Underwater collision-free navigation and dense reconstruction are essential for marine refined exploration. However, existing passive vision-based methods are difficult to apply in low-light and weak-feature underwater environments. In this article, a more adaptable three-dimensional (3-D) dense mapping robotic system based on self-designed scanning binocular structured light (BSL), named ROV-Scanner, is developed to address this challenge. First, the measurement principle based on the refraction model ensures its high accuracy. Second, an underwater 3-D dense mapping algorithm fusing the Doppler velocity log (DVL), inertial measurement unit (IMU), and pressure sensor multifrequency information is proposed to realize dense mapping during robot motion. Then, an air–water two-stage extrinsic calibration algorithm is proposed. In particular, the extrinsic parameters between DVL and camera are innovatively calibrated using BSL, enhancing robustness. Furthermore, for the first time, a framework of BSL-based collision-free navigation is presented to guarantee the safe movement of the system in unknown environments. Experimental results show that our system can simultaneously achieve autonomous collision-free navigation and dense mapping in dark underwater environments, which has great potential for application in marine refined exploration.

Estimating mutual correlations between random variables or data streams is essential for intelligent behavior and decision-making. As a fundamental quantity for measuring statistical relationships, mutual information has been extensively studied and utilized for its generality and equitability. However, existing methods often lack the efficiency needed for real-time applications, such as test-time optimization of a neural network, or the differentiability required for end-to-end learning, like histograms. We introduce a neural network called InfoNet, which directly outputs mutual information estimations of data streams by leveraging the attention mechanism and the computational efficiency of deep learning infrastructures. By maximizing a dual formulation of mutual information through large-scale simulated training, our approach circumvents time-consuming test-time optimization and offers generalization ability. We evaluate the effectiveness and generalization of our proposed mutual information estimation scheme on various families of distributions and applications. Our results demonstrate that InfoNet and its training process provide a graceful efficiency-accuracy trade-off and order-preserving properties. We will make the code and models available as a comprehensive toolbox to facilitate studies in different fields requiring real-time mutual information estimation.

With the continuous exploration and research of researchers in the field of deep learning, deep learning has developed by leaps and bounds. Compared with traditional machine learning technology, deep learning has great advantages. The traditional machine learning algorithm for image classification needs to extract local features by hand. The emergence of deep learning has changed this situation and greatly promoted the development of image classification in the field of computer vision. The main content of this paper is to design a simple convolutional neural network model to classify three different common datasets. Comparative experiments are conducted by changing experimental parameters (such as activation function, pooling mode, size of output size, etc.), and then the influence of different parameters on classification and recognition accuracy is analyzed under different data sets. Experimental results show that rein activation function and maximum pooling are more suitable for the classification of image data sets.

In order to solve the problems of low fault diagnosis accuracy and high false alarm rate in noisy environment, this paper proposes a service fault diagnosis algorithm of noise network under 5G network slice. First, the relationship between the service and the underlying network resources based on the mapping relationship, and fault diagnosis model are established. Secondly, in order to reduce the negative impact of network noise on the fault diagnosis algorithm, the fault propagation model is optimized based on the number of simultaneous faults and the value of the failure rate of each link. Finally, the fault set with the largest interpretation ability is selected as the fault set. In the experimental part, it is verified that the algorithm in this paper improves the accuracy of fault diagnosis and reduces the false alarm rate.

With the development of Internet of Things (IoT), a large number of terminals and devices are connected to the network. Mobile edge computing (MEC) is proposed to assist cloud computing, to relieve the pressure of network and satisfy the requirements of delay-sensitive applications. Considering reasonable allocation of computing resources is the most important aspect corresponding to delay, this paper designs geographic clustering and collaborative scheduling (GC-CS) mechanism. This mechanism can be divided into two parts, which are the decentralized deployment of MEC servers and the resource allocation optimization in MEC. For the first part, this paper designs the load balancing based geographic clustering (LBGC) algorithm which combines the idea of greedy algorithm to realize the initial allocation of computing resources. For the second part, delay minimization oriented collaborative scheduling (DMCS) algorithm is designed to decrease the response delay without increasing system overhead. Finally, the effectiveness of the mechanism is verified by simulation in the IoT scene.