Accurate state estimation for dynamic targets is essential in fields such as target tracking, navigation, and autonomous driving. However, traditional estimation models struggle to handle the nonlinear motion patterns and sensor noise prevalent in real-world environments. To address these challenges, this paper proposes a novel end-to-end estimation model named Variational Neural Network with Planar Flow (VNNPF). The model integrates a Bayesian Gated Recurrent Unit (BGRU) as the process model, a planar flow-based variational autoencoder (PFVAE) as the measurement model, and a Bayesian hyperparameter optimization module inspired by Kalman filtering. The BGRU captures nonlinear temporal depend... More >

Accurate traffic flow prediction plays a critical role in intelligent transportation systems, providing essential support for urban planning, traffic control, and congestion mitigation. To address the challenges of spatial heterogeneity and temporal dynamics inherent in traffic data, this paper proposes AST-GNNFormer, an adaptive spatio-temporal graph neural network that integrates graph attention mechanisms with temporal convolution. The model introduces three key components to enhance predictive accuracy and generalization: (1) a Layer-aware Information Preservation mechanism that mitigates over-smoothing in deep GNNs by retaining original node features across layers; (2) an Inter-Layer At... More >

This paper focuses on the needs of automation professional talent cultivation in the context of the construction of new engineering disciplines, and takes the course “Freshman Engineering Experience” as the research object, and carries out a systematic exploration of teaching reform in response to the problems of insufficient professional cognition and disconnection between theory and practice that exist in the current engineering education. By restructuring the curriculum system, innovating teaching methods and optimizing the practice platform, a progressive cultivation mode of “Cognition-Practice-Innovation” has been constructed. In curriculum design, the combination of professiona... More >

With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tai... More >

On behalf of the Editorial Board, I am very pleased to announce the launch of our new transactions, ICCK Transitions on Sensing, Communication, and Control. This publication aims to serve as a premier platform for researchers, engineers, and scholars to share cutting-edge discoveries, methodologies, and applications in the rapidly evolving fields of sensing, communication, and control. More >