K
Kendall R. Waters
Researcher at National Institute of Standards and Technology
Publications - 68
Citations - 1617
Kendall R. Waters is an academic researcher from National Institute of Standards and Technology. The author has contributed to research in topics: Ultrasonic sensor & Dispersion relation. The author has an hindex of 21, co-authored 68 publications receiving 1564 citations. Previous affiliations of Kendall R. Waters include University of Texas at Austin & Washington University in St. Louis.
Papers
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Journal ArticleDOI
Causality-imposed (Kramers-Kronig) relationships between attenuation and dispersion
TL;DR: The accurate prediction of attenuation and dispersion is demonstrated using several forms of the K-K relations relevant to experimental measurements of media with attenuation coefficients obeying a frequency power law and media consisting of resonant scatterers.
Journal ArticleDOI
On the applicability of Kramers–Krönig relations for ultrasonic attenuation obeying a frequency power law
TL;DR: It is demonstrated that the Kramers-Kronig dispersion relations for application to media with ultrasonic attenuation obeying a frequency power law are available and agreement is found to better than 1 m/s over the experimentally available bandwidth.
Journal ArticleDOI
Temperature estimation using ultrasonic spatial compound imaging
TL;DR: A new imaging technique is proposed that improves the temperature estimation behind the heated region and reduces the variance of the temperature estimates in the entire image.
Patent
Intravascular ultrasound system for co-registered imaging
TL;DR: In this article, an intravascular ultrasound imaging system comprises a catheter having an elongated body having a distal end and an imaging core arranged to be inserted into the catheter.
Journal ArticleDOI
Differential forms of the Kramers-Kronig dispersion relations
TL;DR: For this form of ultrasonic attenuation, the differential Kramers-Kronig dispersion prediction is found to be identical to the (integral) Kramer-Konig disp immersion prediction.