Maximum likelihood positioning algorithm for high-resolution PET scanners

2016 ◽  
Vol 43 (6Part1) ◽  
pp. 3049-3061 ◽  
Author(s):  
Nicolas Gross-Weege ◽  
David Schug ◽  
Patrick Hallen ◽  
Volkmar Schulz
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Positioning Algorithm ◽  
Pet Scanners

scholarly journals Assessing Riyadh’s Urban Change Utilizing High-Resolution Imagery

Land ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 193
Author(s):  
Ali Alghamdi ◽  
Anthony R. Cummings
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Accuracy Assessment ◽  
Middle Eastern ◽  
Object Oriented ◽  
Training Data ◽  
High Resolution Imagery ◽  
Significant Research ◽  
Desert Areas ◽  
Spot 5

The implications of change on local processes have attracted significant research interest in recent times. In urban settings, green spaces and forests have attracted much attention. Here, we present an assessment of change within the predominantly desert Middle Eastern city of Riyadh, an understudied setting. We utilized high-resolution SPOT 5 data and two classification techniques—maximum likelihood classification and object-oriented classification—to study the changes in Riyadh between 2004 and 2014. Imagery classification was completed with training data obtained from the SPOT 5 dataset, and an accuracy assessment was completed through a combination of field surveys and an application developed in ESRI Survey 123 tool. The Survey 123 tool allowed residents of Riyadh to present their views on land cover for the 2004 and 2014 imagery. Our analysis showed that soil or ‘desert’ areas were converted to roads and buildings to accommodate for Riyadh’s rapidly growing population. The object-oriented classifier provided higher overall accuracy than the maximum likelihood classifier (74.71% and 73.79% vs. 92.36% and 90.77% for 2004 and 2014). Our work provides insights into the changes within a desert environment and establishes a foundation for understanding change in this understudied setting.

Estimating SpO2 via Time-Efficient High-Resolution Harmonics Analysis and Maximum Likelihood Tracking

2018 ◽  
Vol 22 (4) ◽  
pp. 1075-1086 ◽  
Author(s):  
Feiyi Fan ◽  
Yuepeng Yan ◽  
Kun Zhao ◽  
Fei Long ◽  
Hao Zhang
Keyword(s):  
Maximum Likelihood ◽  
High Resolution

A novel adaptive discrete cosine transform-domain filter for gap-inpainting of high resolution PET scanners

2014 ◽  
Vol 41 (8Part1) ◽  
pp. 082501 ◽  
Author(s):  
Cheng-Ting Shih ◽  
Jay Wu ◽  
Hsin-Hon Lin ◽  
Shu-Jun Chang ◽  
Keh-Shih Chuang
Keyword(s):  
High Resolution ◽  
Discrete Cosine Transform ◽  
Transform Domain ◽  
Cosine Transform ◽  
Pet Scanners
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