Resistive Random Access Memory (RRAM) is considered one of the most promising candidates for big data storage. By using atomic layer deposition and magnetron sputtering, HfO2 thin films were prepared on ITO first, which exhibited good resistive switching (RS) characteristics in the structure of Ag/HfO2/ITO. By analyzing the RS mechanism, it is found that both metal conductive filaments and oxygen vacancy conductive filaments coexisted and Sn ion in ITO can influence the retention of RRAM. Furthermore, a device in the structure of Ag/HfO2/Pt was proposed and prepared, which exhibited excellent RS characteristics, including an ultrahigh switching ratio averaging up to 108 and low operating voltage. It is concluded that the difference in the work function between the top and bottom electrodes contributes to improving the switching ratio, reducing the operating voltage. In addition, the Ag/HfO2/Pt device is similar to the Ag/HfO2-based threshold switching selector in the structure and in characteristics of high switching ratio, besides non-volatile memory. Hence, the device is functionally equivalent to the combination of an RRAM and a threshold switching selector. It is the potential way to replace the conventional 1S1R structure memory.

In order to solve the problems of low accuracy of the current mainstream target detection algorithms in identifying small target ships, complex background interference such as coastline buildings and trees, and the influence of ship occlusion on ship target detection, an inland river ship detection method based on improved YOLOv10n: CDS-YOLO is proposed under the premise of keeping the model lightweight. Firstly, the CAA attention module is introduced into the Backbone network, and the C2f_CAA module is constructed at the same time to enhance the features of the central region and improve the understanding ability of complex scenes. Then, the Conv of the Backbone network was replaced with DBB to enhance the expression ability of a single convolution and enrich the feature space. Finally, GSConv and VovGSCSP in Slim-Neck are introduced into the Neck network to optimize the network architecture, reduce part of the model complexity, and further improve the performance of the model. Experimental results show that compared with YOLOv10n, CDS-YOLO has the detection accuracy, recall rate and mAP@0.5 increased by 3.7%, 2% and 0.9% respectively, reaching 98.4%, 97.4% and 99.2% respectively, indicating that CDS-YOLO has good accuracy and robustness in the detection and classification of inshore ships.