Search

Currently, measuring either vehicle’s mileage or duration or either one does maintain lubricant viscosity. However, these judgments are inaccurate because there are many other factors like conductivity, humidity and viscosity that may affect the oil quality. This paper proposed one theory of monitoring viscosity quality with Neural Network (NN) modelling by introducing factors like temperature, shear stress and pressure. One deterministic objective will be highlighted that is to develop NN modelling based on those three factors. This research also introduces normalization approach called Along Channel and logarithmic function due to various range of data input. NN modelling, an off-line system is explicitly designed with Backpropagation Algorithm and Multilayer Feedforward Network for learning process while its weight is calculated based on Nguyen Widrow number and Genetic Algorithm. There were 310 sample data, which divided into two sets; 149 data for training set and the rest for testing and vice versa. The application performance has achieved up to 85.91% result approaching real viscosity value.

Vehicle license plate recognition has been a much studied research area in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is rather different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, feature extraction and neural networks. The image processing library is developed in-house which we referred to as vision system development platform (VSDP). The Kirsch edge feature extraction technique is used to extract features from the license plates characters which are then used as inputs to the neural network classifier. The neural network model is the standard multilayered perceptron trained using the back-propagation algorithm. The prototyped system has an accuracy of about 91%, however, suggestions to further improve the system are discussed in this paper based on the analysis of the error.

Vehicle license plat recognition has been a much studied research area in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is rather different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, feature extraction and neural networks. The image-processing library is developed in-house which we referred to as Vision System Development Platform (VSDP). Multi-Cluster approach is applied to locate the license plate at the right position while Kirsch Edge feature extraction technique is used to extract features from the license plates characters which are then used as inputs to the neural network classifier. The neural network model is the standard multilayered perceptron trained using the back-propagation algorithm. The prototyped system has an accuracy of more than 91%, however, suggestions to further improve the system are discussed in this paper based on the analysis of the error.

Robot Soccer is a rich domain for the study in artificial intelligence. Teams of players must work together in order to put the ball in the opposing goal. Learning is essential in this task since the dynamics of the system can change as the opponents’ behaviours change. The players must be able to adapt to new situations. In this paper, we create a passing, avoiding obstacle and shooting strategy for robot soccer coordination. Based on a scenario in robot soccer, we stimulate a mini case study which involves two robots and ball.This research proposes coordination algorithm for robots collaboration in soccer game. The method is based on role, act, and behaviour of the robots. The actions of each robot depend on the created situation. The simulation result shows its probability to be applied in the real robot soccer game.

Off-line writer identification requires transferring the text under consideration into an image file. This represents the only available solution to bring the printed materials to the electronic media. However, the transferring process causes the system to lose the temporal information of that text, which it can be gathered in on-line writer identification. Various techniques have been implemented to achieve high identification rates. These techniques have tackled different aspects of the identification system. Importance of writer identification system is to help mainly in forensic fields, historical document analysis and handwriting recognition system enhancement. Unfortunately, the Arabic writer identification system not achieves a satisfaction rate yet whereas certain process of features and classification still not recognized.

This paper objective is to improve the current method for generating an Arabic Calligraphy text blocks. We test on seven types of Arabic Calligraphy text. We apply projection profiles and a proposed filter to discriminate each line of the Arabic Calligraphy scripts. After performing text detection, skew correction, text and line normalization subsequently, we generate Arabic Calligraphy text blocks for global texture analysis purposes. We compare our proposed filter with current method and median filter. The results show that the proposed filter is outperformed. The proposed method can be further improved to boost the overall performance.

The process of baseline detection has an important role in optical recognition systems and document image analysis systems. It is widely used in many various preprocessing stages as a text normalization including skew, slant and slop corrections, writing lines straightness and characters segmentation, as well as in feature extraction process. In this work, a new framework for baseline detection and straightness for cursive handwritten texts is proposed based on analysis and extraction the directions features from the subwords of the text skeleton. Arabic script is chosen as a case study since it is cursive and widely adopted in many languages all around the world such as Arabic, Jawi, Urdu and Persian. The experiments results on a popular Arabic dataset showed the efficiency of the proposed framework.