Wireless Sensor Networks (WSNs) play a pivotal role in Internet of Things (IoT) applications; however, their lifetime is fundamentally constrained by the limited energy of sensor nodes. This paper introduces a Hybrid Energy-Optimized Clustering Protocol (HEOCP) that combines analytical modeling of radio energy consumption with deep learning–assisted cluster-head (CH) selection. First, an analytical framework is developed to determine the distance-constrained CH eligibility region and the optimal number of clusters, thereby minimizing redundant transmissions and balancing energy consumption. Then, a genetic algorithm (GA) is used to determine the best cluster head configuration. These configurations are then trained by a ResNet-50 deep network and averaged to reduce noise, allowing for real-time cluster head prediction without repeatedly performing expensive heuristic optimization, resulting in more steady performance. Extensive simulations under various network scales demonstrate that HEOCP extends network lifetime by up to 60% compared with conventional LEACH and GA-based approaches, effectively delaying the first-node death and improving overall energy efficiency. Furthermore, the hybrid GA–ResNet framework exhibits high scalability and computational efficiency, making it suitable for large-scale IoT deployments. The results confirm that integrating analytical energy modeling with deep learning provides a powerful and sustainable paradigm for intelligent energy management in future IoT-enabled WSNs.

Accurate and efficient defect detection in printed circuit boards (PCBs) is critical for manufacturing quality control. Existing methods predominantly rely on manually extracted features such as surface texture, color, and shape for defect recognition and classification within small-dimensional feature datasets. A convolutional neural network (CNN) model was developed via transfer learning. Feature extraction involves diverse operations across different CNN layers. Essential features were selected, and dimensionality was reduced via either t-distributed stochastic neighbor embedding (t-SNE) or principal component analysis (PCA). Defect classification was subsequently performed by clustering the reduced features with either the K-means or K-nearest neighbors (KNN) algorithm. Compared with alternative model feature learning classifiers, the proposed small-dimensional CNN model performs significantly better. A defect recognition accuracy of 97.33% was achieved, with processing completed in approximately 60 s. This approach, which integrates transfer learning-based CNN feature extraction with dimensionality reduction and clustering techniques, provides a fast and effective method for high-precision defect detection and classification in PCBs.

This study introduces a dual enhancement mechanism integrating Deformable Convolution v4 (DCNv4) and an Asymmetric Decoupled Detection Head (AsDDet) to significantly improve the detection performance of small objects in high-altitude unmanned aerial vehicle (UAV) imagery using YOLOv11 model architecture. We embedded DCNv4 into YOLOv11 and redesigned its operational modality to establish an efficient and dynamic sparse operator while optimizing memory access processes. This integration enhances both the operational efficiency and overall performance. Furthermore, AsDDet, an efficient and innovative technique, employs an asymmetric dual-branch structure to decouple the classification and regression tasks. This allows the application of distinct convolutional layers tailored to process the positional and complex shape data characteristics of small objects. This dual-improvement design enables the model to accommodate the diverse demands of detection tasks better, thereby increasing the overall detection accuracy. An experimental validation was conducted using a dataset of urban appearance violations, which demonstrated significant improvements in precision, recall, and mean average precision (mAP). Specifically, the precision increased from 2.50% to 84.0%, recall improved from 3.94% to 81.3%, and mAP@0.5, and mAP@0.5:0.95, increased from 2.62% and 4.08% to 87.8% and 61.2%, respectively. Compared with the original YOLOv11 model and other mainstream object detection models, the proposed method exhibited marked enhancements in detection precision, recall, and mAP while effectively reducing both false negatives and false positives. The experimental results substantiate the significant performance gains achieved in UAV-based small-object detection, offering a novel solution for addressing related challenges in the field.

In semiconductor manufacturing, automated wafer defect detection is critical for ensuring chip yield and process stability. In seismically active regions, sub-millimeter conveyor micro-displacements during seismic events can produce complex geometric anomalies on wafer surfaces, and the resulting need for rapid post-event inspection motivates the present work. Conventional convolutional neural networks (CNNs) face inherent limitations in capturing global spatial context and suffer from computational redundancy when processing micro-scale, multi-morphological defects. To address these challenges, this study proposes an improved YOLOv11 framework integrating three enhancements: an attention-based intra-scale feature interaction (AIFI) module at the backbone–neck interface to strengthen global semantic correlations, a lightweight asymmetric detection head (LADH) to decouple classification and localization tasks, and a geometry-aware SIoU loss to constrain bounding box regression from angular, distance, and shape perspectives. Experiments on a public benchmark of industrial wafer maps demonstrate that the proposed method improves mAP50 from 93.0% to 94.5% while maintaining 181.7 FPS on a desktop-class GPU. These results demonstrate the effectiveness of the proposed framework for high-throughput wafer-defect inspection on desktop-class hardware, and its low parameter count makes it a promising candidate for future deployment on resource-constrained edge devices.

The limited energy capacity of sensor nodes remains a central constraint on the operational longevity of Wireless Sensor Networks (WSNs)-a challenge that is more critical in Rechargeable Wireless Sensor Networks (RWSNs), where sustaining long-term functionality depends on delaying the First Node Death (FND), a key point that often signals the beginning of network problems. While prior research has extensively explored heuristic algorithms coupled with energy-aware routing mechanisms to reduce transmission energy consumption and extend network lifetime, these methods typically require repeated iterations to converge. Such computational demands render them unsuitable for real-time applications. To overcome these limitations, we introduce a hybrid optimization framework that integrates Genetic Algorithms (GA) with Deep Learning (DL). This framework can run quickly to improve energy efficiency and extend network lifetime in RWSNs. The proposed approach builds upon an energy-aware clustering architecture that incorporates a lightweight version of the Residual Neural Network (ResNet) model. This clustering strategy promotes a more balanced distribution of energy consumption across the network, delaying early node failures caused by localized energy depletion. Based on this setup, we create an optimization scheme using a novel version of GA that helps the ResNet model-simplified for use on regular CPUs-learns an optimal CH selection policy. The GA component employs a custom fitness function designed to maximize the time to FND latency. The synergy between the clustering method and the GA-DL hybrid selection mechanism enables rapid and effective CH assignment without the iterative overhead typical of heuristic algorithms. Our experimental results demonstrate that the proposed scheme not only reduces energy consumption and extends the time to FND but also achieves near real-time performance. This makes it a promising alternative to traditional heuristic-based approaches, particularly in latency-sensitive RWSN applications. Together, these contributions mark a significant step toward the development of scalable, efficient, and resilient RWSN architectures.