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An ultra-wideband miniature antenna based on circular patch with circular slots has been presented for future generation mm-wave indoor wireless applications. The proposed miniature antenna is fed by probe feed and, the maximum realized gain and the total efficiency throughout the three bands are 7.7 dBi in an upper higher band and 97% in the lower band, respectively. This proposed antenna covers the seven bands (five bands are in the existing allocation to mobile, and the other two bands are to be considered for allocation) for 5G higher bands and lower band covers uplink Ku-band (14GHz -14.5GHz) for satellite communication. The proposed antenna in ultra-wideband has the impedance bandwidth of 31.8 GHz and the fractional bandwidth of 60.61%.

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Segmentation of the whole-cardiac CT image sequence is the key to computer-aided diagnosis and study of lesions in the heart. Due to the dilation, contraction and the flow of the blood, the cardiac CT images are prone to weak boundaries and artifacts. Traditional manual segmentation methods are time-consuming and labor-intensive to produce over-segmentation. Therefore, an automatic cardiac CT image sequence segmentation technique is proposed. This technique was employed using deep learning algorithm to understand the segmentation function from the ground truth data. Using the convolution neural network (CNN) on the central location of the heart, filtering ribs, muscles and other contrasting contrast are not an obvious part of the removal of the heart area. Staked denoising auto-encoders are used to automatically deduce the contours of the heart. Therefore, nine cardiac CT image sequence datasets are used to validate the method. The results showed that the algorithm proposed in this paper has best segmentation impact to such cardiac CT images which have a complex background, the distinctness between the background and the target area which is not obvious; and the internal structure diversification. It can filter out most of the non-heart tissue part, which is more conducive to the doctor observing patient's heart health.

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