High-resolution images provided by synthetic aperture radar (SAR) play an increasingly important role in the field of ship detection. Numerous algorithms have been so far proposed and relative competitive results have been achieved in detecting different targets. However, ship detection using SAR images is still challenging because these images are still affected by different degrees of noise while inshore ships are affected by shore image contrasts. To solve these problems, this paper introduces a ship detection method called N-YOLO, which based on You Only Look Once (YOLO). The N-YOLO includes a noise level classifier (NLC), a SAR target potential area extraction module (STPAE) and a YOLOv5-based detection module. First, NLC derives and classifies the noise level of SAR images. Secondly, the STPAE module is composed by a CA-CFAR and expansion operation, which is used to extract the complete region of potential targets. Thirdly, the YOLOv5-based detection module combines the potential target area with the original image to get a new image. To evaluate the effectiveness of the N-YOLO, experiments are conducted using a reference GaoFen-3 dataset. The detection results show that competitive performance has been achieved by N-YOLO in comparison with several CNN-based algorithms.

In order to improve the spectrum efficiency and secrecy energy efficiency, in this paper, we propose a nonorthogonal multiple access (NOMA) based secure scheme for cognitive radio networks. In the proposed scheme, the secondary users harvest energy from the radio-frequency signals to securely transmit the secondary privacy information with the NOMA technique. Unlike the conventional ideal linear energy harvesting, we employ the practical nonlinear energy harvesting model for energy harvesting. To implement the proposed scheme, the energy transmitter first broadcasts radiofrequency signals to power the secondary users. Then, the secondary users employ the NOMA technique to transmit the uplink privacy information which is threatened by the eavesdropper. Considering two scenarios: two secondary users and more than two secondary users, we first provide comprehensive analysis of the secondary secrecy performances, and derive the closed-form expressions of the secrecy outage probability for both scenarios. Following the above analysis, we develop the optimization problems to optimally allocate the time slot and the secondary transmit power such that the minimum secrecy energy efficiency is maximized under constraints of the transmission security and reliability requirements. A two stages algorithm is proposed to efficiently solve the above optimization problems. Numerical results are presented to verify the analysis in terms of the secrecy rate and secrecy energy efficiency.

Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.

In order to solve the problem of control performance degradation caused by time delay in wave compensation control system, predicting vessel heave motion can be the input vector of the control system to alleviate time delay problem. The vessel heave motion belongs to the problem of time series, this paper proposes an improved Long Short-Term Memory (LSTM) model with a random deactivation layer (dropout), which can deal with the time series problem very well. In order to obtain the vessel heave motion, this paper establishes a wave model suitable for marine operation, and solves the vessel heave motion through the mathematical model of vessel motion. Finally, the paper predicts the vessel heave motion in a short predicted time series. In the process of obtaining the prediction effect of vessel heave motion, the Back Propagation (BP) neural network and the standard LSTM neural network are used to compare with the improved LSTM neural network. While the predicted time series is 0.1 s at sea state 3, the mean absolute percentage (MAPE) errors of BP neural network in the prediction of vessel heave motion is 1.06×10 ^-2 %, the standard LSTM in the prediction of heave motion is 1.43×10 ^-4 %, the improved LSTM in the prediction of heave motion is 7.51×10 ^-6 %. The improved LSTM improves MAPE by 1.05×10 ^-2 % compared with the BP and 1.42×10 ^-4 % compared with the standard LSTM. The prediction results show that the improved LSTM has a strong prediction capability with not easily overfitted in vessel heave motion prediction. The results show that the improved LSTM provides a new idea for vessel motion prediction and solves the problem of time delay, which is useful for the study of stability in marine operations.

Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). This pre-selected network first identifies and extracts a region of interest from input images. In order to efficiently match ship candidates, the principle of our approach is to distinguish suspected areas from the images based on hue, saturation, value (HSV) differences between ships and the background. The whole approach is the basis of an experiment with a large ship dataset, consisting of Google Earth images and HRSC2016 datasets. The experiment shows that the H-YOLO network, which uses the same weight training from a set of remote sensing images, has a 19.01% higher recognition rate and a 16.19% higher accuracy than applying the you only look once (YOLO) network alone. After image preprocessing, the value of the intersection over union (IoU) is also greatly improved.

Aiming at the continuous motion control problem of the upper limb auxiliary exoskeleton. In this paper, we use particle swarm optimization (PSO) to optimize the long short-term memory network (LSTM), and use the optimized network to establish a map between surface electromyography (sEMG) signals and joint angles. The inputs of the model are sEMG feature time series, which have been dimension reduction processed, and the outputs of the model are the joint angles of the elbow and wrist. To validate the effectiveness of the PSO-LSTM model, eight healthy subjects participated in the experiment. Eight channels of sEMG from eight human upper limb muscles were recorded and two joint angles including the elbow joint and wrist joint were acquired. The proposed PSO-LSTM model and back propagation neural network (BP) model were trained and tested, by using the sEMG feature time series. These time series have been processed for continuous estimation of human upper limb movements. Our experimental results showed that the proposed PSO-LSTM model could achieve a significantly lower estimation root mean square error (RMSE) than the BP model. The RMSE of PSO-LSTM is 0.0599 and the RMSE of BP is 0.1986 in the experiment of continuous elbow movement. The RMSE of PSO-LSTM is 0.1025 and the RMSE of BP is 0.2348 in the experiment of continuous wrist movement. These results suggest that the proposed PSO-LSTM model has a good performance on joint angles estimation by using sEMG feature time series that have been processed for subjects. This model would be used on rehabilitation robots for active rehabilitation of spinal cord injury (SCI) patients or stroke patients.