To improve the identification of cardiac regions in Electrical impedance tomography (eit) pulmonary perfusion images, a model of wavelet transform was developed. The main goal was to generate maps of the heart using E...
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To improve the identification of cardiac regions in Electrical impedance tomography (eit) pulmonary perfusion images, a model of wavelet transform was developed. The main goal was to generate maps of the heart using eit images in a controlled animal experiment using a healthy pig and in two human volunteers. The model was capable of identifying the heart regions, demonstrated robustness and generated satisfactory results. The pig images were compared to perfusion images obtained using injection of a hypertonic solution and achieved an average area of the ROC curve of 0.88. The human images were qualitatively compared with Computerized Tomography scan (CT-scan) images.
eit (electrical impedance tomography) problem should be represented by a group of partial differential equation, in numerical calculation: the nonlinear problem should be linearization approximately, and then linear e...
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eit (electrical impedance tomography) problem should be represented by a group of partial differential equation, in numerical calculation: the nonlinear problem should be linearization approximately, and then linear equations set is obtained, so eit image reconstruct problem should be considered as a classical ill-posed, ill-conditioned, linear inverse problem. Its biggest problem is the number of unknown is much more than the number of the equations, this result in the low imaging quality. Especially, it can not imaging in center area. For this problem, we induce the CS technique into eit image reconstruction algorithm. The main contributions in this paper are: firstly, built up the relationship between CS and eit definitely; secondly, sparse reconstruction is a critical step in CS, built up a general sparse regularization model based on eit; finally, gives out some eit imaging models based on sparse regularization method. For different scenarios, compared with traditional Tikhonov regularization (smooth regularization) method, sparse reconstruction method is not only better at anti-noise, and imaging in center area, but also faster and better resolution.
We develop a hybrid scheme of cross phase modulation based on electromagnetically induced transparency(eit)and active Raman gain(ARG)in a multi-level atomic *** cross phase modulation,with low loss and without noise,i...
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We develop a hybrid scheme of cross phase modulation based on electromagnetically induced transparency(eit)and active Raman gain(ARG)in a multi-level atomic *** cross phase modulation,with low loss and without noise,is demonstrated in a room-temperature ^(85)Rb *** show that a p radian nonlinear Kerr phase shift of the signal light relative to a reference light is observed when the signal light is modulated by the phase control field with the low light *** also show that the linear and the third-order absorption can be eliminated via the Raman gain,and the phase noise of the signal light can be ignored when the phase control light is applied in this hybrid scheme.
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