Search Results (1,267)

Pulse compression enhances the signal-to-noise ratio (SNR) in distributed fiber optic acoustic sensing (DAS) by increasing pulse energy through cross-correlation, while maintaining spatial resolution. In DAS systems, linear frequency modulation (LFM) pulses are commonly used; however, their limited sidelobe suppression (SLR) results in increased noise, limiting improvements in SNR and fading noise mitigation. To overcome these limitations, we propose an adaptable NLFM pulse design methodology that optimizes SLR based on specific application requirements. This approach significantly enhances pulse energy injection while reducing system noise, thereby improving overall sensing performance. Additionally, dual-carrier frequency-division multiplexing is employed to maximize energy utilization and mitigate fading effects. The experimental results demonstrate that, compared to the LFM-based detection pulse system, the optimized NLFM pulse improves the SNR by 10 dB. Under identical conditions, the NLFM system also enhances its performance in suppressing fading noise. Furthermore, the use of dual carriers effectively reduces the hardware resource consumption of the sensing system, highlighting the great potential of NLFM pulses in the field of fiber optic sensing. Full article

Electroencephalography-based emotion recognition is essential for brain-computer interface combined with artificial intelligence. This paper proposes a novel algorithm for human emotion detection using a hybrid paradigm of convolutional neural networks and a boosting model. The proposed algorithm employs two subsets of 18 and 14 features extracted from four sub-bands using discrete wavelet transform. These features are identified as the optimal subsets of the most relevant, among 42 original input features extracted from two subsets of 8 and 6 productive channels using a dual genetic algorithm combined with a wise-subject 5-fold cross validation procedure in which the first and second genetic algorithms address the efficient channels and optimal feature subsets. The feature subsets are estimated by differently intelligent models and wise-subject 5-fold cross validation procedure on the validation set. The proposed algorithm produces an accuracy of 70.43%/76.05%, precision of 69.88%/74.57%, recall of 98.70%/99.17%, and F1 score of 81.83%/85.13% for valence/arousal classifications, which suggest that the frontal and left regions of the cortex associate especially to human emotions. Full article

This paper presents a novel dual-band antenna design for simultaneous 5G communication and localization services in high-speed train (HST) scenarios. It operates in the frequency range 1 (FR1) n78 band at 3.5 GHz and the FR2 n258 band at 26.2 GHz. The design combines a dielectric resonator antenna (DRA) and a planar patch antenna to achieve dual-band functionality. This provides efficient performance across both mid-band and millimeter-wave frequencies for advanced 5G applications. The dual-band configuration is motivated by the need to balance wide coverage and high data rates within a single, compact antenna design, addressing the specific challenges of maintaining stable connectivity and efficient spectrum utilization in high-speed, data-intensive environments. A common challenge in dual-band antenna designs is the interference between low- and high-frequency antennas, which can significantly degrade performance or even cause antenna failure. Our design addresses this issue by minimizing interference between the patch and DRA elements, ensuring stable operation across both frequency bands. As a result, the antenna achieves impressive gains and bandwidth, with a maximum gain of 6.8 dBi and an impedance bandwidth of 22.5% for the dual-band configuration. Also, both radiators present high total efficiency above 90%. The compact size of the antenna makes it highly suitable to be mounted on the roof of the train to enable 5G communication and location-based services for both safety-critical and liability-critical applications in HST scenarios. Full article

Accurate information on crop planting and spatial distribution is critical for understanding and tracking long-term land use changes. The method of using deep learning (DL) to extract crop information has been applied in large-scale datasets and plain areas. However, current crop classification methods face some challenges, such as poor image time continuity, difficult data acquisition, rugged terrain, fragmented plots, and diverse planting conditions in complex scenes. In this study, we propose the Complex Scene Crop Classification U-Net (CSCCU), which aims to improve the mapping accuracy of staple crops in complex scenes by combining multi-spectral bands with spectral features. CSCCU features a dual-branch structure: the main branch concentrates on image feature extraction, while the auxiliary branch focuses on spectral features. In our method, we use the hierarchical feature-level fusion mechanism. Through the hierarchical feature fusion of the shallow feature fusion module (SFF) and the deep feature fusion module (DFF), feature learning is optimized and model performance is improved. We conducted experiments using GaoFen-2 (GF-2) images in Xiuwen County, Guizhou Province, China, and established a dataset consisting of 1000 image patches of size 256, covering seven categories. In our method, the corn and rice accuracies are 89.72% and 88.61%, and the mean intersection over union (mIoU) is 85.61%, which is higher than the compared models (U-Net, SegNet, and DeepLabv3+). Our method provides a novel solution for the classification of staple crops in complex scenes using high-resolution images, which can help to obtain accurate information on staple crops in larger regions in the future. Full article

Cognitive loads impact postural control; however, the specific influence of sensory modalities employed in cognitive tasks during motor-cognitive dual tasks remains unclear. This study investigated the distinct effects of visual and auditory cognitive tasks on static postural control while controlling for differences in task content. Twenty-five healthy young adults were instructed to maintain a quiet stance on a force plate under three cognitive task conditions: a single motor task (control), a paced visual serial addition task (visual), and a paced auditory serial addition task (auditory). Center of pressure (COP) displacements were measured, and both linear (e.g., sway area) and non-linear assessments of postural control were analyzed. Results revealed a significant reduction in sway area during cognitive tasks compared to the control condition. However, under the auditory condition, the power spectrum density of COP displacements in the moderate frequency band was significantly higher than those in the control and visual conditions, accompanied by a notable increase in the mean power frequency. These findings suggest that auditory cognitive load exerts a more significant effect on postural control than visual cognitive load during motor-cognitive dual tasks. This highlights the relevance of sensory modalities in cognitive loads for effective fall-risk assessment. Full article

High-sensitivity and large-scale surveys are essential in advancing radio astronomy, enabling detailed exploration of the universe. A Phased Array Feed (PAF) installed in the focal plane of a radio telescope significantly enhances mapping efficiency by increasing the instantaneous Field of View (FoV) and improving sky sampling capabilities. This paper presents the design and optimization of a novel C-Band Phased Array Feed antenna for the Sardinia Radio Telescope (SRT). The system features an 8 × 8 array of dual-polarized elements optimized to achieve a uniform beam pattern and an edge taper of approximately 5 dB for single radiating elements within the 3.0–7.7 GHz frequency range. The proposed antenna addresses key efficiency limitations identified in the PHAROS 2 (PHased Arrays for Reflector Observing Systems) system, including the under-illumination of the Sardinia Radio Telescope’s primary mirror caused by narrow sub-array radiation patterns. By expanding the operational bandwidth and refining the radiation characteristics, this new design enables significantly improved performance across the broader frequency range of 3.0–7.7 GHz, enhancing the telescope’s capability for wide-field, high-resolution observations. Full article

Icebergs pose significant risks to shipping, offshore oil exploration, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean, where darkness and cloud cover are frequent, is particularly challenging. Synthetic aperture radar (SAR) serves as a powerful tool to overcome these difficulties. In this paper, we propose a method for automatically detecting and classifying icebergs in various sea conditions using C-band dual-polarimetric images from the RADARSAT Constellation Mission (RCM) collected throughout 2022 and 2023 across different seasons from the east coast of Canada. This method classifies SAR imagery into four distinct classes: open water (OW), which represents areas of water free of icebergs; open water with target (OWT), where icebergs are present within open water; sea ice (SI), consisting of ice-covered regions without any icebergs; and sea ice with target (SIT), where icebergs are embedded within sea ice. Our approach integrates statistical features capturing subtle patterns in RCM imagery with high-dimensional features extracted using a pre-trained Vision Transformer (ViT), further augmented by climate parameters. These features are classified using XGBoost to achieve precise differentiation between these classes. The proposed method achieves a low false positive rate of 1% for each class and a missed detection rate ranging from 0.02% for OWT to 0.04% for SI and SIT, along with an overall accuracy of 96.5% and an area under curve (AUC) value close to 1. Additionally, when the classes were merged for target detection (combining SI with OW and SIT with OWT), the model demonstrated an even higher accuracy of 98.9%. These results highlight the robustness and reliability of our method for large-scale iceberg detection along the east coast of Canada. Full article

In contrast to conventional remote sensing images, hyperspectral remote sensing images are characterized by a greater number of spectral bands and exceptionally high resolution. The richness of both spectral and spatial information facilitates the precise classification of various objects within the images, establishing hyperspectral imaging as indispensable for remote sensing applications. However, the labor-intensive and time-consuming process of labeling hyperspectral images results in limited labeled samples, while challenges like spectral similarity between different objects and spectral variation within the same object further complicate the development of classification algorithms. Therefore, efficiently exploiting the spatial and spectral information in hyperspectral images is crucial for accomplishing the classification task. To address these challenges, this paper presents a multi-scale feature fusion convolutional neural network (MSFF). The network introduces a dual branch spectral and spatial feature extraction module utilizing 3D depthwise separable convolution for joint spectral and spatial feature extraction, further refined by an attention-based-on-central-pixels (ACP) mechanism. Additionally, a spectral–spatial joint attention module (SSJA) is designed to interactively explore latent dependency between spectral and spatial information through the use of multilayer perceptron and global pooling operations. Finally, a feature fusion module (FF) and an adaptive multi-scale feature extraction module (AMSFE) are incorporated to enable adaptive feature fusion and comprehensive mining of feature information. Experimental results demonstrate that the proposed method performs exceptionally well on the IP, PU, and YRE datasets, delivering superior classification results compared to other methods and underscoring the potential and advantages of MSFF in hyperspectral remote sensing classification. Full article

How to quickly and accurately obtain the basal crop coefficient is the key to estimating evapotranspiration in sparse vegetation. To enhance the accuracy of vineyard evapotranspiration estimation in the subhumid region of Northwest China, this study utilized the actual evapotranspiration (ET_c) measured by the Bowen ratio system as the reference standard. The reference crop evapotranspiration (ET_o) was calculated using the Penman formula, and the grape crop coefficient (K_c) was subsequently derived. The FAO-56 dual crop coefficient method was then employed to determine the soil evaporation coefficient (K_e) and the water stress coefficient (K_s), leading to the acquisition of the basal crop coefficient (K_cb). Concurrently, multispectral remote sensing images captured by unmanned aerial vehicle (UAV) were used to gather grape spectral data, from which the reflectance of multiple bands was extracted to compute four vegetation indices: the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), the Ratio Vegetation Index (RVI), and the Difference Vegetation Index (DVI). Relationship models between the grape basal crop coefficient (K_cb) and these vegetation indices were established using univariate linear regression, polynomial regression, and multiple linear regression. These models were then used to estimate vineyard evapotranspiration and validate the accuracy of the UAV multispectral remote sensing in estimating the grape K_cb. The results indicated that: (1) The growth stage, type of vegetation index, and modeling method were three significant factors influencing the fitting accuracies of the relationship models between the grape basal crop coefficient (K_cb) and vegetation indices. These model fitting accuracies had a notable impact on the estimation accuracies of evapotranspiration. (2) The application of UAV-based multispectral remote sensing to estimate the grape basal crop coefficient in the subhumid region of Northwest China was feasible. Compared to the K_cb values recommended by the FAO-56, the K_cb values derived from the UAV data improved the estimation accuracies of evapotranspiration by more than 11% in 2021 and 13% in 2022. Full article

An S-band antenna has been designed, developed, measured, space-qualified, and integrated into the INTA ANSER satellite constellation and the future ANSER-AT mission. This antenna will be part of the space-to-ground communication link for the constellation, which consists of one Leader and two Followers. The novel antenna, mounted on the Leader, has been designed and manufactured with materials and processes specifically tested for space. It features dual circular polarization over a wide band without requiring a phase-shifting network, making it very compact and straightforward. Additionally, its gain patterns are highly stable within the desired band, improving its link capacity compared to the UHF monopole alternative used in the previous Leader. Currently, the antenna has been qualified and installed on INTA’s Leader-S, set to launch in January 2025, as well as on the future ANSER-AT mission. Full article

Visible/near-infrared spectroscopy is widely used for non-destructive fruit quality detection, but the high cost of spectrometers (400–1100 nm range) in sorting equipment limits its accessibility. This study proposes a dual-channel co-spectroscopy method to address this issue. Using apples’ soluble solids content as the research target, a dual-channel platform was constructed to optimize parameters for full-transmission spectral signal acquisition. Spectral data were collected using dual channels (400–700 nm and 700–1100 nm bands, separated by filters) and a single channel (400–1100 nm range). Preprocessing methods (MSC, SNV, FD, SD, SG) and feature extraction algorithms (CARS, SPA, UVE) were applied, followed by PLSR modeling. The dual-channel method with Raw spectrum + FD + CARS + PLSR achieved optimal results, with R²_v = 0.88, RMSEP = 0.39 for the 400–700 nm band, and R²_v = 0.94, RMSEP = 0.33 for the 700–1100 nm band. The single-channel method with Raw spectrum + MSC + CARS + PLSR achieved R²_v = 0.90, RMSEP = 0.36. These findings validate dual-channel co-spectroscopy as a cost-effective, accurate solution for non-destructive fruit quality detection, providing a practical approach to reduce spectrometer costs and enhance sorting system efficiency. Full article

This paper demonstrates that potential oscillations in various frequency bands of monolithic microwave integrated circuits (MMICs) can be effectively suppressed using well-designed dual RC traps in the bias networks. The proposed approach is applied to the design and development of a highly stable W-band low-noise amplifier (LNA) MMIC for high-precision millimeter-wave applications. The amplifier is fabricated using the 0.1 µm GaAs pHEMT process from Win Semiconductors. The cascaded four-stage design consists of two low-noise-optimized stages, followed by two high-gain-tuned stages. Stability is enhanced through the integration of dual RC traps in the bias networks, which is rigorously evaluated using stability factors (K and μ) and network determinant function (NDF) encirclement analysis. In low-noise mode, the developed low-noise amplifier MMIC achieves a noise figure of 5.6−6.2 dB and a linear gain of 17.8−19.8 dB over the 90−98 GHz frequency range, while only consuming a DC power of 96 mW. In high-gain mode, it has a noise figure of 6.2−6.9 dB and a linear gain of 19.8−21.7 dB. Full article

Decoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different periods. These challenges are exacerbated by the low spatial resolution and high signal redundancy inherent in EEG signals, which traditional linear models struggle to address. To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. The Chebyshev Type II filter decomposes the signal into multiple sub-bands giving the model frequency domain insights. The Adaptive GCN, specifically designed for the MI-EEG context, captures functional connectivity between channels more effectively than conventional GCN models, enabling accurate spatial–spectral feature extraction. Furthermore, combining Bi-GRU and Multi-Head Attention (MHA) captures the temporal dependencies across different time segments to extract deep time–spectral features. Finally, feature fusion is performed to generate the final prediction results. Experimental results demonstrate that our method achieves an average classification accuracy of 80.38% on the BCI-IV Dataset 2a and 87.49% on the BCI-I Dataset 3a, outperforming other state-of-the-art decoding approaches. This approach lays the foundation for future exploration of personalized and adaptive brain–computer interface (BCI) systems. Full article

This paper presents the design of a high-performance dual-band antenna for industrial, scientific, and medical (ISM) band applications. The proposed prototype consists of a low-cost patch antenna, 40 mm × 24 mm in size (i.e., 0.36λ₀ × 0.19λ₀, with λ₀ the wavelength corresponding to the low frequency), with a relatively wideband for both operational bands (up to 140 MHz at 2.45 GHz and 510 MHz at 5.8 GHz), and a radiation efficiency of over 90%. The antenna has a quasi-omnidirectional radiation pattern with gains of 2.41 dBi and 5.22 dBi at 2.45 GHz and 5.8 GHz, respectively. The design methodology is detailed and illustrated by simulation results showing the optimization steps and the characteristics associated with the antenna. Experimental results based on a fabricated prototype are presented and compared with simulation results from the design stage. Finally, the proposed antenna prototype is also compared with similar antennas available in the literature. Full article

Lightning rod configuration is crucial in radar stations. With widespread application of dual-polarisation technology, lightning rods have a significant impact on radar differential reflectivity, particularly for X-band radars with shorter wavelengths. Quantitative analyses and methods for reducing the impact of lightning rods on radar data quality have become particularly important. In this study, lightning rods of two different sizes were configured on Beijing’s Fangshan X-band radar to perform antenna far-field tests and precipitation process comparative observation tests, and to conduct a quantitative impact assessment of the antenna electrical performance parameters and radar differential reflectivity. First, far-field tests were conducted on the impact of small- and original-diameter lightning rods on the Fangshan X-band radar. The results showed that the horizontal polarisation beam width was reduced by 0.081 and 0.08°, while the vertical polarisation beam width was reduced by 0.02 and 0.11°, respectively. Second, light rain or snowfall with a signal-to-noise ratio greater than 15 dB, and a correlation coefficient greater than 0.985, were selected for comparative observation. When other environmental influences could not be isolated, the original lightning rod showed a maximum ZDR value of 1.32 dB and a maximum azimuth span of 35°. The maximum ZDR value of the small-diameter lightning rod was 0.18 dB and the maximum azimuth span was 20°; however, its deviation from the theoretical maximum value is only 0.05 dB. Therefore, once the system configuration is determined, the design of an appropriate lightning rod scheme can effectively improve radar data quality. Full article