Marine oil spills due to ship collisions or operational errors have caused tremendous damage to the marine environment. In order to better monitor the marine environment on a daily basis and reduce the damage and harm caused by oil pollution, we use marine image information acquired by synthetic aperture radar (SAR) and combine it with image segmentation techniques in deep learning to monitor oil spills. However, it is a significant challenge to accurately distinguish oil spill areas in original SAR images, which are characterized by high noise, blurred boundaries, and uneven intensity. Hence, we propose a dual attention encoding network (DAENet) using an encoder–decoder U-shaped architecture for identifying oil spill areas. In the encoding phase, we use the dual attention module to adaptively integrate local features with their global dependencies, thus improving the fusion feature maps of different scales. Moreover, a gradient profile (GP) loss function is used to improve the recognition accuracy of the oil spill areas’ boundary lines in the DAENet. We used the Deep-SAR oil spill (SOS) dataset with manual annotation for training, testing, and evaluation of the network, and we established a dataset containing original data from GaoFen-3 for network testing and performance evaluation. The results show that DAENet has the highest mIoU of 86.1% and the highest F1-score of 90.2% in the SOS dataset, and it has the highest mIoU of 92.3% and the highest F1-score of 95.1% in the GaoFen-3 dataset. The method proposed in this paper not only improves the detection and identification accuracy of the original SOS dataset, but also provides a more feasible and effective method for marine oil spill monitoring.

In the past few years, implanted biomedical devices have been widely used in healthy and medical applications and wireless power transfer (WPT) techniques used in these devices have been become a research hot topic. The printed spiral coils (PSCs), which have characteristics of small volume, low cost, high precision and high-integration, are better suited for WPT system. It is noteworthy that mutual inductance value of PSCs is a significant parameter which can influence the transfer efficiency of WPT system. Considering the geometry of multilayer circular PSCs and the distance between two adjacent layers, the previous methods are not suitable for calculating the mutual inductance of multilayer circular PSCs. In this study, an approximate calculation formula of mutual inductance between two single-layer circular PSCs is presented based on the existing research. On this basis, the self-inductance values of double-layer circular PSCs are calculated by MATLAB, the calculation results are verified by the ANSYS Q3D simulation and measurement results.

In recent years, more and more implantable medical devices have been used in the medical field. Some of these devices, such as brain pacemakers, require long-term power support. The WPT(wireless power transmission) technology which is more convenient and economical than replacing the battery by surgery, has become the first choice of many patients. In this paper, we design a WPT system that can be used in implantable medical devices, simulate the transmission efficiency of the system in the air and in the head model, and simulate the SAR value when the system working in the head model. The results show that when implantation depth of the secondary coil is 3 mm, the efficiency of the system can reach 45%, and the maximum average SAR value is 2.19 W / kg, slightly higher than the standard of IEEE.

Adequate electrical energy can guarantee long-term navigation for the autonomous underwater vehicle (AUV). Compared with the traditional wired charging method, wireless charging has the advantages of high security, simple operation and so on, and is more suitable for underwater application. In this paper, we study the efficiency of wireless power transmission (WPT) system in air and seawater. The transmission efficiency of wireless transmission system in air and seawater is compared with HFSS simulation. The reason of system efficiency decay is analyzed, which is helpful for the application of wireless power transmission technology in AUV.