With the advancement if Internet of Things (IoT) and brain-computer interface (BCI) technologies, BCI-controlled unmanned aerial vehicles (UAVs) have demonstrated significant potential to catalyze innovation across diverse domains. However, the existing BCI-controlled UAV systems are constrained by inherent limitations, including restricted degrees of freedom (DOF) in control, excessive architectural complexity, and non-naturalistic human-machine interaction. This study proposed BCI-controlled UAV system with peripheral high-frequency steady-state visual evoked potential (SSVEP). The visual flicker stimulus regions with 31-33.75 Hz alongside the targets were arranged in an alternating configuration along the edges of the stimulus interface. The modified TDCA algorithm was proposed to decode the different patterns among targets and stimulus regions. System optimization, validation of feasibility and practical utility were conducted through offline, online validation and online control experiments. In the online validation experiment, the accuracy and information transfer rates (ITR) across eleven subjects were 90.40%  7.18% and 55.09  9.79 bits/min, respectively. In the online control experiment, real-time footage captured from the UAV was displayed at the center of the interface as visual feedback, and all subjects were able to complete the assigned flight navigation tasks successfully. In this study, the proposed BCI-controlled UAV system supported naturalistic, user-friendly interaction by employing peripheral high-frequency stimuli, while maintaining comparable performance to existing systems.

Infrared Object Tracking (IOT) is challenging due to the low contrast of infrared images, which limits effective spatial feature extraction. Although recent works have explored frequency-domain information, their utilization remains insufficient, and fusion strategies either retain redundancy or fail to fully explore distinctive differences, thus limiting complementary enhancement. To overcome this, we propose a novel tracker that introduces a Target-guided Frequency Transformation Module (TFTM) and a Dual-domain Interactive Fusion Network (DIFN). The former extracts multi-frequency representations across scales and orientations, guided by an adaptive mask strategy to suppress background interference. The latter fuses the two domains with differentiated attention to achieve complementary enhancement. Extensive experiments show that our approach achieves superior performance over state-of-the-art trackers, highlighting the effectiveness of comprehensive frequency-domain integration in IOT.

Due to the limited storage capacity of Internet of Things (IoT) devices, it is hard for these devices to store all applications locally to meet their various intelligent needs. To solve this deficiency, based on application modularization technology, we propose a Terminal-Edge-Cloud (TEC) collaborative caching framework, which includes module caching, request information uploading, and module loading. Through this framework, IoT devices can load application modules on demand and run diverse and intelligent applications. To manage the caching process, we introduce a utility model consisting of application module request and load delay, caching deployment cost, and caching management cost, and formulate the collaborative caching problem as a utility maximization one, which is NP-complete. To solve the problem, we decouple the problem and propose an effective two-stage collaborative caching strategy. Simulation results show that the proposed algorithm can provide an effective caching strategy by improving users’ Quality of Experience (QoE) and reducing the total system cost.

Autonomous vehicles (AVs) are expected to handle complex tasks in dynamic environments. However, the limited onboard computing capacity of AVs and heterogeneous tasks under strict delay constraints bring performance bottlenecks. To efficiently complete the onboard tasks, we formulate a dynamic Vehicle-to-Vehicle Task Offloading Problem (V2TOP) in Vehicular Ad Hoc Network (VANET), aiming to jointly minimize end-to-end task delay and maximize the average speed of connected vehicles subject to safety constraints. Then, we design a Mobility-Aware Distributed Primal-Dual Offloading Algorithm (MDPDOA) to optimize global resource utilization, explicitly modeling both task diversity and heterogeneous computing capabilities of participating vehicles. Furthermore, a dynamic triggering mechanism is introduced to timely re-allocation in response to topology or resource variations, guaranteeing consistent performance in realtime vehicular networks. Simulation results demonstrate that the V2V collaboration policy efficiently utilizes vehicle computing resources and enhances driving safety.

The geostationary interferometric infrared sounder (GIIRS) onboard the Fengyun-4B (FY-4B) is the first operational geostationary hyperspectral infrared (IR) sounder. This study analyzes the first-year FY-4B/GIIRS on-orbit calibration performance by comparing it to the collocated IR atmospheric sounder interferometer (IASI) observations and radiative transfer (RT) simulations. The results reveal that the mid-wave IR (MWIR) channels had a slightly larger calibration bias compared to the long-wave IR (LWIR) channels. However, the operational FY-4B/GIIRS showed improved performance compared to the experimental FY-4A/GIIRS. Furthermore, this study also found that most channels exhibited negligible annual and weak diurnal variations in calibration bias. However, there was a significant degradation in the LWIR channels (<850 cm1) and the weak diurnal variation in the MWIR channels. Finally, the calibration performance of FY-4B/GIIRS demonstrates a reduced dependence on brightness temperature (BT), except for the channel at wavenumber 703.125 cm1. Overall, it concluded that FY-4B/GIIRS demonstrated consistent calibration stability and high accuracy, highlighting its capability for precise quantitative applications.

To improve the positioning accuracy of the three-axis-stabilized geostationary meteorological satellite (GSMS) images, we developed a geometric precision correction process based on landmarks. For the problem that GSMS images and binary landmark images are difficult to match accurately, we made full use of the local and global spatial information of the image pairs and proposed a dual-threshold matching algorithm based on the misalignment matrix to screen the initial matching results generated by the maximum normalized cross correlation. Under the premise of ensuring matching precision, the number of correctly matched point pairs is effectively increased to provide more sufficient samples for subsequent processing. Finally, we used a shallow neural network to establish the transformation relationship between the GSMS image and the reference image and resampled the GSMS image. We selected observations from five different periods of FY-4A/Advanced Geosynchronous Radiation Imager (AGRI) on-orbit and conducted two verification experiments: 1) in the image matching, compared with several commonly used algorithms, we found that the proposed matching algorithm has obvious advantages in matching precision and relative recall; and 2) in the image correction, the positioning error corrected by our methods is about 1.26 pixels$\left ({3\sigma }\right)$in the central area and about 1.30 pixels$\left ({3\sigma }\right)$in the edge area, which is about 14.9% and 9.1% better than the weighted iterative cubic polynomial fitting algorithm, respectively, with better robustness. In conclusion, the landmark-based geometric precision correction process proposed effectively improves the geometric accuracy of GSMS images, which is an important foundation for subsequent quantitative applications.

In this work, we study the problem of interference in a two-tiered uplink network which contains a macro-cell and multiple small-cells. We null the interference from small-cells to macro-cell by using Vandermonde-subspace frequency division multiplexing (VFDM). VFDM can exploit frequency selectivity and null space generated by the cyclic prefixes (CP) used by the macro-cell communication. We use an interference alignment (IA) scheme with an extension of a grouping method to align the interference between multiple small-cells into a lower dimensional-subspace, by using this grouping method we reduce the number of receive antennas required to null interference. Finally, we derive the achievable degrees of freedom (DoF).

This paper presents a new multiple access control (MAC) protocol called adaptive acquisition multiple access with common-transmission channel assignment (AAMA-CT) and its improved one (AAMA-ICT) for wireless multihop mobile ad hoc networks. In the protocol, every node adaptively chooses a usable channel as its transmission channel in advance in multihop mobile environment and takes advantage of request-to-send (RTS) packet transmitted on a common channel to evoke its recipient to complete their communication process on their transmission channels without packet collisions. With this protocol, resource reservation is flexibly and effectively made on multiple channels by half-duplex radios without any help of powerful central controllers and wired backbone networks in an asynchronous fashion. The protocol can solve hidden and exposed terminal problems perfectly as is validated through analysis and simulation results.