The total least squares (TLS) algorithm is a superior identification tool for low-order errors-in-variables (EIV) systems, where the estimate can be obtained by solving an eigenvector of the minimum eigenvalue of an a...
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The total least squares (TLS) algorithm is a superior identification tool for low-order errors-in-variables (EIV) systems, where the estimate can be obtained by solving an eigenvector of the minimum eigenvalue of an augmented matrix. However, the TLS algorithm demonstrates inefficiency when applied to high-order EIV systems. This study introduces two innovative TLS algorithms: an iterative TLS algorithm, offering superior performance for low-order EIV models, and a two-step TLS algorithm, designed to effectively handle high-order EIV models. In comparison to the conventional TLS algorithm, these proposed methodologies present noteworthy advantages, including: 1) reduced computational costs, 2) the utilization of an iterative technique to calculate the inverse, and 3) the diversification of EIV identification methods. Simulation bench test examples are selected to show the efficacy of the proposed algorithms and transparent procedure for applications.
Current guidelines recommend a two-step algorithm rather than relying solely on a single test for diagnosing Clostridioides difficile infection. This algorithm starts with enzyme immunoassay (EIA) for detecting glutam...
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Current guidelines recommend a two-step algorithm rather than relying solely on a single test for diagnosing Clostridioides difficile infection. This algorithm starts with enzyme immunoassay (EIA) for detecting glutamate dehydrogenase (GDH) and toxins A/B, followed by nucleic acid amplification test (NAAT) for GDH-positive but toxin-negative cases. This study compared the performance of three commercial NAATs: the STANDARD M10 C. difficile, Xpert C. difficile, and BD MAX Cdiff assays, utilized as confirmatory testing of the two-step algorithm. two hundred archived stool specimens, previously tested GDH-positive but toxin-negative by EIA, were analyzed in parallel with these NAATs and toxigenic culture, which served as the reference standard. Sensitivity, specificity, positive predictive value, and negative predictive value were 89.1%, 92.6%, 94.6%, and 85.2%, respectively, for the M10 assay;95.8%, 86.4%, 91.2%, and 93.3%, respectively, for the Xpert assay;and 89.8%, 91.4%, 93.8%, and 86.0%, respectively, for the BD MAX assay. The rates of invalid results were 1.0%, 0.5%, and 1.0% for the M10, Xpert, and BD MAX assays, respectively. In conclusion, the M10 assay is a reliable diagnostic tool, performing comparably to the Xpert and BD MAX assays when used as confirmatory testing in the two-step algorithm.
Measurement automation is essential in various biomedical and biotechnological applications become increasingly important with their intensification and wide utilization. Estimation of both pro-and eukaryotic cells su...
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ISBN:
(纸本)9781538607770
Measurement automation is essential in various biomedical and biotechnological applications become increasingly important with their intensification and wide utilization. Estimation of both pro-and eukaryotic cells sub-populations in different cultures, samples and tissues, including differentiation of live and dead bacterial cells, stem cells in eukaryotic cell culture and so on are essential in multiple biomedical and biotechnological applications. Fluorescent microscopy is a widely used methodology to obtain the above estimates. Wide utilization of biotechnologies increases the importance of automatic microscopic image processing tools design aiming at both qualitative and quantitative assessment of cells sub-populations. Existing methods are mostly based either on cell detection and counting or on the statistical analysis of image areas with similar staining. However, these methods exhibit known drawbacks including their inapplicability to the communities of cells adherent to each other and to external surfaces with biofilms being a prominent example. Another limitation of standard image processing tools in their high level of automation limiting the ability of the operator to adjust the algorithm parameters to particular microscopic imaging conditions as well as to specific features of the studied cells sub-populations. Here we present a two-step algorithm including preliminary adjustment of its parameters to the imaging conditions based on several representative images from the studied cohort in the first step and fully automated analysis of a large series of images with fixed algorithm parameters in the second step. Out results indicate that the suggested methodology is barely sensitive to the decision threshold value that allows to reduce the parameterization of the algorithm.
A new highly efficient two-step algorithm for the stationary incompressible magnetohydro-dynamic equations is studied in this paper. The algorithm uses a lower order finite element pair (i.e., P(1)b-P-1-P-1) to comput...
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A new highly efficient two-step algorithm for the stationary incompressible magnetohydro-dynamic equations is studied in this paper. The algorithm uses a lower order finite element pair (i.e., P(1)b-P-1-P-1) to compute an initial approximation, that is using the Mini-element (i.e., P(1)b-P-1) to approximate the velocity and pressure and P-1 element to approximate the magnetic field, then applies a higher order finite elethent pair (i.e., P-2-P-1-P-2) to solve a linear system on the same mesh. Furthermore, the convergence analyses of standard Galerkin finite element method and the two-step algorithm are addressed. Lastly, numerical experiments are presented to verify both the theory and the efficiency of the algorithm. (C) 2017 Elsevier Inc. All rights reserved.
In this paper, we propose a new highly efficient two-step algorithm based on local Gauss integration for the 2D steady-state natural convection problem. The basic idea of the algorithm is to compute an initial approxi...
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In this paper, we propose a new highly efficient two-step algorithm based on local Gauss integration for the 2D steady-state natural convection problem. The basic idea of the algorithm is to compute an initial approximation for the velocity, pressure and temperature based on a lowest equal-order finite element pair P-1-P-1-P-1, then to solve a linear system based on a quadratic equal-order finite element pair P-2-P-2-P-2 on the same mesh. Next, we give the corresponding stability and convergence of the algorithm, which show that the new two-step algorithm has the same order convergence rate as the quadratic equal-order stabilized finite element method. Finally, some numerical examples show that the new method is efficient, reliable, has good precision and can save a lot of computational time compared with the quadratic equal-order stabilized method. (C) 2016 Elsevier Ltd. All rights reserved.
Spaceborne squinted sliding spotlight synthetic aperture radar (SAR) can achieve high-resolution image with well flexibility. However, traditional imaging algorithm cannot focus its data, as the serious coupling betwe...
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ISBN:
(纸本)9781479979295
Spaceborne squinted sliding spotlight synthetic aperture radar (SAR) can achieve high-resolution image with well flexibility. However, traditional imaging algorithm cannot focus its data, as the serious coupling between the range and the azimuth and the azimuth spectral aliasing problem. To process the data, a refined two-step algorithm is proposed in this paper. Firstly, the linear range cell migration correction (LRCMC) and De-rotation are adopted to solve the azimuth spectral aliasing problem. Then the data is focused with a modified wavenumber domain algorithm (WDA). Also, the azimuth nonlinear chirp scaling (NCS) method is used to solve the limited depth-of-azimuth-focus (DOAF) problem. At last, the geometric correction is adopted to correct the geometrical distortion. The computer simulation results verify the validity of the proposed imaging algorithm.
This study considers an efficient two-step stabilized finite element algorithm for the simulation of Smagorinsky model, which involves solving a stabilized nonlinear Smagorinsky problem by the lowest equal-order P 1 -...
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This study considers an efficient two-step stabilized finite element algorithm for the simulation of Smagorinsky model, which involves solving a stabilized nonlinear Smagorinsky problem by the lowest equal-order P 1 - P 1 finite elements and solving a stabilized linear Smagorinsky problem by the quadratic equal-order P 2 - P 2 finite elements. We theoretically and numerically show that the present two-step algorithm can provide an approximate solution with basically the same accuracy as that of solving the stabilized P 2 - P 2 finite element method, and represent a reduction in CPU time.(c) 2022 Elsevier Inc. All rights reserved.
In this paper, we propose a novel dynamic unsourced random access (URA) framework for massive multiple-input multiple-output (MIMO) uplink access. Unlike the existing studies, where the quasi-static channel models and...
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In this paper, we propose a novel dynamic unsourced random access (URA) framework for massive multiple-input multiple-output (MIMO) uplink access. Unlike the existing studies, where the quasi-static channel models and the unchanged user states (active or idle) are assumed, we take the dynamics in both the channels and the states of user devices into consideration. Such a framework supports the high mobility of user devices, and facilitates their abrupt terminates and accesses during the whole transmission process. To model the dynamics, we adopt steady-state Gaussian Markov processes for all the channel coefficients of user devices, and introduce a series of latent variables to indicate the user states. We design a two-step algorithm, including the approximate message passing (AMP)-based inner decoding algorithm and the variational message passing (VMP)-based outer decoding algorithm, to decode the information sequences for all the user devices that have accessed the network. Simulation results show that our proposed method outperforms all the baselines when there are dynamics in the channels of user devices, and our proposed method has robustness to deal with the abrupt changes of user states by equipping the large number of antennas at the base station.
Purpose: Aiming to identify macrolide and beta-lactam resistance in clinical bacterial isolates rapidly and accurately, a two-step algorithm was developed based on detection of eight antibiotic resistance genes. Metho...
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Purpose: Aiming to identify macrolide and beta-lactam resistance in clinical bacterial isolates rapidly and accurately, a two-step algorithm was developed based on detection of eight antibiotic resistance genes. Methods: Targeting at genes linked to bacterial macrolide (msrA, ermA, ermB, and ermC) and beta-lactam (bla(TEM), bla(SHV), bla(CTX-N-1), bin(CTX-M-9)) antibiotic resistances, this method includes a multiplex real-time PCR, a melting temperature profile analysis as well as a liquid bead microarray assay. Liquid bead microarray assay is applied only when indistinguishable T-m profile is observed. Results: The clinical validity of this method was assessed on clinical bacterial isolates. Among the total 580 isolates that were determined by our diagnostic method, 75% of them were identified by the multiplex real-time PCR with melting temperature analysis alone, while the remaining 25% required both multiplex real-time PCR with melting temperature analysis and liquid bead microarray assay for identification. Compared with the traditional phenotypic antibiotic susceptibility test, an overall agreement of 81.2% (kappa = 0.614, 95% Cl = 0550-0.679) was observed, with a sensitivity and specificity of 87.7% and 73% respectively. Besides, the average test turnaround time is 3.9 h, which is much shorter in comparison with more than 24 h for the traditional phenotypic tests. Conclusions: Having the advantages of the shorter operating time and comparable high sensitivity and specificity with the traditional phenotypic test, our two-step algorithm provides an efficient tool for rapid determination of macrolide and beta-lactam antibiotic resistances in clinical bacterial isolates. (C) 2014 Elsevier B.V. All rights reserved.
Nowadays, adversarial examples of images which are generated by adding special perturbations on their hosts make great impact on many deep neural network (DNN)-based computer vision tasks and bring security risks. Wit...
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Nowadays, adversarial examples of images which are generated by adding special perturbations on their hosts make great impact on many deep neural network (DNN)-based computer vision tasks and bring security risks. With the deep insight into this field, researchers realize that many simple image processing methods can easily reduce the attack success rate. Therefore, studies on robust adversarial examples that are able to defend destruction become the emphasis. However, most existing methods focus more on the distortion in user transmitting procedures and physical deformation such as JPEG compression and brightness. Given that scale transformation is one of the commonest measures in data transformation and enhancement, a two-step algorithm is proposed to eliminate the effect caused by resizing during the model inferring process. The first step is to select pixels that can affect the classification result through a DNN. As a binary classification task, the network predicts whether the pixel deserves to be modified or not. The second one is to strategically compute the amplitude of noise and add it to the host image. Experiments verify that this method resists the scale transformation and guarantees the invisibility of humans as well.
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