为了提高微波光子矢量网络分析仪(MPVNA)进行脉冲测量时的动态范围,提出了一种软件门控方案,该方案利用临时闲置接收通道接收与脉冲激励产生的控制信号同源的脉冲方波,以此生成门控信号。通过这个门控信号对采集到的待测器件脉冲响应进...为了提高微波光子矢量网络分析仪(MPVNA)进行脉冲测量时的动态范围,提出了一种软件门控方案,该方案利用临时闲置接收通道接收与脉冲激励产生的控制信号同源的脉冲方波,以此生成门控信号。通过这个门控信号对采集到的待测器件脉冲响应进行控制,有效提高了信噪比和测量的动态范围。对10 GHz带通滤波器的脉冲散射参数(S参数)进行了测量,实验结果表明:在占空比为1%的脉冲激励下,应用软件门控的测量动态范围相比未应用门控时的提高了约20 d B;门控处理后测得的S11和S22参数曲线也因为信噪比的提升更加光滑和稳定。展开更多
As the smart home is the end-point power consumer, it is the major part to be controlled in a smart micro grid. There are so many challenges for implementing a smart home system in which the most important ones are th...As the smart home is the end-point power consumer, it is the major part to be controlled in a smart micro grid. There are so many challenges for implementing a smart home system in which the most important ones are the cost and simplicity of the implementation method. It is clear that the major share of the total cost is referred to the internal controlling system network; although there are too many methods proposed but still there is not any satisfying method at the consumers＇ point of view. In this paper, a novel solution for this demand is proposed, which not only minimizes the implementation cost, but also provides a high level of reliability and simplicity of operation; feasibility, extendibility, and flexibility are other leading properties of the design.展开更多
To enhance the approximation and generalization ability of artificial neural network (ANN) by employing the principles of quantum rotation gate and controlled-not gate, a quantum-inspired neuron with sequence input is...To enhance the approximation and generalization ability of artificial neural network (ANN) by employing the principles of quantum rotation gate and controlled-not gate, a quantum-inspired neuron with sequence input is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-not gate after being rotated by the quantum rotation gates, control the target qubit for reverse. The model output is described by the probability amplitude of state in the target qubit. Then a quantum-inspired neural network with sequence input (QNNSI) is designed by employing the sequence input-based quantum-inspired neurons to the hidden layer and the classical neurons to the output layer, and a learning algorithm is derived by employing the Levenberg-Marquardt algorithm. Simulation results of benchmark problem show that, under a certain condition, the QNNSI is obviously superior to the ANN.展开更多
文摘为了提高微波光子矢量网络分析仪(MPVNA)进行脉冲测量时的动态范围,提出了一种软件门控方案,该方案利用临时闲置接收通道接收与脉冲激励产生的控制信号同源的脉冲方波,以此生成门控信号。通过这个门控信号对采集到的待测器件脉冲响应进行控制,有效提高了信噪比和测量的动态范围。对10 GHz带通滤波器的脉冲散射参数(S参数)进行了测量,实验结果表明:在占空比为1%的脉冲激励下,应用软件门控的测量动态范围相比未应用门控时的提高了约20 d B;门控处理后测得的S11和S22参数曲线也因为信噪比的提升更加光滑和稳定。
文摘As the smart home is the end-point power consumer, it is the major part to be controlled in a smart micro grid. There are so many challenges for implementing a smart home system in which the most important ones are the cost and simplicity of the implementation method. It is clear that the major share of the total cost is referred to the internal controlling system network; although there are too many methods proposed but still there is not any satisfying method at the consumers＇ point of view. In this paper, a novel solution for this demand is proposed, which not only minimizes the implementation cost, but also provides a high level of reliability and simplicity of operation; feasibility, extendibility, and flexibility are other leading properties of the design.
文摘To enhance the approximation and generalization ability of artificial neural network (ANN) by employing the principles of quantum rotation gate and controlled-not gate, a quantum-inspired neuron with sequence input is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-not gate after being rotated by the quantum rotation gates, control the target qubit for reverse. The model output is described by the probability amplitude of state in the target qubit. Then a quantum-inspired neural network with sequence input (QNNSI) is designed by employing the sequence input-based quantum-inspired neurons to the hidden layer and the classical neurons to the output layer, and a learning algorithm is derived by employing the Levenberg-Marquardt algorithm. Simulation results of benchmark problem show that, under a certain condition, the QNNSI is obviously superior to the ANN.
