http://tainguyenso.vnu.edu.vn/jspui/handle/123456789/2684 Tue, 19 May 2026 13:35:06 GMT 2026-05-19T13:35:06Z http://tainguyenso.vnu.edu.vn/jspui/handle/123456789/4829 Title: Analysis and evaluation of traffic-performance in a backtracked routing network-on-chip Authors: Tran, Tu Xuan Abstract: Analysis and evaluation of traffic-performance in a backtracked routing network-on-chip Tue, 01 Jan 2008 00:00:00 GMT http://tainguyenso.vnu.edu.vn/jspui/handle/123456789/4829 2008-01-01T00:00:00Z http://tainguyenso.vnu.edu.vn/jspui/handle/123456789/4821 Title: Recognizing Vietnamese online handwritten separated characters Authors: Bui, Duy The Abstract: Abstract: Vietnamese alphabet is based on the Latin alphabet with the addition of nine accent marks or diacritics four of them to create additional sounds, and the other five to indicate the tone of each word. Because Vietnamese is a tonal language that uses tone to distinguish words, recognizing diacritics is an important part in recognizing Vietnamese word. However, in written form, diacritics are much smaller then the characters, which make very them hard to recognize. Previous works on Vietnamese characters recognition often pre-process input with a graph-based approach by trying to separate the main characters with their diacritics by determining connected regions at pixel level. his approach, however, only works well where the input contains only characters with separable diacritics, for example, scanned image of printed documents. We propose in this paper a robust method to recognize online Vietnamese characters with diacritics. Using cosine transformation with appropriated sampling algorithms, we represent multiple strokes of a character together in a single set of features. This set of features is then used as the input for a well designed machine learning based system. We have tested our system on the combination of Vietnamese characters with diacritics and Section 1c (isolated characters) of the Unipen data set, and have obtained very competitive results. ?? 2008 IEEE Tue, 01 Jan 2008 00:00:00 GMT http://tainguyenso.vnu.edu.vn/jspui/handle/123456789/4821 2008-01-01T00:00:00Z http://tainguyenso.vnu.edu.vn/jspui/handle/123456789/4775 Title: Easy-setup eye movement recording system for human-computer interaction Authors: Tran, Vinh Quang Abstract: Abstract: Tracking the movement of human eyes is expected to yield natural and convenient applications based on human-computer interaction (HCI). To implement an effective eye-tracking system, eye movements must be recorded without placing any restriction on the user's behavior or user discomfort. This paper describes an eye movement recording system that offers free-head, simple configuration. It does not require the user to wear anything on her head, and she can move her head freely. Instead of using a computer, the system uses a visual digital signal processor (DSP) camera to detect the position of eye corner, the center of pupil and then calculate the eye movement. Evaluation tests show that the sampling rate of the system can be 300 Hz and the accuracy is about 1.8 ?�/s. ??2008 IEEE. Tue, 01 Jan 2008 00:00:00 GMT http://tainguyenso.vnu.edu.vn/jspui/handle/123456789/4775 2008-01-01T00:00:00Z http://tainguyenso.vnu.edu.vn/jspui/handle/123456789/4764 Title: Transformation rule learning without rule templates: A case study in part of speech tagging Authors: Nguyen, Binh Ngoc Abstract: Abstract: Part of speech (POS) tagging is an important problem and is one of the first steps included in many tasks in natural language processing. It affects directly on the accuracy of many other problems such as Syntax Parsing, Word Sense Disambiguation, and Machine Translation. Stochastic models solve this problem relatively well, but they still make mistakes. Transformation-based learning (TBL) is a solution which can be used to improve stochastic taggers by learning a set of transformation rules. However, its rule learning algorithm has the disadvantages that rule templates must be prepared by hand and only rules are instances of rule templates can be generated. In this paper, we propose a model to learn transformation rules without rule templates. This model considers the rule learning problem as a feature selection problem. Experiments on Penn TreeBank showed that the proposal model reduces errors of stochastic taggers with some tags. ?? 2008 IEEE Tue, 01 Jan 2008 00:00:00 GMT http://tainguyenso.vnu.edu.vn/jspui/handle/123456789/4764 2008-01-01T00:00:00Z