Concept Of Principal Component Analysis Research Articles

Sensitivity enhancement of an antenna sensor by using sextuple notch bands for application in cancer detection

In this study, a sextuple band-notched ultra-wideband (UWB) antenna sensor with slots in the feedline and parasitics is proposed. This is used for cancer detection in breast and skin phantom. The antenna sensor operates a bandwidth of 2.45–16 GHz. The dielectric properties of malignant cells are completely distinct from healthy cells, EM waves passing through the cells recognize a malignant. S11 parameters of the antenna are analyzed with the help of a Vector Network Analyzer (VNA) after placing the antenna on the phantoms. Further, the statistical machine learning concept of Principal Component Analysis (PCA) is utilized for more vivid detection of malignant cells. The bands WiMax (2.8–3.4 GHz), radio astronomy (4.75–5.8 GHz), satellite communication (6.7–7.5 GHz, 9.2–10.9 GHz and 12.95–13.55 GHz) and SHF band (15.43–15.63 GHz), create interferences within the UWB spectrum. The interfering bands are removed by slot loading and parasitics. Thus, the sensitivity of the antenna sensor improved.

Crescent shaped slot loaded antenna sensor with tri-band notched for cancer detection

Abstract In this work, a unique tri-band notch and multiband UWB antenna sensor has been designed for microwave sensing system to find out the cancerous tissues. The crescent shaped slot loaded antenna has been designed to avoid interferences between the antennas in UWB ranges. Using notches, the antenna sensitivity increases for detection. The proposed method has been verified through simulation and validated using measurements. S11 plot of the proposed antenna shows three notches at 3 GHz, 4.75 GHz and 7.25 GHz. It also shows four bands and omnidirectional radiation pattern over the UWB frequency range. Ground plane has been chosen to be hexagonal to achieve fourth band. I-shaped and U-shaped parasitics improve the antenna performance in third band. Crescent shaped and C-shaped slots improve the performance of first and second band of the antenna respectively. Additionally, phantom without tumors and with single and multiple tumors are fabricated. S-parameter analysis is a better approach to detect the cancerous tissues in the breast. Concept of principal component analysis of statistical machine learning has also been used to distinguish S-parameter of normal breast phantom from malignant breast phantom.

Multi-criteria decision analysis for pharmaceutical supplier selection problem using fuzzy TOPSIS

PurposeSupplier selection in the supply chain network (SCN) has strategic importance and involves multiple factors. The multi-criteria nature of the problem coupled with environmental uncertainty requires several procedures and considerations. The issue of decision-making in selecting the best among various qualified suppliers remains the major challenge in the pharmaceutical industry. This study investigated the multi-criteria multi-supplier decision-making process and proposed a model for supplier selection problems based on mixed-integer linear programming.Design/methodology/approachThe concept of principal component analysis (PCA) was used to reduce data dimensionality, and the four best criteria have been considered and selected. The result is subjected to decision-makers’ (DMs’) reliability test using the concept of a triangular fuzzy number (TFN). The importance of each supplier to each measure is established using fuzzy technique for order preference by similarity to an ideal solution approach, and the suppliers have ranked accordingly.FindingsThis study proposes a mixed integer linear programming model for supplier selection in a pharmaceutical company. The effectiveness of the proposed model has been demonstrated using a numerical example. The solution shows the model's applicability in making a sound decision in pharmaceutical companies in the space of reality. The model proposed is simple. Readily commercial packages such as LINDO/LINGO and GAMS can solve the model.Research limitations/implicationsThis research contributed to the systematic manner of supplier selection considering DMs’ value judgement under a fuzzy environment and is limited to the case study area. However, interested researchers can apply the study in other related manufacturing industries. However, the criteria have to be revisited to suit that system and might require varying ratings based on the experts' opinions in that field.Practical implicationsThis work suggests more insights practically by considering a realistic and precise investigation based on a real-life case study of pharmaceutical companies with six primary criteria and twenty-four sub-criteria. The study outcome will assist organizations and managers in conducting the best decision objectively by selecting the best suppliers with their various standards and terms among many available contenders in the manufacturing industry.Originality/valueIn this paper, the authors attempted to identify the most critical attributes to be preserved by the top managers (DMs) while selecting suppliers in pharmaceutical companies. The study proposed an MILP model for supplier selection in the pharmaceutical company using fuzzy TOPSIS.

Classification and prediction of wear state based on unsupervised learning of online monitoring data of lubricating oil

With the development of the times, the demand for high efficiency and reliability of machine performance in China’s industry has become higher than ever before, and the traditional equipment condition evaluation method has also encountered a huge challenge. As an important branch of machine learning, cluster analysis is widely used in fault diagnosis and other fields because of its advantages of no prior knowledge and massive data processing. This paper introduces the concept of principal component analysis and K-means method based on time series in the field of oil data analysis. Through the test data on the oil monitoring data of the steam turbine unit in the power plant, the condition evaluation and classification of the equipment are carried out, among which the classification effect of 6μm particles, dielectric constant and 4μm particles is very obvious, and the water content is relatively obvious, which can basically distinguish each state. At the same time, on the basis of feature extraction, use the special cyclic neural network (RNN) in deep learning-long and short-term memory network (LSTM) to build a model of data for data prediction, under the powerful ability of LSTM to process temporal data, they have unearthed more irregular and non-linear trends in a large amount of historical data, and achieved better prediction results than traditional methods (ARIMA). Through clustering and prediction of lubrication parameter data, it can realize early warning and abnormal diagnosis of lubrication status and reduce damage to machinery and equipment.

Experimental Investigation of the Breast Phantom for Tumor Detection Using Ultra-Wide Band–MIMO Antenna Sensor (UMAS) Probe

In this work, a novel Ultra-wideband-Multi-Input-Multi-Output Antenna Sensor (UMAS) probe is designed for the detection of the malignant cells in the breast. The Sensor probe has four radiating elements and it is operated within the 2.8 GHz to 20 GHz ultra-wide band range. Isolation between the radiating element is more than 20 dB. Further, three kinds of the breast phantoms (i.e. normal phantom, phantom with single and multiple tumors) are fabricated using tissue mimicking material. The electrical characteristics of the malignant cells are different from non-malignant cells of the breast. The S-parameter and Specific Absorption Rate (SAR) analysis are best approaches to detect the malignant cells in the breast. The UMAS sensing probe is embedded on the phantoms and S-parameters of the probe are recorded from the Vector Network Analyzer (VNA). Measured S-parameters of the probe for normal and malignant phantoms are differ from each other. The statistical machine learning concept of Principal Component Analysis (PCA) is also applied on the measured S-Parameters. Which exhibits clear detection of normal and malignant breast phantoms. Further verification is done by using Simulation based specific absorption rate (SAR) study of the phantom models for tumor detection. The obtained maximum SAR results are well differentiating the normal phantom.

Characterization and regeneration of 2D general-shape particles by a Fourier series-based approach

The macroscopic mechanical behaviors of granular construction materials are fundamentally linked to the morphology of the particles. Characterization of real particles is important for exploring the linkage through physical investigation, while regeneration of virtual particles is a key step for reliable numerical investigation. This paper proposes a Fourier series-based approach to represent and characterize two-dimensional (2D) general-shape particles, which is more versatile than the traditional complex Fourier analysis for characterizing 2D nonstar-like particles. Additionally, a numerical algorithm that combines the concepts of principal component analysis and empirical cumulative distribution functions is proposed to generate 2D general-shape particles that bears the morphological features of real particles. The approach can consider both the intrinsic relationships among the Fourier coefficients for the x and y coordinates at the first degree of harmonics. The empirical correlation among the Fourier coefficients at different degrees of harmonics can also be taken into account. Precise controlling the elongation property of virtual particles is also readily achievable in this approach. A comprehensive study with two types of construction material particles reveals that the proposed approach is promising.

Multi-pose facial expression recognition using appearance-based facial features

The problem of automatic facial expression recognition is both interesting and challenging with a strong impact on many application areas such as animation and human-computer interaction. The field has shown tremendous growth over the past years with its benchmarking efforts and progress. In this paper, an automated system to recognise facial expressions using the deep convolutional neural network is presented. The proposed system has used the appearance based features to recognise the facial expressions from imagery data with pose variations. The appearance based features are extracted by implementing an integrated approach of Gabor filter with local binary pattern method and the selection process of extracted features is executed using the concept of principal component analysis (PCA). The proposed system performance is accessed using the evaluation metrics of precision, recall, f-measure, and recognition rate for the frontal and half side pose images of the JAFFE and KDEF datasets.

Multi-pose facial expression recognition using appearance-based facial features

The problem of automatic facial expression recognition is both interesting and challenging with a strong impact on many application areas such as animation and human-computer interaction. The field has shown tremendous growth over the past years with its benchmarking efforts and progress. In this paper, an automated system to recognise facial expressions using the deep convolutional neural network is presented. The proposed system has used the appearance based features to recognise the facial expressions from imagery data with pose variations. The appearance based features are extracted by implementing an integrated approach of Gabor filter with local binary pattern method and the selection process of extracted features is executed using the concept of principal component analysis (PCA). The proposed system performance is accessed using the evaluation metrics of precision, recall, f-measure, and recognition rate for the frontal and half side pose images of the JAFFE and KDEF datasets.

New concepts of principal component analysis based on maximum separation of clusters

It is common to apply dimension reduction techniques like principal component analysis before performing cluster analysis of multivariate data. However, it is not guaranteed that the most useful information for separating different groups is concentrated in the first few principal components. To improve the performance of clustering, a novel nonparametric method is constructed by redefining the principal component based on the linear combination of attributes that maximizes a newly proposed measure of separation of clusters. An efficient dynamic programing algorithm with complexity is described, where n is the number of observations and p is the number of attributes. The applications of the proposed methods are discussed with examples in credit card issuance and privacy protection under randomized multiple response techniques.

Fuzzy Space Partitioning Based on Hyperplanes Defined by Eigenvectors for Takagi-Sugeno Fuzzy Model Identification

This article presents a novel method for fuzzy space partitioning and the identification of Takagi-Sugeno fuzzy models. The novelty is in its region-splitting mechanism and membership function definition, which is based on hyperplanes. The proposed algorithm introduces a concept of principal component analysis to define the hyperplanes that split the problem space and uses the distances to these hyperplanes as metrics instead of center-oriented clusters. In contrast with many other methods, the presented method delivers reproducible results and has an easy tuning procedure. The performance is illustrated with analytical examples, benchmark problems from the literature, and real-process data. The obtained results are very promising; however, as with most learning methods, the results depend on the data distribution and input variable selection.

Spatio-Temporal Based Descriptor for Limb Movement-Intent Characterization in EMG-Pattern Recognition System.

Electromyogram (EMG) pattern-recognition (PR) is the most widely adopted prostheses/rehabilitation robots control method that seamlessly support multi-degrees of freedom (MDF) function in an intuitive fashion. The feature extraction framework applied in such PR-based control essentially determines the control performance of the prosthetic device. Based on the drawbacks of the commonly utilized feature extraction methods, this study proposed a spatio-temporal-based feature set (STFS) that might optimally characterize EMG signal patterns even in the presence of white Gaussian noise (WGN) to realize consistently accurate and stable decoding of multiple classes of limb-movements. For benchmark evaluation, the performance of the proposed STFS method was examined in comparison to notable existing popular methods using high density surface EMG recordings from 8 amputees, with metrics such as classification error (CE) and feature-space separability index. Compared to the existing methods, the STFS recorded substantial reduction of up 16.73% even in the presence the inevitable WGN at p<; 0.05. Also, with principal component analysis concept, the proposed STFS feature-space indicates obvious class separability compared to the previous methods. Therefore, the newly proposed STFS method could potentially facilitate the realization of consistently accurate and reliable PR-based control for MDF prostheses/rehabilitation robots.

CLASSIFICATION OF LIDAR DATA OVER BUILDING ROOFS USING K-MEANS AND PRINCIPAL COMPONENT ANALYSIS

Abstract: The classification is an important step in the extraction of geometric primitives from LiDAR data. Normally, it is applied for the identification of points sampled on geometric primitives of interest. In the literature there are several studies that have explored the use of eigenvalues to classify LiDAR points into different classes or structures, such as corner, edge, and plane. However, in some works the classes are defined considering an ideal geometry, which can be affected by the inadequate sampling and/or by the presence of noise when using real data. To overcome this limitation, in this paper is proposed the use of metrics based on eigenvalues and the k-means method to carry out the classification. So, the concept of principal component analysis is used to obtain the eigenvalues and the derived metrics, while the k-means is applied to cluster the roof points in two classes: edge and non-edge. To evaluate the proposed method four test areas with different levels of complexity were selected. From the qualitative and quantitative analyses, it could be concluded that the proposed classification procedure gave satisfactory results, resulting in completeness and correctness above 92% for the non-edge class, and between 61% to 98% for the edge class.

Inference of a compact representation of sensor fingerprint for source camera identification

Sensor pattern noise (SPN) is an inherent fingerprint of imaging devices, which provides an effective way for source camera identification (SCI). Although SPNs extracted from large image blocks usually yield high identification accuracy, their high dimensionality would incur a high computational cost in the matching stage, consequently hindering many applications that require efficient camera matchings. In this work, we employ and evaluate the concept of principal component analysis (PCA) de-noising in SCI tasks. Based on this concept, we present a framework that formulates a compact SPN representation. To enhance the de-noising effect, we introduce a training set construction procedure that minimizes the impact of various interfering artifacts, which is especially useful in some challenging cases, e.g., when only textured reference images are available. To further boost the SCI performance, a novel approach based on linear discriminant analysis (LDA) is adopted to extract more discriminant SPN features. To evaluate our methods, extensive experiments are conducted on the Dresden image database. The results indicate that the proposed framework can serve as an effective post-processing procedure, which not only boosts the performance, but also greatly reduces the computational cost in the matching phase.

Pose Estimation for Spacecraft Relative Navigation Using Model-Based Algorithms

This paper presents the performance assessment of innovative model-based algorithms developed for pose estimation of uncooperative targets by processing sparse three-dimensional point clouds. This topic is of interest in the framework of advanced space applications, e.g., on-orbit servicing and active debris removal, which require a chaser spacecraft to execute autonomous relative navigation maneuvers in close-proximity of a space target. Both the problems of pose acquisition and tracking are addressed. The former one is carried out by combining the concepts of principal component analysis and template matching in order to limit both the computational effort and the amount of on-board data storage compared with traditional approaches. The latter is entrusted to a customized implementation of the iterative closest point algorithm, which adopts multiple model-measurement matching strategies as well as a refinement step, in order to respectively increase robustness and accelerate algorithm convergence. Also, safe transition from acquisition to tracking is implemented by means of autonomous detection of failures of the pose acquisition algorithm. The performance of the proposed techniques is investigated by means of numerical simulations in which the operation of an active LIDAR system as well as the target-chaser relative dynamics are realistically reproduced. Results demonstrate algorithms' effectiveness over a wide range of relative pose conditions and dealing with targets of variable size and shape, in spite of considerable sparseness of the measured datasets.

Face Recognition Using Eigen Face Based Technique Utilizing the Concept of Principal Component Analysis

Face recognition has been an active research area since late 1980s [1]. Eigenface approach is one of the earliest appearance-based face recognition methods, which was developed by M. Turk and A. Pentland [1] in 1991. In this approach we have to perform a lots of computations, which are not feasible with respect to time in many real time system. The concept of principal component analysis (PCA) is used in this approach to reduce the dimension and hence reducing the computation time. Principal component analysis [4] decomposes face images into a small set of characteristic feature images called eigen faces.

English

In the past, the investment portfolio theory regarding stock price deviation was considered to be individual risk that occurred in the market at random. However, the outcome efficiency of portfolios could be influenced by other factors that were found to be interrelated. In this study, we attempted to filter out those random parts of correlation matrix by the walk forward approach (WFA), and applied the Random matrix theory (RMT) that was developed from nuclear physics. We constructed a portfolio and then estimate the accumulation returns and Sharpe ratio. Based on the daily trading records from the Taiwan Stock Exchange (TWSE), the component indexes were grouped into 19 categories from Jan. 2, 2007 to Jan. 29, 2010, and there were a total of 767 data set entries as input information to this study. We employed variance tests, ANOVA, and least squares mean (LSM) to test the results. This study finds that, in general, the random part of stock return increases the portfolio risk, and consequently decreases the efficiency of the portfolio. In addition, this study adapted the concept of principal component analysis to analyze the information eigenvectors, which provided the information sequence for detecting the presence of unusual information that might affect the portfolio. As a result, the new approach could be helpful in forecasting the direction of price fluctuation. Key words: Stock index, Random matrix theory, investment portfolio theory, principal component analysis, interrelated factor.

P‐79: Principal Component Analysis on Characterizing Full‐Color Electrophoretic Display

AbstractA methodology is introduced to characterize colorimetric performance of full‐color electrophoretic display. The concept of principal component analysis benefited simplifying the complicated hybrid color‐mixing system by means of one eigenspectrum for each RGB channel. Proposed model was validated to predict the chromatic feature with high accuracy (ΔEAVE_CIEDE2000&lt;0.2). Due to its simplicity and invertibility, proposed technique will certainly has a promising impact on the color reproduction in emerging novel displays using hybrid color‐mixing system.

Principal Component Analysis of Single-Beam Echo-Sounder Signal Features for Seafloor Classification

Acoustic remote sensing is a useful tool for seafloor characterization. This contribution presents the results of seafloor sediment classification using single-beam echo-sounder (SBES) data based on a phenomenological method. Basic concepts of principal component analysis (PCA) and its applicability to the sediment classification using acoustical data are studied. This mathematical tool, which retains most of the variation of the data, is applied to the SBES echo shape parameters such as total energy, time-spread, skewness and flatness on three low (12 kHz), moderate (38 kHz), and high (200 kHz) frequencies, making 12 features in total. These parameters are dependent on sediment types and can therefore be used as attributes for seafloor classification. To decrease the statistical fluctuations of the extracted features, an averaging over a sufficiently large number of consecutive pings have been applied to the features. The SBES classification results based on the PCA and K-means clustering approach can clearly discriminate between different sediment classes. The signal at 12 kHz contains information on sediment layers (5 m depth). The performance of the method and the results obtained are assessed using the following independent criteria: 1) inspection of the track crossings indicates stable feature extraction and processing strategy; 2) comparison between the class numbers of the classification results and of the grab samples shows a significant correlation coefficient of 0.90; and 3) an error matrix verifies the stability and independence of the classification results from the features considered.

Fast MR parameter mapping using k‐t principal component analysis

Quantification of magnetic resonance parameters plays an increasingly important role in clinical applications, such as the detection and classification of neurodegenerative diseases. The major obstacle that remains for its widespread use in clinical routine is the long scanning times. Therefore, strategies that allow for significant decreases in scan time are highly desired. Recently, the k-t principal component analysis method was introduced for dynamic cardiac imaging to accelerate data acquisition. This is done by undersampling k-t space and constraining the reconstruction of the aliased data based on the k-t Broad-use Linear Acquisition Speed-up Technique (BLAST) concept and predetermined temporal basis functions. The objective of this study was to investigate whether the k-t principal component analysis concept can be adapted to parameter quantification, specifically allowing for significant acceleration of an inversion recovery fast imaging with steady state precession (TrueFISP) acquisition. We found that three basis functions and a single training data line in central k-space were sufficient to achieve up to an 8-fold acceleration of the quantification measurement. This allows for an estimation of relaxation times T(1) and T(2) and spin density in one slice with sub-millimeter in-plane resolution, in only 6 s. Our findings demonstrate that the k-t principal component analysis method is a potential candidate to bring the acquisition time for magnetic resonance parameter mapping to a clinically acceptable level.

A new feature extractor invariant to intensity, rotation, and scaling of color images

This paper proposes a new method for extracting the invariant features of an image based on the concept of principal component analysis and a competitive learning algorithm. The proposed algorithm can be applied to binary, gray-level, or colored-texture images with a size greater than 256 × 256 pixels. In addition to translation, scaling, and rotation invariant extraction, the extraction of a feature invariant to color intensity can be implemented by using this method. In our experiment, the proposed method shows the capability to differentiate images having the same shape but different colored textures. The experimental results report the effectiveness of this technique and its performance as measured by recognition accuracy rate and computational time. These results are also compared with those obtained by classical techniques.