simulation in a sector via DCT using the CR value 32 times requires a computation time of 10.574 seconds.

We propose and experimentally demonstrate a compressive sensing scheme based on optical coherent receiver that recovers sparse optical arbitrary signals with an analog bandwidth up to 25GHz. The proposed scheme uses 16x lower sampling rate than the Nyquist theorem and spectral resolution of 24.4MHz.

We investigate a compressive sampling (CS) stepped frequency ground penetrating radar for detection of underground objects, which uses Bayesian estimation and a probabilistic model for the target support. Due to the underground targets being sparse, the B-scan is a sparse image. Using the CS principle, the stepped frequency radar is implemented using a subset of random frequencies at each antenna position. For image reconstruction we use Markov Chain and Markov Random Field models for the target support in the B-scan, where we also estimate the model parameters using the Expectation Maximization algorithm. The approach is tested using Web radar data obtained by measuring the signal responses scattered off land mine targets in a laboratory experimental setup. Our approach results in improved performance compared to the standard denoising algorithm for image reconstruction.

Compressive Sensing (CS) is a promising investigation field in the communication signal processing domain. It offers an advantage of compression while sampling; hence, data redundancy is reduced and improves sampled data transmission. Due to the acquisition of compressed samples, Analog to Digital Conversions (ADCs) performance also improved at ultra-high frequency communication applications. Several reconstruction algorithms existed to reconstruct the original signal with these sub-Nyquist samples. Orthogonal Matching Pursuit (OMP) falls under the category of greedy algorithms considered in this work. We implemented a compressively sensed sampling procedure using a Random Demodulator Analog-to-Information Converter (RD-AIC). And for CS reconstruction, we have considered OMP and novel Incremental Gaussian Elimination (IGE) OMP algorithms to reconstruct the original signal. Performance comparison between OMP and IGE OMP presented.

Compressive Covariance Sampling (CCS) is a strategy used to recover the covariance matrix (CM) directly from compressive measurements. Several works have proven the advantages of CSS in Compressive Spectral Imaging (CSI) but most of these algorithms require multiple random projections of the scene to obtain good reconstructions. However, several low-resolution copies of the scene can be captured in a single snapshot through a lenslet array. For this reason, this paper proposes a sensing protocol and a single snapshot CCS optical architecture using a lenslet array based on the Dual Dispersive Aperture Spectral Imager(DD-CASSI) that allows the recovery of the covariance matrix with a single snapshot. In this architecture uses the lenslet array allows to obtain different projections of the image in a shot due to the special coded aperture. In order to validate the proposed approach, simulations evaluated the quality of the recovered CM and the performance recovering the spectral signatures against traditional methods. Results show that the image reconstructions using CM have PSNR values about 30 dB, and reconstructed spectrum has a spectral angle mapper (SAM) error less than 15° compared to the original spectral signatures.

The data revolution will continue in the near future and move from centralized big data to "small" datasets. This trend stimulates the emergence not only new machine learning methods but algorithms for processing data at the point of their origin. So the Compressed Sensing Problem must be investigated in some technology fields that produce the data flow for decision making in real time. In the paper, we compare the random and constant frequency deviation and highlight some circumstances where advantages of the random deviation become more obvious. Also, we propose to use the differential transformations aimed to restore a signal form by discrets of the differential spectrum of the received signal. In some cases for the investigated model, this approach has an advantage in the compress of information.

Compressive beamforming has been successfully applied to the estimation of the direction of arrival (DOA) of array signals, and has higher angular resolution than traditional high-resolution beamforming methods. However, most of the existing compressive beamforming methods are based on narrow signal models. Wideband signal processing using these existing compressive beamforming methods is to divide the frequency band into several narrow-bands and add up the beamforming results of each narrow-band. However, for sonar application, signals usually consist of continuous spectrum and line spectrum, and the line spectrum is usually more than 10dB higher than the continuous spectrum. Due to the large difference of signal-to-noise ratio (SNR) of each narrow-band, different regularization parameters should be used, otherwise it is difficult to get an ideal result, which makes compressive beamforming highly complicated. In this paper, a compressive beamforming method based on spatial resampling for uniform linear arrays is proposed. The signals are converted into narrow-band signals by spatial resampling technique, and compressive beamforming is then performed to estimate the DOA of the sound source. Experimental results show the superiority of the proposed method, which avoids the problem of using different parameters in the existing compressive beamforming methods, and the resolution is comparable to the existing methods using different parameters for wideband models. The spatial-resampling compressive beamforming has a better robustness when the regularization parameter is fixed, and exhibits lower levels of background interference than the existing methods.

In order to solve the problem of large data volume brought by the traditional Nyquist sampling theorem in radar signal detection, a compressive detection (CD) model based on compressed sensing (CS) theory is proposed by analyzing the sparsity of the radar target in the range domain. The lower sampling rate completes the compressive sampling of the radar signal on the range field. On this basis, the two-dimensional distribution of the Doppler unit is established by moving target detention moving target detention (MTD), and the detection of the target is achieved with the two-dimensional constant false alarm rate (2D-CFAR) detection algorithm. The simulation experiment results prove that the algorithm can effectively detect the target without the need for reconstruction signals, and has good detection performance.

This paper presents the application of Compressive Sampling (CS) to the realization of a wideband receiver for distributed spectrum monitoring. The proposed prototype performs the non-uniform sampling CS-based technique, while the signal reconstruction is realized by the Orthogonal Matching Pursuit (OMP) algorithm on a personal computer. A first experimental analysis has been conducted on the prototype by assessing several figures of merit, thus characterizing its performance in the time, frequency and modulation domains. The obtained results demonstrate that the proposed prototype can achieve good performance in all specified domains with Compression Ratios (CRs) up to 10 for a 4-QAM (Quadrature Amplitude Modulation) signal having carrier frequency of 350 MHz and working at a symbol rate of 46 MSym/s.

To measure machinery vibration, a sensor system consisting of a 3-axis accelerometer, ADXL345, attached to a self-contained system-on-a-chip with integrated Wi-Fi capabilities, ESP8266, is a low-cost solution. In this work, we first show that in such a system, the widely used direct-read-and-send method which samples and sends individually acquired vibration data points to the server is not effective, especially using Wi-Fi connection. We show that the micro delays in each individual data transmission will limit the sensor sampling rate and will also affect the time of the acquired data points not evenly spaced. Then, we propose that vibration should be sampled in batches before sending the acquired data out from the sensor node. The vibration for each batch should be acquired continuously without any form of interruption in between the sampling process to ensure the data points are evenly spaced. To fill the data gaps between the batches, we propose the use of compressive sampling technique. Our experimental results show that the maximum sampling rate of the direct-read-and-send method is 350Hz with a standard uncertainty of 12.4, and the method loses more information compared to our proposed solution that can measure the vibration wirelessly and continuously up to 633Hz. The gaps filled using compressive sampling can achieve an accuracy in terms of mean absolute error (MAE) of up to 0.06 with a standard uncertainty of 0.002, making the low-cost vibration sensor node a cost-effective solution.

We address the problem of recovering signals from compressed measurements based on generative priors. Recently, generative-model based compressive sensing (GMCS) methods have shown superior performance over traditional compressive sensing (CS) techniques in recovering signals from fewer measurements. However, it is possible to further improve the performance of GMCS by introducing controlled sparsity in the latent-space. We propose a proximal meta-learning (PML) algorithm to enforce sparsity in the latent-space while training the generator. Enforcing sparsity naturally leads to a union-of-submanifolds model in the solution space. The overall framework is named as sparsity driven latent space sampling (SDLSS). In addition, we derive the sample complexity bounds for the proposed model. Furthermore, we demonstrate the efficacy of the proposed framework over the state-of-the-art techniques with application to CS on standard datasets such as MNIST and CIFAR-10. In particular, we evaluate the performance of the proposed method as a function of the number of measurements and sparsity factor in the latent space using standard objective measures. Our findings show that the sparsity driven latent space sampling approach improves the accuracy and aids in faster recovery of the signal in GMCS.

Compressive sensing (CS) is beneficial for unmanned reconnaissance systems to obtain high-quality images with limited resources. The existing prediction methods for block-based compressive sensing of images can be regarded as the particular coefficients of weighted predictive coding. To find better prediction coefficients for BCS, this paper proposes two weighted prediction methods. The first method converts the prediction model of measurements into a prediction model of image blocks. The prediction weights are obtained by training the prediction model of image blocks offline, which avoiding the influence of the sampling rates on the prediction model of measurements. Another method is to calculate the prediction coefficients adaptively based on the average energy of measurements, which can adjust the weights based on the measurements. Compared with existing methods, the proposed prediction methods for BCS of images can further improve the reconstruction image quality.

This paper examines the influence of quantization on the compressive sensing theory applied to the nonuniformly sampled nonsparse signals with reduced set of randomly positioned measurements. The error of the reconstruction will be generalized to exact expected squared error expression. The aim is to connect the generalized random sampling strategy with the quantization effect, finding the resulting error of the reconstruction. Small sampling deviations correspond to the imprecisions of the sampling strategy, while completely random sampling schemes causes large sampling deviations. Numerical examples provide an agreement between the statistical results and theoretical values.

This paper proposes a Time Difference of Arrival (TDoA) based method for the localization of Radio Frequency (RF) emitters working at different carriers, by using wideband spectrum sensors exploiting compressive sampling. The proposed measurement method is based on four or more RF receivers, with known Cartesian positions, performing non uniform sampling on the received signal. By means of simulations, the method has been compared against a localization method adopting RF receivers performing uniform sampling at Nyquist rate. The obtained preliminary results demonstrate that the method is capable of localizing two RF emitters achieving the same results obtained with uniform sampling, with a compression ratio up to CR = 20.

Compressive Sensing (CS) is a promising technology for the acquisition of signals. The number of measurements is reduced by using CS which is needed to obtain the signals in some basis that are compressible or sparse. The compressible or sparse nature of the signals can be obtained by transforming the signals in some domain. Depending on the signals sparsity signals are sampled below the Nyquist sampling criteria by using CS. An optimization problem needs to be solved for the recovery of the original signal. Very few studies have been reported about the reconstruction of the signals. Therefore, in this paper, the reconstruction algorithms are elaborated systematically for sparse signal recovery in CS. The discussion of various reconstruction algorithms in made in this paper will help the readers in order to understand these algorithms efficiently.

We propose a photonic compressive sampling scheme based on multimode fiber for radio spectrum sensing, which shows high accuracy and stability, and low complexity and cost. Pulse overlapping is utilized for a fast detection. © 2020 The Author(s).