Automatic modulation classification (AMC) algorithms based on deep learning (DL) have been widely studied in the past decade, showing significant performance advantage compared to traditional ones. However, the existing DL methods generally behave worse in computational complexity. For this, this paper proposes a lightweight convolutional neural network (CNN) for AMC task, where we design a depthwise separable convolution (DSC) residual architecture for feature extraction to prevent the vanishing gradient problem and lighten the computational burden. Besides that, in order to further reduce model complexity, global depthwise convolution (GDWConv) is adopted for feature reconstruction after the last (non-global) convolutional layer. Compared to recent works, the experimental results show that the proposed network can save approximately 70~98% model parameters and 30~99% inference time on two well-known benchmarks.
Infrared observation is an all-weather, real-time, large-scale precipitation observation method with high spatio-temporal resolution. A high-precision deep learning algorithm of infrared precipitation estimation can provide powerful data support for precipitation nowcasting and other hydrological studies with high timeliness requirements. The “classification-estimation” two-stage framework is widely used for balancing the data distribution in precipitation estimation algorithms, but still has the error accumulation issue due to its simple series-wound combination mode. In this paper, we propose a multi-task collaboration framework (MTCF), i.e., a novel combination mode of the classification and estimation model, which alleviates the error accumulation and retains the ability to improve the data balance. Specifically, we design a novel positive information feedback loop composed of a consistency constraint mechanism, which largely improves the information abundance and the prediction accuracy of the classification branch, and a cross-branch interaction module (CBIM), which realizes the soft feature transformation between branches via the soft spatial attention mechanism. In addition, we also model and analyze the importance of the input infrared bands, which lay a foundation for further optimizing the input and improving the generalization of the model on other infrared data. Extensive experiments based on Himawari-8 demonstrate that compared with the baseline model, our MTCF obtains a significant improvement by 3.2%, 3.71%, 5.13%, 4.04% in F1-score when the precipitation intensity is 0.5, 2, 5, 10 mm/h, respectively. Moreover, it also has a satisfactory performance in identifying precipitation spatial distribution details and small-scale precipitation, and strong stability to the extreme-precipitation of typhoons.
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