Search Results (79)

The harmonized multi-sensor satellite data assists users by providing seamless analysis-ready data with enhanced temporal resolution. The Harmonized Landsat Sentinel (HLS) product has gained popularity due to the seamless integration of Landsat OLI and Sentinel-2 MSI, achieving a temporal resolution of 2.8 to 3.5 days. However, applications that require monitoring intervals of less than three days or cloudy data can limit the usage of HLS data. Gaofen-1 (GF-1) Wide Field of View (WFV) data provides the capacity further to enhance the data availability by harmonization with HLS. In this study, GF-1/WFV data is harmonized with HLS by employing deep learning-based conditional Generative Adversarial Networks (cGANs). The harmonized WFV data with HLS provides an average temporal resolution of 1.5 days (ranging from 1.2 to 1.7 days), whereas the temporal resolution of HLS varies from 2.8 to 3.5 days. This enhanced temporal resolution will benefit applications that require frequent monitoring. Various processes are employed in HLS to achieve seamless products from the Operational Land Imager (OLI) and Multispectral Imager (MSI). This study applies 6S atmospheric correction to obtain GF-1/WFV surface reflectance data, employs MFC cloud masking, resamples the data to 30 m, and performs geographical correction using AROP relative to HLS data, to align preprocessing with HLS workflows. Harmonization is achieved without using BRDF normalization and bandpass adjustment like in the HLS workflows; instead, cGAN learns cross-sensor reflectance mapping by utilizing a U-Net generator and a patchGAN discriminator. The harmonized GF-1/WFV data were compared to the reference HLS data using various quality indices, including SSIM, MBE, and RMSD, across 126 cloud-free validation tiles covering various land covers and seasons. Band-wise scatter plots, histograms, and visual image color quality were compared. All these indices, including the Sobel filter, histograms, and visual comparisons, indicated that the proposed method has effectively reduced the spectral discrepancies between the GF-1/WFV and HLS data. Full article

This article investigates how to exploit the high-frequency mmWave for 5G-advanced and beyond, which requires new filters for the wide bandpass and its multi-sub-band. Based on the substrate-integrated waveguide (SIW), spoof surface plasmon polariton (SSPP), and metastructures, like complementary split-ring resonators (CSRRs), the development of a wide bandpass filter and a multi-sub-band filter is proposed, along with an experimental realization to verify the model. The upper and lower cutoff frequencies of the wide bandpass are controlled through an SIW-SSPP structure, whereas the corresponding wide bandpass and its multi-sub-band filters are designed through incorporating new metastructures. The frequency range of 24.25–29.5 GHz, which covers the n257, n258, and n261 bands for 5G applications, was selected for verification. The basic SIW-SSPP wide bandpass structure of 24.25–29.5 GHz was designed first. Then, by incorporating an Archimedean spiral configuration, the insertion loss within the passband was reduced from 1 dB to 0.5 dB, while the insertion loss in the high-frequency stopband was enhanced from 40 dB to 70 dB. Finally, CSRRs were integrated to effectively suppress undesired frequency components within the bandpass, thereby achieving multi-sub-band filters with low insertion losses with a triple-sub-band filter of 0.5 dB, 0.7 dB, and 0.8 dB in turn. The experimental results showed strong agreement with the design scheme, thereby confirming the rationality of the design. Full article

Effective fault feature extraction and classification methods serve as the foundation for achieving the efficient fault diagnosis of planetary gearboxes. Considering the vibration signals of planetary gearboxes that contain both symmetrical and asymmetrical components, this paper proposes a novel feature extraction method integrating an adaptive multi-bandpass filter (AMBPF) and refined composite multi-scale fuzzy entropy (RCMFE). And a dream optimization algorithm (DOA)–least squares support vector machine (LSSVM) is also proposed for fault classification. Firstly, the AMBPF is proposed, which can effectively and adaptively separate the meshing frequencies, harmonic frequencies, and their sideband frequency information of the planetary gearbox, and is combined with RCMFE for fault feature extraction. Secondly, the DOA is employed to optimize the parameters of the LSSVM, aiming to enhance its classification efficiency. Finally, the fault diagnosis of the planetary gearbox is achieved by the AMBPF, RCMFE, and DOA-LSSVM. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic efficiency and exhibits superior noise immunity in planetary gearbox fault diagnosis. Full article

Underwater object detection with Synthetic Aperture Sonar (SAS) images faces many problems, including low contrast, blurred edges, high-frequency noise, and missed small objects. To improve these problems, this paper proposes a high-accuracy underwater object detection algorithm for SAS images, named the HAUOD algorithm. First, considering SAS image characteristics, a sonar preprocessing module is designed to enhance the signal-to-noise ratio of object features. This module incorporates three-stage processing for image quality optimization, and the three stages include collaborative adaptive Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement, non-local mean denoising, and frequency-domain band-pass filtering. Subsequently, a novel C2fD module is introduced to replace the original C2f module to strengthen perception capabilities for low-contrast objects and edge-blurred regions. The proposed C2fD module integrates spatial differential feature extraction, dynamic feature fusion, and Enhanced Efficient Channel Attention (Enhanced ECA). Furthermore, an underwater multi-scale contextual attention mechanism, named UWA, is introduced to enhance the model’s discriminative ability for multi-scale objects and complex backgrounds. The proposed UWA module combines noise suppression, hierarchical dilated convolution groups, and dual-dimensional attention collaboration. Experiments on the Sonar Common object Detection dataset (SCTD) demonstrate that the proposed HAUOD algorithm achieves superior performance in small object detection accuracy and multi-scenario robustness, attaining a detection accuracy of $95.1\%$, which is $8.3\%$ higher than the baseline model (YOLOv8n). Compared with YOLOv8s, the proposed HAUOD algorithm can achieve $6.2\%$ higher accuracy with only $50.4\%$ model size, and reduce the computational complexity by half. Moreover, the HAUOD method exhibits significant advantages in balancing computational efficiency and accuracy compared to mainstream detection models. Full article

The reduction in disaster risk in urban regions due to natural hazards (e.g., earthquakes, landslides, floods, and tropical cyclones) is primarily a development matter that must be treated within the scope of a broader urban development framework. Natural hazard assessment is one of the turning points in mitigating disaster risk, which typically contributes to stronger urban resilience and more sustainable urban development. Regarding this challenge, our research proposes a new approach in the signal processing chain and feature extraction from microtremor data that focuses mainly on the Horizontal-to-Vertical Spectral Ratio (HVSR) so as to assess liquefaction potential as a natural hazard using AI. The key raw seismic features of site amplification and resonance are extracted from the data via bandpass filtering, Fourier Transformation (FT), the calculation of the HVSR, and smoothing through the use of moving averages. The main novelty is the integration of machine learning, particularly stacked ensemble learning, for liquefaction potential classification from imbalanced seismic datasets. For this approach, several models are used to consider class imbalance, enhancing classification performance and offering better insight into liquefaction risk based on microtremor data. Then, the paper proposes a liquefaction detection method based on deep learning with an autoencoder and stacked classifiers. The autoencoder compresses data into the latent space, underlining the liquefaction features classified by the multi-layer perceptron (MLP) classifier and eXtreme Gradient Boosting (XGB) classifier, and the meta-model combines these outputs to put special emphasis on rare liquefaction events. This proposed methodology improved the detection of an imbalanced dataset, although challenges remain in both interpretability and computational complexity. We created a synthetic dataset of 1000 samples using realistic feature ranges that mimic the Rif data region to test model performance and conduct sensitivity analysis. Key seismic and geotechnical variables were included, confirming the amplification factor (Af) and seismic vulnerability index ($K_g$) as dominant predictors and supporting model generalizability in data-scarce regions. Our proposed method for liquefaction potential classification achieves 100% classification accuracy, 100% precision, and 100% recall, providing a new baseline. Compared to existing models such as XGB and MLP, the proposed model performs better in all metrics. This new approach could become a critical component in assessing liquefaction hazard, contributing to disaster mitigation and urban planning. Full article

Stability problems are increasing in current power systems with a large number of electronic converters, such as microgrids (MGs) and microgrid clusters (MGCs). Frequency-domain methods, commonly used to analyse traditional power system stability, can also be extended to MGs. In particular, the positive-mode-damping (PMD) stability criterion is a simple and practical method to evaluate the stability of multi-terminal power electronics-based systems, making it a powerful tool for addressing stability issues in MGCs. This paper extends the application of the PMD stability criterion to assess stability in MGC-integrated transmission grids. Moreover, it presents two bandpass filter-based active and passive damping compensators and examines their effectiveness in mitigating instabilities in MGCs. A modified IEEE three-bus power system integrating an MGC is used to conduct a small-signal harmonic stability study and apply active and passive damping solutions with the PMD stability criterion. The modified IEEE three-bus power system is implemented in real-time simulations using a hardware-in-the-loop setup with OPAL-RT4512 to validate the results obtained from MATLAB/Simulink R2022a simulations. Full article

In this paper, a capacitively coupled-fed reconfigurable wideband bandpass filter (BPF) is proposed based on liquid crystal (LC) technology, which achieved three transmission poles across varying bias voltages (V_B). An open-ended stepped impedance resonator configuration enables multi-mode resonance, offering significantly wider bandwidth compared to uniform-impedance resonators. The fractional bandwidth (FBW) and transmission pole positions are determined by the impedance ratio of the two resonators, allowing the filter to meet specific design requirements. An analytical methodology employing multilayer transmission line formulations and resonant frequency ratios was used to predict the modal stability of transmission poles under dielectric constant variation, which was subsequently validated through simulation. Experimental results show that the center frequency can be adjusted from 10.76 to 9.47 GHz with a maximum V_B of 30 V, achieving a tuning range of 12.71%. The normalized 3 dB FBW exceeds 64.66%, and the return loss remains above 10 dB from 0 to 30 V, offering the widest FBW among the reported LC BPFs without pole merging or mode collapse. The frequency response of the fabricated filter shows good agreement with the simulation results. Full article

Brain–computer interfaces (BCIs) translate electroencephalography (EEG) signals into control commands, offering potential solutions for motor-impaired individuals. While traditional BCI studies often focus solely on amplitude variations or inter-channel connectivity, movement-related brain activity is inherently dynamic, involving interactions across regions and frequency bands. We propose that combining amplitude-based (filter bank common spatial patterns, FBCSP) and phase-based connectivity features (phase-locking value, PLV) improves decoding accuracy. EEG signals from ten healthy subjects were recorded during arm movements, with electromyography (EMG) as ground truth. After preprocessing (resampling, normalization, bandpass filtering), FBCSP and multi-lag PLV features were fused, and the ReliefF algorithm selected the most informative subset. A feedforward neural network achieved average metrics of: Pearson correlation 0.829 ± 0.077, R-squared value 0.675 ± 0.126, and root mean square error (RMSE) 0.579 ± 0.098 in predicting EMG amplitudes indicative of arm movement angles. Analysis highlighted contributions from both FBCSP and PLV, particularly in the 4–8 Hz and 24–28 Hz bands. This fusion approach, augmented by data-driven feature selection, significantly enhances movement decoding accuracy, advancing robust neuroprosthetic control systems. Full article

Fine-grained sedimentary rocks are ideal carriers for astronomical cycle analysis as they can record and preserve significant astronomical cycle signals. Spectral analysis using the Multi-taper Method (MTM) and Evolutionary Harmonic Analysis (EHA) using the Fast Fourier Transform (FFT) were conducted on natural gamma data from key wells in the Es₃l sub-member in the Bonan Sag, Bohai Bay Basin, China. Gaussian bandpass filtering was applied using a short eccentricity cycle of 100 ka, and a “floating” astronomical time scale for the Es₃l sub-member (Lower 3rd sub-member of Shahejie Formation in Eocene) was established using magnetic stratigraphic ages as boundaries. Stratigraphic divisions were made for single wells in the Es₃l of the Bonan Sag, and a stratigraphic framework was established based on correlations between key wells. The research results indicate the following: Firstly, the Es₃l of the Bonan Sag records significant astronomical cycle signals, with an optimal sedimentation rate of 8.39 cm/ka identified. Secondly, the cyclical thicknesses corresponding to long eccentricity, short eccentricity, obliquity, and precession cycles are 38.9 m, 9.7 m, 4.6–3.4 m, and 1.96–1.66 m, respectively. Thirdly, the Es₃l sub-member stably records 6 long eccentricity cycles and 26 short eccentricity cycles, and the short eccentricity curve is used as a basis for stratigraphic division for high-precision stratigraphic correlations. Fourthly, the quality of sandstone-interbedded mudrock is jointly controlled by the short eccentricity and precession. Eccentricity maximum values result in thicker sandstone interlayers, while minimum precession values promote the thickness of sandstone interlayers. Through astronomical cycle analysis, the depositional evolution mechanism of sandstone-interbedded mudrock is revealed. Combined with the results of high-precision stratigraphic division, this can provide a basis for fine evaluation and “sweet spot” prediction of lacustrine shale oil reservoirs. Full article

Advanced multi-channel acoustic emission (AE) monitoring systems often rely on complex and costly architectures, especially those requiring custom FPGA-based hardware. In this work, we present a digital signal processing (DSP)-based approach to high-performance AE acquisition, implemented using a simplified analog front-end (AFE) and a commercially available synchronous data acquisition (DAQ) card (NI USB-6356). This design eliminates the need for specialized FPGA development, improving accessibility and reducing system complexity. A key feature of the system is the replacement of traditional analog filters with a software-defined digital band-pass filtering module implemented in LabVIEW. This allows for real-time or post-processing filtering with adjustable parameters, enhancing flexibility in data analysis. The system supports 8-channel synchronous sampling at 1.25 MS/s, and performance evaluations demonstrate a dynamic range of 79.22 dB and a signal-to-noise ratio (SNR) of 85.39 dB. These results confirm the system’s ability to maintain high fidelity in AE signal acquisition without the need for dedicated hardware filtering or custom DAQ hardware. The proposed method offers a practical and validated alternative for multi-channel AE monitoring, with potential applications in structural health monitoring, materials testing, and other engineering domains. Full article

The Laser Light Screen System faces critical technical challenges in high-speed, long-range target detection: when a target passes through the light screen, weak light flux variations lead to significantly degraded signal-to-noise ratios (SNRs). Traditional signal processing algorithms fail to effectively suppress phase distortion and boundary effects under extremely low SNR conditions, creating a technical bottleneck that severely constrains system detection performance. To address this problem, this paper proposes a Multi-stage Collaborative Filtering Chain (MCFC) signal processing framework incorporating three key innovations: (1) the design of zero-phase FIR bandpass filtering with forward–backward processing and dynamic phase compensation mechanisms to effectively suppress phase distortion; (2) the implementation of a four-stage cascaded collaborative filtering strategy, combining adaptive sampling and anti-aliasing techniques to significantly enhance signal quality; and (3) the development of a multi-scale adaptive transform algorithm based on fourth-order Daubechies wavelets to achieve high-precision signal reconstruction. The experimental results demonstrate that under −20 dB conditions, the method achieves a 25 dB SNR improvement and boundary artifact suppression while reducing the processing time from 0.42 to 0.04 s. These results validate the proposed method’s effectiveness in high-speed target detection under low SNR conditions. Full article

The transmittance characteristics and the band structure of photonic heterostructures consisting of four distinct dielectric materials are analyzed using the transfer matrix method. An enhanced band structure of such crystals is discovered. It is shown that the band structure is strongly influenced by the arrangement of unit cells in the periodic building blocks of the crystals. The transmission spectra are evaluated for varying layer thicknesses and incident angles to investigate their impact on wave propagation. The symmetrical results for periodicities, sub-layer thickness, and oblique incident angles indicate robust bandgaps with blue shifting and enhanced transmission. Moreover, the periodicity in different cases, followed by the period, has also shown to have a great impact on the emergence of multiple bandgaps. The photonic bandgap and frequency are associated with the lattice elements of the unit cell, shifting naturally as a fundamental property of the structure, which has been achieved by the alteration of unit cells. Hence, the proposed photonic heterostructures offer significant potential for developing efficient band-stop and band-pass filters, facilitating their use in multi-functional integrated optical circuits within the Terahertz spectrum. Full article

A wideband dual-polarized dielectric resonator antenna (DRA) with gain-filtering response was proposed in this paper. First, a differentially fed, low-profile crossed-DRA was used to obtain orthogonal polarizations with two resonant modes. A radiation null at upper band edge was also generalized. Second, with the introduction of four parasitic patches at the top of the crossed DRA, another resonant mode at lower band was excited, and the bandwidth was greatly expanded. Moreover, the introduction of parasitic patches could also help improve the selectivity of realized gain with another radiation null at the upper band edge. Furthermore, four sequentially rotated shorting coupling structures (SRSCSs) were proposed for the first time to generalize two additional radiation nulls. Finally, a wideband bandpass filtering response of the realized gain with four radiation nulls was obtained. Prototypes of the proposed antennas were fabricated, and the testing results showed that the antenna had a wide operation band of 57.1% from 2.75 GHz to 4.95 GHz with sharp roll-off at the band edge. This technique could also be used in wireless communication devices at millimeter/optical front ends and other multi-wavelength fiber lasers with micro structures. Full article

We report a photonic-assisted method for measuring the frequencies of a multi-tone microwave with high accuracy based on pulse identification. The unknown microwave signal and a linearly chirped signal are modulated to an optical carrier using a dual-polarization Mach–Zehnder modulator. Carrier-suppressed single-sideband modulation avoids the generation of undesired frequency components after photodetection. An electrical bandpass filter with a narrow bandwidth selects the beat signal between the unknown signal and the linearly chirped optical tone. A pulse, generated by the beat signal, can be observed using an oscilloscope (OSC). By identifying the beating pulse position, we can accurately determine the frequency of the unknown signal. The single-tone and multi-tone microwave signal ranges of 6–16 GHz and 26–36 GHz are successfully measured, respectively. The measurement errors for single-tone and multi-tone signals are both less than ±1 MHz. Full article

As the application of the Global Navigation Satellite System (GNSS) continues to expand, its stability and safety issues are receiving more and more attention, especially the interference problem. Interference reduces the signal reception quality of ground terminals and may even lead to the paralysis of GNSS function in severe cases. In recent years, Low Earth Orbit (LEO) satellites have been highly emphasized for their unique advantages in GNSS interference detection, and related commercial and academic activities have increased rapidly. In this context, based on the signal-to-noise ratio (SNR) and radio-frequency interference (RFI) measurements data from COSMIC-2 satellites, this paper explores a method of predicting RFI measurements using SNR correlation variations in different GNSS signal channels for application to the detection and localization of civil terrestrial GNSS interference signals. Research shows that the SNR in different GNSS signal channels shows a correlated change under the influence of RFI. To this end, a CNN-BiLSTM-Attention model combining a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and attention mechanism is proposed in this paper, and the model takes the multi-channel SNR time series of the GNSS as the input and outputs the maximum measured value of RFI in the multi-channels. The experimental results show that compared with the traditional band-pass filtering inter-correlation method and other deep learning models, the model in this paper has a root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R²) of 1.0185, 1.8567, and 0.9693, respectively, in RFI prediction, which demonstrates a higher RFI detection accuracy and a wide range of rough localization capabilities, showing significant competitiveness. Since the correlation changes in the SNR can be processed to decouple the signal strength, this model is also suitable for future GNSS-RO missions (such as COSMIC-1, CHAMP, GRACE, and Spire) for which no RFI measurements have yet been made. Full article