Browsing by Author "Wang, Qian"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Regularization Solver Guided FISTA for Electrical Impedance Tomography
    (MDPI, 2023) Wang, Qian; Chen, Xiaoyan; Wang, Di; Wang, Zichen; Zhang, Xinyu; Xie, Na; Liu, Lili; Tianjin University Science & Technology; University of Alabama Tuscaloosa
    Electrical impedance tomography (EIT) is non-destructive monitoring technology that can visualize the conductivity distribution in the observed area. The inverse problem for imaging is characterized by a serious nonlinear and ill-posed nature, which leads to the low spatial resolution of the reconstructions. The iterative algorithm is an effective method to deal with the imaging inverse problem. However, the existing iterative imaging methods have some drawbacks, such as random and subjective initial parameter setting, very time consuming in vast iterations and shape blurring with less high-order information, etc. To solve these problems, this paper proposes a novel fast convergent iteration method for solving the inverse problem and designs an initial guess method based on an adaptive regularization parameter adjustment. This method is named the Regularization Solver Guided Fast Iterative Shrinkage Threshold Algorithm (RS-FISTA). The iterative solution process under the L1-norm regular constraint is derived in the LASSO problem. Meanwhile, the Nesterov accelerator is introduced to accelerate the gradient optimization race in the ISTA method. In order to make the initial guess contain more prior information and be independent of subjective factors such as human experience, a new adaptive regularization weight coefficient selection method is introduced into the initial conjecture of the FISTA iteration as it contains more accurate prior information of the conductivity distribution. The RS-FISTA method is compared with the methods of Landweber, CG, NOSER, Newton-Raphson, ISTA and FISTA, six different distributions with their optimal parameters. The SSIM, RMSE and PSNR of RS-FISTA methods are 0.7253, 3.44 and 37.55, respectively. In the performance test of convergence, the evaluation metrics of this method are relatively stable at 30 iterations. This shows that the proposed method not only has better visualization, but also has fast convergence. It is verified that the RS-FISTA algorithm is the better algorithm for EIT reconstruction from both simulation and physical experiments.
  • Thumbnail Image
    Item
    Ultrasensitive Assay of Alkaline Phosphatase Based on the Fluorescent Response Difference of the Metal-Organic Framework Sensor
    (American Chemical Society, 2020) Yao, Xi-Yuan; Wang, Qian; Liu, Qiao; Pang, Meili; Du, Xiao-Meng; Zhao, Bo; Li, Yue; Ruan, Wen-Juan; Nankai University; University of Alabama Tuscaloosa
    The assay of alkaline phosphatase (ALP) is important in clinical diagnosis because the abnormal expression of this enzyme is related to many serious diseases. In this work, using a luminescent metal-organic framework (MOF) as the sensor, a fluorescent method was developed for the activity assay of ALP. With nanoscale particle size, the prepared MOF sensor exhibited good dispersability and stable photoluminescence in aqueous suspension. The emission of this MOF is inert to p-nitrophenylphosphate (NPP) but could be efficiently quenched by its dephosphorylated product, p-nitrophenol. Taking advantage of this feature, this MOF was added to the system of ALP-catalyzed NPP dephosphorylation to transduce the proceeding of the reaction real-timely to the fluorescent signal. The enzyme activity could be calculated based on the recorded kinetic trace. This method presented a low detection limit (2 x 10(-3) U L-1) and a wide quantification range (0.6-90 U L-1) in our experiments, showing its quantification capability challenges the best of current ALP analytical methods. As a practical application, our method was successfully applied to the ALP analysis in human serum samples.