scholarly journals WILDFIRE DETECTION SYSTEM BASED ON PRINCIPAL COMPONENT ANALYSIS AND IMAGE PROCESSING OF REMOTE-SENSED VIDEO

2016 ◽  
Author(s):  
Ryan F. Radjabi
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis

scholarly journals A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis

2019 ◽  
Vol 9 (22) ◽  
pp. 4733
Author(s):  
Cuiping Shao ◽  
Huiyun Li ◽  
Zheng Wang ◽  
Jiayan Fang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Error Resilience ◽  
Component Analysis ◽  
Cmos Technology ◽  
Error Correcting Codes ◽  
Memory Storage ◽  
Protection Scheme ◽  
Bit Flip

Nanoscale CMOS technology has encountered severe reliability issues especially in on-chip memory. Conventional word-level error resilience techniques such as Error Correcting Codes (ECC) suffer from high physical overhead and inability to correct increasingly reported multiple bit flip errors. On the other hands, state-of-the-art applications such as image processing and machine learning loosen the requirement on the levels of data protection, which result in dedicated techniques of approximated fault tolerance. In this work, we introduce a novel error protection scheme for memory, based on feature extraction through Principal Component Analysis and the modular-wise technique to segment the data before PCA. The extracted features can be protected by replacing the fault vector with the averaged confinement vectors. This approach confines the errors with either single or multi-bit flips for generic data blocks, whilst achieving significant savings on execution time and memory usage compared to traditional ECC techniques. Experimental results of image processing demonstrate that the proposed technique results in a reconstructed image with PSNR over 30 dB, while robust against both single bit and multiple bit flip errors, with reduced memory storage to just 22.4% compared to the conventional ECC-based technique.

scholarly journals Using Principal Component Analysis to Solve a Class Imbalance Problem in Traffic Incident Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Changjiang Zheng ◽  
Shuyan Chen ◽  
Wei Wang ◽  
Jian Lu
Keyword(s):  
Principal Component Analysis ◽  
Detection System ◽  
Class Imbalance ◽  
Principal Component ◽  
Component Analysis ◽  
Incident Detection ◽  
Least Squares Regression ◽  
Class Imbalance Problem ◽  
Significance Level ◽  
Traffic Incident

High imbalances occur in real-world situations when a detection system needs to identify the rare but important event of a traffic incident. Traffic incident detection can be treated as a task of learning classifiers from imbalanced or skewed datasets. Using principal component analysis (PCA), a one-class classifier for incident detection is constructed from the major and minor principal components of normal instances. Experiments are conducted with a real traffic dataset collected from the A12 highway in The Netherlands. The parameters setting, including the significance level, the percentage of the total variation explained, and the upper bound of the eigenvalues for the minor components, is discussed. The test results demonstrate that this method achieves better performance than partial least squares regression. The method is shown to be promising for traffic incident detection.

Principal component analysis in medical image processing: a study

2015 ◽  
Vol 1 (1) ◽  
pp. 65 ◽  
Author(s):  
Dibyadeep Nandi ◽  
Amira S. Ashour ◽  
Sourav Samanta ◽  
Sayan Chakraborty ◽  
Mohammed A.M. Salem ◽  
...  
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Medical Image ◽  
Medical Image Processing ◽  
Principal Component ◽  
Component Analysis
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