This paper presents a novel denoising algorithm with the untrained deep neural network (DNN) for Radio Frequency (RF) signal modulation recognition considering the mobile and Internet of Things (IoT) applications. In ...
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ISBN:
(纸本)9798350340457
This paper presents a novel denoising algorithm with the untrained deep neural network (DNN) for Radio Frequency (RF) signal modulation recognition considering the mobile and Internet of Things (IoT) applications. In denoising with the untrained DNN, which is called deep image prior (DIP), a DNN is used as a deep generative network to generate the less noisy signal from the pure noise. Considering denoiser for RF signal, which can't apply the denoising algorithm selectively, we propose a new DIP algorithm called hybrid DIP to improve the data reproducing capability when the noise level is low. The experimental results show that the proposed method can increase the classification accuracy of the received RF signals, especially with low signal-to-noise ratio (SNR), with minimal impact on the signal with high SNR.
The road extraction from high resolution remote sensing image is of great importance in a variety of applications. Recently, the abundant deep convolutional neural networks are proposed for road extraction task. Howev...
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The road extraction from high resolution remote sensing image is of great importance in a variety of applications. Recently, the abundant deep convolutional neural networks are proposed for road extraction task. However, the existing approaches lack suitable strategy to utilize multiple views road features for road extraction, which fails to extract road with smooth appearance and accurate boundary under complex scenes. To address this problem, the authors propose a novel deep residual and pyramid pooling network (DRPPNet) for extracting road regions from high resolution remote sensing image. The DRPPNet consists of three parts: deep residual network (DResNet), pyramid pooling module (PPM) and deep decoder (DD). Specially, the DResNet uses several residual blocks to extract deep road features from input images, which can enhance learning ability of DRPPNet and avoid gradient vanish. Then, PPM is proposed to fuse road features from multiple views and it aims to address disadvantage of single view feature. Finally, the DD is used to recover size of feature maps to input size. Extensive experiments on two challenging road datasets demonstrate that proposed method outperforms the state-of-the-art methods greatly on performance of road extraction task.
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