Journal of Electronic Research and Application

Enhancing Indoor Object Detection with xLSTM Attention-Driven YOLOv9 for Improved 2D-Driven 3D Object Detection

2025-02-25T11:40:44+08:00

Three-dimensional (3D) object detection is crucial for applications such as robotic control and autonomous driving. While high-precision sensors like LiDAR are expensive, RGB-D sensors (e.g., Kinect) offer a cost-effective alternative, especially for indoor environments. However, RGB-D sensors still face limitations in accuracy and depth perception. This paper proposes an enhanced method that integrates attention-driven YOLOv9 with xLSTM into the F-ConvNet framework. By improving the precision of 2D bounding boxes generated for 3D object detection, this method addresses issues in indoor environments with complex structures and occlusions. The proposed approach enhances detection accuracy and robustness by combining RGB images and depth data, offering improved indoor 3D object detection performance.

Application of Resource Orchestration Theory in the Research of AI and HI to Achieve Value Creation

2025-02-25T11:40:41+08:00

This paper grasps the research theme of artificial intelligence (AI) and human intelligence (HI) synergy to create value and analyzes the development status of AI and HI in the current context of digital intelligence, as well as the significance of their synergy to empower value creation. At the same time, the theory of resource arrangement is introduced, and the connotation and composition of the theory are summarized, as well as the development in the field of research and application. This paper focuses on revealing the intrinsic relationship between resource orchestration theory and AI and HI collaborative work, aiming to fully explore the potential of resource orchestration theory in the collaborative innovation of AI and HI, and put forward practical suggestions based on this.

Research on π/4QPSK Modulation Communication Transmission System and FPGA Implementation

2025-02-25T11:40:36+08:00

This paper examines the π/4QPSK modulation communication transmission system, analyzing the performance advantages and disadvantages of π/4QPSK in comparison to QPSK. It also presents a comprehensive FPGA implementation scheme for a modulation communication transmission system, integrating RS channel coding, framing, frequency conversion, and other modules. This design is based on practical research and development requirements. The Xilinx Spartan6 chip board was used for board-level verification. The π/4QPSK modulated signal was transmitted via D/A conversion and radio frequency, with the transmitted waveform was looped back for reception. After A/D processing, the correctness of the designed modulation transmission scheme was verified.

Electrochemically Driven Nickel-Catalyzed Phenol Synthesis via Sustainable Oxygen Atom Transfer from Nitrous Oxide

2025-03-27T15:16:10+08:00

The valorization of nitrous oxide (N₂O) as an oxygen atom donor presents an attractive opportunity for green chemistry applications, leveraging both its industrial abundance and thermodynamically favorable oxidation potential. However, practical implementation has been constrained by the inherent kinetic inertness and poor coordinating ability of N₂O. While prior studies achieved N₂O-mediated conversion of aryl halides to phenols, such transformations necessitated stoichiometric chemical reductants and elevated pressure (2 atm), posing challenges in operational safety and process scalability. This study focuses on an electrochemical strategy that enables efficient oxygen atom transfer under ambient pressure through controlled current application. This methodology facilitates the selective transformation of aryl iodides to phenols without external reducing agents, establishing an environmentally benign synthetic pathway. By replacing traditional chemical reductants with electrons as the sole reducing equivalent, our approach addresses critical sustainability challenges in aromatic oxygenation chemistry while maintaining operational simplicity under mild conditions.

Variable Speed Limit Plate Signs Based on Depth Recognition Control

2025-04-02T09:38:49+08:00

Predictive control (PC) is an advanced control algorithm, which is widely used in industrial process control. Among them, model-based predictive control (MPC) is an important branch of predictive control. Its basic principle is to use the system model to predict future behavior and determine the current control action by optimizing the objective function. Based on the algorithm combined with three different sections using deep learning technology to identify vehicles and output the optimal speed limit, to achieve the effect of traffic flow optimization.

Application Strategies of Artificial Intelligence and Big Data Technology in Computer Monitoring and Control

2025-03-28T11:41:06+08:00

This article focuses on the current computer monitoring and control as the research direction, studying the application strategies of artificial intelligence and big data technology in this field. It includes an introduction to artificial intelligence and big data technology, the application strategies of artificial intelligence and big data technology in computer hardware, software, and network monitoring, as well as the application strategies of artificial intelligence and big data technology in computer process, access, and network control. This analysis aims to serve as a reference for the application of artificial intelligence and big data technology in computer monitoring and control, ultimately enhancing the security of computer systems.

Motion Analysis of a New Final Coal Sorter Based on Multi-Body Dynamics

2025-04-02T12:00:56+08:00

Research has been conducted on dry coal selection technology to achieve efficient and clean utilization of coal and reduce water resource waste. To investigate the effective separation of 6–1 mm fine coal using this approach, the vibrating cascade sorter was examined, with particular attention given to its dynamic characteristics and motion behavior. Dry coal vibrating cascade sorter motion simulation experiments were carried out and the mechanical system motion characteristics of the vibrating cascade sorter were simulated and analyzed using multi-body dynamics software ADAMS, including the motion curve and spatial trajectory of the sorter body. Theoretical calculations of the sorter body’s motion characteristics, including displacement amplitude, velocity amplitude, and acceleration amplitude, were compared with simulation results derived from multi-body dynamics. The comparison revealed a strong agreement between the computational model and analytical predictions, with a maximum deviation of less than 3.75%. The dynamic behavior of the vibrating cascade sorter at various rotational speeds was evaluated and contrasted against predictive models, with the highest discrepancy between the observed and predicted outcomes being less than 7.6%.

Synergy Between Resilient Networks and Random Forests in Online Fraud Detection

2025-03-28T10:05:14+08:00

This paper explores the synergistic effect of a model combining Elastic Net and Random Forest in online fraud detection. The study selects a public network dataset containing 1781 data records, divides the dataset by 70% for training and 30% for validation, and analyses the correlation between features using a correlation matrix. The experimental results show that the Elastic Net feature selection method generally outperforms PCA in all models, especially when combined with the Random Forest and XGBoost models, and the ElasticNet + Random Forest model achieves the highest accuracy of 0.968 and AUC value of 0.983, while the Kappa and MCC also reached 0.839 and 0.844 respectively, showing extremely high consistency and correlation. This indicates that combining Elastic Net feature selection and Random Forest model has significant performance advantages in online fraud detection.

Exploration of the Application of Artificial Intelligence Technology in the Transformation of Old Objects

2025-04-02T08:49:42+08:00

With the rapid development of technology, artificial intelligence (AI) is increasingly being applied in various fields. In today’s context of resource scarcity, pursuit of sustainable development and resource reuse, the transformation of old objects is particularly important. This article analyzes the current status of old object transformation and the opportunities brought by the internet to old objects and delves into the application of artificial intelligence in old object transformation. The focus is on five aspects: intelligent identification and classification, intelligent evaluation and prediction, automation integration, intelligent design and optimization, and integration of 3D printing technology. Finally, the process of “redesigning an old furniture, such as a wooden desk, through AI technology” is described, including the recycling, identification, detection, design, transformation, and final user feedback of the old wooden desk. This illustrates the unlimited potential of the “AI + old object transformation” approach, advocates for people to strengthen green environmental protection, and drives sustainable development.

Design Discussion of a Wireless Fire Alarm System Based on Data Fusion Technology

2025-03-27T17:03:44+08:00

This article explores the design of a wireless fire alarm system supported by advanced data fusion technology. It includes discussions on the basic design ideas of the wireless fire alarm system, hardware design analysis, software design analysis, and simulation analysis, all supported by data fusion technology. Hopefully, this analysis can provide some reference for the rational application of data fusion technology to meet the actual design and application requirements of the system.

3D Computational Modeling and Stability Analysis of Highway Slope: A Case Study from the X104 Section in Ganxian County

2025-03-28T10:37:00+08:00

Highway planning requires geological surveys and stability analysis of the surrounding area. In the early stage of the survey, the modeling and stability analysis of the survey area can be carried out by using GIS software to intuitively understand the topography of the study area. The use of DEM to extract terrain factors can be used for simple stability analysis and the source data is easy to obtain, simple to operate, fast to analyze, and reliable analysis results. In this paper, taking the X104 road section in Ganxian County as an example, the ArcGIS platform is used to carry out 3D modeling visualization and stability analysis, and the stability evaluation map of the study area is obtained.

Research and Design of Intelligent Inspection System for Thermal Power Plants

2025-03-28T11:13:46+08:00

To meet the demand for intelligent and unmanned development in thermal power plants, an intelligent inspection system has been designed. This system efficiently performs inspection tasks and monitors the operational parameters of key equipment in real-time. The collected data is uploaded to the monitoring center, allowing operation and maintenance personnel to access equipment information promptly. Data analysis is used to provide fault warning and diagnosis for critical equipment. The system employs the Pure Pursuit algorithm, which effectively avoids obstacles and ensures path continuity and stability. Simulation results show that the Pure Pursuit algorithm significantly improves the navigation accuracy and task efficiency of the inspection robot, ensuring the reliability of thermal power plant inspections.

Research on the Prevention and Control Measures of Soil and Water Conservation in Water Conservancy Projects

2025-03-28T11:20:59+08:00

With the development of our country’s social economy, the construction scale of water conservancy project has had an obvious expansion. In the construction of water conservancy projects, certain impacts on the surrounding water and soil conditions are inevitable. These impacts may lead to problems such as soil erosion, which can directly affect local production, livelihoods, and the natural ecological environment on which people depend. In severe cases, such issues may even hinder the progress and quality of the water conservancy project itself. Therefore, in the construction of water conservancy projects, soil and water conservation work is extremely important. Based on this, this paper mainly aimed at the prevention and control of water and soil conservation of water conservancy projects launched the relevant analysis and research.

Research on the Design Scheme of “Boundless Vision” Social App for Visually Impaired People

2025-04-02T10:05:34+08:00

The “Boundless Vision” project focuses on creating novel user interfaces to improve the experience of visually impaired individuals when using smart devices. By incorporating features such as high-contrast color schemes, enlarged text, and voice control, an intuitive and accessible interface is developed. Moreover, the project leverages online volunteer services to offer telephone and video guidance, aiding visually impaired users in navigating urban environments and accessing information. Through a comprehensive process involving needs assessment, UI design, system development, and service evaluation, the project aims to boost the independence of visually impaired individuals and foster greater societal awareness of visual impairment challenges. Ultimately, the project will deliver a suite of user interface designs that can serve as a model for advancing inclusive technology across society.

The Exploration of the Application of Electronic Circuit Simulation Technology in Integrated Circuit Design

2025-03-28T11:05:46+08:00

With the rapid development of Internet technology, the application of electronic circuit simulation technology is more and more extensive, and now it has been applied to integrated circuit design. Because the electronic circuit simulation technology has high efficiency, flexible and simple application, as well as stable performance, it has shown more and more good application prospects in integrated circuit design. Based on the strong development trend of electronic circuit simulation technology, it will be more and more widely used in daily life in the future, so the research on electronic circuit simulation technology is more and more in-depth. In this paper, the application of electronic circuit technology in integrated circuit design is studied, hoping that the technology can provide a more concise and efficient research and development way for electronic applications.

Elevators in the Context of Green Manufacturing

2025-03-28T11:26:59+08:00

With the continuous development and wide application of modern technology, more enterprises and institutions in our country have begun to strengthen the research and manufacturing of intelligent elevators, aiming to build a new mode of modern manufacturing with the concept of green development as the core. Therefore, this paper takes green manufacturing as the background, analyzes the design significance of intelligent elevator lightweight, expounds its design ideas, and in the end introduces its specific design practice path.

Research on Deep Learning-Based Dynamic Load Forecasting and Optimal Dispatch in Smart Grids

2025-04-03T16:00:15+08:00

The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration. This study proposes a hybrid LSTM-Transformer architecture for multi-scale temporal-spatial load prediction, achieving 28% RMSE reduction on real-world datasets (CAISO, PJM), coupled with a deep reinforcement learning framework for multi-objective dispatch optimization that lowers operational costs by 12.4% while ensuring stability constraints. The synergy between adaptive forecasting models and scenario-based stochastic optimization demonstrates superior performance in handling renewable intermittency and demand volatility, validated through grid-scale case studies. Methodological innovations in federated feature extraction and carbon-aware scheduling further enhance scalability for distributed energy systems. These advancements provide actionable insights for grid operators transitioning to low-carbon paradigms, emphasizing computational efficiency and interoperability with legacy infrastructure.

Research on Optimization of Microperforated Acoustic Structures Based on Genetic Algorithm

2025-03-28T09:08:31+08:00

Microperforated panels (MPP) are widely used in noise control applications due to their excellent sound absorption performance. However, traditional single-layer MPPs suffer from a narrow sound absorption bandwidth, making it difficult to meet the demands for broadband sound absorption. To address this limitation, this study proposes a design approach for double-layer MPPs optimized using a genetic algorithm (GA). By optimizing structural parameters such as perforation diameter, panel thickness, perforation ratio, and cavity depth, the sound absorption performance of the double-layer MPP is significantly enhanced. The results demonstrate that the optimized double-layer MPP achieves an average sound absorption coefficient of 0.71 across the 100–5000 Hz frequency range, with a peak absorption coefficient exceeding 0.8 at 500 Hz, outperforming conventional sound-absorbing products of the same category.

Overview of Efficient Numerical Computing Methods Based on Deep Learning

2025-04-02T09:47:46+08:00

This article reviews the application and progress of deep learning in efficient numerical computing methods. Deep learning, as an important branch of machine learning, provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain. The article explores the application of deep learning in solving partial differential equations, optimizing problems, and data-driven modeling, and analyzes its advantages in computational efficiency, accuracy, and adaptability. At the same time, this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency, interpretability, and generalization ability, and proposes strategies and future development directions for integrating with traditional numerical methods.

Research Progress on Particle Behavior and Dynamics in Optical Tweezers

2025-04-02T09:55:26+08:00

Optical tweezers technology utilizes the optical potential well generated by a focused laser beam to achieve precise manipulation of micro and nanoparticles. Based on the optical tweezers platform, the motion behavior and dynamic laws of particles are deeply studied, which can reveal the transport mechanism of complex systems. Based on summarizing the principles and experimental methods of optical tweezers technology, this article systematically summarizes the typical force characteristics of particles in optical tweezers, focusing on the dynamic research progress of single particle non-equilibrium state, double particle coupling, and multi-particle cluster system, laying a theoretical foundation for expanding the application of optical tweezers technology in physics, chemistry, biology, and other fields.

Exploring the Application of Blockchain and IoT Technology in Commodity Management

2025-03-27T16:56:18+08:00

This study provides a detailed analysis of the application of blockchain and Internet of Things (IoT) technologies in various aspects of commodity management, addressing issues such as information asymmetry, data security and privacy challenges, insufficient supply chain transparency, and difficulties in regulation. The study also explores the challenges and strategies associated with the implementation of these technologies. Through this analysis, the article aims to provide theoretical support and practical reference for improving the efficiency and quality of commodity management, thereby promoting the digital transformation of commodity management.

LACC-RCE: A Local Adaptive Color Correction and Rayleigh-Based Contrast Enhancement Method for Underwater Image Enhancement

2025-03-28T09:25:53+08:00

Underwater images are inherently degraded by color distortion, contrast reduction, and uneven brightness, primarily due to light absorption and scattering in water. To mitigate these challenges, a novel enhancement approach is proposed, integrating Local Adaptive Color Correction (LACC) with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE (LACC-RCE). Conventional color correction methods predominantly employ global adjustment strategies, which are often inadequate for handling spatially varying color distortions. In contrast, the proposed LACC method incorporates local color analysis, tone-weighted control, and spatially adaptive adjustments, allowing for region-specific color correction. This approach effectively enhances color fidelity and perceptual naturalness, addressing the limitations of global correction techniques. For contrast enhancement, the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast, while CLAHE is employed to adaptively enhance local regions. A weighted fusion strategy is then applied to synthesize high-quality underwater images. Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration, contrast optimization, and detail preservation, thereby enhancing the visual quality of underwater images. This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.

Leveraging Green AI Technology to Build Sustainable Schools: A Conceptual Model Based on AI Agents

2025-04-02T10:21:30+08:00

The integration of Green Artificial Intelligence (AI) technologies into educational systems offers a promising avenue to enhance operational efficiency while addressing sustainability challenges. Through a rigorous three-phase methodology combining literature review, AI agent development, and participatory workshop-based case analysis, this paper highlights the pivotal role of AI agents, as applications of Green AI technologies, in driving transformative outcomes within schools. By directly improving self-learning efficiency and reducing learning costs for students, enhancing management and service efficiency, reducing labor costs for schools, as well as minimizing resource dependence for both teachers and students, AI agents create a foundation for sustainable operations. These direct effects generate positive spillover effects, cascading into broader outcomes, including innovation performance, economic efficiency, and environmental sustainability, aligning with the United Nations Sustainable Development Goals (SDGs). By presenting a comprehensive conceptual model, this study demonstrates the pathways through which Green AI contributes to sustainable development in education and emphasizes its critical role in bridging technological innovation with sustainability. This framework provides significant theoretical insights for further empirical research while offering actionable strategies for policymakers and educators to harness Green AI for building sustainable schools with a student-centered approach.

A Brief Discussion on Data Encryption and Decryption Technology and Its Applications

2025-03-28T11:33:04+08:00

With the rapid development of information technology, data security issues have received increasing attention. Data encryption and decryption technology, as a key means of ensuring data security, plays an important role in multiple fields such as communication security, data storage, and data recovery. This article explores the fundamental principles and interrelationships of data encryption and decryption, examines the strengths, weaknesses, and applicability of symmetric, asymmetric, and hybrid encryption algorithms, and introduces key application scenarios for data encryption and decryption technology. It examines the challenges and corresponding countermeasures related to encryption algorithm security, key management, and encryption-decryption performance. Finally, it analyzes the development trends and future prospects of data encryption and decryption technology. This article provides a systematic understanding of data encryption and decryption techniques, which has good reference value for software designers.

Algorithm and Application in Vehicle Routing Problem: A Review

2025-04-02T08:34:48+08:00

This paper systematically reviews the latest research developments in Vehicle Routing Problems (VRP). It examines classical VRP models and their classifications across different dimensions, including load capacity, operational characteristics, optimization objectives, vehicle types, and time constraints. Based on literature retrieval results from the Web of Science database, the paper analyzes the current state and trends in VRP research, providing detailed explanations of VRP models and algorithms applied to various scenarios in recent years. Additionally, the article discusses limitations in existing research and provides perspectives on future development trends in VRP research. This review offers researchers in the VRP field a comprehensive overview while identifying future research directions.

Urban Hydrological Extraction Based on Otsu Threshold: A Case Study in Zhejiang Province, China

2025-04-02T13:29:32+08:00

This work uses advanced remote sensing to precisely extract hydrological information, supporting transmission network planning. High-resolution water body mapping lets designers optimize routes to avoid ecologically sensitive areas, achieving environmental protection, cost efficiency, and enhanced operational safety. The methodology provides a scalable, replicable framework for intelligent obstacle avoidance in power grid development, applicable to other regions and sectors with similar planning needs.

An Optimization Method for Reducing Losses in Distribution Networks Based on Tabu Search Algorithm

2025-04-02T13:50:44+08:00

With the continuous growth of power demand and the diversification of power consumption structure, the loss of distribution network has gradually become the focus of attention. Given the problems of single loss reduction measure, lack of economy, and practicality in existing research, this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure. The optimization model is developed with the goal of maximizing comprehensive benefits, incorporating both economic and environmental factors, and accounting for investment costs, including the loss of power reduction. Additionally, the model ensures that constraint conditions such as power flow equations, voltage deviations, and line transmission capacities are satisfied. The solution is obtained through a tabu search algorithm, which is well-suited for solving nonlinear problems with multiple constraints. Combined with the example of 10kV25 node construction, the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system, which provides a theoretical basis for distribution network planning.

The Application of Machine Vision in Defect Detection Systems

2025-04-02T14:08:21+08:00

With the rapid development of computer vision technology, artificial intelligence algorithms, and high-performance computing platforms, machine vision technology has gradually shown its great potential in automated production lines, especially in defect detection. Machine vision technology can be applied in many industries such as semiconductor, automobile manufacturing, aerospace, food, and drugs, which can significantly improve detection efficiency and accuracy, reduce labor costs, improve product quality, enhance market competitiveness, and provide strong support for the arrival of Industry 4.0 era. In this article, the concept, advantages, and disadvantages of machine vision and the algorithm framework of machine vision in the defect detection system are briefly described, aiming to promote the rapid development of industry and strengthen China’s industry.

Web Visualization Application of Large Mesh Models Based on Simplification Algorithms

2025-04-03T15:45:17+08:00

This paper studies polygon simplification algorithms for 3D models, focuses on the optimization algorithm of quadratic error metric (QEM), explores the impacts of different methods on the simplification of different models, and develops a web-based visualization application. Metrics such as the Hausdorff distance are used to evaluate the balance between the degree of simplification and the retention of model details.

Research on the Development Strategies of Real-time Data Analysis and Decision-support Systems

2025-04-03T15:52:01+08:00

With the advent of the big data era, real-time data analysis and decision-support systems have been recognized as essential tools for enhancing enterprise competitiveness and optimizing the decision-making process. This study aims to explore the development strategies of real-time data analysis and decision-support systems, and analyze their application status and future development trends in various industries. The article first reviews the basic concepts and importance of real-time data analysis and decision-support systems, and then discusses in detail the key technical aspects such as system architecture, data collection and processing, analysis methods, and visualization techniques.

Face Expression Recognition on Uncertainty-Based Robust Sample Selection Strategy

2025-04-03T15:58:42+08:00

In the task of Facial Expression Recognition (FER), data uncertainty has been a critical factor affecting performance, typically arising from the ambiguity of facial expressions, low-quality images, and the subjectivity of annotators. Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training. To address this issue, we propose an uncertainty-based robust sample selection strategy, which combines confidence error with RandAugment to improve image diversity, effectively reducing overfitting caused by uncertain samples during deep learning model training. To validate the effectiveness of the proposed method, extensive experiments were conducted on FER public benchmarks. The accuracy obtained were 89.08% on RAF-DB, 63.12% on AffectNet, and 88.73% on FERPlus.

Study on Preparation and Forming of TiC Steel-Bonded Cemented Carbide Paste for Direct Writing Printing

2025-04-10T10:15:05+08:00

TiC steel-bound cemented carbide body was prepared by direct writing printing. The effects of powder content (89.28, 89.49, 89.69, 89.88, and 90.07 wt%) and dispersant content (0.017, 0.034, 0.051, and 0.068 wt%) on the slurry and printing body were studied. The experimental results show that with the increase of powder content, the viscosity of the slurry gradually increases, the settlement rate gradually decreases, and the size and linewidth of the blank body gradually decreases. When the powder content is 89.69 wt%, the sedimentation stability and extrusion stability of the slurry are the best, and the density of the blank body is the highest, which is 3.8275 g/cm³, which is suitable for direct writing printing. The addition of dispersant reduced the viscosity of the slurry; With the increase of dispersant content, the surface line width and size of the printed body gradually increased. When the dispersant content is 0.034 wt%, the extrusion stability of the slurry is the best, and the density of the body is the highest, which is 3.8901 g/cm³.

A Collaborative Protection Mechanism for System-on-Chip Functional Safety and Information Security in Autonomous Driving

2025-04-03T16:16:54+08:00

This article takes the current autonomous driving technology as the research background and studies the collaborative protection mechanism between its system-on-chip (SoC) functional safety and information security. It includes an introduction to the functions and information security of autonomous driving SoCs, as well as the main design strategies for the collaborative prevention and control mechanism of SoC functional safety and information security in autonomous driving. The research shows that in the field of autonomous driving, there is a close connection between the functional safety of SoCs and their information security. In the design of the safety collaborative protection mechanism, the overall collaborative protection architecture, SoC functional safety protection mechanism, information security protection mechanism, the workflow of the collaborative protection mechanism, and its strategies are all key design elements. It is hoped that this analysis can provide some references for the collaborative protection of SoC functional safety and information security in the field of autonomous driving, so as to improve the safety of autonomous driving technology and meet its practical application requirements.

Analysis of Internet of Things Intrusion Detection Technology Based on Deep Learning

2025-04-03T16:30:41+08:00

With the rapid development of modern information technology, the Internet of Things (IoT) has been integrated into various fields such as social life, industrial production, education, and medical care. Through the connection of various physical devices, sensors, and machines, it realizes information intercommunication and remote control among devices, significantly enhancing the convenience and efficiency of work and life. However, the rapid development of the IoT has also brought serious security problems. IoT devices have limited resources and a complex network environment, making them one of the important targets of network intrusion attacks. Therefore, from the perspective of deep learning, this paper deeply analyzes the characteristics and key points of IoT intrusion detection, summarizes the application advantages of deep learning in IoT intrusion detection, and proposes application strategies of typical deep learning models in IoT intrusion detection so as to improve the security of the IoT architecture and guarantee people’s convenient lives.

A Study on the Modeling and Design of Sigma-Delta Modulator for High Precision ADC

2025-04-03T16:40:51+08:00

With the continuous improvement of signal processing accuracy requirements in modern electronic systems, the demand for high-precision analog-to-digital converters (ADCs) is increasing. Sigma-Delta modulator, as the most important component of high-precision ADC, is widely used in high-quality audio, high-precision instrument measurement, and other fields due to its advantages of high precision, strong noise resistance, and low hardware cost. This article designs a discrete structure third-order four-bit high-precision Sigma-Delta modulator through modeling, with an oversampling rate set to 512. Under ideal conditions, the simulation results show that the SDNR reaches 152.7db and the ENOB is 25.24bits. After introducing non-ideal noise, the system performance has decreased. The simulation results show that the SDNR is as high as 124.5db and the ENOB is 20.39bits. This indicates that the design can achieve high-precision conversion and provide assistance for further research in the future.

Manifold-Optimized Error-State Kalman Filter for Robust Pose Estimation in Unmanned Aerial Vehicles

2025-04-03T16:52:17+08:00

This paper presents a manifold-optimized Error-State Kalman Filter (ESKF) framework for unmanned aerial vehicle (UAV) pose estimation, integrating Inertial Measurement Unit (IMU) data with GPS or LiDAR to enhance estimation accuracy and robustness. We employ a manifold-based optimization approach, leveraging exponential and logarithmic mappings to transform rotation vectors into rotation matrices. The proposed ESKF framework ensures state variables remain near the origin, effectively mitigating singularity issues and enhancing numerical stability. Additionally, due to the small magnitude of state variables, second-order terms can be neglected, simplifying Jacobian matrix computation and improving computational efficiency. Furthermore, we introduce a novel Kalman filter gain computation strategy that dynamically adapts to low-dimensional and high-dimensional observation equations, enabling efficient processing across different sensor modalities. Specifically, for resource-constrained UAV platforms, this method significantly reduces computational cost, making it highly suitable for real-time UAV applications.

Design of Differential Signal Processing Circuitry for Single-Frequency Laser Interferometry Displacement Measurement

2025-04-03T17:00:17+08:00

This thesis addresses the issues existing in traditional laser tracking displacement measurement technology in the field of ultraprecision metrology by designing a differential signal processing circuit for high-precision laser interferometric displacement measurement. A stable power supply module is designed to provide low-noise voltage to the entire circuit. An analog circuit system is constructed, including key circuits such as photoelectric sensors, I-V amplification, zero adjustment, fully differential amplification, and amplitude modulation filtering. To acquire and process signals, the PMAC Acc24E3 data acquisition card is selected, which realizes phase demodulation through reversible square wave counting, inverts displacement information, and a visual interface for the host computer is designed. Experimental verification shows that the designed system achieves micrometer-level measurement accuracy within a range of 0–10mm, with a maximum measurement error of less than 1.2μm, a maximum measurement speed of 6m/s, and a resolution better than 0.158μm.

Innovative Research on the Integration of Big Data Technology in Poverty Recurrence Monitoring and Agricultural Product Sales Systems

2025-04-03T15:12:57+08:00

With the advancement of the rural revitalization strategy, preventing poverty recurrence among previously impoverished populations has become a crucial social concern. The application of big data technology in poverty recurrence monitoring and agricultural product sales systems can effectively enhance precise identification and early warning capabilities, promoting the sustainable development of rural economies. This paper explores the application of big data technology in poverty recurrence monitoring, analyzes its innovative integration with agricultural product sales systems, and proposes an intelligent monitoring and sales platform model based on big data, aiming to provide a reference for relevant policy formulation.

Research on Constructing Personalized Learner Profiles Based on Multi-Feature Fusion

2025-04-03T15:35:36+08:00

This study proposes a learner profile framework based on multi-feature fusion, aiming to enhance the precision of personalized learning recommendations by integrating learners’ static attributes (e.g., demographic data and historical academic performance) with dynamic behavioral patterns (e.g., real-time interactions and evolving interests over time). The research employs Term Frequency-Inverse Document Frequency (TF-IDF) for semantic feature extraction, integrates the Analytic Hierarchy Process (AHP) for feature weighting, and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’ interests. Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’ behaviors and provides context-aware learning resource recommendations. The study introduces a novel paradigm for learner modeling in educational technology, combining methodological innovation with a scalable technical architecture, thereby laying a foundation for the development of adaptive learning systems.

Research and Analysis of the Droplet-Based Electricity Generator Based on a Rotating Structure

2025-04-07T16:01:17+08:00

With the development of science and technology, the social demand for energy is also increasing. However, the traditional method of energy supply primarily relies on non-renewable resources for energy conversion. While this conventional approach can expedite the energy conversion process, it also results in irreversible ecological hazards. To solve the above problems, the use of renewable clean energy is proposed. In this paper, a droplet generator is proposed to integrate the rotating structure with the body effect power generation for the tiny energy of raindrops. This droplet generator can increase the speed of droplets leaving the dielectric layer and reduce the effect of continuously falling droplets on the droplet-based electricity generator (DEG). It is demonstrated that the instantaneous power of the generator can reach 0.9 mW, which can be a good solution to the power supply needs of some small power supply equipment, and thereafter is beneficial to the self-powering of the equipment in rainy days.

Research on the Application of a MnO2-Based Flexible Supercapacitor in AC Filtering

2025-04-03T17:12:40+08:00

Aluminum electrolyte capacitors (AEC) are widely used in AC filtering as traditional filter capacitors. However, their strong rigidity, high hardness, and high-risk factor have hindered the flexible development of filter capacitors. Flexible supercapacitors, due to their high energy density and outstanding cycle stability, are becoming one of the main directions for the development of filter capacitors in the future. This paper self-assembles a MnO₂-based flexible supercapacitor with high area capacitance and fast frequency response, and preliminarily verifies the feasibility of using the capacitor for AC filtering through simulation tests in Multisim. Finally, the AC filtering test of the flexible supercapacitor using an oscilloscope demonstrates that the ripple factor in the range of 2.7% to 8%, which confirms that the MnO₂-based flexible supercapacitor has a great AC filtering function and can replace traditional aluminum electrolyte capacitors in AC filtering, promoting the flexible development of electronic products.