Accurate vehicle navigation plays a critical role in vehicle-to-everything (V2X) applications, including connected transportation systems, intelligent traffic management, and autonomous driving. To address the stringent demands of these scenarios, the integration of the global navigation satellite system (GNSS) with the visual-inertial navigation system (VINS) has emerged as a pivotal advancement. Despite these strides, navigation systems remain susceptible to abnormal data. This data, originating from unpredictable external environments and internal device fallibility, poses a threat of substantial errors and system drift. In this paper, we present RF-Nav, a robust fusion-based GNSS-VINS navigation system with enhanced data processing and dynamic factor correction. The framework innovates with a dual-pronged approach: it first applies adaptive gamma correction with bilateral filtering and contrast-limited adaptive histogram equalization (AGCBF-CLAHE) to refine raw images; then, it deploys a long short-term memory (LSTM) denoising network enhanced with an advanced wavelet threshold for IMU data refinement. This dual enhancement of visual and IMU data integrity is further bolstered by a dynamic factor confidence correction mechanism, rooted in factor graph optimization (FGO), designed to counteract the adverse effects of abnormal data. Extensive experiments on large-scale public and real-field dataset demonstrate that RF-Nav exhibits superior robustness and accuracy in various environments.

In this study, the prescribed-time fault-tolerant tracking control problem for quadrotor unmanned aerial vehicle(QUAV) under external disturbances is investigated. Firstly, an error reconstruction mechanism based on adjustable convergence rate is proposed. This mechanism dynamically adjusts the convergence characteristics of the QUAV system, ensuring that the position and attitude tracking errors strictly converge to the prescribed accuracy range before the preset time threshold. Secondly, the disturbance observer is adopted to estimate unknown disturbances and additive faults, thereby reducing the impact of unknown variables on the stability of the system model. Afterwards, an adaptive fault-tolerant control (FTC) strategy is designed. It compensates for the multiplicative faults of the actuator online through the parameter adaptive law, and actively offsets additive faults by combining them with the output of the disturbance observer. This approach forms a composite FTC architecture. Finally, numerical simulation results show that the proposed control scheme can ensure the accurate convergence of the position and attitude system within prescribed-time under multiple actuator faults and unknown external disturbances. This verifies the effectiveness and robustness of the control strategy.

As a forefront core functional module, a bootstrapped sampling switch (BSS) built-in an analog-digital converter (ADC) contributes to realize a high-precision signal sampling in bioelectrical sensing systems. A novel algorithm-based automatic approach to guide the optimal design of a high-performance bootstrapped sampling switch is proposed. Within the first manual topology optimization, a complementary sampling transfer-gate is designed to suppress the clock feed-through effect, and a dynamic body bias control module and an improved bootstrapped closed-loop-path are constructed to effectively improve the linearity. In the secondary core algorithm-based performance solving stage, a support vector regression (SVR) machine is adopted to further explore the best design tradeoff between dynamic noise feature and power dissipation according to the model-training-based optimal solution of the design parameters. SMIC 180 nm/1.8 V standard CMOS technology is employed to implement the front/back-end design of the proposed BSS circuit, and pre-/post-layout simulations for feature verification are performed. Final experimental results show that, targeting a test benchmark signal of 100 Hz and 1.2 V p-p in 51.2 kHz sampling frequency, after mixed-optimization-based ENOB and SNR can be reached up to 12.969 bits and 103.046 dB, respectively. Similar to the other two key dynamic performance indexes, SFDR and THD are also improved to 70.905 dBc and -69.508 dB, respectively. In the design case comparison, with a higher power supply voltage of 1.8 V and kHz-order frequency, the average power consumption is only approximately 0.188 μW. These feature results demonstrated the significant effectiveness of the proposed SVR algorithm aided artificial optimization approach on bootstrapped sampling switch design, and the comprehensive specification of switches can meet the demand of the specific application of highly accurate human bioelectrical signal sampling.

The Internet of Things (IoT) system and path planning algorithm provide a technological foundation for autonomous navigation of uncrewed aerial vehicles (UAVs). Geospatial data from the IoT system is transmitted to UAVs through lightweight protocols, and UAVs make optimal path decisions based on these data through optimization algorithms. The combination of the IoT, UAV, and path planning technology constitutes a UAV delivery system, which offers an efficient and economical solution for last-mile logistics in smart cities. Among these, rapid and accurate optimal path planning is crucial for the autonomous delivery of UAVs. Therefore, this article proposes a multistrategy improved particle swarm optimization (PSO) algorithm called MSIPSO. First, the algorithm incorporates a local deadlock jump strategy to increase the success rate of path planning in dense obstacle environments. Second, to mitigate the influence of parameter selection on the algorithm’s performance, adaptive nonlinear inertia weights and learning factors are introduced to improve the algorithm’s stability. Finally, multiple population differentiation evolution strategies are designed, with different position update equations tailored for populations of varying qualities, which enhances the search efficiency of the algorithm. The simulation results show that MSIPSO outperforms PSO, gray wolf optimizer (GWO), whale optimizer (WOA), elite archive-driven PSO (EAPSO) algorithm, and hybrid GWO and differential evolution (HGWODE) algorithm in terms of convergence speed, accuracy, and stability.

The low resolution of thermal imaging from unmanned aerial vehicles (UAVs) poses a substantial obstacle to the understanding and analysis of ground targets. Utilizing readily available high-resolution visible images presents a promising solution to improve the quality of thermal UAV images. However, current methods primarily focus on simple degradation conditions, neglecting the complexity of real-world degradation scenarios, such as blur and noise, which fail to meet the demands of practical applications. In this paper, we introduce a Degradation-aware Cross-modality Mamba (DC-Mamba) framework to super-resolve (SR) thermal UAV images by integrating degradation information with cross-modality cues. Our approach begins with a self-supervised learning framework that extracts degradation information directly from input images. This information guides the restoration process through the designed degradation-aware modules, which enhance model sensitivity to distorted regions. Additionally, we incorporate a vision-focused state-space module (SSM) to capture long-term spatial dependencies, thereby improving feature adaptability. To address modality disparities, we develop a cross-modality feature integration framework that leverages visible cues at three levels (interaction, refinement, and enhancement) to improve thermal image reconstruction quality. Extensive experiments demonstrate that the proposed method outperforms current state-of-the-art SR methods, providing more realistic details and superior performance across multiple evaluation metrics.

This paper presents an artificial spider-web topology model inspired by the structure and invulnerability of a spider web. A hierarchical clustering routing rule is accordingly established using the vibration transmission features of the natural spider web as a reference. Furthermore, the end-to-end delay is applied as the quantitative indicator of invulnerability for analyzing the communication performance and characteristics of the artificial spider-web topology. The simulation tests of a one-layer and 3-layer artificial spider-web model are implemented to obtain the importance and destructive tolerance of network components based on OPNET, with the change of communication conditions and fault types. This paper can provide a practical analysis method for the invulnerability of the artificial spider-web topology and offer important implications for the construction and maintenance of wireless sensor networks based on the topology.

Through systematic analysis of the structural characteristics and invulnerability of spider web, this paper explores the possibility of combining the advantages of spider web such as network robustness and invulnerability with farmland wireless sensor network. A universally applicable definition and mathematical model of artificial spider web structure are established. The comparison between artificial spider web and traditional networks is discussed in detail. The simulation result shows that the networking structure of artificial spider web is better than that of traditional networks in terms of improving the overall reliability and invulnerability of communication system. A comprehensive study on the advantage characteristics of spider web has important theoretical and practical significance for promoting the invulnerability research of farmland wireless sensor network.