Motor imagery (MI) decoding plays a crucial role in the advancement of electroencephalography (EEG)-based brain-computer interface (BCI) technology. Currently, most researches focus on complex deep learning structures for MI decoding. The growing complexity of networks may result in overfitting and lead to inaccurate decoding outcomes due to the redundant information. To address this limitation and make full use of the multi-domain EEG features, a multi-domain temporal-spatial-frequency convolutional neural network (TSFCNet) is proposed for MI decoding. The proposed network provides a novel mechanism that utilize the spatial and temporal EEG features combined with frequency and time-frequency characteristics. This network enables powerful feature extraction without complicated network structure. Specifically, the TSFCNet first employs the MixConv-Residual block to extract multiscale temporal features from multi-band filtered EEG data. Next, the temporal-spatial-frequency convolution block implements three shallow, parallel and independent convolutional operations in spatial, frequency and time-frequency domain, and captures high discriminative representations from these domains respectively. Finally, these features are effectively aggregated by average pooling layers and variance layers, and the network is trained with the joint supervision of the cross-entropy and the center loss. Our experimental results show that the TSFCNet outperforms the state-of-the-art models with superior classification accuracy and kappa values (82.72% and 0.7695 for dataset BCI competition IV 2a, 86.39% and 0.7324 for dataset BCI competition IV 2b). These competitive results demonstrate that the proposed network is promising for enhancing the decoding performance of MI BCIs.

This paper presents a study on short-term cooperative conflict detection and resolution (CDR) of unmanned aerial vehicles (UAVs). A two-layered CDR mechanism is proposed, which aims at guaranteeing safe separation, minimizing the overall cost of UAVs, and improving computational efficiency. In the first layer, the information from the environment is processed. In the second layer, conflicts among UAVs are resolved by applying the local centralized optimization method, with consideration given to the dynamic constraints of UAVs. This paper studies the safe separation constraints of pairwise conflicts in virtue of a geometry-based method. A heading change and speed change mixed conflict resolution approach is applied. To meet with the online planning requirements, the vectorized stochastic parallel gradient descent-based method is proposed to find the local optimal heading change solutions. The linear safe separation constraints on speeds are derived. A periodicity feature-based method is used to depart the feasible sub-regions for each pairwise conflict. A mixed integer linear programming model is established to find the optimal speed change solutions. The experiments results show that the proposed heading change algorithm could greatly reduce the summation of additional flight distances of UAVs, and influences on air traffic, compared with other short-term algorithms; the computational efficiency of this algorithm satisfies the online planning requirement. Comparing with the existing algorithm, our speed change algorithm reduces the number of feasible sub-regions to 2nc times lower, where nc is the number of pairwise conflict, and therefore, it reduces computation time dramatically.

This letter introduces a novel deep reinforcement learning (DRL) method for collision avoidance problem of fixed-wing unmanned aerial vehicles (UAVs). First, with considering the characteristics of collision avoidance problem, a collision prediction method is proposed to identify the neighboring UAVs with a significant threat. A convolutional neural network model is devised to extract the dynamic environment features. Second, a trajectory tracking macro action is incorporated into the action space of the proposed DRL-based algorithm. Guided by the reward function that considers to reward for closing to the preset flight paths, UAVs could return to the preset flight path after completing the collision avoidance. The proposed method is trained in simulation scenarios, with model updates implemented using a soft actor-critic (SAC) algorithm. Validation experiments are conducted in several complex multi-UAV flight environments. The results demonstrate the superiority of our method over other advanced methods.

The electroencephalography (EEG) signals are easily contaminated by various artifacts and noise, which induces a domain shift in each subject and significant pattern variability among different subjects. Therefore, it hinders the improvement of EEG classification accuracy in the cross-subject learning scenario. Convolutional neural networks (CNNs) have been extensively applied to EEG-based Brain-Computer Interfaces (BCIs) by virtue of the capability of performing automatic feature extraction and classification. However, they have been mainly applied to the within-subject classification which would consume lots of time for training and calibration. Thus, it limits the further applications of CNNs in BCIs. In order to build a robust classification algorithm for a calibration-less BCI system, we propose an end-to-end model that transforms the EEG signals into symmetric positive definite (SPD) matrices and captures the features of SPD matrices by using a CNN. To avoid the time-consuming calibration and ensure the application of the proposed model, we use the meta-transfer-learning (MTL) method to learn the essential features from different subjects. We validate our model by making extensive experiments on three public motor-imagery datasets. The experimental results demonstrate the effectiveness of our proposed method in the cross-subject learning scenario.

This paper addresses the onboard locally centralized conflict resolution problem of heterogeneous unmanned aerial vehicles (UAVs) with variable headings. Firstly, the geometric properties of the conflict resolution problem are analyzed in two-dimensional space. By mapping the nonlinear safe separation constraint to the sine value space, a linear constraint for safe separation is established for pairwise conflicting UAVs. Then, a mixed integer linear programming (MILP) model is built to cope with the conflict resolution problem of heterogeneous UAVs, where the objective is to minimize the overall costs. A coordination coefficient λ is defined to enhance the coordination performance between UAVs. The proposed MILP model is extended to the conflict resolution problem of UAVs in 3D space. By using the CPLEX solver, the MILP model can be efficiently solved. Numerical simulations are presented to demonstrate the effectiveness of the proposed approach. The simulation results show that the proposed approach can generate conflict free solutions more efficiently when compared with the existing short-term coordination algorithm. The proposed approach is able to realize UAV coordination in highly congested air traffic and satisfies the onboard conflict resolution requirement.

The wide applications of Unmanned Aerial Vehicles (UAVs) in the low altitude airspace have brought about the requirement for online conflict resolution coordination. In this study, we propose the decentralized onboard in-depth coordination method. As a premise, we anticipate that each UAV could receive the states of the neighboring UAVs in a wide range with advanced wireless communication devices. Therefore, each UAV is able to determine the surroundings of itself and the UAVs that conflict with it. A two-layered in-depth coordination method is proposed. In the first layer, each conflict-involved UAV determines the maneuver constraints and conflict avoidance responsibilities of itself and its neighbors based on the rules-based coordination method. In the second layer, each UAV determines appropriate conflict avoidance maneuvers according to the in-depth coordination mechanism independently. The safe separation requirements of the neighboring UAVs are considered. Furthermore, we extend the in-depth coordination method to the bio-inspired UAV flocks coordination method with reference to the behaviors of biological flocks. The virtual UAVs are constructed to promote the connections between UAVs in terms of conflict resolution, so that the boundary UAVs would take appropriate heading maneuvers to make room for the practical conflict-involved UAVs. We validate our approach on representative scenarios, and the results demonstrate that our method is beneficial in reducing the impact on the air traffic, and is capable of dealing with complex conflict scenarios.