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 report a tunable polarization-independent broadband absorber in the terahertz regime. The proposed structure consists of periodic all-dielectric array on a gold substrate, sandwiched by a monolayer graphene and an epsilon-near-zero layer. Simulation results show that the absorption that is independent from the incident polarization remains above 90% over a broadband spectral range from 1.6 THz to 4.1 THz, corresponding to a bandwidth of 2.5 THz and a relative bandwidth of 87.7%. By varying the graphenes Fermi energy from 0.2 eV to 0.5 eV, the absorption bandwidth can be turned from 1.5 THz to 2.5 THz. We expect this polarization- independent absorber with dynamically tunable bandwidth can be used a filters in applications such as terahertz detectors.
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.
We propose a novel type of bowtie terahertz antenna based on hyperbolic metamaterials that are composed of multilayers of Indium Antimonide (InSb) and SiO2. The InSb-SiO2 multilayers have hyperbolic dispersion at terahertz frequency range. Compared with the conventional bowtie antenna composed of gold, fully-vectorial simulations show that, the localized field enhancement in the proposed structure is 16 times of that for the gold antenna. We further reveal that this great field enhancement attributes to the significantly enhanced out-of-plane electric field component in the hyperbolic metamaterial antenna. We expect this work will find applications in terahertz sensors, detectors, and nonlinear devices.
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