Multivariate time series (MTS) forecasting is crucial in the finance, energy and transportation sectors. Although current models deliver high prediction accuracy, their complexity and extensive computational demands hinder practical deployment. To address these limitations, this paper introduces a lightweight multiscale spatio-temporal convolutional network (LW-MSTCNN) that leverages deep separable convolution (DSCNN) to build an efficient multiscale pyramid model to decrease model complexity and computational costs. Additionally, a multiscale attention (MA) mechanism is incorporated to selectively emphasize important features across different spatial and temporal scales, improving feature extraction and overall performance. This combined approach enhances the model’s ability to process high-dimensional data efficiently while capturing critical multiscale patterns, addressing key challenges in spatio-temporal data processing. Experimental results on four datasets demonstrate that the proposed model significantly reduces the usage of computational resources, with the parameter count reduced by 4 to 5 times, while maintaining comparable prediction accuracy. Additionally, extensive generalization experiments show that the model exhibits strong robustness. This highlights the effectiveness and practicality of the model in multivariate time series (MTS) forecasting.

Automatic modulation classification (AMC) plays a pivotal role in radar and communication systems. Traditional AMC methods assume an equal number of samples for each modulation during training. However, in real-world scenarios, the number of samples collected for different modulations can vary significantly. This disparity leads the model to overfit to majority classes while underrepresenting minority classes in the optimization process, ultimately leading to degraded classification performance. To tackle this issue, this paper presents a Self-supervised Aligned Data Augmentation Network (SADA-Net) for imbalanced AMC. We leverage Data Augmentation (DA) not only to balance data distribution but also increase data diversity, boosting the model’s robustness in handling minority classes. To amplify the effectiveness of DA, a self-supervised aligned module is incorporated to maintain semantic consistency, preventing the model from focusing on irrelevant variations. Furthermore, an adaptive fusion learning strategy is proposed to dynamically adjust the focus between majority classes and minority classes during training process. This progressive training strategy avoids damaging the learned universal features from majority classes when emphasizing the minority data, ensuring a balanced feature learning process. Comprehensive experiments on simulated, publicly available and real-world datasets demonstrate the effectiveness and generalization of SADA-Net under various class-imbalanced conditions.

The linear dynamical system is a common method for modeling time series whose hidden states evolve linearly. However, it assumes the existence of only a single hidden state at each time step, which constrains its capability to represent a combination of multiple time series. Therefore, we previously proposed the factorial linear dynamical systems and an iterative factorial Kalman filter algorithm for state estimations. Based on previous work, this letter analyzes the convergence property of the factorial Kalman filter, which ensures that the Kalman gain converges when t → ∞. Specifically, a sufficient convergence condition of the Kalman gain is provided without observation biases. Furthermore, a sufficient convergence condition of the average innovation is derived with observation biases. Numerical simulations are provided to corroborate the goodness and effectiveness of the derived results

Decentralized federated learning (DFL) has emerged as a conducive paradigm, facilitating a distributed privacy-preserving data collaboration mode in mobile edge computing (MEC) systems to bolster the expansion of artificial intelligence applications. Nevertheless, the dynamic wireless environment and the heterogeneity among collaborating nodes, characterized by skewed datasets and uneven capabilities, present substantial challenges for efficient DFL model training in MEC systems. Consequently, the design of an efficient collaboration strategy becomes essential to facilitate practical distributed knowledge sharing and cost reduction for MEC. In this paper, we propose an adaptive decentralized federated learning framework that enables heterogeneous nodes to learn tailored collaboration strategies, thereby maximizing the efficiency of the DFL training process in collaborative MEC systems. Specifically, we present an effective option critic-based collaboration strategy learning (OCSL) mechanism by decomposing the collaboration strategy model into two sub-strategies: local training strategy and resource scheduling strategy. In addressing inherent issues such as large-scale action space and overestimation in collaboration strategy learning, we introduce the option framework and a dual critic network-based approximation method within the OCSL design. We theoretically prove that the learned collaboration strategy achieves the Nash equilibrium. Extensive numerical results demonstrate the effectiveness of the proposed method in comparison with existing baselines.

Since the inception of Industry 5.0 in 2021, a growing number of researchers have begun to pay their attention to the revolutionary shift it brings. The principles of Industry 5.0, including human-centric, sustainability, and emphasis on ecological and social values, will become the new paradigm for future industrial development. In this transformative landscape, artificial intelligence (AI) plays a pivotal role, and foundation models based on ChatGPT are set to reshape the organizational structure of industries. In this article, we introduce a multimodal perception and decision-making system built upon a foundational model. This system integrates image and point cloud data to enhance perception accuracy and provide ample information for decision making. It is designed to achieve a deep integration of AI and human-centric autonomous driving within the context of Industry 5.0. We introduce a cross-domain learning approach in the system architecture, along with a model training method from foundation models to handle complex road conditions. The proposed method enables road drivable area segmentation on complex unstructured roads. To address the issue of increased variance caused by the residual structure employed in previous works, this article introduces a distribution correction module, which effectively mitigates this problem. Furthermore, to achieve high-performance perception systems in intricate road scenarios, we put forth a multimodal perception fusion method in this study. The experiments demonstrate the superiority of this approach over single-sensor perception. This work contributes to the ongoing discourse on the convergence of AI, human-centric values, and advanced driving systems within the framework of Industry 5.0.

Remote sensing semantic segmentation plays a significant role in various applications such as environmental monitoring, land use planning, and disaster response. CNNs have been dominating remote sensing semantic segmentation. However, due to the limitations of convolution operations, CNNs cannot effectively model global context. The success of Transformers in the NLP domain provides a new solution for global context modeling. Inspired by Swin Transformer, we propose a novel remote sensing semantic segmentation model called CSTUNet. This model employs a dual-encoder structure consisting of a CNN-based main encoder and a Swin Transformer-based auxiliary encoder. We first utilize a detail-structure preservation module (DPM) to mitigate the loss of detail and structure information caused by Swin Transformer downsampling. Then we introduce a spatial feature enhancement module (SFE) to collect contextual information from different spatial dimensions. Finally, we construct a position-aware attention fusion module (PAFM) to fuse contextual and local information. Our proposed model obtained 70.75% MIoU on the ISPRS-Vaihingen dataset and 77.27% MIoU on the ISPRS-Potsdam dataset.

Effective road maintenance can not only achieve a balance between limited resources and long-term high-efficiency performance of road but also reduce the loss of life and property caused by road damage to vehicles and pedestrians. Due to the lack of a multidimensional dynamic monitoring system and enough extremely special data, the existing road maintenance system cannot accurately assess the road surface condition and provide timely early warning of sudden road damage. In this article, the M-RM system is proposed, that is, a metaverse-enabled road maintenance system based on cyber–physical–social systems (CPSSs), which fully utilizes the social and artificial system information of CPSS, as well as the simulation, monitoring, diagnosis and prediction functions of road systems in the virtual world of the metaverse. Then, in the road damage detection of system model in the virtual world, for the virtual data of the core assets of the metaverse, we propose an adaptive and information-preserving data augmentation (AIDA) algorithm-based nonclassical receptive field suppression and enhancement, an algorithm developed from human visual cognition. This algorithm enables the generation of a large amount of scarce fidelity data and avoids the introduced noise from impairing the performance of nonaugmented data. Finally, a crack detection algorithm named pay attention twice (PAT) is proposed, which uses the generated virtual data for training, and achieves secondary attention to high-frequency targets by fusing frequency-division convolution and mixed-domain attention mechanism. The detection performance of small targets in uncertain environments is enhanced. The metaverse system built in the current research can not only be used for road maintenance but also empower the traffic metaverse by using the traffic flow prediction module embedded in the algorithm. Experimental results demonstrate that the proposed algorithm can be applied to the road damage detection task under different noise and weather conditions, and the performance outweighs other state-of-the-art algorithms.

Vehicle Ad-hoc Networks have laid an essential technical foundation for realizing intelligent transportation. Unexpected mobility change of a specific node often causes a communication link failure. Thus, this work proposes a node backup strategy for routing protocol in software-defined vehicular networks. The core of the strategy is to promote communication link stability through backup nodes. A node backup routing algorithm is designed to search for alternative nodes for each node in a communication link. A node can flexibly select the next-hop node during packet transmission based on actual conditions. When an unexpected mobility change of a specific node causes a communication link failure, we can restore the link by enabling the alternative nodes. The influence of various factors on packet reception rate and communication delay is studied through simulation experiments. By comparing with two existing routing protocols, the effectiveness of the proposed approach is verified.

Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein-protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision-recall curve of 0.63 and a Cohen's Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network's information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

In complex electromagnetic environments, the challenge of signal sorting task for multi-function radars (MFRs) with various work modes has arisen. The previous methods are prone to cause the so-called “increasing batch” problem, which means that the work modes of one MFR may be sorted into multiple emitters. In this letter, a MFR signal sorting method based on complex network is proposed to tackle the problem mentioned above. The novel method utilizes limited penetrable visibility graph to construct the network from interleaved radar pulse sequences, then employs label propagation algorithm and density peak clustering to detect community structures, thus fulfilling deinterleaving of pulses from several MFRs. Simulation results show that the proposed method is effective to alleviate the “increasing batch” problem, and is also robust under non-ideal conditions.

In an ever-increasingly complex electromagnetic environment, multiple low probability of intercept (LPI) radar emitters may transmit their own signals simultaneously on similar bands, resulting in overlapping receiving signals in both time and frequency domain. In this letter, a novel Multi-Instance Multi-Label learning framework based on Deep Convolutional Neural Network (MIML-DCNN) is proposed to automatically recognize the overlapping LPI radar signals,which is trained by single type of signals only. The framework handles signals in an end-to-end manner that is integrated with a well-designed instance generation module, a sophisticated MIML classifier, and an adaptive threshold calibration. Through comprehensive experiments on simulated overlapping signals with four different modulation types, we prove that the proposed framework identifies each individual signal type precisely in the presence of overlapping signals, and is also robust to variation of the signal-to-noise ratio (SNR) and power ratio conditions.

In this study, the target detection based on single surveillance channel is proposed in passive bistatic radar (PBR) system. The reference signal is recovered from the covariance matrix. The cancellation performance of the recovered reference signal is further investigated, and the reason for the poor cancellation performance is also analyzed. In order to improve the performance, the maximization cancellation ratio (CA) criterion is designed, the whole processing is given. Finally, the effectiveness of the proposed scenario has been demonstrated by the real recorded data.

Direct signal and surface clutter cancellation is an important problem in bistatic noise radar. LMS filter is employed to suppress the direct signal and clutter in this work. Direct signal and clutter cancellation ratio (DCR) is used to evaluate the performance of suppression. The impact of two factors, SNR of reference signal and fractional delay of clutter, is analyzed. The analysis is verified by numerical simulation.