Browsing by Author "Wu, Chun-Hao"

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A new readout/driver circuitry for an optical vibration sensor
(2017-01-01)
Wu, Chun-Hao
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Chao, Paul C. -P.
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Wu, Yi-Cheng
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Sauter, Thilo
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Zan, Hsiao-Wen
This work presents research results on designing the drive and readout circuits for a newly-developed optical vibrations sensor. Tes sensor consists of organic light-emitting diodes (OLEDs) and photodetectors (OPDs) and micro-structure to realize the function of vibration sensing. Proper operations of OLEDs and OPDS needs a driver and a readout circuit, respectively. A driver circuit is designed for driving and controlling OLED light; also, a readout circuit is designed to acquire current signal from OPD and then converts it to digital signals by an analog-to-digital converter (ADC). In addition to readout circuit, a front-end amplifier called transimpedance amplifier (TIA) is designed for converting the output current of the OPDs. The TIA is followed by an IA and LPF for better signal quality before a SAR ADC designed for converting the vibration amplitude to digits. The resolution of 10-bit ADC sub-circuit is 1.95mV/LSB. It is suitable for OPD current output. Moreover, a pulse width modulation (PWM) driver circuit which is designed with frequency output 200 KHz is suitable for driving OLED. The proposed circuit is fabricated by TSMC 0.35-mu m CMOS process where the area is 4.5 mm2 for the whole chip while power consumption is 7.808 mW.
Data Compression by Temporal and Spatial Correlations in a Body-Area Sensor Network: A Case Study in Pilates Motion Recognition
(2011-10-01)
Wu, Chun-Hao
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Tseng, Yu-Chee
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資訊工程學系
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Department of Computer Science
We consider a body-area sensor network (BSN) consisting of multiple small, wearable sensor nodes deployed on a human body to track body motions. Concerning that human bodies are relatively small and wireless packets are subject to more serious contention and collision, this paper addresses the data compression problem in a BSN. We observe that, when body parts move, although sensor nodes in vicinity may compete strongly with each other, the transmitted data usually exist some levels of redundancy and even strong temporal and spatial correlations. Unlike traditional data compression approaches for large-scale and multihop sensor networks, our scheme is specifically designed for BSNs, where nodes are likely fully connected and overhearing among sensor nodes is possible. In our scheme, an offline phase is conducted in advance to learn the temporal and spatial correlations of sensing data. Then, a partial ordering of sensor nodes is determined to represent their transmission priorities so as to facilitate data compression during the online phase. We present algorithms to determine such partial ordering and discuss the design of the underlying MAC protocol to support our compression model. An experimental case study in Pilates exercises for patient rehabilitation is reported. The results show that our schemes reduce more than 70 percent of overall transmitted data compared with previous approaches.
Deploying Sensors for Gravity Measurement in a Body-Area Inertial Sensor Network
(2013-05-01)
Wu, Chun-Hao
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Tseng, Yu-Chee
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資訊工程學系
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Department of Computer Science
This paper deals with human posture tracking by deploying accelerometers on a human body. One fundamental issue in such scenarios is how to calculate the gravity. This is very challenging especially when the human body parts keep on moving. Fortunately, it is likely that there is a point of the body that touches the ground in most cases. This allows sensors to collaboratively calculate the gravity vector. Assuming multiple accelerometers being deployed on a rigid part of a human body, a recent work proposes a data fusion method to estimate the gravity vector on that rigid part. However, finding the optimal deployment of sensors that minimizes the estimation error of the gravity vector is not addressed. In this paper, we formulate the deployment optimization problem and propose two heuristics, called Metropolis-based method and largest-inter-distance-based method. Simulation and real experimental results show that our schemes are quite effective in finding near-optimal solutions for a variety of rigid body geometries.
Exploiting Multi-Spatial Correlations of Motion Data in a Body Sensor Network
(2012-05-01)
Wu, Chun-Hao
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Tseng, Yu-Chee
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資訊工程學系
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Department of Computer Science
Human body motions usually exhibit a high degree of coherence and correlation in patterns. This allows exploiting spatial correlations of motion data being captured by a body sensor network. Since human bodies are relatively small, earlier work has shown how to compress motion data by allowing a node to overhear at most kappa = 1 node's transmission and exploit the correlation with its own data for data compression. In this work, we consider multi-spatial correlations by extending kappa = 1 to kappa > 1 and constructing a partial-ordering directed acyclic graph (DAG) to represent the compression dependencies among sensor nodes. While a minimum-cost tree for kappa = 1 can be found in polynomial time, we show that finding a minimum-cost DAG is NP-hard even for kappa = 2. We then propose an efficient heuristic and verify its performance by real sensing data.
VirtualTouch: A Finger Glove to Simulate Touch Screen Commands
(2012)
Wang, Jun-Zhe
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Wu, Chun-Hao
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Tseng, Yu-Chee
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資訊工程學系
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Department of Computer Science
Touch screens have become more and more popular in many end devices. For example, Windows 7, Android, and iOS have adopted several multi-touch commands in their user interfaces. Although touching the screen of a small device is handy, it is not suitable for larger screen environments. In this paper, we propose a glove-shaped input device that can simulate screen-touching operations by hand gestures without actual need of touching the screen. The glove captures hand gestures and reports the sensing data back to a computer, which interprets the data into touch commands. We have implemented commands such as clicking, dragging, flicking, rotating, scrolling, and zooming, and we verified them on Windows 7 to understand user experiences.