该文基于快速卷积算法,提出一种适用于线性相位FIR滤波器的并行结构。该结构采用快速卷积算法减少子滤波器个数,同时让尽可能多的子滤波器具有对称系数,然后利用系数对称的特性减少子滤波器模块中的乘法器数量。对于具有对称系数的FIR...该文基于快速卷积算法,提出一种适用于线性相位FIR滤波器的并行结构。该结构采用快速卷积算法减少子滤波器个数,同时让尽可能多的子滤波器具有对称系数,然后利用系数对称的特性减少子滤波器模块中的乘法器数量。对于具有对称系数的FIR滤波器,提出的并行结构能够比已有的并行FIR结构节省大量的硬件资源,尤其当滤波器的抽头数较大时效果更明显。具体地,对一个4并行144抽头的FIR滤波器,提出的结构比改进的快速FIR算法(Fast FIR Algorithm,FFA)结构节省36个乘法器(14.3%),23个加法器(6.6%)和35个延时单元(11.0%)。展开更多
The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning al...The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.展开更多
文摘该文基于快速卷积算法,提出一种适用于线性相位FIR滤波器的并行结构。该结构采用快速卷积算法减少子滤波器个数,同时让尽可能多的子滤波器具有对称系数,然后利用系数对称的特性减少子滤波器模块中的乘法器数量。对于具有对称系数的FIR滤波器,提出的并行结构能够比已有的并行FIR结构节省大量的硬件资源,尤其当滤波器的抽头数较大时效果更明显。具体地,对一个4并行144抽头的FIR滤波器,提出的结构比改进的快速FIR算法(Fast FIR Algorithm,FFA)结构节省36个乘法器(14.3%),23个加法器(6.6%)和35个延时单元(11.0%)。
文摘The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.
