Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full networ...Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.展开更多
The wireless full-duplex(FD) nodes can transmit and receive at the same time using the same frequency-band. Currently, the latest FD media access control(MAC) protocols mainly focus on how to convert the physical laye...The wireless full-duplex(FD) nodes can transmit and receive at the same time using the same frequency-band. Currently, the latest FD media access control(MAC) protocols mainly focus on how to convert the physical layer gains of FD nodes to the throughput gain of wireless FD networks, but pay little attention to the energy consumptions of FD nodes. In this paper, we propose an energy efficient FD MAC protocol. According to the values of self-interference cancellation coefficients corresponding to the nodes of each FD pair and the signal propagation attenuation, the proposed protocol can adaptively select the communication mode of the FD pair between the full-duplex and half-duplex. Also, the minimum transmit power for FD nodes can be obtained to achieve high energy efficiency. We develop an analytical model to characterize the performance of our protocol. The numerical results show that the proposed MAC protocol can optimize the system throughput and reduce the transmission energy consumptions of nodes simultaneously as compared with those of the existing works.展开更多
Relay in full-duplex(FD) mode can achieve higher spectrum efficiency than that in half-duplex mode,while it is crucial to suppress relay self-interference to ensure transmission quality which requires instantaneous ch...Relay in full-duplex(FD) mode can achieve higher spectrum efficiency than that in half-duplex mode,while it is crucial to suppress relay self-interference to ensure transmission quality which requires instantaneous channel state information(CSI). In this paper,the channel estimation issue in FD amplify-andforward relay networks is considered,where the training-based estimation technique is adopted. Firstly,the least square(LS) estimation is implemented to obtain composite channel coefficients of source-relay-destination(SRD) channel and relay loop-interference(LI) channel in order to assist destination in performing data detection. Secondly,both LS and maximum likelihood estimation methods are utilized to perform individual channel estimation aiming at supporting successive interference cancelation at destination. Finally,simulation results demonstrate the effectiveness of both composite and individual channel estimation,and the presented ML method can achieve lower MSEs than LS solution.展开更多
Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surf...Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surficial sediments（SSs）. Artificial neural network was used to build a model（the determination coefficient square r2 is 0.9977） to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance（ANOVA）, F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model（MRAM）. The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn（–1.993 μmol/mol）. Organic materials（OMs） and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components（Fe, Mn oxides and OMs） in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12271394 and 12071336)the Key Research and Development Program of Shanxi Province(Grant No.202102010101004)。
文摘Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.
基金supported by the National Natural Science Foundation of China (No. 61401330)Natural Science Foundation of Shaanxi Province of China (No. 2016JQ6027)
文摘The wireless full-duplex(FD) nodes can transmit and receive at the same time using the same frequency-band. Currently, the latest FD media access control(MAC) protocols mainly focus on how to convert the physical layer gains of FD nodes to the throughput gain of wireless FD networks, but pay little attention to the energy consumptions of FD nodes. In this paper, we propose an energy efficient FD MAC protocol. According to the values of self-interference cancellation coefficients corresponding to the nodes of each FD pair and the signal propagation attenuation, the proposed protocol can adaptively select the communication mode of the FD pair between the full-duplex and half-duplex. Also, the minimum transmit power for FD nodes can be obtained to achieve high energy efficiency. We develop an analytical model to characterize the performance of our protocol. The numerical results show that the proposed MAC protocol can optimize the system throughput and reduce the transmission energy consumptions of nodes simultaneously as compared with those of the existing works.
基金supported in part by the National High Technology Research and Development Program of China(Grant No.2014AA01A707)the Beijing Natural Science Foundation(Grant No.4131003)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP)(Grant No.20120005140002)the Key Program of Science and Technology Development Project of Beijing Municipal Education Commission of China (KZ201511232036)
文摘Relay in full-duplex(FD) mode can achieve higher spectrum efficiency than that in half-duplex mode,while it is crucial to suppress relay self-interference to ensure transmission quality which requires instantaneous channel state information(CSI). In this paper,the channel estimation issue in FD amplify-andforward relay networks is considered,where the training-based estimation technique is adopted. Firstly,the least square(LS) estimation is implemented to obtain composite channel coefficients of source-relay-destination(SRD) channel and relay loop-interference(LI) channel in order to assist destination in performing data detection. Secondly,both LS and maximum likelihood estimation methods are utilized to perform individual channel estimation aiming at supporting successive interference cancelation at destination. Finally,simulation results demonstrate the effectiveness of both composite and individual channel estimation,and the presented ML method can achieve lower MSEs than LS solution.
基金Supported by the National Natural Science Foundation of China(No.50879025)
文摘Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surficial sediments（SSs）. Artificial neural network was used to build a model（the determination coefficient square r2 is 0.9977） to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance（ANOVA）, F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model（MRAM）. The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn（–1.993 μmol/mol）. Organic materials（OMs） and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components（Fe, Mn oxides and OMs） in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.
文摘应用全光谱测量水体化学需氧量(chemical oxygen demand,COD)、硝酸盐氮(NO_(3)-N)浓度等水环境质量指标容易受水质环境影响,检测模型与特征波长一直是全光谱检测推广关注重点。该文提出一种基于遗传算法-径向基神经网络(genetic algorithm-radial basis function neural network,GA-RBFNN)全光谱水体COD与NO_(3)-N浓度检测方法,鉴于GA搜索能力强、随机性高的特点,对预处理后全光谱吸收数据应用GA进行特征波长选取,以RBFNN神经网络留K法训练过程中平均决定系数作为适应度函数,输出最优特征波长与RBFNN神经网络参数进行部署,从而实现水体COD、NO_(3)-N浓度准确测量。最后,开展GA-RBFNN、偏最小二乘(partial least squares,PLS)、GA-PLS、RBFNN四种模型对160组水样的COD、NO_(3)-N浓度检测实验,结果表明GA-RBFNN模型对COD、NO_(3)-N检测平均决定系数、最大误差分别为0.9964、0.9950和3.9%、4.9%,均优于其他模型,方法具有重要推广价值。
