Internet Technology Letters最新文献

The rapid growth of e-commerce across the world has redefined how users discover, evaluate, and purchase products especially in visually driven domains like fashion and lifestyle. While the traditional product recommendation engines mostly rely on the collaborative or textual content, and many e-commerce platforms, providing the similar visual recommendations and lacks in suggesting the complementary products. The existing CNN baseline models overlook the necessary visual features and contextual relation which are essentials for recommending complementary products. This paper introduces VisCom-Res which is a Pioneering framework using deep learning-based visual complementary product recommendation combining a customized CNN architecture, ResNet50- Efficient Ghost Attention (EGA), with Hierarchical Navigable Small World (HNSW) nearest neighbor indexing technique. The ResNet50-EGA is improved with Ghost modules and Efficient Channel Attention (ECA) to extract semantically rich and fine-grained features from fashion product images. This extracted image embedding's further indexed using HNSW to enable fast, high-precision retrieval of visually compatible items. The Proposed model evaluated on a custom curated multi-class fashion dataset sourced from various Indian e-commerce platforms like Flipkart, Myntra, and Tata CLiQ, and it outperforms conventional models such as VGG16, DenseNet121, and MobileNetV2 in terms of classification accuracy and achieves superior retrieval metrics such as Precision@10 (93.8%), Recall@10 (94.5%), NDCG@10 (0.947), and average query time of 8.2 ms. The results validate the suitability of VisCom-Res for scalable, real-time recommendation systems in visually rich e-commerce recommendation environments. Unlike conventional CNN + attention frameworks, VisCom-Res integrates Ghost feature generation with Efficient Channel Attention (ECA) within a ResNet-50 backbone, reducing redundant map computation by ≈30% while retaining complementary-aware semantics.

To address the positioning accuracy and stability requirements of material storage links at the end of flexible automated production lines in the context of manufacturing intelligent transformation, this paper designs an intelligent warehousing system based on S7-1200 PLC. The technical scheme takes PLC control as the core, integrates 3ND583 and M415B dual stepper drivers to drive a two-dimensional ball screw mechanism, and combines with a pneumatic pushing system and various sensors. Verified by joint simulation with TIA Portal PLCSIM and WinCC, the system supports manual/automatic mode switching, meeting the low-noise and high-stability storage requirements of teaching training and industrial scenarios. The innovation lies in proposing a dual stepper collaborative control algorithm, which realizes multi-axis synchronous motion control through pulse equivalent matching and speed parameter optimization. Although the system is based on the automation and information technology of Industry 4.0, its modular design, easily expandable configuration monitoring interface, and manual mode that supports manual intervention provide a good technical foundation for the transition to the ‘human-machine collaboration’ paradigm advocated by Industry 5.0.

In the English teaching environment driven by the Internet of Things, real-time assessment of students' emotional states based on video data is particularly crucial for understanding student engagement and improving teaching quality. Existing server-based deep networks are limited by long-distance video data transmission, which seriously restricts the real-time performance of sentiment analysis. Moreover, simple video data cannot guarantee robustness in complex scenes. To address these issues, this paper proposes a multimodal fusion emotion recognition framework based on the edge-cloud collaboration mechanism. Firstly, on the edge node, we exploit two complementary modalities of data: video sequences and facial landmark sequences, and design a lightweight dual-stream neural network based on the 3D MobileNetV3 and graph convolutional network to efficiently extract multimodal features. On the server, we adopt the Transformer-based cross fusion mechanism to implement multimodal fusion and emotion evaluation. The edge side is responsible for real-time preprocessing and primary feature extraction. In our proposed framework, the server is responsible for aggregating feature data from multiple edge nodes. The experimental results indicate that the proposed framework can achieve high-precision student engagement assessment with low latency.

The growing use of Wireless Sensor Networks (WSNs) in remote and energy-limited areas calls for compact and efficient power converters. This paper proposes a bidirectional step-up/down DC–DC converter with a Modified Active Switched Inductor (MASL) cell, designed for energy harvesting powered WSNs. By integrating a switched capacitor network and an auxiliary control switch, the converter improves voltage gain, reduces ripple, and lowers voltage stress. It enables efficient energy transfer between renewable sources and storage elements like batteries or supercapacitors. Controlled synchronous rectification enhances efficiency, reaching a peak of 93.5%. MATLAB/Simulink simulations and a 200 W hardware prototype confirm its suitability for reliable, self-powered WSN applications.

Network transmission plays an important role in information system services of IoT, and network transmission quality is also an important indicator of the quality of information export in China. To measure the development of Internet quality in China's foreign trade, this paper calculates and analyzes the time series data of Internet export quality based on open source. This paper first analyzes the necessity and significance of the research. After comparing several types of time series methods, this paper chooses a statistics-based method to describe the Internet quality data, compares the statistical data before and after the epidemic, and draws a conclusion that the epidemic is irrelevant to the Internet quality. Further, this paper combined the cyclic neural network LSTM and principal component analysis methods to carry out calculations. LSTM method completed the analysis and prediction of the quality data of the Internet export, selected packet loss rate, RTT, and delay jitter as the indicators of Internet quality, and completed the preprocessing of the indicator data. To obtain comprehensive network quality indicators, this paper constructs a single index network quality evaluation model based on principal component analysis and completes the analysis and calculation of data analysis. The computation results demonstrate the great prediction accuracy of the LSTM referenced in this research for index data, and the only index result for measuring the quality of Internet exports can be obtained by using principal component analysis. In practical applications, the research in this paper can provide support for the subsequent network performance optimization and bandwidth utilization improvement of the IOT industry. Github link and data source of the paper is https://github.com/superpeace90/iotproject.

In today's world, most individuals are suffering from health complications due to sedentary lifestyles, food habits, and aging. This might have distracted them from their daily activities, and many individuals are not seeking medical help with their busy work schedules. To overcome these challenges, Wireless Body Area Network (WBAN) provides challenging solutions with continuous and remote monitoring systems. Tiny sensors are either embedded in the body or worn by the patients for tracking physiological factors, including heart pulse rate (bilirubin), pressures (alkphos), and levels of hemoglobin (albumin). The inserted or implanted sensors collect the data and transmit it to the central sink node for the aggregation of the readings, and reports are comprehensively generated and transmitted to the medical professionals for analysis. While transmitting the sensitive data, a security breach might occur with the wireless channels. To enhance security, Enhanced Juels and Sudan (EJS) encryption algorithm is deployed to safeguard both the data and sensor systems. This prevents unauthorized access to data. Whale Optimization Algorithm is deployed for improving optimized network performance and security. Simulations are conducted in the Cooja simulator and demonstrate enhanced convergence efficacy, optimal weight computation, and mitigated error rates, including False Rejection Rate (FRR) and False Acceptance Rate (FAR) for a robust health monitoring system.

Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by enabling communication between vehicles and infrastructure. However, ensuring secure, reliable, and consistent data transfer remains challenging due to their dynamic nature. This paper proposes a Clustering-Based Ant Colony Optimization (CB-ACO) and Fuzzy Logic algorithm to address these issues. Clustering reduces network load and enhances stability, while ACO selects optimal cluster heads and routes, supported by Fuzzy Logic-based trust assessments for secure access control. Compared to existing algorithms, the proposed method improves packet delivery, reduces latency, and enhances security, offering a robust solution for next-generation VANETs.

To address overheating and maintenance challenges in wind turbine slip ring systems operating in harsh environments, this study develops an online temperature monitoring system integrating Industrial Internet of Things (IIoT) and edge computing. A deep learning-driven distributed resource allocation algorithm (DDLRA) is deployed on edge devices to achieve real-time slip ring temperature monitoring, intelligent fault prediction, and reduced latency. A multi-layer edge intelligence architecture is constructed, optimizing offloading strategies via distributed neural networks to balance energy efficiency and resource utilization. Experimental simulations using over 1000 temperature datasets from key components demonstrate that the model maintains prediction errors within 0.3°C, effectively identifying anomalies and adapting to variable conditions. The system also enables real-time line load scheduling and carbon brush temperature prediction. This study pioneers the application of online updatable AI models in slip ring monitoring, combining edge responsiveness and cloud collaboration, offering significant engineering value for improving wind turbine reliability.

Cloud-assisted physical education teaching is an important direction for the development of smart education. However, the data processing and transmission of videos severely restrict the real-time performance of action analysis. To this end, this paper proposes an efficient edge cloud-assisted student movement recognition framework based on the graph convolutional network and human skeleton data. On the edge server, we use the YOLO-pose algorithm to generate robust human skeleton sequences and design an improved spatial–temporal dual stream graph convolutional neural network with an early fusion structure, which introduces the node weight module and the dynamic graph module to exploit long-distance dependency relationships of nodes. In the cloud server, we use a federated learning framework based on the density clustering mechanism to collaboratively train and aggregate parameters of models scattered across edge nodes. The experimental results show that our proposed model achieves excellent recognition accuracy on the self-built sports action dataset, providing an effective solution for intelligent and real-time feedback in outdoor sports teaching.

IIoT and digital twin-empowered micro level carbon governance is transforming EU-China carbon neutrality; this study defines the mechanism of DCCNAS (digital cockpits carbon neutral assimilation system) that facilitates real-time vehicle emission behavior interactions; however, it lacks theoretical support and remains unexplained. By employing a mixed-methods, first define the DCCNAS, subsequently describe the first-stage and second-stage structure of DCCNAS mechanism, demonstrate the exploitative and explorative green innovation with dual tacit knowledge transfer in through four cases. Second, develop a tripartite evolutionary model to quantitatively examine the micro-level asymptotic stability of exploratory versus exploitative green innovation strategies under dual tacit knowledge transfer. Automaker achieves dual tacit knowledge transfer through the diffusion of low-carbon technologies in driving behavior and community culture, empowers behavioral optimization and energy efficiency management within the DCCNAS framework to facilitate green transformation from individuals to organization. This study contributes in identifying DCCNAS and offers theoretical support and practical guidance for the green innovation practices of automakers, reveal the mechanism in achieving carbon neutrality goals in product design, technological upgrades, and system optimization.