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.

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

Abstract: Improving localization performance is one of the critical issues in Wireless Sensor Networks (WSN). As a range-free localization algorithm, Distance Vector-Hop(DV-Hop) is well-known for its simplicity but is hindered by its low accuracy and poor stability. Therefore, it is necessary to improve DV-Hop to achieve a competitive performance. However, the comprehensive performance of WSN is limited by computing and storage capabilities of sensor nodes. In this paper, we propose an algorithm with parallel and compact techniques based on Whale Optimization Algorithm (PCWOA) to improve DV-Hop performance. The compact technique saves memory consumption by reducing the original population. The parallel techniques enhance the ability to jump out of local optimization and improve the solution accuracy. The proposed algorithm is tested on CEC2013 benchmark functions and compared with some popular algorithms and compact algorithms. Experimental results show that the improved algorithm achieves competitive results over compared algorithms. Finally, simulation research is conducted to verify the localization performance of our proposed algorithm.

Non-redundant VQ channel coding using modified tabu search approach with simulated annealing

Abstract: Codeword Index Assignment (CIA) is a key issue to vector quantization (VQ). A new algorithm called Modified Tabu Search Algorithm (MTSA) is applied to codeword index assignment for noisy channels for the purpose of minimizing the distortion due to bit errors. Simulated annealing (SA) technique and a new parameter are introduced in the Tabu Search Approach (TSA) to improve the performance of the tabu search approach. Experimental tests show the modified tabu search algorithm is superior to the tabu search algorithm by evaluating the performance of channel distortion after the same number of iterations.

A Kernel-Based Probabilistic Collaborative Representation for Face Recognition

Abstract: A novel classifier for face recognition using an improved probabilistic collaborative representation named IPCR is proposed in this paper. The purpose of this paper is to improve the accuracy of face recognition. The testing sample is assumed to be linearly combined by a part of training samples in feature space. There is two-phase framework in IPCR. In the first phase, an adjusted parameter of the nearest neighbors of the samples is chosen for classification. In the second phase, a linear combination of the features and the sparse coefficients are used for new patterns. In the process of two-phase framework, the weight matrix is obtained according to the distance between all the training samples and each testing sample, and then it is applied to weight probabilistic collaborative representation coefficients. The kernel trick is implemented for the high-dimensional nonlinear information instead of linear information of data to improve the class separability. The second classifier named KPCR uses a kernel probabilistic collaborative representation for face recognition. Several renowned face databases, e.g., AR, GT, PIE, FERET, and LFW-crop are used for evaluating the performances of the proposed classifiers. The experimental results demonstrate that the proposed classifiers outperform the collaborative representation-based classification (CRC), the probabilistic collaborative representation-based classifier (ProCRC), and the other state-of-the-art classifiers in recognition accuracy.

Optimal topology planning of electromagnetic waves communication network for underwater sensors using multi-objective optimization algorithms (MOOAs)

Abstract: “Extremely High Frequency (EHF)” and “Very high frequency (VHF)” bands are mainly utilized with “Underwater Wireless Sensor Networks (UWSNs)” for communication purposes. However, due to the mobility of underwater sensors in water because of the water tide, the EHF/VHF signals may attenuate, lose or fade depending on the condition of the water. Therefore, it is a challenging stint of finding the optimal parameters of UWSN topology planning. In this paper, three “Multi-Objective Optimization Algorithms (MOOAs)” have been utilized to mitigate this problem, namely MOSFP, SPEA2 and NSGA-II. This work also intends to minimize path loss. On the other hand, it intends to maximize the power density of the network. Various network configurations, such as distance between sender and receiver, water conductivity and water permeability, are considered to evaluate the proposed objective models. Qualitative and quantitative tests have been conducted to analyze the results. From the analysis of the intersection point of Pareto-front of the objective functions, it is shown that all the algorithms find the optimal distance between transmitter and receiver, which balances the aforementioned maximization and minimization objective functions. This value is 36 m.

Security Analysis of Rhee et al.'s Public Encryption with Keyword Search Schemes: A Review.

Abstract: Public key encryption with keyword search (PEKS) provides an efficient way to search encrypted files. Recently, Rhee et al. contributed their knowledge to propose several literatures in this research area. In this paper, we first review their three famous schemes and then summarize the security weaknesses of the three schemes. Finally, we discuss the security problems about Rhee et al. like scheme and remain an open problem.

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.