Based on the Gummel-Poon model of BJT, the change of the DC bias as a function of the AC input signal in RF linear power amplifiers is theoretically derived, so that the linearity of different DC bias circuits can be ...Based on the Gummel-Poon model of BJT, the change of the DC bias as a function of the AC input signal in RF linear power amplifiers is theoretically derived, so that the linearity of different DC bias circuits can be interpreted and compared. According to the analysis results, a quantitative adaptive DC bias circuit is proposed, which can improve the linearity and efficiency. From the simulation and test results, we draw conclusions on how to improve the design of linear power amplifier.展开更多
A novel structure based on channel-wise attention mechanism is presented in this paper.With the proposed structure embedded,an efficient classification model that accepts multi-lead electrocardiogram(ECG)as input is c...A novel structure based on channel-wise attention mechanism is presented in this paper.With the proposed structure embedded,an efficient classification model that accepts multi-lead electrocardiogram(ECG)as input is constructed.One-dimensional convolutional neural networks(CNNs)have proven to be effective in pervasive classification tasks,enabling the automatic extraction of features while classifying targets.We implement the residual connection and design a structure which can learn the weights from the information contained in different channels in the input feature map during the training process.An indicator named mean square deviation is introduced to monitor the performance of a particular model segment in the classification task on the two out of five ECG classes.The data in the MIT-BIH arrhythmia database is used and a series of control experiments is conducted.Utilizing both leads of the ECG signals as input to the neural network classifier can achieve better classification results than those from using single channel inputs in different application scenarios.Models embedded with the channel-wise attention structure always achieve better scores on sensitivity and precision than the plain Resnet models.The proposed model exceeds most of the state-of-the-art models in ventricular ectopic beats(VEB)classification performance and achieves competitive scores for supraventricular ectopic beats(SVEB).Adopting more lead ECG signals as input can increase the dimensions of the input feature maps,helping to improve both the performance and generalization of the network model.Due to its end-to-end characteristics,and the extensible intrinsic for multi-lead heart diseases diagnosing,the proposed model can be used for the realtime ECG tracking of ECG waveforms for Holter or wearable devices.展开更多
文摘Based on the Gummel-Poon model of BJT, the change of the DC bias as a function of the AC input signal in RF linear power amplifiers is theoretically derived, so that the linearity of different DC bias circuits can be interpreted and compared. According to the analysis results, a quantitative adaptive DC bias circuit is proposed, which can improve the linearity and efficiency. From the simulation and test results, we draw conclusions on how to improve the design of linear power amplifier.
基金the Key Research and Development Project of Zhejiang Province,China(No.2017C03029)。
文摘A novel structure based on channel-wise attention mechanism is presented in this paper.With the proposed structure embedded,an efficient classification model that accepts multi-lead electrocardiogram(ECG)as input is constructed.One-dimensional convolutional neural networks(CNNs)have proven to be effective in pervasive classification tasks,enabling the automatic extraction of features while classifying targets.We implement the residual connection and design a structure which can learn the weights from the information contained in different channels in the input feature map during the training process.An indicator named mean square deviation is introduced to monitor the performance of a particular model segment in the classification task on the two out of five ECG classes.The data in the MIT-BIH arrhythmia database is used and a series of control experiments is conducted.Utilizing both leads of the ECG signals as input to the neural network classifier can achieve better classification results than those from using single channel inputs in different application scenarios.Models embedded with the channel-wise attention structure always achieve better scores on sensitivity and precision than the plain Resnet models.The proposed model exceeds most of the state-of-the-art models in ventricular ectopic beats(VEB)classification performance and achieves competitive scores for supraventricular ectopic beats(SVEB).Adopting more lead ECG signals as input can increase the dimensions of the input feature maps,helping to improve both the performance and generalization of the network model.Due to its end-to-end characteristics,and the extensible intrinsic for multi-lead heart diseases diagnosing,the proposed model can be used for the realtime ECG tracking of ECG waveforms for Holter or wearable devices.
