scholarly journals Using GIS for Engineering and Geological Zoning for Wells Construction in the Churakovskoye Oil Field

2020 ◽  
Vol 19 (4) ◽  
pp. 372-379
Author(s):  
Sh. Kh. Gaynanov ◽  
◽  
A. V. Konoplev ◽  
S. V. Kozlovskiy ◽  
◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Sliding Window ◽  
Field Work ◽  
Oil Field ◽  
Window Method ◽  
Engineering Geological Zoning ◽  
Geological Situation ◽  
Density Analysis ◽  
Complex Engineering ◽  
Sliding Window Method

In this work, the location of the karst sinkholes not recorded during field work was mapped using satellite imagery data. Based on GIS, engineering-geological zoning of the territory was carried out by the method of distance from the nearest surface karst occurrence. The results of the sinkholes density analysis of the territory conducted using the sliding window method are presented. The complex engineering-geological zonation of the territory was carried out according to the complexity of the engineering-geological situation.

Sliding Window Based Fast Corner Matching Palmprint Authentication

2011 ◽  
Vol 1 (3) ◽  
pp. 16-34
Author(s):  
Jyoti Malik ◽  
G. Sainarayanan ◽  
Ratna Dahiya
Keyword(s):  
Response Time ◽  
Hamming Distance ◽  
Recognition Rate ◽  
Sliding Window ◽  
Phase Congruency ◽  
Window Method ◽  
Harris Corner ◽  
Sliding Window Method ◽  
Authentication System ◽  
Harris Corner Detector

Authentication time is the main and important part of the authentication system. Normally the response time should be fast but as the number of persons in the database increases, there is probability of more response time taken for authentication. The need of fast authentication system arises so that authentication time (matching time) is very less. This paper proposes a sliding window approach to make fast authentication system. The highlight of sliding window method is constant matching time, fast and can match translated images also. Several palmprint matching methods like match by correlation etc. are dependent upon the number of corners detected and so is the matching time. In sliding window method, matching time is constant as the numbers of matching operations are limited and the matching time is independent of the number of corners detected. The palmprint corner features extracted using two approaches Phase Congruency Corner Detector and Harris Corner Detector are binarized so that only useful information (features) is matched. The two approaches of Phase Congruency Corner Detector and Harris Corner Detector, when matched with hamming distance using sliding window can achieve recognition rate of 97.7% and 97.5% respectively.

scholarly journals Iterative sliding window method for shorter number of operations in modular exponentiation and scalar multiplication

2017 ◽  
Vol 4 (1) ◽  
pp. 1304499 ◽  
Author(s):  
Adamu Muhammad Noma ◽  
Abdullah Muhammed ◽  
Zuriati Ahmad Zukarnain ◽  
Muhammad Afendee Mohamed ◽  
Duc Pham
Keyword(s):  
Sliding Window ◽  
Scalar Multiplication ◽  
Modular Exponentiation ◽  
Window Method ◽  
Sliding Window Method
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