Search Results (1,232)

Signal-of-Opportunity (SOP) positioning based on Low-Earth-Orbit (LEO) constellations has gradually become a research hotspot. Due to their large quantity, wide spectral coverage, and strong signal power, LEO satellite SOP positioning exhibits robust anti-jamming capabilities. However, no in-depth studies have been conducted on their anti-jamming performance, particularly regarding the most common type of interference faced by ground receivers—Periodic Frequency Modulation (PFM) interference. Due to the significant differences in signal characteristics between LEO satellite downlink signals and those of Global Navigation Satellite Systems (GNSSs) based on Medium-Earth-Orbit (MEO) or Geostationary-Earth-Orbit (GEO) satellites, traditional interference suppression techniques cannot be directly applied. This paper proposes a Signal Adaptive Iterative Optimization Resampling (SAIOR) algorithm, which leverages the periodicity of PFM jamming signals and the characteristics of LEO constellation signals. The algorithm enhances the concentration of jamming energy by appropriately resampling the data, thereby reducing the overlap between LEO satellite signals and interference. This approach effectively minimizes the damage to the desired signal during anti-jamming processing. Simulation and experimental results demonstrate that, compared to traditional algorithms, this method can effectively eliminates single/multiple-component PFM interference, improve the interference suppression performance under the conditions of narrow bandwidth and high signal power, and holds a high application value in LEO satellite SOP positioning. Full article

The Multiscale Geographically and Temporally Weighted Regression model overcomes the limitation of estimating spatiotemporal variation characteristics of regression coefficients for different variables under a single scale, making it a powerful tool for exploring the spatiotemporal scale characteristics of regression relationships. Currently, the most widely used estimation method for multiscale spatiotemporal geographically weighted models is the backfitting-based iterative approach. However, the iterative process of this method leads to a substantial computational burden and the accumulation of errors during iteration. This paper proposes a non-iterative estimation method for the MGTWR model, combining local linear fitting and two-step weighted least squares estimation techniques. Initially, a reduced bandwidth is used to fit a local linear GTWR model to obtain the initial estimates. Then, for each covariate, the optimal bandwidth and regression coefficients are estimated by substituting the initial estimates into a localized least squares problem. Simulation experiments are conducted to evaluate the performance of the proposed non-iterative method compared to traditional methods and the backfitting-based approach in terms of coefficient estimation accuracy and computational efficiency. The results demonstrate that the non-iterative estimation method for MGTWR significantly enhances computational efficiency while effectively capturing the scale effects of spatiotemporal variation in the regression coefficient functions for each predictor. Full article

Phononic crystals (PnCs) have garnered significant interest owing to their ability to manipulate wave propagation, particularly through phononic band gaps and defect modes. However, conventional defective PnCs are limited by their fixed defect-band frequencies, which restricts their adaptability to dynamic environments. This study introduces a novel approach for temperature-controlled tunability of defective PnCs by integrating shape memory alloys (SMAs) into defect regions. The reversible phase transformations of SMAs, driven by temperature variations, induce significant changes in their mechanical properties, enabling real-time adjustment of defect-band frequencies. An analytical model is developed to predict the relationship between the temperature-modulated material properties and defect-band shifts, which is validated through numerical simulations. The results demonstrate that defect-band frequencies can be dynamically controlled within a specified range, thereby enhancing the operational bandwidth of the ultrasonic sensors. Additionally, sensing-performance analysis confirms that while defect-band frequencies shift with temperature, the output voltage of the sensors remains stable, ensuring reliable sensitivity across varying conditions. This study represents a significant advancement in tunable PnC technology, paving the way for next-generation ultrasonic sensors with enhanced adaptability and reliability in complex environments. Full article

In recent years, missions requiring payload telemetry data transmission to ground stations have increasingly demanded a higher bandwidth. Traditional ranging techniques for spacecraft position determination often use a dedicated spectrum, reducing the available bandwidth for telemetry. To overcome this limitation, a transmission system capable of simultaneously sending high data-rate telemetry and ranging signals within the same bandwidth represents a key advancement for modern space missions, particularly Lagrangian science missions and planetary probes. To enhance the technological readiness of such a system, a hardware demonstrator has been developed using the AMD Xilinx (San Jose, CA, USA) ZCU111 Field Programmable Gate Array (FPGA), selected for its high-speed digital signal processing capabilities and integrated converters. The system, in this preliminary breadboarding phase, operates at a fixed telemetry rate of 4.25 Msym/s and a ranging rate of 2.987 Mchip/s, constrained within a 10 MHz bandwidth typical for science missions. Despite these limitations, tests demonstrated that integrating telemetry with Pseudo Noise (PN) Ranging introduces negligible implementation losses compared to telemetry-only transmission. The system also supports high-order modulations up to 64-APSK, improving spectral efficiency within the available bandwidth. Although some limitations have been found in the use of very high-order modulations, this prototype demonstrates the feasibility of integrating advanced coding techniques with PN Ranging. Full article

With the increasing severity of data privacy and security issues, cross-organizational federated learning is facing challenges in communication efficiency and cost. Knowledge distillation, as an effective model compression technique, can reduce model size without significantly compromising accuracy, thereby lowering communication overhead. However, existing knowledge distillation methods either employ static distillation loss weights, ignoring bandwidth variations in communication networks, or fail to effectively account for bandwidth heterogeneity among different nodes, leading to communication bottlenecks. To enhance the overall system efficiency, there is an urgent need to find new methods that enable models to achieve optimal performance in resource-constrained environments. This paper proposes a communication optimization method based on mutual knowledge distillation (Fed-MKD) to address the bottleneck issues caused by high communication costs in cross-organizational federated learning. By leveraging a mutual distillation mechanism, Fed-MKD enables collaborative training of teacher and student models locally while reducing the frequency and size of global model transmissions to optimize communication. Our experimental results demonstrate that, compared to classical knowledge distillation methods, Fed-MKD significantly improves communication efficiency, with compression ratios ranging from 4.89× to 28.45×. Furthermore, Fed-MKD achieves up to 4.34× acceleration in convergence time across multiple datasets. These findings highlight the significant practical value of Fed-MKD in environments with heterogeneous data distributions and limited communication resources. Full article

This article presents a brief evaluation and discussion of eight proposed printed log-periodic dipole array (PLPDA) antennas that have been introduced in the last decade for EMC applications. These proposed antennas could serve as reference antennas for radiation and immunity tests inside the EMC chamber. Step-by-step design procedures have been detailed with various feeding methods, showing their effect on the wideband characteristic compared to the design complexity. Different miniaturization and bandwidth improvement methods have been utilized to tackle the size reduction and bandwidth enhancement goals. Furthermore, the comprehensive view of the specifications of the reference antenna design inside the EMC chamber has been explained in detail, which presents the motivation for using a printed antenna rather than the classical one for these applications. The achievements of the presented designs have been listed, compared, and discussed with the classical LPDA antenna (HyperLOG 7060) offered for sale. Finally, a brief conclusion presents the recommendations for the design and analysis of the PLPDA antenna for EMC measurements. Full article

In the realm of quantum communication and photonic technologies, the extension of coherence time for photon wave packets is essential for improving system efficacy. This research introduces a methodology for measuring coherence time utilizing an unbalanced fiber interferometer, specifically designed for tunable pulse-width photon wave packets produced by cold atoms. By synchronously generating write pulses, signal light, and frequency-locking light from a single laser source, the study effectively mitigated frequency discrepancies that typically arise from the use of multiple light sources. The implementation of frequency-resolved photon counting under phase-locked conditions was accomplished through the application of polarization filtering and cascaded filtering techniques. The experimental results indicated that the periodicity of frequency shifts in interference fringe patterns diminishes as the differences in delay arm lengths increase, while fluctuations in fiber length and high-frequency laser jitter adversely affect interference visibility. Through an analysis of the correlation between delay and photon counts, the coherence time of the write laser was determined to be 2.56 µs, whereas the Stokes photons produced through interactions with cold atoms exhibited a reduced coherence time of 1.23 µs. The findings suggest that enhancements in laser bandwidth compression and fiber phase stability could further prolong the coherence time of photon wave packets generated by cold atoms, thereby providing valuable technical support for high-fidelity quantum information processing. Full article

This study investigates the impact of material selection on the performance of linear slideways in ultraprecision machines used for freeform surface machining. The primary objective is to address challenges related to load-bearing capacity and limited bandwidth in slow tool servo (STS) techniques. Multi-body dynamic (MBD) simulations are conducted to evaluate the performance of two materials, alloy steel and metal matrix composite (MMC), within the linear slideway system. Key performance parameters, including acceleration, velocity, and displacement, are analyzed to compare the two materials. The findings reveal that MMC outperforms alloy steel in acceleration, velocity, and displacement, demonstrating faster response times and greater linear displacement, which enhances the capabilities of STS-based ultraprecision machining. This study highlights the potential of utilizing lightweight materials, such as MMC, to optimize the performance and efficiency of linear slideways in precision engineering applications. Full article

The realization of broad-wavelength tunability of the structural color in Double layered Cholesteric Liquid Crystal Elastomers (DCLCEs), along with good flexibility and processability, presents a significant challenge. This research introduces a facile and effective fabrication technique, Solvent Evaporation-Induced Self-Assembly (SEISA), for the production of DCLCEs exhibiting broad wavelength tunability, superior flexibility, and robust mechanical characteristics. Focusing on initial color tuning, bubble defect minimization, UV photopolymerization, and coating procedures, this research systematically optimizes the fabrication process through experimental investigation of factors like chiral dopant amount, temperature, UV exposure duration, coating thickness, and speed. The method enabled the successful fabrication of DCLCEs with uniform and controllable coloration, demonstrating the effectiveness of this controlled synthesis approach in significantly enhancing structural color features. Upon stretching to 2.8 times its original length, the center wavelength shifted from 613 nm to 404 nm, yielding a tunable bandwidth of up to 209 nm across the visible spectrum. Full article

In situ-grown SiC nanowires (SiCnws) on SiC porous material (SiCnws@SiC) were prepared using sol–gel and carbothermal reduction methods, which substantially improves the electromagnetic wave absorption property of composite material. The crystallinity and purity of SiCnws are the best when the sintering temperature is 1600 °C. When the ratio of the carbon source (C) to the silicon source (Si) is 1:1, SiCnws@SiC composite exhibits excellent electromagnetic wave absorption performance, the minimum reflection loss is −56.95 dB at a thickness of 2.30 mm, and the effective absorption bandwidth covers 1.85 GHz. The optimal effective absorption bandwidth is 4.01 GHz when the thickness is 2.59 mm. The enhancement of the electromagnetic wave absorption performance of SiCnws is mainly attributed to the increase in the heterogeneous interface and multiple reflection and scattering caused by the network structure, increasing dielectric loss and conduction loss. In addition, defects could occur during the growth of SiCnws, which could become the center of dipole polarization and increase the polarization loss of composite materials. Therefore, in situ growth of SiCnws on SiC porous ceramics is a promising method to improve electromagnetic wave absorption. Full article

The increasing complexity of conventional energy distribution systems, combined with the growing demand for efficient data processing, has necessitated the implementation of smart grid technologies and the integration of advanced computing paradigms such as edge computing. Traditional cloud-based solutions face significant challenges, including high latency, limited bandwidth, and cybersecurity vulnerabilities, rendering them less effective for real-time smart grid applications. Edge computing enables localized data processing, which significantly reduces latency and optimizes bandwidth usage. These capabilities enhance the resilience and intelligence of modern energy systems. This paper presents a systematic review of edge computing in energy distribution systems, examining its architectures, methodologies, and real-world applications. Key application areas consist of real-time data transmission, smart metering, microgrid management, anomaly and fault detection, state estimation, and energy management. The analysis shows how edge computing improves secure communication, supports decentralized intelligence, and facilitates scalable energy optimization. Beyond these advantages, the review also identifies critical challenges such as interoperability issues, resource constraints, and security vulnerabilities. By categorizing edge computing applications, the findings provide a comprehensive reference for both researchers and industry professionals working on the development of next-generation energy management systems. Full article

Fog computing introduces a new dimension to the network edge by pooling diverse resources (e.g., processing power, memory, and bandwidth). However, allocating resources from heterogeneous fog nodes often faces limited capacity. To overcome these limitations, integrating fog nodes with cloud resources is crucial, ensuring that Service Providers (SPs) have adequate resources to deliver their services efficiently. In this paper, we propose a game-theoretic model to describe the competition among non-cooperative SPs as they bid for resources from both fog and cloud environments, managed by an Infrastructure Provider (InP), to offer paid services to their end-users. In our game model, each SP bids for the resources it requires, determining its willingness to pay based on its specific service demands and quality requirements. Resource allocation prioritizes the fog environment, which offers local access with lower latency but limited capacity. When fog resources are insufficient, the remaining demand is fulfilled by cloud resources, which provide virtually unlimited capacity. However, this approach has a weakness in that some SPs may struggle to fully utilize the resources allocated in the Nash equilibrium-balanced cloud solution. Specifically, under a nondiscriminatory pricing scheme, the Nash equilibrium may enable certain SPs to acquire more resources, granting them a significant advantage in utilizing fog resources. This leads to unfairness among SPs competing for fog resources. To address this issue, we propose a price differentiation mechanism among SPs to ensure a fair allocation of resources at the Nash equilibrium in the fog environment. We establish the existence and uniqueness of the Nash equilibrium and analyze its key properties. The effectiveness of the proposed model is validated through simulations using Amazon EC2 instances, where we investigate the impact of various parameters on market equilibrium. The results show that SPs may experience profit reductions as they invest to attract end-users and enhance their quality of service QoS. Furthermore, unequal access to resources can lead to an imbalance in competition, negatively affecting the fairness of resource distribution. The results demonstrate that the proposed model is coherent and that it offers valuable information on the allocation of resources, pricing strategies, and QoS management in cloud- and fog-based environments. Full article

This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and 5G, where NB-IoT is utilized for low-power, reliable data transmission from environmental sensors, such as soil information and weather monitoring, while 5G supports high-bandwidth, low-latency tasks like task scheduling and path tracking to effectively address the diverse communication requirements of modern complex agricultural scenarios. The cloud platform improves operational efficiency and resource utilization through real-time task scheduling, dynamic optimization, and seamless coordination between devices. To accommodate the diverse operational demands of agricultural environments, the system incorporates a real-time data feedback mechanism leveraging sensor data for path tracking and adjustment, enhancing adaptability and stability. Furthermore, a multi-machine collaborative scheduling strategy combining Dijkstra’s algorithm and an improved Harris hawk optimization (IHHO) algorithm, along with a multi-objective optimized path tracking method, is introduced to further improve scheduling efficiency and resource utilization while improving path tracking accuracy and smoothness and reducing external interferences, including environmental fluctuations and sensor inaccuracies. Experimental results demonstrate that the IoT-based cloud platform excels in data transmission reliability, path tracking accuracy, and resource optimization, validating its feasibility in smart agriculture and providing an efficient and scalable solution for large-scale agricultural operations. Full article

The rapid advancement of 3D imaging technology and depth cameras has made depth data more accessible for applications such as virtual reality and autonomous driving. However, depth maps typically suffer from lower resolution and quality compared to color images due to sensor limitations. This paper introduces an improved approach to guided depth map super-resolution (GDSR) that effectively addresses key challenges, including the suppression of texture copying artifacts and the preservation of depth discontinuities. The proposed method integrates both local and nonlocal models within a structured framework, incorporating an adaptive bandwidth mechanism that dynamically adjusts guidance weights. Instead of relying on fixed parameters, this mechanism utilizes a distance map to evaluate patch similarity, leading to enhanced depth recovery. The local model ensures spatial smoothness by leveraging neighboring depth information, preserving fine details within small regions. On the other hand, the nonlocal model identifies similarities across distant areas, improving the handling of repetitive patterns and maintaining depth discontinuities. By combining these models, the proposed approach achieves more accurate depth upsampling with high-quality depth reconstruction. Experimental results, conducted on several datasets and evaluated using various objective metrics, demonstrate the effectiveness of the proposed method through both quantitative and qualitative assessments. The approach consistently delivers improved performance over existing techniques, particularly in preserving structural details and visual clarity. An ablation study further confirms the individual contributions of key components within the framework. These results collectively support the conclusion that the method is not only robust and accurate but also adaptable to a range of real-world scenarios, offering a practical advancement over current state-of-the-art solutions. Full article

Hierarchical Federated Learning (HFL) for the Internet of Vehicles (IoV) leverages roadside units (RSU) to construct a low-latency, highly scalable multilayer cooperative training framework. However, with the rapid growth in the number of vehicle nodes, this framework faces two major challenges: (i) communication inefficiency under bandwidth-constrained conditions, where uplink congestion imposes significant burden on intra-framework communication; and (ii) interference from untrustworthy vehicle nodes, which disrupts model training and affects convergence. Therefore, in order to achieve secure aggregation while alleviating the communication bottleneck problem, we design a hierarchical three-layer federated learning framework with Gradient Quantization (GQ) and secure aggregation, called FedHSQA, which further integrates anomaly scoring to enhance robustness against untrustworthy vehicle nodes. Specifically, FedHSQA organizes IoV devices into three layers based on their respective roles: the cloud service layer, the RSU layer, and the vehicle node layer. During each non-initial communication round, the cloud server at the cloud layer computes anomaly scores for vehicle nodes using a Kullback–Leibler (KL) divergence-based multilayer perceptron (MLP) model. These anomaly scores are used to design a secure aggregation algorithm (ASA) that is robust to anomalous behavior. The anomaly scores and the aggregated global model are then transmitted to the RSU. To further reduce communication overhead and maintain model utility, FedHSQA introduces an adaptive GQ method based on the anomaly scores (ASQ). Unlike conventional vehicle node-side quantization, ASQ is performed at the RSU layer. It calculates the Jensen–Shannon (JS) distance between each vehicle node’s anomaly distribution and the target distribution, and adaptively adjusts the quantization level to minimize redundant gradient transmission. We validate the robustness of FedHSQA against anomalous nodes through extensive experiments on three real-world datasets. Compared to classical aggregation algorithms and GQ methods, FedHSQA reduced the average network traffic consumption by approximately 30 times while improving the average accuracy of the aggregation model by about 5.3%. Full article