Four‐hundred gigahertz broadband multi‐branch waveguide coupler

Abstract

Terahertz technology is a hotspot in the current academic research. In this study, a 400 GHz broadband multi‐branch waveguide hybrid coupler is designed, but it is very difficult to fabricate. In order to release the processing difficulty, a modified five‐branch hybrid coupler has also been designed, fabricated and measured. The hybrid coupler consists of five modified branches and has been optimised to a great performance, which increases the operation bandwidth. Compared to the traditional five‐branch hybrid coupler design, this structure has a wider operation bandwidth and the bandwidth is almost the same as traditional seven‐branch hybrid coupler. Based on this model, the performance of the coupler is optimised by HFSS software. The measurement results show good performance that >18 dB return loss (S11) and isolation (S23), 90° ± 2° phase difference and 0.3 dB amplitude imbalance are obtained in the frequency range of 380–460 GHz, agreeing well with the simulation results.

[Retracted] Real‐Time Analysis of Basketball Sports Data Based on Deep Learning

“…e video interception is performed according to the method described before, the position of the tracking frame is obtained, and the center point is selected as the position of the target, which is sent to the pose estimation network for estimation, and the nodal point data is output. For the later graph convolution action recognition, it is known from the 4 Complexity experiments in Chapter 4 that the network needs consecutive frames as input, and 190 frames are the better input frame length, so let the pose estimation network run 190 frames first, and feed the nodal results into the loop array (the loop array length is the space required for 190 frames) [20][21][22][23]. When the loop array is detected to be full of 190 frames, the system takes out 190 frames of data from the latest video frames of the loop array and puts the video frames into the input cache of the network in reverse order, the graph convolutional network is started, and the action category is finally inferred by the graph convolutional neural network [24][25][26].…”

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

“…It is proven that the ELM’s origin is based on Random Vector Functional Link (RVLF) (Pao et al 1994 ; Wang et al 2021 ), leading to the ultra-fast learning and outstanding generalization capability (Zhang 2020 ; Niu 2020 ). Literature survey shows that ELM has been broadly utilized in many engineering applications (Li et al 2019b ; Liu 2020 ; Yang et al 2020b ).…”

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

“…Based on the observation and analysis of the optimal segmentation thresholds and the segmentation results, it is further demonstrated that CLACO-MIS can obtain better segmentation results and verified that CLACO-MIS has good adaptability to different threshold levels. As exploration and exploration of the method exposes, the experts can also utilize the proposed ACO-based optimizer to tackle multi-faced feature spaces in other families of problems such as works in [ [107] , [108] , [109] ]. For future work, since CLACO is a superior swarm intelligence optimization algorithm, we will consider applying it to more fields, such as face recognition and micro-expression recognition [ 110 , 111 ], 3D deformable shape analysis [ 112 , 113 ], micro-expression spotting [ 111 , 114 ], service ecosystem [ 115 , 116 ], Lunar impact crater identification and age estimation [ 117 ], large scale network analysis [ 118 ], energy storage planning and scheduling [ 119 ], medical diagnosis [ [120] , [121] , [122] ], and prediction of brain-behavior [ 123 , 124 ].…”