Ahmad Ramli

Household survey of container-breeding mosquitoes and climatic factors influencing the prevalence of Aedes aegypti (Diptera: Culicidae) in Makkah City, Saudi Arabia

OBJECTIVE To investigate the prevalence of container breeding mosquitoes with emphasis on the seasonality and larval habitats of Aedes aegypti (Ae. aegypti) in Makkah City, adjoining an environmental monitoring and dengue incidence. METHODS Monthly visits were performed between April 2008 and March 2009 to randomly selected houses. During each visit, mosquito larvae were collected from indoors and outdoors containers by either dipping or pipetting. Mosquitoes were morphologically identified. Data on temperature, relative humidity, rain/precipitations during the survey period was retrieved from governmental sources and analyzed. RESULTS The city was warmer in dry season (DS) than wet season (WS). No rain occurred at all during DS and even precipitations did fall, wetting events were much greater during WS. Larval survey revealed the co-breeding of Aedes, Culex and Anopheles in a variety of artificial containers in and around homes. 32 109 larvae representing 1st , 2nd, 3rd, and 4th stages were collected from 22 618 container habitats. Culicines was far the commonest and Aedes genus was as numerous as the Culex population. Ae. aegypti larval abundance exhibited marked temporal variations, overall, being usually more abundant during WS. Ten types of artificial containers were found with developing larvae. 70% of these habitats were located indoors. 71.42% of indoor containers were permanent and 28.58% was semi-permanent during WS. Cement tanks was the only container type permanent during DS. Ae. aegypti larval indices (CI, HI, BI) recorded were greater during WS. CONCLUSIONS Taken together, these results indicate a high risk of dengue transmission in the holy city.

Bootstrap-based normal reconstruction

We propose a bootstrap-based method for normal estimation on an unorganised point set. Experimental results show that the accuracy of the method is comparable with the accuracy of the widely used Principal Component Analysis. The main advantage of our approach is that the variance of the normals over the bootstrap samples can be used as a confidence value for the estimated normal. In a proposed application, we use the confidence values to construct a bilateral Gaussian filter for normal smoothing.

Constrained data visualization using rational cubic Ball function

The main purpose of this paper is shape-preserving interpolation for visualization of constrained data that will be smooth and pleasant. A rational cubic Ball function with three shape parameters will be introduced and used. A rational cubic Ball function is constructed according to the shape of the data that are constraint between two lines using appropriate conditions on each of the shape parameters.

Comparing reliability of training error and bootstrap error for least square fitting on noisy data

In least square fitting (LSF), increasing the degree of fitting reduces the training error but may lead to overfitting. Therefore, we cannot rely on training error as visual evaluation may be difficult in some cases. Bootstrap error estimation method on 2 dimensional data is applied for LSF. Simulated data is generated with some added noise. In this paper we compare the training error and bootstrap error for different degree of LSF on selected data. We observe that the best polynomial fitting is based on the smallest bootstrap error obtained and the result agree with the original ground truth data.

Analysis on training and bootstrap error evaluation with different parameter values for radial basis function on noisy data

In Radial Basis Function (RBF), user can define a parameter value,c which adjust the shape and smoothness of a data fitting. A larger parameter values increases the training error and may lead to unpleasant result. Therefore, relying on training error and visual evaluation may be difficult in some cases. Bootstrap estimation method on 2 dimensional data is applied for RBF. Simulated data points are generated with some added noise. In this paper, training error and bootstrap error for various parameter values on selected data is evaluated. Using different parameter value, we compute bootstrap error for the RBF fitting. Bootstrap error gives the optimum parameter value which produces the best fit for a specific data set.

Point Set Denoising Using Bootstrap-Based Radial Basis Function.

This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.

Preservation of Sharp Edges on 2D Drawing Using B-Spline Curve

B-spline is widely used in computer graphics in order to construct a smooth curve. In this paper, we discuss about the detection of sharp points on a B-spline curve and the preservation of the sharpness at the corner of the edges as the degree of the curve increases. We have made the removal process for the data that use a huge number of points and then marked the sharp edge points which will be used to calculate the Euclidean distance between the control points. The formulation of automatic knot vector generation by manual marking of points close to the sharp edges is implemented to obtain an algorithm that is capable of detecting the sharp points. The smoothness of the curve is based on the increase in the degree or order of the curve. Thus, to preserve the sharpness of the edges, knot vector repetition is applied corresponding to the sharp points. In addition, knot vectors play an important role in preserving the sharpness of the edges because the changes in knot vectors will produce various shapes of the B-spline curve.

Designing font using radial basis function

A new way of designing font is developed for the font using Radial Basis Function. The approach using Radial Basis Function is used on the interpolation of point. The data point will be used to generate the curve which will then form the shape of the font. To generate the curve, we will use the general cubic spline which will use the data points to generate the curve.