Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Search Publications by: John Lu (Fed)

Search Title, Abstract, Conference, Citation, Keyword or Author
Displaying 26 - 47 of 47

Statistical Analysis of Reader Measurement Variability in Nodule Sizing with CT Phantom Imaging Data

November 23, 2012
Author(s)
John Lu, Charles D. Fenimore, Nicholas Petrick, Rongping Zeng, Marios A. Gavrielides, David Clunie, Kristin Borradaile, Robert Ford, Hyun J. Kim, Michael McNitt-Gray, Binsheng Zhao, Andrew Buckler
RSNA has conducted a phantom quantitative imaging biomarker (QIBA) study to assess reader measurement variability of both spherical and non-spherical nodules using CT imaging. Statistical analysis of intra-reader and inter-reader variability of volume

Big Data Issues in Quantitative Imaging

August 29, 2012
Author(s)
Mary C. Brady, Alden A. Dima, Charles D. Fenimore, James J. Filliben, John Lu, Adele Peskin, Mala Ramaiah, Ganesh Saiprasad, Ram D. Sriram

Development of a Seebeck Coefficient Standard Reference Material (SRM)™

August 1, 2011
Author(s)
Nathan Lowhorn, Winnie Wong-Ng, John Lu, Joshua B. Martin, Martin L. Green, John E. Bonevich, Evan L. Thomas, Neil Dilley, Jeff Sharp
We have successfully developed a Seebeck coefficient Standard Reference Material (SRM™), Bi2Te3, that is essential for interlaboratory data comparison and for instrument calibration. Certification measurements were performed using a differential steady

Development of a Seebeck Coefficient Standard Reference Material

August 7, 2009
Author(s)
Nathan Lowhorn, Winnie Wong-Ng, John Lu, Evan L. Thomas, Makoto Otani, Martin L. Green, Neil Dilley, Jeffrey Sharp, Thanh N. Tran
We have successfully developed a Seebeck coefficient Standard Reference Material (SRM ), Bi2Te3, that is crucial for interlaboratory data comparison and for instrument calibration. Certification measurements were performed using two different techniques on

Statistical analysis of a round-robin measurement survey of two candidate materials for a Seebeck coefficient Standard Reference Material

February 2, 2009
Author(s)
John Lu, Nathan Lowhorn, Winnie Wong-Ng, Weiping Zhang, Evan L. Thomas, Makoto Otani, Martin L. Green, Thanh N. Tran, Chris Caylor, Neil Dilley, Adams Downey, B Edwards, Norbert Elsner, S Ghamaty, Timothy Hogan, Qing Jie, Qiang Li, Joshua B. Martin, George S. Nolas, H Obara, Jeffrey Sharp, Rama Venkatasubramanian, Rhonda Willigan, Jihui Yang, Terry Tritt
In an effort to develop a Standard Reference Material (SRM ) for Seebeck coefficient, we have conducted a round-robin measurement survey of two candidate materials undoped Bi2Te3 and constantan (55% Cu and 45% Ni alloy). Measurements were performed in two

Round-Robin Studies of Two Potential Seebeck Coefficient Standard Reference Materials

January 14, 2009
Author(s)
Nathan Lowhorn, Winnie K. Wong-Ng, Weiping Zhang, John Lu, Makoto Otani, Evan L. Thomas, Martin L. Green, Thanh Tran
The scientific activities of NIST include the development and distribution of standard reference materials (SRM) for instrument calibration and inter-laboratory data comparison. Full characterization of a thermoelectric material requires measurement of the

Form-Profiling of Optics Using the Geometry Measuring Machine and NIST M-48 CMM

January 1, 2006
Author(s)
Nadia Machkour-Deshayes, John R. Stoup, John Lu, Johannes A. Soons, Ulf Griesmann, Robert S. Polvani
We are developing an instrument, the Geometry Measuring Machine (GEMM), to measure the profile errors of aspheric and free form optical surfaces, which require measurement uncertainties near 1nm. Using GEMM, an optical profile is reconstructed from a set

Statistical Methods For Holistic Mass Spectral Analysis

Author(s)
John Lu, Charles M. Guttman
Analysis of molecular mass distribution data for characterizing the complex synthetic polymer structure has demanded new statistical methods. As with many other high throughput measurement devices, typically only very small number of replicates can be