Search Results (3,781)

Wireless Sensor Networks (WSNs) are widely deployed for long-term monitoring in environments characterized by nonstationary sensing dynamics, intermittent connectivity and continuously evolving network topologies, while reliable, fine-grained labeled data capturing faults and adversarial behaviors remain scarce. This survey systematically reviews and synthesizes recent research that integrates autoencoder-based representation learning with self-supervised learning (SSL) objectives to enhance anomaly detection under these practical constraints. We structure the existing literature through a unified taxonomy encompassing autoencoder variants, self-supervised pretext tasks, spatio-temporal encoding mechanisms and the increasing use of graph-structured autoencoders for topology-aware modeling. Across distinct methodological categories, SSL-augmented frameworks consistently demonstrate improved robustness and stability compared to purely reconstruction-driven baselines, particularly in heterogeneous, dynamic and temporally drifting WSN environments. Nevertheless, this review also highlights several unresolved challenges that hinder real-world adoption, including uncertain scalability to large-scale networks, limited model interpretability, nontrivial energy and memory overheads on resource-constrained sensor nodes and a lack of standardized evaluation protocols and reporting practices. By consolidating publicly available datasets, experimental configurations and comparative performance trends, we derive concrete design requirements for robust and resource-aware anomaly detection in operational WSNs and outline promising future research directions, emphasizing lightweight model architectures, explainable learning mechanisms and federated AE–SSL paradigms to enable adaptive, privacy-preserving monitoring in next-generation IoT sensing systems. Full article

The quality of tea bud harvesting directly affects the final quality of the tea; however, due to the small size of tea buds and the complex natural background, accurately detecting them remains challenging. To address this issue, this paper proposes a lightweight and efficient tea bud detection model named TSF-Net. This model adopts the P2-enhanced bidirectional feature pyramid network (P2A-BiFPN) to enhance the recognition ability of small objects and achieve efficient multi-scale feature fusion. Additionally, coordinate space attention (CSA) is embedded in multiple C3k2 blocks to enhance the feature extraction of key regions, while an A2C2f module based on self-attention is introduced to further improve the fine feature representation. Extensive experiments conducted on the self-built WYTeaBud dataset show that TSF-Net increases mAP@50 by 2.0% and reduces the model parameters to approximately 85% of the baseline, achieving a good balance between detection accuracy and model complexity. Further evaluations on public tea bud datasets and the VisDrone2019 small object benchmark also confirm the effectiveness and generalization ability of the proposed method. Moreover, TSF-Net is converted to the RKNN format and successfully deployed on the RK3588 embedded platform, verifying its practical applicability and deployment potential in intelligent tea bud harvesting. Full article

This paper proposes an applied framework for cyberattack and anomaly detection in resource-constrained embedded/IoT environments by combining signal-processing feature construction with supervised and unsupervised AI (Artificial Intelligence) models. The workflow covers dataset preparation and normalization, correlation-driven feature analysis, and compact representations via PCA (Principal Component Analysis), followed by classification and anomaly scoring. In addition to the original UNSW-NB15 (University of New South Wales—Network-Based Dataset 2015) traffic features, Fourier-domain descriptors, wavelet-domain descriptors, and Kalman-based smoothing/innovation features are considered to improve robustness under variability and measurement noise. Detection performance is assessed using classical and ensemble learning methods (SVM (Support Vector Machines), RF (Random Forest), XGBoost (Extreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine)), unsupervised baselines (K-Means and DBSCAN (Density-Based Spatial Clustering of Applications with Noise)), and DL (Deep-Learning) anomaly detectors based on Autoencoder reconstruction and GAN (Generative Adversarial Network)-based scoring. Experimental results on UNSW-NB15 indicate that ensemble-based models provide the strongest overall detection performance, while the signal-processing augmentation and PCA-based compactness support efficient deployment in embedded contexts. The findings confirm that integrating lightweight signal processing with AI-driven models enables effective and adaptable identification of malicious network traffic supporting deployment-oriented embedded cybersecurity and motivating future real-time validation on edge hardware. Full article

Coal gangue sorting is crucial for improving coal quality and reducing environmental pollution; however, traditional methods suffer from resource wastage, high cost, and intensive labor demands. To address these challenges, this paper investigates an image recognition-based coal gangue sorting technique and proposes an improved YOLOv7-tiny detection model tailored for edge GPU devices with limited computational power and memory. YOLOv7-tiny is selected as the baseline due to its balanced performance in detection accuracy, architectural maturity, and deployment stability on edge GPUs. Compared to newer lightweight detectors such as YOLOv8-N and YOLOv6-N, YOLOv7-tiny adopts an ELAN-based modular design, which facilitates structural optimization without relying on anchor-free reconstruction or complex post-training strategies, making it particularly suitable for engineering enhancements in real-time industrial sorting under resource constraints. To tackle the limitations in computing and storage, we first introduce an ELAN-PC feature extraction module based on partial convolution and ELAN. Secondly, a GhostCSP module is proposed by integrating cross-stage aggregation and Ghost bottleneck concepts. These modules replace the original ELAN structures in the backbone and neck networks, significantly reducing floating-point operations (FLOPs) and the number of parameters. Furthermore, the SIoU loss function is adopted to replace the original bounding box loss, enhancing detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv7-tiny, the improved model increases mAP0.5 from 86.9% to 88.7% (a gain of 1.8%), reduces FLOPs from 13.2 G to 9.2 G (a decrease of 30%), and cuts parameters from 6.0 M to 4.3 M (a reduction of 28%). In dynamic sorting tests, the model achieves a coal gangue sorting rate of 82.2% with a misclassification rate of 8.1%, indicating promising practical applicability. Full article

Defect detection on Printed Circuit Boards (PCBs) constitutes a pivotal component of the quality control system in electronics manufacturing. However, owing to the intricate circuitry structures on PCB surfaces and the characteristics of defects—specifically their minute scale, irregular morphology, and susceptibility to background texture interference—existing generic deep learning models frequently fail to achieve an optimal equilibrium between detection accuracy and inference speed. To address these challenges, this study proposes MDEB-YOLO, a lightweight real-time detection network tailored for PCB micro-defects. First, to enhance the model’s perceptual capability regarding subtle geometric variations along conductive line edges, we designed the Efficient Multi-scale Deformable Attention (EMDA) module within the backbone network. By integrating parallel cross-spatial channel learning with deformable offset networks, this module achieves adaptive extraction of irregular concave–convex defect features while effectively suppressing background noise. Second, to mitigate feature loss of micro-defects during multi-scale transformations, a Bidirectional Residual Multi-scale Feature Pyramid Network (BRM-FPN) is proposed. Utilizing bidirectional weighted paths and residual attention mechanisms, this network facilitates the efficient fusion of multi-view features, significantly enhancing the representation of small targets. Finally, the detection head is reconstructed based on grouped convolution strategies to design the Lightweight Grouped Convolution Head (LGC-Head), which substantially reduces parameter volume and computational complexity while maintaining feature discriminability. The validation results on the PKU-Market-PCB dataset demonstrate that MDEB-YOLO achieves a mean Average Precision (mAP) of 95.9%, an inference speed of 80.6 FPS, and a parameter count of merely 7.11 M. Compared to baseline models, the mAP is improved by 1.5%, while inference speed and parameter efficiency are optimized by 26.5% and 24.5%, respectively; notably, detection accuracy for challenging mouse bite and spur defects increased by 3.7% and 4.0%, respectively. The experimental results confirm that the proposed method outperforms state-of-the-art approaches in both detection accuracy and real-time performance, possessing significant value for industrial applications. Full article

Intelligent fruit-picking robots have emerged as a promising solution to labor shortages and the increasing costs of manual harvesting. This review provides a systematic and critical overview of recent advances in three core domains: (i) vision-based fruit and peduncle detection, (ii) motion planning and obstacle-aware navigation, and (iii) robotic manipulation technologies for diverse fruit types. We summarize the evolution of deep learning-based perception models, highlighting improvements in occlusion robustness, 3D localization accuracy, and real-time performance. Various planning frameworks—from classical search algorithms to optimization-driven and swarm-intelligent methods—are compared in terms of efficiency and adaptability in unstructured orchard environments. Developments in multi-DOF manipulators, soft and adaptive grippers, and end-effector control strategies are also examined. Despite these advances, critical challenges remain, including heavy dependence on large annotated datasets; sensitivity to illumination and foliage occlusion; limited generalization across fruit varieties; and the difficulty of integrating perception, planning, and manipulation into reliable field-ready systems. Finally, this review outlines emerging research trends such as lightweight multimodal networks, deformable-object manipulation, embodied intelligence, and system-level optimization, offering a forward-looking perspective for autonomous harvesting technologies. Full article

Identifying floating residual feed is a critical technology in recirculating aquaculture systems, aiding water-quality control and the development of intelligent feeding models. However, existing research is largely based on ideal indoor environments and lacks adaptability to complex outdoor scenarios. Moreover, current methods for this task often suffer from high computational costs, poor real-time performance, and limited recognition accuracy. To address these issues, this study first validates in outdoor aquaculture tanks that instance segmentation is more suitable than individual detection for handling clustered and adhesive feed residues. We therefore propose LMFF–YOLO, a lightweight multi-scale fusion feed segmentation model based on YOLOv8n-seg. This model achieves the first collaborative optimization of lightweight architecture and segmentation accuracy specifically tailored for outdoor residual feed segmentation tasks. To enhance recognition capability, we construct a network using a Context-Fusion Diffusion Pyramid Network (CFDPN) and a novel Multi-scale Feature Fusion Module (MFFM) to improve multi-scale and contextual feature capture, supplemented by an efficient local attention mechanism at the backbone’s end for refined local feature extraction. To reduce computational costs and improve real-time performance, the original C2f module is replaced with a C2f-Reparameterization vision block, and a shared-convolution local-focus lightweight segmentation head is designed. Experimental results show that LMFF–YOLO achieves an mAP50 of 87.1% (2.6% higher than YOLOv8n-seg), enabling more precise estimation of residual feed quantity. Coupled with a 19.1% and 20.0% reduction in parameters and FLOPs, this model provides a practical solution for real-time monitoring, supporting feed waste reduction and intelligent feeding strategies. Full article

Various sensors are increasingly being adopted to support intelligent healthcare systems, which address the growing problem of staff shortages in assisted-living communities. In this context, detecting and assessing pain remain critical yet challenging tasks in both clinical and non-clinical settings. Traditional approaches such as self-reporting, physiological signal monitoring, and facial expression analysis often face limitations related to accessibility, equipment costs, and the need for professional support. To overcome these challenges in this work, we investigate a sensor-assisted system for pain detection and propose a lightweight framework that enables real-time classification of pain levels using acoustic sensors. Our system exploits the spectral features of voice signals that strongly correlate with pain to train Convolutional Neural Network (CNN) models. Our system has been validated through simulations in Jupiter Notebook and a Raspberry Pi-based hardware prototype. The experimental results demonstrate that the proposed three-level pain classification approach obtains an average accuracy of 72.74%, outperforming existing methods with the same pain-level granularity by 18.94–26.74% and achieving performance comparable to that of binary pain detection methods. Our hardware prototype, built from commercial off-the-shelf components for under 100 USD, achieves real-time processing speeds ranging from approximately 6 to 22 s. In addition to CNN models, our experiments demonstrate that other machine learning algorithms, such as Artificial Neural Networks, XGBoost, Random Forests, and Decision Trees, also prove to be applicable within our pain level classification framework. Full article

Intrusion detection in IoT networks is challenged by data heterogeneity, label scarcity, and privacy constraints. Traditional federated learning (FL) methods often assume IID data or require supervised labels, limiting their practicality. We propose G-PFL-ID, a graph-driven personalized federated learning framework for unsupervised intrusion detection in non-IID IoT systems. Our method trains a global graph encoder (GCN or GAE) with a DeepSVDD objective under a federated regularizer (FedReg) that combines proximal and variance penalties, then personalizes local models via a lightweight fine-tuning head. We evaluate G-PFL-ID on the IoT-23 (Mirai-based captures) and N-BaIoT (device-level dataset) under realistic heterogeneity (Dirichlet-based partitioning with concentration parameters $\alpha \in \{0.1,0.5,\infty \}$ and client counts $K\in \{10,15,20\}$ for IoT-23, and natural device-based partitioning for N-BaIoT). G-PFL-ID outperforms global FL baselines and recent graph-based federated anomaly detectors, achieving up to 99.46% AUROC on IoT-23 and 97.74% AUROC on N-BaIoT. Ablation studies confirm that the proximal and variance penalties reduce inter-round drift and representation collapse, and that lightweight personalization recovers local sensitivity—especially for clients with limited data. Our work bridges graph-based anomaly detection with personalized FL for scalable, privacy-preserving IoT security. Full article

To address the challenges of limited detection accuracy and the difficulty of deployment on edge devices caused by dust obstruction, low illumination, and complex background interference in coal mine conveyor belt foreign object detection, this paper proposes an improved algorithm model, RTA-YOLOv11, based on the YOLOv11 framework. First, a Receptive Field Enhancement Module (RFEM) is utilized to expand the field of view by fusing multi-scale perception paths, strengthening the network’s semantic capture capability for subtle targets. Second, a Triplet Attention mechanism is introduced to suppress environmental noise and enhance the saliency of low-contrast foreign objects through cross-dimensional joint modeling of spatial and channel information. Finally, a lightweight detection head based on MBConv is designed, utilizing inverted bottleneck structures and re-parameterization strategies to compress redundant parameters and improve deployment efficiency on edge devices. Experimental results indicate that the mAP@0.5 of the improved RTA-YOLOv11 model is 4.0 percentage points higher than that of the original YOLOv11, with an inference speed of 79 FPS and a reduction in parameters of approximately 22%. Compared with algorithms such as Faster R-CNN, SSD, and YOLOv8, this model demonstrates a superior balance between accuracy and speed, providing an efficient and practical solution for intelligent mine visual perception systems. Full article

Car accidents remain a leading cause of death worldwide, with drowsiness and distraction accounting for roughly 25% of fatal crashes in Ecuador. This study presents a real-time driver monitoring system that uses computer vision and deep learning to detect fatigue, distraction, and emotions from facial expressions. It combines a MobileNetV2-based CNN trained on RAF-DB for emotion recognition and MediaPipe’s 468 facial landmarks to compute the EAR (Eye Aspect Ratio), the MAR (Mouth Aspect Ratio), the gaze, and the head pose. Tests with 27 participants in both real and simulated driving environments showed strong results. There was a 100% accuracy in detecting distraction, 85.19% for yawning, and 88.89% for eye closure. The system also effectively recognized happiness (100%) and anger/disgust (96.3%). However, it struggled with sadness and failed to detect fear, likely due to the subtlety of real-world expressions and limitations in the training dataset. Despite these challenges, the results highlight the importance of integrating emotional awareness into driver monitoring systems, which helps reduce false alarms and improve response accuracy. This work supports the development of lightweight, non-invasive technologies that enhance driving safety through intelligent behavior analysis. Full article

This article presents the design and implementation of a Virtual Cyber-Physical Testbed (VCPT) for transportation systems, featuring an automated level-crossing process. The proposed design improves network fidelity while keeping the platform lightweight. Key components include the Programmable Logic Controller (PLC), sensors, actuators, the Supervisory Control and Data Acquisition (SCADA) system, and OPNsense. Guided by NIST SP 800-115, penetration testing revealed several vulnerabilities and weaknesses that can be exploited and mitigated. Six attack scenarios—enumeration, brute force, remote code execution, ARP poisoning, DoS, and command injection—were executed, demonstrating realistic impacts on process safety and availability. Mitigation strategies using custom firewall and Intrusion Detection and Prevention System (IDPS) rules contributed to improving the security posture of VCPT. Educational evaluation with 41 cybersecurity students showed a 24% increase in average scores and a significant rise in top performers, further supported by positive feedback on engagement and realism. These results validate the VCPT as an effective platform for cybersecurity research, training, and experiential learning. Full article

Accurate detection of pressure-relief boreholes is crucial for evaluating drilling quality and monitoring safety in coal mine roadways. Nevertheless, the highly challenging underground environment—characterized by insufficient lighting, severe dust and water mist disturbances, and frequent occlusions—poses substantial difficulties for current object detection approaches, particularly in identifying small-scale and low-visibility targets. To effectively tackle these issues, a lightweight and robust detection framework, referred to as YOLO-DFBL, is developed using the YOLOv11n architecture. The proposed approach incorporates a DualConv-based lightweight convolution module to optimize the efficiency of feature extraction, a Frequency Spectrum Dynamic Aggregation (FSDA) module for noise-robust enhancement, and a Biformer (Bi-level Routing Transformer)-based routing attention mechanism for improved long-range dependency modeling. In addition, a Lightweight Shared Convolution Head (LSCH) is incorporated to effectively decrease the overall model complexity. Experimental results on a real coal mine roadway dataset demonstrate that YOLO-DFBL achieves an mAP@50:95 of 78.9%, with a compact model size of 1.94 M parameters, a computational complexity of 4.7 GFLOPs, and an inference speed of 157.3 FPS, demonstrating superior accuracy–efficiency trade-offs compared with representative lightweight YOLO variants and classical detectors. Field experiments under challenging low-illumination and occlusion environments confirm the robustness of the proposed approach in real mining scenarios. The developed method enables reliable visual perception for underground drilling equipment and facilitates safer and more intelligent operations in coal mine engineering. Full article

The efficacy of Underwater Camouflaged Object Detection (UCOD) is fundamentally constrained by severe boundary ambiguity, where biological mimicry blends targets into complex backgrounds and aquatic optical degradation erodes edge details. We propose a lightweight boundary perception detector named CAR-YOLO (Camouflage Ambiguity Resolution YOLO). Specifically, a frequency-domain dual-path mechanism (FRM-DWT/EG-IWT) leverages selective wavelet aggregation and dynamic injection to recover high-frequency edges. Subsequently, these high-frequency cues are synergized with low-frequency semantic information via the Low-level Adaptive Fusion (LAF) module. To further address noisy samples, an Uncertainty Calibration Head (UCH) refines supervision via prediction consistency. Finally, we constructed specialized datasets based on public data for training and evaluation, including UCOD10K and UWB-COT220. On UCOD10K, CAR-YOLO achieves 27.1% mAP50–95, surpassing several state-of-the-art (SOTA) methods while reducing parameters from 2.58 M to 2.43 M and GFLOPs from 6.3 to 5.9. On the challenging UWB-COT220 benchmark, the model attains 30.7% mAP50–95, marking a 7.7-point improvement over YOLOv11. Furthermore, cross-domain experiments on UODD demonstrate strong generalization. These results indicate that CAR-YOLO effectively mitigates boundary ambiguity, achieving an optimal balance between accuracy, robustness, and efficiency. Full article

Deploying high-precision deep learning models on resource-constrained edge devices remains a challenge for agricultural disease detection. This study introduces CropHealthyNet, a lightweight hybrid architecture optimized for both accuracy and computational efficiency. The architecture incorporates three key components: the ExGhostConv module, which integrates FReLU and SimAM attention for enhanced feature utilization; a Universal Position Encoding mechanism that adaptively captures spatial information to address variable lesion scales; and a MemoryEfficientTransformer employing chunked attention to mitigate global modeling memory overhead. Experiments on CDC, AGD_256, and CornLeafDisease datasets indicate that CropHealthyNet achieves a weighted average accuracy of 90.55% with 0.47 million parameters. The model outperforms several state-of-the-art lightweight architectures and achieves accuracy comparable to DenseNet121, with approximately 15 times fewer parameters. These results position CropHealthyNet as a viable solution for real-world deployment in resource-limited agricultural environments. Full article