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
Biases affect our judgments and decisions everywhere,because in our daily life,no matter where you are,what kind of occupation you are doing,every decision we make is more or less interfered by cognitive biases,which ...Biases affect our judgments and decisions everywhere,because in our daily life,no matter where you are,what kind of occupation you are doing,every decision we make is more or less interfered by cognitive biases,which even determines the outcome of things.In addition,with the development of the times,the progress of science and technology,and the change of social structure,we have experienced too many processes from rejection to acceptance,from stubbornness to change.However,it often takes time;especially in the commercial field,the timing when users accept products can better reflect this point.This article mainly aims at these phenomena,through the information,examples and data from the online sources,to explore how four kinds of cognitive biases:status quo bias,loss aversion bias,mere-exposure effect,and bounded-rationality that affect the smooth progress of innovation products in the fast consumer market,and how these biases can attack the confidence of merchants,so that the originally widely favored products will eventually end in failure.At the end of the article will also discuss the heuristics that can deal with the biases.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.
基金During this holiday,I participated in two subject project groups,and I would like to thank Dr.Kishore Sengupta for taking me to explore more in innovation fields.I would also like to thank Dr.E.Gallo for telling me a lot about the application of behavioral economics in business.I would also like to thank my supervisor Rick Boutcher,who helps me to do more critical thinking about innovation,and I also thank the teacher Yufan Huang for her guidance on the revision of my paper.Finally,I would like to thank all the publishers of the research materials quoted by me.It is absolutely impossible to have this article without you.
文摘Biases affect our judgments and decisions everywhere,because in our daily life,no matter where you are,what kind of occupation you are doing,every decision we make is more or less interfered by cognitive biases,which even determines the outcome of things.In addition,with the development of the times,the progress of science and technology,and the change of social structure,we have experienced too many processes from rejection to acceptance,from stubbornness to change.However,it often takes time;especially in the commercial field,the timing when users accept products can better reflect this point.This article mainly aims at these phenomena,through the information,examples and data from the online sources,to explore how four kinds of cognitive biases:status quo bias,loss aversion bias,mere-exposure effect,and bounded-rationality that affect the smooth progress of innovation products in the fast consumer market,and how these biases can attack the confidence of merchants,so that the originally widely favored products will eventually end in failure.At the end of the article will also discuss the heuristics that can deal with the biases.
