Zainor Ridzuan Yahya

Reconstruction of generic shape with cubic Bézier using least square method

Reverse engineering procedure has been used to represent the generic shapes of some objects in order to explore its technical principles and mechanism so that an improved system can be develops. Curve reconstruction had immensely used in reverse engineering to reproduce the curves. In this paper, cubic Bezier curve function was used for curve fitting by Least Square Method. Least Square Method is applied to find the optimal values of the parameters in the description of the curve function used.

Craniofacial Reconstruction Using Rational Cubic Ball Curves

This paper proposes the reconstruction of craniofacial fracture using rational cubic Ball curve. The idea of choosing Ball curve is based on its robustness of computing efficiency over Bezier curve. The main steps are conversion of Digital Imaging and Communications in Medicine (Dicom) images to binary images, boundary extraction and corner point detection, Ball curve fitting with genetic algorithm and final solution conversion to Dicom format. The last section illustrates a real case of craniofacial reconstruction using the proposed method which clearly indicates the applicability of this method. A Graphical User Interface (GUI) has also been developed for practical application.

Reconstruction of egg shape using B-spline

In this paper, the reconstruction of egg’s outline by using piecewise parametric cubic B-spline is proposed. Reverse engineering process has been used to represent the generic shape of egg in order to acquire its geometric information in form of a two-dimensional set of points. For the curve reconstruction, the purpose is to optimize the control points of all curves such that the distance of the data points to the curve is minimized. Then, the B-spline curve functions were used for the curve fitting between the actual and the reconstructed profiles.

A hybrid methodology for the mahalanobis-taguchi system using random binary search-based feature selection

The Mahalanobis-Taguchi system (MTS) is a relatively new statistical methodology combining various mathematical concepts and is used in the field of diagnosis and classification in multidimensional systems. MTS is a very efficient method and has already been applied to a wide range of disciplines. However, its feature selection phase (optimization stage), which uses experimental designs (orthogonal array, OA), is susceptible to improvement. In MTS, selection of important features or variables to improve classification accuracy is done using signal-to- noise (S/N) ratios and OA. OA has been noted for limitations in handling a large number of variables. Therefore, in this research, we propose the use of a random binary search (RBS) algorithm incorporated within MTS for optimizing the procedure for selecting the most useful variables. Ensemble is a powerful technique to achieve improvement in the accuracy of predictive models, whereby individual methods, which are not consistently the best performers in different problems and datasets, are brought together to provide predictions which are more accurate than those made by individual methods.

Surface Reconstruction from Parallel Curves with Application to Parietal Bone Fracture Reconstruction

Maxillofacial trauma are common, secondary to road traffic accident, sports injury, falls and require sophisticated radiological imaging to precisely diagnose. A direct surgical reconstruction is complex and require clinical expertise. Bio-modelling helps in reconstructing surface model from 2D contours. In this manuscript we have constructed the 3D surface using 2D Computerized Tomography (CT) scan contours. The fracture part of the cranial vault are reconstructed using GC1 rational cubic Ball curve with three free parameters, later the 2D contours are flipped into 3D with equidistant z component. The constructed surface is represented by contours blending interpolant. At the end of this manuscript a case report of parietal bone fracture is also illustrated by employing this method with a Graphical User Interface (GUI) illustration.

Conic Curve Fitting Using Particle Swarm Optimization: Parameter Tuning

We solve curve fitting problems using Particle Swarm Optimization (PSO). PSO is used to optimize control points and weights of two conic curves to a set of data points. PSO is used to find the best middle control point and weight for both conic curves to provide piecewise conics that preserve G^1 continuity. We present numerical result using parameter changes in PSO scheme. We obtain appropriate parameter values of PSO that provide best error and fastest time to solve curve fitting problem.

Positivity preserving curves using rational cubic Ball interpolant

A rational cubic Ball function which has four shape parameters in its description has been used to preserve the shape of positive data. Conditions are derived on one of the shape parameters in such a way that they preserve shape of the data, while the others three parameters remain free to enable the user to modify the shape of the curve. The degree of smoothness is C1. Outputs from numerical experiments are presented.

Shape Preserving Data Interpolation Using Rational Cubic Ball Functions

A smooth curve interpolation scheme for positive, monotone, and convex data is developed. This scheme uses rational cubic Ball representation with four shape parameters in its description. Conditions of two shape parameters are derived in such a way that they preserve the shape of the data, whereas the other two parameters remain free to enable the user to modify the shape of the curve. The degree of smoothness is . The outputs from a number of numerical experiments are presented.

Positivity preserving rational cubic Ball constrained interpolation

A smooth curve interpolation scheme for positive data is developed. Conditions have been incorporated into this scheme to preserve the shape of the data lying above a line. This scheme uses rational cubic Ball representation. Conditions are derived for preserving positivity and C1 continuity. The outputs from a number of numerical experiments are presented.

Selection of access network using cost function method in heterogeneous wireless network

the challenge for future generation of wireless environment is on how to choose the appropriate wireless access network connection when there are several different wireless networks are existed. Hence, a vertical handover network selection (VHONS) being used to aid in term of service quality for users satisfaction of the mobile terminal. This article intends to propose a new method of choosing the vertical handover network. This method covers the weight distribution and cost factor techniques. The weight distribution is used to measure different weights for existing wireless network based on the users preference and mobile terminal power. The cost factor technique is also used to identify the cost for performing handover target by considering every network parameters and its weight. Results obtained showed that the algorithm has the ability to increase users satisfaction compared to other algorithms, which consistently choose one accessible network.