The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.

Simultaneous Localization and Mapping (SLAM) technology based on LiDAR can achieve real-time robot positioning and establish environmental maps in unknown environments. LiDAR odometry can achieve accurate pose estimation in short distances or small-scale environments, but the accuracy will decrease with the accumulation of errors in large-scale environments. At the same time, under scenes with insufficient structural features, point cloud-based LiDAR SLAM will show degradation phenomena, leading to failures in localization and mapping. We propose a loop closure detection method based on fusion of Inertial Measurement Unit (IMU) and LiDAR information to reduce system cumulative errors and solve environment degradation problems. Firstly, by fusing LiDAR key feature and IMU information, an odometry calculation method is proposed based on Iterative Extended Kalman Filtering (IEKF) to improve the accuracy of FAST-LIO initial pose. Then in loop closure detection, by considering geometric and intensity information simultaneously, a new scan context global descriptor is constructed from dedistortion feature points of front-end IMU to enhance the accuracy of loop closure detection for original point cloud descriptors. Finally, GTSAM is used for global optimization and GPS constraint is introduced to reduce trajectory drifts, obtaining globally consistent trajectories and mapping. Compared with existing LiDAR SLAM on KITTI dataset and self-collected dataset, proposed method has smaller trajectory errors on KITTI sequences, which reduced by 15% than baseline FAST-LIO2, and average time consumption of loop closure detection is reduced by about 50% than SC-ALOAM, and mapping drifts is reduced, which enhanced mapping accuracy and robustness while ensuring global consistency of constructed maps.

The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider’s server contains a lot of valuable resources. LoBSs’ users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs’ risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs’ risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing.