Abstract: Symbolic Circuit Analysis (SCA) is a technique used
to generate the symbolic expression of a network. It has become a
well-established technique in circuit analysis and design. The
symbolic expression of networks offers excellent way to perform
frequency response analysis, sensitivity computation, stability
measurements, performance optimization, and fault diagnosis. Many
approaches have been proposed in the area of SCA offering different
features and capabilities. Numerical Interpolation methods are very
common in this context, especially by using the Fast Fourier
Transform (FFT). The aim of this paper is to present a method for
SCA that depends on the use of Wavelet Transform (WT) as a
mathematical tool to generate the symbolic expression for large
circuits with minimizing the analysis time by reducing the number of
computations.
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.