In recent years,deep learning has been gradually used in communication physical layer receivers and has achieved excellent performance.In this paper,we employ deep learning to establish covert communication systems,en...In recent years,deep learning has been gradually used in communication physical layer receivers and has achieved excellent performance.In this paper,we employ deep learning to establish covert communication systems,enabling the transmission of signals through high-power signals present in the prevailing environment while maintaining covertness,and propose a convolutional neural network(CNN)based model for covert communication receivers,namely Deep CCR.This model leverages CNN to execute the signal separation and recovery tasks commonly performed by traditional receivers.It enables the direct recovery of covert information from the received signal.The simulation results show that the proposed Deep CCR exhibits significant advantages in bit error rate(BER)compared to traditional receivers in the face of noise and multipath fading.We verify the covert performance of the covert method proposed in this paper using the maximum-minimum eigenvalue ratio-based method and the frequency domain entropy-based method.The results indicate that this method has excellent covert performance.We also evaluate the mutual influence between covert signals and opportunity signals,indicating that using opportunity signals as cover can cause certain performance losses to covert signals.When the interference-tosignal power ratio(ISR)is large,the impact of covert signals on opportunity signals is minimal.展开更多
钢渣由于早期活性低,易磨性差,安定性不良,制约了其在水泥混凝土中的大规模利用。本文通过对钢渣进行高温重构,研究了钢渣在不同重构温度下的矿物相转变及易磨性、安定性、活性指数的变化。结果表明:高温可以优化钢渣的矿物相组成,促进...钢渣由于早期活性低,易磨性差,安定性不良,制约了其在水泥混凝土中的大规模利用。本文通过对钢渣进行高温重构,研究了钢渣在不同重构温度下的矿物相转变及易磨性、安定性、活性指数的变化。结果表明:高温可以优化钢渣的矿物相组成,促进难磨相浮氏体(Fe x O)、RO相的转化,促进钢渣中硅酸二钙(C_(2)S)向硅酸三钙(C_(3)S)转变,促进镁铁尖晶石(MgFe_(2)O_(4))的生成;矿物及液相分布均匀,矿物组成良好、边界更清晰的重构钢渣往往表现出更高的强度,试验所用两种钢渣经过1400℃的高温重构,其28 d活性指数可分别达99.03%和96.52%;钢渣中f-CaO含量随重构温度的升高而显著降低;易磨性则随着重构温度的升高呈先升高后降低的趋势。展开更多
基金supported in part by the National Natural Science Foundation of China under Grants U19B2016,62271447 and 61871348。
文摘In recent years,deep learning has been gradually used in communication physical layer receivers and has achieved excellent performance.In this paper,we employ deep learning to establish covert communication systems,enabling the transmission of signals through high-power signals present in the prevailing environment while maintaining covertness,and propose a convolutional neural network(CNN)based model for covert communication receivers,namely Deep CCR.This model leverages CNN to execute the signal separation and recovery tasks commonly performed by traditional receivers.It enables the direct recovery of covert information from the received signal.The simulation results show that the proposed Deep CCR exhibits significant advantages in bit error rate(BER)compared to traditional receivers in the face of noise and multipath fading.We verify the covert performance of the covert method proposed in this paper using the maximum-minimum eigenvalue ratio-based method and the frequency domain entropy-based method.The results indicate that this method has excellent covert performance.We also evaluate the mutual influence between covert signals and opportunity signals,indicating that using opportunity signals as cover can cause certain performance losses to covert signals.When the interference-tosignal power ratio(ISR)is large,the impact of covert signals on opportunity signals is minimal.
文摘钢渣由于早期活性低,易磨性差,安定性不良,制约了其在水泥混凝土中的大规模利用。本文通过对钢渣进行高温重构,研究了钢渣在不同重构温度下的矿物相转变及易磨性、安定性、活性指数的变化。结果表明:高温可以优化钢渣的矿物相组成,促进难磨相浮氏体(Fe x O)、RO相的转化,促进钢渣中硅酸二钙(C_(2)S)向硅酸三钙(C_(3)S)转变,促进镁铁尖晶石(MgFe_(2)O_(4))的生成;矿物及液相分布均匀,矿物组成良好、边界更清晰的重构钢渣往往表现出更高的强度,试验所用两种钢渣经过1400℃的高温重构,其28 d活性指数可分别达99.03%和96.52%;钢渣中f-CaO含量随重构温度的升高而显著降低;易磨性则随着重构温度的升高呈先升高后降低的趋势。
