Xingzeng Cha’s research while affiliated with University of Electronic Science and Technology of China and other places

To improve the image quality of terahertz (THz) continuous wave (CW) computed tomography (CT) under sparse‐view projections, a sparse‐view adaptive‐weighted Bregman Huber total variation (AW‐BHTV) method is proposed and experimentally evaluated at 0.11 THz. The Huber function is served as the fidelity term and TV function is worked for the regularization term, optimized by the adaptive weights, incorporating with iterative algorithms containing the modified simultaneous algebraic reconstruction technique (MSART) and the iterative filtered back‐projection (FBP). Sparse‐view reconstruction experiments are carefully implemented via polystyrene (PS) foam samples: Sample 1 and Sample 2, respectively. Under 20 projection angles by MSART based AW‐BHTV, the values of rooted mean‐square‐error (RMSE), peak signal‐to‐noise ratio (PSNR), structure similarity (SSIM) and feature similarity (FSIM) are 0.0061, 22.1427, 0.8738 and 0.9902 for Sample 1 together with 0.0051, 22.9101, 0.8376 and 0.9885 for Sample 2 separately. All of the results above suggest that the proposed AW‐BHTV method can effectively protect image details and strengthen image quality in sparse‐view THz CW CT.

This paper proposed an accurate series resistance model tailored for Schottky diode‐based terahertz multipliers. Compared to the conventional electrothermal model (E‐T model) only considering thermal effects, this model comprehensively accounts for both thermal and frequency effects of the series resistor components, including the temperature‐dependent epilayer resistance (Repi) and the temperature‐frequency‐dependent spreading resistance (Rspreading). The evaluation of thermal effects relies on steady‐state thermal simulation and the corresponding electrothermal model. Notably, the frequency‐dependent spreading resistance part is derived from the fitting conductivity of doped buffer layers and extracted by auxiliary three‐dimensional electromagnetic simulations. Based on this model, a balanced 225–300 GHz frequency tripler with AlN substrate has been designed and manufactured. By introducing this model, a significant improvement in the consistency between simulated and measured results has been achieved compared to the single E‐T model, regardless of whether the input power is low (80 mW) or high (160 mW).

This letter was to present an attempt of large‐wavelength Gaussian deconvolution phase‐contrast computed tomography (LW‐GD‐PCCT) for promotion of image quality reconstructed in low‐frequency band of terahertz (THz) spectrum at 0.11 THz. The interaction between the imaging samples and the THz Gaussian beam were formulated firstly in this paper, where the unwrapped phase was extracted specifically to portray the spatial structure distribution of the samples. Additionally, a Gaussian deconvolution was employed for the further reduction of spatial distortions. Moreover, an image reconstruction was carried out with the obtained phase sinograms based on phase‐used inverse Radon transform from the different positions on the sample. For an experimental assessment of the concept of LW‐GD‐PCCT, a single ellipsoid reflector‐based THz Gaussian beam generating system was established and samples such as polystyrene (PS) foam cuboid (Sample 1), and cylinder (Sample 2) with hollow defects (air holes and triangles) were prepared carefully in this work. To experimentally evaluate the performance of the contributing to the structural imaging over soft samples. Two‐dimensional topographies of each sample were reconstructed successfully, and the obtained cross root‐mean‐square error (cross‐RMSE), cross peak signal‐to‐noise ratio (cross‐PSNR), and cross structural similarity (cross‐SSIM) were 151.6451, 26.3225, and 0.9616 for Sample 1 with a high dose of 180 projections respectively, as well as 30.3242, 33.3129, and 0.9711 for Sample 2 with a low dose of 36 projections, respectively. The obtained imaging indicators of this work showed a superiority of imaging quality over those of recent works. Furthermore, the investigation of the bearing capacity has shown promise in enhancing image quality even in low‐dose conditions. The presented results suggest that the unwrapped phase combining with Gaussian deconvolution in low‐frequency band of THz imaging would be useful to improve the reconstructed image quality, potential to highly feasible non‐destructive testing of polymer foam via large wavelength.

We propose the equivalent source of reflector in finite‐different time‐domain (ESR‐FDTD) for rapid analysis of corner scattering in terahertz computed tomography (THz‐CT) imaging. The ESR‐FDTD is used instead of the reflector in the THz‐CT imaging system, which can significantly reduce the computational domain of the THz‐CT imaging system and effectively reduce the computational time and memory usage. The electric field distribution of the beam reflected by the reflector and that of the beam radiated from the ESR of the reflector are compared, and the results show that the electric field distributions of the reflector and its ESR are almost the same. The results of the corner scattering show that the phenomenon of corner scattering does not fully satisfy the behaviors of reflection and refraction. Therefore, the corner scattering cannot be accurately analyzed by the ray‐tracing method.

Terahertz (THz) waves are widely used in the field of non-destructive testing (NDT). However, terahertz images have issues with limited spatial resolution and fuzzy features because of the constraints of the imaging equipment and imaging algorithms. To solve these problems, we propose a residual generative adversarial network based on enhanced attention (EA), which aims to pay more attention to the reconstruction of textures and details while not influencing the image outlines. Our method successfully recovers detailed texture information from low-resolution images, as demonstrated by experiments on the benchmark datasets Set5 and Set14. To use the network to improve the resolution of terahertz images, we create an image degradation algorithm and a database of terahertz degradation images. Finally, the real reconstruction of terahertz images confirms the effectiveness of our method.

The diagnosis of hypertrophic cardiomyopathy (HCM) is of great significance for the early risk classification of sudden cardiac death and the screening of family genetic diseases. This research proposed a HCM automatic detection method based on convolution neural network (CNN) model, using single-lead electrocardiogram (ECG) signal as the research object. Firstly, the R-wave peak locations of single-lead ECG signal were determined, followed by the ECG signal segmentation and resample in units of heart beats, then a CNN model was built to automatically extract the deep features in the ECG signal and perform automatic classification and HCM detection. The experimental data is derived from 108 ECG records extracted from three public databases provided by PhysioNet, the database established in this research consists of 14,459 heartbeats, and each heartbeat contains 128 sampling points. The results revealed that the optimized CNN model could effectively detect HCM, the accuracy, sensitivity and specificity were 95.98%, 98.03% and 95.79% respectively. In this research, the deep learning method was introduced for the analysis of single-lead ECG of HCM patients, which could not only overcome the technical limitations of conventional detection methods based on multi-lead ECG, but also has important application value for assisting doctor in fast and convenient large-scale HCM preliminary screening.