A signal denoising method using improved wavelet threshold function is presented for microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C⁴D) device. The evaluation results of denoising effect for the ME-C⁴D simulation signal show that using Daubechies 5 (db5) wavelet at a decomposition level 4 can produce the best performance. Furthermore, the denoising effect is compared with, as well as proved to be superior to, the existing techniques, such as Savitzky–Golay, Fast Fourier Transform, and soft threshold method. This method has been successfully applied to the self-developed ME-C⁴D equipment. After executing this method, the noise is cleanly removed, and the signal peak shape and peak area are well maintained.

When system parameters falling into a certain area, the Synchronous Reluctance Motor drives (SynRM) will experience chaotic behavior, which threatens the secure and stable operation of drive system. To control the undesirable chaos or limit cycles in SynRM, a novel controller is proposed based on tridiagonal structure matrix stability theory. The proposed controller have some advantages: it is simple and easy to be implemented; the control transition time is very short and can be adjusted by change control parameters. Computer simulation results show that the presented controller is very effective. Our research results may help to maintain the SynRM drive system's secure operation.

A fixed split in the second heart sound ( S ₂ ) indicates an aortic septal defect (ASD). This work aims at the shortcomings of using the time interval ( T A₂→P₂ ) between the sounds produced by the aortic valve closure ( A ₂ ), as well as the pulmonary valve closure ( P ₂ ) to evaluate the fixed split S ₂ , which is T A₂→P₂ easily influenced by the heartbeat and difficult to distinguish from other types of splits S ₂ without considering the entire respiratory phase and the third heart sound ( S ₃ ). Thus, this study proposes a novel methodology for detecting an ASD using estimated split coefficients of S ₂ ( S _2split ) combined with the number ( N S₂ ) of S ₂ covered by the moving window with 9 s. The important contributions are highlighted as follows: 1) A novel, simple and efficient methodology based on the Gaussianmixturemodel (GMM) is proposed for estimating the sounds of A ₂ and P ₂ . 2) An overlapping rate ( OLR )-based estimated S _2split is proposed to overcome the split degree influenced by the heartbeat when using the parameter T A₂→P₂ . 3) To achieve high precision of the wide splitting of ASDs, the statistics of N S₂ - S _2split and N S₂ - T A₂→P₂ are proposed for analysis. 4) To avoid drawing a unilateral conclusion, the correct ratio ( CR ) is proposed to predict an ASD. The criteria, derived based on the combination of μ NS₂,S_2split + σ NS₂,S_2split ⩾ 0.45, μ NS₂,S_2split ⩾ 0.6, σ NS₂,S_2split ⩽ 0.01, 0.05 ⩽ μ NS₂,TA₂→P₂ ⩽ 0.1, and CR ⩾ 0.9, are determined to detect ASD. The performance evaluation, as opposed to the state-of-the-art algorithms, demonstrates that the overall accuracy, F1 score and Cohens kappa coefficient achieved by the proposed algorithm all exceed up to 3% compared to the second-highest performance metrics.