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Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks

IEEE Internet of Things Journal 2025年

作者： Yu, Hanzhi Liu, Yuchen Yang, Zhaohui Sun, Haijian Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT), which serves as a virtual representation of the physical network. The considered network includes a set of base stations (BSs) that must allocate its limited spectrum resources to serve a set of users while also transmitting its partially observed physical network information to a cloud server to generate the DNT. Since the DNT can predict the physical network status based on its historical status, the BSs may not need to send their physical network information at each time slot, allowing them to conserve spectrum resources to serve the users. However, if the DNT does not receive the physical network information of the BSs over a large time period, the DNT's accuracy in representing the physical network may degrade. To this end, each BS must decide when to send the physical network information to the cloud server to update the DNT, while also determining the spectrum resource allocation policy for both DNT synchronization and serving the users. We formulate this resource allocation task as an optimization problem, aiming to maximize the total data rate of all users while minimizing the asynchronization between the physical network and the DNT. The formulated problem is challenging to solve by traditional optimization methods, as each BS can only observe a partial physical network, making it difficult to find an optimal spectrum allocation strategy for the entire network. To address this problem, we propose a method based on the gated recurrent units (GRUs) and the value decomposition network (VDN). The GRU component allows the DNT to predict future status using the historical data, effectively updating itself when the BSs do not transmit the physical network information. The VDN algorithm enables each BS to learn the relationship between its local observation and the team reward of all BSs, allowing it to collaborate with others in determining whe

关键词： Resource allocation

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Enhancing Histopathological Image Analysis: A Study on Effect of Color Normalization and Activation Functions 1st

Enhancing Histopathological Image Analysis: A Study on Effe...

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1st International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Sudhamsh, G.V.S. Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031714832

Histopathological images have significant potential for disease diagnosis and prognosis, but their inherent color variations can impede accurate analysis and reliable model performance. Color variations in histopathological images can indeed be a major challenge for accurate diagnosis and reliable model performance. This study investigates the critical role of color normalization in mitigating these inconsistencies, improving feature extraction, the model’s generalizability, and reducing staining bias. More this study examines the impact of activation functions, essential components in deep learning architectures for histopathological image analysis. Selecting the optimal function is crucial for optimizing the model performance. By emphasizing the importance of color normalization and careful activation function selection, this study lays the groundwork for more robust and reliable analysis of histopathological images, ultimately leading to improved disease diagnosis. In the present work, different segmentation architectures are analyzed with and without color normalization and with different activation functions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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GPU-accelerated Evolutionary Many-objective Optimization Using Tensorized NSGA-III

arXiv

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arXiv 2025年

作者： Li, Hao Liang, Zhenyu Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

NSGA-III is one of the most widely adopted algorithms for tackling many-objective optimization problems. However, its CPU-based design severely limits scalability and computational efficiency. To address the limitations, we propose TensorNSGA-III, a fully tensorized implementation of NSGA-III that leverages GPU parallelism for large-scale many-objective optimization. Unlike conventional GPU-accelerated evolutionary algorithms that rely on heuristic approximations to improve efficiency, TensorNSGA-III maintains the exact selection and variation mechanisms of NSGA-III while achieving significant acceleration. By reformulating the selection process with tensorized data structures and an optimized caching strategy, our approach effectively eliminates computational bottlenecks inherent in traditional CPU-based and naïve GPU implementations. Experimental results on widely used numerical benchmarks show that TensorNSGA-III achieves speedups of up to 3629× over the CPU version of NSGA-III. Additionally, we validate its effectiveness in multiobjective robotic control tasks, where it discovers diverse and high-quality behavioral solutions. Furthermore, we investigate the critical role of large population sizes in many-objective optimization and demonstrate the scalability of TensorNSGA-III in such scenarios. The source code is available at https://***/EMI-Group/evomo. Copyright © 2025, The Authors. All rights reserved.

关键词： Cellular automata

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A Modified U-Net for Semantic Segmentation of Liver and Liver Tumors From CT Scans 1st

A Modified U-Net for Semantic Segmentation of Liver and Li...

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1st International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031714832

Medical image analysis algorithms have been quite popular for automating the segmentation of the liver and liver tumours in recent years. A system like this would also lessen radiologists’ workload and subjective interpretations. Deep learning models are commonly used in this field due to their performance improvement over conventional methods of medical image processing. However, deep learning models’ capacity for class imbalance adaptation is rarely studied, particularly in the instance of segmenting liver tumours given their tiny size. Therefore, in the current study, a straightforward Convolutional Neural Network (CNN) model based on the well-known U-Net architecture is suggested in order to capture more contextual information and solve the class imbalance issue. Additionally, a combination of the loss function is intended to guarantee precise segmentation of liver tumours. A detailed qualitative and quantitative analysis is carried out on LiTs dataset to validate the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer Aided Diagnostic Systems CT scan Deep Learning Liver Tumor Semantic Segmentation

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EvoGP: A GPU-accelerated Framework for Tree-based Genetic Programming

arXiv

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arXiv 2025年

作者： Wang, Lishuang Wu, Zhihong Sun, Kebin Li, Zhuozhao Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

Tree-based Genetic Programming (TGP) is a key evolutionary algorithm widely used in symbolic regression, feature engineering, and scientific modeling. Its high computational demands make GPU acceleration essential for scalable and high-performance evolutionary computation. However, GPU acceleration of TGP faces three key challenges: inefficient tree encoding, highly heterogeneous genetic operations, and limited parallelism in fitness evaluation. To address these challenges, we introduce EvoGP, a comprehensive GPU-accelerated TGP framework. First, we design a tensorized encoding scheme to represent tree with different structures as tensors with the same shape, optimizing memory access and enabling efficient parallel execution. Second, we propose a unified parallel framework for genetic operations by leveraging shared computational primitives and implementing dedicated CUDA kernels for scalable performance. Third, we present a fully parallel fitness evaluation strategy for symbolic regression, exploiting both population-level and data-level parallelism to maximize GPU utilization. Moreover, we implement a comprehensive library to provide rich algorithm operators and benchmark problems. EvoGP is extensively tested on various tasks, including symbolic regression, classification, and robotics control, demonstrating its versatility and effectiveness across diverse application scenarios. Experimental results show that EvoGP achieves up to a 140.89× speedup over the state-of-the-art GPU-based TGP implementation, while maintaining or exceeding the accuracy of baseline methods. EvoGP is open-source and accessible at: https://***/EMI-Group/evogp. Copyright © 2025, The Authors. All rights reserved.

关键词： Tensors

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Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization

arXiv

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arXiv 2025年

作者： Liang, Zhenyu Li, Hao Yu, Naiwei Sun, Kebin Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

Evolutionary multiobjective optimization (EMO) has made significant strides over the past two decades. However, as problem scales and complexities increase, traditional EMO algorithms face substantial performance limitations due to insufficient parallelism and scalability. While most work has focused on algorithm design to address these challenges, little attention has been given to hardware acceleration, thereby leaving a clear gap between EMO algorithms and advanced computing devices, such as GPUs. To bridge the gap, we propose to parallelize EMO algorithms on GPUs via the tensorization methodology. By employing tensorization, the data structures and operations of EMO algorithms are transformed into concise tensor representations, which seamlessly enables automatic utilization of GPU computing. We demonstrate the effectiveness of our approach by applying it to three representative EMO algorithms: NSGA-III, MOEA/D, and HypE. To comprehensively assess our methodology, we introduce a multiobjective robot control benchmark using a GPU-accelerated physics engine. Our experiments show that the tensorized EMO algorithms achieve speedups of up to 1113× compared to their CPU-based counterparts, while maintaining solution quality and effectively scaling population sizes to hundreds of thousands. Furthermore, the tensorized EMO algorithms efficiently tackle complex multiobjective robot control tasks, producing high-quality solutions with diverse behaviors. Source codes are available at https://***/EMI-Group/evomo. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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MetaDE: Evolving Differential Evolution by Differential Evolution

arXiv

引用

arXiv 2025年

作者： Chen, Minyang Feng, Chenchen Cheng, Ran Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Data Science and Artificial Intelligence the Department of Computing The Hong Kong Polytechnic University Hong Kong

As a cornerstone in the Evolutionary Computation (EC) domain, Differential Evolution (DE) is known for its simplicity and effectiveness in handling challenging black-box optimization problems. While the advantages of DE are well-recognized, achieving peak performance heavily depends on its hyperparameters such as the mutation factor, crossover probability, and the selection of specific DE strategies. Traditional approaches to this hyperparameter dilemma have leaned towards parameter tuning or adaptive mechanisms. However, identifying the optimal settings tailored for specific problems remains a persistent challenge. In response, we introduce MetaDE, an approach that evolves DE’s intrinsic hyperparameters and strategies using DE itself at a meta-level. A pivotal aspect of MetaDE is a specialized parameterization technique, which endows it with the capability to dynamically modify DE’s parameters and strategies throughout the evolutionary process. To augment computational efficiency, MetaDE incorporates a design that leverages parallel processing through a GPU-accelerated computing framework. Within such a framework, DE is not just a solver but also an optimizer for its own configurations, thus streamlining the process of hyperparameter optimization and problem-solving into a cohesive and automated workflow. Extensive evaluations on the CEC2022 benchmark suite demonstrate MetaDE’s promising performance. Moreover, when applied to robot control via evolutionary reinforcement learning, MetaDE also demonstrates promising performance. The source code of MetaDE is publicly accessible at: https://***/EMI-Group/metade. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Smoke Level Monitoring and Alerting in Automobiles using IoT

Smoke Level Monitoring and Alerting in Automobiles using IoT

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： M. Vani Pujitha Teja Aravind Kumar Rayalavarapu Lal Basha Shaik Sashank Madhabushi Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India Dept. of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Vehicle emissions significantly exacerbate air pollution, releasing harmful gases such as carbon monoxide (CO), nitrogen dioxide (NO 2 ), and hydrocarbons (HC) from poorly maintained engines and inefficient combustion processes. The study presents an Internet of Things (IoT)-based system to monitor the emissions, which mainly includes key pollutants like CO, NO 2 , and HC in real-time. Strategically positioned gas sensors near the vehicle's exhaust capture emission data, which an Arduino microcontroller processes with high accuracy. The system delivers continuous gas level updates via an LCD display and leverages an IoT framework to ensure seamless integration of sensors, microcontrollers, and mobile devices. This connectivity enables real-time data transmission and remote alerting, enhancing emission management. When pollutant levels surpass predefined safety thresholds, the system triggers immediate alerts through a Global System for Mobile Communications (GSM) module (SIM 900) via SMS to the vehicle owner's mobile, complemented by an audible buzzer. This alert notification encourages timely maintenance, reducing emissions and improving vehicle performance and contributing to cleaner air.

关键词： GSM Microcontrollers Real-time systems Sensor systems Mobile handsets Sensors Safety Nitrogen Gas detectors Monitoring

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Effective Early Hypothyroid Disease Prediction Using Traditional and Ensemble Machine Learning Algorithms 15th

Effective Early Hypothyroid Disease Prediction Using Traditi...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Mridul, Aunik Hasan Ahsan, Nowreen Saleh, Md. Abu Asif, Asifuzzaman Afrose, Sonia Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Data Science Clarkson University New York United States Computer Science and Engineering Lovely Professional University Punjab India Nutrition and Food Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783031810794

In recent years, there has been a noticeable rise in the prevalence of physical ailments, most notably hypothyroidism, a condition that has garnered substantial attention due to its substantial impact on a significant portion of the population. To gain a comprehensive understanding of the disease’s severity, distinguishing between standard and affected diagnostic reports is imperative. In this work, we suggest using algorithmic models to facilitate early identification and increase knowledge of possible health hazards linked to hypothyroidism. Our approach is straightforward and well-suited for the prediction of uncomplicated cases of hypothyroid illness in real-world scenarios. Our dataset, sourced from Kaggle, served as the foundation for our research, which involved the utilization of a variety of classifiers, including RF, LR, GB, KN, ABC, DT, GS, and ensemble techniques. The DT classifier proved to be the most accurate, with an astounding accuracy rate of 98.278%. The findings were quite successful. In addition, the accuracy of the Voting Classifier RDGL was 97.218%, while the Stacking Classifier RDAS demonstrated an accuracy of 97.483%. Our optimization efforts, which included hyperparameter tuning, further enhanced the performance of each classifier. Based on extensive experimentation and a review of contemporary research, our findings unequivocally endorse the Decision Tree (DT) boosting classifier as exceptionally proficient, demonstrating a remarkable accuracy rate of 98.278% in the precise prediction of hypothyroid disease. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Bagging Boosting Ensemble Hypothyroidism

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Reliable Vertical Ground Reaction Force Estimation with Smart Insole During Walking

arXiv

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arXiv 2025年

作者： Olugbon, Femi Ghoreishi, Nozhan Huang, Ming-Chun Xu, Wenyao Chen, Diliang Department of Electrical and Computer Engineering University of New Hampshire DurhamNH03824 United States Department of Data and Computational Science Duke Kunshan University Suzhou China Department of Computer Science & Engineering University at Buffalo The State University of New York BuffaloNY14260 United States

The vertical ground reaction force (vGRF) and its characteristic weight acceptance and push-off peaks measured during walking are important for gait and biomechanical analysis. Current wearable vGRF estimation methods suffer from drifting errors or low generalization performances, limiting their practical application. This paper proposes a novel method for reliably estimating vGRF and its characteristic peaks using data collected from the smart insole, including inertial measurement unit data and the newly introduced center of the pressed sensor data. These data were fused with machine learning algorithms including artificial neural networks, random forest regression, and bi-directional long-short-term memory. The proposed method outperformed the state-of-the-art methods with the root mean squared error, normalized root mean squared error, and correlation coefficient of 0.024 body weight (BW), 1.79% BW, and 0.997 in intra-participant testing, and 0.044 BW, 3.22% BW, and 0.991 in inter-participant testing, respectively. The difference between the reference and estimated weight acceptance and push-off peak values are 0.022 BW and 0.017 BW with a delay of 1.4% and 1.8% of the gait cycle for the intra-participant testing and 0.044 BW and 0.025 BW with a delay of 1.5% and 2.3% of the gait cycle for the inter-participant testing. The results indicate that the proposed vGRF estimation method has the potential to achieve accurate vGRF measurement during walking in free-living environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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