Author
Shu-Chuan Chu
Other affiliations: University of South Australia, Sewanee: The University of the South, National Kaohsiung University of Applied Sciences ...read more
Bio: Shu-Chuan Chu is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 28, co-authored 231 publications receiving 3652 citations. Previous affiliations of Shu-Chuan Chu include University of South Australia & Sewanee: The University of the South.
Papers published on a yearly basis
Papers
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TL;DR: Reversible watermarking (RW) based on position determination and three-pixel difference and the incorporation of the predicted value in this estimation set helps to largely enhance the estimation accuracy.
Abstract: Reversible watermarking (RW) based on position determination and three-pixel difference is proposed in this paper. The main idea of this paper is to obtain two difference values depending on a pixel pair. To achieve this purpose, for a pixel pair, its one pixel is predicted by the context of this pair to get its predicted value. By this way, we can obtain a three-pixel set containing one pixel pair and one predicted value, and thus obtain two absolute difference values. For a three-pixel set, no modification is allowed to the predicted value. This predicted value along with all the neighbors surrounding one pair constitute a set used for evaluating the intra-pair correlation. The incorporation of the predicted value in this estimation set helps to largely enhance the estimation accuracy. According to the strength of correlation, we determine if this pair is located into a smooth or complex region. When the desired embedding rate is low, we only modify those pairs located in smooth regions while keeping the others unchanged. Therefore, the PSNR (peak signal to noise ratio) value is largely increased. Experimental results also demonstrate that the proposed method is effective.
2 citations
01 Jan 2017
TL;DR: The flat-gain signal optimization and the semiconductor optical amplifier structure enhance the optical signal and optical signal-to-noise ratio and the proposed system reveals an outstanding with simpler and more economic advantages.
Abstract: A enhanced network quality factors with noise interference elimination over FTTX RoF-WDM Optical Network System. This solution combines the concepts of longdistance transmission and ring topology. It is effective in expanding the bandwidth to overcome network congestion and in addressing the issues in wired optical fibers and wireless microwave communication systems. The potential amplified spontaneous emission broadband light source and spectrum-slicing technology are integrated with the dynamic reconfigurable optical add-drop multiplexer to decentralize the management of FTTX RoF-WDM-wavelength-division multiplexed optical network, which optimizes the transmission performance and eliminates multi-path crosstalk. We proved that the flat-gain signal optimization and the semiconductor optical amplifier structure enhance the optical signal and optical signal-to-noise ratio. The downlink quality factor curves in the Q6 data showed an -18.8 dBm increase in the received optical power. Application of the FTTX RoF-WDM broadband access network system achieved low crosstalk, a clear eye diagram, and an eye 3D graph. Since our proposed system uses only a broadband ASE light source to achieve multi-wavelengths transmissions, it also reveals an outstanding with simpler and more economic advantages.
2 citations
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01 Jan 2022
TL;DR: In this article , a feature selection method using an S-shaped transfer function to process continuous values and convert them into binary form was proposed to improve the speed and accuracy of detection of coronavirus disease.
Abstract: To this day, the prevention of coronavirus disease is still an arduous battle. Medical imaging technology has played an important role in the fight against the epidemic. This paper is to perform feature selection on the CT image feature sets used for COVID-19 detection to improve the speed and accuracy of detection. In this work, the population-based intelligent optimization algorithm Aquila optimizer is used for feature selection. This feature selection method uses an S-shaped transfer function to process continuous values and convert them into binary form. And when the performance of the updated solution is not good, a new mutation strategy is proposed to enhance the convergence effect of the solution. Through the verification of two CT image sets, the experimental results show that the use of the S-shaped transfer function and the proposed mutation strategy can effectively improve the effect of feature selection. The prediction accuracy of the features selected by this method on the two open datasets is 99.67% and 99.28%, respectively.
2 citations
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26 Nov 2007
TL;DR: Experiments implemented on two real datasets show that 2D(PC)2A method is an efficient and practical approach for face recognition.
Abstract: In the real-world application of face recognition system, owing to the difficulties of collecting samples or storage space of systems, only one sample image per person is stored in the system, which is so-called one sample per person problem. In this paper, we propose a novel algorithm, called 2D(PC)2A, to solve this problem. The procedure of 2D(PC)2A can be divided into the three stages: 1) creating the combined image from the original image 2) performing 2DPCA on the combined images; 3) classifying a new face based on assembled matrix distance (AMD). Experiments implemented on two real datasets show that 2D(PC)2A method is an efficient and practical approach for face recognition.
2 citations
01 Jan 2016
TL;DR: This paper analyzes the method of using the random walk framework to establish correspondence between two skeleton graphs and find out matching points between two shapes and shows that the proposed approach clearly outperforms existing algorithms, especially in the presence of noise and outliers.
Abstract: Using graphs to match two feature sets through embedded high-order relations points has many possible applications in criminal justice, security, and high technology. In this paper, we analyze the method of using the random walk framework to establish correspondence between two skeleton graphs and find out matching points between two shapes. The graphs are matched using a skeleton graph with the descriptors of the relationship between the two edges of the end-nodes ranked on an association graph. Through adopting individual jumps with a reweighting scheme, the new proposed approach effectively reflects the one-to-one matching constraints during the random walk process. Experiments on several benchmark data sets show that the proposed approach clearly outperforms existing algorithms, especially in the presence of noise and outliers.
2 citations
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TL;DR: This work presents a comprehensive survey of the advances with ABC and its applications and it is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
Abstract: Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
1,645 citations
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17 Feb 2014
TL;DR: This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences, and researchers and engineers as well as experienced experts will also find it a handy reference.
Abstract: Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm
901 citations
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TL;DR: A novel maximum neighborhood margin discriminant projection technique for dimensionality reduction of high-dimensional data that cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes.
Abstract: We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data. It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters. By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes. To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA. The experimental results demonstrate the effectiveness of our MNMDP in pattern classification.
771 citations