To address the high computational complexity and data association challenges in multi-sensor networks, this paper proposes a spatial-adaptive confidence-weighted Poisson multi-Bernoulli mixture (SACW-PMBM) algorithm that applies a divide-and-conquer fusion strategy, reformulating the global multi-target estimation problem into a series of localized, weighted tasks. Existing distributed PMBM fusion methods, such as those employing the weighted arithmetic average (WAA) or generalized covariance intersection (GCI), utilize a single scalar weight per sensor, which fails to capture spatially varying information quality. The primary contribution of this work is to replace these scalar weights with a state-dependent confidence function, ω(s)(x), that dynamically models sensor reliability across the state space. This confidence function is constructed using a variational autoencoder (VAE) that jointly represents local information entropy and environmental disturbances. We subsequently derive a novel fusion criterion by minimizing the confidence-weighted Kullback-Leibler (KL) divergence between the local sensor posteriors and the fused global density. A computationally tractable hierarchical architecture is also developed to manage hypothesis complexity. Simulation results in challenging scenarios with severe occlusion and non-uniform clutter demonstrate that the proposed SACW-PMBM algorithm achieves superior tracking accuracy and robustness compared to benchmark centralized PMBM (C-PMBM), WAA, and GCI fusion methods.

An electrocardiogram (ECG) is a crucial noninvasive medical diagnostic method that enables real-time monitoring of the electrical activity of the heart. ECGs hold a significant position in the rapid diagnosis and routine monitoring of cardiac diseases due to their user-friendly operation, prompt detection, broad range of diagnosable problems, and cost-effectiveness. However, thorough comprehension of ECG readings requires a high level of medical expertise due to the complex variations in ECG patterns, substantial interindividual differences, and numerous interfering factors. Consequently, current ECG machines and ECG Holters typically provide simplistic indications of ECG anomalies. Nonetheless, current ECG anomaly detection (EAD) algorithms lack precision; therefore, these medical devices cannot accurately report the specific types of diseases reflected in ECG results. In response to these challenges, this paper proposes enhancing the accuracy of electrocardiogram detection by improving algorithms. Therefore, we propose SAPTSTA-AnoECG, a PatchTST-based ECG anomaly detection method with subtractive attention and data augmentation. This method introduces a subtractive attention mechanism to make the Transformer architecture more suitable for time series data. We also use data augmentation to increase the robustness of the model. In addition, a patch-based approach is employed to reduce the algorithm’s computational complexity of the model. Furthermore, we introduce a new publicly available ECG dataset named HCE in this paper and conduct comparative experiments using this dataset along with the PTB-XL and CPSC 2018 datasets. The experimental results demonstrate the effectiveness of this method.

A scarcity of frequencies arises from the increased demand for the Industrial Internet of Things (IIoT) and networked systems in warehouse operations. This puts an additional burden on available bandwidth in cellular networks. This problem can be tackled by cognitive radio networks (CRNs), which use spectrum sensing to track and access unused frequencies, increasing spectrum efficiency. However CRNs have been extensively studied in several IoT projects, this study is the initial to examine the utilisation of CRN to intelligently manage the use of available radio frequencies. Two macro base stations are the central hubs of a network, communicating with IIoT devices in warehouse settings. This paper investigates the utilisation of CRN with machine-learning algorithms to intelligently manage the spectrum for connected IoT devices for intelligent Warehouse settings. A range of machine learning methods, including Support Vector Machine (SVM), k-nearest Neighbors (KNN), Decision Tree, Random Forest, and Naive Bayes, are provided to identify accessible bands for the best possible spectrum allocation and to recognize key users. Based on criteria like accuracy, precision, recall, and score for all ML techniques, the system’s performance is assessed. The numerical results demonstrate a noteworthy 20% reduction in false positives and a substantial improvement in cooperative spectrum sensing accuracy, which in turn improves the effectiveness of IIoT operations in warehouse environments.

The key technologies for applying fifth generation in high-altitude platform station (HAPS) communication systems have many outstanding advantages, especially large-scale antenna array technology. HAPS communication systems have inherent advantages that can perfectly complement large-scale array antenna technology and solve the problems of many existing ground communication systems. The chances of long-packet data users and short-packet data users being serviced are different due to the differing data length of each user's packet. According to the user's channel environment and data length, the user with the smallest delay is selected for transmission. Dynamic and interdependent characteristics between systems and the evaluation of the efficiency of wireless resources are studied in this article. Game theory is applied to the evaluation of the wireless resource efficiency of the system, and an efficiency evaluation model is constructed. In addition, the efficiency of this preliminary work is verified by simulation.