scholarly journals High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis

Sensors ◽  
2016 ◽  
Vol 16 (8) ◽  
pp. 1280 ◽  
Author(s):  
Shaohui Foong ◽  
Zhenglong Sun
Keyword(s):  
Magnetic Field ◽  
Principal Component Analysis ◽  
Feedback Control ◽  
Principal Component ◽  
Component Analysis ◽  
High Accuracy

Matrix Based Feature Measurement and Extraction for Face Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 643-647
Author(s):  
Qi Zhu ◽  
Jin Rong Cui ◽  
Zi Zhu Fan
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Face Recognition ◽  
Measurement Method ◽  
Principal Component ◽  
Component Analysis ◽  
High Accuracy ◽  
Face Image ◽  
Two Dimensional ◽  
The Matrix

In this paper, a matrix based feature extraction and measurement method, i.e.: multi-column principle component analysis (MCPCA) is used to directly and effectively extract features from the matrix. We analyze the advantages of MCPCA over the conventional principal component analysis (PCA) and two-dimensional PCA (2DPCA), and we have successfully applied it into face image recognition. Extensive face recognition experiments illustrate that the proposed method obtains high accuracy, and it is more robust than previous conventional face recognition methods.

scholarly journals Novel Magnetic Resonance Acquisition and Processing Strategies for Biological Tissue Characterisation

2021 ◽  
Author(s):  
◽  
Fangrong Zong
Keyword(s):  
Magnetic Field ◽  
Principal Component Analysis ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Fractional Anisotropy ◽  
Principal Component ◽  
Component Analysis ◽  
Tissue Characterisation ◽  
Diffusion Weighted ◽  
Field Strengths

<p>Proton magnetic resonance techniques have become indispensable for characterising tissues non-invasively. These methods provide abundant information regarding metabolism, morphology and histology of the sample under study. While these techniques were more expensive in the past compared to radioactive methods, modern advances in hardware and methodology provide the potential to use magnetic resonance systems more efficiently and widely. In this context, this thesis explored innovative magnetic resonance technologies from three independent perspectives which are suitable for tissue characterisation, utilising techniques from a wide range of disciplines including physics, engineering, biology and medical sciences.  One strategy relates to compressed sensing magnetic resonance imaging, seeking to recover detailed features at high undersampling rates. A data-adaptive sparse transform facilitated by principal component analysis was introduced as an alternative to the conventional pre-defined sparse transform. Moreover, the principal component analysis was used in a recognition algorithm for the reconstruction of undersampled data. The performances of these approaches were studied in cases of localised changes in the acquired images. The results demonstrated that the recognition reconstruction algorithm performed better than wavelet compressed sensing. This progress can be utilised to accelerate current state of the art imaging protocols at high magnetic field strengths. Furthermore, the prior knowledge contained in high resolution databases may enhance imaging capabilities of technologies at low magnetic field strengths.  A second approach exploits nuclear magnetic resonance diffusion contrast instead of contrast agents for tissue characterisation. Microstructural information and global fractional anisotropy can be obtained from diffusion-diffusion correlation spectroscopy via a novel multi-dimensional gradient scheme. The concept was validated by random walk simulations and experiments of biological samples. Both correlation maps and global fractional anisotropy of in vitro healthy and tumour-bearing mouse brains were found to be different, thus providing a potential application of the proposed scheme in diffusion oncology.  In addition, a threshold algorithm on the selection of a region of interest was implemented to minimise inter-observer variations. This technique was applied to a pilot study of diffusion weighted imaging data which were acquired from patients after x-ray mammography indicated lesions. The statistical analysis revealed an optimal threshold similar to values commonly used in positron emission tomography. Apart from selecting regions automatically, various data processing methods were implemented and compared with each other regarding their diagnostic accuracies. This field study provides opportunities for standardising procedures in diffusion weighted mammography, which may be integrated into clinical analysis in the future.</p>

scholarly journals Development of Application Specific Intelligent Framework for the Optimized Selection of Industrial Grade Magnetic Material

Polymers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 4328
Author(s):  
Muhammad Saleem ◽  
Ali Rizwan
Keyword(s):  
Magnetic Field ◽  
Magnetic Material ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Magnetic Materials ◽  
Principal Component ◽  
Component Analysis ◽  
Soft Magnetic ◽  
Suitability Index

This article attempts to introduce a simple and robust way for the classification of soft magnetic material by using multivariate statistics. The six magnetic properties including coercive magnetic field, relative magnetic permeability, electrical resistivity magnetic inductions, i.e., remanence and saturation along with Curie temperature are used for the classification of 16 soft magnetic materials. Descriptive statistics have been used for defining the prioritization order of the mentioned magnetic characteristics with coercive magnetic field and Curie temperature as the most and least important characteristics for classification of soft magnetic material. Moreover, it has also justified the usage of cluster analysis and principal component analysis for classifying the enlisted materials. After descriptive statistics, cluster analysis is used for classification of materials into four groups, i.e., excellent, good, fair and poor while defining the prioritization order of materials on a relative scale. Principal component analysis reveals that the relative permeability is responsible for defining 99.69% of total variance and is also negatively correlated with the coercive magnetic field. Therefore, these two characteristics are considered the responsible factors for categorically placing the enlisted materials into four clusters. Furthermore, principal component analysis also helps in figuring out the fact that a combined influential consequence of relative permeability, coercive magnetic field, electrical resistivity and critical temperature are responsible for defining prioritization ordering of materials within the clusters. The material’s suitability index is identified while making use of adjacency and decision matrices obtained from material assessment graph and relative importance of magnetic properties, respectively. Afterward this material suitability index is used to rank the enlisted materials based on selected attributes. According to the suitability index, the best choice among enlisted soft magnetic materials is Supermalloy, Magnifer 7904 which is present in group 1 labeled as excellent by multivariate analysis. Therefore, the results of graph theory are in accordance with cluster analysis and principal component analysis, thus confirming the potential of this intelligent approach for the selection application specific magnetic materials.

scholarly journals Novel Magnetic Resonance Acquisition and Processing Strategies for Biological Tissue Characterisation

2021 ◽  
Author(s):  
◽  
Fangrong Zong
Keyword(s):  
Magnetic Field ◽  
Principal Component Analysis ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Fractional Anisotropy ◽  
Principal Component ◽  
Component Analysis ◽  
Tissue Characterisation ◽  
Diffusion Weighted ◽  
Field Strengths

<p>Proton magnetic resonance techniques have become indispensable for characterising tissues non-invasively. These methods provide abundant information regarding metabolism, morphology and histology of the sample under study. While these techniques were more expensive in the past compared to radioactive methods, modern advances in hardware and methodology provide the potential to use magnetic resonance systems more efficiently and widely. In this context, this thesis explored innovative magnetic resonance technologies from three independent perspectives which are suitable for tissue characterisation, utilising techniques from a wide range of disciplines including physics, engineering, biology and medical sciences.  One strategy relates to compressed sensing magnetic resonance imaging, seeking to recover detailed features at high undersampling rates. A data-adaptive sparse transform facilitated by principal component analysis was introduced as an alternative to the conventional pre-defined sparse transform. Moreover, the principal component analysis was used in a recognition algorithm for the reconstruction of undersampled data. The performances of these approaches were studied in cases of localised changes in the acquired images. The results demonstrated that the recognition reconstruction algorithm performed better than wavelet compressed sensing. This progress can be utilised to accelerate current state of the art imaging protocols at high magnetic field strengths. Furthermore, the prior knowledge contained in high resolution databases may enhance imaging capabilities of technologies at low magnetic field strengths.  A second approach exploits nuclear magnetic resonance diffusion contrast instead of contrast agents for tissue characterisation. Microstructural information and global fractional anisotropy can be obtained from diffusion-diffusion correlation spectroscopy via a novel multi-dimensional gradient scheme. The concept was validated by random walk simulations and experiments of biological samples. Both correlation maps and global fractional anisotropy of in vitro healthy and tumour-bearing mouse brains were found to be different, thus providing a potential application of the proposed scheme in diffusion oncology.  In addition, a threshold algorithm on the selection of a region of interest was implemented to minimise inter-observer variations. This technique was applied to a pilot study of diffusion weighted imaging data which were acquired from patients after x-ray mammography indicated lesions. The statistical analysis revealed an optimal threshold similar to values commonly used in positron emission tomography. Apart from selecting regions automatically, various data processing methods were implemented and compared with each other regarding their diagnostic accuracies. This field study provides opportunities for standardising procedures in diffusion weighted mammography, which may be integrated into clinical analysis in the future.</p>

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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