Mingquan Wang

Electrical impedance tomography based on filter back projection improved by means method

Electrical impedance tomography (EIT) is a new kind of noninvasive computer imaging technique. The imaging process of EIT is known as inverse problem, and the back projection algorithm is widely used to solve this problem in practice. However, the image can only be reconstructed with low resolution and interfered by artifacts using this algorithm. This paper proposes a new filter back projection algorithm for improving the quality of the image. Being different with other existing algorithms, this algorithm uses RL (Ramachandran and Lakshminarayanan) filter function to get ride of image artifacts and interference. Because the RL filtering function will take obvious oscillations while image reconstructing, the means method is utilized to modify the filter function in this paper. The results of numerous simulation experiments show the new algorithm can obtain much better resolution and less artifacts compared with existing back projection algorithms and filter algorithms using RL filter function.

Electrical impedance tomography based on Tikhonov regularization method improved by level set method

Electrical impedance tomography (EIT) is a nonlinear and highly ill-posed inverse problem that requires using regularization technology to get solution. The generalized Tikhonov regularization method (TRM) is popular in solving the inverse problem, but its stability is sensitive to the choice of regularization parameter. It usually needs to get a balance between the accuracy and stability of the solution, so additional regularization scheme must be taken for obtaining a stable solution. In this paper, we propose a novel algorithm based on TRM which is improved by level set method (LSM). Level set here is used to represent interfaces between regions with different conductivities, and the regularization parameter can be changed adaptively during the iteration process of region boundary searching. Numerical simulation experiments have shown that the proposed algorithm obtains perfect stability and higher accuracy in reconstruction process compared with the algorithm which is based on TRM only.

A robust T-wave alternans detection algorithm based on the wavelet transform and Bootstrap

In this article, continuous wavelet and Lipschitz indices are used to divide non-stationary T-wave alternans (TWA) in ECG signal into segments. Then Bootstrap method is applied to test the TWA magnitude. Experiments have extracted ECG signal from the database, and form T-wave alternans signal, Gaussian noise, baseline drift; and took a test to MITs TWADB (10 groups) data; and in the last part, we have taken the real ECG to test. The achieved results are satisfactory. It has not only effectively reduced effects on the non-stability of TWA, but also solved the problem that the short-term TWA are difficult to test. The correlation coefficient between measurement and simulation of the magnitude of the true value reached to 0.96.

Extracting of the ultrasound fetal heart rate signal based on wavelet packet mix node threshold

Clinically, fetal heart rate, to a certain extent, reflects the health of the fetus. Therefore, how to extract the fetal heart rate signals in ultrasound fetal heart rate signal processing has been a key issue. In this paper, a new wavelet packet mix node threshold (WPMNT) denoising algorithm based on the maximum wavelet packet energy entropy is proposed. Moreover, based on the threshold given by the new algorithm, a new wavelet packet threshold function is constructed for ultrasound fetal heart rate signal denoising. Experimental results show that this algorithm gives better signal noise ratio (SNR) gains and mean square error (MSE) performance than traditional algorithms.