Abstract: The effects of irrigation with dairy factory wastewater
on soil properties were investigated at two sites that had received
irrigation for > 60 years. Two adjoining paired sites that had never
received DFE were also sampled as well as another seven fields from
a wider area around the factory. In comparison with paired sites that
had not received effluent, long-term wastewater irrigation resulted in
an increase in pH, EC, extractable P, exchangeable Na and K and
ESP. These changes were related to the use of phosphoric acid,
NaOH and KOH as cleaning agents in the factory. Soil organic C
content was unaffected by DFE irrigation but the size (microbial
biomass C and N) and activity (basal respiration) of the soil
microbial community were increased. These increases were
attributed to regular inputs of soluble C (e.g. lactose) present as milk
residues in the wastewater. Principal component analysis (PCA) of
the soils data from all 11sites confirmed that the main effects of DFE
irrigation were an increase in exchangeable Na, extractable P and
microbial biomass C, an accumulation of soluble salts and a liming
effect. PCA analysis of soil bacterial community structure, using
PCR-DGGE of 16S rDNA fragments, generally separated individual
sites from one another but did not group them according to irrigation
history. Thus, whilst the size and activity of the soil microbial
community were increased, the structure and diversity of the
bacterial community remained unaffected.
Abstract: This paper deals with the application of Principal Component Analysis (PCA) and the Hotelling-s T2 Chart, using data collected from a drinking water treatment process. PCA is applied primarily for the dimensional reduction of the collected data. The Hotelling-s T2 control chart was used for the fault detection of the process. The data was taken from a United Utilities Multistage Water Treatment Works downloaded from an Integrated Program Management (IPM) dashboard system. The analysis of the results show that Multivariate Statistical Process Control (MSPC) techniques such as PCA, and control charts such as Hotelling-s T2, can be effectively applied for the early fault detection of continuous multivariable processes such as Drinking Water Treatment. The software package SIMCA-P was used to develop the MSPC models and Hotelling-s T2 Chart from the collected data.
Abstract: This evaluation of land supply system performance in
China shall examine the combination of government functions and
national goals in order to perform a cost-benefit analysis of system
results. From the author's point of view, it is most productive to
evaluate land supply system performance at moments of system
transformation for the following reasons. The behavior and
input-output change of beneficial results at different times can be
observed when the system or policy changes and system performance
can be evaluated through a cost-benefit analysis during the process of
system transformation. Moreover, this evaluation method can avoid
the influence of land resource endowment. Different land resource
endowment methods and different economy development periods
result in different systems. This essay studies the contents, principles
and methods of land supply system performance evaluation. Taking
Beijing as an example, this essay optimizes and classifies the land
supply index, makes a quantitative evaluation of land supply system
performance through principal component analysis (PCA), and finally
analyzes the factors that influence land supply system performance at
times of system transformation.
Abstract: In this paper a novel algorithm is proposed to merit
the accuracy of finger vein recognition. The performances of
Principal Component Analysis (PCA), Kernel Principal Component
Analysis (KPCA), and Kernel Entropy Component Analysis (KECA)
in this algorithm are validated and compared with each other in order
to determine which one is the most appropriate one in terms of finger
vein recognition.
Abstract: Face recognition is a technique to automatically
identify or verify individuals. It receives great attention in
identification, authentication, security and many more applications.
Diverse methods had been proposed for this purpose and also a lot of
comparative studies were performed. However, researchers could not
reach unified conclusion. In this paper, we are reporting an extensive
quantitative accuracy analysis of four most widely used face
recognition algorithms: Principal Component Analysis (PCA),
Independent Component Analysis (ICA), Linear Discriminant
Analysis (LDA) and Support Vector Machine (SVM) using AT&T,
Sheffield and Bangladeshi people face databases under diverse
situations such as illumination, alignment and pose variations.
Abstract: Principal Component Analysis (PCA) has many
different important applications especially in pattern detection
such as face detection / recognition. Therefore, for real time
applications, the response time is required to be as small as
possible. In this paper, new implementation of PCA for fast
face detection is presented. Such new implementation is
designed based on cross correlation in the frequency domain
between the input image and eigenvectors (weights).
Simulation results show that the proposed implementation of
PCA is faster than conventional one.
Abstract: In this paper, an efficient local appearance feature
extraction method based the multi-resolution Curvelet transform is
proposed in order to further enhance the performance of the well
known Linear Discriminant Analysis(LDA) method when applied
to face recognition. Each face is described by a subset of band
filtered images containing block-based Curvelet coefficients. These
coefficients characterize the face texture and a set of simple statistical
measures allows us to form compact and meaningful feature vectors.
The proposed method is compared with some related feature extraction
methods such as Principal component analysis (PCA), as well
as Linear Discriminant Analysis LDA, and independent component
Analysis (ICA). Two different muti-resolution transforms, Wavelet
(DWT) and Contourlet, were also compared against the Block Based
Curvelet-LDA algorithm. Experimental results on ORL, YALE and
FERET face databases convince us that the proposed method provides
a better representation of the class information and obtains much
higher recognition accuracies.
Abstract: In this paper, we propose a robust scheme to work face alignment and recognition under various influences. For face representation, illumination influence and variable expressions are the important factors, especially the accuracy of facial localization and face recognition. In order to solve those of factors, we propose a robust approach to overcome these problems. This approach consists of two phases. One phase is preprocessed for face images by means of the proposed illumination normalization method. The location of facial features can fit more efficient and fast based on the proposed image blending. On the other hand, based on template matching, we further improve the active shape models (called as IASM) to locate the face shape more precise which can gain the recognized rate in the next phase. The other phase is to process feature extraction by using principal component analysis and face recognition by using support vector machine classifiers. The results show that this proposed method can obtain good facial localization and face recognition with varied illumination and local distortion.
Abstract: Analyses carried out on examples of detected defects
echoes showed clearly that one can describe these detected forms according to a whole of characteristic parameters in order to be able to make discrimination between a planar defect and a volumic defect.
This work answers to a problem of ultrasonics NDT like Identification of the defects. The problems as well as the objective of
this realized work, are divided in three parts: Extractions of the parameters of wavelets from the ultrasonic echo of the detected defect - the second part is devoted to principal components analysis
(PCA) for optimization of the attributes vector. And finally to establish the algorithm of classification (SVM, Support Vector Machine) which allows discrimination between a plane defect and a
volumic defect. We have completed this work by a conclusion where we draw up a summary of the completed works, as well as the robustness of the
various algorithms proposed in this study.
Abstract: This paper describes a method to improve the robustness of a face recognition system based on the combination of two compensating classifiers. The face images are preprocessed by the appearance-based statistical approaches such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). LDA features of the face image are taken as the input of the Radial Basis Function Network (RBFN). The proposed approach has been tested on the ORL database. The experimental results show that the LDA+RBFN algorithm has achieved a recognition rate of 93.5%
Abstract: This Paper proposes a new facial feature extraction approach, Wash-Hadamard Transform (WHT). This approach is based on correlation between local pixels of the face image. Its primary advantage is the simplicity of its computation. The paper compares the proposed approach, WHT, which was traditionally used in data compression with two other known approaches: the Principal Component Analysis (PCA) and the Discrete Cosine Transform (DCT) using the face database of Olivetti Research Laboratory (ORL). In spite of its simple computation, the proposed algorithm (WHT) gave very close results to those obtained by the PCA and DCT. This paper initiates the research into WHT and the family of frequency transforms and examines their suitability for feature extraction in face recognition applications.
Abstract: Current image-based individual human recognition
methods, such as fingerprints, face, or iris biometric modalities
generally require a cooperative subject, views from certain aspects,
and physical contact or close proximity. These methods cannot
reliably recognize non-cooperating individuals at a distance in the
real world under changing environmental conditions. Gait, which
concerns recognizing individuals by the way they walk, is a relatively
new biometric without these disadvantages. The inherent gait
characteristic of an individual makes it irreplaceable and useful in
visual surveillance.
In this paper, an efficient gait recognition system for human
identification by extracting two features namely width vector of
the binary silhouette and the MPEG-7-based region-based shape
descriptors is proposed. In the proposed method, foreground objects
i.e., human and other moving objects are extracted by estimating
background information by a Gaussian Mixture Model (GMM) and
subsequently, median filtering operation is performed for removing
noises in the background subtracted image. A moving target classification
algorithm is used to separate human being (i.e., pedestrian)
from other foreground objects (viz., vehicles). Shape and boundary
information is used in the moving target classification algorithm.
Subsequently, width vector of the outer contour of binary silhouette
and the MPEG-7 Angular Radial Transform coefficients are taken as
the feature vector. Next, the Principal Component Analysis (PCA)
is applied to the selected feature vector to reduce its dimensionality.
These extracted feature vectors are used to train an Hidden Markov
Model (HMM) for identification of some individuals. The proposed
system is evaluated using some gait sequences and the experimental
results show the efficacy of the proposed algorithm.
Abstract: Face Recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal Component Analysis (PCA), Self Organizing Maps (SOM) and Independent Component Analysis (ICA) are the three techniques among many others as proposed by different researchers for Face Recognition, known as the unsupervised techniques. This paper proposes integration of the two techniques, SOM and PCA, for dimensionality reduction and feature selection. Simulation results show that, though, the individual techniques SOM and PCA itself give excellent performance but the combination of these two can also be utilized for face recognition. Experimental results also indicate that for the given face database and the classifier used, SOM performs better as compared to other unsupervised learning techniques. A comparison of two proposed methodologies of SOM, Local and Global processing, shows the superiority of the later but at the cost of more computational time.
Abstract: In the last few years, three multivariate spectral
analysis techniques namely, Principal Component Analysis (PCA),
Independent Component Analysis (ICA) and Non-negative Matrix
Factorization (NMF) have emerged as effective tools for oscillation
detection and isolation. While the first method is used in determining
the number of oscillatory sources, the latter two methods
are used to identify source signatures by formulating the detection
problem as a source identification problem in the spectral domain.
In this paper, we present a critical drawback of the underlying linear
(mixing) model which strongly limits the ability of the associated
source separation methods to determine the number of sources
and/or identify the physical source signatures. It is shown that the
assumed mixing model is only valid if each unit of the process gives
equal weighting (all-pass filter) to all oscillatory components in its
inputs. This is in contrast to the fact that each unit, in general, acts
as a filter with non-uniform frequency response. Thus, the model
can only facilitate correct identification of a source with a single
frequency component, which is again unrealistic. To overcome
this deficiency, an iterative post-processing algorithm that correctly
identifies the physical source(s) is developed. An additional issue
with the existing methods is that they lack a procedure to pre-screen
non-oscillatory/noisy measurements which obscure the identification
of oscillatory sources. In this regard, a pre-screening procedure
is prescribed based on the notion of sparseness index to eliminate
the noisy and non-oscillatory measurements from the data set used
for analysis.