Chin Ming Hong

Interactive Augmented Reality Game for Enhancing Library Instruction in Elementary Schools

Due to limited budgets and manpower, most elementary schools in Taiwan do not plan or provide library instruction for students. Although students can use libraries, they typically lack the knowledge needed to use library resources effectively. Consequently, students have difficulty finding the books they need and can easily become overwhelmed by the massive amount of information in libraries. Computer-assisted instruction for teaching basic library skills to large numbers of students is an appealing method. Particularly, game-based learning has garnered considerable attention in education research. Many researchers and scholars believe that integrating teaching and games enhances student learning performance and motivation. This work develops an educational gaming system based on situated learning theory, and applies innovative augmented reality interactive technology to a librarys learning environment. Student library knowledge can be enhanced via the proposed game-based augmented reality library instruction system (GARLIS). Experimental results demonstrate that student learning performance is improved significantly by using the proposed GARLIS. Moreover, this work demonstrates that using the proposed GARLIS for library instruction results in the same learning performance as conventional librarian instruction. The proposed library instruction system overcomes shortcomings of personal teaching skills of librarians that may adversely affect student learning performance by conveying the same learning content to all students. Additionally, the proposed system results in better learning performance for learners with the field-dependent cognitive style than learners with the field-independent cognitive style. Further, the proposed system provides more benefits in terms of library skills training than conventional librarian instruction.

Intelligent Web-based Tutoring System with Personalized Learning Path Guidance

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist online web-based learning and adaptively provide learning paths in order to promote the learning performance of individual learners. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, compared with the freely browsing learning mode used in most web-based learning systems, this paper assesses whether the proposed genetic-based personalized e-learning system which can generate appropriate learning paths according to the incorrect testing responses of individual learners in a pre-test provides benefits in terms of learning performance promotion. Experiment results indicated that applying the proposed genetic-based personalized e-learning system for Web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.

Efficient auto-focus algorithm utilizing discrete difference equation prediction model for digital still cameras

Most digital still cameras employ the passive auto-focus scheme to achieve these goals. Measuring the sharpness of an image using passive auto-focus is time-consuming, particularly for digital still cameras with mega-pixels resolutions. However, the auto-focus search algorithm for digital still cameras needs to be fast and real-time. This study presents an efficient real-lime auto-focus (AF) algorithm combining the discrete difference equation prediction model (DDEPM) and bisection search method for digital still cameras. The proposed auto-focus algorithm uses both the coarse search procedure with DDEPM and the fine search procedure with bisection search method to perform the auto-focus process for digital still cameras. The coarse search procedure utilizes the precise forecasting ability of the DDEPM model to determine the turning points of the focus value curve, thus skipping some unnecessary detection points on the focus value curve. This procedure can be used to move the lens quickly to the neighborhood region of the best-focused lens position during AF processes. After the coarse search is finished, the fine search procedure is used to find the best-focused lens position according to the coarse search result. The proposed scheme increases the auto-focus speed since it reduces the number of detection points required for the focus value curve when searching the neighboring region of the best-focused lens position, and also reduces the lens backlash times. The proposed algorithm is implemented herein on an HP digital camera including control software. The experimental results indicate that the proposed AF algorithm is very reliable and requires few focusing iterations

Context-Aware Ubiquitous English Learning in a Campus Environment

Many researches have indicated that the wireless technology can enhance the learning performance of teaching, learning and self-study. Therefore, the web-based learning has been evolved into mobile learning and even ubiquitous learning in recent years. Certainly, performing language teaching and learning actions by handheld devices in cooperating with advanced wireless technology has been a novel trend. Therefore, the research proposes a context-aware ubiquitous English learning system in a campus environment, which can detect the learner location by wireless positioning techniques, then retrieve specific learning content based on location information to individual learners through wireless networks. Currently, the system prototype has been successfully implemented on PDA and it has been applied to assist English situated learning for learning performance promotion in a campus environment.

Portable real-time homecare system design with digital camera platform

An ideal homecare system needs to process multiple diagnostic signals using a portable battery-driven device and provide a real-time audio/video interface for telemedicine applications. In this paper, we present a low cost real-time homecare system based on a commercial digital camera platform. In addition to the typical multimedia recording functions of a digital camera, such as JPEG and MPEG-1/MPEG-4 recording, the proposed system can further process and compress phonocardiogram (PCG) signal concurrently.

Design of a static reactive power compensator using fuzzy sliding mode control

This paper demonstrates a fuzzy sliding mode control (FSMC) scheme for a static reactive power compensator (static VAR compensator or SVC) to improve the damping of a synchronous generator. A robust sliding regime is derived to guarantee the stability of the proposed FSMC under parameter uncertainties. Two types of FSMC, the fuzzy switching gain compensation and the fuzzy control signal compensation, are recommended to regulate the reactive power of the SVC. A synchronous generator system with the proposed FSMC-SVC has been studied and its dynamic responses are simulated. The results show that the suggested FSMC scheme possesses the advantages of fuzzy control and sliding mode control, e.g. insensitivity to parameter variations and load disturbances. Note that the dynamic performance of the synchronous machine system can be improved dramatically over a wide range of operating conditions. Furthermore, for simplification of practical implementation, the reducing switches (or local feedback) control is adopted.

Web-based remote human pulse monitoring system with intelligent data analysis for home healthcare

Many studies had indicated that applying intelligent systems with physiology signal monitoring for e-health care is a current developing trend. A physiology signal monitoring system can help medical staffs to monitor and analyze humanpsilas physiology signal effectively, such that they can not only monitor the patientspsila physiology states immediately, but also reduce medical cost and save a lot of time of patients to visit hospitalpsilas doctors. Therefore, the study employed system on chip (SOC) techniques to develop an embedded human pulse monitoring system with intelligent data analysis mechanism for disease detection and long-term health care, which can be applied to monitor and analyze humanpsilas pulse signal in daily life. Meanwhile, the proposed system also developed a friendly web-based interface that is convenient to the observation of immediate human physiological signals. Moreover, this study also proposes an intelligent data analysis scheme based on the modified cosine similarity measure to diagnose abnormal human pulses for exploring potential chronic diseases. Therefore, the proposed system provides benefits in terms of aiding long-distance medical treatment, exploring trends of potential chronic diseases, and urgent situation informing for sudden diseases.

Intelligent Location-Based Mobile News Service System with Automatic News Summarization

Developing location-based services (LBSs) on mobile devices can convey location related information to individual users, thus helping users obtain useful and adaptive information using mobile devices with small screen. Based on the requirement, this study proposes an intelligent location-based service system which can provide local news with summarization to personal handheld PDA (Personal Digital Assistant) device based on user’s location. According to experimental results in multi-document summarization and user location identification, news summarization with satisfied quality is up to 86%, and the accuracy rate of user location awareness scheme is up to 90%. The experimental results demonstrate that the proposed system can be successfully applied in real-world applications for personalized location-based news services.

Automatic extraction of new words based on Google News corpora for supporting lexicon-based Chinese word segmentation systems

Chinese word segmentation is an essential step in a processing of Chinese natural language because it is beneficial to the Chinese text mining and information retrieval. Currently, the lexicon-based Chinese word segmentation scheme is widely adopted, which can correctly identify Chinese sentences as distinct words from Chinese language texts in real-word applications. However, the word identification ability of the lexicon-based scheme is highly dependent with a well prepared lexicon with sufficient amount of lexical entries which covers all of the Chinese words. In particular, this scheme cannot perform Chinese word segmentation process well for highly changeable texts with time, such as newspaper articles and web documents. This is because highly changeable documents often contain many new words that cannot be identified by a lexicon-based Chinese word segmentation system with a constant lexicon. Moreover, to maintain a lexicon by manpower is an inefficient and time-consuming job. Therefore, this study proposes a novel statistics-based scheme for extraction of new words based on the categorized corpora of Google News retrieved automatically from the Google News site to promote the word identification ability for lexicon-based Chinese word segmentation systems. Since corpora of news almost contain all words used in daily life, to extract news words from corpora of news and to incrementally add them into lexicon for lexicon-based Chinese word segmentation systems provide benefits in terms of automatically constructing a professional lexicon and enhancing word identification capability. Compared to another proposed scheme of new word extraction, the experimental results indicated that the proposed extraction scheme of new words not only more correctly retrieves new words from the categorized corpora of Google News, but also obtains larger amount of new words. Moreover, the proposed scheme of new word extraction has been applied to automatically expand the lexicon of the Chinese word segmentation system ECScanner (A Chinese Lexicon Scanner with Lexicon Extension). Currently, the ECScanner has been published on the Web to provide Chinese word segmentation service based on Web service. Experimental results also confirmed that ECScanner is superior to CKIP (Chinese knowledge information processing) in identifying meaningful Chinese words.

Mining interactive social network for recommending appropriate learning partners in a Web-based cooperative learning environment

In Web-based cooperative learning environments, peer-to-peer interaction often suffers from the difficulty due to lack of exploring useful social interaction information, so that peers cannot find appropriate learning partners to make an effective cooperative learning. This problem easily results in poor learning outcomes in Web-based cooperative learning environments. Generally, learning partners assigned by instructors cannot ensure to compose suitable learning groups for individual learners in cooperative learning environments. Inappropriate learning partners not only easily lead to poor learning interaction and achievement, but also lose the meaning of cooperative learning. As a result, this study presents a novel scheme of mining social interactive networks for recommending appropriate learning partners for individual learners in a cooperative problem-based learning environment. The preliminary experimental results reveal that the proposed scheme provides likely benefits in terms of promoting learnerspsila learning interaction and learning performance in cooperative learning environments.