基于TMR传感器的冠字码提取与识别算法
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摘要
针对第5套人民币冠字码,采用TMR传感器获取冠字码磁信号,经小波变换对信号降噪,采用能量差法截取冠字码有效磁信号,对其提取多个时域特征,构建特征判据样本库,利用BP神经网络识别冠字码磁信号,实现真假币分类。实验结果表明,所采集的不同面额纸币的冠字码磁信号稳定饱和,小波变换对磁信号降噪效果良好,能量差法可以获取完整、有效的冠字码磁信号,BP神经网络算法运算速度快、识别率达100%。
According to the fifth set RMB serial numbers,tunnel magneto resistance( TMR) is used to acquire the magnetic signal of serial numbers,the approach of wavelet transform is adopted to reduce the signal noise,the method based on energy D-value is used to cut out the effective signal,to extract several time domain features and build characteristic criterion sample library,BP neural network is used to recognize magnetic signal of serial numbers and classify counterfeit and banknote. The study shows that the collected character magnetic signal of different RMB is stable and saturate,the method of wavelet transform reduces the signal noise well,complete and effective magnetic signal can be acquired by using the method based on energy D-value,computing speed of BP neural network algorithm is fast and recognition currency is 100%.
引文
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