scholarly journals An adaptive threshold estimation scheme for abrupt changes detection algorithm in a cement rotary kiln

2014 ◽  
Vol 259 ◽  
pp. 835-842 ◽  
Author(s):  
Abdelmalek Kouadri ◽  
Abderazak Bensmail ◽  
Aissa Kheldoun ◽  
Larbi Refoufi
Keyword(s):  
Rotary Kiln ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Threshold Estimation ◽  
Abrupt Changes ◽  
Estimation Scheme ◽  
Changes Detection

scholarly journals A New Approach to Detection of Changes in Multidimensional Patterns

2020 ◽  
Vol 10 (2) ◽  
pp. 125-136 ◽  
Author(s):  
Tomasz Gałkowski ◽  
Adam Krzyżak ◽  
Zbigniew Filutowicz
Keyword(s):  
Kernel Estimation ◽  
Detection Algorithm ◽  
Heterogeneous Data ◽  
Data Types ◽  
New Approach ◽  
Abrupt Changes ◽  
Changes Detection ◽  
Data Collections ◽  
Derivatives Of

AbstractNowadays, unprecedented amounts of heterogeneous data collections are stored, processed and transmitted via the Internet. In data analysis one of the most important problems is to verify whether data observed or/and collected in time are genuine and stationary, i.e. the information sources did not change their characteristics. There is a variety of data types: texts, images, audio or video files or streams, metadata descriptions, thereby ordinary numbers. All of them changes in many ways. If the change happens the next question is what is the essence of this change and when and where the change has occurred. The main focus of this paper is detection of change and classification of its type. Many algorithms have been proposed to detect abnormalities and deviations in the data. In this paper we propose a new approach for abrupt changes detection based on the Parzen kernel estimation of the partial derivatives of the multivariate regression functions in presence of probabilistic noise. The proposed change detection algorithm is applied to oneand two-dimensional patterns to detect the abrupt changes.

scholarly journals An automated cloud detection method based on the green channel of total-sky visible images

2015 ◽  
Vol 8 (11) ◽  
pp. 4671-4679 ◽  
Author(s):  
J. Yang ◽  
Q. Min ◽  
W. Lu ◽  
W. Yao ◽  
Y. Ma ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Mie Scattering ◽  
Brightness Distribution ◽  
Adaptive Threshold ◽  
Cloud Detection ◽  
Blue Band ◽  
Green Channel ◽  
The Past ◽  
Visible Images ◽  
Cloud Mask

Abstract. Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.

A WT-Based Edge Detection with Adaptive Threshold

2011 ◽  
Vol 204-210 ◽  
pp. 1386-1389
Author(s):  
Deng Yin Zhang ◽  
Li Xiao ◽  
Shun Rong Bo
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Value Iteration ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Image Edge ◽  
Threshold Setting

The existing edge detection algorithms with wavelet transform need to artificially set the threshold value and are lack of flexibility.To salve the limitations, in this paper, we propose a WT(wavelet transform)-based edge detection algorithm with adaptive threshold, which uses threshold value iteration method to achieve adaptive threshold setting. Comparison of experiment results for the CT image shows that the method which improve the clarity and continuity of the image edge can effectively distinguish edge and noise, and get more completely information of the edge. It has good application value in the fields of medical clinical diagnosis and image processing.

scholarly journals Performance of Threshold Detection in Cognitive Radio with Improved Otsu’s and Recursive One-Sided Hypothesis Testing Technique

2019 ◽  
Vol 8 (9S3) ◽  
pp. 343-346
Keyword(s):  
Cognitive Radio ◽  
Hypothesis Testing ◽  
Threshold Energy ◽  
Energy Detection ◽  
Adaptive Threshold ◽  
Spectrum Efficiency ◽  
Threshold Estimation ◽  
Testing Technique ◽  
Noise Statistics ◽  
Signal Threshold

Cognitive radio (CR) is a new technology proposed to enhance spectrum efficiency by enabling unlicensed secondary users to access the licensed frequency bands without getting involved with the primary users licensed. Although considered optimal, in order to calculate the signal threshold, this approach requires prior noise statistics information. Even though considered optimal, in order to calculate the signal threshold, this approach requires prior noise statistics information. A prominent example of an Adaptive Threshold Estimation Technique (ATT) for energy detection in Cognitive Radio (CR) is the Recursive One-sided Hypothesis Testing Technique (ROHT). Accurate threshold values are known to be calculated based on the correct choice of their parameter values, which include the standard deviation coefficient and the stop criteria. In this paper, for efficient threshold estimation, the improved Otsu and ROHT are combined for estimating threshold even in the presence of noise floor without need of prior knowledge. The proposed methodology for enactment in cognitive radio sensor networks (CRSN) system based on the adaptive threshold energy detection model with noise variance estimation. The simulation is carried out with the help of Matlab 2017a with the improved Otsu and ROHT techniques. The results obtained shows that improved Otsu and ROHT techniques outperforms that of fixed threshold energy detection in terms of different probability of false alarm rates and miss detections

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