现有数字信号自动调制识别方法大多只适用于无记忆信号，如PSK、ASK、FSK信号等。将有记忆 信号(MSK信号)和无记忆信号一起考虑，提出了一种改进的数字信号自动识别方法。该方法采用信号的瞬时统 计量作为特征参数，采用多层神经网络作为分类器。计算机仿真表明：当噪声采用高斯白噪声，并且信噪比大于 l5 dB时，识别率高于96％ ；当信噪比不低于l0 dB时，识别率不低于90％。,Existing digital signal automatic modulation recognition

The decoding of Low Density Parity Check (LDPC) codes through the iterative process of belief propagation presents practical challenges for designers looking for real time performance in communication systems. Due to the geometric and finite pattern

图像融合论文，基于小波变换和多通道脉冲耦合神经网络的高光谱图像融合-Image fusion papers, coupled wavelet transform and neural network-based multi-channel pulse hyperspectral image fusion