A fiber-optic gyroscope (FOG) north finder for teaching experiment is designed and implemented. The FOG designed and implemented in this paper is a device that achieves north seeking by measuring the Earth's rotational angular velocity using a single fiber optic gyroscope. The hardware system consists of a fiber optic gyroscope, an air-floating vibration isolation platform, a rotating mechanism, a host computer, and a supporting power supply. In terms of software implementation, C # programming language is used to implement control of the rotating mechanism, communication with the gyroscope, data parsing, data acquisition and processing, and embedding commonly used two-position and four-position north seeking algorithms. The experimental results show that the fiber optic gyro north finder can well achieve the function of north seeking, has a simple and reliable structure, and is easy to implement and develop further.
Detecting roads from high-resolution photographs can serve forestry, agriculture, traffic and even military areas, and produce significant social and economic value. In this paper, we present a novel method that utilizes the flatness and the connectivity to detect the road in high-resolution aerial images. The method iterates the probable locations of the roads by using the flatness and connects the roads by using the connectivity. Firstly, we introduce a concept of ‘footprint’, which reveals the probable location and extension direction of a road. Given an initial footprint, we assess the flatness between locations to search the resulting footprint. By iterating and connecting the footprints, our approach produces a set of connected line segments that reflect the road to be detected. In addition, a footprints initialization algorithm is introduced to make our method totally automatic, and a road network pruning algorithm is designed to make the result clearer and more accurate. Tested under three high-resolution aerial photographs, our method achieved an accuracy of more than 80%. The algorithm is adapted for road detection and still linear target detection in high-resolution aerial photographs. Since the algorithm does not require artificial features or training data, it can be quickly deployed in application.
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