Showing papers by "Jeng-Shyang Pan published in 2011"

Reversible data hiding based on multilevel histogram modification and sequential recovery

Abstract: This paper proposes a reversible data hiding method for natural images. Due to the similarity of neighbor pixels’ values, most differences between pairs of adjacent pixels are equal or close to zero. In this work, a histogram is constructed based on these difference statistics. In the data embedding stage, a multilevel histogram modification mechanism is employed. As more peak points are used for secret bits modulation, the hiding capacity is enhanced compared with those conventional methods based on one or two level histogram modification. Moreover, as the differences concentricity around zero is improved, the distortions on the host image introduced by secret content embedding is mitigated. In the data extraction and image recovery stage, the embedding level instead of the peak points and zero points is used. Accordingly the affiliated information is much smaller than in those methods of the kind. A sequential recovery strategy is exploited for each pixel is reconstructed with the aid of its previously recovered neighbor. Experimental results and comparisons with other methods demonstrate our method's effectiveness and superior performance.

Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems

Abstract: Inspired by Bat Algorithm, a novel algorithm, which is called Evolved Bat Algorithm (EBA), for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, we redefine the corresponding operation to the bats’ behaviors. EBA is a new method in the branch of swarm intelligence for solving numerical optimization problems. In order to analyze the improvement on the accuracy of finding the near best solution and the reduction in the computational cost, three well-known and commonly used test functions in the field of swarm intelligence for testing the accuracy and the performance of the algorithm, are used in the experiments. The experimental results indicate that our proposed method improves at least 99.42% on the accuracy of finding the near best solution and reduces 6.07% in average, simultaneously, on the computational time than the original bat algorithm.

Overview of algorithms for Swarm intelligence

Abstract: Swarm intelligence (SI) is based on collective behavior of selforganized systems. Typical swarm intelligence schemes include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search (SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Besides the applications to conventional optimization problems, SI can be used in controlling robots and unmanned vehicles, predicting social behaviors, enhancing the telecommunication and computer networks, etc. Indeed, the use of swarm optimization can be applied to a variety of fields in engineering and social sciences. In this paper, we review some popular algorithms in the field of swarm intelligence for problems of optimization. The overview and experiments of PSO, ACS, and ABC are given. Enhanced versions of these are also introduced. In addition, some comparisons are made between these algorithms.

Characteristic region based watermark embedding with RST invariance and high capacity

Abstract: This paper presents a novel feature point based image watermarking scheme to achieve high capacity information hiding and generalized watermark robustness. The key idea is to embed a binary watermark image into multi-scale feature point based local characteristic regions in transform domain. Watermark synchronization is first achieved by the characteristic regions, which can be extracted using the scale-invariant feature transform and image normalization. Then the watermark image is embedded in a content-based manner by modifying the wavelet transform coefficients. In the detector, the watermark can be extracted from the distorted image directly. Experimental results on Stirmark benchmark 4.0 show that the proposed scheme can resist both traditional signal processing attacks and geometric attacks. Comparisons also demonstrate the advantages of the scheme.

Neighborhood Discriminant Nearest Feature Line Analysis for Face Recognition

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

New Anti-phishing Method with Two Types of Passwords in OpenID System

Abstract: with the rapid development of IT, the demand for online services is growing. In order to obtain network services, each person must register a lot of accounts and passwords on the site. To meet the need that user effectively manage more and more accounts and passwords, OpenID was born. OpenID is a convenient, simple, user-centric ID management system. OpenID provides single sign-on (SSO) service, that is, we login only once and can enjoy the service of multiple sites. But OpenID is vulnerable to phishing attacks. To avoid phishing attacks, many methods have been proposed, but there is no satisfactory method. In this paper, we propose a model of two types of passwords for anti-phishing. Through analysis, this method can effectively avoid phishing.

A New Framework for Optimization Based-On Hybrid Swarm Intelligence

Abstract: A hybrid optimization algorithm based on Cat Swarm Optimization (CSO) and Artificial Bee Colony (ABC) is proposed in this chapter. CSO is an optimization algorithm designed to solve numerical optimization problems, and ABC is an optimization algorithm generated by simulating the behavior of bees finding foods. By hybridizing these two algorithms, the hybrid algorithm called Hybrid PCSOABC is presented. Five benchmark functions are used to evaluate the accuracy, convergence, the speed, and the stabilization of the Hybrid PCSOABC. In this chapter, the literature review regarding CSO, AS, ACS, BF, PSO, ABC, and the parallel version of CSO are given at the beginning. The proposed hybrid framework combining different algorithms is given in the fourth section. And the experimental results are presented at the end of the chapter with the conclusions.

The Structure Optimization of Main Beam for Bridge Crane Based on An Improved PSO

Abstract: The structure optimization of main beam is a nonlinear constrained optimization problem, which is important for bridge crane to save manufacturing cost on quality assurance. The modified particle swarm optimization (MPSO) with feasibility-based rules (1), which was advanced to solve mixed-variable optimization problems, is proposed to optimize the structure of main beam in order to find the optimal parameters so as to make minimize the deadweight of main beam. The comparison results with the enumeration algorithm illustrated that MPSO can get best optimal solutions in much less calculation numbers.

Computational Collective Intelligence. Technologies and Applications

Abstract: Nowadays, agreements and all the processes and mechanisms implicated in reaching agreements between different kinds of agents are a subject of perspective interdisciplinary scientific research. Newest trend in Agent Technology is to enhance agents with "social" abilities. Agreement Technologies brings new flavor in implementation of more sophisticated autonomous software agents that negotiate to achieve acceptable agreements. The paper presents key concepts in this area and highlights influence of Agreement Technologies on development of more sophisticated multi-agent systems.

A New Robust Watermarhing Scheme Based on Shuffled Frog Leaping Algorithm

Abstract: A new robust watermarking method, named SFLA-QIM, is proposed based on the shuffled frog leaping algorithm (SFLA) and quantization index modulation (QIM). The shuffled frog leaping algorithm is utilized to find out the optimal embedding position and adaptive quantization step for embedding watermark into a carrier image in the framework of QIM. A carefully chosen fitness function is designed in terms of the Peak Signal to Noise Ratio (PSNR) and the Normalized Correlation (NR) value to achieve high transparency and robustness. The proposed scheme is blind. Compared with other quantization index related watermarking methods, SFLA-QIM exhibits satisfactory robustness against a wide variety of attacks such as amplitude scaling, filtering, noise addition, cropping and JPEG compression.

Biometric driven initiative system for passive continuous authentication

Abstract: In this paper, a passive continuous authentication system based on both the hard and the soft biometrics is implemented. The passive continuous authentication system keeps verifying the user without interrupting the user concentrating on his work. It also provides the capacity for the machine to recognize who is in front of the terminal, reduces the potential security leak of the user is temporarily absent front the terminal, and denies the invader to login the system with the stolen account and password. Our system forces to logoff the user when the authentication result identifies the user leaves his seat, but the system won't logoff the user if the user only turns his face to some other directions. The reliability of the system is verified by 7 registered users. According to the experimental results, combining the soft and the hard biometrics in our theoretical framework achieves the goal of continuous authentication efficiently and correctly.

A Parallel Hybrid Evolutionary Particle Filter for Nonlinear State Estimation

Abstract: Particle filters (PF) are widely used for state estimation in non-linear and non-Gaussian environments. However, conventional particle filters possess some drawbacks such as sample impoverishment and sample size dependency. In this paper, a novel parallel hybrid evolutionary particle filter is proposed to solve those problems from the perspective of evolutionary computation. In the proposed algorithm, an effort has been made to fuse a genetic algorithm (GA) and particle swarm optimization (PSO) together to improve the standard particle filter. Genetic operators such as crossover and mutation are utilized to maintain the particle diversity and PSO is used to optimize the particle distribution. A parallel scheme is employed to reduce the computation time so it is more suitable to implement by multithreaded programming for real-time system. The simulation results demonstrate the effectiveness of the proposed algorithm.

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.

A new scheme of ant colony system algorithm to discovery optimal solution with flip-flop search

Abstract: In this study, we propose an ACS with flip-flop search strategy to find the route from source node to destination node in an Ad hoc network topology. A flip-flop search strategy is to alternate the search direction towards either high pheromone area or low pheromone area iteratively in the evolution process. The proposed Flip-Flop search strategy effectively solves the pheromone-excess problem in ACS. The ants are allowed to select reverse path to avoid the ants affected by the high pheromone concentration and disable the ability of discover new search area in routing phase. In simulations, the proposed Flip-Flop Ant Colony System (FFACS) is compared with Traditional Ant Colony System in conditions of various deployment densities and topologies of wireless sensor network. The results show that the FFACS has promising ability of discover new search area to reach the better optimal solution than the TACS has. In addition, the robustness and the stability of FFACS are better than TACS.

High Capacity Watermark Embedding Based on Invariant Regions of Visual Saliency

Abstract: A new image watermarking scheme is presented to achieve high capacity information hiding and geometric invariance simultaneously. Visually salient region is introduced into watermark synchronization. The saliency value of a region is used as the quantitative measure of robustness, based on which the idea of locally most salient region (LMSR) is proposed to generate the disjoint invariant regions. A meaningful binary watermark is then encoded using Chinese Remainder Theorem (CRT) in transform domain. Simulation results and comparisons demonstrate the effectiveness of the proposed scheme.

Biometric Electrocardiogram Card for Access Control System

Abstract: With the rapid development of access control system based on biometric technologies, the conventional identification exposed more and more weakness while they are easy to be counterfeited and imitated. In this paper, we bring the access control systems with electrocardiogram identification into a practical application. Moreover, a new prompt based ECG algorithm are proposed to seek a more secure and accurate identification. In our evaluation, a hardware board were designed to verified its feasibility, then the analysis and comparison among various biometric identification and previous ECG identification researches were further disused as well. The result shown our proposed design could provide a more secure, low cost and convenient identification for access control system.

An optimization of adaptive transmission with guarantee connection degree for wireless sensor networks

Abstract: In this paper, an optimization of adaptive transmission with guarantee connection degree of nodes for wireless sensor networks is proposed. The proposed optimization realizes a novel approach to topology control based a power-adaptation scheme. According to the density of deployed nodes, an expected connection degree of nodes and the operational condition are derived. Afterward, the transmission power is adapted to the minimum strength but is sufficient to guarantee node degrees. The benefits of the proposed approach include the reduction and the equalization of power dissipation in nodes. The proposal is applied on a hierarchical network composed of node clusters to manage transmission distances of nodes in a cluster. The present approach guarantees each pair of nodes in a cluster to be connected. The simulation results are obtained in case of uniform-distribution node deployment. The error, defined as the difference of the actual connection degree of nodes and the expected, is around 10%. Besides, the error decreases as the expected connection degree of nodes increasing. The evidence shows that the proposed approach is reliable and stable.

PSO with Constraint-Preserving Mechanism for Mixed-Variable Optimization Problems

Abstract: A new algorithm (called CPMPSO for short), in which PSO with constraint-preserving mechanism is used as a global search algorithm and PSO itself is used as local search one, is proposed in this paper to solve mixed-variable optimization problems. The values of non-continuous variables are got according to the velocity of the particle, the constraint-preserving method is used as the mechanism for handling the constraint violations, and the particle swarm optimization itself is used as local search algorithm to obtain the consistent optimal results for mixed-variable optimization problems. The performance of CPMPSO is evaluated against two real-world mixed-variable optimization problems, and it is found to be highly competitive compared with other existing algorithms.

Indoor Water-Dispensing Robot with Two-Stage Localization

Abstract: With improvement in the quality of people's life and the change of their lifestyle, the demand for intelligent water dispensers is increased. This study, therefore, proposes to de-sign a service robot that can not only move autonomously but also serve people intelligently. In this paper, a solution to de-sign an intelligent water-dispensing service robot is proposed. A new integrated system architecture is introduced, and a two-stage localization, a novel method of indoor robot localization, is described to solve the localization problem of the robot. Ow-ing to the detailed description of the implementation, it is clearly indicated that the proposed solution is reasonable and feasible. Besides, comparisons of various indoor localization methods and indoor robot applications demonstrate that the water-dispensing service robot has advantages over the existing designs and application.

Symmetry Auto-detection Based on Contour and Corner Models

Abstract: In this paper we present a novel symmetry auto-detection approach which is used for the symmetry detection of the objects in images. Rather than relying on gradients and pixel matching of an image, symmetry can be automatically detected by using corner detection on a symmetry function which is proposed in Hsu's paper, and a result will be shown to demonstrate the probability that the object in the image is bilateral symmetry. In this paper, we first use modified active contour model to get the contour of the object in an image. Then we use a symmetries evaluation function to get the symmetry function of the contour. Last, we detect corner in the proposed function. By matching corner points, the symmetry function shows us the symmetry-probability of the object. We apply our method in several natural or man-made objects, and they all exhibit significant performance.

Face recognition with one sample per person based on contourlet

Abstract: In this paper, a novel algorithm for face recognition with one sample per person is proposed. The proposed algorithm is based on contourlet. Multiple training images for each class are constructed through the decomposition and reconstruction of original training images by contourlet. Thus neighborhood discriminant nearest feature line analysis can be performed on the new database. The experimental results demonstrate the efficiency of the proposed algorithm.

A Double Particle Swarm Optimization for Mixed-Variable Optimization Problems

Abstract: A double particle swarm optimization (DPSO), in which MPSO proposed by Sun et al [1] is used as a global search algorithm and PSO with feasibility-based rules is used to do local searching, is proposed in this paper to solve mixed-variable optimization problems MPSO can solve the non-continuous variables very well However, the imprecise values of continuous variables brought the inconsistent results of each run A particle swarm optimization with feasibility-based rules is proposed to find optimal values of continuous variables after the MPSO algorithm finishes each independent run, in order to obtain the consistent optimal results for mixed-variable optimization problems The performance of DPSO is evaluated against two real-world mixed-variable optimization problems, and it is found to be highly competitive compared with other existing algorithms

Optimization-Based Audio Watermarking Using Low-Frequency Amplitude Modification

Abstract: Based on Karush-Kuhn-Tucker Theorem, a new blind digital audio watermarking scheme is proposed. In order to guarantee the robustness of watermarks, this scheme embeds information into low-frequency coefficients of discrete wavelet transform. For the modification of low-frequency amplitude, this work uses Karush-Kuhn-Tucker Theorem to minimize the difference between the original and the modified coefficients. Consequently, the watermarked audio has strong robustness under sufficient watermarked-audio quality and high embedding capacity. In addition, the system can extract the hidden data without the knowledge of original audio signal. Experimental results indicate that the performance of proposed system is mostly better than other amplitude modification method.

Evolutionary Interval Fingerprint for Wireless Network

Abstract: In this paper, we have proposed a novel evolutionary random interval fingerprint for active RFID and ZigBee systems. This new approach can enable more secure communication in a multi-party communication, if the packet is forged by another communication party, the interval fingerprint can provide another way to detect the spoofing packet. Moreover, the evolutionary random algorithms - genetic and memetic random algorithms are also proposed to generate the random interval fingerprint. Comparing with the conventional random generator, our approach is flexible to generate uniform random numbers, and more robust for the cracking. The forged party is difficult to produce the fake random intervals. Finally, we provide an application example, completed related work survey, pseudo-code and analysis result to prove our concept is feasible for the pervasive communication.

Wavelet-Based Entropy for Digital Audio Watermarking

Abstract: Unlike traditional entropy in information theory, this work uses the normalized energy instead of probability to obtain a low-frequency amplitude transform (LAT) on coefficients of discrete wavelet transform (DWT). The watermark is embedded based on the properties and characteristics of this transform. Finally, performance of the proposed scheme is assessed by signal-to-watermark (SWR) and bit error rate (BER). Experimental results demonstrate that the embedded data are robust against most signal processing and attacks, such as re-sampling, low-pass filtering, and amplitude-scaling.