Browsing by Author "Chou, Chia-Wei"

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A Fully Integrated 8-Channel Closed-Loop Neural-Prosthetic CMOS SoC for Real-Time Epileptic Seizure Control
(2014-01-01)
Chen, Wei-Ming
;
Chiueh, Herming
;
Chen, Tsan-Jieh
;
Ho, Chia-Lun
;
Jeng, Chi
An 8-channel closed-loop neural-prosthetic SoC is presented for real-time intracranial EEG (iEEG) acquisition, seizure detection, and electrical stimulation in order to suppress epileptic seizures. The SoC is composed of eight energy-efficient analog front-end amplifiers (AFEAs), a 10-b delta-modulated SAR ADC (DMSAR ADC), a configurable bio-signal processor (BSP), and an adaptive high-voltage-tolerant stimulator. A wire-less power-and-data transmission system is also embedded. By leveraging T-connected pseudo-resistors, the high-pass (low-pass) cutoff frequency of the AFEAs can be adjusted from 0.1 to 10 Hz (0.8 to 7 kHz). The noise-efficiency factor (NEF) of the AFEA is 1.77, and the DMSAR ADC achieves an ENOB of 9.57 bits. The BSP extracts the epileptic features from time-domain entropy and frequency spectrum for seizure detection. A constant 30-mu A stimulus current is delivered by closed-loop control. The acquired signals are transmitted with on-off keying (OOK) modulation at 4 Mbps over the MedRadio band for monitoring. A multi-LDO topology is adopted to mitigate the interferences across different power domains. The proposed SoC is fabricated in 0.18-mu m CMOS and occupies 13.47 mm(2). Verified on Long Evans rats, the proposed SoC dissipates 2.8 mW and achieves high detection accuracy 92% within 0.8 s.
A Fully Integrated 8-Channel Closed-Loop Neural-Prosthetic SoC for Real-Time Epileptic Seizure Control
(2013-01-01)
Chen, Wei-Ming
;
Chiueh, Herming
;
Chen, Tsan-Jieh
;
Ho, Chia-Lun
;
Jeng, Chi
A MedRadio-Band Low-Energy-Per-Bit 4-Mbps CMOS OOK Receiver for Implantable Medical Devices
(2013)
Chou, Chia-Wei
;
Liu, Li-Chen
;
Wu, Chung-Yu
;
交大名義發表
;
電子工程學系及電子研究所
A 4-Mbps 400-MHz On-Off Keying (OOK) receiver implemented in 0.18-um CMOS technology for implantable epilepsy sense-and-stimulation devices is presented. The proposed receiver is composed of a new current-mode full-wave envelope detector and differential cascaded gain amplifiers which is operated at MedRadio band. The fabricated receiver has power consumption of 0.27 mW and energy consumption of 0.07 nJ per bit at 4-Mbps. The sensitivity of receiver is -45.67 dBm.
Machine Learning Hyperparameter Fine Tuning Service on Dynamic Cloud Resource Allocation System - taking Heart Sounds as an Example
(2018-01-01)
Peng, Yu-Hsiang
;
Chuang, Chia-Chuan
;
Wu, Zhou-Jin
;
Chou, Chia-Wei
;
Chen, Hui-Shan
The hyperparameters tuning of machine learning has always been a difficult and time-consuming task in deep learning area. In many practical applications, the hyperparameter tuning directly affects the accuracy. Therefore, the tuning optimization of hyperparameters is an important topic. At present, hyperparameters can only be set manually based on experience, and use Violent Enumeration, Random Search or through Grid Search to try and error, lack of effective automatic search parameters. In this study, we proposed a machine learning hyperparameter fine tuning service on dynamic cloud resource allocation system, which leverages several mainstream hyperparameter tuning methods such as flyperopt and Optunity. In the meanwhile, various tuning methods are measured and compared by example application in this work. Finally, we dedicated actual case - Heart Sounds, and then tested it. In order to verify that the system service can not only automate the task of tuning, but also break through the limitation of the number of adjustable parameters. Furthermore the proposed hyperparameter fine tune system makes optimization process more efficient.