Patents by Inventor Nicolas W. Hengartner

Nicolas W. Hengartner has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Robust statistical reconstruction for charged particle tomography
    Patent number: 8552370
    Abstract: Systems and methods for charged particle detection including statistical reconstruction of object volume scattering density profiles from charged particle tomographic data to determine the probability distribution of charged particle scattering using a statistical multiple scattering model and determine a substantially maximum likelihood estimate of object volume scattering density using expectation maximization (ML/EM) algorithm to reconstruct the object volume scattering density. The presence of and/or type of object occupying the volume of interest can be identified from the reconstructed volume scattering density profile. The charged particle tomographic data can be cosmic ray muon tomographic data from a muon tracker for scanning packages, containers, vehicles or cargo. The method can be implemented using a computer program which is executable on a computer.
    Type: Grant
    Filed: October 24, 2007
    Date of Patent: October 8, 2013
    Assignee: Los Alamos National Security, LLC
    Inventors: Larry Joe Schultz, Alexei Vasilievich Klimenko, Andrew Mcleod Fraser, Christopher Morris, John Christopher Orum, Konstantin N. Borozdin, Michael James Sossong, Nicolas W. Hengartner
  • Robust statistical reconstruction for charged particle tomography
    Publication number: 20130238291
    Abstract: Systems and methods for charged particle detection including statistical reconstruction of object volume scattering density profiles from charged particle tomographic data to determine the probability distribution of charged particle scattering using a statistical multiple scattering model and determine a substantially maximum likelihood estimate of object volume scattering density using expectation maximization (ML/EM) algorithm to reconstruct the object volume scattering density. The presence of and/or type of object occupying the volume of interest can be identified from the reconstructed volume scattering density profile. The charged particle tomographic data can be cosmic ray muon tomographic data from a muon tracker for scanning packages, containers, vehicles or cargo. The method can be implemented using a computer program which is executable on a computer.
    Type: Application
    Filed: October 24, 2007
    Publication date: September 12, 2013
    Inventors: Larry Joe Schultz, Alexei Vasilievich Klimenko, Andrew McLeod Fraser, Christopher Morris, John Christopher Orum, Konstantin N. Borozdin, Michael James Sossong, NIcolas W. Hengartner
  • Imaging and sensing based on muon tomography
    Patent number: 8288721
    Abstract: Techniques, apparatus and systems for detecting particles such as muons for imaging applications. Subtraction techniques are described to enhance the processing of the muon tomography data.
    Type: Grant
    Filed: April 23, 2008
    Date of Patent: October 16, 2012
    Assignees: Decision Sciences International Corporation, Los Alamos National Security, LLC
    Inventors: Christopher L. Morris, Alexander Saunders, Michael James Sossong, Larry Joe Schultz, J. Andrew Green, Konstantin N. Borozdin, Nicolas W. Hengartner, Richard A. Smith, James M. Colthart, David C. Klugh, Gary E. Scoggins, David C. Vineyard
  • IMAGING AND SENSING BASED ON MUON TOMOGRAPHY
    Publication number: 20080315091
    Abstract: Techniques, apparatus and systems for detecting particles such as muons for imaging applications. Subtraction techniques are described to enhance the processing of the muon tomography data.
    Type: Application
    Filed: April 23, 2008
    Publication date: December 25, 2008
    Inventors: Christopher L. Morris, Alexander Saunders, Michael James Sossong, Larry Joe Schultz, J. Andrew Green, Konstantin N. Borozdin, Nicolas W. Hengartner, Richard A. Smith, James M. Colthart, David C. Klugh, Gary E. Scoggins, David C. Vineyard