A Signal-Based One-Dimensional Convolutional Neural Network (SB 1D CNN) Model for Seizure Prediction

Moghadam, Ali Derogar; Karami Mollaei, Mohammad Reza; Hassanzadeh, Mohammadreza

doi:10.1007/s00034-024-02700-7

Circuits Syst Signal Process

2024

DOI: 10.1007/s00034-024-02700-7

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A Signal-Based One-Dimensional Convolutional Neural Network (SB 1D CNN) Model for Seizure Prediction

Ali Derogar Moghadam,

Mohammad Reza Karami Mollaei,

Mohammadreza Hassanzadeh

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Cited by 1 publication

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Real-Time Ubiquitous Radar Target Classification with 1D ResNet-SE-Based Multi-Channel Network

Song,

Zhou,

Zhang

et al. 2024

Remote Sensing

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Ubiquitous radar has significant advantages over traditional radar in detecting and identifying low, slow, and small (LSS) targets in a strong clutter environment. It effectively addresses challenges faced in low-altitude target monitoring within the low-altitude economy (LAE). The working mode of ubiquitous radar, which tracks first and identifies later, provides high-resolution Doppler data to the target identification module. Utilizing high-resolution Doppler data allows for the effective identification of LSS targets. To meet the needs of real-time classification, this paper first designs a real-time classification process based on sliding window Doppler data. This process requires the classifier to classify targets based on multiple rows of high-resolution Doppler spectra within the sliding window. Secondly, a multi-channel parallel perception network based on a 1D ResNet-SE network is designed. This network captures features within the rows of sliding window data and integrates inter-row features. Experiments show that the designed real-time classification process and multi-channel parallel perception network meet real-time classification requirements. Compared to the 1D CNN-MLP multi-channel network, the proposed 1D ResNet-MLP multi-channel network improves the classification accuracy from 98.71% to 99.34%. Integrating the 1D Squeeze-and-Excitation (SE) module to form the 1D ResNet-SE-MLP network further enhances accuracy to 99.58%, with drone target accuracy, the primary focus of the LAE, increasing from 97.19% to 99.44%.

show abstract

Real-Time Ubiquitous Radar Target Classification with 1D ResNet-SE-Based Multi-Channel Network

Song,

Zhou,

Zhang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

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A Signal-Based One-Dimensional Convolutional Neural Network (SB 1D CNN) Model for Seizure Prediction

Cited by 1 publication

References 38 publications

Real-Time Ubiquitous Radar Target Classification with 1D ResNet-SE-Based Multi-Channel Network

Real-Time Ubiquitous Radar Target Classification with 1D ResNet-SE-Based Multi-Channel Network

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