11 February 2013
My main research interests can be divided into two parts. The first part is on wireless body sensor networks which includes VLSI designs of power controllers, multi-sensor controllers, lossless body signal encoders, and channel coding. The second part is on endoscopic systems which includes VLSI designs of real-time colour correctors, barrel distortion correctors, image scalars, and lossless image encoders. Related to my research interests, I am focusing on a high performance system on chip (SoC) design and implementation..
Traditional medical instruments are designed to transmit medical signals or images using cables in hospital; a system which is not suitable for real-time monitoring in mobile scenarios or for 24-hour healthcare. By using wireless healthcare solutions, hospitals can monitor physiology signals for 24-hours a day without affecting the life of a patient. Wireless healthcare solutions can also be used to take care of chronic disease patients in houses. In addition, the health department of a national government could control an epidemic disease efficiently, such as SARS. The motivation of my current work is to develop wireless healthcare solutions which will contribute to the human life.
The work reported in our Letter achieved a high performance, low-power, and low-cost lossless VLSI encoder design for wireless healthcare monitoring applications. The main contribution of this work includes a novel lossless ECG compression algorithm and an efficient lossless ECG encoder design.
This work is designed to compress ECG data in real time. It could be applied to wireless body sensor networks, wireless healthcare systems, home-care services, and hospitals. It could also be enabled in some new applications such as sports player training, on treadmills, or in straight-face tests. As well as the ECG signal, this work can be also applied to other physiological signals such as heart rate, blood pressure, body temperature, brain waves and EEG.
This work was designed based on prediction theory and entropy coding techniques. In order to improve the performance of this work, more effective prediction and entropy techniques, such as an arithmetic encoding and dictionary encoding, will be built up in the future. Moreover, with the advance of VLSI techniques, this work can achieve better performance and lower power consumption in 45-nm or 28-nm CMOS process techniques.
We are working on a VLSI design of a real-time image processor for medical video-endoscopic system. The real-time image processor consists of three parts: a real-time colour interpolator, a real-time barrel distortion corrector and a real-time high-quality image scalar. By utilising this project, doctors can diagnose using real-time images, which are corrected by a colour correction, distortion correction, and high-quality scaling, and displayed in a high-quality monitor or TV.
As VLSI techniques advance, mixed-signal and system-on-a-chip research will increase over the next few years. In other words, it is necessary for our work to be integrated with analogue circuits, such as a readout circuit or ADC, into a mixed-signal chip. Furthermore, the integrated mixed-signal chip will be integrated into a SoC chip. We are looking forward to see the wireless healthcare systems achieving higher performance, less power, and lower cost in the future.
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