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  • Open Access


    Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

    Fangjun Luan1,2,3, Xuewen Mu1,2,3, Shuai Yuan1,2,3,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 695-712, 2024, DOI:10.32604/cmc.2024.048502

    Abstract Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries. However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. To address these issues, we propose a novel approach for online signature verification, using a one-dimensional Ghost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolution with a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residual structure is introduced to leverage both self-attention and convolution mechanisms for capturing global feature information and extracting local information, effectively complementing whole and… More >

  • Open Access


    Straw Segmentation Algorithm Based on Modified UNet in Complex Farmland Environment

    Yuanyuan Liu1,2, Shuo Zhang1, Haiye Yu3, Yueyong Wang4,*, Yuehan Feng1, Jiahui Sun1, Xiaokang Zhou1

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 247-262, 2021, DOI:10.32604/cmc.2020.012328

    Abstract Intelligent straw coverage detection plays an important role in agricultural production and the ecological environment. Traditional pattern recognition has some problems, such as low precision and a long processing time, when segmenting complex farmland, which cannot meet the conditions of embedded equipment deployment. Based on these problems, we proposed a novel deep learning model with high accuracy, small model size and fast running speed named Residual Unet with Attention mechanism using depthwise convolution (RADw–UNet). This algorithm is based on the UNet symmetric codec model. All the feature extraction modules of the network adopt the residual… More >

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