Journal of Applied Automation Technologies
The Journal of Applied Automation Technologies (JAAT) eISSN:3105-5524 is an international, peer-reviewed journal dedicated to publishing high-quality research and application-focused studies in the field of automation and intelligent systems. The journal aims to bridge the gap between theoretical innovation and practical implementation in industrial automation, smart manufacturing, and cyber-physical systems. JAAT welcomes original research articles, review papers, case studies, and technical reports that address cutting-edge developments and real-world challenges in the design, integration, and deployment of automation technologies. The journal serves as a platform for academic researchers, engineers, and industry professionals to share advances that drive automation-enabled productivity, flexibility, and intelligence.
European Federation of Engineering Associations
Context-Aware Cascade RCNN for Robust Pedestrian Detection in Complex Urban Environments
In bustling cities, autonomous pedestrian detection needs to consider changes in lighting conditions, multiple occlusions, and the simultaneous appearance of multiple objects. This paper proposes a context-aware cascaded RCNN architecture to improve the accuracy and robustness of heterogeneous urban environment detection. To accommodate local uncertainties and recognize multi-scale spatial dependencies, the framework includes a unique context aggregation module and a dynamic threshold adjustment mechanism. A large-scale dataset has been prepared for comprehensive experiments, including adverse weather, nighttime scenes, and congested traffic. The new method outperforms previous detectors in many cases. Under conditions with significant occlusion or environmental changes, it achieves higher average precision and recall rates. Not only is each module effective individually, but they are also very effective when combined. In real-world environments, on-site deployment demonstrated good inference speed and stability, and validated real-time operational conditions in various street settings. The aforementioned framework plays an important role in urban pedestrian detection tasks and provides new pathways for intelligent transportation and safety applications. It has good application value in urban surveillance and autonomous driving.
Copyright (c) 2026 Hana Hájek, Adéla Svoboda https://creativecommons.org/licenses/by-nc-sa/4.0
Real-Time Semantic Segmentation for Autonomous Driving Based on Swin Transformer
Real-time semantic segmentation helps safely construct detailed maps of dynamic cities and improves the robustness of autonomous driving perception. To meet the high precision and high efficiency requirements of automotive hardware, a universal Swin Transformer framework has been developed for autonomous driving. This new method is optimized at runtime by using small decoder modules, hierarchical window self-attention mechanisms, quantization-aware training, channel pruning, and adaptive normalization. By using these additions, global and local contextual data can be obtained at a relatively low computational cost. A large number of experiments were conducted on the built-in city driving dataset, aiming to improve its generalization ability under various lighting and weather conditions, and to enhance its capability to recognize fine-grained object boundaries and rare semantic categories. The system will run stably at a frame rate of over 30 FPS and can operate on both GPUs and embedded devices. Comprehensive performance evaluations indicate that it outperforms traditional CNNs and transformer-based baselines, with significant improvements in mean Intersection over Union (IoU) and boundary delineation. As shown above, scalable attention, controllable model compression, and system-level acceleration constitute the practical application of this framework in safety-critical environments. According to the above research, advanced vision transformers can be used for efficient and high-performance semantic segmentation in intelligent vehicles.
Copyright (c) 2026 Sophia Koch, Jakob Bauer, Jonas Fischer https://creativecommons.org/licenses/by-nc-sa/4.0
Automatic Scientific Literature Text Summarization Based on GPT-4
Using advanced transformer-based language models, automatic scientific document summarization has begun to address the information overload problem in the big data era. To summarize English and Chinese academic papers, this paper develops a complete system based on the GPT-4 model. Hierarchical tokenization, paragraph-aware encoding, and gated paragraph scoring are methods by which the new system effectively addresses the discourse and logical differences between different academic papers. Perform domain-adaptive masked language modeling using a two-step training strategy. The model is first trained on specialized terminology, and then fine-tuned with annotated full texts and summaries. Prompt engineering strategies can help create summaries and meet user needs. Many experiments were conducted on benchmark datasets such as arXiv, PubMed, and CSL, using a unified preprocessing pipeline and evaluation protocol. Based on the above results, the ROUGE and BERTScore metrics indicate an improvement in coverage and semantic accuracy. The system improves the accuracy and clarity of the summaries thru robust post-processing and entity normalization. Strict human evaluations also indicate that, compared to leading baseline models, there is an increase in the amount of information and unsupported content. Based on the above findings, the framework demonstrates strong generalization capabilities across many languages and scientific domains. Therefore, it is very suitable for large-scale, high-fidelity literature summarization and knowledge extraction.
Copyright (c) 2026 Rajesh Joshi, Manoj Iyer, Rakesh Verma, Chunbo Lin https://creativecommons.org/licenses/by-nc-sa/4.0
Parallax-Aware Urban Road Scene Segmentation Based on DeepLabv3+
In order to promote the development of intelligent transportation and autonomous driving systems, clear semantic segmentation is essential. Due to depth discontinuities and parallax artifacts from dynamic viewpoints, as well as spatial misalignment and segmentation errors, accurately analyzing complex urban scenes is very challenging. This paper proposes a complete urban road segmentation framework. By introducing a separate disparity removal module in DeepLabv3+, the issue of disparity effects is clearly addressed. Model and correct the geometric distortions caused by depth, and then perform multi-scale semantic feature fusion for detailed scene analysis. All standard public datasets contain different levels of annotation details, including various urban areas and environments. According to the experimental results, the proposed method significantly improves the mean Intersection over Union (IoU) and boundary accuracy under conditions of strong perspective distortion, occlusion, and multi-layer structures. These conditions are contrary to the current baseline model. The architecture is very reliable under various weather conditions and urban environments, and it can run in real-time on embedded systems. Some improvements have been made, but there are still issues under extremely low visibility conditions. In the future, by integrating multimodal sensors and enhancing data augmentation, it is expected to provide more solutions. This study proposes a disparity-aware segmentation method for urban road scenes, directly supporting smart mobility and smart infrastructure.
Copyright (c) 2026 Izabela Rutkowski https://creativecommons.org/licenses/by-nc-sa/4.0
Adaptive Nonlinear Model Predictive Control for Soft Robotics Applications
Computer-based adaptive control technology has made significant progress in the real-time management of complex nonlinear system dynamics and the inevitable uncertainties in soft robots. This paper proposes a method for achieving precise and reliable control of soft robotic systems Through the use of adaptive nonlinear model predictive control schemes. Develop a continuous-time dynamic model to identify changes in the core deformation drive state of a multi-link flexible arm; integrate online parameter estimation and model predictive control algorithms to counteract dynamic disturbances caused by environmental or model changes. Adaptive estimation continuously updates its own parameters based on sensor data to achieve an accurate predictive control system under various conditions or uncertainties. The experimental setup uses numerical simulations and a pneumatically driven soft robotic arm to validate the effectiveness of the proposed controller. Compared to traditional MPC and PID control methods, adaptive NMPC can improve trajectory tracking accuracy, anti-interference performance, and execution safety levels. This feature achieves high-reliability real-time computing performance and is suitable for embedded hardware devices. This study proposes a comprehensive approach to ensure the robustness, accuracy, and efficiency of soft robotic systems in resilient control under uncertain environments.
Copyright (c) 2026 Andrzej Zielinski, Dorota Urban https://creativecommons.org/licenses/by-nc-sa/4.0
Integration and Application of 5G Ultra-Reliable and Low-Latency Communications in Networked Control Systems for Intelligent Factories
Computer-based Network Control Systems (NCS) are key technologies for achieving smart factory operations. NCS can operate stable-state coordination, distributed monitoring, and automated control in an Industry 4.0 environment. This paper analyzes how 5G ultra-reliable low-latency communication technology (URLLC) can best be integrated into industrial application network control systems. To ensure response determinism in critical mission applications, a hierarchical system architecture is developed, integrating edge, fog, and cloud layers to achieve an enhanced 5G wireless network. Considering the overall model of protocol layer optimization for reliability and latency, dynamic resource management redundancy solutions are suitable for complex industrial environments. In terms of operational risk, conduct probabilistic risk assessments on various attack surface dimensions to determine the hazards. Experimental validation conducted Through scale simulators and hardware-in-the-loop testing platforms shows that the control accuracy and reliability of 5G URLLC significantly surpass those of existing systems. Implement multi-layer security protection, reliable network data security transmission methods, and systems that identify abnormal behaviors that may threaten operational continuity. The conclusion of this study is: 5G URLLC meets the stringent requirements of industrial automation and can serve as a long-term flexible foundation to drive smart manufacturing processes in digital and integrated cyber-physical systems (CPS).
Copyright (c) 2026 Jakub Grzegorz Zieliński, Damian Piotr Dąbrowski https://creativecommons.org/licenses/by-nc-sa/4.0
Automated Classification and Fiber Recovery Process Redesign for Waste Textiles Based on Computer Vision Sorting Algorithms
Due to the intensification of globalization in production and consumption, the continuous growth of post-industrial textile waste has become an environmental issue. To address the urgent need for efficient and accurate classification of used textiles, this paper will introduce a fully automated classification and fiber recovery system based on advanced computer vision technology. High-resolution color near-infrared (RGB-NIR) imaging, multi-stage feature extraction, and an embedded module classifier network optimized for large-scale industrial applications are components of the designed system. A rigorous experiment used a dataset containing over 19,000 textile samples, which included various types of fibers, to simulate real-world environmental pollution around the globe. Compared to traditional manual operations or mechanized processes, the intelligent sorting system has excelled with a classification accuracy of over 96% and a fiber recovery rate of over 80%. The volume of operations and the error rate of pollutants have significantly improved. The recycling process will become more efficient. This study provides an implementation method for an industrial framework, with broader application potential beyond textile materials. Thru this work, an effective technical system is established to promote the sustainable reuse value chain of low-carbon recycled resources and to advance the equation of green industrial standards.
Copyright (c) 2026 Bartosz Dabrowski https://creativecommons.org/licenses/by-nc-sa/4.0
Application of an Improved Kalman Filter in Non-Gaussian Noise Sensor Signal Acquisition
In many fields of modern engineering activities, such as industrial automation, robotics, and intelligent vehicles, the reliable acquisition of sensor signals is crucial. The traditional Kalman filter, with its strong computational power and mathematical rigor, is usually considered effective when the sensor noise is Gaussian distributed and has constant statistical properties. Compared to traditional state estimation methods, in real sensor environments, examples of non-Gaussian, heavy-tailed, and impulsive noise are often more prevalent. This paper introduces an improved Kalman filter for non-Gaussian noise environments commonly encountered in sensor network-based applications. Robust innovation domain processing and adaptive statistical transformation can simultaneously eliminate the impact of outliers while mitigating heavy-tailed interference. The evaluation results using synthetic and real datasets indicate that the modified method has higher estimation accuracy and lower sensitivity to non-Gaussian outliers. Compared to classical and more stable robust extensions, this system can achieve higher accuracy for instruments with stringent real-time requirements. It also has stronger adaptability (to noise variations). From a scientific and engineering perspective, the improved Kalman filter provides a solid foundation for the stable operation of sensors in uncertain and harsh environments, as well as for precise measurements in rich environments.
Copyright (c) 2026 Panagiotis Christodoulou, Anastasios Ioannidis, Stavros Tsiolis https://creativecommons.org/licenses/by-nc-sa/4.0
RoBERTa-Based Sentiment Mining for Engineering Technical Reports
Engineering technical reports document the operations and changes, providing a basis for decision-making. Due to the highly specialized nature of these documents, sentiment extraction becomes challenging because they contain complex jargon, formal structures, and the implicit nature of evaluative language. This paper uses a customized RoBERTa model to build a sentiment analysis system for engineering technical reports. These three methods are used to extract subtle evaluative expressions in technical papers. These methods include expanding domain-specific vocabularies, adaptive segmentation and hierarchical embeddings, as well as customized attention mechanisms. A multi-source engineering corpus containing over 30,000 real technical documents was evaluated, and detailed annotation and segmentation were performed. The proposed model significantly outperforms traditional baseline models on datasets with higher term density, achieving an average accuracy of 93% and a macro F1 score of over 0.81 in negative sentiment detection. Without domain vocabulary adaptation and structural encoding, the F1 score would drop by more than 10%. Targeted model adaptation can improve the accuracy of sentiment mining in the engineering field and support the implementation of more reliable safety management, compliance assessment, and other measures.
Copyright (c) 2026 Piotr Aleksander Kamiński, Natalia Joanna Dąbrowska, Anna Maria Nowicka, Natalia Woźniak https://creativecommons.org/licenses/by-nc-sa/4.0
Defect Recognition Method for Automated Optical Inspection Systems Based on Generative Adversarial Networks
Optical Inspection Automation (AOI) has already been able to support the construction of large-scale precision electronic assembly production lines and is now receiving increasing attention. Traditional AOI solutions cannot handle visual noise, large defects, or variations in small defect sizes; moreover, they cannot adapt to product miniaturization and increased process complexity. An efficient defect recognition system based on Generative Adversarial Networks (GANs) is used to improve industrial image resource limitations, reduce class distribution imbalance, and handle complex environmental conditions. To generate more realistic defect images and improve the quality of any area of interest (AOI) module, the system integrates a class-conditional GAN. To determine the robustness, accuracy, and precision of the system on these real industrial application datasets. Compared to state-of-the-art deep learning model baselines and traditional visual methods, this study is able to improve the recall rate of rare defects while reducing false positives. This design does not require additional funding and can evolve over time. The solution can be retrained in real-time, directly compatible with existing production IT systems, and provide data support for process management. The above results indicate that generative models are very useful in industrial inspection systems. The adaptation of the AOI method is being expanded to be applied to more advanced safety and intelligent processes over time.
Copyright (c) 2026 Michał Tomasz Dąbrowski https://creativecommons.org/licenses/by-nc-sa/4.0
Neural Radiance Fields (NeRF) for Dynamic Lighting Effects and Real-Time Rendering Optimization in Virtual Fashion Show Garment Design
Due to the presence of many physical fashion stores and virtual fashion exhibitions on the internet, virtual clothing research is becoming increasingly common. Rendering fabrics with photorealistic quality under dynamic lighting and its practical application remain the main obstacles that must be overcome. Based on the new technology of neural radiance fields, a method for real-time digital rendering of very delicate clothing is proposed. By encoding the characteristics of clothing materials through a special network structure, and then using dynamic lighting techniques to achieve precise rendering of appearance and fabric light response. A more optimized real-time rendering pipeline, using GPU acceleration and adaptive sampling techniques, ensures smooth operation of clothing in terms of image quality. Based on various environmental lighting and dynamic changes in similar clothing, as well as a complete dataset containing real and hypothetical clothing items. Compared to traditional physics-based rendering methods and previous neural rendering methods, it improves image clarity, structural consistency, and perceptual effects. Based on this paper, the framework can support interactive e-commerce, digital fashion design, and virtual fitting programs. In the future, various complex textiles and lighting variations will be added. Provide a reliable and scalable system design to achieve the visualization of high-quality digital garment images.
Copyright (c) 2026 Agnieszka Julia Jankowska, Monika Elżbieta Chmielewska https://creativecommons.org/licenses/by-nc-sa/4.0
Multi-Source Environmental Data Flood Forecasting System Based on ConvLSTM Networks
To lessen or avoid fatalities, injuries, and property damage brought on by heavy rainfall-induced floods, improve the precision and timeliness of flood early warning notifications. In this project, develop a sophisticated flood forecasting system using deep learning and a variety of environmental data. To investigate the upper reaches of the X River, a new system has been set up in collaboration with the departments of hydrology, meteorology, and remote sensing. For high-resolution, real-time flood forecasting, spatial-temporal features are learned using a ConvLSTM-Attention neural network. With an average Nash-Sutcliffe Efficiency of 0.84 and an average Root Mean Square Error (RMSE) of 0.18 meters, the system can outperform conventional hydrodynamic and other machine learning models, according on studies conducted over the last five years and 14 flood events. In certain locations, the model has been able to provide an early warning of peak flood more than four hours in advance, and it works well in all Basin areas and meteorological conditions. The aforementioned analysis indicates that the suggested course is workable and will be the foundation for developing a functional emergency response and flood early warning system.
Copyright (c) 2026 Adéla Svoboda https://creativecommons.org/licenses/by-nc-sa/4.0
Vision-Based Grasping for Industrial Robots Guided by Deep Q-Networks
This paper proposes a vision-to-grasp framework driven by a Deep Q-Network (DQN). The framework is designed for industrial robots in complex and dynamic factory environments. Multimodal visual perception, robust scene understanding, and adaptive deep reinforcement learning-based grasp planning are the three components of the new framework. The experiments were conducted in both simulated and real environments, using a UR5e robotic arm equipped with an RGB-D camera and a standard industrial gripper. In the 200,000 training steps in the simulation, the asymptotic grasping success rate of the Double DQN variant was 93.4%, which is 6.2 %age points higher than the standard DQN, and the reward variance was reduced by over 38%. Boltzmann's study achieved a success rate of 92.8% within 80,000 steps, far surpassing the noise network and ε-greedy methods. The framework achieved a success rate of over 90% for grasping both simple and complex objects on physical hardware, with success rates of 95.1%, 91.3%, and 87.6% for acrylic boxes and ceramic cups, respectively. In mixed and cluttered environments, the system achieved a total success rate of 92.5% over 300 rounds, with a relatively short grasp execution time of 2.7 to 3.7 seconds. Robustness tests indicate that it can generalize well to new objects, partial occlusions, and lighting changes. Failure analysis pointed out the main defects in spatial alignment and reflection. The above results indicate that DQN-based visual-guided grasping has been applied in practice and is feasible in industrial environments.
Copyright (c) 2026 Emilia Zdzisława Dąbrowska, Karolina Alicja Bąk, Paweł Jankowski, Aleksandra Król https://creativecommons.org/licenses/by-nc-sa/4.0
Application of Differential Privacy-Enhanced FedAvg Algorithm in Medical Sensor Data Processing
Federated learning is increasingly being used in computer-driven medical analysis to facilitate collaborative model training without sharing sensitive patient data. This paper discusses the privacy protection issues in distributed medical sensor networks and proposes a federated averaging algorithm that integrates adaptive differential privacy. The privacy budget can be dynamically allocated, distributing privacy resources most reasonably across rounds and devices based on the gradient norm and client participation. Experiments were conducted on the Intensive Care Unit (ICU) and benchmark Electrocardiogram (ECG) sensor datasets, simulating over a hundred heterogeneous and non-independent and identically distributed (non-IID) client scenarios. The above results indicate that under moderate privacy constraints, the proposed framework achieved a global accuracy of 89.7% on ECG data and 87.2% on ICU data, with macro F1 scores exceeding 0.92 and 0.90, respectively. Compared to fixed allocation, adaptive allocation of privacy budgets reduced the fairness gap per client by over 60%. Convergence remains stable, with only a 5% increase in computational cost. In the case of a privacy budget of 0.5, the attack success rate is close to the level of random guessing. The above results indicate that in federated learning, well-tuned differential privacy can provide robust privacy protection while maintaining high model utility. Therefore, this can be implemented in secure medical analysis across various regions of society.
Copyright (c) 2026 Michał Adam Kaczmarek https://creativecommons.org/licenses/by-nc-sa/4.0
GraphSAGE-Enhanced Security Authentication Protocol for Internet of Things(loT) Devices
A new model for device authentication in large-scale Internet of Things (IoT) systems has been proposed based on graph representation learning methods. In heterogeneous and resource-constrained IoT environments, the efficiency, flexibility, and security of authentication protocols are the three main topics investigated in this paper. Using GraphSAGE with context-aware feature aggregation and neighborhood encoding, dynamic device interactions are modeled as evolving graphs. The purpose of the ablation study is to build a full-climate experimental system to simulate and benchmark adversarial attacks on multiple networks. The results show that under minimal pressure, the success rate of impersonation attacks on this protocol is very low, at only 1%, and it consistently blocks over 97% of privilege escalation attempts. Authentication delay is stable; as the number of devices increases from 100 to 20,000, it increases sub-linearly, with the median delay rising from only 120 milliseconds to 310 milliseconds. Compared to the previous lightweight protocol, this protocol reduces both communication and computational costs by over 30%. Through ablation experiments, key design elements such as the context aggregation module have been shown to be crucial for system stability and error suppression. In summary, this paper provides an excellent high-scalability solution for trusted IoT authentication, offering quantitative security metrics and performance data across various deployment environments.
Copyright (c) 2026 Abdullah Al Nahyan, Saeed Al Maktoum, Ayesha Al Mansouri https://creativecommons.org/licenses/by-nc-sa/4.0
Robust Visual Product Matching in Intelligent Warehouses via Siamese Networks and Adaptive Data Augmentation
In order to meet the ever-changing demands and higher accuracy requirements of intelligent warehouse management systems, automatic product identification technology will be added. The lack of stable visual matching in large-scale logistics is the main issue this paper aims to address. Lighting, occlusion, or any changes to the product can reduce the stability of the solution. Propose an enhanced Siamese network structure while establishing an augmented data pipeline to simulate warehouse changes. Strict labeling and partitioning rules have already been adopted, and a large number of real warehouse images have been used for system validation. In conditions of poor lighting or partial occlusion, the average exceeds 91%, with inference speeds below 50 milliseconds on typical hardware. Category-level accuracy and overall robustness have significantly improved compared to traditional convolutional and metric learning baselines. Adaptive decision logic and context-aware network training can reduce failure rates and ensure stable operation of visually similar or label-ambiguous products. The aforementioned findings lay the foundation for a highly scalable platform for the next generation of warehouse automation. To scale, continuous learning and various data types will be added in the future.
Copyright (c) 2026 Mohamed Al Khalifa, Aziza Al Romaithi https://creativecommons.org/licenses/by-nc-sa/4.0
Real-Time Defect Detection in Intelligent Manufacturing Systems Based on YOLOv8
In high-end manufacturing, deep learning-based detection systems are gradually being used for automated quality control. In order to create a fast, real-time defect detection system, a robust backbone network and a powerful data preparation module were established in this study. The large intra-class variability, dynamic imaging environment, and strict requirements for real-time inference are serious issues faced by current work in industrial defect detection. Geometric transformations, photometric variations, and synthetic defect generation are types of data augmentation techniques that can enhance the model's generalization ability. To ensure stable convergence and avoid overfitting, the model training will use an adaptive learning rate scheduler, a composite loss function, and reasonable regularization methods. Architecture optimization: Multi-scale feature fusion and anchor-free detection heads improve the sensitivity to sparse surface defect recognition. Using the same method across multiple industries, each with its own products and defects. According to the experimental results, the average accuracy, recall rate, and processing speed of these two frameworks are all above average. The new pipeline runs stably on GPU servers and in edge cases. By using error and ablation analysis, the individual impact of the new components was revealed, providing a reference for future research. In summary, the aforementioned research indicates that the integrated framework can meet the high demands of large-scale automated quality inspection in modern manufacturing.
Copyright (c) 2026 Alessio Moretti, Luca Ferrari https://creativecommons.org/licenses/by-nc-sa/4.0
Real-Time Object Detection Deployment of YOLOv7-Tiny for Onboard UAV Applications
New intelligent and more reliable perception systems for autonomous unmanned aerial vehicles (UAVs) are being developed by computer vision technology for embedded systems in aerospace. This project will use an optimized YOLOv7-Tiny network on board a UAV to accomplish real-time detection while taking into account its computational and power limitations. Multi-objective channel pruning, sensitivity-aware mixed-precision quantization, and high-performance dataflow fusion for the model structure have all been studied concurrently to address the engineering challenges in small-scale model development. Numerous experiments have been carried out on a custom UAV testbed that is outfitted with an NVIDIA Jetson Xavier NX and a high-definition imaging system. The testbed has been placed in a variety of locations, including urban regions, agricultural zones, and low-visibility settings. The optimized model outperforms the unmodified YOLOv7-Tiny and other lightweight baselines, according to the results, with a mean Average Precision of 73.5% at 0.5 IoU. Jetson Xavier NX has an average inference speed of 41 frames per second and uses 32.5% less energy than the regular design. After network compression, ablation and robustness testing reveal that the detection accuracy remains over 94% in over 90% of real-world use situations. For real-time object detection on edge UAVs, the current work suggests a high-reliability and high-integrity deployment pipeline and confirms that it can satisfy the requirements of different missions in terms of model efficiency and detection accuracy.
Copyright (c) 2026 Giorgos Katsaros, Georgios Papadopoulos, Katerina Papageorgiou, Dimitris Nikolaidis https://creativecommons.org/licenses/by-nc-sa/4.0
Lightweight PhiNet-Based Neural Network for Image Classification on Edge Devices
Many AI applications still require substantial computation and memory, making edge computing unsuitable for resource-limited devices. Therefore, this paper will design a relatively lightweight image classification framework based on the enhanced PhiNet architecture. Dynamically select filters and perform hierarchical quantization to achieve the best balance between accuracy and efficiency. In order to evaluate the framework, the benchmark datasets ImageNet-1K and CIFAR-100 were selected using the aforementioned standard experimental procedures. The above results indicate that the model based on PhiNet achieved a Top-1 accuracy of 77.0% on ImageNet-1K and 85.1% on CIFAR-100, surpassing the corresponding Top-1 accuracies of MobileNetV3 and EfficientNet-Lite. On the NVIDIA Jetson Xavier NX platform, the average inference latency per image is 23.1 milliseconds, with a throughput of 42.4 frames per second. In addition, the average power consumption is 2.7 watts. When batch processing up to 32 photos, the memory usage remains below 95 MB. In addition, many tests in various real-world environments have shown good consistency and robustness. These environments include recognizing pedestrians in low light and recognizing license plates in motion. Increase hardware-aware optimizations so that the system can more easily adapt to various hardware. Finally, this paper demonstrates that the PhiNet-based solution achieves an ideal balance in terms of accuracy, efficiency, and robustness, making it suitable for practical applications in edge intelligence.
Copyright (c) 2026 Aleksandra Danuta Król https://creativecommons.org/licenses/by-nc-sa/4.0
A Security Analysis Method for Access Control Graphs Based on DeepWalk Node Embedding
In this article, we will discuss how large-scale dynamic information systems can detect access control anomalies. An automatic security analysis based on the DeepWalk node embedding framework is proposed for enterprise-level access control graphs. In the extraction of the graph structure, random walks map the relationships between explicit and implicit users, resources, and permissions into dense vectors. By using the aforementioned embeddings for unsupervised anomaly scoring and permission path risk assessment, the system can effectively identify configuration errors and suspicious access patterns. For the experiment, a large-scale dataset was obtained from multiple commercial cloud platforms in fields such as healthcare and banking, containing over 100,000 accounts and more than 1 million audit objects. Based on the above results, the three models for medical, financial, and cloud scenarios achieved AUC values of 0.96, 0.95, and 0.94, respectively. Compared to previous methods, the current detection time averages 2 hours, and the false positive rate has also been reduced by 60%. Ablation and cross-domain robustness studies indicate that the time module and embedding module are crucial for the accuracy and stability of system detection. It is an easy-to-understand real-time analysis method that can be used for enterprise security processes and SIEM platforms. In summary, the aforementioned framework enhances automated access governance and risk monitoring, providing an effective and intelligent foundation for large-scale permission management and policy compliance analysis in modern digital infrastructure.
Copyright (c) 2026 Cristian Radu, Elena Constantin, Maria Dragomir https://creativecommons.org/licenses/by-nc-sa/4.0
High-Resolution Multi-Level Feature Fusion Network for Robust Facial Landmark Detection
Facial keypoint detection is relatively easy, but not completely accurate. In practical applications, occlusion, pose variations, and image noise are obstacles to accuracy. To achieve more accurate and stable keypoint detection, this paper will address the aforementioned issues by constructing a high-resolution neural network. The network uses a multi-layer feature fusion structure to extract global facial features and local details at different scales. In order to balance the trade-off between overall smoothness and high-resolution accuracy in model optimization, a new combined loss function was adopted. It will be more robust to blurred and occluded samples. 300-W, COFW, and WFLW are public data experimental datasets. The normalized mean error (NME) on 300-W is 2.89, on COFW is 3.56, and on WFLW is 3.19, all three methods have set new records. Common interferences such as blur and noise have been better resisted. Ablation experiments demonstrated the fusion strategy and loss components. Qualitative visualizations and application cases indicate that the system can perform well in various environments and under different conditions. These two methods are both practical and reliable, and can be used in many fields, such as mobile health monitoring, biometric authentication, and human-computer interaction.
Copyright (c) 2026 Julia Magdalena Kozłowska, Dawid Jerzy Chmielewski, Patryk Waldemar Mazurek https://creativecommons.org/licenses/by-nc-sa/4.0
Dual-Domain Denoising Transformer for Precise Anti-Jamming Signal Reconstruction
In modern wireless communication, radar, and intelligent sensing, recovering signals exposed to complex and adaptive interference is an issue that needs to be addressed. This paper proposes a novel dual-domain denoising transformer structure (D-DDT) that can effectively integrate time-frequency domain information, thereby enhancing anti-jamming capabilities. Considering the urgent need for accurate signal recovery in dynamic electromagnetic fields, a dual-branch encoder path and adaptive cross-domain attention mechanism are introduced. Conducted on multiple datasets, including real-world and simulated interference environments, with rigorous testing using filter-based and learning-based algorithms. DDT enhances noise suppression effects, improving the signal-to-noise ratio (SNR) compared to other methods in the comparison. Bilateral domain representation and adaptive attention are two necessary conditions for achieving good stability. In practical application tests, it can effectively handle various resource levels under time constraints. As a new participant in the complex anti-jamming signal recovery benchmark test, it contributes to subsequent communication and detection capabilities. Future research will enhance the adaptability of interference models and the optimization of edge deployments.
Copyright (c) 2026 Raluca Preda, Bianca Ionescu, Simona Marin https://creativecommons.org/licenses/by-nc-sa/4.0
Adaptive Honeypot Deployment in Software-Defined Networks Based on Deep Q-Learning
The conventional deployment approach for static honeypots is no longer appropriate, and their detection performance and resource utilization have declined in modern network defense as software-defined networks (SDN) have progressively grown more complex and dynamic. In order to enable intelligent, context-aware cyber deception, this study presents an adaptive honeypot orchestration architecture that combines SDN programmability and deep Q-learning reinforcement learning. A deep Q-network agent dynamically adjusts decoy sites based on observed adversarial behavior and the real-time network status. The general form of the core methodology is a high-dimensional Markov decision process for honeypot deployment. The aforementioned technique can increase the average detection rate to 0.85 and improve it by almost 20% when compared to that attained by static and periodic techniques, according to numerous experiments conducted in the simulated SDN testbed. The false-positive rate is remains less than 4.3% in many assault scenarios, and the detection delay has been reduced by around 50%. According to the aforementioned data, the framework maintains a comparatively high detection rate as network size and traffic volume increase and is comparatively stable in the face of zero-day assaults. Deep reinforcement learning will therefore enhance the effectiveness and flexibility of SDN-based honeypot systems based on the aforementioned experiments. The design can facilitate the development of a high-performance autonomous and proactive network protection system, according to the research mentioned above.
Copyright (c) 2026 Aleksandar Popović, Jelena Simić https://creativecommons.org/licenses/by-nc-sa/4.0
Transformer-Based Security Anomaly Detection for Wireless Sensor Networks
Although Wireless Sensor Networks (WSNs) are already being used in the development of cyber-physical systems, they are susceptible to several security risks due to their open architecture and few resources. In order to handle high-dimensional time-series data and different kinds of assaults, this study introduces a security anomaly detection technique for WSNs based on Transformer architecture. Eleven different types of attacks and over 240,000 labeled time-series windows were created by merging real data from several sensors and simulated attack situations in multiple networks. Richer information can be extracted from sensor nodes and sequential data using a Multi-Head Self-Attention-based Model and Spatial-Temporal Feature Encoding. With an average area under the ROC curve (AUC) of 0.976, an F1-score of 0.942, and more than 93% recall on an unbalanced test set, the Transformer-based framework outperforms the basic model, Long Short-Term Memory network, and Isolation Forest, according to the experiment results. Additionally, the detection rate decreases by less than 7% under adversarial attacks or increased signal noise, and the system operates steadily in the presence of noise and variations in the environment or parameters. As a result, it is evident that the Transformer approach has improved detection stability and accuracy and is still viable for high-speed use in WSN defense. In order to enhance the security of wireless sensor networks for monitoring vital infrastructure and intelligent environments, this study suggests an effective and comprehensive solution.
Copyright (c) 2026 Sara Kovačević, Milena Đorđević, Dunja Gajić https://creativecommons.org/licenses/by-nc-sa/4.0
Real-Time Assembly Line Balancing Optimization Based on Deep Reinforcement Learning
The issue of real-time optimisation for assembly-line balancing in large-scale, high-throughput production can be effectively resolved with deep reinforcement learning. The goal of this research is to address the dynamic scheduling problem with varying station restrictions and a fluctuating workload. The concepts of work allocation and the actual restrictions of a system in discrete manufacturing are integrated in a comprehensive mathematical model. In this paper, a digital twin-driven simulation platform constructed from actual industrial data and factory deployment records was used to develop a graph-based neural network with prioritised experience replay. The method outperforms traditional integer linear programming, DQN, and metaheuristic baselines by up to 6.3% in terms of throughput and reduces load imbalance by over 28% under stress, according to experiments conducted on over 60,000 production cycles. It achieves a median task throughput of 0.942 and an average decision latency of 38ms. According to robustness study, the system will continue to function steadily with a throughput loss of less than 5% even in the event of large loads and equipment failures. The resources have been optimised and the response time to environmental changes has been improved. The technical viability of deep reinforcement learning for intelligent assembly line balance has been confirmed based on the aforementioned findings, and real-time, data-driven production optimisation support for industrial deployment is offered.
Copyright (c) 2026 Franciszka Joanna Truskolaska, Zuzanna Czesława Latocha https://creativecommons.org/licenses/by-nc-sa/4.0