To solve the lights positioning problem for the cleaning system of aviation embedded aeronautical ground lights in the civil airport, a light positioning system based on vision was designed. Firstly, according to the installation standards, characteristics of nighttime lighting and impacts of external interference, the geometric modeling of the positioning system was designed and the parameters of cameras were optimized. Furthermore, the feasibility of system was taken into account at the same time. Then an improved algorithm of maximum between-class variance method named Otsu was proposed to achieve the adaptive threshold segmentation, and the step distance method was used to calculate the centroid of aeronautical ground lights. Finally, in order to improve the system accuracy, the system was calibrated after its errors had been analyzed. The actual experiment for the positioning system of embedded aeronautical ground lights was carried out in the nighttime environment. The experimental results show that the designed system has the advantages of high positioning speed, high accuracy with average error of 16.3mm, and it also has strong environment adaptability. This visual positioning system can effectively meet the positioning demands of aviation embedded aeronautical ground lights.

Aiming at the problem of high misdiagnosis rate, high false positive rate and low detection accuracy in pulmonary nodule computer-aided detection, a method of nodular detection based on three-dimensional shape index and Hessian matrix eigenvalue was proposed. Firstly, the parenchyma region was extracted and the eigenvalues and eigenvectors of the Hessian matrix were calculated. Secondly, the three-dimensional shape index formula was deduced by the two-dimensional shape index, and the improved three-dimensional spherical like filter was constructed. Finally, in the parenchyma volume, the suspected nodule region was detected, and more false-positive regions were removed. The nodules were detected by the three-dimensional volume data, and the detected coordinates were input as the seeds of belief connect, and the three-dimensional data was splited to pick out three-dimensional nodules. The experimental results show that the proposed algorithm can effectively detect different types of pulmonary nodules, and has better detection effect on the ground glass nodules which are more difficult to detect, reduces the false positive rate of nodules, and finally reaches 92.36% accuracy rate and 96.52% sensitivity.

New difficulties are met when establishing accurate behavioral models of a transport robot. To solve this problem, behavioral models of a transport robot were built using Petri Nets (PN) with inhibitor arcs. There exist coupling, constraint, and asynchronization relationships among the behaviors of a transport robot. A Petri net metamodel with inhibitor arcs of interactive behaviors as well as a token flow control mechanism were utilized for modeling the behaviors of a transport robot. The Petri net models were converted into LabVIEW programs using LabVIEW2012 and the Robotics module. The robot behaviors were verified using a transport robot platform. The experimental results demonstrate that the transport robot's behaviors and interaction logic are achieved, and that the robot has behavioral identification, decision-making and implementation capabilities, and it is a suitable method model the behaviors of a transport robot using Petri nets with inhibitor arcs. The reference models of Petri nets are given for designing related behaviors of transport robots.

A new image retrieval method based on enhanced micro-structure and context-sensitive similarity was proposed to overcome the shortcoming of high dimension of combined image feature and intangible combined weights. A new local pattern map was firstly used to create filter map, and then enhanced micro-structure descriptor was extracted based on color co-occurrence relationship. The descriptor combined several features with the same dimension as single color feature. Based on the extracted descriptor, normal distance between image pairs was calculated and sorted. Combined with the iterative context-sensitive similarity, the initial sorted image series were re-ranked. With setting the value of iteration times as 50 and considering the top 24 images in the retrieved image set, the comparative experiments with Multi-Texton Histogram (MTH) and Micro-Structure Descriptor (MSD) show that the retrieval precisions of the proposed algorithm respectively are increased by 13.14% and 7.09% on Corel-5000 image set and increased by 11.03% and 6.8% on Corel-10000 image set. By combining several features and using context information while keeping dimension unchanged, the new method can enhance the precision effectively.