Search Results (1,738)

Metasurface absorbers play a critical role in microwave electromagnetic control, yet conventional designs suffer from fixed performance and strong cross-coupling between tunable parameters, limiting their adaptability in dynamic environments. Here, we propose a dual-band reconfigurable metasurface absorber with independent modulation of absorption intensity and frequency. The absorber adopts a double-layer metallic structure integrated with PIN diodes and varactors, realizing independent regulation of absorption intensity and frequency. In the lower band (4.1–7.7 GHz, S₁₁ < −10 dB), the absorption intensity is continuously tunable via the PIN diode bias without frequency shift, while in the upper band (13.4–14.4 GHz), the absorption frequency is continuously tunable via the varactor bias without intensity variation. Quantitative cross-sensitivity analysis yields a frequency shift of less than 1.5% during intensity tuning and an intensity variation of less than 0.8 dB during frequency tuning. The absorber exhibits polarization insensitivity and stable performance under oblique incidence up to 45°. An equivalent circuit model is developed and validated against full-wave simulations. Numerical analyses of fabrication tolerance for the active components confirm that the highly decoupled behavior is robust, with absorption peak shifts below 0.15 GHz and intensity variations below ±1.2 dB. Our conceptual design highlights the potential towards independent multi-parametric control in reconfigurable metasurface absorbers for adaptive electromagnetic shielding, smart radomes, and frequency-agile sensing. Full article

Red tide events pose substantial threats to marine ecosystems, aquaculture, and coastal public health. Timely and accurate delineation of red tide extent from satellite imagery is therefore essential for operational monitoring and early warning. However, existing deep learning-based semantic segmentation methods generally treat multispectral bands as homogeneous inputs and do not fully exploit the domain knowledge embodied in spectral indices commonly used in traditional remote sensing analysis. To address this limitation, this study proposes a spectral-index-guided network (SIG-Net) that explicitly incorporates spectral-index priors into deep feature extraction through a dual-branch architecture. SIG-Net comprises three components: a spectral encoder based on a Mix Vision Transformer (MiT-B2) that learns spatial-spectral representations from the original Sentinel-2 bands; a lightweight CNN-based index encoder that extracts discriminative features from four spectral indices, namely the red-green index (RGI), blue-green index (BGI), normalized difference vegetation index (NDVI), and the normalized difference Noctiluca index (NDNI) proposed in this study; and a spectral-index-guided fusion (SIGF) module that adaptively integrates multi-scale features from the two branches using spatial-reduction cross-attention and a gated fusion mechanism. Experiments on a Sentinel-2 red tide dataset show that SIG-Net outperforms single-branch baselines, including U-Net, DeepLabV3+, and SegFormer, as well as naive multi-source fusion strategies. Ablation studies further confirm the contributions of the SIGF module, the gating mechanism, and the proposed NDNI to performance improvements. The proposed method provides an effective framework for integrating domain knowledge with deep learning for red tide remote sensing monitoring. Full article

Microfinance institutions (MFIs) are vital in promoting financial inclusion for underserved populations. However, these institutions face growing security threats, including sophisticated burglary tactics like underground tunneling. In the Philippines, notable incidents, such as the “Termite Gang” heist in Marikina City and a mall robbery in Ozamiz, highlight the limitations of conventional security systems in addressing subterranean intrusions. This study addresses the gap in existing security technologies by developing a real-time detection system that integrates a vibration sensor, a Global System for Mobile Communications (GSM) module for sending real-time SMS alerts, an audible alarm, and a solar-powered backup system for continuous operation. The system was simulated in the electrical technology laboratory to enhance classroom learning. The system’s core is an Arduino Uno microcontroller that processes inputs from the SW-420 vibration sensor, activating alarms and triggering SMS notifications via the SIM900A module when it detects unusual vibrations. Simulations A, B, and C were conducted to evaluate the system’s response time, with results showing a progressive reduction in detection time from five seconds to one second, indicating improved calibration and system efficiency. These findings also support the existing literature on user interaction with vibration alerts, demonstrating high accuracy in interpreting haptic notifications and the cognitive trade-offs involved. The proposed solution offers a proactive, energy-resilient, and cost-effective security system specifically designed to address underground burglary attempts. It applies to MFIs, pawnshops, and other high-risk financial environments. Future research should explore the application of machine learning for adaptive threat detection, expand the system’s scalability, and integrate mobile applications to enable user customization and enhance alert management. Full article

Shortwave infrared (SWIR) imagery plays an important role in land–water boundary delineation, coastal monitoring, and complex aquatic environment observation. However, the spatial resolution of SWIR bands is usually lower than that of visible bands, which limits their capability to represent fine-scale targets and boundary structures. To address this problem, this study proposes MLE-ResUNet, a SWIR image super-resolution method that integrates along-track oversampling with visible-light-guided deep learning. The proposed method first exploits dual-view SWIR observations with sub-pixel displacement generated by increasing the sampling line rate in the push-broom imaging process. A maximum likelihood estimation (MLE)-based physical prior module is then introduced to transform multi-view degraded observations into a physically consistent latent high-resolution prior. Finally, high-resolution visible images are used to provide edge, texture, and structural guidance, and a ResUNet-based network is employed for multi-source feature fusion and residual reconstruction. Based on multi-region measured data acquired by the LHRSI (Lightweight High-Resolution Spectral Imager) payload onboard the BlueCarbon-1A satellite, a SWIR super-resolution dataset covering typical urban, farmland, and coastal scenarios was constructed. Comparative experiments were conducted against PCA, BDSD, PanNet, GPPNN, and two additional lightweight-guided deep learning baselines, namely LGPConv and a CANConv-style visible-guided baseline. The results show that MLE-ResUNet achieves the best performance across different scenarios and consistently outperforms the comparison methods in terms of SSIM, SAM, ERGAS, and Q-index. The proposed method effectively enhances spatial detail recovery while maintaining favorable spectral consistency. Ablation experiments further demonstrate that both along-track oversampling information and the MLE-based physical prior contribute to improved reconstruction quality and more stable training convergence. These findings indicate that the proposed method can enhance fine-scale SWIR observation capability without substantially increasing hardware complexity, providing an effective technical solution for shoreline identification, land–water boundary extraction, and complex surface target monitoring. Full article

Electroencephalography (EEG) is a non-invasive technique used to monitor brain activity but is prone to physiological artifacts, especially eye movements (EOG) and muscle contractions (EMG). These artifacts are non-stationary and frequently overlap with neural oscillation bands, making them difficult to separate accurately from genuine EEG activity. Conventional single-domain filters often fail to eliminate such interference, resulting in either residual noise or the unintended suppression of authentic EEG data. To address these limitations, we propose a Frequency-Aware Residual U-Net (FARU-Net), a dual-domain, frequency-aware residual architecture for EEG artifact removal designed to improve restoration fidelity. Unlike models based solely on temporal features, FARU-Net explicitly modulates the spectral properties of the signal in the latent space through a Frequency-aware Bottleneck Module (FBM), while simultaneously refining temporal details. Additionally, Attention Gates (AGs) are integrated into the skip connections to refine feature fusion and reduce residual noise while preserving salient waveform structures. Comparative experiments on the EEGdenoiseNet benchmark demonstrate that FARU-Net achieves strong overall performance for single-channel EEG restoration. Across five independent test groups, the proposed model attains a mean Pearson correlation coefficient (CC) of 0.9681 and a mean signal-to-noise ratio improvement (ΔSNR) of 26.66 dB. These results indicate that the proposed method effectively preserves both waveform morphology and spectral structure compared with conventional U-Net variants and CNN-based models. Full article

To address the dual dilemmas of energy shortage and environmental pollution caused by excessive consumption of fossil fuels, semiconductor photocatalysis has been regarded as a promising sustainable technical route. As a novel metal-free polymeric semiconductor, graphitic carbon nitride (g-C₃N₄) has become a benchmark material in photocatalysis due to its suitable visible light response, excellent band structure, high stability, and low-cost raw materials. This review systematically elaborates the structural characteristics, photocatalytic mechanism and mainstream synthetic methods of g-C₃N₄, summarizes the performance optimization strategies, sorts out its application progress in environmental remediation and energy conversion, analyzes the core bottlenecks of current research and prospects the future directions, providing a systematic reference for the fundamental research and industrial application of g-C₃N₄-based photocatalysts. Full article

Short-term wind speed forecasting plays a critical role in enabling the reliable integration of renewable energy and supporting the sustainable operation of power systems. However, traditional dual-frequency decomposition methods oversimplify wind speed dynamics by separating them into only high-frequency disturbances and low-frequency trends, making it difficult to capture intermediate-frequency transitional dynamics. Additionally, single models struggle to adapt to multi-scale temporal features, limiting forecasting performance. To address these issues, this paper proposes a tri-band frequency-aware heterogeneous expert collaboration framework. First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed for signal denoising, followed by Particle Swarm Optimization-Time Varying Filtering-based Empirical Mode Decomposition (PSO-TVF-EMD) for multi-scale signal disentanglement. Then, Permutation Entropy (PE) is used to construct a tri-band structure consisting of high-, intermediate-, and low-frequency components. A frequency-aware expert routing mechanism assigns Bayesian Optimization Long Short-Term Memory (BO-LSTM), an improved Markov model, and Auto-Regressive Integrated Moving Average (ARIMA) to the corresponding frequency bands. Finally, a reliability-aware cooperative aggregation strategy integrates predictions from multiple experts. Experimental results show that representative baseline models, including BO-LSTM, Markov, ARIMA, Gated Recurrent Unit (GRU) and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), achieve MAE values ranging from 0.308 to 0.429, while the proposed framework reduces the Mean Absolute Error (MAE) to 0.193 and Root Mean Square Error (RMSE) to 0.274, with a Mean Absolute Percentage Error (MAPE) of 7.35% and R² of 0.927. Compared with the dual-frequency decomposition scheme (MAE = 0.266), the proposed tri-band framework achieves an average improvement of approximately 28.1%. The results suggest that explicitly modeling intermediate-frequency dynamics and aligning model inductive biases with multi-scale signal characteristics can effectively enhance short-term wind speed forecasting performance. Full article

Three new heteroleptic copper(I) complexes of the form [Cu(N,N)(XantPhos)]PF₆ were synthesized and characterized, where N,N refers to phenyl-substituted imidazo[4,5-f][1,10]phenanthroline. All complexes were obtained as yellow powders in yields ranging 82–95% and were fully characterized by NMR spectroscopy, FT-IR, and mass spectrometry. The complexes were also redox-optically characterized. Their absorption profiles display a lower-energy metal-to-ligand charge-transfer (MLCT) band at approximately 412 nm. In solution, weak dual emission is observed, combining ligand-centered and MLCT contributions, with oxygen-dependent quenching supporting the presence of triplet character in the latter. Temperature- and solvent-dependent studies reveal thermally coupled emissive states, in which a relaxed ³MLCT state dominates at low temperatures. In the solid state, intense orange-to-red emission arises from restricted molecular motion and stabilized ³MLCT states, with C3 showing the highest efficiency. Additionally, aggregation-induced emission (AIE) is observed in solvent mixtures. These results suggest that remote substitution can influence the excited-state dynamics and aggregation-driven emission in Cu(I) complexes. Full article

Waterbody extraction is of great significance for water resource investigation and monitoring. In addition to RGB bands, most common satellite images have a near-infrared (NIR) band. By combining these RGB-NIR bands, certain water, vegetation, and shadow indices can be calculated. The near-infrared band and these indices are very similar to the X modality in RGB+X data (common examples include RGB-D and RGB-Thermal). However, at present, no studies have thoroughly examined multimodal feature fusion from the RGB+X perspective in order to extract waterbodies with high precision. As a result, existing algorithms do not fully utilize satellite image information and have limited generalization ability. To overcome this limitation, we propose a dual-complexity backbone for waterbody extraction from the perspective of RGB+X data semantic segmentation. Its complex Transformer branch is used to extract RGB modality features, while its simple CNN branch is used to extract X modality features. This network structure can effectively capture multimodal, global, and local features in remote sensing images. It can also fully leverage the fact that the scale of RGB image datasets in computer vision is significantly larger than that of remote sensing waterbody extraction datasets. If a large pretrained model is used in the RGB branch, it is unnecessary to freeze the weights. Instead, both branches can be trained jointly, allowing the RGB branch to better adapt to the remote sensing waterbody extraction task without raising concerns that fine-tuning might undermine the pretrained model’s strong representation capability. We also propose two X modality configurations with strong generalization performance. To fully fuse multimodal features, we design a hybrid fusion module combining a CNN and a cross-attention mechanism. To integrate the multi-scale features, we employ a multi-scale Transformer structure in the RGB branch and design a multi-scale decoder. Our algorithm achieves state-of-the-art performance on the GID-5 dataset and competitive performance on the S1S2-Water dataset. Furthermore, it significantly outperforms existing methods in cross-dataset zero-shot transfer between the two datasets, with IoU/F1-score gains of 26.08%/27.33% on GID-5 and 38.74%/31.37% on S1S2-Water over previous SOTA methods. Our processing paradigm of modeling RGB-NIR remote sensing images as RGB+X data shows potential for generalization to other multi-modal remote sensing tasks. The dual-complexity backbone we design also has potential to be extended to other tasks that transfer large pretrained RGB models to remote sensing imagery with RGB-NIR four bands or even more spectral bands. We have open-sourced the code and trained models used in this research. Full article

A synthesis method for compact dual-band bandpass filters based on split-ring resonators (SRRs) is presented. The method combines coupling-matrix synthesis with an energy-based SRR model with a control technique of the center frequencies and the bandwidth ratio (BWR) of the two passbands. The proposed methodology is experimentally validated for prototypes implemented on Rogers RO3010. Although the synthesis procedure is general in formulation, any change of substrate requires re-optimization of the SRR dimensions, couplings, and achievable bandwidth ratio. Two third-order microstrip prototypes were fabricated on Rogers RO3010 (${ϵ}_r=10.2$, $h=0.64$ mm) to validate the approach. The first prototype operates at 1.9 and 2.4 GHz with measured −3 dB bandwidths of 200 and 100 MHz, insertion losses of 1.0 and 1.95 dB, and BWR ≈ 0.5. The second prototype operates at 1.9 and 2.4 GHz with measured bandwidths of 100 and 200 MHz, insertion losses of 1.8 and 0.6 dB, and BWR ≈ 1.9. The corresponding footprints are 32 × 12.37 ${\mathrm{mm}}^2$ and 27.87 × 12.42 ${\mathrm{mm}}^2$, respectively. The measured responses agree well with electromagnetic simulations and confirm that asymmetric dual-band bandwidths can be achieved in a compact planar topology without additional reconfigurable elements. Full article

Objective: Rapid vertical manoeuvres and intermittent vibration in autonomous electric vertical take-off and landing (eVTOL) aircraft can provoke pronounced psychological anxiety in passengers. To address this, we propose a closed-loop adaptive system that integrates an in-ear wearable sensor with dynamic regulation of the cabin microenvironment, enabling real-time monitoring of each passenger’s autonomic state and delivering individualised mitigation through a continuous sense–analyse–intervene–feedback loop. Methods: The system is built around a pair of custom in-ear modules that integrate dual-wavelength photoplethysmography (PPG; 525 nm green and 940 nm infrared), galvanic skin response (GSR), and a six-axis inertial measurement unit (IMU) sampled at 200 Hz. To suppress the 20–80 Hz vibration generated by the distributed electric propulsion system, a compliant silicone damping sleeve attenuates high-frequency components at the hardware level, while a Kalman filter fuses the IMU and PPG streams and an adaptive notch filter removes residual rotor harmonics. The pipeline raises the heart-rate-variability (HRV) signal-to-noise ratio (SNR) to 24.1 dB, with a Pearson correlation of 0.96 against a medical-grade chest strap. A hybrid CNN–LSTM network—two convolutional layers (32 filters each) followed by two LSTM layers (128 hidden units)—predicts impending anxiety from HRV time-domain features (RMSSD, pNN50) and frequency-domain features (LF/HF ratio), triggering intervention 8.2 s in advance on average. According to the predicted anxiety level (mild/moderate/severe), a fuzzy controller modulates transcutaneous auricular vagus nerve stimulation (1–5 mA), the binaural-beat frequency (4–8 Hz, theta band), and the cabin lighting colour temperature (2700–6500 K) in real time. The intervention parameters are continuously refined by SPSA-based stochastic optimisation of the HRV recovery rate (step size 0.01; updated every 30 s). Results: In a randomised controlled experiment conducted in a simulated flight environment (N = 50; aged 22–45 years; 1:1 sex ratio), the active group reached physiological recovery in 52.3 s on average, compared with 98.6 s for the sham-controlled group—a 47% reduction (Cohen’s d = 1.24, p < 0.001). User acceptance reached 94%. Conclusions: The proposed in-ear platform enables closed-loop adaptive regulation of anxiety in the eVTOL cabin and overcomes the limitations of conventional passive mitigation strategies. By combining vibration-tolerant physiological sensing with multimodal environmental control, the work offers a practical pathway for improving passenger experience in urban air mobility and provides a useful reference for human-factors standards governing autonomous aircraft. Full article

Radar quantitative precipitation estimation (QPE) is a crucial product for nowcasting and disaster warning. However, its accuracy is constrained by factors such as radar band, attenuation effects, and variations in the phase and microphysical properties of precipitation particles. Based on X-band phased-array radar data from Zhongshan City, Guangdong Province, this study compares and evaluates the QPE correction performance of three deep learning models: stacking ensemble learning, gated recurrent unit (GRU), and three-dimensional convolutional neural network (3D CNN). The aim is to explore the applicability of different model types under complex precipitation conditions. Data from August 2023 to August 2024 were used to construct the samples, with records from May 2024 held out as an independent test set and excluded from model training and hyperparameter tuning. Model performance was assessed under different radar combinations (three-radar, dual-radar, and single-radar configurations), temporal scales (minute and hourly), and precipitation intensities. The results show that: (1) at the minute scale, all three models improved the original QPE, reducing average relative error (RE) by approximately 24.6–29.5%, mean absolute error (MAE) by 23.2–27.7%, and root-mean-square error (RMSE) by 19.7–22.8%, while increasing correlation coefficient (CC) by approximately 20.4–20.9%. Specifically, GRU achieved the largest reduction in RE, stacking showed slight advantages in controlling MAE and RMSE, and 3D CNN and GRU showed similar improvements in CC. (2) At the hourly scale, the correction effect varied with precipitation intensity. In the light-to-moderate rainfall range ($0.1\le R<8.0\mathrm{mm}{\mathrm{h}}^{-1}$, where R denotes hourly rainfall), 3D CNN generally showed better error-control performance, whereas the advantage of GRU was less consistent among radar combinations. In the heavy-rainfall range ($R\ge 16.0\mathrm{mm}{\mathrm{h}}^{-1}$), stacking and GRU provided complementary value in some radar configurations, although model performance remained configuration dependent. (3) Case analysis shows that stacking can improve the original QPE at some extreme-precipitation stations, but correction performance in the extreme high-value range remains unstable, and GRU and 3D CNN are more prone to underestimation. Oriented toward operational applications, this study systematically evaluates the applicability and limitations of three model types under different scenarios while considering computational-resource constraints and timeliness requirements, thereby providing a reference for model selection and operational application in radar QPE correction. Full article

In practical imaging applications, low-light and overexposure are two common types of image degradation problems with inherent conflicts, and existing methods struggle to achieve accurate restoration of both degradations within a unified framework. To address this challenge, this paper proposes DFE-Net based on explicit frequency decoupling. The network adopts a symmetric U-Net architecture and embeds discrete wavelet transform (DWT) and inverse discrete wavelet transform (IWT) to construct an explicit dual-frequency processing mechanism, which optimizes the low-frequency information carrying global illumination and the high-frequency information containing detailed textures, respectively. In the encoder, DWT decouples features into low-frequency and high-frequency sub-bands and feeds them into dedicated enhancement modules. The low-frequency enhancement block integrates SS2D and a gated convolutional feed-forward network to efficiently model global contextual dependencies with linear complexity and accurately restore image illumination and contrast; the high-frequency enhancement block adopts CMT attention combined with a matching convolutional feed-forward network, enabling the detail restoration process to be guided by the optimized low-frequency information and ensuring the collaborative optimization of global structure and local textures. The decoder completes the reconstruction and fusion of the processed sub-bands through IWT. The quantitative and qualitative experimental results on the MSEC, SICE, and LOLv1 datasets demonstrate that DFE-Net achieves or surpasses existing state-of-the-art methods in various metrics while maintaining low model complexity. Full article

The electromechanical transmission (EMT) systems of hybrid special vehicles are highly susceptible to severe transient torsional resonance under frequent start-stop operating conditions. Traditional entire-frequency domain H_∞ active vibration reduction strategies are often limited by insufficient gain, failing to achieve ultimate suppression within the core resonance frequency band. To address this issue, this paper proposes a finite-frequency H_∞ active torsional vibration suppression strategy based on a motor dual-loop control architecture. This strategy achieves a profound physical decoupling between torsional vibration suppression and steady-state driving tasks. Furthermore, by introducing the Generalized Kalman–Yakubovich–Popov (GKYP) lemma and Linear Matrix Inequalities (LMIs) into the secondary loop, the control degrees of freedom are precisely concentrated on the 8–30 Hz frequency band, where the transient resonance energy is highly localized. This thoroughly eliminates the conservatism inherent in entire-frequency designs. To mitigate the instability risks caused by unmeasurable states and actuator response lags in practical engineering applications, a robust controller integrating input time-delay compensation and dynamic output feedback is subsequently constructed. Numerical case studies and hardware-in-the-loop (HIL) test results based on a specific EMT configuration demonstrate that the proposed strategy effectively overcomes the instability induced by system delays. It achieves an outstanding resonance peak attenuation of up to 93% and strictly constrains output shaft torque fluctuations within a safe threshold of 50 N·m. Ultimately, this study provides an efficient and robust closed-loop engineering solution for the transient vibration management of high-power electromechanical transmission systems and the enhancement of overall vehicle NVH performance. Full article

Power amplifiers are one of the most important microwave components and key equipment in satellite transponder subsystems. It plays a significant role in enhancing the overall capabilities of satellite systems, optimizing thermal design, and ensuring reliability. The rapid development of High Throughput Satellites (HTS) and global mobile communication satellites imposes challenges to power amplifier design. This paper presents a dual-band Doherty power amplifier (DPA) with a hybrid GaN HEMT device and a commercial transistor that can operate simultaneously at 0.9 GHz and 2.14 GHz. At 6 dB output power back-off (OBO), the proposed amplifier achieves drain efficiencies of 42% and 37% at the two frequency bands respectively. When excited by a 20 MHz 16 QAM signal, it exhibits adjacent channel power ratios (ACPR) of −45.4 dBc and −48.6 dBc at output power levels of 34.8 dBm and 34.9 dBm respectively. A novel dual-band offset line structure was employed to achieve the required dual-band load modulation. The proposed DPA is well-suited for application in dual-band wireless communication systems. Full article