1,510 research outputs found

    Hydrogen : a metastable donor in TiO₂ single crystals

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    2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    An Integrated ISFETs Instrumentation System in Standard CMOS Technology

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    Water-induced degradation in lead zirconate titanate piezoelectric ceramics

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    2001-2002 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Dielectric properties of barium titanate ceramics doped by B₂O₃ vapor

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    2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Hydrogen-related dynamic dielectric behavior of barium titanate single crystals

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    2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    A cooperative feature gene extraction algorithm that combines classification and clustering

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    In feature gene selection, filtering model concerns classification accuracy while ignoring gene redundancy problem. On the other hand, gene clustering finds correlated genes without considering their predictive abilities. It is valuable to enhance their performances by the help of each other. We report a new feature gene extraction algorithm, namely Double-thresholding Extraction of Feature Gene (DEFG), that combines gene filtering and gene clustering. It firstly pre-select feature gene set from the original dataset. A modified gene clustering is then applied to refine this set. In the gene clustering, specific designs are employed to balance the predictive abilities and the redundancies of the extracted feature gene. We have tested DEFG on a microarray dataset and compared its performance with that of two benchmark algorithms. The experimental results show that DEFG is superior to them in terms of internal validation accuracy and external validation accuracy. Also, DEFG can generalize the pattern structure by a small number of training samples. ©2009 IEEE.published_or_final_versio

    VISJET & VISFLOOD: Software for environment hydraulic modeling & visualization

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    We present two general interactive PC-based modeling and visualization software systems developed for the study of two types of environmental water flows: buoyant jet mixing and urban drainage problems. VISJET (http://www.aoe-water.hku.hk/visjet) is arguably the most robust software with advanced graphics for the prediction of mixing and transport of effluent discharges into a stratified crossflow. The prediction engine is a Lagrangian model for buoyant jets with three-dimensional trajectories, and is based on extensive basic experiments and turbulence model calculations. It can be used in outfall design and environmental impact assessment, and as an educational or training tool. VISFLOOD (http://www.aoe-water.hku.hk/visflood) is based on the numerical solution of the Saint-Venant equations, and caters for the simulation of unsteady flood propagation in urban drainage systems. Both software systems are fully interactive with data interrogation; the 3D visualization is fully integrated with the model engine, and enables the user to appreciate the context of the problem in a most effective way. Both models have been well-validated against laboratory and field data, and have been applied to many actual engineering projects. This software product is an outcome of a grant by the Hong Kong Innovation and Technology Fund (ITF).published_or_final_versio

    Adopting RFID for body parts tagging: a Local Association Network approach

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    Adopting an innovative technology often requires extensive intelligence research. A major value indicator for RFID Technology adoption is how the potentials of RFID can translate into actions to improve business operational efficiency [1]. This paper presents a Local Association Network (LAN) approach to developing RFID enabled visibility systems for body tagging. On site testing validates the proposed approach. User feedback strengthens our belief that the proposed approach would help facilitate RFID technology adoption in body tagging. © 2010 IEEE.published_or_final_versio
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