Based on a Bell Laboratories layered space-time (BLAST) technique, a room configuration model of the indoor multiple-input multiple-output (MIMO) infrared diffuse channels is established, and all the surface elements in the room are assumed to act as ambient light noise sources. The performance of two transmitters and two receivers MIMO ) 2 2 ( × system is compared against single-input singleoutput (SISO) system with the same total transmitter power. Numerical results show that different position leads to different bit error rate (BER) due to the variety of received ambient optical power. We also show that the MIMO ) 2 2 ( × system can achieve better bit error rate (BER) performance than the SISO system in the proposed diffuse channels model.

Cross-age image generation technology is to generate cross-age face images on the basis of the original face image. The synthetic face image can show facial details such as skin, wrinkles and hair at a certain age. The technology can be widely used in film and television, animation, public safety and other fields. Cross-age face synthesis techniques can be divided into traditional cross-age face synthesis techniques and cross-age face synthesis techniques based on generative adversarial network models. With the continuous development of GAN, the technologies based on generative adversarial network models have made more progress and advantages in the field of face synthesis. The model in this paper, based on the generation of the adversarial network model, combines the advantages of the conditional autoencoder and the StyleGAN model, and innovates in the use of the feature contrasting device, which can generate HD face images consistent with the change logic across ages, and effectively avoid the emergence of problems such as organ deformation and identity inconsistency.

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Cardiovascular disease (CVD) is a common disease related to the heart and blood vessels. Due to the lack of research on the characteristics and effect of cardiovascular calcification on hemodialysis patients in China, it is almost impossible to accurately extract, segment, and measure the cardiovascular calcified regions from the cardiovascular calcification image. This article proposed an algorithm to extract and segment the calcified regions based on image characteristics. Firstly, the dome method was used to obtain the approximate region of calcification and the basic extraction and segmentation algorithm was used to preprocess the calcified region. Then, the region of calcification was enhanced using the image enhancement method before being further processed by the basic algorithm. After that, the preprocessed segmented image was compared back to the original image and only the initial grey value of the common area was kept in the original image. Finally, the basic segmentation algorithm was utilized again to process the original image before the threshold division and binarization being performed to obtain the final segmentation results. The results indicated that our method can segment the calcified region more accurately and thus more accurate distinguishment of the calcified regions from the non-calcified regions can be achieved.

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