ICA and PCA-Based Cryptology

2014 ◽  
pp. 200-217
Author(s):  
Sattar B. Sadkhan Al Maliky ◽  
Nidaa A. Abbas
Keyword(s):  
Principal Component Analysis ◽  
Blind Source Separation ◽  
Biomedical Engineering ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Mathematical Problems ◽  
Mixing Matrix ◽  
Speech Encryption ◽  
High Level

Blind Source Separation (BSS) represented by Independent Component Analysis (ICA) has been used in many fields such as communications and biomedical engineering. Its application to image and speech encryption, however, has been rare. In this chapter, the authors present ICA and Principal Component Analysis (PCA) as a category of BSS-based method for encrypting images and speech by using Blind Source Separation (BSS) since the security encryption technologies depend on many intractable mathematical problems. Using key signals, they build a suitable BSS underdetermined problem in the encryption and then circumvent this problem with key signals for decoding. The chapter shows that the method based on the BSS can achieve a high level of safety right through building, mixing matrix, and generating key signals.

scholarly journals A Principal Component Analysis of Acoustic Emission Signals from a Landing Gear Component

2008 ◽  
Vol 13-14 ◽  
pp. 41-47 ◽  
Author(s):  
Rhys Pullin ◽  
Mark J. Eaton ◽  
James J. Hensman ◽  
Karen M. Holford ◽  
Keith Worden ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Acoustic Emission ◽  
Background Noise ◽  
Principal Component ◽  
Component Analysis ◽  
Landing Gear ◽  
Fracture Detection ◽  
Artificial Fracture ◽  
High Level ◽  
Ae Signals

This work forms part of a larger investigation into fracture detection using acoustic emission (AE) during landing gear airworthiness testing. It focuses on the use of principal component analysis (PCA) to differentiate between fracture signals and high levels of background noise. An artificial acoustic emission (AE) fracture source was developed and additionally five sources were used to generate differing AE signals. Signals were recorded from all six artificial sources in a real landing gear component subject to no load. Further to this, artificial fracture signals were recorded in the same component under airworthiness test load conditions. Principal component analysis (PCA) was used to automatically differentiate between AE signals from different source types. Furthermore, successful separation of artificial fracture signals from a very high level of background noise was achieved. The presence of a load was observed to affect the ultrasonic propagation of AE signals.

scholarly journals Principal Component Analysis on Soil Fertility Parameters of Vegetable Growing Panchayats/ Locations in Ernakulam District of Kerala

2020 ◽  
pp. 58-65
Author(s):  
A. Muhsina ◽  
Brigit Joseph ◽  
Vijayaraghava Kumar
Keyword(s):  
Principal Component Analysis ◽  
Soil Fertility ◽  
Soil Ph ◽  
Principal Component ◽  
Component Analysis ◽  
Organic Carbon Content ◽  
Score Plot ◽  
Five Factors ◽  
Total Variability ◽  
High Level

The present paper used Principal Component Analysis (PCA) on 13 soil fertility parameters including soil pH and electrical conductivity of 17 vegetable growing panchyat/locations in Ernakulam district of Kerala based on 583 soil samples. Soil pH of panchayats varied from 4.2- 5.8 with a coefficient of variation 3.16-12.23 per cent and it was inferred that most of the panchayats in the district had very strongly acidic (pH: 4.2-5) and strongly acidic soils (pH: 5-5.5). High level of organic carbon content was noticed in most of the panchayats except in four panchayats. The results of PCA revealed that five PC’s together explained a total variability of 80 per cent and the remaining PCs accounted for 20 per cent of the variability in the data which has been discarded from further analysis. First principal component accounted for 25 per cent variance followed by PC 2(21%), PC 3(14%), PC 4(10%) and PC 5(10%). Factor analysis generated five factors and they explained 85 per cent of variability. Score plot drawn as part of PCA showed that Chengamanadu, Manjapra and Thirumaradi panchayats had high content of soil available S and B. EC was also found to be higher in these panchayats. Amount of OC, Fe and Mn were more in Kalady, Keerampara and Mudakkuzha of Ernakulam district whereas Thuravur, Piravom and Pothanikkad had highly acidic and Mg rich soils. Amount of Zn was more in Vengoor panchayat. Available K, Ca, P and Cu were found to be higher in Kakkad, Nedumbassery, Vengola and Kadungalloor. Based on the fertility status of each panchayats, they could be classified into different groups.

scholarly journals Application of Characteristic Model-Based Principal Component Analysis in Optimization of Flowmeter Parameters

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenping Jiang ◽  
Zhencun Jiang ◽  
Lingyang Wang ◽  
Jun Min ◽  
Yi Zhu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Measurement Error ◽  
Measurement Accuracy ◽  
Structural Parameters ◽  
Principal Component ◽  
Component Analysis ◽  
Characteristic Model ◽  
Industrial Systems ◽  
Model Based ◽  
High Level

In complex industrial processes, it is necessary to perform modeling analysis on some industrial systems and find and optimize the factors that have the greatest impact on the results, in order to achieve the optimization of the industrial systems. However, due to the high-level nature or complex working mechanism of complex industrial systems, traditional principal component analysis methods are difficult to apply. Therefore, this paper proposes a characteristic model-based principal component analysis (CMPCA) to perform principal component analysis on complex industrial systems. The differential pressure flowmeter is taken as an example to verify the effectiveness of the method. Flowmeter is an indispensable instrument in measurement, and its accuracy depends on its own structural parameters. However, the measurement accuracy of some flow meters is not high, and the measurement error is large, which affects the normal industrial production process. This method is used to analyze the influence of the structural parameters of the flowmeter on its measurement accuracy, and the four most important structural parameters are found and optimized. The measurement error of the Bitoba flowmeter is reduced from 1% to 0.2%, and the measurement repeatability is reduced from 0.3 to 0.06, which proves the effectiveness of the method.

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