Passivated SDD-Based Detection Unit to Improve Reliability in Scintillation Detection

2019 ◽  
Author(s):  
L. Buonanno ◽  
M. Carminati ◽  
C. Fiorini ◽  
P. King ◽  
G. Borghi ◽  
...  
Keyword(s):  
Scintillation Detection ◽  
Detection Unit ◽  
Improve Reliability

Improving Electricity Access through Policy Reform

2018 ◽  
Author(s):  
Sam Amadi
Keyword(s):  
Policy Reform ◽  
Social Needs ◽  
Private Capital ◽  
Power Sector ◽  
Legal Regime ◽  
Sector Reform ◽  
State Institutions ◽  
Reform Programme ◽  
Electricity Access ◽  
Improve Reliability

In Nigeria, an estimated 170 million people depend on less than 4,000 megawatts of electricity from the grid for economic and social needs. Since 2000 the country has embarked on an ambitious power sector reform programme, the main objective of which is to ensure adequate, available, and reliable electricity. The power sector reform adopts a neo-liberal development model that is based on the triple strategy of liberalization, commercialization, and privatization. This strategy has relied heavily on the reform of the existing legal regime of state institutions so as to attract foreign private capital to increase capacity, expand connection, and improve reliability. This chapter reviews the incompletely theorized neo-liberal assumptions in the reform policies and shows how these assumptions have undermined the efficacy of legal reform in the electricity industry and resulted in failed expectation.

scholarly journals Improved Visual Localization via Graph Filtering

2021 ◽  
Vol 7 (2) ◽  
pp. 20
Author(s):  
Carlos Lassance ◽  
Yasir Latif ◽  
Ravi Garg ◽  
Vincent Gripon ◽  
Ian Reid
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Visual Localization ◽  
Localization Accuracy ◽  
Additional Information ◽  
Feature Representations ◽  
The Mean ◽  
Classical Image ◽  
Vision Based Localization ◽  
Improve Reliability

Vision-based localization is the problem of inferring the pose of the camera given a single image. One commonly used approach relies on image retrieval where the query input is compared against a database of localized support examples and its pose is inferred with the help of the retrieved items. This assumes that images taken from the same places consist of the same landmarks and thus would have similar feature representations. These representations can learn to be robust to different variations in capture conditions like time of the day or weather. In this work, we introduce a framework which aims at enhancing the performance of such retrieval-based localization methods. It consists in taking into account additional information available, such as GPS coordinates or temporal proximity in the acquisition of the images. More precisely, our method consists in constructing a graph based on this additional information that is later used to improve reliability of the retrieval process by filtering the feature representations of support and/or query images. We show that the proposed method is able to significantly improve the localization accuracy on two large scale datasets, as well as the mean average precision in classical image retrieval scenarios.

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