Novel method of Euclidean distance calculation for bilateral filtering based on CMOS sensor noise profiles

2008 ◽  
Author(s):  
Radu Gheorghe ◽  
Milivoje Aleksic ◽  
Maxim Smirnov
Keyword(s):  
Euclidean Distance ◽  
Bilateral Filtering ◽  
Sensor Noise ◽  
Distance Calculation ◽  
Cmos Sensor ◽  
Novel Method

Measuring fiber orientations in nonwoven web images using corner detection by Bézier fitting curves

2017 ◽  
Vol 88 (18) ◽  
pp. 2120-2131 ◽  
Author(s):  
Jue Hou ◽  
Bugao Xu ◽  
Hanchao Gao ◽  
RongWu Wang
Keyword(s):  
Curve Fitting ◽  
Fiber Orientation ◽  
Euclidean Distance ◽  
High Accuracy ◽  
Corner Detection ◽  
Bézier Curve ◽  
Corner Points ◽  
Novel Method ◽  
Web Images ◽  
Orientation Statistics

This paper describes a novel method for measuring fiber orientations in nonwoven web images by using Bézier fitting curves to detect corners of fiber edges and to separate crossing fiber edges. First, the Canny detector was adopted to extract fiber edges. Second, Bézier curve fitting was used to fit each fiber edge for calculating the curvature of every point on the edge. Third, corner points were detected by locating points where the curvatures were minimal on various edges and below the threshold to divide edges into segments for orientation calculations. Last, a formula calculating the fiber orientation statistics based on the Euclidean distance was established. The experiment results demonstrated that the proposed method is robust for analyzing different nonwoven web images, and has a high accuracy for corner detection and fiber orientation calculation.

Evidence Theory of One-Dimensional Compression KNN Classification Method

2010 ◽  
Vol 143-144 ◽  
pp. 1337-1341
Author(s):  
Wei Feng Yan ◽  
Gen Xiu Wu ◽  
Can Ze Li ◽  
Li Zhou
Keyword(s):  
Euclidean Distance ◽  
Evidence Theory ◽  
Numerical Data ◽  
Calculation Methods ◽  
Data Set ◽  
One Dimensional ◽  
Actual Distance ◽  
Distance Calculation ◽  
New Methods ◽  
Knn Classification

As only using Euclidean distance KNN algorithm has its limits, many researchers use other distance calculation methods as the replacement it to improve the accuracy of Data Classification. While combining the DS evidence theory with a series of KNN algorithm which discussed in this paper, we found that every algorithm has their merits. All of them ignore the analysis of the data set, through deeply analysis we found that the actual distance is determined by the larger value when two attribute values are in great difference. Therefore, what we do next is to compress the large-dimensional numerical data values. By this way, the accuracy of KNN, VSMKNN, KERKNN algorithm are obviously improved after experiment and then these new methods are called CDSKNN, CDSVSMKNN, CDSKERKNN.

Research on Recommendation System Selection Based on Fuzzy VIKOR with Multiple Distances

2014 ◽  
Vol 598 ◽  
pp. 481-485 ◽  
Author(s):  
Bao Wen Sun ◽  
Ming Li ◽  
Wei Zhang
Keyword(s):  
Hausdorff Distance ◽  
Euclidean Distance ◽  
Recommendation System ◽  
Hamming Distance ◽  
Evaluation Criteria ◽  
Calculation Methods ◽  
Distance Calculation ◽  
Fuzzy Vikor ◽  
Mcdm Method ◽  
System Selection

Nowadays, there are several different kinds of methodology in selecting recommendation systems (CRS), and every method has its own evaluation criteria to pick up the best one. In this paper, a new MCDM method for recommendation system selection based on fuzzy VIKOR with multiple distances is introduced. It selects the best system by calculating values using three different distance calculation methods, which are Hamming distance, Euclidean distance and Hausdorff distance, and voting via Condorcet method. It minimizes the effect of distance and offers a more objective result than other methods and helps enterprises to select the most suitable recommendation system.

INCREASING CHEMICAL SELECTIVITY OF CARBON NANOTUBE-BASED SENSORS BY FLUCTUATION-ENHANCED SENSING

2010 ◽  
Vol 09 (03) ◽  
pp. 277-287 ◽  
Author(s):  
DÁNIEL MOLNÁR ◽  
PÉTER HESZLER ◽  
RÓBERT MINGESZ ◽  
ZOLTÁN GINGL ◽  
ÁKOS KUKOVECZ ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Environmental Protection ◽  
Output Signal ◽  
Gas Detection ◽  
Chemical Information ◽  
Wide Spread ◽  
Sensor Noise ◽  
Main Disadvantage ◽  
Novel Method ◽  
Chemical Selectivity

Nowadays gas detection in the ppm and sub-ppm domain is essential in terms of environmental protection as well as reducing sanitary risks. However, detecting systems to perform these measurements (e.g., gas chromatographs) are expensive and take up too much space, thus their use is not likely to become wide-spread. Small, cheap and easily mountable sensors, such as resistive sensors are more applicable for this purpose. But the main disadvantage of these sensors is the lack of chemical selectivity. Yet, a novel method called fluctuation-enhanced sensing (FES), which considers the sensor noise as the source of chemical information, can be used to improve selectivity. Since carbon nanotube (CNT)-based sensors are regarded as promising devices for FES measurements, we investigated whether stationary fluctuations in output signal (dc-resistance) of a CNT sensor could be used to increase chemical selectivity. In this work we prove that FES is applicable to increase selectivity of CNT sensors: air polluting gases ( N2O , NH 3 and H2S ) and their mixtures can be distinguished. Furthermore, we also show that different concentrations of the same analyte can be differentiated and chemical selectivity can be extended into the sub-ppm region.

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