We proposed a one-dimensional convolutional neural network (CNN) model, which divides heart sound signals into normal and abnormal directly independent of ECG. The deep features of heart sounds were extracted by the denoising autoencoder (DAE) algorithm as the input feature of 1D CNN. The experimental results showed that the model using deep features has stronger anti-interference ability than using mel-frequency cepstral coefficients, and the proposed 1D CNN model has higher classification accuracy precision, higher F-score, and better classification ability than backpropagation neural network (BP) model. In addition, the improved 1D CNN has a classification accuracy rate of 99.01%.
We present terahertz reference-free phase imaging for identification of three explosive materials (HMX, RDX, and DNT. We propose a feature extraction technique to locate the spectral position of an unknown material’s absorption lines without using the reference signal. The samples are identified by their absorption peaks extracted from the negative first-order derivative of the sample signal phase divided by the frequency at each pixel. This technique will greatly benefit the future development of standoff distance, large size focal-plane terahertz imaging system.
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