Search Results (343)

This paper presents the multi-triangular ring and self-routing protocol (MTRSRP), which is a new decentralized strategy designed to boost throughput and network efficiency in multiring scatternets. MTRSRP comprises two primary phases: leader election and scatternet formation, which collaborate to establish an effective multi-triangular ring topology. In the leader election phase, nodes exchange broadcast messages to gather neighbor information and elect coordinators through a competitive process. The scatternet formation phase determines the optimal number of rings based on the coordinator’s collected node information and predefined rules. The master nodes then send unicast connection requests to establish piconets within the scatternet, following a predefined role table. Intra- and inter-bridge nodes were activated to interconnect the piconets, creating a cohesive multi-triangular ring scatternet. Additionally, MTRSRP incorporates a self-routing addressing scheme within the triangular ring architecture, optimizing packet transmission paths and reducing overhead by utilizing master/slave relationships established during scatternet formation. Simulation results indicate that MTRSRP with dual-bridge connectivity outperforms the cluster-based on-demand routing protocol and Bluetooth low-energy mesh schemes in key network transmission performance metrics such as the transmission rate, packet delay, and delivery ratio. In summary, MTRSRP significantly enhances throughput, optimizes routing paths, and improves network efficiency in multi-ring scatternets through its multi-triangular ring topology and self-routing capabilities. Full article

Edge computing (EC) plays a critical role in advancing the next-generation Industrial Internet of Things (IIoT) by enhancing production, maintenance, and operational outcomes across heterogeneous network boundaries. This study builds upon EC intelligence and integrates graph-based learning to propose a Cross-Layer Controller Tasking Scheme (CLCTS). The scheme operates through two primary phases: task grouping assignment and cross-layer control. In the first phase, controller nodes executing similar tasks are grouped based on task timing to achieve monotonic and synchronized completions. The second phase governs controller re-tasking both within and across these groups. Graph structures connect the groups to facilitate concurrent tasking and completion. A learning model is trained on inverse outcomes from the first phase to mitigate task acceptance errors (TAEs), while the second phase focuses on task migration learning to reduce task prolongation. Edge nodes interlink the groups and synchronize tasking, migration, and re-tasking operations across IIoT layers within unified completion periods. Departing from simulation-based approaches, this study presents a fully implemented framework that combines learning-driven scheduling with coordinated cross-layer control. The proposed CLCTS achieves an 8.67% reduction in overhead, a 7.36% decrease in task processing time, and a 17.41% reduction in TAEs while enhancing the completion ratio by 13.19% under maximum edge node deployment. Full article

Broadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these limitations, in this paper, we conduct a comparative study of two innovative broadcasting schemes that enhance adaptability through dynamic fuzzy logic membership functions for the broadcasting problem. The first approach (Model A) dynamically adjusts membership functions based on changing network parameters and fine-tunes the broadcast (BC) versus do-not-broadcast (DNB) ratio. Model B, on the other hand, introduces a multi-profile switching mechanism that selects among distinct fuzzy parameter sets optimized for various macro-level scenarios, such as energy constraints or node density, without altering the broadcasting ratio. Reinforcement learning (RL) is employed in both models: in Model A for BC/DNB ratio optimization, and in Model B for action decisions within selected profiles. Unlike prior fuzzy logic or reinforcement learning approaches that rely on fixed profiles or static parameter sets, our work introduces adaptability at both the membership function and profile selection levels, significantly improving broadcasting efficiency and flexibility across diverse MANET conditions. Comprehensive simulations demonstrate that both proposed schemes significantly reduce redundant broadcasts and collisions, leading to lower network overhead and improved message delivery reliability compared to traditional static methods. Specifically, our models achieve consistent packet delivery ratios (PDRs), reduce end-to-end Delay by approximately 23–27%, and lower Redundancy and Overhead by 40–60% and 40–50%, respectively, in high-density and high-mobility scenarios. Furthermore, this comparative analysis highlights the strengths and trade-offs between reinforcement learning-driven broadcasting ratio optimization (Model A) and parameter-based dynamic membership function adaptation (Model B), providing valuable insights for optimizing broadcasting strategies. Full article

The rapid growth of Internet of Things (IoT) environments has increased security challenges due to heightened exposure to cyber threats and attacks. A key problem is the class imbalance in attack traffic, where critical yet underrepresented attacks are often overlooked by intrusion-detection systems (IDS), thereby compromising reliability. We propose Feature-Importance GAN SMOTE (FIGS), an innovative, realistic intrusion-detection framework designed for IoT environments to address this challenge. Unlike other works that rely only on traditional oversampling methods, FIGS integrates sensitivity-based feature-importance analysis, Generative Adversarial Network (GAN)-based augmentation, a novel imbalance ratio (GIR), and Synthetic Minority Oversampling Technique (SMOTE) for generating high-quality synthetic data for minority classes. FIGS enhanced minority class detection by focusing on the most important features identified by the sensitivity analysis, while minimizing computational overhead and reducing noise during data generation. Evaluations on the CICIoMT2024 and CICIDS2017 datasets demonstrate that FIGS improves detection accuracy and significantly lowers the false negative rate. FIGS achieved a 17% improvement over the baseline model on the CICIoMT2024 dataset while maintaining performance for the majority groups. The results show that FIGS represents a highly effective solution for real-world IoT networks with high detection accuracy across all classes without introducing unnecessary computational overhead. Full article

With the rapid expansion of high-speed rail infrastructure, maintaining the structural integrity of insulators is critical to operational safety. However, conventional defect detection techniques typically rely on extensive labeled datasets, struggle with class imbalance, and often fail to capture large-scale structural anomalies. In this paper, we present an unsupervised insulator defect detection framework based on a masked autoencoder (MAE) architecture. Built upon a vision transformer (ViT), the model employs an asymmetric encoder-decoder structure and leverages a high-ratio random masking scheme during training to facilitate robust representation learning. At inference, a dual-pass interval masking strategy enhances defect localization accuracy. Benchmark experiments across multiple datasets demonstrate that our method delivers competitive image- and pixel-level performance while significantly reducing computational overhead compared to existing ViT-based approaches. By enabling high-precision defect detection through image reconstruction without requiring manual annotations, this approach offers a scalable and efficient solution for real-time industrial inspection under limited supervision. Full article

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments. Full article

This study introduces Logarithmic Positional Partition Interval Encoding (LPPIE), a novel lossless compression methodology employing iterative logarithmic transformations to drastically reduce data size. While conventional dictionary-based algorithms rely on repeated sequences, LPPIE translates numeric data sequences into highly compact logarithmic representations. This achieves significant reduction in data size, especially on large integer datasets. Experimental comparisons with established compression methods—such as ZIP, Brotli, and Zstandard—demonstrate LPPIE’s exceptional effectiveness, attaining compression ratios nearly 13 times superior to established methods. However, these substantial storage savings come with elevated computational overhead due to LPPIE’s complex numerical operations. The method’s robustness across diverse datasets and minimal scalability limitations underscore its potential for specialized archival scenarios where data fidelity is paramount and processing latency is tolerable. Future enhancements, such as GPU-accelerated computations and hybrid entropy encoding integration, are proposed to further optimize performance and broaden LPPIE’s applicability. Overall, LPPIE offers a compelling alternative in lossless data compression, substantially redefining efficiency boundaries in high-volume numeric data storage. Full article

This study presents a unique and comprehensive application of ANSYS Mechanical R19.2’s SMART crack growth feature, leveraging its capabilities to conduct an unprecedented parametric investigation into fatigue crack propagation behavior under a wide range of positive and negative stress ratios, and to provide detailed insights into the influence of hole positioning on crack trajectory. By uniquely employing an unstructured mesh method that significantly reduces computational overhead and automates mesh updates, this research overcomes traditional fracture simulation limitations. The investigation breaks new ground by comprehensively examining an unprecedented range of both positive (R = 0.1 to 0.5) and negative (R = −0.1 to −0.5) stress ratios, revealing previously unexplored relationships in fracture mechanics. Through rigorous and extensive numerical simulations on two distinct specimen configurations, i.e., a notched plate with a strategically positioned hole under fatigue loading and a cracked rectangular plate with dual holes under static loading, this work establishes groundbreaking correlations between stress parameters and fatigue behavior. The research reveals a novel inverse relationship between the equivalent stress intensity factor and stress ratio, alongside a previously uncharacterized inverse correlation between stress ratio and von Mises stress. Notably, a direct, accelerating relationship between stress ratio and fatigue life is demonstrated, where higher R-values non-linearly increase fatigue resistance by mitigating stress concentration, challenging conventional linear approximations. This investigation makes a substantial contribution to fracture mechanics by elucidating the fundamental role of hole positioning in controlling crack propagation paths. The research uniquely demonstrates that depending on precise hole location, cracks will either deviate toward the hole or maintain their original trajectory, a phenomenon attributed to the asymmetric stress distribution at the crack tip induced by the hole’s presence. These novel findings, validated against existing literature, represent a significant advancement in predictive modeling for fatigue life assessment, offering critical new insights for engineering design and maintenance strategies in high-stakes industries. Full article

The Basic Fractal Image Compression (BFIC) method is widely known for its high computational complexity and long encoding time under a fixed block segmentation. To address these limitations, we propose an enhanced fractal image compression algorithm based on adaptive non-uniform rectangular partition (FICANRP). This novel approach adaptively partitions the image into variable-sized range blocks (R-blocks) and non-overlapping domain blocks (D-blocks) guided by local texture and feature. By converting the similarity-matching process for R-blocks into a localized search strategy based on block size and feature classification, the FICANRP method significantly reduces computational overhead. Moreover, employing a non-overlapping partition strategy for D-blocks drastically reduces the number of D-blocks and the associated spatial coordinate data while preserving high matching accuracy. This reduction, coupled with the block similarity matching algorithm that overcomes traditional fractal computation redundancy, significantly decreases algorithmic complexity and encoding time. Additionally, by adaptively segmenting R-blocks into varying sizes according to local texture, the proposed method minimizes redundancy in smooth regions while preserving fine details in complex areas. The experimental results show that compared with BFIC, FICANRP has a compression ratio (CR) improvement range of 0.84–2.29 times, a PSNR improvement range of 0.25–4.8 dB, and an acceleration encoding time efficiency improvement of 54.14×–1448.73×. Compared with QFIC, under the same PSNR, the FICANRP compression ratio (CR) improvement range is 0.87–19.12 times, and the accelerated encoding time (ET) efficiency is increased by 37.26×–114.83×. Full article

In response to the rising volume and sophistication of cyber intrusions, data-oriented methods have emerged as critical defensive measures. While machine learning—including neural network-based solutions—has demonstrated strong capabilities in identifying malicious activities, several fundamental challenges remain. Chief among these difficulties are the substantial resource demands related to data preprocessing and inference procedures, limited scalability beyond centralized environments, and the necessity of deploying multiple specialized detection models to address diverse stages of the cyber kill chain. This paper introduces a contextual bandit-based reinforcement learning approach, designed to reduce operational expenditures and enhance detection cost-efficiency by introducing an adaptive decision boundary within a layered detection scheme. The proposed framework continually measures the confidence of each participating detection model, applying a reward-driven mechanism to balance cost and accuracy. Specifically, each potential action, representing a particular decision boundary, earns a reward reflecting its overall cost-to-effectiveness ratio, thereby prioritizing reduced overheads. We validated our method using two highly representative datasets that capture prevalent modern-day threats: phishing and malware. Our findings show that this contextual bandit-based strategy adeptly regulates the frequency of resource-intensive detection tasks, significantly lowering both inference and processing expenses. Remarkably, it achieves this reduction with minimal compromise to overall detection accuracy and efficacy. Full article

This article presents a low-noise amplifier (LNA) with high image rejection ratio (IRR) operating in the 5G millimeter-wave band using a 65 nm CMOS process. The circuit adopts an inter-stage notch filtering structure composed of a transformer and a switched capacitor array to achieve image suppression and impedance matching with no die area overhead. By adjusting the values of the switch capacitor array, the transmission zeros are positioned in the stopband while the poles are placed in the passband, thereby realizing image rejection. Furthermore, the number and distribution of poles under the both real and complex impedance conditions are analyzed. Moreover, the quality factor (Q) of the zero is derived to establish the relationship between Q and the image rejection ratio, guiding the optimization of both gain and IRR of the circuit design. Measurement results demonstrate that the LNA exhibits a gain of 18 dB and a noise figure (NF) of 4.4 dB at 40 GHz, with a corresponding IRR of 53.4 dB when the intermediate frequency (IF) is 6 GHz. The circuit demonstrates a 3 dB bandwidth from 36.3 to 40.7 GHz, with an IRR greater than 42 dB across this frequency range. The power consumption is 25.4 mW from a 1 V supply, and the pad-excluded core area of the entire chip is 0.13 mm². Full article

Multi-hop inter-domain wireless networks play a vital role in future heterogeneous communication systems by improving data transmission efficiency and security assurance. Despite the advances in secure routing techniques in areas such as node authentication and encryption, they still suffer from the shortcomings of frequent key updates, high computational overhead, and poor adaptability to large-scale dynamic topologies. To address these limitations, we propose a new routing method—the Secure Cross-Layer Route—designed for multi-hop inter-domain wireless networks to achieve unified optimization of security, delay, and throughput. First, we construct a multi-objective optimization model that integrates authentication delay, link load, and resource states, enabling balanced trade-offs between security and transmission performance in dynamic conditions. Second, we introduce a cross-layer information fusion mechanism that allows nodes to adapt routing costs in real time under heterogeneous network conditions, thereby improving path reliability and load balancing. Furthermore, a risk-aware dynamic key update strategy is developed to handle behavioral uncertainty among nodes, reducing authentication overhead and enhancing attack resilience. Experimental evaluations conducted on four datasets with varying network scales demonstrate the superior performance of the proposed method. Experimental results demonstrated that the proposed method achieves at least 28% improvement in effective throughput, reduces average authentication delay by approximately 30%, and increases the secure link ratio by at least 10%, outperforming mainstream routing algorithms under multi-constraint conditions. Full article

Addressing the significant challenge of speech enhancement in ultra-low-Signal-to-Noise-Ratio (SNR) scenarios for Unmanned Aerial Vehicle (UAV) voice communication, particularly under edge deployment constraints, this study proposes the Edge-Deployed Band-Split Rotary Position Encoding Transformer (Edge-BS-RoFormer), a novel, lightweight band-split rotary position encoding transformer. While existing deep learning methods face limitations in dynamic UAV noise suppression under such constraints, including insufficient harmonic modeling and high computational complexity, the proposed Edge-BS-RoFormer distinctively synergizes a band-split strategy for fine-grained spectral processing, a dual-dimension Rotary Position Encoding ($\mathrm{RoPE}$) mechanism for superior joint time–frequency modeling, and $\mathrm{FlashAttention}$ to optimize computational efficiency, pivotal for its lightweight nature and robust ultra-low-SNR performance. Experiments on our self-constructed DroneNoise-LibriMix (DN-LM) dataset demonstrate Edge-BS-RoFormer’s superiority. Under a −15 dB SNR, it achieves Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) improvements of 2.2 dB over Deep Complex U-Net (DCUNet), 25.0 dB over the Dual-Path Transformer Network (DPTNet), and 2.3 dB over HTDemucs. Correspondingly, the Perceptual Evaluation of Speech Quality (PESQ) is enhanced by 0.11, 0.18, and 0.15, respectively. Crucially, its efficacy for edge deployment is substantiated by a minimal model storage of 8.534 MB, 11.617 GFLOPs (an 89.6% reduction vs. DCUNet), a runtime memory footprint of under 500MB, a Real-Time Factor (RTF) of 0.325 (latency: 330.830 ms), and a power consumption of 6.536 W on an NVIDIA Jetson AGX Xavier, fulfilling real-time processing demands. This study delivers a validated lightweight solution, exemplified by its minimal computational overhead and real-time edge inference capability, for effective speech enhancement in complex UAV acoustic scenarios, including dynamic noise conditions. Furthermore, the open-sourced dataset and model contribute to advancing research and establishing standardized evaluation frameworks in this domain. Full article

Background/objective: Tennis involves repetitive overhead movements, and understanding the relationship between shoulder mobility, dynamic stability, and isometric strength could be crucial for developing targeted training programmes to enhance performance and reduce injury risk. This study aimed to assess shoulder rotational mobility, dynamic stability, and isometric strength profiles in elite and national-level tennis players. Methods: Twenty-four male and female athletes were grouped by competitive level: National-Level Female Group (NFG); National-Level Male Group (NMG); and Elite Male Group (EMG). Shoulder isometric strength was evaluated using an electromechanical dynamometer (Dynasystem), while rotational mobility and dynamic stability were assessed using standardised protocols. Results: Significant anthropometric differences in height, weight, and leg length were identified between NFG and the other groups (p < 0.001). NMG showed reduced external rotation compared to NFG and EMG in the dominant shoulder (p < 0.05). EMG exhibited significant asymmetries in external rotation between the dominant and non-dominant shoulders, which may be attributed to higher training volumes (p < 0.05; ES = 0.994). No significant differences were found in isometric strength across the groups, although NFG showed lower internal rotation strength and ER/IR ratio asymmetry between the dominant and non-dominant shoulder (p < 0.05). Dynamic stability scores were consistently low, with asymmetries between the dominant and non-dominant sides in most cases. Conclusions: These findings suggest the need for targeted training to address asymmetries and enhance dynamic stability. Caution is advised when generalising these results due to the limited sample size. Future research should include more participants and explore associations with performance metrics, such as serve speed and playing style. Full article

The global transition toward clean energy has driven the extensive deployment of overhead tower-lines in desserts, where such structures face unique challenges from wind–sand interactions. The current design standards often overlook these combined loads due to oversimplified collision models and inadequate computational frameworks. These gaps are bridged in the present study through the development of a refined impact force model grounded in Hertz contact theory, which captures transient collision mechanics and energy dissipation during sand–structure interactions. Validated against field data from northwest China, the model enables a comprehensive parametric analysis of wind speed (5–60 m/s), sand density (1000–3500 kg/m³), elastic modulus (5–100 GPa), and Poisson’s ratio (0.1–0.4). Our results show that peak impact forces increase by 66.7% (with sand density) and 148% (with elastic modulus), with higher wind speeds amplifying forces nonlinearly, reaching 8 N at 30 m/s. An increased elastic modulus shifts energy dissipation toward elastic rebound, reducing the penetration depth by 28%. The dynamic analysis of a 123.6 m transmission tower under wind–sand coupling loads demonstrated significant structural response amplifications; displacements and axial forces increased by 28% and 41%, respectively, compared to pure wind conditions. These findings reveal the importance of integrating coupling load effects into design codes, particularly for towers in sandstorm-prone regions. The proposed framework provides a robust basis for enhancing structural resilience, offering practical insights for revising safety standards and optimizing maintenance strategies in arid environments. Full article