The research on mechanism of color management system based on iCAM color appearance model

Since color recording and color representing abilities of various media are very different, color information is often lost or misrepresented in the process of image transmission and reproduction. A Color Management System (CMS) based on iCAM color appearance model is presented in this paper. It can be used either in computer operation system software or application software for obtaining color consistency across different software applications, imaging devices, imaging media and viewing conditions.     

Indirect augmented reality

Developing augmented reality (AR) applications for mobile devices and outdoor environments has historically required a number of technical trade-offs related to tracking. One approach is to rely on computer vision which provides very accurate tracking, but can be brittle, and limits the generality of the application. Another approach is to rely on sensor-based tracking which enables widespread use, but at the cost of generally poor tracking performance. In this paper we present and evaluate a new approach, which we call Indirect AR, that enables perfect alignment of virtual content in a much greater number of application scenarios.To achieve this improved performance we replace the live camera view used in video see through AR with a previously captured panoramic image. By doing this we improve the perceived quality of the tracking while still maintaining a similar overall experience. There are some limitations of this technique, however, related to the use of panoramas. We evaluate these boundaries conditions on both a performance and experiential basis through two user studies. The result of these studies indicates that users preferred Indirect AR over traditional AR in most conditions, and when conditions do degrade to the point the experience changes, Indirect AR can still be a very useful tool in many outdoor application scenarios.     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.     

Mapping of user interfaces on electronic appliances

The purpose of this study was to investigate mapping of user interfaces on consumer electronic appliances. A digital clock with five different interface modes was chosen for the experiment. Forty elderly subjects (above 65 years) and 40 middle-aged subjects (45-64 years) participated in the study. Every subject had to complete three different tasks, namely time setting, alarm time setting and date setting. The researchers investigated what problems were encountered when performing these tasks and collected data such as operational time, the number of total inputs, subjective estimation and interview information. The findings of this study indicated that the interface modes had significant effects on operational time, the number of total inputs and subjective estimation. Finally, the study confirms that when the spatial relationship between buttons and display contents are clearly indicated, the operation is easier to complete.     

On the optimal design of fuzzy neural networks with robust learningfor function approximation

A novel robust learning algorithm for optimizing fuzzy neural networks is proposed to address two important issues: how to reduce the outlier effects and how to optimize fuzzy neural networks, in the function approximation. This algorithm is able to reduce the outlier effects by cooperating with a conventional robust approach, and then to optimize fuzzy neural networks by determining the optimal learning rates which can minimize the next-step mean error at each iteration of our algorithm.     

A new hybrid and dynamic fusion of multiple experts for intelligent porch system

Intelligent porch research is an important issue in smart home development; however, such a field was rarely investigated in the literature. This investigation proposes a new hybrid and dynamic fusion of multiple experts for the intelligent porch system. First, a new hybrid priority tree with decision fusion (HPTD-fusion) is proposed to eliminate the problems of tag-based authentication outdoors. The HPTD-fusion first verifies the vocal entrance code (VEC), and subsequently the remaining experts are performed in the cases of AND, OR or majority voting for decision fusion. Second, the post-mapping dynamic weighted fusion (PMDW-fusion) scheme is presented to adapt the indoor porch audio-visual environment. The PMDW-fusion dynamically assigns the higher weight to experts with higher performance, and then sums all participating experts for score fusion. The experimental results demonstrate that FRR and FAR can reach up to 0.18 and 0.19, respectively, when the system is tested in the outdoor environment. Furthermore, the indoor recognition accuracy can be increased to 86.1% using the proposed fusion scheme. The experiments have verified the effectiveness and feasibility of the proposed system. Restated, the contribution of this work is to develop a novel intelligent porch system incorporating a natural and unobtrusive method for identity recognition. The proposed system has been installed and tested in a real-world environment in the Technologies for Ubiquitous Computing and Humanity (TOUCH) Center at National Cheng Kung University.     

An interactive agent-based system for concept-based web search

Search engines are useful tools in looking for information from the Internet. However, due to the difficulties of specifying appropriate queries and the problems of keyword-based similarity ranking presently encountered by search engines, general users are still not satisfied with the results retrieved. To remedy the above difficulties and problems, in this paper we present a multi-agent framework in which an interactive approach is proposed to iteratively collect a user's feedback from the pages he has identified. By analyzing the pages gathered, the system can then gradually formulate queries to efficiently describe the content a user is looking for. In our framework, the evolution strategies are employed to evolve critical feature words for concept modeling in query formulation. The experimental results show that the framework developed is efficient and useful to enhance the quality of web search, and the concept-based semantic search can thus be achieved.     

Oscillation in nonlinear difference equations

In this paper, we shall discuss oscillatory behavior of the solutions of difference equations, including the self-adjoint second-order linear equation and the discrete version of the nonlinear wave equation. Our work is to give sufficient conditions such that every nontrivial solution of the equations oscillates.     

Electrostatic actuation of three-dimensional MEMS mirrors using sidewall electrodes

We propose and demonstrate electrostatic sidewall-electrodes actuation of three-dimensional (3-D) microelectromechanical systems (MEMS) gimbal mirrors. The linearity of the mirror angle dependence on actuation voltage is improved with the sidewall-electrodes actuation. In addition, the undesired spring-softening effect commonly found in electrostatic actuation, where the mirror resonance frequency decreases with increased tilt angle, is significantly reduced. Sidewall actuation enables superior performance of 3-D MEMS mirrors including large pull-in angles, reduced actuation voltages, improved device reliability, and fast switching times.