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- ItemOverlapping Vehicle Tracking via Adaptive Particle Filter with Multiple Cues(2011) W.L. Khong; W.Y. Kow; Y.K. Chin; I. Saad; K.T.K. TeoVehicle tracking is a vital approach to assist the onroad traffic surveillance system. Since the on-road vehicles is increasing, occlusion and overlapping of vehicles is often happen in the traffic surveillance scene. Therefore, segmentation and tracking of the occlusion or overlapped vehicle can be a challenging task in surveillance system via image processing. In this paper, a multiple cues overlapping vehicle tracking algorithm is proposed to continuously track the occluded vehicle effectively. The earlier vehicle tracking systems are normally based on colour feature which will leads to inaccurate results when the background colour is complex or too similar with the target vehicle. On the other hand, shape feature will increase the accuracy but consume more computation time in the resampling process during overlapping. The experimental results show that enhancement of the particle filter resampling process with multiple cues is capable to track the overlapped vehicle with higher accuracy and without compromising the processing time.
- ItemVariational Level Set Algorithm in Image Segmentation for Foetus Ultrasound Imaging System(2012) Mei Yeen Choong; May Chin Seng; Aroland Kiring; Soo Siang Yang; Kenneth Tze Kin TeoSegmentation on ultrasound image is difficult when the image is not clear and contains unwanted noise. Since the object to be segmented out can be changing in shape for a period of time, there is a need to apply a computerised segmentation method for future analysis without any assumptions about the object’s topology is made. In general, when performing pregnancy ultrasound scanning, seeking a snapshot with best position or angle of the foetus is often a task done by obstetrician. This snapshot is useful for the obstetrician to locate the crown and the rump of the foetus for specific measurement. In this paper, a computerized segmentation using variational level set algorithm (VLSA) is proposed here. Results showed the variational level set contour evolved well on the low contrast and noise consisting ultrasound image.
- ItemSimulation of SPWM Fed Three-phase Induction Motor Drive Mathematical Model Using MATLAB Simulink(2020) Amir Rasyadan; Sazali bin Yaacob; Pranesh Krishnan; Mohamed Rizon; Chun Kit AngThree-phase induction motors are used in a vast area of applications mainly due to their simplicity, ruggedness and high reliability. With recent advancement in semiconductor technologies, the use of fixed speed induction motor drive is becoming obsolete, majority of the applications now requires inverter-based drives for variable speed operation. In the study of induction motor drive operation, mathematical models are often used to simulate the steady state and transient behavior of induction motor. However, developing such models are not as simple. One must develop the understanding of the mathematical equations to be used and also having the knowledge to solve the equations with the use of computer simulation software. To address this issue, this work presents an approach to implement the mathematical model of a Sinusoidal Pulse Width Modulation (SPWM) fed three-phase induction motor drive in MATLAB Simulink. The sub models include induction motor direct quadraturemodel and a voltage source inverter fed by SPWM signal generator. The presented model implementation is able to simulate the dynamic behavior of an induction motor operation, this mathematical model implementation would be useful for further studies on the development of induction motor drive system.
- ItemQ-Learning Traffic Signal Optimization within Multiple Intersections Traffic Network(2012) Yit Kwong Chin; Wei Yeang Kow; Wei Leong Khong; Min Keng Tan; Kenneth Tze Kin TeoTraffic flow optimization within traffic networks has been approached through different kinds of methods. One of the methods is to reconfigure the traffic signal timing plan. However, dynamic characteristic of the traffic flow is not able to be resolved by the conventional traffic signal timing plan management. As a result, traffic congestion still remains as an unsolved problem. Thus, in this study, artificial intelligence algorithm has been introduced in the traffic signal timing plan to enable the traffic management systems’ learning ability. QLearning algorithm acts as the learning mechanism for traffic light intersections to release itself from traffic congestions situation. Adjacent traffic light intersections will work independently and yet cooperate with each other to a common goal of ensuring the fluency of the traffic flows within traffic network. The experimental results show that the Q-Learning algorithm is able to learn from the dynamic traffic flow and optimized the traffic flow.
- ItemQ-Learning Based Traffic Optimization in Management of Signal Timing Plan(2011) Yit Kwong Chin; Nurmin Bolong; Aroland Kiring; Soo Siang Yang; Kenneth Tze Kin TeoOccurrences of traffic congestions within the urban traffic network are increasing in a rapid rate due to the rising traffic demands of the outnumbered vehicles on road. The effectiveness of management from traffic signal timing planner is the key solution to solve the traffic congestions, but unfortunately the current traffic light signal system is not fully optimized based on the dynamic traffic conditions on the road. Adaptable traffic signal timing plan system with ability to learn from their past experiences is needed to overcome the dynamic changes of the urban traffic network. The ability of Q-learning to prospect gains from future actions and obtain rewards from its past experiences allows Q-learning to improve its decisions for the best possible actions. A good valuable performance has been shown by the proposed learning algorithm that able to improve the traffic signal timing plan for the dynamic traffic flows within a traffic network.