Problems such as low light, similar background colors, and noisy image acquisition often occur when collecting images of lunar surface obstacles. Given these problems, this study focuses on the AD-Census algorithm. In the original Census algorithm, in the bit string calculated with the central pixel point, the bit string will be affected by the noise that the central point is subjected to. The effect of noise results in errors and mismatching. We introduce an improved algorithm to calculate the average window pixel for solving the problem of being susceptible to the central pixel value and improve the accuracy of the algorithm. Experiments have proven that the object contour in the grayscale map of disparity obtained by the improved algorithm is more apparent, and the edge part of the image is significantly improved, which is more in line with the real scene. In addition, because the traditional Census algorithm matches the window size in a fixed rectangle, it is difficult to obtain a suitable window in the image range of different textures, affecting the timeliness of the algorithm. An improvement idea of area growth adaptive window matching is proposed. The improved Census algorithm is applied to the AD-Census algorithm. The results show that the improved AD-Census algorithm has been shown to have an average run time of 5.3% and better matching compared to the traditional AD-Census algorithm for all tested image sets. Finally, the improved algorithm is applied to the simulation environment, and the experimental results show that the obstacles in the image can be effectively detected. The improved algorithm has important practical application value and is important to improve the feasibility and reliability of obstacle detection in lunar exploration projects.

Nitric oxide is one of the atmospheric pollutants and an important gas messenger molecule in the human body, involved in many physiological and pathological processes. Therefore, detecting nitric oxide rapidly and accurately has been one of the popular topics in recent decades. In this study, we synthesized CuCo-PTC MOF materials using a solvothermal method based on the mechanism of triazole ring formation from o-phenylenediamine (OPD) and nitric oxide. The synthesized CuCo-PTC MOF materials show high sensitivity and good selectivity for detecting nitric oxide in vitro and in cell lysates. The results indicate the potential for applying this sensing strategy to detect nitric oxide in the internal environment. n is ± standard deviation (n = 3)

The low light conditions, abundant dust, and rocky terrain on the lunar surface pose challenges for scientific research. To effectively perceive the surrounding environment, lunar rovers are equipped with binocular cameras. In this paper, with the aim of accurately detect obstacles on the lunar surface under complex conditions, an Improved Semi-Global Matching (I-SGM) algorithm for the binocular cameras is proposed. The proposed method first carries out a cost calculation based on the improved Census transform and an adaptive window based on a connected component. Then, cost aggregation is performed using cross-based cost aggregation in the AD-Census algorithm and the initial disparity of the image is calculated via the Winner-Takes-All (WTA) strategy. Finally, disparity optimization is performed using left–right consistency detection and disparity padding. Utilizing standard test image pairs provided by the Middleburry website, the results of the test reveal that the algorithm can effectively improve the matching accuracy of the SGM algorithm, while reducing the running time of the program and enhancing noise immunity. Furthermore, when applying the I-SGM algorithm to the simulated lunar environment, the results show that the I-SGM algorithm is applicable in dim conditions on the lunar surface and can better help a lunar rover to detect obstacles during its travel.

Nitric oxide is one of the atmospheric pollutants and an important gas messenger molecule in the human body, involved in many physiological and pathological processes. Therefore, detecting nitric oxide rapidly and accurately has been one of the popular topics in recent decades. In this study, we synthesized CuCo-PTC MOF materials using a solvothermal method based on the mechanism of triazole ring formation from o-phenylenediamine (OPD) and nitric oxide. The synthesized CuCo-PTC MOF materials show high sensitivity and good selectivity for detecting nitric oxide in vitro and in cell lysates. The results indicate the potential for applying this sensing strategy to detect nitric oxide in the internal environment.