In this paper, we utilize a terahertz (THz) reflection detection structure to examine multilayer heterogeneous samples and propose an improved non-destructive testing algorithm. Firstly, the Rouard equation is introduced to establish a THz wave reflection model. To enhance the model's accuracy, the Debye model is employed to calculate the dielectric constant, determining the refractive index of the samples. The model parameters are solved using genetic algorithms and the African vulture optimization algorithm. Experiments were conducted using a six-axis robotic arm and a THz spectrometer to detect non-metallic composite material samples. The results demonstrate that the proposed algorithm can accurately detect the samples, with a thickness measurement precision of 1 micrometer and an error margin within 4%. This study proposes a more reliable non-destructive testing algorithm based on terahertz time-domain spectroscopy technology, advancing the application of terahertz technology in the power industry and industrial production.
Terahertz-based non-destructive testing utilizes a point-scanning imaging process to capture comprehensive regional information through high-density grid sampling. Accurate estimation of characteristic parameters such as refractive index, dielectric constant, and absorption coefficient requires addressing a multi-parameter numerical problem with micrometer-scale precision in sample thickness. Given the demands of high frame rate imaging, purely CPU-based computing frameworks are insufficient for the required rapid processing. Consequently, this paper introduces a GPUaccelerated terahertz spectroscopic characteristic parameter measurement algorithm using CUDA technology. The system enhances performance by streamlining data transfers between devices and performing batch Fourier transformations on multiple short one-dimensional sequences. It also leverages shared memory to efficiently execute distributed detection of signal peaks and spectral matching. Furthermore, a fitness function based on the distribution of local extrema across multiple consecutive data buckets is proposed, facilitating rapid comparison and optimization of results. Experimentally, the system was tested on both simulated data of single and double-layer media and real data from the MenloSystems terahertz time-domain spectrometer. Experimental results indicate that NVIDIA GeForce 30 series GPU acceleration increases processing speed by 5 to 8 times relative to traditional CPU methods. Additionally, by exploring and sampling in a more refined parameter space while maintaining a computation speed of 100ms per measurement point, the accuracy of refractive index determination improved by 80%. These achievements not only underscore the potential of GPU acceleration in enhancing terahertz spectroscopy performance but also provide substantial support for the continued advancement of related technologies.
Terahertz (THz) technology, due to its non-ionizing nature and high sensitivity to the water content in substances, demonstrates significant potential in areas such as tumor analysis, skin tissue examination, burn assessment, and the analysis of traditional Chinese medicine components. This study investigates a novel method for detecting organic materials using THz technology. The experiment involved both normal and skin barrier-damaged mice. Samples were prepared using different methods, including direct slicing, 60°C water bath skin scraping, and enzymatic treatment, and were observed using THz spectral imaging. The study found that mice with damaged skin barriers exhibited significant differences in THz imaging and THz time-domain spectroscopy, reflecting changes in water distribution and tissue structure within the skin. This method not only non-invasively and efficiently reflects differences in skin barrier function but also verifies the accuracy of imaging results through THz time-domain spectroscopy. It shows promise for early diagnosis and monitoring of skin diseases, advancing the fields of dermatology and biomedicine.
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