Mohd Zubir Mat Jafri

Simulation of an Ultra-Compact Multimode Interference Power Splitter Based on Kerr Nonlinear Effect

We propose an ultra-compact multimode interference (MMI) power splitter based on the Kerr nonlinear effect from simulations using modal propagation analysis. Crystalline polydiacetylene is used as the core layer to allow for the creation of a power splitter with a high number of outputs with the shortest possible multimode waveguide length operating in the nonlinear regime. The 11 high-contrast, high-resolution images at the end of the multimode waveguide in the simulated power splitter have a high power balance, whereas access to a high number of self-images is not possible under the linear regime in the proposed length range. The compact dimensions and ideal performance of the device are established according to optimized parameters. The proposed regime can be extended to the design of M × N power splitters. The results of this study indicate that nonlinear modal propagation analysis solves the miniaturization problem for all-optical devices based on MMI couplers to achieve multiple functions in a compact planar integrated circuit and also overcomes the limitations of previously proposed methods for nonlinear MMI. The results are verified using a numerical method.

Structural and optical properties of ITO/TiO2 anti-reflective films for solar cell applications

Indium tin oxide (ITO) and titanium dioxide (TiO2) anti-reflective coatings (ARCs) were deposited on a (100) P-type monocrystalline Si substrate by a radio-frequency (RF) magnetron sputtering. Polycrystalline ITO and anatase TiO2 films were obtained at room temperature (RT). The thickness of ITO (60 to 64 nm) and TiO2 (55 to 60 nm) films was optimized, considering the optical response in the 400- to 1,000-nm wavelength range. The deposited films were characterized by X-ray diffraction (XRD), Raman spectroscopy, field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), and atomic force microscopy (AFM). The XRD analysis showed preferential orientation along (211) and (222) for ITO and (200) and (211) for TiO2 films. The XRD analysis showed that crystalline ITO/TiO2 films could be formed at RT. The crystallite strain measurements showed compressive strain for ITO and TiO2 films. The measured average optical reflectance was about 12% and 10% for the ITO and TiO2 ARCs, respectively.

Comparison of Neural Network and Maximum Likelihood classifiers for land cover classification using landsat multispectral data

Land cover classification is one of the remote sensing applications, in order to identify features such as land use by utilizing typically multispectral satellite data. Numerous algorithms have been developed for classification purpose and different classifiers have their own characteristics. Different data and study area, especially the landscape complexity bring different impact on the different classifiers. Therefore, the aim of this study is to compare Neural Network and Maximum Likelihood approaches and find a suitable classifier in land cover classification using medium spatial resolution satellite images in equatorial tropical region. These two classifiers were tested using Landsat Thematic Mapper (TM) data in Penang Island, Malaysia using the same training sample data sets. Five land cover classes — forest, grassland, urban, water, and cloud — were classified. In addition, the study also been carried out in order to obtain the performances of both classifiers for the purpose of land cover mapping. Overall classification accuracy and Kappa Coefficient were calculated. The results indicated that Neural Network algorithm provided better classification accuracy than Maximum Likelihood algorithm. The overall accuracy of Neural Network approach reaches 93.5 % associated with 0.909 Kappa coefficient, which is more reliable than Maximum Likelihood, with 80.5 % overall accuracy and 0.722 Kappa coefficient.

Multispectral absorption algorithm for retrieving TSS concentrations in water

The environmental problems caused by the increase of pollutant. It is the result of industrial waste and environmental accidents. One of the main sources of water pollution is suspended solids. When these suspended particles settle to the bottom of a water body, they become sediments. Optical methods offer two possibilities: scattering and transmission. The design of optical sensor systems is based on the interaction between the photons of the electromagnetic radiation and suspended particles in water. Pollution usually results in higher total suspended solids (TSS) concentrations or turbidity. Water pollution is caused by any chemical, physical or biological change in the quality of water. Suspended solids are small particles of solid pollutants that float on the surface of, or are suspended in waters. Water pollution has become increasingly critical in this present-day, whether in the developed or the developing countries. Total suspended solids (TSS) in water can be detected by a number of optical sensing techniques, which involve light interaction with suspended particles in water. The measurements of the transmittance give a method for determining the concentration of the particles. The objectives of this study are to derive multispectral absorption algorithm and to develop an optical system, which is based on absorption measurements for measuring the concentration of total suspended solids,TSS in water. The multispectral absorption algorithm has been developed and a new multispectral optical sensor system designed for measuring total suspended solids TSS concentrations in polluted water. The development of the optical algorithm was based on the Beer-Lambert law. Firstly, the water samples used for calibration were filtered and dried to obtain the TSS values. In this study, three light emitting diodes were used as sensing emitters (sources) and a silicon phototransistor as radiations detector. The radiation values were determined from the output voltage readings of the sensor system. An electronic circuit was designed and the readings were measured by using a digital multimeter. The major advantages in using an LED as the light sources are its relatively low power consumption and ability to be modulated electronically at rapid rates. Standard polluted samples were prepared for sensors calibration. As the concentration of total suspended solids P was increased, the intensity of the transmitted light decreased. The level of the photocurrent was linearly proportional to the pollutants concentration. The proposed multispectral algorithm was calibrated using the measured parameters. Its accuracy was determined based on correlation coefficient,R and the value of the root-mean square errors,RMSE. The results showed a good correlation between the radiation values and the total suspended solids concentrations. This optical system provides various advantages over the current portable turbidity meter, which uses a single infrared source. The main advantage is its capability provide water pollution levels accurately (i.e. total suspended solids concentration). It only requires inexpensive components and can be assembled easily. The proposed algorithm produced a high correlation coefficient and low root mean square error value. This new methodology is very important for measuring and monitoring TSS in polluted water areas.

Multiple regression analysis in modeling of columnar ozone in Peninsular Malaysia

This study aimed to predict monthly columnar ozone (O3) in Peninsular Malaysia by using data on the concentration of environmental pollutants. Data (2003–2008) on five atmospheric pollutant gases (CO2, O3, CH4, NO2, and H2O vapor) retrieved from the satellite Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) were employed to develop a model that predicts columnar ozone through multiple linear regression. In the entire period, the pollutants were highly correlated (R = 0.811 for the southwest monsoon, R = 0.803 for the northeast monsoon) with predicted columnar ozone. The results of the validation of columnar ozone with column ozone from SCIAMACHY showed a high correlation coefficient (R = 0.752–0.802), indicating the model’s accuracy and efficiency. Statistical analysis was utilized to determine the effects of each atmospheric pollutant on columnar ozone. A model that can retrieve columnar ozone in Peninsular Malaysia was developed to provide air quality information. These results are encouraging and accurate and can be used in early warning of the population to comply with air quality standards.

Regression analysis in modeling of air surface temperature and factors affecting its value in Peninsular Malaysia

Abstract. This study encompasses air surface temperature (AST) modeling in the lower atmosphere. Data of four atmosphere pollutant gases (CO, O3, CH4, and H2Ovapor) dataset, retrieved from the National Aeronautics and Space Administration Atmospheric Infrared Sounder (AIRS), from 2003 to 2008 was employed to develop a model to predict AST value in the Malaysian peninsula using the multiple regression method. For the entire period, the pollutants were highly correlated (R=0.821) with predicted AST. Comparisons among five stations in 2009 showed close agreement between the predicted AST and the observed AST from AIRS, especially in the southwest monsoon (SWM) season, within 1.3 K, and for in situ data, within 1 to 2 K. The validation results of AST with AST from AIRS showed high correlation coefficient (R=0.845 to 0.918), indicating the model’s efficiency and accuracy. Statistical analysis in terms of β showed that H2Ovapor (0.565 to 1.746) tended to contribute significantly to high AST values during the northeast monsoon season. Generally, these results clearly indicate the advantage of using the satellite AIRS data and a correlation analysis study to investigate the impact of atmospheric greenhouse gases on AST over the Malaysian peninsula. A model was developed that is capable of retrieving the Malaysian peninsulan AST in all weather conditions, with total uncertainties ranging between 1 and 2 K.

Mangrove mapping in Penang Island by using Artificial Neural Network technique

Environmental study is crucial in order to understand deeply about the flora and fauna living in the Earth. Mangrove forest is a unique and natural ecosystem that can be used to produce forestry product such as charcoal, timber, supply food to their surrounding marine life, and protect the inland from disturbance like erosion, flood, and tsunami. Due to the uncontrolled planning of human activity, many mangrove forests had been deforested for development of industry area, urban land and agriculture. In this study, Multi-layer Feed Forward/Multilayer Perceptrons (MLP) network system in Artificial Neural Network technique was used to map out the current state of mangrove trees. This network system require user to have a ground truth data such as in supervised classification in order to generate the training area for classification. Generally, this network comprise of a simple structure layer which consist of three layers namely input layer, hidden layer and output layer. Multi-layer Feed Forward algorithm has at least one hidden layer of neuron between the input and output layer. Each successive layer of neurons is fully interconnected with connection weight determine the strength of the connection. 2010 Thailand Earth Observing System (THEOS) satellite imagery was used as the source for the data processing with the aid of PCI Geomatica version 10.3.2 software packaging. The classification result of Multi-layer Feed Forward yield 5 category of classes. Post-classification analysis was further carried out to validate the classified data with reference data. High overall accuracy of 93.5% and kappa coefficient of 0.900 was obtained for the mangrove cover mapping. Final thematic map was produce to quantify and display the current distribution of mangrove land. The result indicates that neural network approach is suitable and reliable used for mangrove mapping.

Water quality mapping using digital camera images

The objective of this study was to implement a low-cost airborne remote sensing system to provide an alternative solution for remote sensing given the difficulty in obtaining cloud-free satellite scenes of the equatorial region. An inexpensive sensor, a Kodak DC 290 digital camera, was used, onboard a light aircraft, Cessna 172Q. The feasibility of using camera images for remote sensing applications was tested for quantifying total suspended solids (TSS) from three estuaries located in the northern region of Peninsular Malaysia. The aircraft was flown at altitudes of 914.4 to 2438.4 m for digital image acquisition of the study areas. Water samples were collected simultaneously with the aircraft overpasses and their locations were determined by using a hand-held Global Positioning System (GPS). Oblique images were corrected for brightness variation. A simple relative atmospheric correction was performed on the multidate images. The captured colour images were then separated into three bands (red, green and blue) for multispectral analysis. The digital numbers (DNs) were extracted corresponding to the sea data locations for each band. A multiband regression algorithm was developed based on a reflectance model, which is a function of the inherent optical properties of water, and this in turn can be related to the concentration of the TSS. Data from different scenes were combined and then divided into two sets, one for calibration of the algorithm and the other for a validation analysis. The calibrated algorithm had a correlation coefficient (R) of 0.96 and a root mean square error (RMSE) of approximately 17 mg l−1. The validation analysis showed that the algorithm could estimate the TSS concentration within an RMSE of about 23 mg l−1 and an R value of 0.95. The calibrated algorithm was used to generate water quality maps for all images. The maps were geometrically corrected and colour coded for visual interpretation.

Segmentation and estimation of brain tumor volume in computed tomography scan images using hidden Markov random field Expectation Maximization algorithm

Brain tumors have been created by abnormal and uncontrolled cell division inside the brain. A crucial and lengthy task is the segmentation of brain tumors, which can be gained manually with the help of Computed Tomography (CT). Treatment, diagnosis, signs and symptoms of the brain tumors mainly depend on the volume, shapes and location of the tumors. The accuracy and time of detecting brain tumor are vital contributions in the successful diagnosis and treatment of tumors. Therefore, the detection of brain tumor needs to be fast and accurate. Brain tumor segmentation and volume estimation have been considered a challenge mission in medical image processing. The main aim of this paper is that with the help of hidden Markov random field- Expectation Maximization (HMRF-EM) and threshold method, a novel approach of improving the segmentation of brain tumors from CT scan images is produced. The segmentation and volume estimation images are obtained by the study of 2D images. We calculate the volume of tumor using a new approach based on 2D images estimations and voxel space. In order to validate the proposed approach a comparison is carried out with a manual method using Mango software which, the noise or impurities are less than Mango software in measurement of tumor volume.

Multiple regression analysis in modelling of carbon dioxide emissions by energy consumption use in Malaysia

Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model’s accuracy and efficiency. These result...