It is difficult to reflect the properties of samples from the signal directly collected by the low field nuclear magnetic resonance (NMR) analyzer. People must obtain the relationship between the relaxation time and the original signal amplitude of every relaxation component by inversion algorithm. Consequently, the technology of T2 spectrum inversion is crucial to the application of NMR data. This study optimized the regularization factor selection method and presented the regularization algorithm for inversion of low field NMR relaxation distribution, which is based on the regularization theory of ill-posed inverse problem. The results of numerical simulation experiments by Matlab7.0 showed that this method could effectively analyze and process the NMR relaxation data.
The inverse problem of electrical impedance tomography (EIT) is seriously ill-posed, which restricts the clinical application of EIT. Regularization is an important numerical method to improve the stability of the EIT inverse problem as well as the resolution of the imaging. This paper proposes a self-diagnosis regularization method based on Tikhonov regularization and diagonal weight regularization method (DWRM). Firstly, the ill-posedness of the inverse problem is analyzed by sensitivity. Then, the performance of the self-diagnosis regularization is analyzed through the singular value theory. Finally, some simulated experiments including simulations and flume experiment are carried out and verify that the self-diagnosis regularization has better image quality and anti-noise ability than those of traditional regularization methods. The self-diagnosis regularization method weakens the ill-posedness of inverse problem of EIT and can prompt the practical application of EIT.
To assess the background field removal method usually used in quantitative susceptibility mapping (QSM), and to analyze the cause of serious artifacts generated in the truncated k-space division (TKD) method, this paper discusses a variety of background field removal methods and proposes an improved method to suppress the artifacts of susceptibility inversion. Firstly, we scanned phase images with the gradient echo sequence and then compared the quality and the speed of reconstructed images of sophisticated harmonic artifact reduction for phase data (SHARP), regularization enable of SHARP (RESHARP) and laplacian boundary value (LBV) methods. Secondly, we analyzed the reasons for reconstruction artifacts caused by the multiple truncations and discontinuity of the TKD method, and an improved TKD method was proposed by increasing threshold truncation range and improving data continuity. Finally, the result of susceptibility inversion from the improved and original TKD method was compared. The results show that the reconstruction of SHARP and RESHARP are very fast, but SHARP reconstruction artifacts are serious and the reconstruction precision is not high and implementation of RESHARP is complicated. The reconstruction speed of LBV method is slow, but the detail of the reconstructed image is prominent and the precision is high. In the QSM inversion methods, the reconstruction artifact of the original TKD method is serious, while the improved method obtains good artifact suppression image and good inversion result of artifact regions.
Speech feature learning is the core and key of speech recognition method for mental illness. Deep feature learning can automatically extract speech features, but it is limited by the problem of small samples. Traditional feature extraction (original features) can avoid the impact of small samples, but it relies heavily on experience and is poorly adaptive. To solve this problem, this paper proposes a deep embedded hybrid feature sparse stack autoencoder manifold ensemble algorithm. Firstly, based on the prior knowledge, the psychotic speech features are extracted, and the original features are constructed. Secondly, the original features are embedded in the sparse stack autoencoder (deep network), and the output of the hidden layer is filtered to enhance the complementarity between the deep features and the original features. Third, the L1 regularization feature selection mechanism is designed to compress the dimensions of the mixed feature set composed of deep features and original features. Finally, a weighted local preserving projection algorithm and an ensemble learning mechanism are designed, and a manifold projection classifier ensemble model is constructed, which further improves the classification stability of feature fusion under small samples. In addition, this paper designs a medium-to-large-scale psychotic speech collection program for the first time, collects and constructs a large-scale Chinese psychotic speech database for the verification of psychotic speech recognition algorithms. The experimental results show that the main innovation of the algorithm is effective, and the classification accuracy is better than other representative algorithms, and the maximum improvement is 3.3%. In conclusion, this paper proposes a new method of psychotic speech recognition based on embedded mixed sparse stack autoencoder and manifold ensemble, which effectively improves the recognition rate of psychotic speech.