scholarly journals Sequence images hiding technique of three-dimensional object based on principal component analysis in wavelet domain

2011 ◽  
Vol 60 (3) ◽  
pp. 034203
Author(s):  
Shuai Wen-Juan ◽  
Feng Shao-Tong ◽  
Nie Shou-Ping ◽  
Zhu Zhu-Qing
Keyword(s):  
Principal Component Analysis ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Wavelet Domain ◽  
Object Based ◽  
Dimensional Object

scholarly journals Principal Component Analysis of Munich Functional Developmental Diagnosis

2021 ◽  
Vol 13 (2) ◽  
pp. 227-233
Author(s):  
Grażyna Pazera ◽  
Marta Młodawska ◽  
Jakub Młodawski ◽  
Kamila Klimowska
Keyword(s):  
Principal Component Analysis ◽  
Social Skills ◽  
Motor Development ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Three Dimensions ◽  
Matrix Analysis ◽  
Psychomotor Development ◽  
First Year Of Life

Objectives: Munich Functional Developmental Diagnosis (MFDD) is a scale for assessing the psychomotor development of children in the first months or years of life. The tool is based on standardized tables of physical development and is used to detect developmental deficits. It consists of eight axes on which the following skills are assessed: crawling, sitting, walking, grasping, perception, speaking, speech understanding, social skills. Methods: The study included 110 children in the first year of life examined with the MFDD by the same physician. The score obtained on a given axis was coded as a negative value (defined in months) below the child’s age-specific developmental level. Next, we examined the dimensionality of the scale and the intercorrelation of its axes using polychoric correlation and principal component analysis. Results: Correlation matrix analysis showed high correlation of MFDD axes 1–4, and MFDD 6–8. The PCA identified three principal components consisting of children’s development in the areas of large and small motor skills (axis 1–4), perception (axis 5), active speech, passive speech and social skills (axis 6–8). The three dimensions obtained together account for 80.27% of the total variance. Conclusions: MFDD is a three-dimensional scale that includes motor development, perception, and social skills and speech. There is potential space for reduction in the number of variables in the scale.

scholarly journals Competitive and Recreational Running Kinematics Examined Using Principal Components Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1321
Author(s):  
Wenjing Quan ◽  
Huiyu Zhou ◽  
Datao Xu ◽  
Shudong Li ◽  
Julien S. Baker ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Sagittal Plane ◽  
Three Dimensional ◽  
Frontal Plane ◽  
Principal Component ◽  
Component Analysis ◽  
Ankle Inversion ◽  
Motion System ◽  
Recreational Runners

Kinematics data are primary biomechanical parameters. A principal component analysis (PCA) of waveforms is a statistical approach used to explore patterns of variability in biomechanical curve datasets. Differences in experienced and recreational runners’ kinematic variables are still unclear. The purpose of the present study was to compare any differences in kinematics parameters for competitive runners and recreational runners using principal component analysis in the sagittal plane, frontal plane and transverse plane. Forty male runners were divided into two groups: twenty competitive runners and twenty recreational runners. A Vicon Motion System (Vicon Metrics Ltd., Oxford, UK) captured three-dimensional kinematics data during running at 3.3 m/s. The principal component analysis was used to determine the dominating variation in this model. Then, the principal component scores retained the first three principal components and were analyzed using independent t-tests. The recreational runners were found to have a smaller dorsiflexion angle, initial dorsiflexion contact angle, ankle inversion, knee adduction, range motion in the frontal knee plane and hip frontal plane. The running kinematics data were influenced by running experience. The findings from the study provide a better understanding of the kinematics variables for competitive and recreational runners. Thus, these findings might have implications for reducing running injury and improving running performance.

scholarly journals Improving three‐dimensional mechanical imaging of breast lesions with principal component analysis

2017 ◽  
Vol 44 (8) ◽  
pp. 4194-4203 ◽  
Author(s):  
Mohit Tyagi ◽  
Yuqi Wang ◽  
Timothy J. Hall ◽  
Paul E. Barbone ◽  
Assad A. Oberai
Keyword(s):  
Principal Component Analysis ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Breast Lesions ◽  
Mechanical Imaging

scholarly journals A three-dimensional principal component analysis approach for exploratory analysis of hyperspectral data: identification of ovarian cancer samples based on Raman microspectroscopy imaging of blood plasma

The Analyst ◽  
2019 ◽  
Vol 144 (7) ◽  
pp. 2312-2319 ◽  
Author(s):  
Camilo L. M. Morais ◽  
Pierre L. Martin-Hirsch ◽  
Francis L. Martin
Keyword(s):  
Ovarian Cancer ◽  
Principal Component Analysis ◽  
Blood Plasma ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Data ◽  
Exploratory Analysis ◽  
Hyperspectral Images ◽  
Analysis Approach

Three-dimensional principal component analysis (3D-PCA) for exploratory analysis of hyperspectral images.

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