Lin Zhijian Gao Xuewei Chen Xiaopei Zhu Zhipeng Du Xiaoyong Chen Pingping
The Journal of China Universities of Posts and Telecommunications, 2022, 29 (5). doi： 10.19682/j.cnki.1005-8885.2022.0026
Zhang Xin, Lou Haoran, Jiang Li, Xiao Qianhao, Cai Zhuwen
The Journal of China Universities of Posts and Telecommunications, 2022, 29 (5). doi： 10.19682/j.cnki.1005-8885.2022.0025
Li Na, Wu Yangyang, Liu Ying, Li Daxiang, Gao Jiale
The Journal of China Universities of Posts and Telecommunications, 2022, 29 (5). doi： 10.19682/j.cnki.1005-8885.2022.0007
Lin Zihan, Zheng Jiali, Xie Xiaode, Feng Minyu, He Siyi
The Journal of China Universities of Posts and Telecommunications, 2022, 29 (5). doi： 10.19682/j.cnki.1005-8885.2022.0008
Xie Xiaode Zheng Jiali Lin Zihan He Siyi Feng Minyu
The Journal of China Universities of Posts and Telecommunications, 2022, 29 (5). doi： 10.19682/j.cnki.1005-8885.2022.0010
In order to improve robustness and efficiency of the radio frequency identification (RFID) network, a random mating mayfly algorithm (RMMA) was proposed. Firstly, RMMA introduced the mechanism of random mating into the mayfly algorithm (MA), which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage, and find a better solution to the RFID nework planning (RNP) problem. Secondly, in RNP, tags are usually placed near the boundaries of the working space, so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction, as to enhance the ability of searching near the boundary. Lastly, in order to measure the performance of RMMA, the algorithm is then benchmarked on three well -known classic test functions, and the results are verified by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), and MA. The results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics, RMMA is also applied to solve RNP problems. The performance evaluation shows that RMMA achieves higher coverage than the other three algorithms. When the number of readers is the same, RMMA can obtain lower interference and get a better load balance in each instance compared with other algorithms. RMMA can also solve RNP problem stably and efficiently when the number and position of tags change over time.
Zhang Chengchang Xu Yu Yang Jianpeng Li Xiaomeng
The Journal of China Universities of Posts and Telecommunications, 2022, 29 (5). doi： 10.19682/j.cnki.1005-8885.2022.0009
Deep learning (DL) requires massive volume of data to train the network. Insufficient training data will cause serious overfitting problem and degrade the classification accuracy. In order to solve this problem, a method for automatic modulation classification ( AMC) using AlexNet with data augmentation was proposed. Three data augmentation methods is considered, i. e. , random erasing, CutMix, and rotation. Firstly, modulated signals are converted into constellation representations. And all constellation representations are divided into training dataset and test dataset. Then, training dataset are augmented by three methods. Secondly, the optimal value of execution probability for random erasing and CutMix are determined. Simulation results show that both of them perform optimally when execution probability is 0.5. Thirdly, the performance of three data augmentation methods are evaluated. Simulation results demonstrate that all augmentation methods can improve the classification accuracy. Rotation improves the classification accuracy by 13.04% when signal noise ratio (SNR) is 2 dB. Among three methods, rotation outperforms random erasing and CutMix when SNR is greater than - 6 dB. Finally, compared with other classification algorithms, random erasing, CutMix, and rotation used in this paper achieved the performance significantly improved. It is worth mentioning that the classification accuracy can reach 90.5% with SNR at 10 dB.
Chao Hao Lian Weifang Liu Yongli
The Journal of China Universities of Posts and Telecommunications, 2022, 29 (5). doi： 10.19682/j.cnki.1005-8885.2022.0017
Li Yujie Zhang Jingjing Jiang Wei Wang Chunxiao
The Journal of China Universities of Posts and Telecommunications, 2022, 29 (5). doi： 10.19682/j.cnki.1005-8885.2022.0006
Emotional space refers to a multi-dimensional emotional model that describes a group of subjective feelings or emotions. Since the existing discrete emotional space is mainly aimed at human’s primary emotions, it cannot describe the complex emotions evoked when watching movies. In order to solve this problem, an emotional fusion space for videos was constructed by selecting movies and TV dramas with rich emotional semantics as the research objects. Firstly, emotional words based on movie and TV drama videos are acquired and analyzed by using subjective evaluation and semantic analysis methods. Then, the emotional word vectors obtained from the above analysis are fused, reduced dimension by t-distributed stochastic neighbor embedding (t-SNE) algorithm, and clustered by bisecting K-means clustering algorithm to get a discrete emotional space for movie and TV drama videos. This emotional fusion space can obtain different categories by changing the value of the emotion classification number without re-labeling and calculation.
Li Fucheng Deng Junyong Zhu Yun Luo Jiaying Ren Han
The Journal of China Universities of Posts and Telecommunications, 2022, 29 (5). doi： 10.19682/j.cnki.1005-8885.2022.0005
In order to solve the hole-filling mismatch problem in virtual view synthesis, a three-step repairing (TSR) algorithm was proposed. Firstly, the image with marked holes is decomposed by the non-subsampled shear wave transform ( NSST), which will generate high-/ low-frequency sub-images with different resolutions. Then the improved Criminisi algorithm was used to repair the texture information in the high-frequency sub-images, while the improved curvature driven diffusion (CDD) algorithm was used to repair the low-frequency sub-images with the image structure information. Finally, the repaired parts of high-frequency and low-frequency sub-images are synthesized to obtain the final image through inverse NSST. Experiments show that the peak signal-to-noise ratio (PSNR) of the TSR algorithm is improved by an average of 2 - 3 dB and 1 - 2 dB compared with the Criminisi algorithm and the nearest neighbor interpolation (NNI) algorithm, respectively.
Zhang Han Jing Yinji Zhao Yongli
The Journal of China Universities of Posts and Telecommunications, 2022, 29 (5). doi： 10.19682/j.cnki.1005-8885.2022.0018
Han Yunan Liang Peiyun Lin Yang Cheng Chunyue Yao Yuchen Jin Ming Dai Lin
The Journal of China Universities of Posts and Telecommunications, 2022, 29 (5). doi： 10.19682/j.cnki.1005-8885.2022.0016
Compact dual-band bandpass filter (BPF) for the 5th generation mobile communication technology (5G) radio frequency (RF) front-end applications was presented based on multilayer stepped impedance resonators (SIRs). The multilayer dual-band SIR BPF can achieve high selectivity and four transmission zeros (TZs) near the passband edges by the quarter-wavelength tri-section SIRs. The multilayer dual-band SIR BPF is fabricated on a 3-layer FR-4 substrate with a compact dimension of 5.5 mm ×5.0 mm ×1.2 mm. The measured two passbands of the multilayer dual-band SIR BPF are 3.3 GHz -3.5 GHz and 4.8 GHz -5.0 GHz with insertion loss (IL) less than 2 dB respectively. Both measured and simulated results suggest that it is a possible candidate for the application of 5G RF front-end at sub-6 GHz frequency band.