Browsing by Author "FANG Wai-Chi"

Now showing 1 - 2 of 2

DEVICE AND METHOD FOR DIFFUSION OPTICAL TOMOGRAPHY
(2013-06-20)
FANG Wai-Chi
;
CHEN Tien-Ho
;
KANG Shih
;
WU Shih-Yang
;
CHENG Ching-Ju
A device and method for diffusion optical tomography are disclosed. The device includes a sensing circuit with a plurality of light sources and sensors and an optical tomography element having a control unit, a computation unit and an image reconstruction unit. First, the computation unit constructs an image model of an object using optical parameters of the object, and performs decomposition on the image model. Then, the control unit instructs the light sources to emit light to the object, and receives a plurality of optical signals generated by the object in response to the light. Finally, the image reconstruction unit combines the optical signals and the decomposed image model and reconstructs an image of the object based on the combination of the optical signals and the decomposed image model.
REAL-TIME MULTI-CHANNEL EEG SIGNAL PROCESSOR BASED ON ON-LINE RECURSIVE INDEPENDENT COMPONENT ANALYSIS
(2014-11-27)
FANG Wai-Chi
;
SHIH Wei-Yeh
;
LIAO Jui-Chieh
;
HUANG Kuan-Ju
;
CHEN Chiu-Kuo
A real-time multi-channel EEG signal processor based on an on-line recursive independent component analysis is provided. A whitening unit generates covariance matrix by computing covariance according to a received sampling signal. A covariance matrix generates a whitening matrix by a computation of an inverse square root matrix calculation unit. An ORICA calculation unit computes the sampling signal and the whitening matrix to obtain a post-whitening sampling signal. The post-whitening sampling signal and an unmixing matrix implement an independent component analysis computation to obtain an independent component data. An ORICA training unit implements training of the unmixing matrix according to the independent component data to generate a new unmixing matrix. The ORICA calculation unit may use the new unmixing matrix to implement an independent component analysis computation. Hardware complexity and power consumption can be reduced by sharing registers and arithmetic calculation units.