GPU parallel implementation of improved noise adaptive principal component algorithm for feature extraction of hyperspectral images

2020 ◽  
Author(s):  
Chunchao Li ◽  
Luting Wu ◽  
Yuanxi Peng ◽  
Yu Liu ◽  
Yunpeng Xu
Keyword(s):  
Feature Extraction ◽  
Parallel Implementation ◽  
Principal Component ◽  
Hyperspectral Images

scholarly journals GPU Parallel Implementation for Real-Time Feature Extraction of Hyperspectral Images

2020 ◽  
Vol 10 (19) ◽  
pp. 6680
Author(s):  
Chunchao Li ◽  
Yuanxi Peng ◽  
Mingrui Su ◽  
Tian Jiang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Hyperspectral Image ◽  
Parallel Implementation ◽  
Image Data ◽  
Computer Experiments ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Real Time Processing ◽  
Time Processing

As the application of real-time requirements gradually increases or real-time processing and responding become the bottleneck of the task, parallel computing in hyperspectral image applications has also become a significant research focus. In this article, a flexible and efficient method is utilized in the noise adaptive principal component (NAPC) algorithm for feature extraction of hyperspectral images. From noise estimation to feature extraction, we deploy a complete CPU-GPU collaborative computing solution. Through the computer experiments on three traditional hyperspectral datasets, our proposed improved NAPC (INAPC) has stable superiority and provides a significant speedup compared with the OpenCV and PyTorch implementation. What’s more, we creatively establish a complete set of uncrewed aerial vehicle (UAV) photoelectric platform, including UAV, hyperspectral camera, NVIDIA Jetson Xavier, etc. Flight experimental results show, considering hyperspectral image data acquisition and transmission time, the proposed algorithm meets the feature extraction of real-time processing.

scholarly journals Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2748
Author(s):  
Jersson X. Leon-Medina ◽  
Maribel Anaya ◽  
Núria Parés ◽  
Diego A. Tibaduiza ◽  
Francesc Pozo
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Wind Turbine ◽  
Structural Damage ◽  
Principal Component ◽  
Gradient Boosting ◽  
Damage Classification ◽  
Linear Feature ◽  
Linear Feature Extraction ◽  
Extreme Gradient Boosting

Damage classification is an important topic in the development of structural health monitoring systems. When applied to wind-turbine foundations, it provides information about the state of the structure, helps in maintenance, and prevents catastrophic failures. A data-driven pattern-recognition methodology for structural damage classification was developed in this study. The proposed methodology involves several stages: (1) data acquisition, (2) data arrangement, (3) data normalization through the mean-centered unitary group-scaling method, (4) linear feature extraction, (5) classification using the extreme gradient boosting machine learning classifier, and (6) validation applying a 5-fold cross-validation technique. The linear feature extraction capabilities of principal component analysis are employed; the original data of 58,008 features is reduced to only 21 features. The methodology is validated with an experimental test performed in a small-scale wind-turbine foundation structure that simulates the perturbation effects caused by wind and marine waves by applying an unknown white noise signal excitation to the structure. A vibration-response methodology is selected for collecting accelerometer data from both the healthy structure and the structure subjected to four different damage scenarios. The datasets are satisfactorily classified, with performance measures over 99.9% after using the proposed damage classification methodology.

scholarly journals PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Kanokmon Rujirakul ◽  
Chakchai So-In ◽  
Banchar Arnonkijpanich
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Expectation Maximization ◽  
High Speed ◽  
Expectation Maximization Algorithm ◽  
Parallel Architecture ◽  
Principal Component ◽  
Eigenvalue Decomposition ◽  
Computational Time ◽  
Recognition Systems

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

2016 ◽  
Vol 231 (1) ◽  
pp. 98-108 ◽  
Author(s):  
Ke Li ◽  
Yalei Wu ◽  
Shimin Song ◽  
Yi sun ◽  
Jun Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Electrical Characteristics ◽  
Complex Signals ◽  
Fcm Clustering

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

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