Showing papers by "Shu-Chuan Chu published in 2013"

Kernel Learning Algorithms for Face Recognition

Abstract: Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its newest applications.

Invariability of Mean Value Based Reversible Watermarking

Abstract: By employing invariant relation between the mean value of the first (n − 1) pixels and the last one pixel (also called the remaining pixel) for every image block containing n pixels, a new reversible watermark scheme capable of mostly carrying 2n−3 bits into one n-sized image block in a single embedding process is presented in this paper. First, the mean value of the first (n − 1) pixel is calculated. Next, the difference value between the last one pixel and this obtained mean value is applied to distinguish which classification (i.e., smooth or complex sub-block) any sub-block belongs to. Consequently, it is determined to embed (n − 2) bits or 2(n − 2) bits into each sub-block according to its final classification results. And meanwhile, the mean value is reapplied to predict this last one pixel. 1-bit watermark is embedded into this last one pixel in accordance with the magnitude of prediction-error value. By multi-employing invariability of the mean value of (n− 1) pixels, the embedding rate can approach to (2− 3 n ) bpp (bit per pixel) for a single embedding process. Meanwhile, the embedding distortion is greatly controlled by embedding more bits into smooth image blocks and fewer bits into the other blocks with complex texture. Experimental results reveal the proposed scheme is effective.

A Reduce Identical Event Transmission Algorithm for Wireless Sensor Networks

Abstract: This paper proposed a Reduce Identical Event Transmission Algorithm (RIET). The algorithm can decide that which sensor nodes could send the event to sink node when sensor nodes sense a same even. Moreover, other nodes can save power because they didn’t send the same event. In our simulation, the RIET algorithm can enhance sensor nodes’ life time about 12.9 times and saving power consumption about 52.43 % than tradition algorithms.

A Transmission Power Optimization with a Minimum Node Degree for Energy-Efficient Wireless Sensor Networks with Full-Reachability

Abstract: Transmission power optimization is the most significant factor in prolonging the lifetime and maintaining the connection quality of wireless sensor networks. Un-optimized transmission power of nodes either interferes with or fails to link neighboring nodes. The optimization of transmission power depends on the expected node degree and node distribution. In this study, an optimization approach to an energy-efficient and full reachability wireless sensor network is proposed. In the proposed approach, an adjustment model of the transmission range with a minimum node degree is proposed that focuses on topology control and optimization of the transmission range according to node degree and node density. The model adjusts the tradeoff between energy efficiency and full reachability to obtain an ideal transmission range. In addition, connectivity and reachability are used as performance indices to evaluate the connection quality of a network. The two indices are compared to demonstrate the practicability of framework through simulation results. Furthermore, the relationship between the indices under the conditions of various node degrees is analyzed to generalize the characteristics of node densities. The research results on the reliability and feasibility of the proposed approach will benefit the future real deployments.

An Echo-Aided Bat Algorithm to Support Measurable Movement for Optimization Efficiency

Abstract: An Echo-Aided Bat Algorithm (EABA) based on measurable movement is proposed to improve optimization efficiency in this study. The conception is to employ the echo time to measure the distance from bats and objective. The bats emit an ultrasound to objective to measure the time of a round trip between their position and objective position. The echo time can guide the bats to correct velocity, direction and movement step. And the bats can more accurately measure the position of objective to adjust its step to find the better solution. There are many scenarios with different population sizes and objective functions to verify the performance of the proposed EABA. The experimental numeric result shows that EABA has better ability of search to improve the quality of the best solution than BA. The solution performance is improved by 45% and 30% for the functions of low complexity and high complexity in comparison with the original bat algorithm, respectively.

Neighborhood Discriminant Nearest Feature Line Analysis and Its Application to Face Recognition

Abstract: In this paper, a novel subspace learning algorithm, called neighborhood discriminant nearest feature line analysis (NDNFLA), is proposed. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) scatter and minimizing the within-class FL scatter. At the same time, the neighborhood is preserved in the feature space. Experimental results demonstrate the efficiency of the proposed algorithm.

Bit-Reduced Automaton Inspection for Cloud Security

Abstract: With the development of the cloud computing, its security issues have got more and more attention. There is a great demand for the examining the content of data or packets in order to improve cloud security. In this paper, we propose a new algorithm about pattern matching for cloud security named Bit-Reduced au- tomaton, First it performs inexact matching to filter out the part of nonattack information and then do exact matching to get the final attack information. Finally, Bit-Reduced automaton is feasible through a prelimi- nary evaluation.