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Physical Layer Security in AmBC-NOMA Networks with Random Eavesdroppers

IEEE Internet of Things Journal 2025年

作者： Pei, Xinyue Wang, Xingwei Huang, Min Chen, Yingyang Li, Xiaofan Tsiftsis, Theodoros A. Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110819 China Jinan University College of Information Science and Technology Guangzhou510632 China Jinan University School of Intelligent Systems Science and Engineering Zhuhai519070 China University of Thessaly Department of Informatics Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China

In this work, we investigate the physical layer security (PLS) of ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) networks where non-colluding eavesdroppers (Eves) are randomly distributed. In the proposed system, a base station (BS) transmits a superimposed signal to a typical NOMA user pair, while a backscatter device (BD) simultaneously transmits its unique signal by reflecting and modulating the BS’s signal. Meanwhile, Eves passively attempt to wiretap the ongoing transmissions. Notably, the number and locations of Eves are unknown, posing a substantial security threat to the system. To address this challenge, the BS injects artificial noise (AN) to mislead the Eves, and a protected zone is employed to create an Eve-exclusion area around the BS. Theoretical expressions for outage probability (OP) and intercept probability (IP) are provided to evaluate the system’s reliability-security trade-off. Asymptotic behavior at high signal-to-noise ratio (SNR) is further explored, including the derivation of diversity orders for the OP. Numerical results validate the analytical findings through extensive simulations, demonstrating that both the AN injection and protected zone can effectively enhance PLS. Furthermore, analysis and insights of different key parameters, including transmit SNR, reflection efficiency at the BD, power allocation coefficient, power fraction allocated to desired signal, Eve-exclusion area radius, Eve distribution density, and backscattered AN cancellation efficiency, on OP and IP are also provided. © 2014 IEEE.

关键词： Signal to noise ratio

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Cloud Service Providers: Evaluating Scalability, Security, and Cost for Business Growth 15th

Cloud Service Providers: Evaluating Scalability, Security, a...

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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： Kumar, Naween Dash, Yajnaseni Abraham, Ajith Saini, Vaibhav School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810855

Cloud computing brought a radical change in information technology by enabling infinite growth in terms of scalability and flexibility and by making new technologies available and easily accessible. Although the identification of Cloud Service Providers (CSPs) and their services is straightforward, there are difficulties for clients when they attempt to traverse the immense market share of CSPs. This paper empowers users by abstracting the leading CSPs such as Amazon, Microsoft, and Google, by developing a comparative view under the influence of various service parameters. When the features and offerings of various cloud providers are known, enabling the business to determine its suitability for the cloud, informed decisions are made. Assessing the range of services that each vendor provides and classifying them based on the type of services offered can be either under the IaaS, PaaS, or SaaS. It emphasizes evaluating key aspects of versatility to accommodate the business expansion, highly secure protection that meets legislative requirements, and affordable rates for expenditures. Based on this analysis, the paper recommends that businesses should make the right choices to work with the right cloud partner so it should promote a lot of growth and development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloud Computing CSPs Expansion Flexibility Growth PaaS SaaS Security Service Delivery Models such as IaaS

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Enhancing Respiratory Monitoring by CNN Using Mel Frequency Cepstral Coefficients 15th

Enhancing Respiratory Monitoring by CNN Using Mel Frequency ...

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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： Dash, Yajnaseni Abraham, Ajith Gupta, Shivam Rathore, Shaurya Vardhan Patil, Harsh School of Artificial Intelligence Bennett University UP Greater Noida India School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810794

This study aims to provide a comprehensive analysis of the distribution of respiratory sounds using a large number of recordings. The data set that we used includes normal breathing sounds, wheezing sounds, and coughing sounds, all of which indicate a different breathing pattern. The method involved first removing audio data and making the data consistent, then extracting features using Mel Frequency Cepstral Coefficients (MFCC), Based on these features, breath sounds are classified using a convolutional neural networks (CNN) model. The CNN model has shown high accuracy in distinguishing all the various respiratory sounds, showing and evaluating that it could be useful in diagnosis. This work has demonstrated how well CNN can identify breath sounds and suggests ways to develop automatic, non-invasive respiratory monitoring tools to help in the field of respiratory problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional Neural Networks (CNNs) Deep learning Early identification Machine learning Medical imaging

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The Art of Perception: Cross-Modality Learning for Accurate Tumour Segmentation in Breast Imaging

The Art of Perception: Cross-Modality Learning for Accurate ...

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Women in Data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Mudassar Ali Iqra Mariam Kainat Wajahat Ghulam Murtaza Muhammad Awais Muhammad Ahsan Qureshi College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Electronics and Information Engineering Liaoning University of Technology Jinzhou China Liaquat university of medical and health science Jamshoro Pakistan Walton Institute South East Technological University Waterford Ireland Department of Computer Science and Information Technology Virtual University Lahore Pakistan Department of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Unlocking the potential of advanced imaging techniques, this study proposes an improved version of TransUNet, which includes a Multi-Scale Attention Module and Cross-Modality Adaptation, enabling the unleashing of advanced imaging for the segmentation of tumors in the breast. Our model is designed to cope with the challenges related to DBT, MRI, and ultrasound, capturing both fine and large-scale tumor features while generalizing seamlessly to multiple imaging modalities. The experimental results have shown that the performance of the proposed model outperforms all state-of-the-art methods with a Dice score of 65.50%, mIoU of 60.30%, and HD95 of 6.00 mm, while it still keeps computationally efficient: 25.40G FLOPs and 43.50M parameters, which makes it very promising for large-scale clinical deployment.

关键词： Image segmentation Adaptation models Ultrasonic imaging Accuracy Breast tumors Three-dimensional displays Magnetic resonance imaging Computational modeling Computational efficiency Biomedical imaging

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AI-Driven Framework for Multi-Service Multi-Modal Devices in NextG ORAN Systems

arXiv

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arXiv 2025年

作者： Gain, Mrityunjoy Kim, Kitae Raha, Avi Deb Adhikary, Apurba Saad, Walid Han, Zhu Hong, Choong Seon Department of Artificial Intelligence School of Computing Kyung Hee University Yongin17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Yongin17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Yongin-si17104 Korea Republic of Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States

Next-generation (NextG) networks will adopt cloud-based, disaggregated designs with open interfaces, thereby enabling tailored data-driven control. The open radio access network (ORAN) paradigm enhances RAN optimization by enabling intelligent and flexible network management. The ORAN Alliance standardizes open and virtualized architectures, supporting services such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (uRLLC), and massive machine-type communication (mMTC). Traditionally, eMBB, uRLLC, and mMTC services are tied to specific use equipment (UE) optimized for one service. NextG networks, however, aim to enable multiple services on a single device, crucial for applications like the metaverse. To fill this, in this paper, an artificial intelligence (AI)driven efficient RAN management framework is proposed. This framework introduces the concept of the multi-service-modal UE (MSMU) system, which allows a single UE to handle both eMBB and uRLLC services. The proposed framework integrates traffic demand prediction, route optimization, RAN slicing, service identification, and radio resource management under uncertainty. The challenge of dynamic environments in such a system is addressed by decomposing the optimization problem into long-term (L-SP) and short-term (S-SP) subproblems. Using a long short-term memory (LSTM) model, the proposed approach allows the prediction of eMBB and uRLLC traffic demands and optimal routes for RAN slicing in the L-SP. For the S-SP, another LSTM model is employed to handle real-time service type identification and resource management based on long-term predictions. To support continuous adaptation, continual learning is incorporated into the S-SP framework, allowing the model to learn new service types while retaining prior knowledge. Experimental results show that the proposed framework efficiently manages dual-mode UEs, achieving low mean square error for traffic demand (0.003), resource block prediction

关键词： Resource allocation

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Lyapunov-Based Adaptive Neural Network Optimized Backstepping Control of Uncertain Unmanned Fire Fighting Robot

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Chen, Jiannan Hua, Changchun Mu, Dianrui Sun, Fuchun Yanshan University School of Electrical Engineering Qinhuangdao 066000 China Tsinghua University Department of Computer Science and Technology Beijing 100084 China

This paper studies the Lyapunov-based adaptive neural network optimized tracking control problem for a class of unmanned fire fighting robots. Firstly, by reasonably simplifying the unmanned fire fighting robot (UFFR) and combining it with its actual working scene, a novel system model is created that takes into consideration both system uncertainties and external disturbances, including unknown friction factors and drag force. Then, the optimized tracking control scheme for the UFFR is devised by integrating both adaptive neural networks and the backstepping technique. The objective of introducing adaptive neural network technique is to overcome the challenge posed by solving the Hamilton-Jacobi-Bellman (HJB) equation. Based on Lyapunov stability theory, it is demonstrated that all signals in the closed-loop system are semi-globally ultimately bounded and the output variables follow the reference signals to the desired accuracy. In the end, to validate the effectiveness of our designed control scheme, numerical simulations and practical platform experiments have been conducted. © 2000-2011 IEEE.

关键词： backstepping control modeling neural networks optimized control Unmanned fire fighting robots

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning Algorithms

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Chinnasamy Patan Feridoz Khan C Sasikala Mavalluru Swathi M Prema Kumar Ajay Kumar Yadav Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of ECE Shri Vishnu Engineering College for Women (A) Bhimavaram Andhra Pradesh India Department of Computer Application United Institute of Management Prayagraj India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time.

关键词： Cloud computing Machine learning algorithms Automation Accuracy Linear regression Energy efficiency Virtual machines Time factors Industrial Internet of Things Smart manufacturing

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Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments

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IEEE Access 2025年 13卷 90362-90388页

作者： Dash, Ritesh Sinha, Anupa Jyotheeswara Reddy, K. Dhanamjayulu, C. Kamwa, Innocent Kalinga University Research Scholar Computer Science Engineering Raipur India Kalinga University Computer Science Engineering Raipur India REVA University School of Electrical and Electronics Engineering Bangalore India Vellore Institute of Technology School of Electrical Engineering Vellore 632014 India Laval University Department of Electrical Engineering and Computer Engineering Quebec G1V 0A6 Canada

The study address the challenge of forecasting per unit energy prices in a microgrid environment consisting of solar and hydro power resources under multi-seasonal *** deep learning techniques such as LSTM,GRU and ESN often struggle with non-linear dependencies and volatility in energy market. To overcome these we propose a hybrid framework incorporating Adiabatic Quantum Computing (AQC) for electricity price forecasting. The proposed AQC model encodes-32 system and market related variables into quantum states and applies adiabatic evolution to derive optimized price prediction. Simulation results using real microgrid data set-up based on HIL shows that AQC reduces forecasting error by 17.03% compared to LSTM, 14.29% to GRU and 13.88% to ESN over 24-hrs and 48-hrs horizons. The enhanced accuracy and robustness of the quantum assisted model demonstrates its potential for next generation energy market forecasting and decisions making *** entire framework is tested using a synthetic microgrid dataset designed to emulate real-world seasonal and operational dynamics. While this enables controlled validation of the models, the generalizability of the results to real world deployment requires further empirical evaluations on physical microgrid data set. © 2013 IEEE.

关键词： AQC Energy Trading ESN GRU LSTM Microgrid Spot Market

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Multi-modal Long-Short Distance Attention-based Transformer-GAN for PET Reconstruction with Auxiliary MRI

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zeng, Pinxian Zeng, Xinyi Wang, Yan Zhou, Luping Zu, Chen Wu, Xi Zhou, Jiliu Shen, Dinggang Sichuan University School of Computer Science China University of Sydney School of Electrical and Information Engineering Australia Department of Risk Controlling Research *** China Chengdu University of Information Technology School of Computer Science China ShanghaiTech University School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd Shanghai200232 China Shanghai Clinical Research and Trial Center Shanghai201210 China

To obtain high-quality PET scans while minimizing potential radiation hazards for patients, various GAN-based methods have been developed to reconstruct high-quality standard-count PET (SPET) images from low-count PET (LPET) ones. While recent efforts try to integrate MRI or CT to enhance reconstruction in a multi-modal way, current architectures mainly face two limitations: (1) CNN backbones or simple Transformer bottleneck layers are insufficient for robust semantic understanding, and (2) the identical strategies for multi-modal feature extraction and fusion overlook each modality's respective importance for the reconstruction task. In this work, we propose the Multi-modal Long-Short Distance Attention-based Transformer-GAN (MLSDA-GAN), a novel network combining 3D transformer and CNN architecture for PET image reconstruction. Specifically, to extract fine-grained features with a small number of parameters, our MLSDA-GAN integrates multi-scale convolution into the embedding part of the transformer. As for our multi-modal design, given the strong correlation between LPET and SPET in structural characteristics, we treat MRI as an auxiliary modality to LPET and achieve effective multi-modal extraction and fusion strategies. These strategies include (1) a PET-specific Self-attention Extraction (PSE) block for comprehensive feature extraction of the primary LPET and (2) a Multi-modality Cross-attention Fusion (MCF) block for effective multi-modal interaction and fusion, enabling us to more efficiently model both long- and short-range relationships in the corresponding feature extraction and fusion processes. Experiments demonstrate superiority of our method quantitatively and qualitatively. Code is available at https://***/Aru321/MLSDA-GAN. © 1991-2012 IEEE.

关键词： Radiation hazards

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Temporal Dependency Modeling in Air Quality Index Prediction Using LSTM Networks 15th

Temporal Dependency Modeling in Air Quality Index Prediction...

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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： Dash, Yajnaseni Abraham, Ajith Kumar, Naween Purohit, Gayatri Mittal, Arnav Bagla, Aarav School of Artificial Intelligence Bennett University UP Greater Noida India School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810824

The Air Quality Index (AQI) forecasting is a very significant area of research as it has an impact on worldwide ecosystems and human health. The close monitoring of AQI is necessary to develop different mitigation strategies. The present study investigated the efficacy of different techniques such as Linear Regression, Support Vector Regression, Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks to forecast AQI patterns. After modeling the historical AQI climate data it is found that LSTM models can accurately predict AQI as these models can capture temporal dependencies. As per this study, LSTM models can be used to make predictions and to support policies for tackling related issues of the dynamism of Climate science. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Climate science Deep learning Gradient Boosting Linear Regression Long Short-Term Memory (LSTM) networks Machine learning Random Forest Support Vector Regression Temporal Dependency

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