Sharifah Lailee Syed Abdullah

A modified algorithm for species specific motif discovery

Motif discovery can be used to categorize unknown DNA sequences into their corresponding families. For this study, PSO was modified for discovering motif. The modified Linear-PSO is chosen even though it is a slower because linear search is not a choice but a necessary criteria for identifying motif of pig (Sus Scrofa). Pig motif identification is a critical for halal authentication. The modified Linear-PSO algorithm used linear number for population initializing and next position updating. For each cycle, only a particle called ‘target motif’ was selected and compared with other DNA sequences for fitness calculation. Motif discovered can be used as a standard motif for species identification. Experimental results show that the modified algorithm is able to identify motifs as expected. This study showed that a slower algorithm is still needed and has value based on how critical the problem is.

A rule-based segmentation method for fruit images under natural illumination

Image segmentation is a process that significantly important for machine vision system such as automatic fruit grading system. This process separates an image into several areas to extract the interest object from its background. However, the segmentation task is difficult for isolating the images that captured in outdoor environment. This is due to the existence of non-uniform illumination on the object surface. Technically, different illuminations lead to different intensity on the object surface colour. This condition leads to low quality segmented images and therefore reduces the accuracy of object classification. Image segmentation can be accomplished using several methods such as Otsu, K-means and Fuzzy C-means. However, these three traditional methods have limitations in producing accurate segmented areas due to the existence of illumination on the object surface. Therefore, this paper developed a rule-based segmentation method that is able to segment natural images correctly and accurately. This method uses IF-THEN algorithm to segment the images of interest object. All four segmentation methods are implemented on fruit images and their performance are compared based on visual and quantitative evaluations. The analysis results showed that the new method is capable to produce segmented images with high accuracy rate.

An accurate thresholding-based segmentation technique for natural images

Segmentation is a process of dividing an image into distinct regions with the aim to extracts object of interest from the background. The traditional thresholding and clustering segmentation techniques that were widely used are Otsu and K-means, respectively. However, the segmentation process becomes more challenging for segmenting natural images. Both Otsu and K-means methods failed to produce good quality of segmented areas under natural environment due to the complex background and non-uniform illumination on the images. Therefore, this paper proposed an improved thresholding-based segmentation with inverse technique (TsTN) that was able to partition natural images. A comparison between Otsu, K-means and TsTN techniques was conducted using colour-based image processes on the quality of the segmented images. The analysis results showed that TsTN has the ability to produce good quality of segmented images. Furthermore, this improved technique was proved to be more accurate than the traditional thresholding and clustering techniques.

Integrating GIS technology and 3D animation in an Event Information System

The integration of spatial and non spatial data in GIS (Graphical Information System) applications has huge contributions in many areas. In this study, we apply an integration technique to incorporate three types of applications: 1) application that manipulates non spatial data, 2) application that generates spatial data (maps and images) and lastly application that generates 3D animation. This integration technique allows us to develop Event Information System (EvIS) which could extract maps and distances, images of respective buildings and 3D animation of route directions. This prototype will revolutionise the way users have access to event information which include views of beautiful buildings and routes to respective events.

Thermal Gravimetric Analysis (Tga) of Kenaf Core and Its Cellulose for Membrane Fabrication

Membranes can be fabricated using three main materials, namely solvent, polymer, and additives. The function of the solvent is to dissolve the polymer so it becomes homogenous with another material that is required to form the membrane layer. Various types of additives can be used to enhance the surface of the membrane layer. Examples of commonly used additives in membrane are polyethylene glycol (PEG), monosodium glutamates, sulfonated poly(ether ether ketone), and polyvinylpyrrolidone (PVP). However, these are synthetic additives; therefore, there is a need to search for a sustainable organic additive that would be greener for the environment, such as cellulose from suitable plants. In this study, cellulosic material was extracted from kenaf core, to be used as the organic additive for membrane formation. In order to investigate the suitability of this material, thermal gravimetric analyses (TGA) were performed in two parts. The first TGA was done on the kenaf core to determine the amount of cellulose that can be extracted from it. Then, a second TGA was done on a sample of the extracted cellulose to determine its weight and quantified loss of water, loss of solvent, loss of plasticizer, decarboxylation, pyrolysis, oxidation, decomposition, weight % filler, amount of metallic catalytic residue remaining on carbon nanotubes, and weight percentage of ash. TGA results on cellulose from kenaf core have shown that the crystalline melt was defined by the peak temperature at 333.07 °C. After the melt transition, the baseline had returned to a slightly lower position than the premelt baseline. The slope of the post-melt baseline had changed as the sample began to decompose. Meanwhile, TGA has extrapolated the onset temperature to 421.61 °C as the sample decomposes.

Formulation of Nanofiltration Flat Sheet Membrane Using Cellulose from Kenaf Core

This study was conducted in order to determine the suitable formula in developing a nanofiltration membrane using additive from kenaf core. Kenaf core was processed and analyse the suitable component to be used as the additive in formulation the nanofiltration membrane. Kenaf taken from National Kenaf and Tobacco Board (NKTB) Malaysia with code reference of V36 was used in this study. Cellulose of Kenaf core was extracted using Microcrystalline Cellulose (MCC) process at the Forestry Research Institute of Malaysia (FRIM). From few percentage of Kenaf cellulose with analyses using Flux Purewater Test, NaCl Test (Rejection Test), Molecular Weight Cut Off (MWCO) Test, Scanning Electron Microscopy (SEM) and Fourier Transform Infra Red (FTIR), the suitable percentage of Kenaf cellulose for nanofiltration flat sheet membrane was obtained.

Improved technique for segmenting images under natural environment

Segmentation is an essential step used for analyzing images in computer vision because digital images must be partitioned into different regions. The objective of this study is to investigate the techniques in thresholding-based method for segmenting images under natural environment. Although thresholding is popular and widely used for image segmentation, it was discovered that modifications to threshold value must be made for several images to produce clearer segmented images.