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Full-Text Articles in Engineering
Compressive Sensing Via Variational Bayesian Inference Under Two Widely Used Priors: Modeling, Comparison And Discussion, Mohammad Shekaramiz, Todd K. Moon
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
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 …
Details On Csa-Sbl: An Algorithm For Sparse Bayesian Learning Boosted By Partial Erroneous Support Knowledge, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
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
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 …
Side Information In Coded Aperture Compressive Spectral Imaging, Laura Galvis, Henry Arguello, Daniel L. Lau, Gonzalo R. Arce
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 …
Video Compressive Sensing With 3-D Wavelet And 3-D Noiselet, Dao Lam, Donald C. Wunsch
Video Compressive Sensing With 3-D Wavelet And 3-D Noiselet, Dao Lam, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
A new compressive video sampling method is investigated. as opposed to other video sampling methods for which processing is conducted on a single-frame basis, this method is applied on multiple frames of the video stream. by exploiting the extension of Wavelet to 3-D with the support of Noiselet 3-D and combining it with fast reconstruction algorithms, this framework produces successful results quickly while maintaining the quality of the video stream. Despite its simplicity, this new approach outperforms other sophisticated methods. © 2012 IEEE.