skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Recovery guarantees for compressed sensing with unknown errors

 [1]; ORCiD logo [2];  [1]
  1. Simon Fraser University, Canada
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Sampling Theory and Applications - Tallinn, , Estonia - 7/3/2017 4:00:00 AM-
Country of Publication:
United States

Citation Formats

Brugiapaglia, S., Archibald, Richard K., and Adcock, Ben. Recovery guarantees for compressed sensing with unknown errors. United States: N. p., 2017. Web. doi:10.1109/SAMPTA.2017.8024421.
Brugiapaglia, S., Archibald, Richard K., & Adcock, Ben. Recovery guarantees for compressed sensing with unknown errors. United States. doi:10.1109/SAMPTA.2017.8024421.
Brugiapaglia, S., Archibald, Richard K., and Adcock, Ben. Sat . "Recovery guarantees for compressed sensing with unknown errors". United States. doi:10.1109/SAMPTA.2017.8024421.
title = {Recovery guarantees for compressed sensing with unknown errors},
author = {Brugiapaglia, S. and Archibald, Richard K. and Adcock, Ben},
abstractNote = {},
doi = {10.1109/SAMPTA.2017.8024421},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2017},
month = {Sat Jul 01 00:00:00 EDT 2017}

Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • D-Cell, which contained the head end for an obsolete, batch aluminum fuel dissolution system, was to be decontaminated and dismantled. During prior periods, the cell had been flushed to minimal residual uranium; then the piping connections had been disconnected and capped. Cell instrumentation had been removed. The work crew connected a decontamination line to the first vessel followed by a routine sampling. The sample showed significant amounts of uranium in the vessel. At this point, analysis of the situation was required to determine the current margin of safety of the vessel, and a general procedure was required for adding decontaminationmore » solutions to the remaining vessels in the cell with possible unknown uranium contents.« less
  • The Ultrasonic Propagation Imaging (UPI) System is a unique, non-contact, laser-based ultrasonic excitation and measurement system developed for structural health monitoring applications. The UPI system imparts laser-induced ultrasonic excitations at user-defined locations on a structure of interest. The response of these excitations is then measured by piezoelectric transducers. By using appropriate data reconstruction techniques, a time-evolving image of the response can be generated. A representative measurement of a plate might contain 800x800 spatial data measurement locations and each measurement location might be sampled at 500 instances in time. The result is a total of 640,000 measurement locations and 320,000,000 uniquemore » measurements. This is clearly a very large set of data to collect, store in memory and process. The value of these ultrasonic response images for structural health monitoring applications makes tackling these challenges worthwhile. Recently compressed sensing has presented itself as a candidate solution for directly collecting relevant information from sparse, high-dimensional measurements. The main idea behind compressed sensing is that by directly collecting a relatively small number of coefficients it is possible to reconstruct the original measurement. The coefficients are obtained from linear combinations of (what would have been the original direct) measurements. Often compressed sensing research is simulated by generating compressed coefficients from conventionally collected measurements. The simulation approach is necessary because the direct collection of compressed coefficients often requires compressed sensing analog front-ends that are currently not commercially available. The ability of the UPI system to make measurements at user-defined locations presents a unique capability on which compressed measurement techniques may be directly applied. The application of compressed sensing techniques on this data holds the potential to reduce the number of required measurement locations, reduce the time to make measurements, reduce the memory required to store the measurements, and possibly reduce the computational burden to classify the measurements. This work considers the appropriate selection of the signal dictionary used for signal reconstruction, and performs an evaluation of compressed sensing technique's ability to reconstruct ultrasonic images using fewer measurements than would be needed using traditional Nyquist-limited data collection techniques.« less
  • We present an innovative way to autonomously classify LiDAR points into bare earth, building, vegetation, and other categories. One desirable product of LiDAR data is the automatic classification of the points in the scene. Our algorithm automatically classifies scene points using Compressed Sensing Methods via Orthogonal Matching Pursuit algorithms utilizing a generalized K-Means clustering algorithm to extract buildings and foliage from a Digital Surface Models (DSM). This technology reduces manual editing while being cost effective for large scale automated global scene modeling. Quantitative analyses are provided using Receiver Operating Characteristics (ROC) curves to show Probability of Detection and False Alarmmore » of buildings vs. vegetation classification. Histograms are shown with sample size metrics. Our inpainting algorithms then fill the voids where buildings and vegetation were removed, utilizing Computational Fluid Dynamics (CFD) techniques and Partial Differential Equations (PDE) to create an accurate Digital Terrain Model (DTM) [6]. Inpainting preserves building height contour consistency and edge sharpness of identified inpainted regions. Qualitative results illustrate other benefits such as Terrain Inpainting s unique ability to minimize or eliminate undesirable terrain data artifacts. Keywords: Compressed Sensing, Sparsity, Data Dictionary, LiDAR, ROC, K-Means, Clustering, K-SVD, Orthogonal Matching Pursuit« less