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Zhang X.,University of Sussex | Wang W.,Micro Image U.K. Ltd. | Sze G.,Hong Kong Applied Science and Technology Research Institute ASTRI | Barber D.,University of Sheffield | Chatwin C.,University of Sussex
IEEE Transactions on Medical Imaging | Year: 2014

The Sussex MK4 electrical impedance mammography system is especially designed for 3-D breast screening. It aims to diagnose breast cancer at an early stage when it is most treatable. Planar electrodes are employed in this system. The challenge with planar electrodes is the inaccuracy and poor sensitivity in the vertical direction for 3-D imaging. An enhanced image reconstruction algorithm using a duo-mesh method is proposed to improve the vertical accuracy and sensitivity. The novel part of the enhanced image reconstruction algorithm is the correction term. To evaluate the new algorithm, an image processing based error analysis method is presented, which not only can precisely assess the error of the reconstructed image but also locate the center and outline the center and outline the shape of the objects of interest. Although the enhanced image reconstruction algorithm and the image processing based error analysis method are designed for the Sussex MK4 system, they are applicable to all electrical impedance tomography systems, regardless of the hardware design. To validate the enhanced algorithm, performance results from simulations, phantoms and patients are presented. © 1982-2012 IEEE. Source


Chao S.-C.,Hong Kong Applied Science and Technology Research Institute ASTRI
IEEE Technology Time Machine Symposium on Technologies Beyond 2020, TTM 2011 | Year: 2011

In Hong Kong, an aging population and rising healthcare cost have put a lot of stress on the healthcare infrastructure. By 2033, almost one in four in the population will be 65 or older, while the healthcare expenditure will take up 9.2% of the GDP. It becomes an increasingly challenging problem to provide sustainable, accessible and good quality of health care to the elderly citizens. © 2011 IEEE. Source


Zhu L.,Chinese University of Hong Kong | Yum T.-S.P.,Hong Kong Applied Science and Technology Research Institute ASTRI
IEEE Transactions on Communications | Year: 2010

The anti-collision algorithm is an important part of the Radio-Frequency Identification (RFID) system. Of the various possible algorithms, the Framed Aloha based (FA) algorithms have been most widely used due to their simplicity and robustness. Previous studies have focused mainly on the tag population estimation, choosing the frame size based on the classical results of Random Access (RA) systems. We show that a new theory is needed for algorithm design for RFID systems, because RFID and RA systems are fundamentally different. The Philips RFID system is studied in this paper. We model the reading process as a Markov Chain and derive the optimal reading strategy by first-passage-time analysis. The optimal frame sizes are derived analytically and numerically. © 2010 IEEE. Source


Liu Y.,Hong Kong Applied Science and Technology Research Institute ASTRI
IEEE Transactions on Circuits and Systems for Video Technology | Year: 2010

This letter proposes a unified loop filter for video coding, which suppresses the quantization noise optimally and improves the objective and subjective qualities of the reconstructed picture simultaneously. The proposed filter unifies nonlinear enhancement filter and linear restoration filter within the classical optimization framework of least mean square error. To adapt to locally diverse quantization error characteristics, classification-based strategy is employed to design unified loop filters with different characteristics, which further improves the capability of picture restoration. Experimental results show that the proposed filter achieves superior objective coding gain and better visual quality improvement around edges and textures, compared with H.264/AVC high profile. © 2010 IEEE. Source


Chan C.-F.,City University of Hong Kong | Yu E.W.M.,Hong Kong Applied Science and Technology Research Institute ASTRI
European Signal Processing Conference | Year: 2010

A detection and classification system for sound surveillance is presented. A human/non-human voice classifier is firstly applied to separate the input sound into human voice sound or non-human emergency sound. It utilizes a sliding window Hidden Markov Model (HMM) with trained background, human voice and non-human sound templates. In case of human voice, a scream/non-scream classification is performed to detect screaming in an abnormal situation such as screaming for help during bank robbery. In case of nonhuman sound, an emergency sound classifier capable of identifying abnormal sounds such as gun shot, glass breaking, and explosion, is employed. HMM is used in both scream/non-scream classification and emergency sound classification but with different sound feature sets. In this research, a number of useful sound features are developed for various classification tasks. The system is evaluated under various SNR conditions and low error rates are reported. © EURASIP, 2010. Source

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