Although both GPs and diabetic patients are aware of the need for regular fundal screening, just over half the patients had been screened. There exists a need for programmes in Myanmar to induce a behavioural change in diabetic patients with regards to screening examinations.
In fifth-generation wireless communications, data transmission is challenging due to the occurrence of burst errors and packet losses that are caused by multipath fading in multipath transmissions. To acquire more efficient and reliable data transmissions and to mitigate the transmission medium degradation in the 5G networks, it is important to study the error patterns or burst the error sequences that can provide insights into the behavior of 5G wireless data transmissions. In this paper, a two-state Markov-based 5G error model is investigated and developed to model the statistical characteristics of the underlying error process in the 5G network. The underlying 5G error process was obtained from our 5G wireless simulation, which was implemented based on three different kinds of modulation methods, including QPSK, 16QAM, and 64QAM, and was employed using the LDPC and TURBO coding methods. By comparing the burst or gap error statistics of the reference error sequences from the 5G wireless simulations and those of the generated error sequences from the two-state Markov error model, we show that the error behaviors of the coded OFDM 5G simulations can be adequately modeled by using the two-state Markov error model. Our proposed two-state Markov-based wireless error model can help to provide a more thorough understanding of the error process in 5G wireless communications and to evaluate the error control strategies with less computational complexity and shorter simulation times. INDEX TERMS 5G, burst error statistics, two-state Markov model, wireless error model.
The Internet of Health Things (IoHT) enables health devices to connect to the Internet and communicate with each other, which provides the high-accuracy and high-security diagnosis result in the medical area. As essential parts of the IoHT, computed tomography (CT) images help doctors diagnose disease. In the traditional disease diagnosing process, low-resolution medical CT images produce lowaccuracy diagnosis results for microlesions. Moreover, CT images can only provide 2D information about organs, and doctors should estimate the 3D shape of a lesion based on experience. To solve these problems, we propose a 3D reconstruction method for secure super-resolution computed tomography (SRCT) images in the IoHT using deep learning. First, we use deep learning to obtain secure SRCT images from lowresolution images in the IoHT. To this end, we adopt a conditional generative adversarial network (CGAN) based on the edge detection loss function (EDLF) in the deep learning process, namely EDLF-CGAN algorithm. In this algorithm, the CGAN is employed to generate SRCT images with luminance and contrast as the input auxiliary conditions, which can improve the accuracy of super-resolution (SR) images. An EDLF is proposed to consider the edge features in the generated SRCT images, which reduces the deformation of generated image. Second, we apply the secure SR images generated from the deep learning method to perform 3D reconstruction. An advanced ray casting 3D reconstruction algorithm that can reduce the number of rays by selecting the appropriate bounding box is proposed. Compared with the traditional algorithm, the proposed ray casting 3D reconstruction algorithm can reduce the time and memory cost. The experimental results show that our EDLF-CGAN has a better SR reconstruction effect than other algorithms via the indicators of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In addition, our advanced ray casting 3D reconstruction algorithm greatly improves the efficiency compared with the traditional ray casting algorithm.
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