Browsing by Author "Chen, Yi-Chung"

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A Novel Readout Chip with Extendability for Multi-Channel EEG Measurement
(2013-01-01)
Chen, Yi-Chung
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Tsai, Chung-Han
;
Hsieh, Zong-Han
;
Fang, Wai-Chi
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電子工程學系及電子研究所
This paper proposes an extendable front-end readout chip (EFRC) for electroencephalography (EEG) measurements. An EFRC is developed for EEG measurement with features including low power consumption, a high signal-to-noise ratio, and highly efficient chip area usage. A chopper-stabilized differential difference amplifier (CHDDA) is used in the first stage to amplify signals and then during another adjustable amplification stage and filter are used to process biomedical signals. A 10-bit successive approximation register analog-to-digital converter (SAR-ADC) then links to the back-end for digital signal processing. In the last stage, shift-register pairs are used to transmit data to the next chip and receive data from the previous chip. The shift register design allows the number of channels to be extended. A TSMC 0.18 um CMOS process is used to design the EFRC and it operates with a 1.8 V supply voltage. The results shows that the total power consumption for the EFRC chip is approximately 80.268 uW and the chip area is approximately 944 x 863 um(2).
A Wireless Readout Front-End Device for Portable EEG Acquisition System
(2013)
Chen, Yi-Chung
;
Hsieh, Zong-Han
;
Fang, Wai-Chi
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
In this paper, the 32-channel readout front-end device with a Bluetooth 2.0 module and a MSP430 ultra-low power microcontroller for portable Electroencephalograph (EEG) acquisition is presented. In addition to the consideration of low power, low noise, and high efficient chip area usage, the extendable readout front-end chip is presented with a design of chopper-stabilized differential difference amplifier (CHDDA) for high common mode rejection ratio (CMRR) and the 10-bit successive approximation register analog-to-digital converter (SAR-ADC) for high Signal-to-Noise Ratio (SNR). The power consumption of each readout chip is about 80.268 mu W, and the total system is about 134.6 mW under the power supply of +/- 1.8 V.
An EEG Analog Front-End Design with Wireless Communication Module for a Portable EEG Monitoring System
(2015)
Chen, Wei-Chen
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Chen, Yi-Chung
;
Chou, Chia-Ching
;
Fang, Wai-Chi
;
電機學院
In this paper, a wireless electroencephalography (EEG) acquisition analog front-end (AFE) circuit design is presented. The wireless EEG acquisition system includes an analog front-end readout chipset (AFERC) with extendable channel, a low-power MSP430 microcontroller from Texas Instruments and a commercial Bluetooth 2.0 module (BTM). The front-end readout chipset not only applies the ability of noise attenuation but also modulates the EEG signal into appropriate amplitude. It has individual 10-bit successive approximation register analog-to-digital converter (SAR-ADC) for each EEG channel, and it transmits the EEG signals which have been serially converted to digital format to MSP430. MSP430 is responsible for rearranging the converted digital bits and controlling the BTM with built-in Universal Asynchronous Receiver/Transmitter (UART) interface. The AFERC maximumly reaches to 64 channels, and single channel consumes 80.268 uW. Total power consumption of AFERC is 2.6 mW at with 32 channels, and its Common-Mode-Reject-Ratio (CMRR) is 75 dB.
HEAD MOTION RECOGNITION USING A SMART HELMET FOR MOTORCYCLE RIDERS
(2019-01-01)
Wong, K. I.
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Chen, Yi-Chung
;
Lee, Tzu-Chang
;
Wang, Sheng-Min
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運輸與物流管理系註：原交通所+運管所
This paper presents a head motion detection and recognition study using a smart helmet for motorcycle rider which can potential be used for the analysis of behavior of motorcycle riders. The smart helmet is a full face motorcycle helmet integrated with an intelligent system embedded an Inertial Measurement Unit (IMU) sensor. In the analysis, the motions and the corresponding signals are assessed with the video footage with a data acquisition and visualization platform. We introduce a feature extraction methodology to extract the most discriminant features from the signal data, and the head motion recognition problem is formulated as a machine-learning based classification model. Experiment results show that gyroscope sensor data is more useful than accelerometer sensor data for head motion recognition and the classification accuracy for different head motions ranges from 95.9% to 99.1%.
Linearity Characteristics of Field-Plated AlGaN/GaN High Electron Mobility Transistors for Microwave Applications
(2010)
Huang, Jui-Chien
;
Hsu, Heng-Tung
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Chang, Edward-Yi
;
Lu, Chung-Yu
;
Chang, Chia-Ta
A field-plated (FP) AlGaN/GaN high electron mobility transistor (HEMT) was fabricated. Investigations on the linearity characteristics were performed through two-tone and wide band code division multiple access (WCDMA) modulated excitations. The FP-HEMT exhibited an improved breakdown voltage of 160 V compared with that of the conventional HEMT. Additionally, a higher output power of 25.4 dBm with 43% power added efficiency at a 30 V drain bias at 2 GHz was achieved. When biased at 30 V and 15 mA/mm current density, the third-order intermodulation (IM3) level was measured to be -27.1 dBc (at P(1dB)) and the adjacent channel power rejection (ACPR) was -33.8 dBc (at P(1dB)) under WCDMA modulation at 2 GHz. Measurement results revealed that the field-plated structure improved the linearity performance over the conventional structure at high output power levels even beyond P(1dB). (C) 2010 The Japan Society of Applied Physics