scholarly journals Miniaturized high frequency phased array devices for high resolution neonatal and intraoperative imaging

1990 ◽  
Vol 15 (2) ◽  
pp. A10 ◽  
Author(s):  
David J. Sahn ◽  
Diana Tasker ◽  
Sandra Hagen-Ansert ◽  
Axel Brisken ◽  
Scott Corbett
Keyword(s):  
High Resolution ◽  
High Frequency ◽  
Phased Array ◽  
Intraoperative Imaging

scholarly journals High-Resolution Black-Blood MRI of the Carotid Vessel Wall Using Phased-Array Coils at 1.5 and 3 Tesla1

2005 ◽  
Vol 12 (12) ◽  
pp. 1521-1526 ◽  
Author(s):  
SeshaSailaja Anumula ◽  
Hee Kwon Song ◽  
Alexander C. Wright ◽  
Felix W. Wehrli
Keyword(s):  
High Resolution ◽  
Vessel Wall ◽  
Phased Array ◽  
Black Blood ◽  
Black Blood Mri ◽  
Carotid Vessel Wall ◽  
Phased Array Coils ◽  
Array Coils ◽  
Carotid Vessel

scholarly journals PCDRN: Progressive Cascade Deep Residual Network for Pansharpening

2020 ◽  
Vol 12 (4) ◽  
pp. 676 ◽  
Author(s):  
Yong Yang ◽  
Wei Tu ◽  
Shuying Huang ◽  
Hangyuan Lu
Keyword(s):  
High Resolution ◽  
Loss Function ◽  
High Frequency ◽  
Experimental Results ◽  
Superior Performance ◽  
Residual Network ◽  
High Quality ◽  
Convolution Approach ◽  
Visual Assessments ◽  
Coarse To Fine

Pansharpening is the process of fusing a low-resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image. In the process of pansharpening, the LRMS image is often directly upsampled by a scale of 4, which may result in the loss of high-frequency details in the fused high-resolution multispectral (HRMS) image. To solve this problem, we put forward a novel progressive cascade deep residual network (PCDRN) with two residual subnetworks for pansharpening. The network adjusts the size of an MS image to the size of a PAN image twice and gradually fuses the LRMS image with the PAN image in a coarse-to-fine manner. To prevent an overly-smooth phenomenon and achieve high-quality fusion results, a multitask loss function is defined to train our network. Furthermore, to eliminate checkerboard artifacts in the fusion results, we employ a resize-convolution approach instead of transposed convolution for upsampling LRMS images. Experimental results on the Pléiades and WorldView-3 datasets prove that PCDRN exhibits superior performance compared to other popular pansharpening methods in terms of quantitative and visual assessments.

scholarly journals High-resolution monitoring of nutrients in groundwater and surface waters: process understanding, quantification of loads and concentrations, and management applications

2016 ◽  
Vol 20 (9) ◽  
pp. 3619-3629 ◽  
Author(s):  
Frans C. van Geer ◽  
Brian Kronvang ◽  
Hans Peter Broers
Keyword(s):  
High Resolution ◽  
High Frequency ◽  
Temporal Trends ◽  
Surface Water Quality ◽  
Mitigation Measures ◽  
Nutrient Concentrations ◽  
Special Issue ◽  
Monitoring Strategies ◽  
Frequency Monitoring ◽  
Water Quality And Quantity

Abstract. Four sessions on "Monitoring Strategies: temporal trends in groundwater and surface water quality and quantity" at the EGU conferences in 2012, 2013, 2014, and 2015 and a special issue of HESS form the background for this overview of the current state of high-resolution monitoring of nutrients. The overview includes a summary of technologies applied in high-frequency monitoring of nutrients in the special issue. Moreover, we present a new assessment of the objectives behind high-frequency monitoring as classified into three main groups: (i) improved understanding of the underlying hydrological, chemical, and biological processes (PU); (ii) quantification of true nutrient concentrations and loads (Q); and (iii) operational management, including evaluation of the effects of mitigation measures (M). The contributions in the special issue focus on the implementation of high-frequency monitoring within the broader context of policy making and management of water in Europe for support of EU directives such as the Water Framework Directive, the Groundwater Directive, and the Nitrates Directive. The overview presented enabled us to highlight the typical objectives encountered in the application of high-frequency monitoring and to reflect on future developments and research needs in this growing field of expertise.

scholarly journals Assessing Impacts of the High-Frequency Assimilation of Surface Observations for the Forecast of Convection Initiation on 3 April 2014 within the Dallas–Fort Worth Test Bed

2018 ◽  
Vol 146 (11) ◽  
pp. 3845-3872 ◽  
Author(s):  
Nicholas A. Gasperoni ◽  
Xuguang Wang ◽  
Keith A. Brewster ◽  
Frederick H. Carr
Keyword(s):  
High Resolution ◽  
High Frequency ◽  
Weather Prediction ◽  
National Research Council ◽  
Future Application ◽  
Fort Worth ◽  
Sources Of Information ◽  
Test Bed ◽  
Convection Initiation ◽  
The Impact

Abstract The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system and the continued advancement toward accurate high-resolution numerical weather prediction. The research test bed known as the Dallas–Fort Worth (DFW) Urban Demonstration Network was created to experiment with many kinds of mesoscale observations that could be used in a data assimilation system. Many nonconventional observations, including Earth Networks and Citizen Weather Observer Program surface stations, are combined with conventional operational data to form the test bed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information. In this study, the GSI-based EnKF system was used together with the WRF-ARW Model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hail storm case. Data denial experiments tested the impact of high-frequency (5 min) assimilation of nonconventional data on the timing and location of CI and subsequent storm evolution. Results showed nonconventional observations were necessary to capture details in the dryline structure causing localized enhanced convergence and leading to CI. Diagnosis of denial-minus-control fields showed the cumulative influence each observing network had on the resulting CI forecast. It was found that most of this impact came from the assimilation of thermodynamic observations in sensitive areas along the dryline gradient. Accurate metadata were found to be crucial toward the future application of nonconventional observations in high-resolution assimilation and forecast systems.

Sign in / Sign up

Export Citation Format

Share Document