Search Results (6,128)

In this paper, a method for the design of tunable multifunctional attenuators is proposed by analyzing the characterization of dual-mode microstrip resonators loaded by a graphene-sandwiched structure (GSS). Firstly, the odd–even mode method is applied to analyze the resonance characteristics of two common GSS-loaded dual-mode resonators, which clearly describe the influence of graphene on these resonators. Then, two kinds of multifunctional attenuator with dynamically tunable attenuation are proposed based on graphene-integrated dual-mode resonators, which enables controllable characteristics and multi-frequency transmission options for traditional attenuating devices. Finally, all the proposed multifunctional attenuators are fabricated and measured. The experimental results are in good agreement with the simulation results, which further verifies the conclusions and design method proposed in this paper. Full article

Major earthquakes and rainfall may occur at the same time, necessitating further investigation into the dynamic characteristics and responses of reinforced soil retaining walls subjected to the combined forces of rainfall and seismic activity. Three sets of shaking table tests on model retaining walls were designed, a modular reinforced earth retaining wall was utilized as the subject of this study, and a custom-made device was made to simulate rainfall conditions of varying intensities. These tests monitored the rainwater infiltration pattern, macroscopic phenomena, panel displacement, tension behavior, dynamic characteristics, and acceleration response of the modular reinforced earth retaining wall during vibration under different rainfall intensities. The results indicated the following. (1) Rainwater infiltration can be categorized into three stages: rapid rise, rapid decline, and slow decline to stability. The duration for infiltration to reach stability increases with greater rainfall. (2) An increase in rainfall intensity enhances the seismic stability of the retaining wall panel, as higher rainfall intensity results in reduced sand leakage from the panel, thereby diminishing panel deformation during vibration. (3) Increased rainfall intensity decreases the shear strength of the soil, leading to a greater load on the reinforcement. (4) The natural vibration frequencies of the three groups of retaining walls decreased by 0.21%, 0.54%, and 2.326%, respectively, indicating some internal damage within the retaining walls, although the degree of damage was not severe. Additionally, the peak displacement of the panel increased by 0.91 mm, 0.63 mm, and 0.61 mm, respectively. (5) The amplification effect of rainfall on internal soil acceleration is diminished, with this weakening effect becoming more pronounced as rainfall intensity increases. These research findings can provide a valuable reference for multi-disaster risk assessments of modular reinforced soil retaining walls. Full article

One of the predominant technologies for multi-source navigation in vehicles involves the fusion of GNSS/IMU/ODO through a factor graph. To address issues such as the asynchronous sampling frequencies between the IMU and ODO, as well as diminished accuracy during GNSS signal loss, we propose a GNSS/IMU/ODO integrated navigation method based on an adaptive sliding window factor graph. The measurements from the ODO are utilized as observation factors to mitigate prediction interpolation errors associated with traditional ODO pre-integration methods. Additionally, online estimation and compensation for both installation angle deviations and scale factors of the ODO further enhance its ability to constrain pose errors during GNSS signal loss. A multi-state marginalization algorithm is proposed and then utilized to adaptively adjust the sliding window size based on the quality of GNSS observations, enhancing pose optimization accuracy in multi-source fusion while prioritizing computational efficiency. Tests conducted in typical urban environments and mountainous regions demonstrate that our proposed method significantly enhances fusion navigation accuracy under complex GNSS conditions. In a complex city environment, our method achieves a 55.3% and 29.8% improvement in position and velocity accuracy and enhancements of 32.0% and 61.6% in pitch and heading angle accuracy, respectively. These results match the precision of long sliding windows, with a 75.8% gain in computational efficiency. In mountainous regions, our method enhances the position accuracy in the three dimensions by factors of 89.5%, 83.7%, and 43.4%, the velocity accuracy in the three dimensions by factors of 65.4%, 32.6%, and 53.1%, and reduces the attitude errors in roll, pitch, and yaw by 70.5%, 60.8%, and 26.0%, respectively, demonstrating strong engineering applicability through an optimal balance of precision and efficiency. Full article

The Global Navigation Satellite System (GNSS) provides critical positioning, navigation, and timing (PNT) services worldwide, enabling a wide range of applications from everyday use to advanced scientific and military operations. The importance of Low Earth Orbit (LEO) PNT systems lies in their ability to enhance the GNSS by implementing signals in additional frequency bands, offering increased signal strength, reduced latency, and improved accuracy and coverage, particularly in challenging environments such as urban canyons or polar regions, thereby addressing the limitations of the traditional Medium Earth Orbit (MEO) GNSS. This paper details the system engineering of a novel CubeSat-based multi-regional PNT system tailored for deployment in LEO. The proposed system leverages on a miniaturized CubeSat-compatible PNT payload that includes a chip-scale atomic clock (CSAC) and relies on MEO GNSS technologies to deliver positioning and timing information across multiple regions. The findings indicate that the proposed CubeSat-based PNT system offers a viable solution for enhancing global navigation and timing services, with potential commercial and scientific applications. This work contributes to the growing body of knowledge on LEO-based PNT systems and lays the groundwork for future research and development in this rapidly evolving field. Full article

Piezoelectric micromachined ultrasonic transducers (PMUTs) show considerable promise for application in ultrasound imaging, but the limited bandwidth of the traditional PMUTs largely affects the imaging quality. This paper focuses on how to arrange cells with different frequencies to maximize the bandwidth and proposes a multi-frequency PMUT (MF-PMUT) linear array. Seven cells with gradually changing frequencies are arranged in a monotonic trend to form a unit, and 32 units are distributed across four lines, forming one element. To investigate how the arrangement of cells affects the bandwidth, three different arrays were designed according to the extent of unit aggregation from the same frequency. Underwater experiments were conducted to assess the acoustic performance, especially the bandwidth. We found that the densest arrangement of the same cells produced the largest bandwidth, achieving a 92% transmission bandwidth and a 50% burst-echo bandwidth at 6 MHz. The mechanism was investigated from the coupling point of view by finite element analysis and laser Doppler vibrometry, focusing on the cell displacements. The results demonstrated strong ultrasound coupling in the devices, resulting in larger bandwidths. To exploit the advanced bandwidth but reduce the crosstalk, grooves for isolation were fabricated between elements. This work proposes an effective strategy for developing advanced PMUT arrays that would benefit ultrasound imaging applications. Full article

The remarkable development of deepfake models has facilitated the generation of fake content with various modalities, such as forged images, manipulated audio, and modified video with (or without) corresponding audio. However, many existing methods only analyze content with known and fixed modalities to identify deepfakes, which restricts their focus on intra-domain inconsistencies, and they fail to explore diverse modal and inter-domain hierarchical inconsistencies. In this work, we propose a novel unified neural network named MGDL-Net (Modal-Guided Domain Learning Network), which contains a spatial branch, a temporal branch, and a frequency branch. This diverse combination of branches endows our network with the ability to detect face-related input with flexible modalities and perceive both intra- and inter-domain inconsistencies, such as unimodal, bimodal, and trimodal modalities. To effectively and comprehensively capture the various inconsistencies, we propose implementing heterogeneous inconsistency learning (HIL) with a three-level joint extraction paradigm. In particular, HIL performs heterogeneous learning from spatial, temporal, and frequency perspectives to generate more generalized representations of forgery and eliminate the interference of static redundant information. Furthermore, a multi-modal deepfake dataset is also constructed. We have conducted extensive experiments, and our results have demonstrated that the proposed method can achieve an outstanding performance compared to that of numerous state-of-the-art methods, which implies that the cross-modal inconsistency learning we propose is beneficial for multi-modal face forgery detection. Full article

A stochastic energetics framework is applied to examine how periodically shifting the frequency of a time-dependent oscillating temperature gradient affects heat transport in a nanoscale molecular model. We specifically examine the effects that frequency switching, i.e., instantaneously changing the oscillation frequency of the temperature gradient, has on the shape of the heat transport hysteresis curves generated by a particle connected to two thermal baths, each with a temperature that is oscillating in time. Analytical expressions are derived for the energy fluxes in/out of the system and the baths, with excellent agreement observed between the analytical expressions and the results from nonequilibrium molecular dynamics simulations. We find that the shape of the heat transport hysteresis curves can be significantly altered by shifting the frequency between fast and slow oscillation regimes. We also observe the emergence of features in the hysteresis curves such as pinched loops and complex multi-loop patterns due to the frequency shifting. The presented results have implications in the design of thermal neuromorphic devices such as thermal memristors and thermal memcapacitors. Full article

The design of street spaces significantly influences public behavior and quality of life. Understanding how various urban street spatial characteristics affect public behavior, alongside the role of multi-sensory perception, enables designers and planners to create more human-centered urban environments. Grounded in landsenses ecology, this study employs correlation analysis, regression analysis, and Partial Least-Squares Structural Equation Modeling (PLS-SEM) to examine the effects of different urban street spatial characteristics on public behavior and the mediating role of multi-sensory perception. The findings reveal that street spatial characteristics, particularly the Water Surface Ratio (WSR) and Waterfront Density (WD), have a pronounced impact on behavioral traits, with higher public activity frequencies in areas with elevated WSR and WD. Notably, WSR significantly affects static behaviors, such as sunbathing (β = 0.371, p < 0.001), and dynamic behaviors, such as walking (β = 0.279, p < 0.001). While road and water characteristics directly influence behavior, buildings and green spaces mainly affect public behavior through multi-sensory perception. Different sensory perceptions show varying effects, with olfactory perception playing a significant role in these experiences, alongside a notable chain-mediated effect between tactile perception and psychological cognition. These results provide valuable insights for integrating multi-sensory experiences into urban design. Full article

This study introduces a quantitative approach to evaluating the resilience of oil pipeline systems against various natural and physical disruptions. Resilience is increasingly essential in critical infrastructure to ensure continuous operations and minimize disruption impacts. However, existing quantitative methods often need specific time-dependent data, making measuring resilience in pipeline infrastructure challenging. To address this gap, this paper proposed a comprehensive framework by integrating the existing incident database with key features of assessing failure probabilities based on historical events and developing multi-event resilience indicators based on system performance under various disruptions. The methodology employs event tree analysis to quantify the probabilities of multiple failure scenarios and their impact on pipeline operations and recovery efforts. The practical application of the proposed approach was demonstrated using real-world oil pipeline incident data from across the United States, covering the period from 2010 to 2022. The focus was on multiple event scenarios involving pipeline disruptions, followed by shutdowns, examining how these events collectively impact pipeline resilience. The results indicate that corrosion failure, equipment failure, and natural hazard damage significantly impact oil pipeline resilience. Corrosion and equipment failures affect resilience primarily due to their frequency, while natural hazard damage, despite its lower occurrence rate, is more unpredictable and often requires more frequent shutdowns. Understanding these failure causes and their impacts is essential for enhancing the resilience and sustainable operation of oil pipeline systems. Full article

Remote sensing of land surface temperature (LST) is a fundamental variable in analyzing temperature variability in urban areas. Geostationary sensors provide sufficient observations throughout the day for a diurnal analysis of temperature, however, lack the spatial resolution needed for highly heterogeneous areas such as cities. Polar orbiting sensors have the advantage of a higher spatial resolution, enabling a better characterization of the surface while only providing one to two observations per day. This work aims at using a multi-layer perceptron-based method to downscale geostationary-derived LST based on a polar-orbit-derived one. The model is trained on a pixel-by-pixel basis, which reduces the complexity of the model while requiring fewer auxiliary data to characterize the surface conditions. Results show that the model is able to successfully downscale LST for the city of Madrid, from approximately 4.5 km to 750 m. Performance metrics between training and validation datasets show no overfitting. The model was applied to a different time period and compared to data derived from three additional sensors, which were not used in any stage of the training process, yielding a R² of 0.99, root mean square errors between 1.45 and 1.58 and mean absolute errors ranging from 1.07 to 1.15. The downscaled LST is shown to improve the representation of both the temporal variability and spatial heterogeneity of temperature, when compared to geostationary- and polar-orbit-derived LST individually. The resulting downscaled data take advantage of the high observation frequency of geostationary data, combined with the spatial resolution of polar orbiting sensors and may be of added value for the study of diurnal and seasonal patterns of LST in urban environments. Full article

Urbanization growth and climate change have increased the frequency and severity of floods in urban areas. One of the effective methods for reducing stormwater volume and managing urban floods is the low-impact development best management practice (LID-BMP). This study aims to mitigate flood volume and peak discharge caused by land use changes in the Darabad basin located in Tehran, Iran, using LID-BMPs. For this purpose, land use maps were extracted for a period of 23 years from 2000 to 2022 using Landsat satellite images. Then, by using a combination of geographic information system-based multi-criteria decision analysis (GIS-MCDA) method and spatial criteria, four types of LID-BMPs, including bioretention basin, green roof, grass swale, and porous pavement, were located in the study area. Next, rainfall–runoff modeling was applied to calculate the changes in the mentioned criteria due to land use changes and the application of LID-BMPs in the area using soil conservation service curve number (SCS-CN) method. The simulation results showed that the rise in built-up land use from 43.49 to 56.51 percent between the period has increased the flood volume and peak discharge of 25-year return period by approximately 60 percent. The simulation results also indicated that the combined use of the four selected types of LID-BMPs will lead to a greater decrease in stormwater volume and peak discharge. According to the results, LID-BMPs perform better in shorter return periods in a way that the average percentage of flood volume and peak discharge reduction in a 2-year return period were 36.75 and 34.96 percent, while they were 31.37 and 26.5 percent in a 100-year return period. Full article

The accurate detection and localization of polyps during endoscopic examinations are critical for early disease diagnosis and cancer prevention. However, the presence of artifacts and noise, along with the high similarity between polyps and surrounding tissues in color, shape, and texture complicates polyp detection in video frames. To tackle these challenges, we deployed multivariate regression analysis to refine the model and introduced a Noise-Suppressing Perception Network (NSPNet) designed for enhanced performance. NSPNet leverages wavelet transform to enhance the model’s resistance to noise and artifacts while improving a multi-frame collaborative detection strategy for dynamic polyp detection in endoscopic videos, efficiently utilizing temporal information to strengthen features across frames. Specifically, we designed a High-Low Frequency Feature Fusion (HFLF) framework, which allows the model to capture high-frequency details more effectively. Additionally, we introduced an improved STFT-LSTM Polyp Detection (SLPD) module that utilizes temporal information from video sequences to enhance feature fusion in dynamic environments. Lastly, we integrated an Image Augmentation Polyp Detection (IAPD) module to improve performance on unseen data through preprocessing enhancement strategies. Extensive experiments demonstrate that NSPNet outperforms nine SOTA methods across four datasets on key performance metrics, including F1Score and recall. Full article

In this paper, we propose a random frequency division multiplexing (RFDM) method for multicarrier modulation in mobile time-varying channels. Inspired by compressed sensing (CS) technology which use a sensing matrix (with far fewer rows than columns) to sample and compress the original sparse signal simultaneously, while there are many reconstruction algorithms that can recover the original high-dimensional signal from a small number of measurements at the receiver. The approach choose the classic sensing matrix of CS–Gaussian random matrix to compress the signal. However, the signal is not sparse which makes the reconstruction algorithms ineffective. We take full account of the great power of deep neural networks (DNN) to detect the signal as it is an underdetermined equation. The proposed RFDM establishes a novel signal modulation and detection method to target better transmission efficiency, and the simulation results show that the proposed method can achieve good BER, offering a new research paradigm to improve the spectrum efficiency of a multi-subcarrier, multi-antenna, multi-user system. Full article

Wavelet-based edge detection methods have evolved significantly over the years, contributing to advances in image processing, computer vision, and pattern recognition. This paper proposes a new local optimal spline wavelet (LOSW) and the dual wavelet of the LOSW. Then, a pair of dual filters can be obtained, which can provide distortion-free signal decomposition and reconstruction, while having stronger denoising and feature capture capabilities. The coefficients of the pair of dual filters are calculated for image edge detection. We propose a new LOSW-based edge detection algorithm (LOSW-ED), which introduces a structural uncertainty–aware modulus maxima (SUAMM) to detect highly uncertain edge samples, ensuring robustness in complex and noisy environments. Additionally, LOSW-ED unifies multi-structure morphology and modulus maxima to fully exploit the complementary properties of low-frequency (LF) and high-frequency (HF) components, enabling multi-stage differential edge refinement. The experimental results show that the proposed LOSW and LOSW-ED algorithm has better performance in noise suppression and edge structure preservation. Full article

Coal remains a crucial component of China’s energy supply, with production exceeding half of the global output in 2023. Despite safety improvements, the fatality rate in coal mining rose significantly, underscoring ongoing safety challenges. A total of 174 coal mine explosion investigation reports from China between 2000 and 2024 were analyzed, extracting and mining text related to coordination failures. The texts were categorized by time and region, creating two temporal datasets (2000–2018 and 2019–2024) and six regional datasets (Northeast, East, Central South, Southwest, Northwest, and North China). Using frequent itemset mining and social network construction, the concept of risk propagation was applied to identify the critical paths that lead to coal mine explosions. Over time, coordination failures in China’s coal mine explosions have evolved from localized issues among a few stakeholders to complex, multi-layered challenges involving broader governmental oversight and systemic management issues. Based on regional findings, balancing the frequency and severity of penalties, ensuring meaningful safety inspections, and alleviating the policy pressure on small coal mines are key points for addressing coordination failures. Full article