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Author: chih-ming wang
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1. Parameter Estimation and Uncertainty Evaluation in a Low Rician K-Factor Reverberation-Chamber Environment
Published: 10/15/2014
Authors: Chih-Ming Wang, Catherine A Remley, Ansgar T. Kirk, Ryan J. Pirkl, Christopher L Holloway, Dylan F Williams, Paul D Hale
Abstract: In this paper we study statistical methods for estimating the Rician K-factor when this parameter is small. A fiducial approach for making statistical inference on the K-factor is discussed. The approach requires a Monte Carlo method to compute the u ...

2. Calibrated Oscilloscope Measurements for System-Level Characterization of Weakly Nonlinear Sources
Published: 4/4/2014
Authors: Catherine A Remley, Dylan F Williams, Paul D Hale, Chih-Ming Wang, Jeffrey A Jargon, Youngcheol Park
Abstract: We present a technique to characterize and correct for linear and weakly nonlinear distortion introduced by the nonideal response of a wideband, high-frequency modulated-signal source. The magnitude and phase relationships between frequency component ...

3. Statistical Sampling of Carbon Nanotube Populations by Thermogravimetric Analysis
Published: 12/25/2013
Authors: Elisabeth Mansfield, Aparna Kar, Chih-Ming Wang, Ann Chiaramonti Chiaramonti Debay
Abstract: Carbon nanotubes are one of the most promising nanomaterials on the market, with applications in electronics devices, sensing, batteries, composites and medical communities. Strict control of the carbon nanotube chemistry and properties is neces ...

4. Baseband Corrections for Precision Millimeter Wave Signal Measurements
Published: 8/12/2013
Authors: Catherine A Remley, Saeed Farsi, Dominique Schreurs, Dylan F Williams, Paul D Hale, Chih-Ming Wang
Abstract: NA

5. A Precision Millimeter-Wave Modulated-Signal Source
Published: 6/6/2013
Authors: Catherine A Remley, Paul D Hale, Dylan F Williams, Chih-Ming Wang
Abstract: We develop and characterize a modulated-signal source for use at millimeter-wave frequencies. Components within the source are phase locked by a 10 GHz reference source to minimize drift and improve synchronization. The complex frequency response of ...

6. Assessing the Effect of Mode-Stirring Method on Uncertainty in Reverberation-Chamber Measurements
Published: 1/14/2013
Authors: Catherine A Remley, Ryan J. Pirkl, Chih-Ming Wang, Haider Shah
Abstract: We develop methods for assessing the standard uncertainty arising from various combinations of mode-stirring methods used in reverberation-chamber measurements. By use of a straightforward measurement-based technique, the combination of mode-stirring ...

7. Sequential Estimation of Timebase Corrections for an Arbitrarily Long Waveform
Published: 10/1/2012
Authors: Chih-Ming Wang, Paul D Hale, Jeffrey A Jargon, Dylan F Williams, Catherine A Remley
Abstract: We present a procedure for correcting the timebase distortion and jitter of temporal waveforms of arbitrary lengths. This is achieved by estimating the timebase distortion and jitter sequentially with overlapping measurements and using the informatio ...

8. Traceability of high-speed electrical waveforms at NIST, NPL, and PTB
Published: 7/6/2012
Authors: Paul D Hale, Dylan F Williams, Andrew M Dienstfrey, Chih-Ming Wang, Jeffrey A Jargon, David Humphreys, Matthew Harper, Heiko Fuser, Mark Bieler
Abstract: Instruments for measuring high-speed waveforms typically require calibration to obtain accurate results. The national metrology institutes of the United States of America, the United Kingdom, and Germany offer measurement services based on electro-op ...

9. Pivotal Methods in the Propagation of Distributions
Published: 4/24/2012
Authors: Chih-Ming Wang, Jan Hannig, Hariharan K Iyer
Abstract: We propose a method for assigning a probability distribution to an input quantity. The distribution is used in the Monte Carlo method for uncertainty evaluation. The proposed method provides an alternative to other methods, such as the principle of m ...

10. Fiducial Prediction Intervals
Published: 2/18/2012
Authors: Chih-Ming Wang, Jan Hannig, Hariharan K Iyer
Abstract: This paper presents an approach for constructing prediction intervals for any given distribution. The approach is based on the principle of fiducial inference. We use several examples, including the normal, binomial, gamma, and Weibull distributions, ...

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