Browsing by Author "Zhang, Xinyu"

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    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.
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    Ultrasensitive electrochemical biosensors based on zinc sulfide/graphene hybrid for rapid detection of SARS-CoV-2
    (Springer Nature, 2023) Sarwar, Shatila; Lin, Mao-Chia; Amezaga, Carolina; Wei, Zhen; Iyayi, Etinosa; Polk, Haseena; Wang, Ruigang; Wang, Honghe; Zhang, Xinyu; Auburn University; University of Alabama Tuscaloosa; Tuskegee University
    The coronavirus disease 2019 (COVID-19) is a highly contagious and fatal disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In general, the diagnostic tests for COVID-19 are based on the detection of nucleic acid, antibodies, and protein. Among different analytes, the gold standard of the COVID-19 test is the viral nucleic acid detection performed by the quantitative reverse transcription polymerase chain reaction (qRT-PCR) method. However, the gold standard test is time-consuming and requires expensive instrumentation, as well as trained personnel. Herein, we report an ultrasensitive electrochemical biosensor based on zinc sulfide/graphene (ZnS/graphene) nanocomposite for rapid and direct nucleic acid detection of SARS-CoV-2. We demonstrated a simple one-step route for manufacturing ZnS/graphene by employing an ultrafast (90 s) microwave-based non-equilibrium heating approach. The biosensor assay involves the hybridization of target DNA or RNA samples with probes that are immersed into a redox active electrolyte, which are detectable by electrochemical measurements. In this study, we have performed the tests for synthetic DNA samples and, SARS-CoV-2 standard samples. Experimental results revealed that the proposed biosensor could detect low concentrations of all different SARS-CoV-2 samples, using such as S, ORF 1a, and ORF 1b gene sequences as targets. This microwave-synthesized ZnS/graphene-based biosensor could be reliably used as an on-site, real-time, and rapid diagnostic test for COVID-19.