Ultra wide band (UWB) positioning technology establishes a mathematical model of the target location based on distance parameters. The problem of accurately solving the tag point positioning is transformed into an optimization problem of nonlinear equations due to ranging errors. Existing optimization methods may fail when certain distance errors are large. Therefore, this paper proposes an optimization method based on redundant distance screening by considering the distances between multiple positioning tags as constraints, filtering the redundant distances by weighting, and using a gradient descent method to optimize the initial value calculated by the Caffery method. In simulation experiments, the positioning error of this method is only 70% of the Caffery-Taylor (CT) method. In real data experiments, this method outperforms the CT method in terms of the optimization effect.

This paper describes an identification system for Chinese Materia Medicas (CMMs) in microscopic powder images. The imaging processing of the microscopic powder image is very complex because of the low contrast, blurry boundaries, overlapping objects, and messy background. Therefore, the object detection must segment the significant microscopic structures from the complex image. The objects are detected in these images using an adaptable interactive method. After identifying the significant microscopic structures, the system identifies 14 features belonging to three main characteristics. These features form a 14-dimensional vector that represents the microscopic structures. The multi-dimensional vector is then analyzed using a feature assignment algorithm that picks the most notable features to construct a decision tree with thresholds. The identification system consists of a coarse classifier based on the decision tree and a fine classifier using similarity measurements to rank the possible results. Tests on 528 images from 24 different kinds of microscopic structures show the system effectiveness and applicability.