Use Of Compressive Sensing Research Articles

Consensus and Sectioning-Based ADMM With Norm-1 Regularization for Imaging With a Compressive Reflector Antenna

This paper presents three distributed techniques to find a sparse solution of the underdetermined linear problem $\textbf{g}=\textbf{Hu}$ with a norm-1 regularization, based on the Alternating Direction Method of Multipliers (ADMM). These techniques divide the matrix $\textbf{H}$ in submatrices by rows, columns, or both rows and columns, leading to the so-called consensus-based ADMM, sectioning-based ADMM, and consensus and sectioning-based ADMM, respectively. These techniques are applied particularly for millimeter-wave imaging through the use of a Compressive Reflector Antenna (CRA). The CRA is a hardware designed to increase the sensing capacity of an imaging system and reduce the mutual information among measurements, allowing an effective imaging of sparse targets with the use of Compressive Sensing (CS) techniques. Consensus-based ADMM has been proved to accelerate the imaging process and sectioning-based ADMM has shown to highly reduce the amount of information to be exchange among the computational nodes. In this paper, the mathematical formulation and graphical interpretation of these two techniques, together with the consensus and sectioning-based ADMM approach, are presented. The imaging quality, the imaging time, the convergence, and the communication efficiency among the nodes are analyzed and compared. The distributed capabitities of the ADMM-based approaches, together with the high sensing capacity of the CRA, allow the imaging of metallic targets in a 3D domain in quasi-real time with a reduced amount of information exchanged among the nodes.

Spatially modulated illumination allows for light sheet fluorescence microscopy with an incoherent source and compressive sensing.

Light sheet fluorescence microscopy has become one of the most widely used techniques for three-dimensional imaging due to its high speed and low phototoxicity. Further improvements in 3D microscopy require limiting the light exposure of the sample and increasing the volumetric acquisition rate. We hereby present an imaging technique that allows volumetric reconstruction of the fluorescent sample using spatial modulation on a selective illumination volume. We demonstrate that this can be implemented using an incoherent LED source, avoiding shadowing artifacts, typical of light sheet microscopy. Furthermore, we show that spatial modulation allows the use of Compressive Sensing, reducing the number of modulation patterns to be acquired. We present results on zebrafish embryos which prove that selective spatial modulation can be used to reconstruct relatively large volumes without any mechanical movement. The technique yields an accurate reconstruction of the sample anatomy even at significant compression ratios, achieving higher volumetric acquisition rate and reducing photodamage biological samples.

Enhancing cooperation in MANET using neighborhood compressive sensing model

This paper presents the use of Compressive Sensing (CS) in the reduction of resource consumption to minimize battery and bandwidth usage. It also focuses on how attacks and misbehavior can be nullified. The proposed Neighborhood Compressive Sensing (NCS) model compresses the neighborhood sparse data such as routing table updates, advertisement and trust information. It minimizes resource consumption because major computations are performed by the leader node. The use of compressive sensing gives the reduction in resource consumption because it reduces the amount of transmitting data in the network. It also prevents a network from unwanted advertisement and attacks because the neighborhood nodes do not accept the advertisements and updates directly, rather it uses leader node’s processed information. The proposed NCS model is implemented in “GloMoSim” on top of the DSR protocol, resulting its effectiveness, as compared to the DSR protocol when the network is misconducting for its selfish needs. Simulation result shows that the proposed NCS model is outperformed DSR in terms of the energy consumption, network lifetime and packet dropping ratio. This work is the extended version of Reduction in Resource Consumption to enhance cooperation in MANET using Compressive Sensing (Akhtar and Sahoo, 2015) [68].

A New Methodology for 3D Target Detection in Automotive Radar Applications.

Today there is a growing interest in automotive sensor monitoring systems. One of the main challenges is to make them an effective and valuable aid in dangerous situations, improving transportation safety. The main limitation of visual aid systems is that they do not produce accurate results in critical visibility conditions, such as in presence of rain, fog or smoke. Radar systems can greatly help in overcoming such limitations. In particular, imaging radar is gaining interest in the framework of Driver Assistance Systems (DAS). In this manuscript, a new methodology able to reconstruct the 3D imaged scene and to detect the presence of multiple targets within each line of sight is proposed. The technique is based on the use of Compressive Sensing (CS) theory and produces the estimation of multiple targets for each line of sight, their range distance and their reflectivities. Moreover, a fast approach for 2D focus based on the FFT algorithm is proposed. After the description of the proposed methodology, different simulated case studies are reported in order to evaluate the performances of the proposed approach.

Hierarchical Data Aggregation Using Compressive Sensing (HDACS) in WSNs

Energy efficiency is one of the key objectives in data gathering in wireless sensor networks (WSNs). Recent research on energy-efficient data gathering in WSNs has explored the use of Compressive Sensing (CS) to parsimoniously represent the data. However, the performance of CS-based data gathering methods has been limited since the approaches failed to take advantage of judicious network configurations and effective CS-based data aggregation procedures. In this article, a novel Hierarchical Data Aggregation method using Compressive Sensing (HDACS) is presented, which combines a hierarchical network configuration with CS. Our key idea is to set multiple compression thresholds adaptively based on cluster sizes at different levels of the data aggregation tree to optimize the amount of data transmitted. The advantages of the proposed model in terms of the total amount of data transmitted and data compression ratio are analytically verified. Moreover, we formulate a new energy model by factoring in both processor and radio energy consumption into the cost, especially the computation cost incurred in relatively complex algorithms. We also show that communication cost remains dominant in data aggregation in the practical applications of large-scale networks. We use both the real-world data and synthetic datasets to test CS-based data aggregation schemes on the SIDnet-SWANS simulation platform. The simulation results demonstrate that the proposed HDACS model guarantees accurate signal recovery performance. It also provides substantial energy savings compared with existing methods.

Multiresolution Image Fusion: Use of Compressive Sensing and Graph Cuts

In this paper, we propose a new approach for multiresolution fusion, i.e., obtaining a high spatial and spectral resolution multispectral (MS) image using the available low spatial resolution MS and the high spatial resolution Panchromatic (Pan) image. Our approach is based on the idea of compressive sensing (CS) and graph cuts. Assuming that both the MS and Pan images have the same sparseness, a close approximation to the MS image is obtained from the Pan image using the theory of compressive sensing and l1 minimization. We then use regularization framework to obtain fused image. The low resolution (LR) MS image is modeled as degraded and noisy version of fused image in which degradation matrix entires estimated using the close approximation are used. The regularization is carried out by using truncated quadratic smoothness prior which takes care of preservation of the discontinuities in the fused image. A suitable energy function is then formed consisting of data fitting term and prior term. Minimization of the energy function is carried out using a computationally efficient graph cuts optimization to obtain final fused image. Advantage of our approach is that the Pan and MS images need not be registered. This is because, we are not directly using the Pan digital numbers to derive the fused image. The effectiveness of the proposed method is illustrated by conducting experiments on real satellite images. Subjective and quantitative comparison of the proposed method with the state-of-the-art approaches indicates efficacy of our approach.

Compressive Sensing for Spread Spectrum Receivers

With the advent of ubiquitous computing there are two design parameters of wireless communication devices that become very important power: efficiency and production cost. Compressive sensing enables the receiver in such devices to sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in the two design parameters. This paper investigates the use of Compressive Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We show that when using spread spectrum codes in the signal domain, the CS measurement matrix may be simplified. This measurement scheme, named Compressive Spread Spectrum (CSS), allows for a simple, effective receiver design. Furthermore, we numerically evaluate the proposed receiver in terms of bit error rate under different signal to noise ratio conditions and compare it with other receiver structures. These numerical experiments show that though the bit error rate performance is degraded by the subsampling in the CS-enabled receivers, this may be remedied by including quantization in the receiver model. We also study the computational complexity of the proposed receiver design under different sparsity and measurement ratios. Our work shows that it is possible to subsample a CDMA signal using CSS and that in one example the CSS receiver outperforms the classical receiver.

Sensing, Compression, and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework

We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples. We propose a sparsity model that allows the use of Compressive Sensing (CS) for the online recovery of large data sets in real WSN scenarios, exploiting Principal Component Analysis (PCA) to capture the spatial and temporal characteristics of real signals. Bayesian analysis is utilized to approximate the statistical distribution of the principal components and to show that the Laplacian distribution provides an accurate representation of the statistics of real data. This combined CS and PCA technique is subsequently integrated into a novel framework, namely, SCoRe1: Sensing, Compression and Recovery through ON-line Estimation for WSNs. SCoRe1 is able to effectively self-adapt to unpredictable changes in the signal statistics thanks to a feedback control loop that estimates, in real time, the signal reconstruction error. We also propose an extensive validation of the framework used in conjunction with CS as well as with standard interpolation techniques, testing its performance for real world signals. The results in this paper have the merit of shedding new light on the performance limits of CS when used as a recovery tool in WSNs.

Reconstruction of complete connectivity matrix for connectomics by sampling neural connectivity with fluorescent synaptic markers

Physical organization of the nervous system is a topic of perpetual interest in neuroscience. Despite significant achievements here in the past, many details of the nervous system organization and its role in animals’ behavior remain obscure, while the problem of complete connectivity reconstructions has recently re-emerged as one of the major directions in neuroscience research (i.e. connectomics). We describe a novel paradigm for connectomics reconstructions that can yield connectivity maps with high resolution, high speed of imaging and data analysis, and significant robustness to errors. In essence, we propose that physical connectivity in a neural circuit can be sampled using anatomical fluorescent synaptic markers localized to different parts of the neural circuit with a technique for randomized genetic targeting, and that high-resolution connectivity maps can be extracted from such datasets. We describe how such an approach can be implemented and how neural connectivity matrix can be reconstructed statistically using the methods of Compressive Sensing. Use of Compressive Sensing is the key to allow accurate neural connectivity reconstructions with orders-of-magnitude smaller volumes of experimental data. We test described approach on simulations of neural connectivity reconstruction experiments in C. elegans, where real neural wiring diagram is available from past electron microscopy studies. We show that such wiring diagram can be in principle re-obtained using described approach in 1–7 days of imaging and data analysis. Alternative approaches would require currently at least 1–2 years to produce a single comparable reconstruction. We discuss possible applications of described approach in larger organisms such as Drosophila.