We propose and demonstrate a low-cost single-pixel terahertz imaging method based on near-field photomodulation and compressed sensing. By using monolayer graphene on a silicon substrate as the photomodulator, and a low-cost continuous-wave laser and digital micromirror device for effective patterned photomodulation, we achieve fast single-pixel terahertz imaging based on the compressed sensing algorithm. We further show that adopting a graphene on silicon substrate leads to deeper modulation depth and thus better image quality than a high-resistance silicon substrate. We expect this work will advance the development of low-cost single-pixel terahertz imaging and promote this technique into practical applications.
Here, we propose an effective classification strategy for THz pulsed signals of breast tissues based on wavelet packet energy (WPE) feature exaction and machine learning classifiers. The parafin-embedded breast tissue samples were adopted in this study and identified as tumor (226 samples), healthy fibrous tissue (233 samples) or adipose tissue (178 samples) based on the histological results. Firstly, the THz pulsed signals of tissue samples were acquired using a standard transmission THz time-domain spectrometer. Then, the signals were decomposed by the wavelet packet transform (WPT) and the features of the WPE were extracted. To reduce the dimensionality of extracted features, the principal components analysis (PCA) method was employed. Six different machine learning classifiers were then performed and compared for automatic classification of different tissue samples. The highest classification accuracy is up to 97% using the fine Gaussian support vector machine (SVM) approach. The results indicate that the WPE feature exaction combined with machine learning classifier can be used for automatic evaluation of biological tissue THz signals with good accuracy.
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