scholarly journals Single shot fringe pattern phase demodulation using Hilbert-Huang transform aided by the principal component analysis

2016 ◽  
Vol 24 (4) ◽  
pp. 4221 ◽  
Author(s):  
Maciej Trusiak ◽  
Łukasz Służewski ◽  
Krzysztof Patorski
Keyword(s):  
Principal Component Analysis ◽  
Fringe Pattern ◽  
Principal Component ◽  
Component Analysis ◽  
Single Shot ◽  
Hilbert Huang Transform ◽  
Phase Demodulation

scholarly journals Laser-Induced Breakdown Spectroscopy and Principal Component Analysis for the Classification of Spectra from Gold-Bearing Ores

2019 ◽  
Vol 74 (1) ◽  
pp. 42-54 ◽  
Author(s):  
Daniel Diaz ◽  
Alejandro Molina ◽  
David W. Hahn
Keyword(s):  
Principal Component Analysis ◽  
Limit Of Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Emission Lines ◽  
Laser Induced Breakdown Spectroscopy ◽  
Single Shot ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Laser-induced breakdown spectroscopy (LIBS) and principal component analysis (PCA) were applied to the classification of LIBS spectra from gold ores prepared as pressed pellets from pulverized bulk samples. For each sample, 5000 single-shot LIBS spectra were obtained. Although the gold concentrations in the samples were as high as 7.7 µg/g, Au emission lines were not observed in most single-shot LIBS spectra, rendering the application of the usual ensemble-averaging approach for spectral processing to be infeasible. Instead, a PCA approach was utilized to analyze the collection of single-shot LIBS spectra. Two spectral ranges of 21 nm and 0.15 nm wide were considered, and LIBS variables (i.e., wavelengths) reduced to no more than three principal components. Single-shot spectra containing Au emission lines (positive spectra) were discriminated by PCA from those without the spectral feature (negative spectra) in a spectral range of less than 1 nm wide around the Au(I) 267.59 nm emission line. Assuming a discrete gold distribution at very low concentration, LIBS sampling of gold particles seemed unlikely; therefore, positive spectra were considered as data outliers. Detection of data outliers was possible using two PCA statistical parameters, i.e., sample residual and Mahalanobis distance. Results from such a classification were compared with a standard database created with positive spectra identified with a filtering algorithm that rejected spectra with an Au intensity below the smallest detectable analytical LIBS signal (i.e., below the LIBS limit of detection). The PCA approach successfully identified 100% of the data outliers when compared with the standard database. False identifications in the multivariate approach were attributed to variations in shot-to-shot intensity and the presence of interfering emission lines.

Application of Principal Component Analysis and Adaptive Median Filter to Improve Real-Time Prostate Capsula Detection

2020 ◽  
Vol 10 (2) ◽  
pp. 336-347 ◽  
Author(s):  
Shixiao Wu ◽  
Chengcheng Guo ◽  
Xinghuan Wang
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Data Augmentation ◽  
Median Filter ◽  
Principal Component ◽  
Component Analysis ◽  
Single Shot ◽  
Convolutional Network ◽  
Adaptive Median Filter ◽  
Multiple Network

This study investigated the potential for using Principal Component Analysis (PCA) and Adaptive Median Filter (AMF) to improve real-time prostate capsula detection with the traditional Region-based Fully Convolutional Network (R-FCN), Faster Region-based Convolutional Neural Network (Faster R-CNN), You Only Look Once-Version 3 (YOLOv3) and Single Shot Multibox Detector (SSD) algorithms. The processing steps included data augmentation (rotation, vertical flip, and horizontal flip) to increase the size of the dataset from 149 to 596 images, PCA-based feature extraction, AMF-based image denoising and a training phase incorporating the sample image set. The data were then used to test a series of combined methods that were applied to the detection of prostate capsula (PC). The results showed that application of PCA and AMF to Faster R-CNN increased the mean average precision (mAP) for the PC images by 9.4%. The application of PCA and AMF to R-FCN, YOLOv3 and SSD increased the mAP by 7.22%, 7.14% and 3.29% for the same dataset, respectively. This study represents the first application of PCA and AMF to traditional object detection algorithms, such as R-FCN, Faster R-CNN, YOLOv3, or SSD, and the improved mAP results suggest that this approach is a robust tool for improving multiple network architectures.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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