The brain–computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011–2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals.

In oceanographic mapping, underwater exploration, and military reconnaissance scenarios, it is necessary to deploy multiple autonomous underwater vehicles (AUVs) to cooperatively fulfill some operational tasks. In the presence of uncertain dynamics, time-varying ocean environmental disturbances, input saturations, and communication delays, this article innovatively solves the 3-D time-varying formation problem of underactuated AUVs by creating a data-driven motion state predictor (DDMSP), a modified super-twisting algorithm (STA), and a new auxiliary dynamic system (ADS). In lieu of the indirect way of handling the communication delays in most of the existing works, a novel way of directly handling them is proposed in this article with help of the created DDMSP. Based on the modified STA, a super-twisting extended state observer is constructed to accurately estimate the total disturbances lumped by the uncertain dynamics and disturbances. The ADS contributes to mitigating the adverse effect of input saturations. By virtue of the above, a novel distributed robust prescribed performance 3-D time-varying formation control law is developed, which ensures that the AUVs maintain the desired 3-D time-varying formation pattern, while presetting the time desired for the formation errors to reach and stay within the prescribed error tolerance band. Simulation studies on a group of AUVs demonstrate the remarkable performance of our developed formation control law.

Effective communication between multiple autonomous underwater vehicles (AUVs) is necessary for formation control. As the most reliable underwater communication method, acoustic communication still has many constraints compared with radio communication, which affects the effectiveness of formation control. Therefore, this paper proposes a formation control scheme for multiple AUVs under communication delay, packet discreteness and dropout. Firstly, the communication delay is estimated based on the kernel density estimation method. To solve the problem of packet discreteness and dropout, the curve fitting method is used to predict the states of the AUV. Secondly, a follower controller is designed based on the leader–follower approach using input–output feedback linearization, which is proven to be stable with Lyapunov stability theory. Then, some simulation results are presented to demonstrate the stability and accuracy of the formation control in different communication environments. Finally, the field tests on the lake show that the scheme introduced in this paper is valid and practical.

The media access control (MAC) protocol is a primary technology to achieve a collision-free transmission and improve the channel utilization for underwater acoustic networks (UANs). Most relative studies currently focus on solving underwater acoustic sensor networks (UASNs) with a limited mobility and fixed topology, where nearly all the data is transmitted from the network edge to the sink node. However, multiple underwater unmanned vehicle (multi-UUV) networks have complex and variable topologies and usually contain both unicast and multicast transmission. The MAC protocol designed for it should be flexible and achieve a low latency while avoiding signal collisions. This article proposes a distributed channel sensing MAC (DCSM) protocol based on the aforementioned requirement, which allows each node to sense changes in surrounding traffic by sharing the slot usage table. The proposed adaptive transmission scheduling algorithm makes each vehicle select the optimal transmission slot based on a predictive value function, which utilizes long propagation delays to achieve spatial-temporal reuse and is adaptive to the network load. The simulation results demonstrate that compared to the other four distributed MAC protocols, DCSM had significant advantages in the transmission success rate (15% higher) and throughput (35% higher) and achieved a short latency. The adaptability and flexibility of DCSM have also been verified through simulations.

In systems of multiple autonomous underwater vehicles (AUVs), to achieve cooperative operation and cluster intelligence, information is often disseminated via broadcasting. However, due to the long propagation delay and slow transmission rate of underwater acoustic communication, traditional broadcast scheduling algorithms require a long broadcast period to avoid signal collision. To improve the channel utilization rate as much as possible and improve the update rate for broadcast information, we propose an optimal broadcast scheduling algorithm. This algorithm uses the location information of AUVs to adjust the broadcast sequence and broadcast schedule, to achieve the shortest possible collision-free broadcast period in the broadcast network for the current node distribution. Simulation experiments show that this algorithm can achieve a broadcast period much shorter than that of traditional TDMA and higher channel utilization without signal collision. In addition, the simulations prove the feasibility of applying the algorithm in an actual MAC protocol.