Performance analysis of a parallel algorithm for restoring large-scale CT images

The performance of the underwater optical wireless communication (UOWC) system is highly affected by seawater´s inherent optical properties and the solar radiation from sunlight, especially for a shallow environment. The multipath effect and degradations in signal-to-noise ratio (SNR) due to absorption, scattering, and ambient noises can significantly limit the viable communication range, which poses key challenges to its large-scale commercial applications. To this end, this paper proposes a unified model for underwater channel characterization and system performance analysis in the presence of solar noises utilizing a photon tracing algorithm. Besides, we developed a generic simulation platform with configurable parameters and self-defined scenarios via MATLAB. Based on this platform, a comprehensive investigation of underwater channel impairments was conducted including temporal and spatial dispersion, illumination distribution pattern, and statistical attenuation with various oceanic types. The impact of ambient noise at different operation depths on the bit error rate (BER) performance of the shallow UOWC system was evaluated under typical specifications. Simulation results revealed that the multipath dispersion is tied closely to the multiple scattering phenomenon. The delay spread and ambient noise effect can be mitigated by considering a narrow field of view (FOV) and it also enables the system to exhibit optimal performance on combining with a wide aperture.

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A parallel algorithm for large-scale linear programs with a special structure

Proceedings of IEEE Scalable High Performance Computing Conference ◽

10.1109/shpcc.1994.296716 ◽

2002 ◽

Author(s):

Seyoung Oh ◽

S.Y. Shin

Keyword(s):

Parallel Algorithm ◽

Large Scale ◽

Special Structure ◽

Linear Programs

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Assisting scalable diagnosis automatically via CT images in the combat against COVID-19

Scientific Reports ◽

10.1038/s41598-021-83424-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bohan Liu ◽

Pan Liu ◽

Lutao Dai ◽

Yanlin Yang ◽

Peng Xie ◽

...

Keyword(s):

Large Scale ◽

Area Under The Curve ◽

Independent Set ◽

Reference Method ◽

Ct Images ◽

Loss Of Life ◽

Case Identification ◽

Reverse Transcription Pcr ◽

Using Data

AbstractThe pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

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