Obstacle Avoidance in Vehicle Intelligent Auxiliary Drive System

This paper studies the problem of computer vision in the vehicle intelligent auxiliary driving system. An effective and real time method in highway to search and track obstacle is presented. Obstacle candidates are tracked and analyzed, false alarms generated by the object detector are recognized and rejected. It also supplies some valuable discussion on problems existing in its experiments.     

Lane Detection in Navigation System of Vehicle

To solve the problem of lane detection in intelligent auxiliary driving system and as the common ways have some shortcoming such as less real-time and adaptive, the paper raises a new way. Based on the edge being extracted, the feature model of lane markings is built by combining the Median of the Intercepts and the spline. One effective and real time method in highway surface searching and following is presented. The result proves that the method can figure out the way stretching correctly. And therefore, it provides reliable information for the vehicle to be self-anti-deviation. At the same time, it also supplies some valuable discussion on problems existing in its experiments.     

BP Neural Networks and Its Application in Lung Cancer Intelligent Diagnosis System

The learning algorithm of networks is discussed. The programming example of 3 layer BP networks is given with Visual C++6.0 program langue. Based on this model, a lung cancer intelligent diagnosis system is successfully implemented. Furthermore, the paper introduces network's structure design, preferencesand the source of stylebookdatum in factual applications. The ameliorative arithmetic is applied to the study of networks and BP dynamic evolving process is designed. The experiments indicatecell images are recognized and classified by the trained neural network. The study illustrates the system has feasibility and clinical value in lung cancer diagnosis.