Browsing by Author "Chen, Yu-Jui"

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A Wireless Multi-Channel Physiological Signal Acquisition System-on-Chip for Wearable Devices
(2016-01-01)
Lee, Sheng-Cheng
;
Lin, Yu -Shan
;
Chen, Yu-Jui
;
Chiueh, Herming
;
電機工程學系
A novel wireless ExG sensor tag with a multichannel physiological signals acquisition (PSA) system is presented. The presented system, optimized for power efficiency, contains mixed-signal processor system-on-chip (SoC) and Bluetooth Low Energy (BLE) chip for real time recording and wireless transmission. The versatility of the proposed wireless ExG sensor tag is being demonstrated in several biopotential signals, such as electrocardiogram (ECG), electrooculography (EOG), and electromyogram (EMG). The ExG wireless tag with Tablet can be easily achieve 12 hours with 200mAH battery of continuous recording of ExG signals in healthcare application.
Live Demonstration: A Wireless Multi-channel Physiological Signal Acquisition System-on-Chip for Wearable Devices
(2016-01-01)
Lee, Sheng-Cheng
;
Lin, Yu-Shan
;
Chen, Yu-Jui
;
Chiueh, Herming
;
電機工程學系
this demonstration presents a power-efficient multi-channel physiological signals acquisition system-onchip (SoC). The proposed system is implemented to achieve continuous real-time processing for acquired biosignals and analysis the results on computer or mobile devices through wireless transmission by Bluetooth low energy (BLE) protocol. The ultra-low power consumption SoC with features of lightweight, wireless, portable, rechargeable battery-based can be applied on many wearable biomedical implants.
Live Demonstration: A Wireless Multi-channel Physiological Signal Acquisition System-on-Chip for Wearable Devices
(2016-01-01)
Lin, Yu-Shan
;
Lee, Sheng-Cheng
;
Chen, Yu-Jui
;
Huang, Chia-Ming
;
Chiueh, Herming
A wireless ExG sensor tag with a multi-channel physiological signals acquisition system is demonstrated. The system contains a mixed-signal system-on-chip (SoC) and Bluetooth Low Energy (BLE) chip for data analyzing and wireless transmission. The demonstration presents acquisition of different physiological signals, such as electrocardiogram (ECG), electrooculography (EOG), surface electromyogram (EMG) being real-time recorded and wirelessly displayed on tablet. The power-efficient ExG wireless tag can be easily achieve 12 hours with 200mA battery for continuous recording of ExG signals in healthcare applications.
Low Computational Complexity Pitch Based VAD for Dynamic Environment in Hearing Aids
(2010-01-01)
Chen, Yu-Jui
;
Wei, Cheng-Wen
;
Meng, Yi-Le
;
Jou, Shyh-Jye
;
電機學院
This paper presents a low computational complexity and high robust voice activity detection (VAD) algorithm. The algorithm is based on an efficient time-domain pitch detection and harmonic structure discrimination exploiting frequency decomposition by 1/3 octave filter bank defined in ANSI S1.11 standard. In addition, a simple yet efficient phoneme keeper (PK) is adopted for the detection of monosyllable languages, such as Mandarin. Simulation results reveal that the proposed VAD has very robust performance for Mandarin speeches in different environments, even for dynamic SNR (signal to noise ratio) and noise type. Furthermore, in white noise with 0dB SNR, the proposed VAD still has about 90 percent accuracy.
Low Computational Complexity Pitch Based VAD for Dynamic Environment in Hearing Aids
(2011)
Chen, Yu-Jui
;
Wei, Cheng-Wen
;
Meng, Yi-Le
;
Jou, Shyh-Jye
;
電子工程學系及電子研究所
This paper presents a low computational complexity and high robust voice activity detection (VAD) algorithm. The algorithm is based on an efficient time-domain pitch detection and harmonic structure discrimination exploiting frequency decomposition by 1/3 octave filter bank defined in ANSI S1.11 standard. In addition, a simple yet efficient phoneme keeper (PK) is adopted for the detection of monosyllable languages, such as Mandarin. Simulation results reveal that the proposed VAD has very robust performance for Mandarin speeches in different environments, even for dynamic SNR (signal to noise ratio) and noise type. Furthermore, in white noise with 0dB SNR, the proposed VAD still has about 90 percent accuracy.
Neuromorphic Pitch Based Noise Reduction for Monosyllable Hearing Aid System Application
(2014-02-01)
Chen, Yu-Jui
;
Wei, Cheng-Wen
;
Yi FanChiang
;
Meng, Yi-Le
;
Huang, Yi-Cheng
This paper presents a low computational complexity hardware-oriented neuromorphic pitch based noise reduction (NR) algorithm and hardware implementation for monosyllable hearing aid system applications. The proposed NR design consists of a pitch-based voice activity detector (pitch-based VAD) for speech detection and a neuromorphic noise attenuator for speech enhancement. The pitch-based VAD is developed on ANSI S1.11 based filter bank architecture and employs the characteristics of monosyllable and nonlinear energy operator (NEO) to improve the accuracy of VAD. The neuromorphic noise attenuator reduces the background noise by using the characteristics of human hearing system and the clues of speech. Simulation results show that the proposed algorithm has better SNR and PESQ performance than other non-pitch based NR algorithms in non-stationary background noise environments. Compared with multiband (mband) spectral subtraction and minimum mean square error (mmse) algorithms, the computational complexity of the proposed algorithm can save 90% computational complexity. The hardware implementation consumes 47.74 mu W at 0.5 V operation with 65 nm HVT standard cell library.