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Search Results: 1 - 10 of 601 matches for " Kah Phooi SENG "
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MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition
King Hann Lim,Kah Phooi Seng,Li-Minn Ang
Applied Computational Intelligence and Soft Computing , 2012, DOI: 10.1155/2012/793176
Abstract: Lyapunov theory-based radial basis function neural network (RBFNN) is developed for traffic sign recognition in this paper to perform multiple inputs multiple outputs (MIMO) classification. Multidimensional input is inserted into RBF nodes and these nodes are linked with multiple weights. An iterative weight adaptation scheme is hence designed with regards to the Lyapunov stability theory to obtain a set of optimum weights. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Weight gain is formed later to obey the Lyapunov stability theory. Detail analysis and discussion on the proposed classifier’s properties are included in the paper. The performance comparisons between the proposed classifier and some existing conventional techniques are evaluated using traffic sign patterns. Simulation results reveal that our proposed system achieved better performance with lower number of training iterations. 1. Introduction Traffic sign recognition is important in autonomous vehicular technology for the sake of identifying a sign functionality through visual information capturing via sensors. The usage of neural networks has become increasingly popular in traffic sign recognition recently to classify various kinds of traffic signs into a specific category [1–3]. The reason of applying neural networks in traffic sign recognition is that, they can incorporate both statistical and structural information to achieve better performance than a simple minimum distance classifier [4]. The adaptive learning capability and processing parallelism for complex problems have led to the rapid advancement of neural networks. Among all neural networks, radial basis function neural network (RBFNN) has been applied in many engineering applications with the following significant properties: (i) universal approximators [5]; (ii) simple topological structure [6] which allows straightforward computation using a linearly weighted combination of single hidden-layer neurons. The learning characteristic of RBFNN is greatly related to the associative weights between hidden-output nodes. Therefore, an optimal algorithm is required to update the weights relative to an arbitrary training input. Conventionally, the training process for RBFNN is mainly dependent on the optimization theory. The cost function of this network, for instance, the sum of squared errors or mean squared error between network’s output and targeted input is firstly defined. It is followed by minimizing the cost function in weight parameter space to search
River Flow Lane Detection and Kalman Filtering-Based B-Spline Lane Tracking
King Hann Lim,Kah Phooi Seng,Li-Minn Ang
International Journal of Vehicular Technology , 2012, DOI: 10.1155/2012/465819
Abstract: A novel lane detection technique using adaptive line segment and river flow method is proposed in this paper to estimate driving lane edges. A Kalman filtering-based B-spline tracking model is also presented to quickly predict lane boundaries in consecutive frames. Firstly, sky region and road shadows are removed by applying a regional dividing method and road region analysis, respectively. Next, the change of lane orientation is monitored in order to define an adaptive line segment separating the region into near and far fields. In the near field, a 1D Hough transform is used to approximate a pair of lane boundaries. Subsequently, river flow method is applied to obtain lane curvature in the far field. Once the lane boundaries are detected, a B-spline mathematical model is updated using a Kalman filter to continuously track the road edges. Simulation results show that the proposed lane detection and tracking method has good performance with low complexity. 1. Introduction Automation of vehicle driving is being developed rapidly nowadays due to the vast growth of driver assistance systems (DASs) [1]. In conjunction with the development of low-cost optical sensors and high-speed microprocessors, vision-based DASs become popular in the vehicular area to detect apparent imaging cues from various road scenes for visual analysis and therefore warn a driver of an approaching danger and simultaneously perform autonomous control to the vehicle’s driving. Of all fatal errors happened, driver’s inattention and wrong driving decisions making are the main factors of severe crashes and casualties on road [2]. The deviation of a vehicle from its path without a signal indication has threatened the nearby moving vehicles. As a consequence, vision-based lane detection and tracking system becomes an important mechanism in vehicular autonomous technology to alert a driver about road physical geometry, the position of the vehicle on the road, and the direction in which the vehicle is heading [3]. In the last few decades, a lot of vision-based lane detection and tracking techniques [4–8] have been developed in order to automatically allocate the lane boundaries in a variety of environmental conditions. It can broadly be divided into three major categories, that is, region-based method, feature-driven method, and model-driven method. Region-based method [9–14] basically classifies the road and nonroad pixels using color or texture information. Although it has simple algorithm, it may suffer from color inconstancy and illumination problem. Feature-driven method [15–18]
Audio-Visual Authentication System over the Internet Protocol
Yee Wan Wong,Kah Phooi Seng,Li Minn Ang
Lecture Notes in Engineering and Computer Science , 2009,
Abstract:
Nose Tip Detection on a Three-Dimensional Face Range Image Invariant to Head Pose
Wei Jen Chew,Kah Phooi Seng,Li Minn Ang
Lecture Notes in Engineering and Computer Science , 2009,
Abstract:
Embedded Descendent-Only Zerotree Wavelet Coding for Image Compression
Wai Chong Chia,Li Minn Ang,Kah Phooi Seng
Lecture Notes in Engineering and Computer Science , 2009,
Abstract:
Lossless Image Compression using Tuned Degree-K Zerotree Wavelet Coding
Li Wern Chew,Li Minn Ang,Kah Phooi Seng
Lecture Notes in Engineering and Computer Science , 2009,
Abstract:
A New Configuration of Adaptive Arithmetic Model for Video Coding with 3D SPIHT
Wai Chong Chia,Li Minn Ang,Kah Phooi Seng
Lecture Notes in Engineering and Computer Science , 2009,
Abstract:
Audio-Visual Recognition System Insusceptible to Illumination Variation over Internet Protocol
Yee Wan Wong,Kah Phooi Seng,Li Minn Ang
IAENG International Journal of Computer Science , 2009,
Abstract:
Performance Evaluation of Ant-Based Routing Protocols for Wireless Sensor Networks
Adamu Murtala Zungeru,Li-Minn Ang,Kah Phooi Seng
International Journal of Computer Science Issues , 2012,
Abstract: High efficient routing is an important issue in the design of limited energy resource Wireless Sensor Networks (WSNs). Due to the characteristic of the environment at which the sensor node is to operate, coupled with severe resources; on-board energy, transmission power, processing capability, and storage limitations, prompt for careful resource management and new routing protocol so as to counteract the differences and challenges. To this end, we present an Improved Energy-Efficient Ant-Based Routing (IEEABR) Algorithm in wireless sensor networks. Compared to the state-of-the-art Ant-Based routing protocols; Basic Ant-Based Routing (BABR) Algorithm, Sensor-driven and Cost-aware ant routing (SC), Flooded Forward ant routing (FF), Flooded Piggybacked ant routing (FP), and Energy-Efficient Ant-Based Routing (EEABR), the proposed IEEABR approach has advantages in terms of reduced energy usage which can effectively balance the WSN node€ s power consumption, and high energy efficiency. The performance evaluations for the algorithms on a real application are conducted in a well known WSN MATLAB-based simulator (RMASE) using both static and dynamic scenario.
A Very Compact AES-SPIHT Selective Encryption Computer Architecture Design with Improved S-Box
Jia Hao Kong,Li-Minn Ang,Kah Phooi Seng
Journal of Engineering , 2013, DOI: 10.1155/2013/785126
Abstract: The “S-box” algorithm is a key component in the Advanced Encryption Standard (AES) due to its nonlinear property. Various implementation approaches have been researched and discussed meeting stringent application goals (such as low power, high throughput, low area), but the ultimate goal for many researchers is to find a compact and small hardware footprint for the S-box circuit. In this paper, we present our version of minimized S-box with two separate proposals and improvements in the overall gate count. The compact S-box is adopted with a compact and optimum processor architecture specifically tailored for the AES, namely, the compact instruction set architecture (CISA). To further justify and strengthen the purpose of the compact crypto-processor’s application, we have also presented a selective encryption architecture (SEA) which incorporates the CISA as a part of the encryption core, accompanied by the set partitioning in hierarchical trees (SPIHT) algorithm as a complete selective encryption system. 1. Introduction In the year 1972, the National Institute of Standards and Technology (NIST) has identified and further concluded the study of the US government’s computer security needs its own standard for encrypting government-class sensitive information. After various proposal submissions which did not meet their vigorous design requirements, a cipher candidate developed in IBM was deemed suitable and the NSA worked closely with IBM to strengthen that algorithm. Eventually, the Data Encryption Standard (DES) was approved as a federal standard in November 1976. From there onwards, the pillar and model of the encryption for data are formed and established as DES having influenced the advancements of the modern cryptography for many years on. Since cryptographic solutions are often used to offer integrity and security over the transmission of sensitive data in our communication mediums, it is important for them to have consistent and nondecaying cryptographic strength over time. However, the strength of the encryption is weighted on the key itself, resulting in the strength being exploitable given massive computation strength to search for the key within a finite key space. Over time, the advances of computing technology have dramatically improved the computer processing power and have rendered the earlier DES with the small-sized 56-bit key as no longer safe. This is because of the far more superior computing power we have today, compared to those computers in the earlier days when the DES is proposed. This was quickly rectified later by replacing
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