Abstract Objective pain assessment remains challenging, particularly in patients with limited capacity for self-report. Electroencephalography (EEG), with its high temporal resolution, non-invasiveness, and clinical accessibility, has become an important tool for investigating pain-related neural dynamics and developing objective pain decoding models. This review summarizes recent advances in EEG-based pain decoding, focusing on neural oscillatory mechanisms, signal processing strategies, and machine learning (ML) and deep learning (DL) approaches. Current evidence indicates that acute pain is mainly associated with stimulus-evoked oscillatory responses, especially gamma-band activity related to pain intensity, whereas chronic pain is characterized by altered resting-state rhythms, abnormal low-frequency activity, and reorganization of large-scale brain networks. Traditional ML methods offer interpretability and are suitable for small-sample studies but rely heavily on handcrafted features. In contrast, DL models can automatically learn complex spatiotemporal representations and generally achieve higher decoding performance, although their generalizability and interpretability remain limited. Key challenges include signal artifacts, inter-individual variability, inconsistent experimental paradigms, and a lack of standardized public datasets. Future studies should emphasize multimodal fusion, transfer learning, explainable artificial intelligence, standardized validation, and clinical translation toward real-time and personalized pain monitoring.
Abstract Addressing the inherent low stiffness of flexible manipulators, existing control schemes often face an intrinsic contradiction where rapid convergence leads to increased vibration amplitudes, making it challenging to achieve high-precision trajectory tracking while effectively suppressing elastic vibrations. To tackle this issue, this paper proposes a neural network-based Fixed-Time learning control strategy. This strategy is capable of simultaneously handling output constraints, model uncertainties, and input dead-zone nonlinearities of the system. The designed controller effectively compensates for the adverse effects of the input deadzone, ensuring that all system states converge to a small neighborhood around the origin within a fixed time, thereby significantly improving the system’s convergence speed and transient performance. By introducing a logarithmic Barrier Lyapunov Function (BLF), the prescribed tracking error constraints are strictly guaranteed. Furthermore, high-frequency chattering is mitigated through a smooth approximation of the sign function. Experimental results demonstrate that, compared with the PSF controller, the proposed Fixed-Time control scheme reduces the steady-state tracking errors by 61.9% and 69.2%, respectively. In terms of vibration suppression, the steady-state values of elastic vibrations are reduced by 49.2% and 32.6%, respectively. These results fully validate the superiority and robustness of the proposed control strategy in balancing rapid convergence with vibration suppression.
Abstract Underwater small object detection is one of the emerging core technologies at the intersection of computer vision and underwater imaging detection, which aims to achieve efficient and accurate detection and identification of faint, small targets in underwater environments, and has been widely applied in fields of aquatic organism detection, mineral resource exploration and other fields. Among them, underwater object detection methods based on optical images, with their high resolution and good flexibility, have demonstrated significant advantages in underwater biological detection, recognition and other scenarios. However, there is currently a relative lack of comprehensive reviews on existing research regarding underwater small object detection. To address this gap, this paper presents a systematic review of small object detection in underwater optical images. Firstly, at the level of problems and data, the specific problems faced by underwater small detection are comprehensively sorted out and classified into two categories: the inherent characteristics of underwater small objects, and the degradation characteristics of underwater images. Then 16 existing typical datasets are sorted out and analyzed in detail. Subsequently, at the methodological level, existing underwater small object detection algorithms are systematically summarized from the perspectives of traditional machine learning and deep learning, and an in-depth analysis of improved deep learning-based strategies is conducted by covering five aspects: data augmentation and pre-processing, multi-scale feature fusion, attention mechanism introduction, loss function optimization, and lightweight design. Finally, the challenges faced by underwater small object detection at present are pointed out, and the potential directions for future development are explored, aiming to provide valuable references for subsequent related research.
Abstract Decentralized federated learning (DFL) allows a group of distributed users to train a shared model without relying on a central server. Most existing decentralized aggregation methods, however, still treat the communication network as a flat Euclidean space and overlook the geometric differences that may arise in the model parameter landscape. To better capture these differences, we develop a curvature-aware decentralized federated learning framework, termed CA-DFL, which introduces geometric structure into the aggregation process. The main idea is to view the communication graph from a discrete Riemannian perspective. According to the divergence between neighboring model parameters, each communication edge is associated with one of three constant-curvature geometries: the sphere S2, the Euclidean plane R2, or the pseudosphere P S2. This classification allows model updates to be transferred between tangent spaces through closed-form parallel transport operators, so that the aggregation step respects the local geometry rather than mixing parameters in a purely Euclidean manner. Building on this, we further design a tensor-field-based geometric weighting scheme to assign adaptive aggregation weights using manifold inner products. This weighting scheme only borrows the idea of adaptive neighbor weighting, while the core operation remains curvature-aware transport and aggregation on manifolds. A subtree partition scheme is also introduced to encourage parameter sharing while reducing communication overhead. We establish convergence guarantees for the proposed method under standard assumptions. Experiments on MNIST and CIFAR-10 under ring, exponential, and random communication topologies demonstrate that CA-DFL achieves accuracy competitive with standard decentralized methods while providing adaptive geometric aggregation. A curvature-threshold sensitivity analysis confirms the robustness of the framework to hyperparameter choice.
Electroencephalograph (EEG)-based vigilance estimation methods have achieved significant progress. In vigilance-associated EEG signals, non-adjacent electrodes exhibit strong coupling, and distant time points also demonstrate significant dependence, not limited the adjacent ones. Therefore, fully extracting such rich globalu2212local spatiotemporal characteristics is critical. In this paper, we propose the global enhanced LSTM Network (GE-LSTM-Net), in which synergizes the Transformeru2019s attention mechanism with LSTM to enhance the extraction of spatiotemporal features. Firstly, a specialized sample partitioning strategy along with the designed feature fusion module is adopted to reorganize raw EEG signals into structured 3D differential entropy (DE) feature representations, effectively preserving spatiotemporal and frequency dependencies across electrode channels and time points. Secondly, the attention mechanism and LSTM are encapsulated into a novel module (GE-LSTM module), serving as the core of the proposed GE-LSTM-Net to simultaneously extract spatiotemporal features from 3D representations. In this module, the attention mechanism will extract global information and integrate it into each unit of the LSTM, enabling LSTM to focus on more critical electrode channels and time points and extract richer globalu2212local features. Subsequently, the GE-LSTM-Net demonstrate competitive performance and achieved SOTA results compared to existing methods on two public vigilance datasets. The codes are available at: https://github.com/Lanhao23-nudt/GE-LSTM-Net.
