scholarly journals Temporary Gastric Stimulation in Patients With Gastroparesis Symptoms: Low-Resolution Mapping Multiple Versus Single Mucosal Lead Electrograms

2019 ◽  
Vol 12 (2) ◽  
pp. 60-66
Author(s):  
Mohsen Hasanin ◽  
Om Amin ◽  
Hamza Hassan ◽  
Archana Kedar ◽  
Michael Griswold ◽  
...  
NeuroImage ◽  
2018 ◽  
Vol 164 ◽  
pp. 131-143 ◽  
Author(s):  
Laurentius Huber ◽  
Dimo Ivanov ◽  
Daniel A. Handwerker ◽  
Sean Marrett ◽  
Maria Guidi ◽  
...  

2017 ◽  
Vol 4 (2) ◽  
pp. 021103 ◽  
Author(s):  
Antonio M. Chiarelli ◽  
Edward L. Maclin ◽  
Kathy A. Low ◽  
Sergio Fantini ◽  
Monica Fabiani ◽  
...  

1995 ◽  
Vol 6 (7) ◽  
pp. 454-458 ◽  
Author(s):  
N. Urosevic ◽  
J. P. Mansfield ◽  
J. S. Mackenzie ◽  
G. R. Shellam

2021 ◽  
Vol 2 (4) ◽  
pp. 27-33
Author(s):  
Rafaa Amen Kazem ◽  
Jamila H. Suad ◽  
Huda Abdulaali Abdulbaqi

Super Resolution is a field of image analysis that focuses on boosting the resolution of photographs and movies without compromising detail or visual appeal, instead enhancing both. Multiple (many input images and one output image) or single (one input and one output) stages are used to convert low-resolution photos to high-resolution photos. The study examines super-resolution methods based on a convolutional neural network (CNN) for super-resolution mapping at the sub-pixel level, as well as its primary characteristics and limitations for noisy or medical images.


2006 ◽  
pp. 105-114 ◽  
Author(s):  
M. Rosa Ponce ◽  
Pedro Robles ◽  
Francisca M. Lozano ◽  
M. Asunción Brotóns ◽  
José L. Micol

2021 ◽  
Vol 1 (4) ◽  
pp. 27-33
Author(s):  
Rafaa Amen Kazem ◽  
Jamila H. Suad ◽  
Huda Abdulaali Abdulbaqi

Super Resolution is a field of image analysis that focuses on boosting the resolution of photographs and movies without compromising detail or visual appeal, instead enhancing both. Multiple (many input images and one output image) or single (one input and one output) stages are used to convert low-resolution photos to high-resolution photos. The study examines super-resolution methods based on a convolutional neural network (CNN) for super-resolution mapping at the sub-pixel level, as well as its primary characteristics and limitations for noisy or medical images.


1996 ◽  
Vol 111 ◽  
pp. 1086 ◽  
Author(s):  
C. Giovanardi ◽  
L. K. Hunt

2020 ◽  
Vol 8 (2) ◽  
pp. 162-173
Author(s):  
Cecilia Smith

AbstractArchaeologists are tasked with balancing a call to open data and the need to maintain confidentiality of sensitive archaeological site locations. Low-resolution mapping and data aggregation are the methods most commonly used to hide site locations; however, we understand little of the effectiveness of these practices. Trends in geomasking, obscuring observed geographic points, to anonymize public health data are suggested as a source of methods for sharing archaeological site data. Archaeologists have available to them a number of geomasking methods that balance open data and site security in different ways. Low-resolution mapping at several scales and random direction with fixed radius, random perturbation donut, and Gaussian donut techniques are tested on a set of archaeological site locations. Random perturbation donuts resulted in the best balance between obscuring archaeological locations and conveying observed spatial patterning. Researchers should carefully consider how they convey archaeological location data, as commonly used low-resolution scales may not provide the desired level of obscurity. Researchers should also be explicit as to how and why their methods of site visualization are chosen.


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