As a two-way transmission channel, Universal Asynchronous Receiver/Transmitter (UART) not only greatly improves the efficiency of information transmission between computers and external devices, but also ensures the accuracy and consistency of information by eliminating metastable state, setting baud rate and other means. In this paper, on the basis of fully understanding the definition and function of UART, based on Verilog HDL language to build UART, and through Modelsim simulation, image and data. The experimental results show that the receiving and sending module of this module works well and meets the requirements of full-duplex serial communication equipment. There is no doubt that the design of this paper has made a more detailed explanation of the basic operating principle of UART, which will contribute to its further development.

Since computed tomography (CT) provides the most sensitive radiological technique for diagnosing COVID-19, CT has been used as an efficient and necessary aided diagnosis. However, the size and number of publicly available COVID-19 imaging datasets are limited and have problems such as low data volume, easy overfitting for training, and significant differences in the characteristics of lesions at different scales. Our work presents an image segmentation network, Pyramid-and-GAN-UNet (PGUNet), to support the segmentation of COVID-19 lesions by combining feature pyramid and generative adversarial network (GAN). Using GAN, the segmentation network can learn more abundant high-level features and increase the generalization ability. The module of the feature pyramid is used to solve the differences between image features at different levels. Compared with the current mainstream method, our experimental results show that the proposed network achieved more competitive performances on the CT slice datasets of the COVID-19 CT Segmentation dataset and CC-CCII dataset.

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