Shu-Chuan Chu

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.

Lossless Data Hiding for Halftone Images

Abstract: With the development of image halftoning techniques and computer networks, a large quantity of digital halftone images are produced and transmitted in the Internet Meanwhile, hiding data in images becomes a powerful approach for covert communications, copyright protection, owner announcement, content authentication, traitor tracing, etc This chapter proposes two look-up table based methods to hide data in the cover media of halftone images Both of them belong to lossless data hiding techniques, ie, not only the secret data but also the original cover image can be accurately recovered in data extraction when the stego image is intact Besides, an application example of the proposed method in lossless content authentication is illustrated Furthermore, a data capacity enhancement strategy is introduced based on a statistical property of error diffused halftone image micro structure

Prediction of Chaotic Time Series of RBF Neural Network Based on Particle Swarm Optimization

Abstract: Radial basis function (RBF) neural network has very good performance on prediction of chaotic time series, but the precision of prediction is great affected by embedding dimension and delay time of phase-space reconstruction in the process of predicting. Based on hereinbefore problems, we comprehensive optimize embedding dimension and delay time by particle swarm optimization, to get the optimal values of embedding dimension and delay time in RBF single-step and multi-step prediction models. In addition, we made single step and multi-step prediction to the Lorenz system by this method, the results show that the prediction accuracy of optimized prediction model is obvious improved.

Solving Constrained Optimization Problems by an Improved Particle Swarm Optimization

Abstract: Constrained optimization problems compose a large part of real-world applications. More and more attentions have gradually been paid to solve this kind of problems. An improved particle swarm optimization (IPSO) algorithm based on feasibility rules is presented in this paper to solve constrained optimization problems. The average velocity of the swarm and the best history position in the particle's neighborhood are introduced as two turbulence factors, which are considered to influence the fly directions of particles, into the algorithm so as not to converge prematurely. The performance of IPSO algorithm is tested on 13 well-known benchmark functions. The experimental results show that the proposed IPSO algorithm is simple, effective and highly competitive.

Diversity Enhanced Ion Motion Optimization for Localization in Wireless Sensor Network.

Abstract: A new optimization algorithm (named DIMO) based on diversity enhanced Ion Motion Optimization (IMO) is proposed. Diversity learning strategy and random perturbations are applied to improve IMO by modifying its individual evolutionary information. A set of selected benchmark functions and an estimation localization in Wireless sensor network (WSN) are used to test the performance of the proposed algorithm. The experimental results compared with the others algorithms in the literature shows that the proposed approach are superior to the other algorithms in optimization accuracy, convergence speed, and robustness.

SimSim: A Service Discovery Method Preserving Content Similarity and Spatial Similarity in P2P Mobile Cloud

Abstract: Mobile cloud has become a new computing paradigm such that services are accessible in any place and at any time. Despite its promising prospect, challenges arise due to unreliable channel condition and limited bandwidth in wireless communication, dynamic route establishment due to node mobility, difficulties in associating request to relevant service providers, and complication in service deployment. To ensure the fairness of resource allocation and network load balance, it is necessary to consider strategies for distributing services. In this paper, we propose SimSim, a service discovery scheme based on keywords search which preserves content similarity and spatial similarity. A mapping from a keyword set of services to a bit vector with identical hash is designed to preserve content similarity. The proposed technique applies a hierarchical hash clustering model and investigates the strategies of service deployment and discovery. By mapping the services characterized by keywords to the Gray space, SimSim offers similar services at close geographical proximity. Extensive simulations have been conducted to assess the proposed system.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

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.

Nature-Inspired Optimization Algorithms

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

Maximum neighborhood margin discriminant projection for classification

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.