Cognitive Language Processing (CLP), situated at the intersection of Natural Language Processing (NLP) and cognitive science, plays a progressively pivotal role in the domains of artificial intelligence, cognitive intelligence, and brain science. Among the essential areas of investigation in CLP, Cognitive Signal Decoding (CSD) has made remarkable achievements, yet there still exist challenges related to insufficient global dynamic representation capability and deficiencies in multi-domain feature integration. In this paper, we introduce a novel paradigm for CLP referred to as Disentangled Frequency-Spatial-Temporal Attention(D-FaST). Specifically, we present an novel cognitive signal decoder that operates on disentangled frequency-space-time domain attention. This decoder encompasses three key components: frequency domain feature extraction employing multi-view attention, spatial domain feature extraction utilizing dynamic brain connection graph attention, and temporal feature extraction relying on local time sliding window attention. These components are integrated within a novel disentangled framework. Additionally, to encourage advancements in this field, we have created a new CLP dataset, MNRED. Subsequently, we conducted an extensive series of experiments, evaluating D-FaST’s performance on MNRED, as well as on publicly available datasets including ZuCo, BCIC IV-2A, and BCIC IV-2B. Our experimental results demonstrate that D-FaST outperforms existing methods significantly on both our datasets and traditional CSD datasets including establishing a state-of-the-art accuracy score 78.72% on MNRED, pushing the accuracy score on ZuCo to 78.35%, accuracy score on BCIC IV-2A to 74.85% and accuracy score on BCIC IV-2B to 76.81%.

This paper addresses the problem of cooperative coverage control for heterogeneous Autonomous Underwater Vehicle (AUV) swarms operating in complex underwater environments. The objective is to achieve optimal coverage of a target region while simultaneously ensuring collision avoidance—both among AUVs and with static obstacles—and satisfying the inherent dynamic constraints of the AUVs. To this end, we propose a hierarchical control framework that fuses Control Barrier Functions (CBFs) with consensus theory. First, addressing the heterogeneity and limited sensing ranges of the AUVs, a cooperative coverage model based on a modified Voronoi partition is constructed. A nominal controller based on consensus theory is designed to balance the ratio of task workload to individual capability for each AUV. By minimizing a Lyapunov-like function via gradient descent, the swarm achieves self-organized optimal coverage. Second, to guarantee system safety, multiple safety constraints are designed for the AUV double-integrator dynamics, utilizing Zeroing Control Barrier Functions (ZCBFs) and High-Order Control Barrier Functions (HOCBFs). This approach unifies the handling of collision avoidance and velocity limitations. Finally, the nominal coverage controller and safety constraints are integrated into a Quadratic Programming (QP) formulation. This constitutes a safety-critical layer that modifies the control commands in a minimally invasive manner. Theoretical analysis demonstrates the stability of the framework, the forward invariance of the safe set, and the convergence of the coverage task. Simulation experiments verify the effectiveness and robustness of the proposed method in navigating obstacles and efficiently completing heterogeneous cooperative coverage tasks in complex environments.

Unmanned Surface Vehicle (USV) swarms face significant challenges during dynamic target encirclement in complex marine environments, particularly regarding strong target maneuverability and strict safety constraints. To address these issues, this paper proposes a novel encirclement and capture method. The proposed approach integrates a bio-inspired pursuit-evasion dynamic game with fixed-time safety control. Drawing on the behavioral mechanism of dynamic cooperative blocking and escape space compression in group predation in nature, a multi-pursuer single-evader dynamic game model is established. The payoff functions of pursuers and the evader are constructed to characterize the strategic interaction between both sides, and adaptive cooperative encirclement strategies for the USV swarm are generated based on dynamic game equilibrium. To address environmental disturbances and the requirement of rapid response in complex marine environments, a fixed-time safety controller is designed. Taking the desired encirclement positions generated by the dynamic game as control objectives, a safety constraint mechanism is constructed by introducing control barrier functions, which achieves rapid convergence and cooperative collision avoidance of the USV swarm. Furthermore, the fixed-time convergence of position errors and the satisfaction of safety constraints for the control system are proven via Lyapunov stability theory. Finally, dynamic target encirclement numerical simulations under ocean current disturbances verify the effectiveness of the proposed method. The results demonstrate that the USV swarm can adapt to continuous target maneuvering, realize stable encirclement and capture, and satisfy safety constraints.