Journal of Computer Science and Electrical Engineering
Journal of Computer Science and Electrical Engineering (JCSEE) is an international peer-reviewed, open access journal.
THE EVOLUTION OF LARGE MODEL INFERENCE ARCHITECTURES: FROM CENTRALIZED CLOUDS TO DECENTRALIZED ON-DEVICE INTELLIGENCE
The proliferation of large-scale AI models, particularly Large Language Models (LLMs), has made inference a critical and resource-intensive workload. This survey provides a comprehensive review of the historical evolution of inference architectures, charting a distinct trajectory from centralized, cloud-native paradigms to fully decentralized, on-device intelligence. We systematically analyze four key architectural epochs: (1) Device-Cloud, (2) Device-Edge-Cloud, (3) Device-Edge, and (4) pure On-Device inference. For each paradigm, we conduct an in-depth examination of its dominant systems, key enabling technologies, and the inherent advantages and limitations that catalyzed the transition to the subsequent stage. Our analysis reveals that this evolution is driven by a persistent set of trade-offs between computational power, latency, data privacy, cost, and energy efficiency. This paper concludes that the future of AI inference lies not in a single monolithic architecture but in a heterogeneous "compute continuum," where workloads are dynamically orchestrated across a spectrum of resources to meet diverse application demands.
Copyright (c) 2026 ZeYi Luo https://creativecommons.org/licenses/by/4.0
INTELLIGENT DETECTION METHOD FOR FOREIGN OBJECTS ON OVERHEAD LINES OF URBAN BUILDINGS
To address practical engineering challenges of overhead power lines near urban residential buildings, including low erection height, close proximity to building facades, lightweight litter such as plastic bags easily tangled on cables, complex background interference from building walls, vegetation and outdoor air-conditioning units, as well as variable shapes of tangled foreign objects, tiny target sizes and imbalanced scarce fault samples, this paper proposes an intelligent foreign object detection method for overhead lines combining improved EfficientDet and improved Vision Transformer (ViT). Firstly, the improved EfficientDet is adopted to locate regions containing bird nests and tangled plastic bags. Dilated convolutions are embedded into shallow backbone layers P1 and P2 to enlarge the receptive field, and the CBAM attention mechanism replaces the original SE module to strengthen the extraction of contour features of foreign objects under cluttered backgrounds. Secondly, the improved Vision Transformer performs refined discrimination on the presence or absence of foreign objects based on cropped local images. The rigid hard patch embedding of raw images is substituted by multi-layer small convolutions, the number of multi-head attention heads is optimized, and Focal Loss is introduced to alleviate sample imbalance. Actual test results demonstrate that the mean average precision (mAP) of foreign object detection reaches 96.41%, and the classification accuracy of foreign objects is 96.73%. The proposed method exhibits outstanding stability in typical urban scenarios including backlight, building occlusion and dense shrub interference.
Copyright (c) 2026 ZhiPeng Li; ZhiYong Liu; ZhaoYun Liu; JiaXing Hao; ShuJun Yang https://creativecommons.org/licenses/by/4.0
DYNAMIC TRUST EVALUATION AND RISK CONTROL TECHNOLOGY FOR BOUNDARIES OF NOVEL POWER SYSTEMS
Against the backdrop of the full advancement of the "dual carbon" strategy, the new power system has gradually formed an operation pattern featuring multi-stakeholder collaboration across source, grid, load and storage. With the ubiquitous access of massive distributed terminals and the open, interconnected network architecture, the traditional static security protection mode can no longer cope with the increasingly complex cybersecurity risks. Targeting practical problems such as blurred power protection boundaries, rapid risk propagation, lagging static rule-based defense and single disposal methods, this paper proposes an integrated security protection scheme combining dynamic trust evaluation and grayscale risk control. Based on multi-dimensional features, a quantitative trust evaluation system is constructed, and a layered grayscale disposal strategy is designed tailored to business scenarios. The full-link system covering data collection, transmission, intelligent analysis, decision issuance and closed-loop optimization is built, and core modules including subject-object identification, trust assessment and intelligent risk control are deeply designed and engineered. Verified by multiple sets of simulation experiments, the system achieves millisecond-level risk identification and policy response, and significantly improves boundary security protection capabilities while ensuring continuous operation of power businesses. It can provide feasible technical ideas and engineering references for the construction of cybersecurity systems for new power systems.
Copyright (c) 2026 Yang Cao; Yang Su; Peng Zhou; XueFei Tian; ShuXiang Wen; KaiMin Zheng; JinYu Wu https://creativecommons.org/licenses/by/4.0
TIME-SERIES PREDICTION AND PERFORMANCE EVALUATION OF OPTICAL COMMUNICATION CHANNEL STATES BASED ON AN IMPROVED LSTM MODEL
Optical communication channel states are highly dynamic and are easily affected by noise, nonlinear impairments, received power fluctuations, atmospheric turbulence, and link instability. Accurate time-series prediction of channel states is therefore important for link quality assessment, performance optimization, and intelligent communication control. To improve prediction accuracy and stability, this study proposes an improved CNN-BiLSTM-Attention model for time-series prediction and performance evaluation of optical communication channel states. In the proposed framework, convolutional neural networks extract local fluctuation features from multidimensional channel state sequences, bidirectional long short-term memory networks capture temporal dependencies, and an attention mechanism emphasizes critical time steps related to future channel degradation. Channel state indicators, including received optical power, OSNR, SNR, Q-factor, BER, and EVM, are constructed as model inputs or evaluation variables. Experimental results show that the proposed model outperforms ARIMA, SVR, GRU, LSTM, and BiLSTM in terms of MAE, RMSE, MAPE, and R2. In addition, the prediction-assisted evaluation results indicate that the proposed method can reduce BER and outage probability while improving Q-factor, demonstrating its potential for intelligent optical link monitoring and adaptive communication control.
Copyright (c) 2026 HaoYu Tian https://creativecommons.org/licenses/by/4.0
DESIGN AND IMPLEMENTATION OF A PYTHON-BASED SYSTEM FOR NANYANG HAN DYNASTY STONE RELIEF RESOURCES RESTORATION AND VISUALIZATION
The Han dynasty stone reliefs in Nanyang are an important part of China's historical and cultural heritage, with high historical, artistic, and archaeological value. However, due to long-term natural weathering, environmental erosion, and human damage, many of these stone reliefs have problems like cracks, missing parts, dirt, and blurred patterns, which seriously affect their digital preservation and academic research. Currently, the management of Han painting resources generally faces issues such as scattered data, low search efficiency, and a lack of intelligent analysis tools. To address this, we designed and implemented a Python-based system for restoring and visually analyzing Nanyang Han paintings. The system uses a B/S structure, is developed with the Django framework, uses a MySQL database as a central data storage, combines Bootstrap for front-end design, and employs ECharts for multi-dimensional visual analysis. For image restoration, the system introduces a deep learning image repair model that can automatically fill in damaged areas of the stone reliefs and reconstruct patterns. For resource management, it allows adding, deleting, updating, and retrieving Han painting resource information along with categorized management. For visual analysis, it supports multi-dimensional statistical analysis like distribution by era, theme classification, excavation sites, and preservation status, and also provides word cloud analysis, combined searches, and Excel data export. Test results show that the system is stable, user-friendly, and can effectively improve the digital preservation, resource management, and academic research efficiency of the Nanyang Han dynasty stone reliefs.
Copyright (c) 2026 Rui Li; Xiang Li https://creativecommons.org/licenses/by/4.0
BUILDING AN ACCOUNTING EDUCATION ECOSYSTEM FOR LIFELONG LEARNING SUPPORTED BY VIRTUAL SIMULATION TECHNOLOGY: A TRIADIC INTERACTIONIST FRAMEWORK
The rapid digital transformation of the accounting profession, driven by artificial intelligence, blockchain, and continuous regulatory changes, has created an urgent demand for lifelong learning ecosystems that transcend traditional educational boundaries. Virtual simulation technology offers unprecedented opportunities to bridge the gap between theoretical accounting knowledge and practical professional competencies within a lifelong learning paradigm. This study proposes and empirically validates a novel Triadic Interactionist Framework for Accounting Education Ecosystems, integrating Bandura's social cognitive theory with ecosystem theory and technology-mediated learning paradigms. Using a mixed-methods design combining structural equation modeling with longitudinal experimental data from one thousand two hundred forty-eight participants across eighteen Chinese universities and forty-two accounting firms, we demonstrate that virtual simulation technology-enabled accounting education ecosystems significantly enhance learners' self-efficacy, professional competence transfer, and lifelong learning orientation. The ecosystem's modular architecture, comprising virtual simulation laboratories, AI-adaptive learning pathways, blockchain-verified credentialing, and industry-university collaborative platforms, explains a substantial portion of the variance in lifelong learning outcomes. Our findings reveal that the mediating role of immersive experiential learning and the moderating effect of digital readiness are critical mechanisms through which virtual simulation technology transforms accounting education. This research contributes to educational technology theory by extending ecosystem thinking to discipline-specific lifelong learning and provides actionable design principles for institutions seeking to build future-ready accounting education infrastructures.
Copyright (c) 2026 YongHui Li; HaiBo Zhang https://creativecommons.org/licenses/by/4.0
CROP PEST AND DISEASE IDENTIFICATION SYSTEM BASED ON MUTUAL LEARNING
As the fundamental industry supporting the national economy, agricultural production is seriously restricted by frequent occurrences of crop pests and diseases, which have been proven to cause enormous economic losses worldwide every year. Therefore, the rapid and accurate identification of crop pests and diseases is widely regarded as a crucial prerequisite for the sustainable development of green agriculture and high-quality agricultural production. Traditional pattern classification algorithms and representative deep learning models including VGG and ResNet have been gradually applied in the field of pest and disease identification, yet most deep networks are confronted with great challenges in real-world deployment on mobile and edge devices due to their excessively large number of parameters and high computational complexity. To effectively tackle this application bottleneck, a lightweight BMV3 network based on the mutual learning framework is proposed in this paper. After heterogeneous knowledge interaction and mutual learning with ResNet50, the identification accuracy of the proposed model is significantly improved by 2.6%, and the dual optimization of high identification accuracy and flexible terminal deployment is successfully realized.
Copyright (c) 2026 HanQiu Shen; Zhuo Xie; XiaoYu Guo; XinYing Liu https://creativecommons.org/licenses/by/4.0
DESIGN AND REALIZATION OF MONITORING AND CONTROL SYSTEM FOR SMART FARMS USING ESP32-P4 AND LVGL
Aiming at the problems of poor real-time performance, weak local interaction and limited data processing capacity of traditional agricultural monitoring systems, this paper presents an intelligent farm monitoring and control system based on ESP32-P4 and a 7-inch IPS touch screen. Taking the dual-core RISC-V ESP32-P4 with a main frequency of 400 MHz as the main controller, the system drives the display screen via the MIPI-DSI interface and adopts the LVGL graphics library to realize a high-frame-rate local human-machine interaction interface. It integrates five types of sensors for temperature, humidity, soil moisture, light intensity and carbon dioxide concentration, and supports Wi-Fi cloud data transmission as well as closed-loop control functions including automatic irrigation and environmental regulation. The test results show that the interface refresh rate is no less than 28 FPS, and the control response time is less than 100 ms. The system can satisfy the requirements of real-time monitoring and precise control for small and medium-sized intelligent farms.
Copyright (c) 2026 YuanJu Zhou https://creativecommons.org/licenses/by/4.0
PROPAGATION CHARACTERISTICS AND SYSTEM PERFORMANCE OF TERAHERTZ–OPTICAL HYBRID COMMUNICATION CHANNELS
This paper investigates the propagation characteristics and system performance of terahertz–optical hybrid communication channels. To meet the increasing demand for high capacity and high reliability in future high-speed wireless communication systems, a hybrid communication model consisting of a terahertz link and an optical wireless link is established. First, the dominant propagation impairments are analyzed, including free-space path loss, molecular absorption, and pointing errors in the terahertz link, as well as atmospheric attenuation, turbulence effects, and pointing errors in the optical wireless link. Then, a hybrid channel model is developed based on an instantaneous signal-to-noise-ratio-based link selection mechanism, and outage probability, bit error rate, and channel capacity are selected as the main performance metrics. Numerical results show that the terahertz and optical wireless links exhibit clear complementarity in terms of propagation impairments. Compared with single-link transmission, the terahertz–optical hybrid communication system can effectively reduce the outage probability and maintain more stable system capacity under different transmission distances and propagation conditions. The results of this work may provide useful guidance for the design of future 6G high-speed wireless backhaul and reliable communication systems in complex environments.
SMART HOME SPACE DESIGN INTEGRATING XR AND AIGC
With the rapid advancement of extended reality (XR), artificial intelligence-driven generation (AIGC), and digital twin technologies, home space design is transitioning from "digital assistance" to a new phase of "intelligent symbiosis." Traditional virtual reality (VR) design faces limitations such as lengthy modeling cycles, conflicts between realism and real-time performance, and insufficient user engagement, making it inadequate for contemporary personalized and immersive design demands. This paper first analyzes the technological evolution of current home space design and highlights the necessity of integrating XR with AIGC; secondly, it establishes an intelligent design framework based on 3D Gaussian Spatter (3DGS) and generative spatial models (e.g., PanoWorld), enabling end-to-end generation of high-fidelity virtual scenes from user intent comprehension; thirdly, it employs an improved collaborative filtering algorithm to quantitatively evaluate spatial design preferences and similarity; finally, it compares simulation experiments with traditional design methods. Results demonstrate that the novel design approach achieves approximately 11% improvement in comprehensive metrics for spatial practicality, intuitiveness, and functional diversity compared to conventional methods, enhances user satisfaction by nearly 12%, and reduces the solution generation cycle from days to minutes. This study provides a novel technical pathway for advancing theoretical frameworks and engineering applications in smart home space design.
Generative Artificial Intelligence (AIGC)
Copyright (c) 2026 LingJuan Wang https://creativecommons.org/licenses/by/4.0
DEEP LEARNING-BASED MALICIOUS TRAFFIC ANALYSIS: A COMPREHENSIVE SURVEY
Malicious traffic analysis has become increasingly challenging due to encryption-by-default communication, evolving attack behaviors, and distribution shifts across heterogeneous environments. At present, many studies still do not share consistent pipeline designs, traffic representations, benchmark datasets, or assessment methods. Based on this problem, this paper systematically examines deep-learning-based malicious-traffic-analysis technologies. Existing works are organized into four main parts: data acquisition and preprocessing, traffic representation, learning models, and performance evaluation methods. In particular, we compare representative methods, including traditional machine learning, deep learning, and hybrid forms, in terms of detection accuracy, computational cost, cross-scenario generalization ability, and suitable metrics. We also identify key problems in practical applications, such as encrypted traffic monitoring capability, biased datasets, and adversarial attacks, and summarize related research paths. This paper introduces the research area and provides a reference for reproducible literature-based evaluation design.
Copyright (c) 2026 WenCai He; ZhiJie Peng; MingShen Zhang; He Zhu https://creativecommons.org/licenses/by/4.0
AI-ENABLED DESIGN AND OPTIMIZATION OF AEROSPACE ELECTRIC MOTORS: A TASK-ORIENTED REVIEW
Artificial intelligence (AI) becomes an effective tool in electric motor design. It can speed up design updates, improve optimization in complex conditions, and support physical modeling. However, most existing review papers discuss AI in motor design in a general way. These studies usually do not focus on the special needs of aerospace systems. This paper presents a task-based review of AI methods for aerospace electric motor design under extreme constraints which mainly covers data-efficient learning, surrogate models, multi-objective optimization, constraint-aware optimization, and physics-informed methods. These methods help designers deal with difficult trade-offs among power density, thermal behavior, mechanical strength, and fault tolerance, even when training data are limited. This paper also discusses the main problems in current studies. For example, many methods still lack validation under real flight conditions. In addition, this paper points out several future directions, such as the use of digital twins and AI workflows that can support certification. This review can provide a clear reference for researchers and engineers who apply AI to next-generation aerospace electric propulsion and actuation systems.
Copyright (c) 2026 SaiBo Wang; HaoZheng Yu; JinXiao Wang; SiYu Wang; Chao Gong; Ke Tang https://creativecommons.org/licenses/by/4.0
SIGNAL DENOISING BASED ON QUANTUM ADAPTIVE TRANSFORMATION AND SINGULAR VALUE DECOMPOSITION
The denoising algorithm plays an important role in biomedical signal processing such as spectrophotometry, and is an important means to improve signal quality and detection accuracy. This article combines singular value decomposition denoising and quantum adaptive transformation to process Gaussian spectral data. The results indicate that denoising evaluation indicators such as signal-to-noise ratio, mean square error, and normalized cross-correlation coefficient have all improved. Moreover, the stronger the noise, the more obvious the advantages of the proposed algorithm compared to other denoising algorithms. And this method can be extended to pre-processing biomedical signals, such as electrocardiograms and electroencephalograms.
Copyright (c) 2026 YingYing Sun; MengYao Lv; ChenLin Wu; WeiXian Xie; ZiYuan Fang; RuiTong Zhao https://creativecommons.org/licenses/by/4.0
DEEP REINFORCEMENT LEARNING FOR TEST CASE GENERATION AND PRIORITIZATION
With the increasing scale and complexity of software systems, improving testing efficiency and fault detection capability under limited testing resources has become a critical issue in software testing research. To address the limitations of traditional approaches, which are often static, separately designed, and unable to make full use of dynamic execution feedback in test case generation and prioritization, this paper proposes a unified testing optimization framework based on deep reinforcement learning. The proposed method formulates both test case generation and test case prioritization as a sequential decision-making problem aimed at maximizing testing utility, and develops a feedback-driven strategy learning mechanism through state representation, action selection, and reward design, enabling the model to dynamically adjust testing behavior according to coverage information, fault detection outcomes, and budget constraints. Experimental results show that the proposed method effectively improves statement coverage, branch coverage, and the number of detected faults in test case generation, while also achieving higher APFD and earlier fault detection under limited budgets in test case prioritization. Further ablation analysis demonstrates that coverage reward, fault-related reward, and budget constraint play complementary roles in optimizing the testing strategy. Overall, this study shows that deep reinforcement learning provides an adaptive and unified solution for automated decision-making in software testing and offers a promising direction for the development of intelligent software testing methods.
Copyright (c) 2026 JingXuan Guo; JingJing Liu https://creativecommons.org/licenses/by/4.0
THE ALGORITHM FOR DETECTING OIL STAINS AND FOREIGN OBJECTS ON THE BOTTOM OF EMU CARS BASED ON DEEP LEARNING
The operation safety of EMU (Electric Multiple Unit) trains is the core guarantee of the high-speed railway transportation system. Currently, the widely deployed EMU operation fault dynamic image detection system (TEDS) in China mainly relies on manual image interpretation, resulting in low detection efficiency, prone to missed detections and false detections. To address the challenges in detecting oil stains under EMU car bottoms, such as scarce samples, insufficient accuracy of a single model, and difficulty in identifying reflective oil stains, this paper proposes an oil stain detection algorithm based on the YOLOv12 and DeepLabV3+ dual-model collaboration. In terms of data augmentation, to address the deficiency of only 356 original oil stain samples, this paper designs a three-stage data augmentation strategy. This strategy expands the training set to 6786 images through basic geometric transformations, noise addition and blurring processing, as well as a composite enhancement pipeline based on the Albumentations library, effectively enhancing the generalization ability of the model. This paper uses the YOLOv12 as the target detection model and trains an oil stain detector on the expanded dataset. Experimental results show that the YOLOv12 model achieves an accuracy of0.88 on the validation set, a recall rate of 0.70, and a recall rate of 0.79 for the oil stain category, effectively identifying most oil stain targets. The oil stain candidate regions detected by YOLOv12 are input into the DeepLabV3+ network, using MobileNetV2 as a lightweight backbone network, and training a pixel-level oil stain segmentation model. Experimental results show that the model achieves an average IoU of 0.4535 and an average Dice coefficient of 0.4872 on the validation set. The test results show that the model can effectively identify reflective oil stains that are difficult for humans to distinguish and reduces false alarms for dried traces. The joint detection framework combining target detection and semantic segmentation proposed in this paper integrates the characteristics of YOLOv12’s rapid localization and DeepLabV3+’s fine segmentation. This model can adapt to the oil stain defect detection requirements in complex EMU operation environments and provide a reference path for research on railway image intelligent detection technologies.
Copyright (c) 2026 ZhiJian Wei; SongTao Zhang; ZiYi Xu; Hang Zhou https://creativecommons.org/licenses/by/4.0
A MULTI-LABEL IMAGE RECOGNITION NETWORK DRIVEN BY LABEL-IMAGE SEMANTIC ALIGNMENT
Multi-label image recognition aims to predict a set of semantic labels for an image and has wide applications in other fields. Existing methods have two main problems: the attention regions generated by attention-based methods are insufficiently correlated with label semantics; methods based on label correlation modeling lack dynamic interaction with visual image content, making it difficult to achieve precise alignment between label semantics and image regions. To address these issues, this paper proposes a novel multi-label image recognition algorithm. First, we construct a label graph and leverage a graph convolutional network to learn label semantic priors, modeling the dependencies among labels. Second, we design a semantic decoupling module that adaptively focuses on relevant image regions under the guidance of label semantics to generate label semantic representations. Finally, we introduce a semantic association reasoning module that employs a multi-head self-attention mechanism to dynamically capture semantic correlations among labels, thereby enhancing the discriminative ability of features. Experimental results on the PASCAL VOC 2007 dataset show that our method achieves 95.0% mAP, surpassing existing state-of-the-art methods and improving by 1.6 percentage points over the baseline SSGRL. Ablation studies further validate the effectiveness of each module.
Copyright (c) 2026 YuYu Chen; LinJun Wu; LianZhi Chu https://creativecommons.org/licenses/by/4.0
APPLICATION OF LARGE LANGUAGE MODELS IN SMART CONTRACT VULNERABILITY DETECTION
With the rapid iteration of blockchain technology, smart contracts, as core components of decentralized applications, directly impact the stability of on-chain assets and ecosystems through their security. Traditional vulnerability detection methods primarily rely on expert rules and static analysis, facing bottlenecks such as high false positive rates and poor adaptability to complex logical vulnerabilities. In recent years, Large Language Models (LLMs), with their exceptional code understanding and reasoning capabilities, have provided new technical pathways for smart contract security auditing. This paper focuses on LLM-driven smart contract vulnerability detection technologies, systematically reviewing mainstream application paradigms from prompt engineering to model fine-tuning. The paper first reviews the current state of smart contract security and the limitations of traditional methods; subsequently, it provides in-depth analysis of the architectural design and core mechanisms of representative frameworks such as GPTLens and SmartVD, evaluating their performance in detection accuracy and recall rate; finally, addressing current challenges including data scarcity, model hallucinations, and computational overhead, it proposes future evolution directions such as multimodal fusion and human-in-the-loop auditing, providing reference for research and practice in related fields.
Copyright (c) 2026 Yun Li; YanLing Liu; ChangEr Liu; XinMan Luo https://creativecommons.org/licenses/by/4.0
SMART CONTRACTS IN INDUSTRY APPLICATIONS AND ECONOMIC ECOSYSTEMS: CURRENT STATUS AND TRENDS
As a core technology of blockchain ecosystems, smart contracts are fundamentally reshaping the operational logic and commercial landscape of the digital economy. This paper systematically analyzes the current economic ecosystem of smart contracts from four dimensions: business value creation, multi-dimensional industry applications, legal and regulatory challenges, and frontier technological evolution. Research indicates that smart contracts, by reducing transaction costs and enhancing trust mechanisms, have achieved maturity in financial applications (DeFi) and are demonstrating significant enabling effects in real-economy sectors such as supply chains, healthcare, and energy internet. However, their widespread implementation still faces institutional obstacles including ambiguous jurisdictional boundaries, compatibility between code and law, and liability attribution. Looking ahead, Layer-2 scaling solutions have significantly improved cost-effectiveness, while the integration of cross-chain interoperability and AI-driven intelligent decision-making will become key trends driving the expansion of "contractability" boundaries. Smart contracts are not merely technical tools but rather institutional infrastructure driving the transformation of business models from intermediary-dominated to algorithm-autonomous paradigms.
Copyright (c) 2026 YiWen Gao; ChangEr Liu https://creativecommons.org/licenses/by/4.0
A SPATIOTEMPORAL DUAL-BRANCH NETWORK BASED ON CHANNEL ATTENTION AND RESIDUAL CONNECTION IN MOTOR IMAGERY EEG CLASSIFICATION
Accurate decoding of motor imagery (MI) electroencephalogram (EEG) signals is a core bottleneck for the practical application of brain-computer interface (BCI) technology. Due to the characteristics of low signal-to-noise ratio, non-stationarity, and multi-dimensional feature coupling of these signals, a single feature extraction method is difficult to fully mine discriminative information. To address this problem, this study proposes a spatiotemporal dual-branch MI-EEG decoding method based on hierarchical channel attention and learnable residual connections. Taking a 3D EEG tensor as input, the method captures scalp electrode topological features and temporal dynamic dependencies through parallel spatial and temporal branches, respectively. It also introduces a channel attention mechanism with learnable residual connections to adaptively enhance the representation ability of key feature channels. Experimental results on the public BCI Competition IV 2A dataset show that the proposed method achieves an average classification accuracy of 79.63% and a Kappa coefficient of 0.7284 in four types of motor imagery tasks, significantly outperforming mainstream models such as EEGNet and FBCNet. Research indicates that the organic combination of the spatiotemporal dual-branch structure and the hierarchical channel attention mechanism can effectively improve the ability to extract spatiotemporal features and channel dependency relationships of EEG signals. The method exhibits excellent classification performance and cross-subject generalization in motor imagery EEG decoding tasks, providing strong support for feature construction and model design in related fields.
Copyright (c) 2026 HuiXi Mo https://creativecommons.org/licenses/by/4.0
OPTIMIZATION OF SCATTERING CHARACTERISTICS OF SUPPORT ROD BASED ON CST SOFTWARE
This paper addresses the problem of strong electromagnetic scattering from traditional diamond-shaped/cylindrical support rods in electromagnetic testing. Low-scattering optimization research is carried out based on finite integration software CST (Computer Simulation Technology) simulation. The scattering mechanism is analyzed by combining geometric diffraction and traveling wave theories, the "convex" water-drop-shaped cross-section is optimized, and a segmented coarse-fine alternating cylindrical structure is designed to disperse the scattering field. Simulations show that the designed water-drop-shaped cross-section segmented support rod has lower scattering intensity in the target frequency band, with significantly reduced scattering in high-frequency bands compared to traditional structures.
Copyright (c) 2026 ZhongXin Yang; MengMeng Zhang; QiPeng Xu https://creativecommons.org/licenses/by/4.0
MODELING THE EVOLUTIONARY DYNAMICS OF SMARTPHONE BATTERY TIMING
Addressing the complexity of smartphone battery life prediction, this study constructs a continuous-time dynamic model based on the law of energy conservation. The model defines the rate of change in battery state of charge as the ratio of total power consumption to effective capacity, which is influenced by temperature and aging. A first-order ordinary differential equation describes its evolution over time. Through physical modular modeling of core components—including the display, processor, network communication, and background applications—the study achieves a refined decomposition of total transient power. Parameters are calibrated using high-precision datasets, and numerical solutions are obtained via the fourth-order Runge-Kutta method. The resulting state-of-charge evolution curves exhibit smoothness and stable convergence, with locally truncated errors controlled at extremely low levels. Empirical analysis demonstrates the model's ability to accurately capture instantaneous power fluctuations triggered by user behavior transitions, revealing the dominant role of processor power consumption across different usage phases. This research establishes a robust physical foundation for achieving high-precision remaining power prediction and energy efficiency attribution analysis.
Copyright (c) 2026 MingGuang Jia https://creativecommons.org/licenses/by/4.0
DESIGN OF A DYNAMIC MONITORING AND TIERED EARLY WARNING SYSTEM FOR CULVERT WATERLOGGING BASED ON A CLOUD-EDGE COLLABORATIVE ARCHITECTURE
In response to the frequent waterlogging of urban culverts induced by extreme rainfall, the vulnerability of traditional monitoring methods, and the lack of targeted early warnings, this paper designs and implements an Internet of Things (IoT)-based dynamic monitoring and tiered early warning system utilizing a cloud-edge collaborative architecture. At the perception layer, the system employs a non-contact computer vision approach. By deploying dual lightweight YOLOv8 object detection models on edge computing nodes (Raspberry Pi 4B), it synchronously extracts on-site water depth and the chassis height of passing vehicles in real time. At the decision layer, a tiered early warning logic based on dynamic "water level-vehicle type" matching is proposed. This approach overcomes the limitations of traditional uniform static thresholding strategies and enables sub-second local audio-visual traffic control directly at the edge. Furthermore, the edge nodes transmit lightweight structured data to the Huawei Cloud IoT platform for aggregation, ultimately delivering public services via a WeChat Mini Program. Users can leverage the embedded map component to monitor the real-time waterlogging status across various monitoring sites and receive vehicle-specific routing recommendations. Prototype testing results demonstrate that the system's visual perception error is maintained within a reasonable margin, and the overall response latency at the edge is less than 500 ms. Crucially, the system retains offline resilience and local warning capabilities even in extreme scenarios involving network interruptions. This research provides a low-cost, robust engineering solution for smart urban flood control and emergency traffic dispatching.
Copyright (c) 2026 YiTao Li https://creativecommons.org/licenses/by/4.0
MOTION COMPENSATION FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY BASED ON MULTI-DIMENSIONAL MATRIX PENCIL METHOD AND ADAPTIVE EN-FACE IMAGE ENHANCEMENT
This paper proposes a two-stage algorithm combining the multi-dimensional matrix pencil method (MDMP) with En-face image enhancement to address the problem of eye motion artifacts severely degrading image quality in optical coherence tomography angiography (OCTA). In the first stage, a two-dimensional Hankel matrix pencil in the depth-time domain is constructed to jointly estimate Doppler frequency shifts and motion parameters of blood flow signals. The Centro-Hermitian property is exploited via unitary transformation to reduce computational complexity by approximately 75%, and an automatic parameter pairing mechanism is introduced to avoid mispairing problems inherent in conventional approaches. In the second stage, En-face image enhancement is applied to the MDMP-compensated retinal volume data, sequentially performing FFT difference calculation, weighted layered projection, CLAHE adaptive enhancement, column-wise median normalization for stripe removal, and FAZ region cosine gradient darkening to generate high-quality En-face vascular images. Experimental validation on a retinal OCTA dataset demonstrates that compared with multiple existing methods, the proposed method achieves a 65.1% improvement in signal-to-noise ratio (SNR), an 80.2% improvement in contrast-to-noise ratio (CNR), a structural similarity index (SSIM) of 0.94, and a 34.0% reduction in motion artifact index (MAI). The results confirm that the proposed two-stage algorithm effectively suppresses eye motion artifacts and significantly enhances OCTA En-face vascular imaging quality.
Optical coherence tomography angiography
Copyright (c) 2026 LinKang Du; Lei Liu; HaiYi Bian https://creativecommons.org/licenses/by/4.0
INNOVATIVE APPLICATION OF AI AGENT BASED ON MARKOV DECISION PROCESS IN DYNAMIC OPTIMIZATION DECISION-MAKING FOR ENTERPRISE SERVICES
Against the backdrop of the booming digital economy, the enterprise service environment is increasingly characterized by dynamics, high uncertainty, and multi-objective collaboration. Facing core challenges such as fluctuating service demands, changing resource constraints, and rapidly iterating customer preferences, traditional static decision-making methods struggle to adapt to actual operational needs, gradually revealing their limitations. This paper focuses on key issues in dynamic enterprise service decision-making, including resource allocation, service priority setting, and precise matching of customer needs. Targeting the regional characteristics and operational pain points of enterprise service scenarios in South China, we innovatively improve the traditional Markov Decision Process (MDP) model. Leveraging the intelligent computing power and data simulation support provided by Shenzhen Qicheng Zhiyuan Network Technology Co., Ltd. through the "LiQiCloud" AI-powered Sci-Tech Innovation Policy Platform, we construct an AI agent decision-making framework that integrates Deep Reinforcement Learning (DRL) and Digital Twin technology. Furthermore, we propose an improved value iteration algorithm based on function approximation, which effectively overcomes the limitations of traditional models and significantly enhances decision-making efficiency and adaptability. Through rigorous mathematical derivation, we elucidate the theoretical foundations for state representation, action output, reward function design, and optimal policy solutions. Simulation and empirical analysis were conducted using real-world service scenarios from a small-to-medium-sized clothing e-commerce enterprise in South China (daily order volume of 1,500–2,200; peak order volume of 4,000 during major promotions in 2025; customer service team of 25). Experimental results demonstrate that the proposed model significantly outperforms traditional MDP models and manual decision-making methods in core metrics, including service cost control (reduced by 22.8%), customer waiting time (shortened by 56.1%), resource utilization (improved by 14.7%), and effective problem resolution rate (increased by 5.9 percentage points). This study provides an implementable and replicable solution for optimizing service decision-making in the digital transformation of SMEs in South China.
Copyright (c) 2026 XueYong Li; WeiBin Zhao; Xing Xu; JinHan Cai https://creativecommons.org/licenses/by/4.0
OPTIMIZATION OF ESPRIT ALGORITHM AND FAST DOA ESTIMATION FOR UNIFORM LINEAR ARRAYS
To tackle the problems of high computational complexity and cumbersome engineering deployment posed by traditional subspace-based direction-of-arrival (DOA) estimation algorithms, this study presents a low-complexity DOA estimation approach designed specifically for uniform linear arrays (ULA). The key innovation of the proposed method is the elimination of eigenvalue decomposition or singular value decomposition (EVD/SVD)—two core components of classical subspace-based techniques. Instead, it employs a strategy of array segmentation and matrix reconstruction. In detail, the ULA is first split into two separate subarrays, and their cross-covariance matrix is used to build a joint cross-covariance matrix. An equivalent signal subspace is then directly generated via a linear transformation, which greatly simplifies the computational procedure. Finally, by exploiting the rotation invariance principle of ESPRIT, the proposed approach realizes fast and precise DOA estimation. Theoretical analyses and simulation results indicate that, in comparison with traditional subspace-based algorithms, the proposed method reduces computational complexity significantly while retaining comparable estimation accuracy, thus improving the overall efficiency of DOA estimation.
Copyright (c) 2026 XinYu Weng https://creativecommons.org/licenses/by/4.0
DUAL-FAIRNESS DRIVEN 3D TRAJECTORY DESIGN AND RESOURCE ALLOCATION FOR UAV-ASSISTED ISAC SYSTEMS
Unmanned Aerial Vehicles (UAVs), recognized for their high flexibility and rapid deployment, are increasingly utilized as key platforms for Integrated Sensing and Communication (ISAC). However, balancing on-demand service quality for users with robust sensing coverage for targets remains a critical challenge, especially in emergency scenarios where blind spots must be avoided. This paper proposes a dual-fairness framework for a UAV-assisted ISCC system. We formulate a joint optimization problem of sensing, computation, communication scheduling, and UAV 3D trajectory. To ensure service fairness among users, we adopt a logarithmic utility function to maximize the sum of the logarithm of user-received data volume. Simultaneously, to guarantee sensing fairness, we enforce a Minimum Sensing Rate Ratio (MSRR) constraint, ensuring that no target is persistently neglected. The formulated non-convex problem is solved via a proposed four-stage Alternating Optimization (AO) algorithm. Simulation results demonstrate that the proposed scheme effectively balances user service fairness with sensing robustness, achieving high-quality data delivery while strictly satisfying the sensing fairness requirements.
Copyright (c) 2026 LuLu Jing; Hai Wang https://creativecommons.org/licenses/by/4.0
ST-GNAD: MULTI-LEVEL ANOMALY DETECTION FOR 5G CORE NETWORKS BASED ON SPATIAL-TEMPORAL GRAPH NEURAL NETWORK
The rapid expansion of 5G infrastructure has intensified the need for robust security within the Service-Based Architecture (SBA). Critical signaling interfaces, specifically N2 (connecting the Radio Access Network to the Control Plane) and N4 (linking the Control and User Planes), are increasingly targeted by exploits such as signaling storms, session hijacking, and unauthorized access. Traditional security measures often fail to account for the complex, non-Euclidean relationships between decentralized Network Functions. This research addresses these vulnerabilities by proposing a multi-level anomaly detection framework grounded in Graph Neural Networks (GNN). By modeling the 5G core network as a dynamic graph, the proposed ST-GNAD model effectively aggregates spatial dependencies across N2 and N4 interfaces while capturing temporal signaling evolutions.The performance of this framework was rigorously evaluated through a series of experiments on a high-fidelity simulation platform utilizing Open5gs and UERANSIM. The experimental campaign involved simulating diverse attack vectors targeting the NGAP and PFCP protocols to reflect authentic network perturbations. Quantitative results demonstrate that the model excels in identifying multi-stage anomalies, achieving superior detection precision and lower false-alarm rates compared to traditional sequential models. This approach provides a scalable and resilient solution for securing the signaling backbone of modern 5G architectures.
Copyright (c) 2026 HuiYuan Ke https://creativecommons.org/licenses/by/4.0
LINK QUALITY EVALUATION AND FAULT PREDICTION OF COMMUNICATION NETWORKS BASED ON MACHINE LEARNING
As a core infrastructure of modern society, the stability of communication networks directly affects the operation of key areas. Traditional passive maintenance methods suffer from drawbacks such as delayed fault detection and difficulty in real-time monitoring. Machine learning's strong data processing and pattern recognition capabilities offer a new path to solve this problem. This study aims to construct a machine learning-based communication network link quality evaluation and fault prediction system to achieve accurate link quality assessment and early fault warning. The research designs a complete research framework including data preprocessing, feature engineering, model building, and prediction evaluation. Key performance indicators such as link bandwidth utilization and packet loss rate are collected, and multi-dimensional data quality control is implemented. A deep learning model combining traditional machine learning algorithms such as support vector machines and random forests with CNN-LSTM is used to conduct link quality evaluation, and a fault prediction model is constructed based on continuous-time Markov chains. Experimental results show that the fault detection accuracy of the proposed machine learning method reaches 92.3%, while the deep learning method further improves it to 95.8%. Compared with traditional methods, this significantly shortens the average fault location time, significantly increases the fault prediction lead time, and reduces the false alarm rate. The link quality assessment and fault prediction framework constructed in this study provides an efficient technical solution for communication network fault prevention and control, promoting the intelligent development of communication network operation and maintenance.
Copyright (c) 2026 ZhiYi Tan https://creativecommons.org/licenses/by/4.0
INTELLIGENT TRANSPORTATION SYSTEM OF MEDICAL WASTE BASED ON UWB POSITIONING AND CLOUD COLLABORATION
To address the pain points of traditional medical waste transportation, such as high reliance on manual labor, irrational routes, prominent dumping risks, and difficulties in traceability, this paper designs an intelligent transportation system integrating multiple technologies. The core innovation lies in constructing an integrated intelligent architecture of "perception-localization-scheduling-traceability-risk prevention." This system breaks the traditional human-dominated model of medical waste transportation and achieves intelligent control of the entire transportation process through deep integration of software and hardware. The system primarily uses ESP32C3 as the main controller, innovatively integrating UWB high-precision positioning, K230 visual recognition, and anti-dumping mechanical structures to form a triple innovation system: First, the linkage between UWB and K230 visual recognition enables precise positioning and classification identification of waste bins; Second, the integration of improved genetic algorithms with A* algorithms achieves dynamic route scheduling and obstacle avoidance; Third, a cloud-based traceability system is constructed based on the MQTT protocol, complemented by anti-dumping structures to mitigate risks. After debugging, the system's positioning accuracy reaches the centimeter level. The classification recognition accuracy of the medical waste trash bins is relatively high. The single transportation efficiency has been improved, and the transportation process can be traced, meeting the digital supervision requirements of the "Medical Waste Management Regulations". This provides a reliable solution for the intelligent upgrade of medical waste transportation.
Copyright (c) 2026 YuHan Chen; XinYuan Gao; BoYu Shan; XianXing Luo; ShangXuan Li; Yi Zhang https://creativecommons.org/licenses/by/4.0
NONLINEAR CHANNEL EQUALIZATION AND ADAPTIVE LEARNING METHODS FOR LOW-POWER VLC SYSTEMS
To address the challenges of limited modulation bandwidth and nonlinear channel impairments in low-power visible light communication (VLC) systems, this paper proposes a hardware-software co-designed adaptive learning equalization scheme. At the physical layer, a hardware pre-equalization circuit is designed to compensate for Light Emitting Diode (LED) high-frequency attenuation, thereby expanding the system's physical bandwidth. At the algorithmic layer, an adaptive gradient descent learning algorithm arctan-softsign variable-step least mean square (A-SVSLMS) based on the softsign activation function is proposed. This algorithm leverages the nonlinear mapping mechanism between the step size factor and the error gradient to achieve dynamic optimization of weight updates, effectively resolving the challenge faced by traditional algorithms in balancing convergence speed and steady-state accuracy. Experimental results demonstrate that under a 0.06 W light source, the system's -3 dB bandwidth increases from 1.6 MHz to 13.5 MHz. Compared to traditional LMS algorithms, the proposed algorithm exhibits faster learning rates and enhanced robustness, successfully achieving 2 Mbps error-free transmission at a 0.55 m distance. This validates the application potential of lightweight intelligent algorithms in resource-constrained devices.
Copyright (c) 2026 LiuYang Niu; Lei Guo; XiangYu Liu; GuoDong Zhao https://creativecommons.org/licenses/by/4.0