基于手掌-手背-静脉系统在基于云物联网的移动健康环境中的结果与讨论

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引用次数: 0

摘要

在云计算环境中基于手掌背静脉的移动医疗系统的结果在本书的这一章中进行了详细的讨论和分析。样本图像S1、S2、S3和S4用于重采样、分割、中值滤波、细化和顶部静脉的硬件设计和性能评估,将用于基于云物联网的移动健康环境中危重患者和普通患者的身份验证。使用ModelSim-Altera硬件设计语言作为仿真工具,用采样静脉图像模拟硬件设计。此外,将ModelSim-Altera仿真结果与MATLAB实现进行比较,以评估所述算法的硬件设计在基于云物联网的移动健康环境中的性能。对结果进行了分析,并在本章中详细讨论了这些结果。
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Results and Discussions of Palm-Dorsa-Veins-Based Systems in the Cloud IoT-Based M-Health Environment
The results of palm-dorsa-veins-based m-health systems in a cloud-computing environment are discussed and analyzed in a detailed way in this chapter of the book. The sample images S1, S2, S3, and S4 are being used for hardware designs and performance evaluation in the cases of re-sampling, segmentation, median filters, thinning and Top veins, which will be used for critically ill and general patients' identity verification in the cloud IoT-based m-health environments. The ModelSim-Altera hardware design language is used as a simulator tool to simulate the hardware design with sample veins images. Further, the ModelSim-Altera simulation outcomes are compared with MATLAB implementations for evaluating the performances of hardware designs of the described algorithms in the cloud IoT-based m-health environment. The outcomes are analyzed, and the details of these outcomes are discussed in this chapter.
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Approaches for M-Health Environment Results and Discussions of Palm-Dorsa-Veins-Based Systems in the Cloud IoT-Based M-Health Environment The Panoramic Views of Cloud IoT-Based M-Health Biometrics A Glimpse of Hardware Design Approaches Future Generation Computing in M-Health
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