Least-squares multiple imaging constrained jointly by OBN and towed-streamer data

Author(s):  
G. Poole
Author(s):  
Xiao Zhang

Polymer microscopy involves multiple imaging techniques. Speed, simplicity, and productivity are key factors in running an industrial polymer microscopy lab. In polymer science, the morphology of a multi-phase blend is often the link between process and properties. The extent to which the researcher can quantify the morphology determines the strength of the link. To aid the polymer microscopist in these tasks, digital imaging systems are becoming more prevalent. Advances in computers, digital imaging hardware and software, and network technologies have made it possible to implement digital imaging systems in industrial microscopy labs.


VASA ◽  
2018 ◽  
Vol 47 (5) ◽  
pp. 361-375 ◽  
Author(s):  
Harold Goerne ◽  
Abhishek Chaturvedi ◽  
Sasan Partovi ◽  
Prabhakar Rajiah

Abstract. Although pulmonary embolism is the most common abnormality of the pulmonary artery, there is a broad spectrum of other congenital and acquired pulmonary arterial abnormalities. Multiple imaging modalities are now available to evaluate these abnormalities of the pulmonary arteries. CT and MRI are the most commonly used cross-sectional imaging modalities that provide comprehensive information on several aspects of these abnormalities, including morphology, function, risk-stratification and therapy-monitoring. In this article, we review the role of state-of-the-art pulmonary arterial imaging in the evaluation of non-thromboembolic disorders of pulmonary artery.


Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 110-115 ◽  
Author(s):  
Rand R. Wilcox ◽  
Jinxia Ma

Abstract. The paper compares methods that allow both within group and between group heteroscedasticity when performing all pairwise comparisons of the least squares lines associated with J independent groups. The methods are based on simple extension of results derived by Johansen (1980) and Welch (1938) in conjunction with the HC3 and HC4 estimators. The probability of one or more Type I errors is controlled using the improvement on the Bonferroni method derived by Hochberg (1988) . Results are illustrated using data from the Well Elderly 2 study, which motivated this paper.


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