Engineering Commons^™

High-Accuracy Reconstruction Of Missing Ground-Penetrating Radar Signals Based On Projection Onto Convex Sets In The Curvelet Domain, Wu Qiming, Wang Honghua, Xi Yuhe, Wang Yucheng

Coal Geology & Exploration

Influence by the acquisition environments and instrument performance, missing signals and destructed channels are inevitable in measured ground-penetrating radar (GPR) profiles. They can cause event discontinuity of reflected and diffracted waves generated by targets, severely impairing the accuracy and resolution of subsequent processing and imaging. Hence, by combining the projection onto convex sets (POCS) algorithm extensively used in image processing with the curvelet transform exhibiting high sparsity, this study proposed a high-accuracy reconstruction method for missing GPR signals based on curvelet-domain POCS. Building on the compressive sensing theory, the objective function for missing signal reconstruction based on discrete curvelet transform …

Compressive Sensing Via Variational Bayesian Inference Under Two Widely Used Priors: Modeling, Comparison And Discussion, Mohammad Shekaramiz, Todd K. Moon

Electrical and Computer Engineering Faculty Publications

Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying priors such as Bernoulli-Gaussian-inverse Gamma (BGiG) and Gaussian-inverse Gamma (GiG) priors on the compounds of the signal. With the introduction of variational Bayesian inference, the sparse Bayesian learning (SBL) methods for solving the inverse problem of compressive sensing have received significant interest as the SBL methods become more efficient in terms of execution time. In this paper, we consider the sparse signal recovery …

Short Word-Length Entering Compressive Sensing Domain: Improved Energy Efficiency In Wireless Sensor Networks, Nuha A. S. Alwan, Zahir M. Hussain

Research outputs 2014 to 2021

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows …

Performance Of Fft-Ofdm Versus Dwt-Ofdm Under Compressive Sensing, Zainab Hdeib Al-Shably, Zahir M. Hussain

Research outputs 2014 to 2021

In this work, we present a comparative study on the performance of Fourier-based OFDM (FFT-OFDM) and wavelet-based OFDM (DWT-OFDM) under compressive sensing (CS). Transmission over FFT-OFDM and DWT-OFDM, which has been made under different baseband modulation schemes such as Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Key (QPSK), Quadrature amplitude modulation (16QAM) and (64QAM) has been considered. From numerical simulation results, it is observed that the Wavelet-based OFDM system outperforms Fourier based OFDM when the Quadrature Amplitude Modulation is 16QAM and 64QAM within the signal to noise ratios range 30 to 40 dB. Although CS is more efficient in …

Deep Learning For Compressive Sar Imaging With Train-Test Discrepancy, Morgan R. Mccamey

Browse all Theses and Dissertations

We consider the problem of compressive synthetic aperture radar (SAR) imaging with the goal of reconstructing SAR imagery in the presence of under sampled phase history. While this problem is typically considered in compressive sensing (CS) literature, we consider a variety of deep learning approaches where a deep neural network (DNN) is trained to form SAR imagery from limited data. At the cost of computationally intensive offline training, on-line test-time DNN-SAR has demonstrated orders of magnitude faster reconstruction than standard CS algorithms. A limitation of the DNN approach is that any change to the operating conditions necessitates a costly retraining …

Two Image Watermarkingmethodsbased On Compressive Sensing, Yidi Miao, Lü Ju, Xiumei Li

Journal of System Simulation

Abstract: As an emerging sample theory, compressive sensing attracts wide attention because it breaks through the Nyquist sampling theorem. , Two different methods of watermark embedding and extraction are presented by measuring the carrier image and watermark image respectively based on compressive sensing. Moreover, the attack tests, such as the Gaussian noise, pepper and salt noise, filtering, compression, and cropping, are implemented to watermarked images. Experiment results show that although the two different methods for image watermarking have different processing procedure, both can guarantee the robustness and security of embedded digital watermark.

Sparse And Random Sampling Techniques For High-Resolution, Full-Field, Bss-Based Structural Dynamics Identification From Video, Bridget Martinez, Andre Green, Moises Felipe Silva, David Mascareñas, Yongchao Yang

Michigan Tech Publications

Video-based techniques for identification of structural dynamics have the advantage that they are very inexpensive to deploy compared to conventional accelerometer or strain gauge techniques. When structural dynamics from video is accomplished using full-field, high-resolution analysis techniques utilizing algorithms on the pixel time series such as principal components analysis and solutions to blind source separation the added benefit of high-resolution, full-field modal identification is achieved. An important property of video of vibrating structures is that it is particularly sparse. Typically video of vibrating structures has a dimensionality consisting of many thousands or even millions of pixels and hundreds to thousands …

Improving Performance Of Indoor Localization Using Compressive Sensing Andnormal Hedge Algorithm, Saeid Hassanhosseini, Mohammad Reza Taban, Jamshid Abouei, Arash Mohammadi

Turkish Journal of Electrical Engineering and Computer Sciences

Accurate indoor localization technologies are currently in high demand in wireless sensor networks, which strongly drive the development of various wireless applications including healthcare monitoring, patient tracking and endoscopic capsule localization. The precise position determination requires exact estimation of the time varying characteristics of wireless channels. In this paper, we address this issue and propose a three-phased scheme, which employs an optimal single stage TDOA/FDOA/AOA indoor localization based on spatial sparsity. The first contribution is to formulate the received unknown signals from the emitter as a compressive sensing problem. Then, we solve an $\ell_1$ minimization problem to localize the emitter's …

Distributed Compressive Sensing Algorithm For Photoacoustic Tomography, Maha Abdulwahab Hassan Shehada

Electrical Engineering Theses

Biomedical imaging techniques are playing an essential role in diagnosing different kinds of diseases, which always motivates the search for improving their sensitivity and accuracy. Photoacoustic Tomography (PAT) is one of the most powerful techniques. PAT has many advantages as it is less expensive and faster than Magnetic Resonance Imaging (MRI). It combines the advantages of optical imaging and ultrasound imaging as it provides high contrast, high penetration, and high-resolution images for biological tissues. Also, it uses non-ionizing radiation which is very safe for human health. The main challenge in PAT is that human tissues can be exposed only to …

Details On Csa-Sbl: An Algorithm For Sparse Bayesian Learning Boosted By Partial Erroneous Support Knowledge, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther

Electrical and Computer Engineering Faculty Publications

This report provides details on CSA-SBL(VB) algorithm for the recovery of sparse signals with unknown clustering pattern. More specifically, we deal with the recovery of sparse signals with unknown clustering pattern in the case of having partial erroneous prior knowledge on the supports of the signal. In [1], we provided a modified sparse Bayesian learning model to incorporate prior knowledge and simultaneously learn the unknown clustering pattern. For this purpose, we added one more layer to support-aided sparse Bayesian learning algorithm (SA-SBL) that was proposed in [2]. This layer adds a prior on the shape parameters of Gamma distributions, those …

Details On O-Sbl(Mcmc): A Compressive Sensing Algorithm For Sparse Signal Recovery For The Smv/Mmv Problem Using Sparse Bayesian Learning And Markov Chain Monte Carlo Inference, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther

Electrical and Computer Engineering Faculty Publications

This report provides details on O-SBL(MCMC) algorithm for the recovery of jointly-sparse signals for the multiple measurement vector (MMV) problem. For the MMVs with this structure, the solution matrix, which is a collection of sparse vectors, is expected to exhibit joint sparsity across the columns. The notion of joint sparsity here means that the columns of the solution matrix share common supports. This algorithm employs a sparse Bayesian learning (SBL) model to encourage the joint sparsity structure across the columns of the solution. While the proposed algorithm is constructed for the MMV problems, it can also be applied to the …

Sparse Signal Recovery Based On Compressive Sensing And Exploration Using Multiple Mobile Sensors, Mohammad Shekaramiz

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The work in this dissertation is focused on two areas within the general discipline of statistical signal processing. First, several new algorithms are developed and exhaustively tested for solving the inverse problem of compressive sensing (CS). CS is a recently developed sub-sampling technique for signal acquisition and reconstruction which is more efficient than the traditional Nyquist sampling method. It provides the possibility of compressed data acquisition approaches to directly acquire just the important information of the signal of interest. Many natural signals are sparse or compressible in some domain such as pixel domain of images, time, frequency and so forth. …

Side Information In Coded Aperture Compressive Spectral Imaging, Laura Galvis, Henry Arguello, Daniel L. Lau, Gonzalo R. Arce

Electrical and Computer Engineering Faculty Publications

Coded aperture compressive spectral imagers sense a three-dimensional cube by using two-dimensional projections of the coded and spectrally dispersed source. These imagers systems often rely on FPA detectors, SLMs, micromirror devices (DMDs), and dispersive elements. The use of the DMDs to implement the coded apertures facilitates the capture of multiple projections, each admitting a different coded aperture pattern. The DMD allows not only to collect the sufficient number of measurements for spectrally rich scenes or very detailed spatial scenes but to design the spatial structure of the coded apertures to maximize the information content on the compressive measurements. Although sparsity …

Sparsity Based Methods For Target Localization In Multi-Sensor Radar, Haley H. Kim

Dissertations

In this dissertation, several sparsity-based methods for ground moving target indicator (GMTI) radar with multiple-input multiple-output (MIMO) random arrays are proposed. MIMO random arrays are large arrays that employ multiple transmitters and receivers, the positions of the transmitters and the receivers are randomly chosen. Since the resolution of the array depends on the size of the array, MIMO random arrays obtain a high resolution. However, since the positions of the sensors are randomly chosen, the array suffers from large sidelobes which may lead to an increased false alarm probability. The number of sensors of a MIMO random array required to …

Compressive Sensing Framework For Mass Spectrometry Data Analysis, Khalfalla Ahmad Kh. Awedat

Dissertations

Mass Spectrometry (MS) data is ideal for identifying unique bio-signatures of diseases. However, the high dimensionality of MS data hinders any promising MS-based proteomics development. The goal of this dissertation is to develop an accurate classification tool by employing compressive sensing (CS). Not only can CS significantly reduce MS data dimensionality, but it also will allow for full reconstruction of original data. The framework developed in this work is based on using L2 and a mixed L2-L1 norms, allowing an overdetermined system to be resolved. The results show that the L2- based algorithm with regularization terms has a better performance …

Compressive Sensing For Feedback Reduction In Mimo Broadcast Channels, Mohammed Eltayeb, Tareq Al-Naffour, Hamid Bahrami

Hamid Bahrami

In multi-antenna broadcast networks, the base sta-tions (BSs) rely on the channel state information (CSI) of the users to perform user scheduling and downlink transmission. However, in networks with large number of users, obtaining CSI from all users is arduous, if not impossible, in practice. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing (CS), which permits the BS to obtain CSI with acceptable recovery guarantees under substantially reduced feedback overhead. Additionally, assuming noisy CS measurements at the BS, inexpensive ways for improving post-CS detection are explored. The proposed techniques are shown to reduce the …

Optimization Methods For Active And Passive Localization, Nil Garcia

Dissertations

Active and passive localization employing widely distributed sensors is a problem of interest in various fields. In active localization, such as in MIMO radar, transmitters emit signals that are reflected by the targets and collected by the receive sensors, whereas, in passive localization the sensors collect the signals emitted by the sources themselves. This dissertation studies optimization methods for high precision active and passive localization.

In the case of active localization, multiple transmit elements illuminate the targets from different directions. The signals emitted by the transmitters may differ in power and bandwidth. Such resources are often limited and distributed uniformly …

Global Optimization Methods For Localization In Compressive Sensing, Marco Rossi

Dissertations

The dissertation discusses compressive sensing and its applications to localization in multiple-input multiple-output (MIMO) radars. Compressive sensing is a paradigm at the intersection between signal processing and optimization. It advocates the sensing of “sparse” signals (i.e., represented using just a few terms from a basis expansion) by using a sampling rate much lower than that required by the Nyquist-Shannon sampling theorem (i.e., twice the highest frequency present in the signal of interest). Low-rate sampling reduces implementation’s constraints and translates into cost savings due to fewer measurements required. This is particularly true in localization applications when the number of measurements is …

Compressive Parameter Estimation With Emd, Dian Mo

Masters Theses 1911 - February 2014

In recent years, sparsity and compressive sensing have attracted significant attention in parameter estimation tasks, including frequency estimation, delay estimation, and localization. Parametric dictionaries collect signals for a sampling of the parameter space and can yield sparse representations for the signals of interest when the sampling is sufficiently dense. While this dense sampling can lead to high coherence in the dictionary, it is possible to leverage structured sparsity models to prevent highly coherent dictionary elements from appearing simultaneously in a signal representation, alleviating these coherence issues. However, the resulting approaches depend heavily on a careful setting of the maximum allowable …

A Study Of Compressive Sensing For Application To Structural Health Monitoring, Vaahini Ganesan

Electronic Theses and Dissertations

One of the key areas that have attracted attention in the construction industry today is Structural Health Monitoring, more commonly known as SHM. It is a concept developed to monitor the quality and longevity of various engineering structures. The incorporation of such a system would help to continuously track health of the structure, indicate the occurrence/presence of any damage in real time and give us an idea of the number of useful years for the same. Being a recently conceived idea, the state of the art technique in the field is straight forward - populating a given structure with sensors …

Adaptive Sampling With Mobile Sensor Networks, Shuo Huang

Dissertations, Master's Theses and Master's Reports - Open

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed …

Compressive Sensing With Prior Information Applied To Magnetic Resonance Imaging, Cristiano Jacques Miosso

Open Access Theses & Dissertations

This dissertation introduces the theory of compressive sensing with prior information about a signal's sparse representation. We show, mathematically and in numerical simulations, that prior information improves signal reconstruction, in terms of number of required measurements, computation time and signal-to-noise ratios. Following, we present a set of methods for enhanced magnetic resonance imaging and tomography that can be combined with prior information, for enhanced image quality.

In developing the theory of compressive sensing with prior information, we provide a mathematical proof of the required condition (in terms of number of linear measurements) for reconstruction using the ideal approach of l0-minimization, …