Data Compression and Mixed Signal ASIC Design for Energy-efficient Low Power Applications
博士 === 中原大學 === 電子工程研究所 === 105 === Huge data processing contributes many factors in wireless sensor network (WSN) such as network traffic and energy constraint. Wearable devices have become widely used to monitor body signals for long-term health care and home care applications. They detect vital s...
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| Format: | Others |
| Language: | en_US |
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2017
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| Online Access: | http://ndltd.ncl.edu.tw/handle/50156857704852533590 |
| Summary: | 博士 === 中原大學 === 電子工程研究所 === 105 === Huge data processing contributes many factors in wireless sensor network (WSN) such as network traffic and energy constraint. Wearable devices have become widely used to monitor body signals for long-term health care and home care applications. They detect vital signals through physiological sensors and then transmit them to a cloud database for evaluation and monitoring purposes through wireless communication systems. These wearable devices also need to pay attention their power consumption. This dissertation is divided into parts, applying two different data compression techniques for reducing the power consumption of WSN and wearable device. Using compressive sensing a new technique in data acquisition which reduced the required sampling rate to reconstruct the original signal will therefore lessen the power consumption of the WSN. The primary objective of the design is to reduce the power consumption on wireless system network by maximizing the data packet payloads while minimizing the transmission activity of the Wireless Sensor Network. The sensor and receiver node consume more power when transmission of data is taking place. Fast Fourier Transform (FFT) will determine the sparsity of the signal; the measurement matrix contains the large coefficients and orthogonal matching pursuit was used for the recovery of the original signals. Matlab was used to simulate the results of the compressive sensing algorithm. On the other hand, a smart analog-to-digital converter (ADC) was realized by a mixed-signal application-specific integrated circuit (ASIC) based on adaptive resolution and lossless compression techniques for electrocardiogram (ECG) signal monitoring. The sampling clock for the ADC can be adaptively selected according to the characteristic of the signals. The lossless encoder consists of trend forecasting and entropy coding modules. The transmission data rate was decreased efficiently by adaptive resolution and lossless compression techniques. The chip aims to meet the low power consumption for the design because it reduced the signal transmission rate and maintained high-quality ECG signal detection. The mixed-signal ASIC design was realized using a 0.18 μm CMOS process with a total power consumption of 78.8 µW when operating at 1 kHz and a total chip area of 850 × 850 μm2.
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