In this paper, we improve traditional generative adversarial networks (GAN) with reference to residual networks and convolutional neural networks to facilitate the reconstruction of complex objects that cannot be reco...In this paper, we improve traditional generative adversarial networks (GAN) with reference to residual networks and convolutional neural networks to facilitate the reconstruction of complex objects that cannot be reconstructed by traditional associative imaging methods. Unlike traditional ghost imaging to reconstruct objects from bucket signals, our proposed method can use simple objects (such as EMNIST) as a training set for GAN, and then recognize objects (such as faces) of completely different complexity than the training set. We use traditional ghost imaging and neural network to reconstruct target objects respectively. According to the research results in this paper, the method based on neural network can reconstruct complex objects very well, but the method based on traditional ghost imaging cannot reconstruct complex objects. The research scheme in this paper is of great significance for the reconstruction of complex object-related imaging under low sampling conditions.展开更多
在无线通信环境中压制式复合干扰信号对通信系统的正常工作有着严重的影响,针对其特征提取和识别较为困难的问题,提出一种基于短时傅里叶变换(short time Fourier transform,STFT)和残差卷积网络的复合干扰识别算法。该算法将STFT得到...在无线通信环境中压制式复合干扰信号对通信系统的正常工作有着严重的影响,针对其特征提取和识别较为困难的问题,提出一种基于短时傅里叶变换(short time Fourier transform,STFT)和残差卷积网络的复合干扰识别算法。该算法将STFT得到的时频域信息作为输入,同时对复合干扰信号的种类和干噪比进行识别,为了使模型更加适合部署在移动端上,采用幻影卷积代替普通卷积。仿真结果表明,在干噪比为-15~10 dB的范围内,该算法在5种单一干扰及其复合而成的10种复合干扰信号种类识别任务上准确率可以达到99.97%,在干噪比识别任务上准确率可以达到99.04%。相比于残差卷积网络,该算法在几乎不降低准确率的前提下可以使模型参数量减小38.4%,计算复杂度降低46.6%,更加符合移动端的要求。展开更多
文摘In this paper, we improve traditional generative adversarial networks (GAN) with reference to residual networks and convolutional neural networks to facilitate the reconstruction of complex objects that cannot be reconstructed by traditional associative imaging methods. Unlike traditional ghost imaging to reconstruct objects from bucket signals, our proposed method can use simple objects (such as EMNIST) as a training set for GAN, and then recognize objects (such as faces) of completely different complexity than the training set. We use traditional ghost imaging and neural network to reconstruct target objects respectively. According to the research results in this paper, the method based on neural network can reconstruct complex objects very well, but the method based on traditional ghost imaging cannot reconstruct complex objects. The research scheme in this paper is of great significance for the reconstruction of complex object-related imaging under low sampling conditions.
文摘在无线通信环境中压制式复合干扰信号对通信系统的正常工作有着严重的影响,针对其特征提取和识别较为困难的问题,提出一种基于短时傅里叶变换(short time Fourier transform,STFT)和残差卷积网络的复合干扰识别算法。该算法将STFT得到的时频域信息作为输入,同时对复合干扰信号的种类和干噪比进行识别,为了使模型更加适合部署在移动端上,采用幻影卷积代替普通卷积。仿真结果表明,在干噪比为-15~10 dB的范围内,该算法在5种单一干扰及其复合而成的10种复合干扰信号种类识别任务上准确率可以达到99.97%,在干噪比识别任务上准确率可以达到99.04%。相比于残差卷积网络,该算法在几乎不降低准确率的前提下可以使模型参数量减小38.4%,计算复杂度降低46.6%,更加符合移动端的要求。
