Search Results (11,320)

A series of Ni_xCo_3−xO₄ (x = 0.75, 1, 1.5, 2, 2.25) catalysts with different Ni/Co molar ratios was fabricated via the co-precipitation method. The catalytic performance for CO oxidation and SO₂ resistance of the prepared catalysts was systematically investigated, and their physicochemical properties were characterized by XRD, SEM, BET, H₂-TPR, CO-TPD and in situ DRIFTS. The experimental results reveal that the Ni₂.₂₅Co₀.₇₅O₄ catalyst exhibits the optimal CO oxidation activity, achieving a CO conversion of 93.25% at 120 °C. The superior catalytic performance can be attributed to its large specific surface area, low reduction temperature, and easily activated lattice oxygen species. When exposed to SO₂ at a concentration 10 times the industrial emission limit, all catalysts exhibited varying degrees of activity loss. Among them, NiCo₂O₄ exhibited the slowest deactivation rate and showed the greatest recovery in CO conversion after SO₂ was cut off, suggesting relatively better sulfur tolerance and recoverability among the investigated catalysts. In conclusion, tuning the Ni/Co molar ratio can effectively optimize the low-temperature CO oxidation activity and sulfur resistance of Ni-Co composite oxides. This work provides a useful reference for the structural composition design and practical application of such catalysts in flue gas purification. Full article

Unmanned aerial vehicles (UAVs) are progressively evolving from remotely operated platforms into intelligent autonomous systems. This research addresses the role of Artificial Intelligence (AI) in advancing UAV capabilities and expanding their applications across diverse sectors. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology, studies retrieved from Scopus and relevant academic books and book chapters were screened, resulting in 217 publications retained for final analysis. The analysis introduces a three-layer framework linking AI techniques, UAV functional capabilities, and application domains. The first layer covers the list of adopted AI and ML approaches in the UAV applications. The second layer maps these approaches to key UAV capabilities, including perception, autonomous navigation, control and stability, swarm coordination, communication, and energy optimization. The third layer examines applications in agriculture, logistics, disaster response, environmental monitoring, surveillance, defense, and wireless network systems. The findings show that deep learning enhances aerial perception, reinforcement learning supports adaptive navigation and control, federated learning improves distributed intelligence, and swarm intelligence enables cooperative multi-UAV missions. Despite these advances, AI-enabled UAVs still face challenges related to energy consumption, onboard computation, data availability, communication reliability, safety, ethics, privacy, and regulation. Future progress is expected to be driven by edge AI, Tiny Machine Learning (TinyML), quantum-inspired optimization, explainable artificial intelligence (XAI), human-AI collaboration, and robust swarm coordination. Overall, this review provides a structured synthesis of AI-enabled UAV research and identifies key directions for future innovation. Full article

Unmanned aerial vehicle (UAV) inspection of transmission towers requires joint analysis of corridor geometry, tower components, and localized defects, whereas available datasets provide incompatible annotations at different scales. Tower-HMT is a hierarchical multitask network that shares a ConvNeXt-Tiny encoder and feature pyramid across corridor parsing, component parsing, missing-bolt localization, and component-condition recognition. Four public real-image datasets are organized by native supervision rather than merged into a flat label space. Task-conditioned feature modulation separates dataset statistics; topology and boundary losses preserve thin conductors and lattice edges; and a detached tower-probability gate supplies structural context to a high-resolution bolt head. Source-image groups define non-overlapping training, validation, and test partitions. On held-out data, the network achieved foreground mIoU values of 0.597 for tower-conductor parsing and 0.561 for box-conditioned component parsing, an AP50 of 0.123 for missing-bolt localization, and a macro-F1 of 0.925 for component-condition recognition. Boundary, class-wise, robustness, threshold-sensitivity, and latency results quantify the effects and limitations of hierarchical learning. The framework provides a reproducible real-image baseline for review-oriented inspection, while low missing-bolt localization accuracy and weak component masks remain the principal constraints. Full article

Small cargo unmanned aerial vehicles (UAVs) operating in narrow confined spaces suffer instantaneous collision impacts that induce gyroscope heteroscedastic noise and abrupt angular velocity derivatives, triggering severe dynamic attitude errors in traditional strapdown inertial navigation systems (SINS). Existing algorithms separate angular velocity fitting and noise suppression, adopt unified three-axis weighting, and lack adaptive segmentation for collision disturbances, limiting navigation accuracy without raising computational costs. This paper proposes an integrated heteroscedastic decoupling algorithm for UAV SINS under collision interference. Hermite orthogonal polynomials are utilized to fit non-stationary angular velocity with derivative matching constraints, an optimized single-pass CUSUM detector with steady-state residual compensation is proposed to identify collision-induced variance change points. An axis-differentiated weighting strategy is developed to suppress heteroscedastic noise. Recursive least-squares is adopted to lower online computation overhead. Multi-condition coning motion simulations show Hermite polynomials achieve the lowest attitude RMSE under steady flight; the improved CUSUM detector delivers shorter detection delay, fewer false alarms, and lighter computation than mainstream detection methods, and segmented differentiated weighting eliminates collision-induced noise distortion at the raw measurement stage. The proposed algorithm unifies signal fitting and noise correction with minimal computational overhead, effectively mitigating dynamic errors for lightweight airborne navigation hardware and offering a high-precision front-end preprocessing solution for cargo UAVs operating in cluttered obstacle environments. The proposed algorithm is positioned as a gyro-only front-end preprocessing module; accelerometer-related error compensation and full multi-sensor back-end integration are addressed in ongoing work. Full article

Reliable coordination of unmanned aerial vehicle (UAV) swarms is significantly challenged by random denial-of-service (R-DoS) attacks, which introduce stochastic packet loss, time-varying communication interruptions, and strong concealment. Existing fault-tolerant consensus approaches typically assume known attack information or treat attack detection, topology recovery, and control design as separate processes, resulting in limited resilience under dynamically evolving attack conditions. To address this issue, this paper proposes a detection-guided resilient consensus control framework for UAV swarms under R-DoS attacks. A dual-dimensional statistical detection method is developed by jointly modeling packet reception rate (PRR) and inter-arrival time (IAT), enabling real-time identification of attack-induced anomalies through spatio-temporal feature fusion. Based on the detection results, a distributed topology reconstruction strategy is designed, incorporating redundant node identification and cluster-based dynamic communication reconfiguration. The communication graph is adaptively updated via online adjustment of adjacency and Laplacian matrices, and robustness guarantees for the resulting consensus process are analytically established. Hardware-in-the-loop simulation experiments under both single-leader and multi-leader architectures demonstrate that the proposed method can accurately detect attacked nodes, effectively reconstruct the communication topology, and maintain stable formation coordination under severe R-DoS attacks. The position tracking error is constrained within 0.4 m, validating the effectiveness and robustness of the proposed framework. This study is limited to defensive cyber-resilience in a closed hardware-in-the-loop simulation environment and does not address reconnaissance payloads, weaponization, target selection, or operational attack execution. Unlike methods that assume known attack schedules or treat detection, topology recovery, and control separately, this study focuses on their online coupling under unknown random packet loss; its validation is limited to the stated closed HIL impairment model. Full article

Unmanned aerial vehicles (UAVs), also referred to as unmanned aerial pesticide application systems (UAPASs), are increasingly used for crop protection applications because of their operational efficiency and precision. However, UAV spraying is typically conducted at very-low volumes (VLVs) (10–20 L ha⁻¹), resulting in highly concentrated spray solutions that may alter formulation behavior relative to conventional ground applications. In this study, a total of nineteen commercially available herbicide, insecticide, and fungicide formulations representing multiple formulation classes were evaluated under UAV-relevant (10 L ha⁻¹) and conventional ground application conditions. Tank-mix compatibility, sprayability, droplet size distribution, driftable fines, dynamic surface tension (DST), and droplet spreading were assessed. Tank-mix incompatibility was most frequently observed in mixtures containing emulsifiable concentrate (EC) formulations, with five of 11 commonly used tank mixes exhibiting severe incompatibility at UAV rates despite compatibility at conventional application volumes. Formulations containing suspended actives, including suspension emulsions (SEs), suspension concentrates (SCs), oil dispersions (ODs), and water-dispersible granules (WDGs), showed the greatest risk of filter and screen clogging, whereas EC and soluble liquid (SL) formulations exhibited acceptable sprayability. UAV-rate spray solutions generally produced comparatively finer droplet spectra than ground-rate solutions, increasing driftable fines by up to 36.6% depending on formulation type. DST decreased by 2.5–35.2% under UAV conditions, with the largest reductions observed for SC and EC formulations. Reduced DST was associated with increased droplet spreading, particularly for fungicide formulations, where droplet spreading increased up to 718.8% relative to ground-rate preparations. These results demonstrate that formulation behavior can differ substantially under VLV conditions and that formulation-specific evaluation of compatibility, sprayability, atomization characteristics, and surface-tension-dependent behavior is required when products are deployed through UAV spray systems. Full article

UAV (Unmanned Aerial Vehicle)-to-satellite self-localization is commonly treated as satellite tile retrieval. This makes large-area search tractable, but it also forces a continuous localization problem into a discrete ranking form. Once the task is defined this way, training naturally relies on hard positive–negative tile labels, and inference tends to read coordinates from the top-ranked tile center. The model, therefore, learns image identity more than geographic continuity, while the final estimate remains vulnerable to tile-center quantization and top-1 retrieval errors. We propose PFIRNet (Probability Field Inference Network), a continuous geographic posterior inference framework that reformulates retrieval outputs as evidence for coordinate estimation rather than discrete tile selection. It uses distance-aware geographic supervision to shape candidate responses according to metric proximity, lifts top-k candidates into a coordinate-space probability field, and applies risk-calibrated multi-peak verification to update the estimate only when an alternative posterior peak is sufficiently supported. On DenseUAV, PFIRNet reduces the median localization error to 9.84 m and outperforms both one-stage retrieval methods and two-stage matching baselines. It also remains more robust under sparse and non-aligned satellite galleries. Full article

Unmanned aerial vehicles (UAVs) are increasingly used for wildfire and disaster monitoring, enabling rapid data collection in hazardous, inaccessible areas. Secure and reliable communication is a major challenge in wildfire monitoring, as heat, smoke, terrain obstacles, and electromagnetic interference can degrade or interrupt data transmission. This paper analyzes communication failures in drone-based wildfire-monitoring systems, examining their causes, evolution, and operational implications. A multi-layered analytical framework integrating physical, technical, network, and security aspects is proposed. The study highlights cascading failure processes and supports the design of more resilient UAV communication architectures for dynamic wildfire environments. Full article

As an emerging and powerful technology, unmanned aerial vehicles (UAVs) have tremendous potential applications in real-time monitoring, instant communication, data transmission, and more, providing ground terminal users with more efficient services and support. In this paper, we analyze the optimal networking scheme for user association with UAVs based on matching algorithms, which offer higher throughput and satisfaction. Particularly, to minimize algorithm complexity and enhance the efficiency of user devices, we propose a novel approach for stable UAV–user device pairing. This framework combines the concepts of density-based clustering algorithms and utility-driven matching algorithms. Firstly, we address the issue of large-scale scenarios with numerous and unevenly distributed user devices by proposing a clustering algorithm. This clustering algorithm divides the geographical area into multiple grids and clusters based on local density and relative distance within each grid. Next, we introduce a hierarchical matching game, where user clusters and UAVs are the players in the game. Each player ranks the other based on their individual utility functions, constructing preference lists of UAVs for users and vice versa. The network resource balancing and efficiency maximization are achieved through the matching process of bilateral selection. Simulation results demonstrate that this method exhibits low average required transmit power per user and the highest throughput among the five compared schemes under hotspot user distributions. Full article

Machine learning intrusion detection for unmanned aerial vehicles (UAVs) reports extremely high accuracy on public benchmarks, but such figures depend critically on how models are evaluated, and they are often inflated by identifier leakage or collection artifacts. We study the harder, more realistic problem of leakage-safe zero-day detection of identity-based attacks, using the Sybil attack as the representative case. When evaluating every attack in a UAV benchmark as an unseen target, we find that point-wise novelty detectors detect volumetric and routing attacks but fail for Sybil attacks because a single Sybil flow lies inside the region occupied by the known attacks; adding model capacity does not help. We propose a leakage-safe, split-invariant representation of per-source behavioral rates—derived from identity fields without using their values—and score novelty against the benign profile rather than extrapolating a known attack direction. Under a source-disjoint leave-one-attack-class-out protocol, both a hyperparameter-free Mahalanobis detector and an attention autoencoder recover unseen-source Sybil flow (ROC of 0.91 and 0.98, respectively), where per-flow autoencoding fails and the known attack direction inverts. That two different model families succeed once the representation is right shows the representation, not the model complexity, to be decisive. We further find that a public UAV benchmark records device identities too degenerately to evaluate identity-based attacks, motivating identity-preserving benchmarks. Full article

Reliable stability assessment of structurally complex rock masses increasingly relies on advanced remote sensing techniques integrated with detailed geotechnical analysis. This study presents a combined remote geotechnical workflow applied to rock masses surrounding natural cavities, with the study area located in Greece, aiming to evaluate the stability of coupled cavity–slope systems under varying conditions. The methodology combines Unmanned Aerial Vehicle (UAV) photogrammetry and SLAM-based LiDAR surveying to acquire centimetre-scale surface and underground opening data. These datasets are fused into a geometrically consistent three-dimensional representation of the slope–portal–cavity system, enabling improved documentation of slope morphology, internal cave geometry and externally exposed discontinuity patterns. The fused spatial dataset was then used to extract a representative two-dimensional cavity–slope section for plane-strain finite element analysis. The numerical model was formulated as an equivalent-continuum model using the Hoek–Brown failure criterion, with stability assessed through the Shear Strength Reduction technique across multiple scenarios. Overall, the study demonstrates that integrated geotechnical and remote-sensing approaches improve geometric completeness and consistency, enhance reproducibility, and reduce geometry-related uncertainty in scenario-based stability assessments of complex rock masses with natural cavities and underground openings. Full article

Accurate rice-lodging mapping from unmanned aerial vehicle (UAV) imagery supports post-disaster loss assessment, crop insurance, and precision field management. Existing deep-learning methods typically require dense pixel-level annotations, which are costly and time-consuming to produce. Moreover, RGB imagery alone often fails to distinguish lodged from healthy rice when their canopy colors and textures are similar. To address these challenges, we propose Geometry-Guided UniMatch (GUMatch), a semi-supervised multimodal segmentation framework that leverages registered RGB imagery and UAV-derived digital surface models (DSMs). Unlike conventional approaches that treat DSMs as uniformly fused auxiliary channels, GUMatch uses them as reliability-aware geometric priors, incorporating height and boundary evidence to guide lodging segmentation. Our framework integrates three key components. First, Adaptive Geometric Prompting (AGP) injects DSM-based prompt features into the decoder based on local geometric reliability and RGB–DSM compatibility. Second, Geometry-Calibrated Pseudo-Label Learning (GPL) down-weights uncertain pseudo-label supervision within teacher-identified boundary-risk regions. Third, Boundary-Aware Geometric Regularization (BGR) refines boundary localization exclusively where pseudo-labels and geometric evidence are jointly reliable. Experiments are conducted on a three-parcel UAV rice-lodging collection. The main semi-supervised benchmark trains and selects models on the Huai’an parcel HA-P2 under labeled ratios of 10%, 20%, and 40%, and evaluates them on the held-out HA-P1 parcel. The external Wuxi parcel WX-P3 is reserved solely for direct cross-region testing. With an RN-101 backbone, GUMatch achieves 80.12%, 82.98%, and 85.06% mIoU under the three labeled ratios, consistently outperforming representative semi-supervised baselines including UniMatch V2 and RSProtoSemiSeg. With a DINOv2-B backbone, GUMatch further reaches 82.64%, 84.95%, and 86.72% mIoU. On WX-P3 under the 40% labeled setting with models trained on HA-P2, GUMatch with DINOv2-B achieves 68.34% mIoU, improving over UniMatch V2 by 5.39 points. These results demonstrate that reliability-aware geometric guidance enhances annotation-efficient UAV-based rice-lodging mapping. Full article

This study aims to autonomously detect and classify Unmanned Aerial Vehicles (UAVs), which are becoming increasingly widespread, based on their emitted radio frequency (RF) signals in highly noisy and interference-prone RF environments where traditional optical and radar-based methods prove insufficient. The dataset used in this study is the “Noisy Drone RF Signal Classification” dataset, which consists of seven classes in total—six different drone models and one noise class—and is balanced across classes. As the methodological approach, a multimodal hybrid deep learning (DL) architecture was developed, integrating raw time-domain IQ signals (1D-CNN) with frequency-domain spectrogram representations (2D-CNN). To enhance model performance and generalization capability, techniques such as Batch Normalization, Dropout, Label Smoothing, and Gradient Clipping were incorporated into the architecture. The model was trained using the AdamW optimization algorithm alongside a cosine annealing learning rate scheduler. In order to efficiently process large-scale datasets, memory management optimizations including memory-mapped files (mmap) and lazy loading were implemented. Experimental results demonstrate that the proposed multimodal model achieves an accuracy of 87.42% and an F1-score of 0.87, indicating strong detection performance. SNR-based analyses reveal that the model achieves accuracy levels ranging between 64.52% and 82.40% under negative-SNR conditions (−20 to 0 dB), while exceeding 89% accuracy under positive-SNR conditions. The findings indicate that multimodal DL approaches can achieve high performance in UAV detection and classification even in noisy RF environments. Full article

Radio-spectrum prediction can support proactive channel verification, sensing scheduling, and access decisions in unmanned aerial vehicle (UAV) systems. However, existing evidence remains fragmented across UAV-oriented prediction, label-efficient learning, and deployment-oriented efficiency. This article presents a structured critical review of studies identified in IEEE Xplore, Scopus, and the Web of Science Core Collection from database inception to 24 August 2026. Studies were included when they evaluated the prediction of a future spectrum-related condition and were excluded when they addressed only current-state sensing, static spectrum mapping, UAV detection or classification, localization, or superseded study versions. The final corpus comprised 76 retained publications, including 63 original studies and 13 background references. The original studies included 14 direct UAV-related prediction studies, 38 transferable radio-spectrum studies, and 11 UAV-scenario studies. The review shows that transfer learning currently provides the most consistent support for reducing target-domain data requirements when related source bands, sensing stations, or radio environments are available. Self-supervised and other unlabeled-data methods are particularly relevant to UAV missions that can continuously collect spectrum traces but cannot obtain extensive labels, whereas meta-learning remains promising but lacks a standardized support–query evaluation protocol for UAV spectrum prediction. Generative augmentation can expand limited training data, but its effectiveness depends on whether the generated samples preserve the temporal, spectral, spatial, and propagation characteristics of the target environment. Lightweight architectures, online learning, model compression, knowledge distillation, FPGA implementation, and embedded execution provide complementary efficiency mechanisms, but their benefits should be distinguished from one another. Embedded prediction-related processing has been demonstrated on Raspberry Pi and software-defined-radio platforms; however, no end-to-end validation of a UAV-mounted predictor during flight was identified. Overall, the evidence suggests a conditional trade-off among target-domain data requirements, prediction generalization, adaptation cost, and deployment efficiency rather than a universal conflict between few-shot learning and lightweight models. The principal research gap is the limited joint validation of UAV-acquired data, target-domain adaptation, efficient inference, uncertainty-aware decision making, and onboard hardware under representative flight conditions. Full article

To address slow convergence, local optimum stagnation, and multi-objective imbalance problems for unmanned aerial vehicle (UAV) three-dimensional (3D) path planning in complex obstacle environments, an improved adaptive two-stage pigeon swarm optimization (IPIO) algorithm is proposed. Firstly, a hybrid initialization strategy integrating Latin hypercube sampling and obstacle avoidance constraints is adopted to improve initial population diversity and the quality of feasible solutions. Secondly, in the map compass stage, a linearly decreasing adaptive map factor and population diversity-based dynamic perturbation strategy are introduced to balance global exploration and local exploitation while preventing premature convergence. In the landmark stage, an inverse fitness weighting elite center updating mechanism and linearly decreasing elite quantity strategy are designed to enhance the guidance of high-quality individuals and accelerate convergence. A multi-objective fitness function integrating path length, obstacle avoidance safety, and flight smoothness is constructed, whose weight coefficients (${\omega }_{\mathrm{L}}=0.3$, ${\omega }_{\mathrm{C}}=0.5$, ${\omega }_{\mathrm{S}}=0.2$) are calibrated through parameter-sensitivity analysis and Pareto frontier comparison across six representative weight combinations. Combining ablation validation for each improved module, single-UAV multi-scenario tests, and preliminary multi-UAV trials, these coordinated improvements realize targeted optimization for UAV 3D flight characteristics. Specifically, the preliminary multi-UAV trials involve three UAVs performing independent trajectory planning in shared obstacle environments without explicit inter-UAV collision avoidance constraints, and the reported improvements are based on single-UAV experiments. Finally, comparative experiments are conducted with a standard 100 × 100 × 50 m space, and varying obstacle densities are demonstrated in six diverse 3D test scenarios, where the proposed IPIO achieves an average path length reduction of 12.8% and 15.3% compared to the standard PIO and PSO, respectively. The average fitness improvement is 14.2% over PIO, 16.8% over PSO, 19.5% over GWO, 24.1% over CO, and 38.7% over CS. Key path-quality metrics include a minimum obstacle clearance of 2.37 m, average smoothness cost of 0.34, average convergence time of 0.60 s, and computational cost of O(N*D*MaxIter). Statistical tests confirm that these improvements are significant (p < 0.05) in all tested scenarios. This study presents an efficient and robust algorithm for autonomous three-dimensional path planning of UAVs in complex obstacle environments. Full article