Several 2DPCA-based face recognition algorithms have been proposed hoping to achieve the goal of improving recognition rate while mostly at the expense of computation cost. In this paper, an approach named SI2DPCA is proposed to not only reduce the computation cost but also increase recognition performance at the same time. The approach divides a whole face image into smaller sub-images to increase the weight of features for better feature extraction. Meanwhile, the computation cost that mainly comes from the heavy and complicated operations against matrices is reduced due to the smaller size of sub-images. The reduced amount of computation has been analyzed and the integrity of sub-images has been discussed thoroughly in the paper. The experiments have been conducted to make comparisons among several better-known approaches and SI2DPCA. The experimental results have demonstrated that the proposed approach works well on reaching the goals of reducing computation cost and improving recognition performance simultaneously.

People have the need to use a novel consumer clock device for home automation, personal security and convenience. This paper presents the design and implementation of the innovative system. The system architecture which is controlled by micro-controller unit (MCU) includes main alarm clock module, lucky module and security module. In the main alarm clock module, we create a novel hourglass clock model and intelligent alarm clock features based on grey relational analysis. In the lucky module, we design a unique structure of live round according to the medical theory of psychology and hashing function for randomization. In the live round, it includes the fortune of love, career, family, and health. It adopts funny and encouraged voice to transmit messages. The system also provides the function of security using infrared sensors and third-generation (3G) mobile phone unit. The system has been successful and gets the award in Taiwan.

Abstract This article presents an adaptive learning framework for Phonetic Similarity Modeling (PSM) that supports the automatic construction of transliteration lexicons. The learning algorithm starts with minimum prior knowledge about machine transliteration and acquires knowledge iteratively from the Web. We study the unsupervised learning and the active learning strategies that minimize human supervision in terms of data labeling. The learning process refines the PSM and constructs a transliteration lexicon at the same time. We evaluate the proposed PSM and its learning algorithm through a series of systematic experiments, which show that the proposed framework is reliably effective on two independent databases.

This article proposes an approach for the automatic extraction of transliteration pairs from Chinese Web corpora. In this approach, we formulate the machine transliteration process using a syllable-based phonetic similarity model which consists of phonetic confusion matrices and a Chinese character n -gram language model. With the phonetic similarity model, the extraction of transliteration pairs becomes a two-step process of recognition followed by validation: First, in the recognition process, we identify the most probable transliteration in the k -neighborhood of a recognized English word. Then, in the validation process, we qualify the transliteration pair candidates with a hypothesis test. We carry out an analytical study on the statistics of several key factors in English-Chinese transliteration to help formulate phonetic similarity modeling. We then conduct both supervised and unsupervised learning of a phonetic similarity model on a development database. The experimental results validate the effectiveness of the phonetic similarity model by achieving an F -measure of 0.739 in supervised learning. The unsupervised learning approach works almost as well as the supervised one, thus allowing us to deploy automatic extraction of transliteration pairs in the Web space.

This paper presents an adaptive learning framework for Phonetic Similarity Modeling (PSM) that supports the automatic construction of transliteration lexicons. The learning algorithm starts with minimum prior knowledge about machine transliteration, and acquires knowledge iteratively from the Web. We study the active learning and the unsupervised learning strategies that minimize human supervision in terms of data labeling. The learning process refines the PSM and constructs a transliteration lexicon at the same time. We evaluate the proposed PSM and its learning algorithm through a series of systematic experiments, which show that the proposed framework is reliably effective on two independent databases.

In this paper, a new robust fuzzy clustering approach is proposed for better performing principal component analysis (PCA) on function curves and character images that not only have loops, sharp corners, and intersections but also are bound of noise and outlier data. The proposed method is composed of two phases: firstly, input data are clustered using the proposed distance analysis to get good initial cluster centers and a reasonable number of clusters; secondly, the input data are further reclustered by the proposed robust fuzzy c-means (RFCM) based on the results obtained in the first phase to overcome the influence of noise and outlier data so that a good result of principal components can be found. Several function curves and Chinese character images are given to illustrate the effectiveness of the proposed method. Experimental results have demonstrated that the proposed approach works very well on PCA for both curves and images despite the fact that their input data sets may include loops, corners, intersections, noise, and outlier information.

In the area of fuzzy set theory, one of the important themes is how to extend Dempster-Shafer (D-S) theory to include the process of fuzzy events. In this paper, instead of extending D-S rules, we propose a direction of arguing that elements in a fuzzy domain are all singletons based on the statistical viewpoint and are independent of one another. Therefore, the D-S combination rule can consequently be applied as regularly as in non-fuzzy cases. We first examine the characteristics of how membership functions are defined, which leads to the conclusion that every element in a fuzzy domain is independent, mutually exclusive of each other and, consequently, a singleton. We then discuss the relationships existing between the grades of a membership function and its corresponding statistical probability, and between the probability of a fuzzy event and the probability of every element in its associated data domain. The equations are then derived to calculate the probability of every element in a fuzzy event data domain when both the probability of the event and its membership functions are known. An example is finally given to illustrate discussions.

A low-cost geometry-preserving image database system is proposed in this paper. This scheme adapts, but not suppresses, the unique characteristics of images. In addition, an image data storage scheme which requires only linear order space-complexity is described to provide the benefits of not only saving storage space but also preserving geometrical features of images. The proposed database system provides more functions and easier and faster in image manipulations.