Fairul Nazmie Osman

Development of Novel Data Compression Technique for Accelerate DNA Sequence Alignment Based on SmithWaterman Algorithm

This paper presents the development of high performance accelerating technique for DNA sequences alignment. The scope of the paper focuses on speed optimization and memory reduction of the existing technique on initialization module. The novel development of the optimization using data compression technique for accelerates the Smith-Waterman (SW) algorithm has been revealed through this paper. This technique has been implemented on hardware based acceleration device. The development is targeted to Altera Cyclone II 2C70 FPGA and using 50MHz oscillator. The code is written in verilog HDL syntax using Quartus 2 version 7.2 and the simulation is verified using Quartus 2 version 7.2 simulator tool. The theoretical analysis and simulation result based on implementation of the design using FPGA are presented and well organized in this paper. The comparative study based on theoretical and simulation results of this technique has been made to accomplish result of analysis. The compilation result for data compression technique development of SW algorithm consisting of 73 logic elements with 93.75% reduction in memory space requirement.

Optimization of DNA Sequences Data to Accelerate DNA Sequence Alignment on FPGA

This paper presents the design and development of high performance accelerating and optimization technique for accelerate the process of DNA sequences alignment. The scope of the paper focuses on memory and speed optimization by optimizing and mapping the DNA sequences data before alignment. This optimization technique is designed based on novel data compression technique in optimizing the numbers of data transmitted from computer to DNA sequences alignment accelerator. Consequently, the numbers of data per serial data transmission for alignment can be optimize and accelerate up to 400% greater than conventional DNA sequences alignment accelerator system. This technique has been design and implemented on hardware based acceleration device and targeted to Altera Cyclone II 2C70 FPGA and using 50MHz oscillator for clock source. The code is written in verilog HDL syntax using Quartus 2 version 7.2 and the simulation is verified using Quartus 2 version 7.2 simulator tools. As a result, the theoretical analysis, simulation and implementation result based on development and implementation of the proposed design on FPGA are presented and well organized in this paper. The comparative study based on theoretical, simulation and implementation results of this technique has been made to accomplish result of analysis.

Classification of Rubber Tree Leaf Disease Using Spectrometer

In this paper, the classification of five types of rubber leaf disease by using the spectrometer and SPSS are presented. There are five of leaf disease that have been used as samples which are Oidium secondary leaf fall, Fusicoccum Leaf Blight, Bird eye’s spot and Anthracnose. The reflectance of the infected leaves sample is measured by using MCS600 Carl Zeiss spectrometer. Besides, Aspect Plus, a universal spectroscopy program from Zeiss manages to measure the spectral regions of the leaves sample. Further analysis and justification are completed by using approximate statistical tools from SPSS. The results obtained show that there are strong evidences that these diseases can be discriminated from each other using a spectrometer.

An Intelligent Classification Model for Rubber Seed Clones Based on Shape Features through Imaging Techniques

This paper describes research work in developing an intelligent model for classifying selected rubber tree series clones based on shape features using image processing techniques. Sample of rubber tree seeds are captured using digital camera where the RGB color image are processed involving segmentation algorithm which includes thresholding and morphological technique. Shape features such as area, perimeter and radius are extracted from each image. Two models are being designed. Model 1 is represented by 38 input features while Model 2 is represented by a reduction of input size using Principle Component Analysis (PCA). The inputs for both models are then used to train a multi-layer perceptron Artificial Neural Network (ANN) using Levenberg-Marquardt algorithm. 160 samples are used as training set while another 100 samples are used for testing. The optimized ANN models are then evaluated and validated through analysis of performance indicators regularly applied in classification research work via pattern recognition. Findings in this work have shown that the optimized Model 2 has the best accuracy of 84% with more than 70% achievement for sensitivity and specificity.

Comparative study for DNA data minimization technique on FPGA

Data minimization technique and existing DNA sequence data representation is compared and presented in this paper. The architecture of these two modules is compared and analyzed to study the complexity and performance characteristics of the system when dealing with large DNA sequences data on FPGA. These two technique is designed, synthesized and simulated using Altera Quartus II version 9.1 and targeted to Altera Cyclone II FPGA EP2C35 for determine the performance and complexity characteristic when running with DNA sequences data. Simulation results presented in this paper show it effects and feasibility when dealing with DNA sequences data on FPGA platform.

High-sensitivity gas detection and monitoring system for high-risk welding activity

This study attempted to investigate and design a new mechanism in monitoring safety by detecting gas concentration levels to prevent an explosion during welding activities at a high-risk site. The objective of this project was to design and develop a new safety precaution monitoring system during welding activity using a low-cost microcontroller to improve the current practice. The study was conducted by dividing the project into three stages: sensing, controlling, and notification. At the sensing stage, different gas concentration levels have been tested and analyzed to determine the safety range or level. However, the controller part managed the responses based on the detection of gas in the environment. Indicator and secure digital memory have been used to notify and record the activity during the operation. In the environment test, the system starts to trigger when sensing the level produced at 1.416 V, which represents 30% of the gas concentration. Nevertheless, the gas sensor required a 10-second initialized time to stabilize and produce the desired sensing level. At the end of this paper, the system has been successfully designed, developed, and tested at the prototype level.

Implementation of Real-Time Kinematic Data to Determine the Ionospheric Total Electron Content

Ionosphere layer is an important medium in communication system. At this layer where the total electron content exist because of ionization by the Suns extreme ultraviolet radiation, meanwhile the night side ionosphere electron content is reduced by chemical recombination. Maximum Total Electron Content (TEC) was detected at 0500 Local Time Clock (LTC) and will decrease gradually and reached its minimum at 2200 LTC. TEC during the day is higher than at night. The ultraviolet (UV) radiation from the sun ionizes fractions of the neutral atmosphere and forms the ionosphere. The elevation angle of satellite will also influence the TEC value. The higher elevation angle, the value of vertical TEC will also increase. The data from GPS Stations in RINEX format is supplied by Department of Survey and Mapping Malaysia (JUPEM). The data is taken from two different stations located at GPS Station Arau, Perlis and GPS Station Universiti Teknologi Malaysia Skudai, Johor.

A characterization of watermelon leaf diseases using Fuzzy Logic

This paper presents a characterization of watermelon leaf diseases through the RGB color. The aim of this study is to perform identification of selected critical watermelon leaf diseases in Malaysia namely the Downy Mildew and Anthracnose diseases. Several samples of infected leaves images were put under digital RGB color extraction where the images were captured under standardized and controlled environment. This study involves 200 samples of infected leaves of which the classification of the diseases was carried out using Fuzzy Logic technique. Fuzzy Logic was used to handle the uncertainty and vagueness as it provides a means of translating qualitative and imprecise information into quantitative (linguistic) terms. The results have shown that the percentage of accuracy for both types of disease were more than 67%.

A Statistical Approach for Rubber Seed Clones Classification Using Reflectance Index

This paper presents a statistical study for rubber seed clones classification. There are five types of clones from the same series of rubber seed being used as samples in this work which are the PB360, RRIM2009, RRIM2011, RRIM2016 and RRIM2025. The main objective is to identify significant features based on reflectance indices of both lateral and dorsal of the rubber seed surfaces from the application of ZEISS spectrometer instrument. The instrument measures the percentage of reflectance with respect to intensity of safe radiation light being reflected from the seed surface. Empirical analysis is done using SPSS software in order to identify discrimination between the clones. From the observed error plots and one-way ANOVA measurements, it is shown that reflectance indices of lateral surface can be used to recognize significantly the RRIM2009 from the other rubber seed clones.

Artificial Neural Networks Based Algorithm for Identifying Engine Oil Parameters

Engine oil is an essential type of lubricant normally used by internal combustion engines. The main job is to lubricate moving part of the engine. Engine oil is also performed to clean, inhibits corrosion and cools the engine by sinking the heat away from the main structure of the engine. However, the viscosity of the engine oil varies with respect to changes in mileage and the change of lubricant on schedule is always inaccurate. As a result, engine oil becomes out of condition for after a period of usage, resulting poor performance and increase cost and maintenance. Viscosity is a parameter to measure resistance of a fluid to flow. Through the theoretical and experimental method, this paper verifies the measurement of transmittance percentages of viscosity in terms of infrared wavelength with respect to several classes’ mileage distances. The most robust networks are also identified to be applied in this study. Finally, this information can be used in designing sensor to detect the degree of viscosity and development of the decay of lubricant evaluation index.