Search Results (53)

Rapid detection of unauthorized unmanned aerial vehicles (UAVs) via radio frequency (RF) spectrograms is critical for low-altitude security. However, standard object detectors struggle to locate transient, frequency-hopping UAV signals because their microscopic spatial footprints are easily discarded by conventional lossy downsampling and overwhelmed by complex background noise. To overcome this limitation, we propose SPA-DETR, a custom architecture based on the RT-DETR framework. The core of our design is the Spatial-Preserving Attention (SPA) block, which integrates Space-to-Depth Convolution (SPDConv) with a Parallel Patch-Aware Attention (PPA) module. By replacing traditional pooling mechanisms, the SPA block preserves the spatial details of weak signals without information loss, while the PPA module concurrently filters out ambient background interference. Furthermore, to address the severe foreground–background imbalance in RF spectrograms, we introduce an Adaptive Threshold Focal Loss (ATFL). Operating exclusively during training, ATFL prevents background noise gradients from dominating the learning process, forcing the network to focus on hard-to-detect signal patches without adding computational overhead during inference. Experiments on our public RFUAV dataset validate the approach. SPA-DETR achieves an $mA{P}_{50:95}$ of 86.8% and an $A{P}_S$ of 85.6%, improving upon the baseline RT-DETR-R18 by 4.9% and 5.2%, respectively. Operating at 235.2 FPS with only 23.74 M parameters, SPA-DETR outperforms contemporary detectors such as YOLOv10m, as well as heavier models like YOLOv8m and RT-DETR-R50, highlighting its efficiency and practical value for real-time low-altitude security applications. Full article

This paper investigates frequency manoeuvring as a method to improve the resilience of unmanned aerial vehicle (UAV) networks operating in contested electromagnetic environments. The study considers scenarios in which the network initially operates on a single channel and is then exposed to intentional jamming or unintentional interference affecting the primary channel, adjacent channels or a wider frequency range. Several response policies are compared, including no channel change, immediate switching after quality degradation is detected, delayed switching after a defined loss-of-connectivity interval, and periodic frequency hopping. In addition to channel switching, the analysis also considers changes in channel bandwidth, comparing narrower channels with lower throughput but potentially higher resistance to interference against wider channels with greater capacity but increased susceptibility to disruption. The evaluation includes the switching cost, which is modelled as temporary packet loss, additional delay and jitter during reconfiguration. Performance is assessed using the packet delivery ratio, latency, jitter, packet loss and communication continuity. The main objective is to identify the interference conditions under which frequency manoeuvring becomes operationally beneficial and to determine which policy offers the best trade-off between resilience and communication performance. In quantitative terms, immediate switching under environmental interference achieved a PDR of 0.961 and a mean latency of 123.6 ms compared with a PDR of 0.946 and a mean latency of 138.7 ms for fixed-channel operation. Manoeuvring gave a substantial 12.2-percentage-point PDR gain under jamming (periodic hopping: 0.780 vs. 0.658) and a 6.7-percentage-point gain under combined interference (0.674 vs. 0.607). These results indicate that manoeuvring is most worthwhile once interference is persistent and channel-focused rather than purely environmental. Full article

Existing target sorting methods suffer severe performance degradation or even failure under inherent severe spectrum overlap, homogeneous protocol parameters, and scarce single-source points in Synchronous Non-Orthogonal Frequency Hopping (SNOFH) scenarios. To address this challenge, this paper proposes a passive sorting framework for SNOFH UAV swarm signals based on multi-station relative hopping time difference. The proposed framework constructs a spatial-location-driven sorting feature system, designs a kernel joint distribution adaptation module to eliminate inter-station measurement discrepancies, and develops a multi-scale wavelet-based method to achieve sub-sampling level hopping time extraction, reducing the dependence on prior FH parameters and hardware radio frequency fingerprints. Experimental comparisons between the proposed and reference sorting methods are conducted on a simulated SNOFH dataset to validate the performance of the proposed sorting framework. The experimental results show that the proposed method achieves the highest sorting accuracy of 98%, outperforming adopted baselines in most SNOFH cases. The proposed method exhibits favorable robustness with noise interference, clock-synchronization error, carrier-frequency offset and multipath influence. It is a suitable choice for UAV swarm sorting under regular and slow-varying UAV formations. Full article

This article presents a fast-locking phase-locked loop (PLL) that incorporates a low-power extended phase frequency detector (LPEPFD) and a discriminator-aided phase detector (DAPD) to simultaneously address cycle slippage and frequency overshoot issues during frequency and phase acquisition, respectively. Specifically, the proposed LPEPFD introduces a novel finite state machine architecture that extends the linear range of a conventional PFD without requiring a power-hungry counter, thereby eliminating cycle slippage and reducing the time required for frequency acquisition while maintaining switching activity and power consumption comparable to those of the conventional design. Moreover, after frequency convergence, the DAPD quantizes the accumulated phase error, which is corrected by adaptively tuning the programmable delay lines without causing significant frequency overshoot seen in conventional PLLs, resulting in improved settling time. Fabricated using a 28 nm complementary metal oxide semiconductor (CMOS) process, the proposed fast-locking PLL occupies an area of 0.36 mm² and operates over a frequency range of 2.6 to 3.2 GHz. Experimental results demonstrate a 0.84-μs settling time for a frequency hop from 2.6 to 3.1 GHz. The designed PLL consumes 5.6 mW of power from a supply of 1 V with an integral root-mean-square jitter of 1.27 ps from 1 kHz to 100 MHz. Full article

Ka-band carrier-frequency-agility (CFA) synthetic aperture radar (SAR) employs pulse-to-pulse random wide-range frequency hopping to enhance anti-interference capability. However, the random hopping disrupts the azimuth phase continuity, and the millimeter-wave wavelength of the Ka band makes the imaging quality extremely sensitive to motion errors. To address these challenges, this paper proposes an auto-focusing imaging framework and performs a performance analysis for Ka-band CFA SAR. First, a back-projection (BP)-based imaging model is derived to restore the coherent phase history from the hopped echoes. Second, to compensate for the residual phase errors inevitable in high-resolution millimeter-wave imaging, an auto-focusing framework is developed. This framework incorporates a dynamic sub-aperture strategy and an adaptive spectral notching mechanism to ensure precise phase error estimation in complex scattering environments. Furthermore, the imaging performance under different frequency-selection modes is analyzed to provide a guideline for the parameter selection of the Ka-band CFA SAR. Experiments with a vehicle-mounted Ka-band SAR system demonstrate that the proposed method achieves well-focused images with 5 cm resolution. Full article

Nested Named Entity Recognition (nested NER) frequently encounters challenges like boundary conflicts, complications in modeling long-distance dependencies, and inadequate representation of deep nested semantics resulting from overlapping spans and hierarchical inclusion relationships of entities. This research presents a multi-scale heterogeneous graph attention network to facilitate end-to-end recognition of nested entities through the collaborative modeling of structure and semantics. The model initially presents the structural integration mechanism, which consolidates the hierarchical restrictions of the syntactic tree and the inter-word relationships of the dependency tree within a singular heterogeneous graph space. It subsequently generates 1/2/3-hop multi-scale subgraphs and employs multi-scale subgraph attention to adaptively integrate information from various structural receptive fields, harmonizing the local cues of shallow entities with the global dependencies of deep entities. The experimental findings on the ACE2004, ACE2005, and GENIA benchmark datasets indicate that the proposed method surpasses several robust baselines regarding overall performance and nested entity recognition, particularly exhibiting notable advantages in identifying long entities and low-frequency entities. We further evaluate MHGAT on KBP2017 and GermEval2014 to validate generalization across datasets and languages. Full article

Conventional message-driven frequency-hopping (MDFH) systems are vulnerable to partial-band jamming, particularly when the jamming simultaneously affects both active and idle subcarriers, which disrupts energy-based detection. To address this limitation, this paper proposes a novel randomized intra-group subcarrier selection with joint suppression (RIJS-MDFH) scheme. In this framework, subcarriers are dynamically organized into configurable groups, and active carriers are randomized within each group. This structure decouples the jamming signal into distinct mainlobe and sidelobe components. The mainlobe is mitigated via rate-adaptive channel coding, whose rate is matched to the jamming bandwidth and the subcarrier mapping configuration. The sidelobe is suppressed using a filter-bank-based technique, effectively accelerating its roll-off. Simulation results demonstrate that the proposed scheme significantly outperforms existing MDFH systems in anti-jamming robustness under identical partial-band jamming conditions. At the same time, it preserves high spectral efficiency through flexible parameter adjustment. The work confirms that jointly addressing both jamming components enables reliable communication under low signal-to-jamming ratios, overcoming a key weakness of conventional MDFH designs. Full article

Unmanned Aerial Vehicles (UAVs) have made significant advancements in communication stability and security through techniques such as frequency hopping, signal spreading, and adaptive interference suppression. However, challenges remain in modeling spectrum competition, integrating expert knowledge, and predicting opponent behavior. To address these issues, we propose UAV-FPG (Unmanned Aerial Vehicle–Frequency Point Game), a game-theoretic environment model that simulates the dynamic interaction between interference and anti-interference strategies of opponent and ally UAVs in communication frequency bands. The model incorporates a prior expert knowledge base to optimize frequency selection and employs large language models for episode-level opponent trajectory generation and planning within UAV-FPG, serving as an operationally more challenging simulator adversary for stress-testing anti-jamming policies under our evaluation protocol. Experimental results highlight the effectiveness of integrating the expert knowledge base and the large language model: relative to fixed-path baselines, iterative feedback-conditioned LLM planning tends to generate more adaptive trajectories and achieve higher opponent rewards in UAV-FPG. These findings are confined to the proposed simulation environment and are not intended as general claims about real-world jamming capability or onboard planning performance. UAV-FPG provides a robust platform for advancing anti-jamming strategies and intelligent decision-making in UAV communication systems. Full article

Recent developments in unmanned aerial vehicle (UAV) activity highlight the need for advanced electromagnetic spectrum monitoring systems that can detect drones operating near sensitive or restricted areas. Such systems can identify emissions from drones even under frequency-hopping conditions, providing an early warning system and enabling a timely response to protect critical infrastructure and ensure secure operations. In this context, the present work proposes the development of a high-performance multichannel broadband monitoring system with real-time analysis capabilities, designed on an SDR architecture based on USRP with three acquisition channels: two broadband (160 MHz and 80 MHz) and one narrowband (1 MHz) channel, for simultaneous, of extended spectrum segments, aligned with current requirements for analyzing emissions from drones in the 2.4 GHz and 5.8 GHz ISM bands. The processing system was configured to support cumulative bandwidths of over 200 MHz through a high-performance hardware platform (powerful CPU, fast storage, GPU acceleration) and fiber optic interconnection, ensuring stable and lossless transfer of large volumes of data. The proposed spectrum monitoring system proved to be extremely sensitive, flexible, and extensible, achieving a reception sensitivity of −130 dBm, thus exceeding the values commonly reported in the literature. Additionally, the parallel multichannel architecture facilitates real-time detection of signals from different frequency ranges and provides a foundation for advanced signal classification. Its reconfigurable design enables rapid adaptation to various signal types beyond unmanned aerial systems. Full article

Accurate ultra-short-term wind power forecasting is vital for the secure and economic operation of power systems with high renewable penetration. Conventional models, however, struggle with multi-scale frequency feature extraction, dynamic cross-variable dependencies, and simultaneously capturing local fluctuations and global trends. This study proposes a novel hybrid framework termed VMD–ALIF–GraphBlock–MLLA–Transformer. A dual-decomposition strategy combining variational mode decomposition and adaptive local iterative filtering first extracts dominant periodic components while suppressing high-frequency noise. An adaptive GraphBlock with MixHop convolution then models structured and time-varying inter-variable dependencies. Finally, a multi-scale linear attention-enhanced Mamba-like module and Transformer encoder jointly capture short- and long-range temporal dynamics. Experiments on a real wind farm dataset with 10-min resolution demonstrate substantial superiority over State-of-the-Art baselines across 1-, 4-, and 8-step forecasting horizons. SHAP analysis further confirms excellent consistency with underlying physical mechanisms. The proposed framework provides a robust, accurate, and highly interpretable solution for intelligent wind power forecasting. Full article

Two-legged hopping is a well-established model for assessing leg stiffness; however, in existing studies, it is unclear whether the trial segment selection affects the results. This study aimed to assess if the selected hopping segment alters the value and individual variability (%CVind) of leg stiffness and kinetic performance metrics. Elite women athletes (42, volleyball, basketball, handball) and 14 non-athletic women performed barefoot two-legged hopping (130 bpm) on a force-plate (Kistler, 9286AA, sampling at 1000 Hz). Leg stiffness was estimated from the Fz registration (resonant frequency method). Four cumulative range segments (1–10, 1–20, 1–30, and 1–40 hops) and three segments of 10-hop subranges (11–20, 21–30, and 31–40) were analyzed (repeated measures one-way Anova, p ≤ 0.05, SPSS v30.0). The hopping segment did not significantly alter the leg stiffness value (segment average 30.6 to 31.2 kN/m) or its %CVind (segment average ≈ 3%). The kinetic performance metrics depicted a solid foundation for the extracted leg stiffness value, with %CVind not exceeding 6.2%. The results indicate a data collection of just 15 hops, in continuance reduced to a 10 hops segment (after excluding the first five to avoid neuromuscular adaptation) as a robust reference choice. Full article

In dynamic, high-interference industrial and logistics environments, multi-AGV cooperative tasks are often affected by communication delays and data loss, leading to information staleness and reduced control accuracy. Traditional handshake frequency hopping communication strategies introduce additional overhead in high-load environments, and channel selection strategies struggle to adapt to dynamic changes. To address challenges related to communication delay, task coordination, and real-time information exchange, we propose a control and communication co-optimization method based on a nonlinear Age of Information (AoI) penalty and an adaptive handshake frequency hopping mechanism. The method constructs a coupled control-communication model, designs an adaptive handshake period and multi-channel frequency hopping strategy to reduce channel conflicts, and introduces a nonlinear AoI penalty function that prioritizes the update of critical timely information, improving communication success rates and path control accuracy. Furthermore, by integrating the differential dynamics model, state estimation under communication delay and control error modeling, we propose a cooperative optimization algorithm for perception control and communication based on nonlinear AoI optimization (PPO-CCBNA). The algorithm achieves efficient solution based on approximate policy optimization (PPO). Simulation results demonstrate that PPO-CCBNA significantly outperforms benchmark algorithms in communication success rates, control stability, and energy efficiency, validating its effectiveness and feasibility in complex multi-AGV cooperative tasks. Full article

This paper challenges the conventional wisdom of using uniform random resource selection for collision resolution in distributed scheduling, particularly in wireless protocols. Bluetooth, being one such technology, is analyzed through its frequency hopping mechanism to explore for a better alternative in random access MAC (medium access control). Using diffusion theory, we characterize Bluetooth’s original frequency hopping as exhibiting maximum diffusivity, which correlates with unnecessarily high collision rates and a short mean first encounter time (MFET) between nodes. MFET, defined as the expected time until two independent hopping sequences first collide on the same channel, serves as an intuitive metric for evaluating collision likelihood. This insight leads to the proposal of a new collision avoidance mechanism with reduced diffusivity, effectively increasing MFET while maintaining efficient spectrum utilization. Our analysis and simulation results demonstrate that it can significantly lower packet collisions, outperforming existing techniques such as adaptive frequency hopping. The results are further corroborated by a real-life prototype implementation that closely replicates the predicted performance. The proposed diffusion-based MAC, by explicitly targeting longer MFETs, is expected to better handle dense Bluetooth environments, which are becoming increasingly common. Full article

Enhancing user access capacity in satellite beam-hopping systems remains challenging due to dynamic traffic and limited beam dwell times. Conventional Multi-Frequency Time-Division Multiple Access (MF-TDMA) proves highly inefficient under such constraints. To overcome this, we propose a novel scheme that integrates power-domain Non-Orthogonal Multiple Access (NOMA) with MF-TDMA, employing Successive Interference Cancelation (SIC) for multi-user signal separation. A bi-directional adaptive carrier synchronization method and optimized burst structure are introduced, which collectively reduce synchronization overhead by over 40% compared to MF-TDMA. Simulations demonstrate a dramatically improved frame error rate of 0.0005% at 4 dB SNR—30 times lower than the 0.016% achieved by MF-TDMA—and a transmission efficiency of 92–97%, significantly outperforming conventional MF-TDMA. These results validate the proposed method’s substantial gains in capacity and efficiency for next-generation satellite systems. Full article

In practical applications, the clarity of analog dial images is often compromised due to factors such as lighting conditions, leading to low precision and poor segmentation of dial scales and pointers. This results in segmentation outcomes that fail to meet the real-time requirements of substation inspection systems. To address these challenges, we propose an improved U-Net segmentation algorithm. The key innovation of our approach is the insertion of a layer-hopping connection module between the Encoder and Decoder to capture feature information across multiple scales, enhancing semantic expressiveness and optimizing feature fusion. Additionally, we replace traditional convolution operations with wavelet convolution, which improves the network’s ability to capture low-frequency information, essential for understanding the overall dial structure. An adaptive attention mechanism is also incorporated in the upsampling stage of the network, enabling the model to dynamically focus on salient features, further improving generalization. These improvements enable the network to more accurately detect target regions within dial images, significantly enhancing segmentation accuracy and robustness. Experimental results demonstrate that the proposed method outperforms traditional U-Net models in segmentation tasks, achieving superior precision in segmenting scales and pointers, effectively addressing issues of low precision and poor segmentation, and making it suitable for real-time substation inspection systems. Full article