Plasmonic Talbot imaging exhibits unique advantages over conventional Talbot imaging owing to the potential possibility of the miniaturization and integration of optoelectronic devices. However, the fixed structure parameters limit imaging manipulation, and the defects of masks also affect imaging integrity. Here, we propose a plasmonic Talbot imaging scheme based on the periodic metal nanopore arrays to achieve spatially tunable and self-healing performance evaluated by the FDTD method. The results reveal that the transverse field distribution at the Talbot planes can be offset in the 500 nm range with the incident light angle changed from 0°to 25°, exhibiting a tunability of the spatial position. Meanwhile, the randomly missing and misaligned defects of such periodic metal nanopore arrays can be effectively restored by the presence of low density or defects located at the center. The feasibility of spatial tunability and self-healing may provide an opportunity for optical imaging, nanolithography, and phase manipulation.
Automatic modulation recognition technology with deep learning has a broad prospective owing to big data and computing power. However, the accuracy of modulation recognition largely depends on the massive volume of data and the applicability of the model. Here, to eliminate the difficulties of manual feature extraction, a low accuracy, and a small sample dataset, we propose an effective recognition method that combines time series data augmentation with a spatiotemporal multi-channel learning framework. Compared with other advanced network models, the results showed that the method gave a positive index in the order of 93.5% for ten modulation signal types, which was increased by at least 15%. Especially for QAM16 and QAM64 signals, the average recognition accuracy was improved by nearly 50% at SNRs as low as −2 dB, showing a significant recognition performance. The proposed method provides an attractive method for signal modulation recognition in wireless or wired communication fields.
Automatic modulation recognition with deep learning has great prospective owing to computing power and big data. However, modulation recognition accuracy depends highly extent on massive volume of data and model applicability. Here, to overcome difficulties such as small sample dataset, manual extraction of features and low accuracy, we proposed an efficient recognition method that combined time-series data augmentation with spatiotemporal multi-channel learning framework. The results showed that the method provided positive indicators on the order of 93.5 percent for ten modulation signal types, which can be improved by at least 15 percent. Especially for QAM16 and QAM64 signals, the average recognition accuracy is increased by nearly 50 percent at SNRs as low as -2 dB, revealing remarkable recognition performance. Effectiveness of the proposed method provides an attractive approach for signal modulation recognition for wired or wireless communication fields.
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