Voice synthesizers still present several challenges in the speech of mathematical content, as spoken mathematics has quite peculiar rules. In the synthesized speech, pauses help blind and visually impaired students id...
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We present a new method that integrates subspace modeling and a pre-learned spatiotemporal denoiser for reconstruction from highly noisy magnetic resonance spectroscopic imaging (MRSI) data. The subspace model imposes...
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Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a patholog...
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A novel diagnostic system is introduced for assessing the required level of respiratory support for COVID-19 patients. It bases its assessments on the correlation between detected COVID-19 lesions and the respiratory ...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
A novel diagnostic system is introduced for assessing the required level of respiratory support for COVID-19 patients. It bases its assessments on the correlation between detected COVID-19 lesions and the respiratory support levels administered to the patients. The correlation with computed tomography (CT) scans will focus on three respiratory support levels, including minimal support (Level 0), non-invasive support (Level 1, such as soft oxygen), and invasive support (Level 3, e.g., mechanical ventilation). The system initially outlines the pulmonary regions from CT scans, then identifies COVID-19 lesions within these segmented lung regions. Subsequently, three second-order texture features are extracted from the identified COVID-19 lesion regions to detect differences and abnormalities across varying severity levels in COVID-19 cases. To obtain the suitable level of respiratory support for each patient, a fusion mechanism based on backpropagation neural network is utilized to integrate the diagnosis of these three features, individually generated by the support vector machine (SVM) classifier. The system’s performance is evaluated on 307 COVID-19 patients using a hold-out validation approach. This evaluation included various metrics, such as sensitivity, specificity, F1-score, Cohen’s kappa, and accuracy, demonstrating impressive results. Specifi-cally, it achieved a score of 97.25%, 98.56%, 97.26%, 95.79%, and 97.25%, respectively. The results demonstrate the effectiveness of the integrated system, which uses various second-order features, in predicting respiratory support needs for COVID-19 patients, outperforming both its individual components and other machine learning-based classification systems.
Objective: Recent advancements demonstrate the significant role of digital microfluidics in automating laboratory work with DNA and on-site viral testing. However, since commercially available instruments are limited ...
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The characteristic mode analysis (CMA) is formulated and implemented for the hydrodynamic volume integral equation (HDVIE) that is used to mathematically model electromagnetic field interactions and conduction current...
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ISBN:
(数字)9798350369908
ISBN:
(纸本)9798350369915
The characteristic mode analysis (CMA) is formulated and implemented for the hydrodynamic volume integral equation (HDVIE) that is used to mathematically model electromagnetic field interactions and conduction current dynamics on nanoantennas and nanoscatterers. The proposed method produces excitation-independent characteristic hydrodynamic currents and the corresponding modal significance curves, providing useful information that can be used to optimize the performance of a nanoantenna. Numerical results demonstrate the reliability and the applicability of the proposed approach.
Terahertz time-domain imaging was performed of stereotactic body radiotherapy-treated murine pancreatic ductal adenocarcinoma (PDAC) with a high spatial resolution. To generate 2D maps of the tissue samples, the refra...
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We present an uncertainty quantification method for neural networks solving inverse imaging problems. We leverage the physical forward model to establish forward-backward cycles to quantify inference uncertainty and d...
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The problem of finding the minimum amount of fanout needed to realize a switching function f is investigated. Fanout-free functions are defined, and necessary and sufficient conditions for a function to be fanout-free...
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