Search Results (308)

This paper presents a microwave sensing platform based on a 32-element dipole array designed for near-field dielectric contrast mapping. The sensor utilizes an $8\times 8$ tightly coupled dipole array (TCDA) topology, where pairs of dipoles form unit cells that exploit electromagnetic coupling variations. A 32-way equal power divider network ensures uniform excitation across the aperture. Operating at 830 MHz, the dipole array exhibits high absorption (>90%), which enhances near-field intensity and sensitivity to surface perturbations. Experimental validation with dielectric samples, saline liquids of varying concentrations (${ϵ}_r\approx$ 70–78), and biological tissues demonstrates the array’s capability to map spatial variations in electromagnetic properties through rectified DC voltage shifts. When compared to a state-of-the-art multi-port Vector Network Analyzer (VNA) configurations, the proposed architecture offers a robust, low-complexity, proof-of-concept alternative by eliminating complex RF routing networks and multi-port switches. Full article

Micro-Unmanned Aerial Vehicles (micro-UAVs) require compact radar arrays under severe Size, Weight, Power, and Cost (SWaP-C) constraints. Spread Spectrum Digital Beamforming (SSDBF) reduces receiver hardware by multiplexing multiple antenna channels before a shared RF chain, but the receiver-side switching operation folds wideband noise and injects switch-related transient noise before digital demultiplexing. This paper develops a hardware-waveform co-design framework that links the transition density of bipolar spreading sequences to high-frequency code energy and switching-event count, while explicitly distinguishing modulation-induced thermal-noise folding from switch-transient injection. Transition density is used as a physically interpretable design surrogate rather than a sufficient statistic for folded noise, and it is constrained jointly with non-zero-shift cross-correlation to preserve spatial isolation under receiver-side timing skew. An $ϵ$-constrained Greedy Coordinate Space Search ($ϵ$-GCSS) algorithm is proposed to synthesize low-transition-density SSDBF code sets. Commercial circuit-level transient simulations and system-level MATLAB R2024b simulations show that the proposed code set reduces the PRN-like transition-density level from about $0.50$ to about $0.19$, lowers the circuit-simulation-derived integrated IF noise power by $9.63$ dB relative to the Gold/PRN-like baseline, and improves the normalized maximum detection range by $8.2$ percentage points at $K=4$ without adding analog front-end hardware. Full article

Reconfigurable radio-frequency (RF) front ends employ N-path filters to achieve digitally tunable frequency selectivity, high linearity, and low static power. However, their linear periodically time-varying (LPTV) operation complicates analysis because an input tone is translated to multiple output harmonics. This tutorial review synthesizes the principal methods for analyzing N-path filters, comparing continuous-time window function analysis, discrete-time ordinary differential equation (ODE) modeling, and adjoint network methods. We evaluate and compare their underlying assumptions, outputs, and computational burdens. Additionally, we present an educational time-domain interpretation based on orthogonal sine/cosine excitation. This viewpoint connects capacitor averaging and path-to-path phase cancellation with harmonic transfer functions (HTFs). Rather than replacing rigorous HTF formulations, this interpretation provides a physically intuitive explanation for the fundamental coefficient $H_0\left(f\right)$ and the gain-null condition at $f_{\mathrm{in}}=kN{f}_s$. The numerical integration of the switched-RC equations serves as a consistency check. For a four-path example with $\Gamma =\tau /\left(RC\right)=0.02$, the numerical values of $|H_0\left(f_s\right)|$ and $|H_0\left(2f_s\right)|$ differ from the intuitive limits by less than $0.001$ dB. The residual responses at $4f_s$ and $8f_s$ are $-49.95$ dB and $-55.97$ dB, respectively. Finally, we extend the orthogonal-excitation relationship to extract higher-order HTFs. This tutorial synthesis clarifies how these established analytical methods relate and guides selection for specific applications. Full article

Yttrium iron garnet (Y₃Fe₅O₁₂, YIG) films were deposited on gadolinium gallium garnet (Gd₃Ga₅O₁₂, GGG) through room-temperature RF magnetron sputtering and annealed in flowing oxygen. We examined the effects of annealing temperature and film thickness using X-ray diffraction (XRD), scanning electron microscopy (SEM), longitudinal magneto-optical Kerr effect (MOKE) loops and multi-frequency field-swept ferromagnetic resonance (FMR). Representative SEM images showed laterally uniform surface backgrounds with sparse localized particulates. Gaussian switching-field fits with residual-bootstrap analysis were used to obtain Kerr-derived coercive fields, switching-field standard deviations and their normalized ratios. In the temperature series, the 900 °C sample gave the smallest fitted σ_SFD/H_c, whereas the 850 °C sample gave the lowest effective damping of α = (4.2 ± 0.27) × 10⁻³. In the thickness series annealed at 850 °C, the 75 nm film combined the smallest σ_SFD/H_c (3.11 ± 0.38%) with the lowest measured α, (5.84 ± 0.16) × 10⁻³. The different temperature optima show that switching uniformity and dynamic loss should be assessed separately. The results provide an empirical comparison for selecting processing conditions within the investigated RF-sputtered YIG/GGG series. Full article

This paper proposes a high-sensitivity and sensitivity-tunable vibration sensing system based on a switchable loop length optoelectronic oscillator (OEO). Carrier-sideband separation is realized by using an acousto-optic modulator (AOM), and the resonant cavity length is designed to be independent of the sensing fiber arm. Compared with a conventional 10 GHz OEO under the same total loop delay condition, the proposed architecture provides a theoretical sensitivity enhancement of approximately $1.93\times {10}^4$, without requiring a high RF oscillation frequency. Meanwhile, the system oscillates at only 80 MHz, which greatly reduces the implementation difficulty of the frequency detection circuit. The proposed scheme further introduces a mechanical optical switch (MOS) to select intra-loop fibers of different lengths, thereby reconfiguring the equivalent loop delay and the free spectral range of the OEO. Experimental results show that stable single-mode oscillation is achieved at 80.42 MHz with a side-mode suppression ratio of 51 dB. By selecting loop fiber lengths of 1200 m, 500 m and 0 m, frequency-to-displacement sensitivities of 0.892 GHz/cm, 1.93 GHz/cm and 9.27 GHz/cm are obtained respectively, with excellent linearity. A 600 Hz vibration signal is successfully demodulated with a signal-to-noise ratio of 72.1 dB. The proposed method provides a simple and reconfigurable solution for high-precision vibration measurement under different operating conditions. Full article

As frequent and highly destructive geologic hazards, debris flows necessitate effective monitoring alongside rapid and accurate detection to support disaster prevention and mitigate losses of life and property. Current detection technologies, however, are often limited by high false alarm rates, insufficient accuracy, considerable model complexity that complicates deployment, and the elevated costs of contact-based detection. To overcome these limitations, this paper introduces a lightweight real-time debris flow detection model based on the Roboflow Detection Transformer (RF-DETR). First, we constructed a dataset of realistic debris flow scenarios including debris flow disaster events worldwide. Based on this, the lightweight vision transformer model EfficientFormerV2 is adopted as the backbone. By employing a dimension-consistent architecture, the model avoids the frequent switching between 4D and 3D features found in traditional Vision Transformers (ViTs), thereby reducing a significant number of inefficient operations. Additionally, we optimized the multiscale projection layer by removing downsampling and feature aggregation operations, which reduces redundant computations and improves feature extraction efficiency. Furthermore, the introduction of the Efficient Intersection over Union (EIoU) loss function achieves faster convergence and improved detection accuracy for debris flows. Ablation studies performed on our debris flow dataset demonstrate that the improved model reduces parameters by 54.2% and computational load by 36.4% while ensuring acceptable losses in detection accuracy and latency. This significant reduction in model size and complexity achieves effective lightweighting, fulfilling the requirements for deployment on edge devices and enabling real-time debris flow detection. Full article

In recent years, there has been growing interest in applying vision–language models (VLMs) to quantitative remote sensing. This study evaluates whether three commercial VLMs (GPT-4o, GPT-5.5, and Claude Sonnet 4.6) can detect and classify the severity of harmful algal blooms (HABs) from Sentinel-2 satellite imagery of western Lake Erie and compares them against classical machine learning classifiers (Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost)) trained on both a three-band red, green, blue (RGB) composite representation of the imagery and a 10-band multi-spectral reflectance representation. Forty bloom events identified from the National Oceanic and Atmospheric Administration (NOAA) Harmful Algal Bloom Operational Forecast System (HAB-OFS) severity assessments were assembled into the evaluation dataset, spanning seven bloom seasons (2019–2025). For binary bloom detection, the VLMs did not match the classical RGB classifiers; their F1 scores (0.69–0.75) fell below the best RGB classifier (Random Forest, 0.76) and below a trivial always-present baseline (F1 = 0.77), and they carried false positive rates of 73–93% on bloom-absent images, against 27–40% for the RGB classifiers. The VLMs reached high recall by labeling most scenes as bloom-positive, which makes them operationally unreliable in this configuration. For severity classification, the VLMs assigned 60–70% of their predictions to the “moderate” category regardless of actual conditions and identified at most one of the two severe blooms, whereas the classical classifiers tracked the ground-truth distribution and delivered two to nearly three times the exact-match accuracy (0.44–0.59 vs. 0.20–0.225). The strongest method across all metrics was the multi-spectral SVM (F1 = 0.833, false positive rate 27%, accuracy 0.795). Switching the same SVM from RGB to multi-spectral features raised accuracy from 0.675 to 0.795, a 12-percentage-point gain that measures the spectral information carried by red-edge and shortwave infrared bands that are accessible through multi-spectral sensors but unavailable to standard VLM vision encoders. Feature-importance analysis showed that the multi-spectral classifiers ranked chlorophyll-specific indices, the Normalized Difference Chlorophyll Index (NDCI) and the Floating Algae Index (FAI), among their top predictors, the same signatures used in established operational algorithms, while the RGB classifiers relied on red-channel variability and green-dominant pixel fractions because RGB inputs cannot compute those indices. Two compounded limitations therefore constrain off-the-shelf VLMs for aquatic remote sensing: the limited spectral information available through standard RGB channels and a mismatch between the land-dominated training distributions of these models and aquatic optical conditions. Domain-specific classifiers operating on multi-spectral data remain the more suitable tools for continued development of HAB monitoring and water-quality retrieval. Full article

In modern radar and electronic countermeasure systems, frequency-agile (FA) signal generators with low phase noise are of vital importance. The optoelectronic oscillator (OEO) is restricted by the periodic boundary condition (PBC), despite its superior performance in phase noise and frequency tunability. This paper proposes a new FA optoelectronic hybrid oscillator scheme, which employs a reconfigurable aperiodic FA filter and a dynamic frequency compensation module to collaboratively break the PBC limitation. It achieves fast switching and fine-grained frequency hopping at the 100 kHz level while maintaining low phase noise. Theoretical and experimental verification show that the system can generate arbitrary FA radio frequency (RF) signals from 1.95 GHz to 2.05 GHz with a tuning range of 10³ times the free spectral range (FSR), and the phase noise reaches −120 dBc/Hz at 10 kHz offset. This study provides a novel technical route for generating narrow-step frequency-agile signals and effectively improves target detection accuracy and anti-jamming performance in electronic warfare applications. Full article

Indoor visible light communication (VLC) has attracted increasing attention as a promising wireless access technology because of its large unlicensed bandwidth and dual functionality of illumination and data transmission. However, practical VLC systems are vulnerable to line-of-sight (LoS) blockage caused by user mobility, human shadowing, and indoor obstacles, which may degrade link reliability and service continuity. Although hybrid VLC/RF networks can improve robustness by using RF transmission as a backup link, excessive RF fallback under severe optical blockage may overload the bandwidth-limited RF interface and reduce the service quality of RF-associated users. To address this issue, this paper investigates a risk-aware illumination-constrained resource allocation scheme for hybrid VLC/RF indoor networks under random optical blockage. A unified system model is developed by considering Lambertian optical propagation, random optical blockage, RF backup transmission, and working-plane illumination constraints. Based on this model, a joint user association and power allocation problem is formulated under QoS, transmit-power, and illumination requirements. The proposed scheme evaluates VLC service utility under blockage uncertainty, controls RF fallback to avoid excessive backup-link loading, allocates VLC/RF transmission power, and performs illumination feasibility adjustment to preserve the required lighting level. Simulation results show that, under severe blockage conditions, the proposed scheme reduces the outage probability to approximately 0.26, compared with 0.68 for VLC-only transmission and 0.47 for threshold-based VLC/RF switching. For a 20-user network, the proposed scheme achieves an average sum rate of approximately 277 Mbps, maintains a 100% illumination compliance ratio, and achieves higher energy efficiency than the benchmark schemes. Further RF backup analysis shows that the proposed scheme can maintain the service quality of RF-associated users by avoiding excessive RF fallback. These results demonstrate the effectiveness of the proposed framework for reliable and illumination-feasible hybrid VLC/RF indoor communication. Full article

Dense direct-current (DC) bias routing and numerous radio-frequency (RF) choke inductors pose major challenges to the practical implementation of phase-shifter-free beam steering using PIN-controlled phase switching. To address this issue, a compact vertical DC biasing network is proposed, in which most DC bias lines are routed beneath the ground plane. The DC signals are fed to the PIN diodes through vertical bias lines passing through metallized vias in the dielectric substrate. This arrangement reduces routing congestion and simplifies array-level bias integration. The number of required RF choke inductors is decreased from 112 to 22 per dual-polarized element while preserving the required beam-steering functionality. For experimental validation, a 1 × 3 prototype operating at 3.5 GHz is fabricated and measured. The measured beam directions of −14°, 0°, and +14° agree well with simulations, confirming that the proposed bias network provides the phase control required for beam steering. The proposed network, therefore, offers a compact, low-complexity, and practical solution for scalable phase-shifter-free beam-steering systems. Full article

By leveraging the complementary advantages of free-space optical (FSO) and radio frequency (RF) links, hybrid FSO/RF systems exhibit broad application prospects. However, maintaining robustness while performing trade-off optimization between reliability and transmission efficiency under dynamic conditions with power constraints remains challenging. To address this, we propose a weather-aware multi-objective adaptive power allocation approach for hybrid FSO/RF systems based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a deep neural network (DNN). Closed-form expressions for the average bit-error rate (ABER) and average channel capacity (ACAP) are derived and used to evaluate NSGA-II objectives, generating a labeled optimal allocation dataset across diverse scenarios. A DNN is then trained on the dataset to learn adaptive power allocation strategies for dynamic environments. Numerical results demonstrate that the proposed scheme effectively achieves adaptive power allocation and significantly outperforms existing benchmark schemes. In dynamic scenarios, it reduces the ABER by 1–2 orders of magnitude and substantially lowers the outage probability (OP), while improving the overall ACAP by more than 0.5 Gbps under the same transmit power. Full article

Reconfigurable radio frequency (RF) attenuators are critical passive components for 5G-Advanced and emerging 6G wireless systems. Conventional tunable attenuators rely on solid-state switches combined with fixed resistor networks, which suffer from unavoidable static power consumption and severe parasitic degradation at high frequencies. Here, we systematically demonstrate HfO₂-based non-volatile memristors as RF switches with tunable ON-state resistance (R_ON), enabling a switching-attenuation-integrated multistate attenuator. The fabricated Au/HfO₂/Ag devices exhibit stable bipolar resistive switching with an ON/OFF ratio exceeding 10⁹, reliable retention of 10⁵ s, and programmable R_ON continuously tuned from 5.8 Ω to 197.5 Ω. On-wafer RF characterizations from 10 MHz to 43.5 GHz reveal low insertion loss (−0.53 dB), high isolation (−26.8 dB), and clear scaling laws governing the effects of device geometry and R_ON on RF performance. Leveraging these unique characteristics, we propose a symmetric π-type programmable all-memristor attenuator architecture with a cascaded 2-unit configuration. The design achieves 12 discrete attenuation levels from 2 dB to 24 dB, a return loss better than 10 dB across the full band, and zero static power consumption without additional passive components or bias networks. This work establishes the fundamental material-device-RF performance relationship in HfO₂-based RF switches and provides a compact, low-power, and highly integrable solution for next-generation reconfigurable RF front-ends. Full article

With the rapid development of software-defined radio (SDR) technology, a digital, software-reconfigurable, and flexible solution is provided for microwave radiometers, particularly suitable for atmospheric water vapor and oxygen detection with wideband, multi-channel requirements, significantly improving system efficiency. Meanwhile, digitization helps improve channel consistency and address nonlinearity issues, while the digital zero-balancing mechanism implemented through adaptive integration is more suitable for digital platforms. This paper proposes a digital Dicke-type radiometer system based on an SDR platform, using Xilinx RFSoC XCZU47DR (AMD, San Jose, CA, USA) as the core hardware to achieve single-chip integration of RF signal sampling, digital local oscillator generation, and signal processing. The system implements a 46-channel channelized receiver (23 channels each for K-band and V-band) on an FPGA using a polyphase filter bank. The prototype filters achieve 70 dB stopband attenuation and 0.5 dB passband ripple, with each polyphase branch requiring only 25 coefficients, significantly reducing hardware resource consumption. An adaptive integration method is proposed, where an adaptive switch controller dynamically adjusts the hot source injection time ratio by calculating the power difference between adjacent integration periods, enabling the Dicke zero-balancing mechanism to operate entirely in the digital domain. Furthermore, a complete hardware transfer model is established for three signal branches (antenna, hot source, and matched load), and full-chain calibration of all 46 channels is performed using a liquid nitrogen cold source, with calibration reliability verified through blackbody measurements. Experimental results demonstrate brightness temperature consistency better than 0.7 K, with a sensitivity of less than 0.15 K for the K-band and less than 0.21 K for the V-band at 1 s integration time. Full article

As a core piece of equipment for marine monitoring, search and rescue missions, and other applications, the motion state prediction accuracy of Unmanned Surface Vehicles (USVs) directly determines mission reliability and safety. However, existing methods fail to fully consider the motion characteristic differences in various vessel sizes and variable-speed navigation under complex sea conditions, and struggle to capture the spatiotemporal dynamic features of state variations. This paper proposes a hybrid prediction algorithm based on Random Forest-Long Short-Term Memory (RF-LSTM), which utilizes Random Forest for key feature selection while employing LSTM to excavate temporal correlations. An intelligent routing mechanism based on the dominant frequency energy ratio (P_d) is introduced to achieve adaptive prediction mode switching, enabling comprehensive characterization of state variations. Under the 20 kn high-speed condition of a 7.5 m USV, the proposed algorithm achieves a Circular RMSE for heading prediction that is 1.9 times lower than the Extended Kalman Filter (EKF) and 1.2 times lower than a standalone LSTM, with pitch and roll prediction RMSE reduced to 0.36° and 0.85°, respectively. On a 14.5 m-long USV at 23 kn, it maintains a heading prediction accuracy of 0.10°, verifying favorable scale generalization capability. Furthermore, the algorithm demonstrates strong robustness against Gaussian white noise and synthetic ocean noise. Experimental results indicate that RF-LSTM significantly outperforms traditional methods, effectively breaking through the application limitations of fixed-architecture models, substantially enhancing USV autonomy and adaptability in complex marine environments, and providing robust guarantees for mission reliability and safety. Full article

Outdoor jogging is a significant component of daily physical activities that benefit public health and urban living environments. However, it is still challenging to untangle the intricate associations between environmental variables and jogging paces, due to nonlinear interactions, spatial heterogeneity, and inadequacy in model interpretability. To this end, an interpretable spatial machine learning framework based on the integration of the Geographically Weighted Random Forest (GW-RF) model and SHapley Additive exPlanations (SHAP) is proposed. Drawing on multi-source urban datasets and Beijing’s large-scale jogging trajectory data, this model allows for global and local interpretation of environmental effects on the built, natural, and visual dimensions. The findings are as follows: (1) Built environment variables demonstrate the greatest explanatory power, with street network configuration (GAC, GAI) and population density identified as the dominant predictors of jogging intensity; (2) All environmental variables exhibit nonlinear threshold effects, with SHAP analysis revealing sign-switching points and optimal ranges—moderate NDVI and sky openness promote jogging while extreme values suppress it; (3) Natural and visual variables operate within distinct comfort thresholds, where moderate annual mean temperature, green view index, and sky openness are consistently associated with higher jogging intensity; and (4) The GW-RF model achieves superior predictive performance (R² = 0.7939, RMSE = 8.54, MAE = 5.72) over five benchmark models, confirming the necessity of spatial weighting in nonlinear ensemble learning. By revealing nonlinear response patterns and effective environmental ranges, the study presents quantitative evidence for the understanding urban physical activities and providing methodological guidance for fostering healthier and more activity-supportive urban environments. Full article