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Reducing peak to average power ratio in optical NOMA based 5G system using advanced SLM method

Optical and Quantum Electronics 2024年第10期56卷 1-13页

作者： Kumar, Arun Chakravarty, Sumit Nanthaamornphong, Aziz Department of Electronics and Communication Engineering New Horizon College of Engineering Bengaluru India Department of Electrical Engineering and Computer Engineering Kennesaw State University KennesawGA United States College of Computing Prince of Songkla University Phuket Thailand

One of the most critical challenges in optical fiber communication systems using non-orthogonal multiple access (NOMA) techniques is reducing the peak-to-average power ratio (PAPR). Optical NOMA often leads to high PAPR, causing non-linear distortion and limiting the system’s gain. The advanced Selected Mapping (SLM) algorithm is introduced in this work to reduce the PAPR of the O-NOMA. In conventional SLM, multiple phase sequences are applied to the input data, which requires numerous Inverse Fast Fourier Transform, increasing computational complexity. The proposed approach simplifies this by selecting a central reference phase sequence, reducing the number of candidate sequences and the number of IFFT operations required. This reduction in complexity does not significantly compromise the PAPR reduction capability, making the method more efficient. The SLM algorithm addresses this issue by applying the phase rotation factor to the transmitted signals, redistributing the power, and reducing the peak levels. This article highlights the benefits and challenges of implementing SLM in optical NOMA systems, including its compatibility with existing NOMA schemes. Matlab 2016 analyzes parameters such as bit error rate (BER), PAPR, and out-of-band radiation. A 66% PAPR reduction can be obtained using the suggested SLM with various phase variables. Furthermore, there is a −2650 reduction in spectrum leakage. The simulation outcomes of the work reveal the potential of SLM to reduce PAPR and enhance the BER and PSD performance of optical NOMA compared with the existing algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： MATLAB

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Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks

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International Journal of Intelligent Networks 2024年第1期5卷 255-266页

作者： Ahmad, Rizwana Anwar, Dil Nashin Bany Salameh, Haythem Elgala, Hany Ayyash, Moussa Almajali, Sufyan El-Khazali, Reyad Electronic and Electrical Engineering Department University of Cambridge United Kingdom Electrical and Computer Engineering Department University at Albany-State University of New York United States Artificial Intelligence Research Center Al Ain University Al Ain United Arab Emirates Telecommunications Engineering Department Yarmouk University Irbid Jordan Department of Computing Information and Technology Chicago State University ChicagoIL United States Department of Computer Science Princess Sumaya University for Technology Amman Jordan Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates

Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy. © 2024 The Authors

关键词： Data transfer

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering Systems

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computer Modeling in engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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Intelligent Energy Management Strategies for Renewable Energy Sources 1

Intelligent Energy Management Strategies for Renewable Energ...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Patil, Sangita Deotale, Disha Mali, Ramesh MIT School of Computing MIT ADT University Department of Electrical & Electronics Engineering Pune412201 India MIT School of Computing MIT ADT University Department of Computer Science & Engineering Pune412201 India

ISBN: (纸本)9798350355611

There have been several developments in renewable resources, standby sources of energy, and storage technologies. Because renewable sources are inconsistent, the best method to ensure supply continuity is to combine them with traditional energy to serve mini-grids or the isolated loads that are not linked to the main grid. In hybrid power system, many different energy sources are blended. Every time, effective and efficient Energy Management Strategies (EMS) are necessary when more than a source of energy is employed to supply a given load. This strategy directs the energy flow via the supply chain. This is necessary not just for standalone hybrid systems, but for hybrid power system that are linked to the main network. The intelligent control of hybrid MG has been the subject of a large number of serious research projects. A comprehensive assessment of the approaches used by a number of writers in their research work on applying energy management approaches is conducted. Standalone hybrid energy systems and the hybrid systems those are linked to the electrical grids are examples of these solutions. The aim of this paper is to give summary of various energy management schemes utilized in sustainable hybrid systems. Brief study is conducted for a number of freestanding and grid-linked hybrid system designs in order to make sure and find that which energy management technique that works well and will be applied for one renewable source is not always the best for another. © 2025 IEEE.

关键词： Hybrid power

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Inverse kinematics solution for a six-degree-of-freedom upper limb rehabilitation robot using deep learning models

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Neural computing and Applications 2025年第18期37卷 12991-13009页

作者： Shah, Muhammad Faizan Khan, Naveed Ahmad Jamwal, Prashant K. Chetty, Girija Goecke, Roland Hussain, Shahid School of Information Technology & Systems University of Canberra Canberra Australia Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan School of Systems and Computing University of New South Wales Canberra Australia

The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there are no systematic approaches for solving the inverse kinematics problem. Furthermore, for some morphology of the upper limb rehabilitation robots, the inverse kinematics problem is particularly challenging to solve. Conventional methods to solve the inverse kinematics problem reported in the literature are computationally expensive. In the present work, we propose a deep learning-based model to acquire the joint angles for a given end-effector position. The proposed approach exhibits high efficacy in determining the joint angles for various target positions and can accurately predict the end-effector positions once trained, improving the ability of the upper limb rehabilitation robot to adapt to varying patient needs. Due to its improved capability and effectiveness to track positions, the proposed algorithm lays the foundation for the development of efficient controllers in future. © The Author(s) 2025.

关键词： Inverse kinematics

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Quantifying the Impact of Incentives on Service Availability at the Extreme Edge

Quantifying the Impact of Incentives on Service Availability...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Azmy, Sherif B. Allahham, M.H.D. Saria Zorba, Nizar Hassanein, Hossam S. Queen's University Department of Electrical and Computer Engineering KingstonON Canada Queen's University School of Computing KingstonON Canada Qatar University Electrical Engineering Department Doha Qatar

ISBN: (纸本)9798350351255

Edge computing seeks to optimize service provision over enterprise-owned infrastructure near the end-user at the network's edge. However, it misses out on the opportunity to utilize user-owned hardware at the extreme edge of the network as workers in a sharing economy. In this work, we build upon the existing Incentive Vacation Queueing (IVQ) model and develop the Virtual Kiosk Model (VKM) to analyze service availability and the dynamics of multiple workers' participation in the provision of a service on the extreme edge. We formulate an optimization problem to minimize total cost of incentive payments while maintaining service availability under temporal constraints. We propose the Model-based Incentive Strategy at the Edge (MISE) algorithm to iteratively adjust incentives in real-time. MISE is compared against traditional numerical optimizers and a baseline naive approach that greedily focuses on minimizing incentives. Our findings demonstrate that MISE ensures sustained service availability without overburdening the workers at a cost acceptable to the service provider, striking a crucial balance in the management of extreme edge computing resources. © 2024 IEEE.

关键词： Virtual corporation

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Hardware Acceleration for Fully Homomorphic Encryption Scheme Switching from CKKS to FHEW 58

Hardware Acceleration for Fully Homomorphic Encryption Schem...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Zhang, Kaiyuan Wang, Antian Parhi, Keshab K. Lao, Yingjie Tufts University Department of Electrical and Computing Engineering MedfordMA02155 United States Purdue University Fort Wayne Department of Electrical and Computer Engineering Fort WayneIN46805 United States University of Minnesota Department of Electrical and Computer Engineering MinneapolisMN55455 United States

ISBN: (纸本)9798350354058

Fully Homomorphic Encryption (FHE) presents a paradigm-shifting framework for performing computations on encrypted data, offering revolutionary implications for privacy-preserving technologies. This paper introduces a novel hardware implementation of scheme switching between two leading FHE schemes targeting different computational needs, i.e., arithmetic HE scheme CKKS, and Boolean HE scheme FHEW. The proposed architecture facilitates dynamic switching between the schemes with improved throughput and latency compared to the software baseline. The proposed architecture computation modules support scheme switching operations involving coefficient conversion, modular switching, and key switching. We also optimize the hardware designs for the pre-processing and post-processing blocks, involving key generation, encryption, and decryption. The effectiveness of our proposed design is verified on the Xilinx U280 Datacenter Acceleration FPGA. We demonstrate that the proposed scheme switching accelerator yields a 365× performance improvement over the software counterpart. © 2024 IEEE.

关键词： Privacy-preserving techniques

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A SPRINT-Based Single Photon Subtractor for PNS Attacks on QKD

A SPRINT-Based Single Photon Subtractor for PNS Attacks on Q...

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2024 Photonics North, PN 2024

作者： Pasharavesh, Abdolreza Bajcsy, Michal Institute for Quantum Computing University of Waterloo WaterlooON Canada Electrical and Computer Engineering Department University of Waterloo WaterlooON Canada

ISBN: (纸本)9798350366365

We present a photon subtraction scheme designed to deterministically extract single photons from multiphoton states within arbitrary input pulses of light using single-photon Raman interaction (SPRINT) [1]. The proposed system comprises two cascaded Lambda-type atoms with transitions selectively coupled to distinct modes of a single chiral waveguide. Through numerical simulations, we evaluate the device's performance in a potential application involving photon-number-splitting (PNS) attacks against quantum key distribution (QKD). © 2024 IEEE.

关键词： Quantum cryptography

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Invoking an Efficient Deep Learning Approach for Real-Time Detection of False Data Injection Attacks

Invoking an Efficient Deep Learning Approach for Real-Time D...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Bcgovic, Miroslav Ghrayeb, Ali Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States Hamad Bin Khalifa University Information and Computing Technology Division Doha Qatar University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States

ISBN: (纸本)9798331541125

With the increasing complexity of power systems and their vulnerability to cyber-attacks, robust detection mechanisms are crucial for ensuring the grid's transient and steady-state stability and resiliency. This study introduces an efficient deep learning framework to detect false data injection attacks (FDIAs) in power systems. Leveraging the MATPOWER environment, we simulate a realistic power grid scenario to generate datasets comprising normal operations and various FDIA scenarios. Utilizing advanced deep learning techniques, including convolutional neural networks enhanced with channel attention mechanisms and transformer encoders, our model processes sequential power system data to discern subtle attack signatures from normal fluctuations. The proposed methodology employs a stratified k-fold cross-validation approach on resampled datasets to address class imbalance, ensuring robust model evaluation. The model's performance is meticulously assessed using accuracy metrics and confusion matrices, demonstrating its capability to effectively identify and differentiate between normal and compromised states in power systems. Results on the IEEE 39-bus and 300-bus systems highlight that this approach significantly enhances detection capabilities, achieving high accuracy and practical detection times of less than 2 milliseconds for the IEEE 39-bus system, which addresses the real-time requirements. These promising results suggest that such models can be pivotal for preemptive security measures and real-time monitoring of power grids. © 2025 IEEE.

关键词： Convolutional neural networks

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