The performance of current liquid zoom systems is severely limited by their initial structure’s construction and solution. In this study, an automatic search method based on genetic algorithm (GA) was proposed for obtaining the optimal initial structure of a double liquid lens zoom optical system. This method was used to design a zoom telescopic objective with a fast response characteristic. The zoom equation of the zoom system was derived based on the Gaussian bracket method, and an initial structure evaluation function that integrated the aberration, the system length, and the smoothness of the focal power change in the liquid lenses was designed. This evaluation function was used as the fitness function in GA to automatically retrieve the optimal initial structure of the zoom system. Finally, an optical design software was used to optimize the design of the zoom system to obtain the final structure of the zoom system. Image quality analysis and tolerance analysis of the zoom system revealed that the system exhibited excellent imaging capability and could be manufactured easily. In addition, the analysis of the zoom curve revealed that the optical system exhibited a smooth continuous zooming capability.

Digital imaging systems (DISs) have been widely used in industrial process control, field monitoring, and other domains, and the autofocusing capability of DISs is a key factor affecting the imaging quality and intelligence of the system. In view of the deficiencies of focusing accuracy and speed in current imaging systems, this paper proposes a fast autofocus method of bionic vision on the basis of the liquid lens. First, the sharpness recognition network and sharpness comparison network are designed based on the consideration of a human visual focusing mechanism. Then a sharpness evaluation function combined with the distance-aware algorithm and an adaptive focusing search algorithm are proposed. These lead to the construction of our proposed autofocus method with the introduction of the memory mechanism. In order to verify the effectiveness of the proposed method, an experimental platform based on a liquid lens is built to test its performance. Experiment confirms that the proposed autofocus method has obvious advantages in robustness, accuracy, and speed compared with traditional methods.

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The accurate identification of moving target types in alert areas is a fundamental task for unattended ground sensors. Considering that the seismic and sound signals generated by ground moving targets in urban areas are easily affected by environmental noise and the power consumption of unattended ground sensors needs to be reduced to achieve low-power consumption, this paper proposes a ground moving target detection method based on evolutionary neural networks. The technique achieves the selection of feature extraction methods and the design of evolving neural network structures. The experimental results show that the improved model can achieve high recognition accuracy with a smaller feature vector and lower network complexity.

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