Terahertz technology has broad application prospects in biomedical detection. However, the mixed characteristics of actual samples make the terahertz spectrum complex and difficult to distinguish, and there is no practical terahertz detection method for clinical medicine. Here, we propose a three-step one-way terahertz model, presenting a detailed flow analysis of terahertz technology in the biomedical detection of renal fibrosis as an example: 1) biomarker determination: screening disease biomarkers and establishing the terahertz spectrum and concentration gradient; 2) mixture interference removal: clearing the interfering signals in the mixture for the biomarker in the animal model and evaluating and retaining the effective characteristic peaks; and 3) individual difference removal: excluding individual interference differences and confirming the final effective terahertz parameters in the human sample. The root mean square error of our model is three orders of magnitude lower than that of the gold standard, with profound implications for the rapid, accurate and early detection of diseases.

With the advent of the era of big data, artificial intelligence has attracted continuous attention from all walks of life, and has been widely used in medical image analysis, molecular and material science, language recognition and other fields. As the basis of artificial intelligence, the research results of neural network are remarkable. However, due to the inherent defect that electrical signal is easily interfered and the processing speed is proportional to the energy loss, researchers have turned their attention to light, trying to build neural networks in the field of optics, making full use of the parallel processing ability of light to solve the problems of electronic neural networks. After continuous research and development, optical neural network has become the forefront of the world. Here, we mainly introduce the development of this field, summarize and compare some classical researches and algorithm theories, and look forward to the future of optical neural network.

纳米材料是20世纪末期发展起来的新型材料，其以优异的特殊性能得到了广泛关注。纳米材料的制备是纳米科技中的重要领域。本文介绍了脉冲激光烧蚀法制备纳米材料的两个重要途径——脉冲激光沉积法和脉冲激光液相烧蚀法，包括这两种方法的原理、特点、应用领域以及国内外的研究进展，最后阐述了脉冲激光沉积法和脉冲激光液相烧蚀法在制备工艺和材料性能方面仍需面对的挑战以及未来的发展趋势。

条纹非均匀性是红外焦平面阵列成像系统中一种较为常见的固定图案噪声,其对成像质量会产生很大的影响。通常认为图像的非均匀性主要体现在像素间的灰度差,即非均匀性所引起的灰度差。为了解决这一问题,提出一种利用灰度差估计的条纹非均匀性校正方法。该方法根据条纹噪声的空间特性,对每一帧图像的非均匀性条纹的所在位置进行判断;根据条纹位置处相邻像素间的灰度差,对该处存在的非均匀性进行初步估计,并对下一帧的估计结果进行评价以进一步完善估计值。实验结果表明,所提方法能够显著减少条纹非均匀性,并且能够有效地保护图像的边缘信息。

提出了一种新的基于多尺度角点和Harris变换相结合的角点检测的图像配准方法。其核心思想是采用Harris算子和Laplace图像金字塔相结合，对原始图像进行不同尺度的向下采样并对得到的图像进行角点检测，得到角点的角度和尺度响应的最值点比(尺度精定位)，对图像进行旋转和平移变换，完成图像配准。理论分析和仿真实验表明，该算法具有较高的配准精度且计算量较小。