We propose a sub-wavelength range-based dual-band tunable ideal terahertz metamaterial perfect absorber. The absorber structure consists of three main layers, with the absorber layer consisting of a metal I-shaped structure. By simulating the incident wave absorbance of the structure, we found that the structure has more than 99% absorption peaks in both bands. In addition, we have investigated the relationship between structural absorbance and the structural geometrical parameters. We have studied the relationship between the thickness of the metal absorber layer hb and the absorbance of the metamaterial structure in the 4–14 THz band. Secondly, we have studied the relationship between the thickness of the SiO2 dielectric layer and structural absorbance. Afterwards, we have studied the relationship between the incident angle of the incident electromagnetic wave and structural absorbance. Finally, we have studied the relationship between the length of the metal structure and structural absorbance. The structure can be effectively used for detectors, thermal emitters, terahertz imaging and detection.

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In the application of X-ray industrial flaw detection, the exposure parameters directly affect the image quality. The voltage of the tube is the most important factor, which is difficult to be accurately calculated. Especially in the detection of a workpiece composed of both high absorption coefficient and low absorption coefficient materials, the improper symmetric balance of the tube voltage would lead to an overexposure or underexposure phenomenon. In this paper, based on the X-ray absorption model, combined with the performance of the X-ray imaging detector, and taking the optimal symmetry and contrast as the model constraint condition, the key factors of high absorption ratio material imaging are decomposed. Through expansion and iteration, the calculation process is simplified, the optimal imaging convergence surface is found, and then the optimal energy input conditions of high absorptivity materials are obtained and symmetrically balanced. As a result, this paper solves the problem of fast selection and symmetric factor chosen of the optimal tube voltage when imaging materials with high absorption ratios. It reduces the subsequent complications of the X-ray image enhancement process and obtains a better image quality. Through experimental simulation and measurement verification, the error between the theoretical calculation results and the measured data was better than 5%.

Blood samples are easily damaged in traditional bloodstain detection and identification. In complex scenes with interfering objects, bloodstain identification may be inaccurate, with low detection rates and false-positive results. In order to meet these challenges, we propose a bloodstain detection and identification method based on hyperspectral imaging and mixed convolutional neural networks, which enables fast and efficient non-destructive identification of bloodstains. In this study, we apply visible/nearinfrared reflectance hyperspectral imaging in the 380-1000 nm spectral region to analyze the shape, structure, and biochemical characteristics of bloodstains. Hyperspectral images of bloodstains on different substrates and six bloodstain analogs are experimentally obtained. The acquired spectral pixels are pre-processed by Principal Component Analysis (PCA). For bloodstains and different bloodstain analogs, regions of interest are selected from each substance to obtain pixels, which are further used in convolutional neural network (CNN) modeling. After the mixed CNN modeling is completed, pixels are selected from the hyperspectral images as a test set for bloodstains and bloodstain analogs. Finally, the bloodstain recognition ability of the mixed 2D-3D CNN model is evaluated by analyzing the kappa coefficient and classification accuracy. The experimental results show that the accuracy of the constructed CNN bloodstain identification model reaches 95.4%. Compared with other methods, the bloodstain identification method proposed in this study has higher efficiency and accuracy in complex scenes. The results of this study will provide a reference for the future development of the bloodstain online detection system.