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.     

Registration of challenging image pairs: initialization, estimation, and decision

Our goal is an automated 2D-image-pair registration algorithm capable of aligning images taken of a wide variety of natural and man-made scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and physical changes in the scene. An important component of this is the ability to automatically reject pairs that have no overlap or have too many differences to be aligned well.We propose a complete algorithm, including techniques for initialization, for estimating transformation parameters, and for automatically deciding if an estimate is correct. Keypoints extracted and matched between images are used to generate initial similarity transform estimates, each accurate over a small region. These initial estimates are rank-ordered and tested individually in succession. Each estimate is refined using the Dual-Bootstrap ICP algorithm, driven by matching of multiscale features. A three-part decision criteria, combining measurements of alignment accuracy, stability in the estimate, and consistency in the constraints, determines whether the refined transformation estimate is accepted as correct. Experimental results on a data set of 22 challenging image pairs show that the algorithm effectively aligns 19 of the 22 pairs and rejects 99.8% of the misalignments that occur when all possible pairs are tried. The algorithm substantially out-performs algorithms based on keypoint matching alone.     

Large-scale protein identification of human urine proteome by multi-dimensional LC and MS/MS

Urine is a human specimen that is easily obtained non-invasively for clinical diagnosis. We attempted to enhance the resolution of current human urine proteomes and construct a comprehensive reference database for advanced studies, such as the discovery of biomarkers for renal diseases. Multi-dimensional LC-MS/MS was coupled with de novo sequencing and database matching. The proposed approach improved the identification of not only the proteins, but also the post-translational sites of urinary proteins. We identified 165, 200 and 259 unique gene products in the urine proteomes from males, females and pregnant women, respectively. When all of the results were combined and the redundancies removed, a total of 1095 distinct peptides were identified. Of these, 1016 peptides were associated with 334 unique gene products. In this study, over 100 gene products, including some disease-related proteins, were detected in urine for the first time by proteomic approaches. Various proteins with novel post-translational hydroxylation were identified using the MASCOT program and de novo sequencing. All proteins with peptide information were summarized into a comprehensive urine protein database. We believe that this comprehensive urine proteome database will assist in the identification of urinary proteins/polypeptides whose spectra are difficult to interpret in the discovery of urinary biomarkers.     

Suppression of sidelobe levels for guided-wave acousto-optic tunable filters using weighted coupling

An analysis of guided-wave acousto-optic tunable filters (AOTFs) that employ simple acousto-optic (AO) weighted coupling techniques for sidelobe reduction and the calculated and experimental results from a specific example that involves only variation of the width of a surface acoustic wave (SAW) slot waveguide are presented. The calculations on single- and multi-stage AOTFs consisting of an optical channel waveguide and a SAW slot waveguide in LiNbO(3) substrate show that waveguide width weighting using generalized Hamming functions would provide significant improvement in sidelobe suppression. Calculated results together with the design, fabrication, and measured performance characteristics of a single-stage AOTF that utilizes a weighted-aperture SAW slot waveguide in YX-LiNbO(3) substrate at the optical wavelength of 1.55 mum and the acoustic center frequency of 175 MHz are reported. The measured sidelobe level is -13.6 dB and the measured FWHM bandwidth is 26 A, as compared to the theoretical values of -15.0 dB and 15 A, respectively. The RF drive power was measured to be 1.0 W at a mode-conversion efficiency of 100%     

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.     

Interactive augmented reality system 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, developing augmented reality (AR) technologies for learning have garnered considerable attention in education research. Many researchers and scholars believe that integrating teaching and AR enhances student learning performance and motivation. This work develops an educational AR system based on situated learning theory, and applies innovative augmented reality interactive technology to a library’s learning environment. Student library knowledge can be enhanced via the proposed augmented reality library instruction system (ARLIS). Experimental results demonstrate that student learning performance is improved significantly by using the proposed ARLIS. Moreover, this work demonstrates that using the proposed ARLIS for library instruction results in the same learning performance as conventional librarian instruction and there is no gender difference on learning performance between the proposed ARLIS and conventional librarian instruction. Moreover, 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 of application and comprehension than conventional librarian instruction. Moreover, the learning performance of students is not affected by their gaming skills. Therefore, student gaming skills do not need to be considered when adopting the proposed system in library instruction programs.     

Preliminary cost estimates for thin-film transistor liquid–crystal display inspection and repair equipment: A hybrid hierarchical approach

The thin-film transistor liquid–crystal display (TFT-LCD) industry has developed rapidly in recent years. Because TFT-LCD manufacturing is highly complex and requires different tools for different products, accurately estimating the cost of manufacturing TFT-LCD equipment is essential. Conventional cost estimation models include linear regression (LR), artificial neural networks (ANNs), and support vector regression (SVR). Nevertheless, in accordance with recent evidence that a hierarchical structure outperforms a flat structure, this study proposes a hierarchical classification and regression (HCR) approach for improving the accuracy of cost predictions for TFT-LCD inspection and repair equipment. Specifically, first-level analyses by HCR classify new unknown cases into specific classes. The cases are then inputted into the corresponding prediction models for the final output. In this study, experimental results based on a real world dataset containing data for TFT-LCD equipment development projects performed by a leading Taiwan provider show that three prediction models based on HCR approach are generally comparable or better than three conventional flat models (LR, ANN, and SVR) in terms of prediction accuracy. In particular, the 4-class and 5-class support vector machines in the first-level HCR combined with individual SVR obtain the lowest root mean square error (RMSE) and mean average percentage error (MAPE) rates, respectively.     

Optimal expansion of competence sets with multilevel skills

The purpose of competence set expansion is to find an optimal expansion process at the minimal cost and then obtain the required competence set from the acquired competence set to solve a problem. Several models have been proposed to address the competence set expansion problem of only a single decision maker or multiple decision makers without considering multilevel skills. However, a practical competence set expansion model should involve multiple decision makers and multilevel skills. This study discusses an optimal expansion model of incorporating competence sets of group decision makers with multilevel skills. The proposed method not only obtains the optimal competence set expansion of all decision makers with the maximal total benefit but finds all optimal alternatives of the competence set expansion model. A cooperative alliance problem is solved to illustrate the usefulness of the proposed method.