Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior know...Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.展开更多
文章提出一种基于小波变换和卷积神经网络-双向长短期记忆(Convolutional Neural Network-Bidirectional Long Short Term Memory,CNN-BiLSTM)的电力电缆故障定位算法,结合小波变换的时频局部化特性和CNN与BiLSTM的深度学习能力,以提升...文章提出一种基于小波变换和卷积神经网络-双向长短期记忆(Convolutional Neural Network-Bidirectional Long Short Term Memory,CNN-BiLSTM)的电力电缆故障定位算法,结合小波变换的时频局部化特性和CNN与BiLSTM的深度学习能力,以提升故障定位的精准性。为验证提出算法的有效性,将True、BiLSTM、极值域均值模式分解(Extremum field Mean Mode Decomposition,EMMD)+小波变换算法与本文算法进行对比实验分析。实验结果表明,基于小波变换和CNN-BiLSTM的电力电缆故障定位算法能够将定位误差控制在0.02 km以内,显著提高了故障定位的精度。展开更多
Atrial fibrillation is the most common persistent form of arrhythmia.A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms.Si...Atrial fibrillation is the most common persistent form of arrhythmia.A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms.Since the ECG signal is easily inferred,the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function,and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise.A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes,and finally the softmax classifier is used to classify them.This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge.After cross validation,this method can obtain 87.1%accuracy and the F1 score is 86.46%.Compared with the existing classification method,our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62005307 and 61975228).
文摘Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.
文摘文章提出一种基于小波变换和卷积神经网络-双向长短期记忆(Convolutional Neural Network-Bidirectional Long Short Term Memory,CNN-BiLSTM)的电力电缆故障定位算法,结合小波变换的时频局部化特性和CNN与BiLSTM的深度学习能力,以提升故障定位的精准性。为验证提出算法的有效性,将True、BiLSTM、极值域均值模式分解(Extremum field Mean Mode Decomposition,EMMD)+小波变换算法与本文算法进行对比实验分析。实验结果表明,基于小波变换和CNN-BiLSTM的电力电缆故障定位算法能够将定位误差控制在0.02 km以内,显著提高了故障定位的精度。
基金This work is supported by Key Research and Development Project of Shandong Province(2019JZZY020124),ChinaNatural Science Foundation of Shandong Province(23170807),China.
文摘Atrial fibrillation is the most common persistent form of arrhythmia.A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms.Since the ECG signal is easily inferred,the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function,and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise.A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes,and finally the softmax classifier is used to classify them.This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge.After cross validation,this method can obtain 87.1%accuracy and the F1 score is 86.46%.Compared with the existing classification method,our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification.
