Abstract: In this paper, we introduce a new method for elliptical
object identification. The proposed method adopts a hybrid scheme
which consists of Eigen values of covariance matrices, Circular
Hough transform and Bresenham-s raster scan algorithms. In this
approach we use the fact that the large Eigen values and small Eigen
values of covariance matrices are associated with the major and minor
axial lengths of the ellipse. The centre location of the ellipse can be
identified using circular Hough transform (CHT). Sparse matrix
technique is used to perform CHT. Since sparse matrices squeeze zero
elements and contain a small number of nonzero elements they
provide an advantage of matrix storage space and computational time.
Neighborhood suppression scheme is used to find the valid Hough
peaks. The accurate position of circumference pixels is identified
using raster scan algorithm which uses the geometrical symmetry
property. This method does not require the evaluation of tangents or
curvature of edge contours, which are generally very sensitive to
noise working conditions. The proposed method has the advantages of
small storage, high speed and accuracy in identifying the feature. The
new method has been tested on both synthetic and real images.
Several experiments have been conducted on various images with
considerable background noise to reveal the efficacy and robustness.
Experimental results about the accuracy of the proposed method,
comparisons with Hough transform and its variants and other
tangential based methods are reported.
Abstract: In this paper we present a new method for coin
identification. The proposed method adopts a hybrid scheme using
Eigenvalues of covariance matrix, Circular Hough Transform (CHT)
and Bresenham-s circle algorithm. The statistical and geometrical
properties of the small and large Eigenvalues of the covariance
matrix of a set of edge pixels over a connected region of support are
explored for the purpose of circular object detection. Sparse matrix
technique is used to perform CHT. Since sparse matrices squeeze
zero elements and contain only a small number of non-zero elements,
they provide an advantage of matrix storage space and computational
time. Neighborhood suppression scheme is used to find the valid
Hough peaks. The accurate position of the circumference pixels is
identified using Raster scan algorithm which uses geometrical
symmetry property. After finding circular objects, the proposed
method uses the texture on the surface of the coins called texton,
which are unique properties of coins, refers to the fundamental micro
structure in generic natural images. This method has been tested on
several real world images including coin and non-coin images. The
performance is also evaluated based on the noise withstanding
capability.
Abstract: A fuzzy classifier using multiple ellipsoids approximating decision regions for classification is to be designed in this paper. An algorithm called Gustafson-Kessel algorithm (GKA) with an adaptive distance norm based on covariance matrices of prototype data points is adopted to learn the ellipsoids. GKA is able toadapt the distance norm to the underlying distribution of the prototypedata points except that the sizes of ellipsoids need to be determined a priori. To overcome GKA's inability to determine appropriate size ofellipsoid, the genetic algorithm (GA) is applied to learn the size ofellipsoid. With GA combined with GKA, it will be shown in this paper that the proposed method outperforms the benchmark algorithms as well as algorithms in the field.
Abstract: We study the spatial design of experiment and we want to select a most informative subset, having prespecified size, from a set of correlated random variables. The problem arises in many applied domains, such as meteorology, environmental statistics, and statistical geology. In these applications, observations can be collected at different locations and possibly at different times. In spatial design, when the design region and the set of interest are discrete then the covariance matrix completely describe any objective function and our goal is to choose a feasible design that minimizes the resulting uncertainty. The problem is recast as that of maximizing the determinant of the covariance matrix of the chosen subset. This problem is NP-hard. For using these designs in computer experiments, in many cases, the design space is very large and it's not possible to calculate the exact optimal solution. Heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective and exact solution not possible. We developed a GA algorithm to take advantage of the exploratory power of this algorithm. The successful application of this method is demonstrated in large design space. We consider a real case of design of experiment. In our problem, design space is very large and for solving the problem, we used proposed GA algorithm.
Abstract: The dynamic or complex modulus test is considered
to be a mechanistically based laboratory test to reliably characterize
the strength and load-resistance of Hot-Mix Asphalt (HMA) mixes
used in the construction of roads. The most common observation is
that the data collected from these tests are often noisy and somewhat
non-sinusoidal. This hampers accurate analysis of the data to obtain
engineering insight. The goal of the work presented in this paper is to
develop and compare automated evolutionary computational
techniques to filter test noise in the collection of data for the HMA
complex modulus test. The results showed that the Covariance
Matrix Adaptation-Evolutionary Strategy (CMA-ES) approach is
computationally efficient for filtering data obtained from the HMA
complex modulus test.
Abstract: Quality control charts are very effective in detecting
out of control signals but when a control chart signals an out of
control condition of the process mean, searching for a special cause
in the vicinity of the signal time would not always lead to prompt
identification of the source(s) of the out of control condition as the
change point in the process parameter(s) is usually different from the
signal time. It is very important to manufacturer to determine at what
point and which parameters in the past caused the signal. Early
warning of process change would expedite the search for the special
causes and enhance quality at lower cost. In this paper the quality
variables under investigation are assumed to follow a multivariate
normal distribution with known means and variance-covariance
matrix and the process means after one step change remain at the new
level until the special cause is being identified and removed, also it is
supposed that only one variable could be changed at the same time.
This research applies artificial neural network (ANN) to identify the
time the change occurred and the parameter which caused the change
or shift. The performance of the approach was assessed through a
computer simulation experiment. The results show that neural
network performs effectively and equally well for the whole shift
magnitude which has been considered.
Abstract: One of the primary uses of higher order statistics in
signal processing has been for detecting and estimation of non-
Gaussian signals in Gaussian noise of unknown covariance. This is
motivated by the ability of higher order statistics to suppress additive
Gaussian noise. In this paper, several methods to test for non-
Gaussianity of a given process are presented. These methods include
histogram plot, kurtosis test, and hypothesis testing using cumulants
and bispectrum of the available sequence. The hypothesis testing is
performed by constructing a statistic to test whether the bispectrum
of the given signal is non-zero. A zero bispectrum is not a proof of
Gaussianity. Hence, other tests such as the kurtosis test should be
employed. Examples are given to demonstrate the performance of the
presented methods.
Abstract: In recent years, the use of vector variance as a
measure of multivariate variability has received much attention in
wide range of statistics. This paper deals with a more economic
measure of multivariate variability, defined as vector variance minus
all duplication elements. For high dimensional data, this will increase
the computational efficiency almost 50 % compared to the original
vector variance. Its sampling distribution will be investigated to make
its applications possible.
Abstract: The purpose of this study was to investigate effects of
modality and redundancy principles on music theory learning among
pupils of different anxiety levels. The lesson of music theory was
developed in three different modes, audio and image (AI), text with
image (TI) and audio with image and text (AIT). The independent
variables were the three modes of courseware. The moderator
variable was the anxiety level, while the dependent variable was the
post test score. The study sample consisted of 405 third-grade pupils.
Descriptive and inferential statistics were conducted to analyze the
collected data. Analyses of covariance (ANCOVA) and Post hoc
were carried out to examine the main effects as well as the
interaction effects of the independent variables on the dependent
variable. The findings of this study showed that medium anxiety
pupils performed significantly better than low and high anxiety
pupils in all the three treatment modes. The AI mode was found to
help pupils with high anxiety significantly more than the TI and AIT
modes.
Abstract: Autoregressive Moving average (ARMA) is a parametric based method of signal representation. It is suitable for problems in which the signal can be modeled by explicit known source functions with a few adjustable parameters. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Autocorrelation, Covariance and most recently, the use of Artificial Neural Network technique. In this paper, the method of using Artificial Neural network (ANN) technique is compared with some known and widely acceptable techniques. The comparisons is entirely based on the value of the coefficients obtained. Result obtained shows that the use of ANN also gives accurate in computing the coefficients of an ARMA system.
Abstract: The innovative intelligent fuzzy weighted input
estimation method (FWIEM) can be applied to the inverse heat
transfer conduction problem (IHCP) to estimate the unknown
time-varying heat flux of the multilayer materials as presented in this
paper. The feasibility of this method can be verified by adopting the
temperature measurement experiment. The experiment modular may
be designed by using the copper sample which is stacked up 4
aluminum samples with different thicknesses. Furthermore, the
bottoms of copper samples are heated by applying the standard heat
source, and the temperatures on the tops of aluminum are measured by
using the thermocouples. The temperature measurements are then
regarded as the inputs into the presented method to estimate the heat
flux in the bottoms of copper samples. The influence on the estimation
caused by the temperature measurement of the sample with different
thickness, the processing noise covariance Q, the weighting factor γ ,
the sampling time interval Δt , and the space discrete interval Δx ,
will be investigated by utilizing the experiment verification. The
results show that this method is efficient and robust to estimate the
unknown time-varying heat input of the multilayer materials.
Abstract: This paper presents comparative study on recent
integer DCTs and a new method to construct a low sensitive structure
of integer DCT for colored input signals. The method refers to
sensitivity of multiplier coefficients to finite word length as an
indicator of how word length truncation effects on quality of output
signal. The sensitivity is also theoretically evaluated as a function of
auto-correlation and covariance matrix of input signal. The structure of
integer DCT algorithm is optimized by combination of lower sensitive
lifting structure types of IRT. It is evaluated by the sensitivity of
multiplier coefficients to finite word length expression in a function of
covariance matrix of input signal. Effectiveness of the optimum
combination of IRT in integer DCT algorithm is confirmed by quality
improvement comparing with existing case. As a result, the optimum
combination of IRT in each integer DCT algorithm evidently improves
output signal quality and it is still compatible with the existing one.