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High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

Tsinghua science and Technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

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Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising

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computers, Materials & Continua 2023年第5期75卷 4535-4552页

作者： Anoud A.Mahmoud Hanaa A.Sayed Sara S.Mohamed Department of Mathematics and Computer Science Faculty of ScienceNew Valley UniversityNew Valley72511Egypt Department of Computer Science College of Computer Science and Engineering Taibah UniversityMedina42221KSA Department of Mathematics Faculty of ScienceAssiut UniversityAssiut71516Egypt

Computed Tomography(CT)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are *** the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients;on the other hand,decreasing it by using a Low-Dose CT(LDCT)image may cause more noise and increased artifacts,which can compromise the ***,image reconstruction from LDCT image data is necessary to improve radiologists’judgment and *** study proposed three novel models for denoising LDCT images based on Wasserstein Generative Adversarial Network(WGAN).They were incorporated with different loss functions,including Visual Geometry Group(VGG),Structural Similarity Loss(SSIM),and Structurally Sensitive Loss(SSL),to reduce noise and preserve important information on LDCT images and investigate the effect of different types of loss ***,experiments have been conducted on the Graphical Processing Unit(GPU)and Central Processing Unit(CPU)to compare the performance of the proposed *** results demonstrated that images from the proposed WGAN-SSIM,WGAN-VGG-SSIM,and WGAN-VGG-SSL were denoised better than those from state-of-the-art models(WGAN,WGAN-VGG,and SMGAN)and converged to a stable equilibrium compared with WGAN and *** proposed models are effective in reducing noise,suppressing artifacts,and maintaining informative structure and texture details,especially WGAN-VGG-SSL which achieved a high peak-signalto-noise ratio(PNSR)on both GPU(26.1336)and CPU(25.8270).The average accuracy of WGAN-VGG-SSL outperformed that of the state-ofthe-art methods by 1 *** prove that theWGAN-VGG-SSL is more stable than the other models on both GPU and CPU.

关键词： Machine learning deep learning image denoising low dose CT loss function

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Enhanced Physical Layer Security for Full-Duplex Symbiotic Radio with AN Generation and Forward Noise Suppression

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IEEE Transactions on communications 2024年第7期72卷 3905-3918页

作者： Jin, Chi Chang, Zheng Hu, Fengye Chen, Hsiao-Hwa Hamalainen, Timo University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Jilin University College of Communication Engineering Changchun130012 China National Cheng Kung University Department of Engineering Science Tainan70101 Taiwan

Due to the constraints on power supply and limited encryption capability, data security based on physical layer security (PLS) techniques in backscatter communications has attracted a lot of attention. In this work, we propose to enhance PLS in a full-duplex symbiotic radio (FDSR) system with a proactive eavesdropper, which may overhear the information and interfere legitimate communications simultaneously by emitting attack signals. To deal with the eavesdroppers, we propose a security strategy based on pseudo-decoding and artificial noise (AN) injection to ensure the performance of legitimate communications through forward noise suppression. A novel AN signal generation scheme is proposed using a pseudo-decoding method, where AN signal is superimposed on data signal to safeguard the legitimate channel. The phase control in the forward noise suppression scheme and the power allocation between AN and data signals are optimized to maximize security throughput. The formulated problem can be solved via problem decomposition and alternate optimization algorithms. Simulation results demonstrate the superiority of the proposed scheme in terms of security throughput and attack mitigation performance. 0090-6778 © 2024 IEEE.

关键词： Backscattering

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Blockchain Integration in IoT:Applications,Opportunities,and Challenges

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computers, Materials & Continua 2025年第5期83卷 1561-1605页

作者： Mozhgan Gholami Ali Ghaffari Nahideh Derakhshanfard Nadir iBRAHIMO GLU Ali Asghar Pourhaji Kazem Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabriz***Iran Department of Computer Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbul34396Türkiye Department of Computer Science Khazar UniversityBakuAZ1096Azerbaijan MSDC Department Huawei R&D Center˙Istanbul34768Türkiye

The Internet has been enhanced recently by blockchain and Internet of Things(IoT)*** Internet of Things is a network of various sensor-equipped *** gradually integrates the Internet,sensors,and cloud *** is based on encryption algorithms,which are shared database technologies on the *** technology has grown significantly because of its features,such as flexibility,support for integration,anonymity,decentralization,and independent *** nodes in the blockchain network are used to verify online ***,this integration creates scalability,interoperability,and security *** the last decade,several advancements in blockchain technology have drawn attention fromresearch communities and *** technology helps IoT networks become more reliable and enhance security and *** also removes single points of failure and lowers the *** recent years,there has been an increasing amount of literature on IoT and blockchain technology *** paper extensively examines the current state of blockchain technologies,focusing specifically on their integration into the Internet of ***,it highlights the benefits,drawbacks,and opportunities of recent studies on security issues based on blockchain solutions into *** survey examined various research papers fromdifferent types of ***,a review of the other IoT applications has been included,focusing on the security requirements and challenges in IoT-based *** research directions are gathered for the effective integration of Blockchain and IoT.

关键词： Internet ofThings blockchain cybersecurity privacy security

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Securing IoT Systems with AI-Infused Software and Virtual Replica Models 2

Securing IoT Systems with AI-Infused Software and Virtual Re...

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2nd IEEE International Conference on Integrated Intelligence and communication Systems, ICIICS 2024

作者： Supriya, K. Sangeetha Jeno Lovesum, S.P. Arora, Renuka Bhatia, Rishu Yadwad, Sunita Natrayan, L. Department of Computer Science & Engineering Christ University Bengaluru India Department of Computer Science & Engineering Jagannath University NCR Bahadurgarh India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Jhajjar India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9798331504960

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges due to the increased attack surface and the inherent vulnerabilities of interconnected systems. This paper proposes a novel security framework that integrates Artificial Intelligence (AI)-infused software and Virtual Replica Models, or Digital Twins, to enhance the robustness of IoT systems. The framework leverages AI-driven anomaly detection and predictive analysis to identify potential threats in real-time, while Digital Twins provide a virtual replica of the IoT ecosystem for continuous monitoring and simulation-based threat assessment. By fusing these technologies, the proposed solution enables proactive security measures, ensuring rapid response to cyberattacks and continuous system optimization. Extensive simulations and experimental results demonstrate the system’s ability to reduce attack detection times by 30%, while improving overall system resilience and minimizing false positive rates. This approach provides a scalable and adaptable solution for securing IoT infrastructures in various industries. © 2024 IEEE.

关键词： Risk assessment

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Image Restoration and Enhancement of Retinal images Using Multi-Resolution Dual CNN

Image Restoration and Enhancement of Retinal images Using Mu...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Tojo, Laya Devi, Manju Sujith, Annie The Oxford College of Engineering Department of Electronics & Communication Communication Bengaluru India Jyothy institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331540067

In this paper we propose a Multi-resolution Dual Convolutional Neural Network (MR-DCNN) which is used to denoise the images. By incorporating sparse mechanism in dual networks, implementing a 2-tier architecture and by downscaling the width and height of CNN layers, we could reduce the computational complexity and achieve better performance and speed. The observed values prove that the novel MR-DCNN method can upgrade the visual quality without even compromising on PSNR, SSIM and MSE values. The suggested MR DCNN method outperforms numerous modern image enhancing techniques, based on objective and subjective results. Later, we propose the MR-DCNN method to boost the visual quality of low contrast retinal images. The MR-DCNN incorporated helps to enhance the visual quality by denoising the images and also removing fading effects. This novel formulation attains further improvement than existing schemes, and proves significant development in PSNR, SSIM and MSE values and visual quality perception. The presented scheme enhances the retinal image quality by improving the clarity. The MR-DCNN approach could deliver ophthalmologists with novel strategies to rectify retinal diseases from fundus images. This will lessen the amount of manual grading required, which will assist to cut down on pointless, expensive referrals. © 2024 IEEE.

关键词： Convolutional neural networks

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Incremental Least-Recently-Used Algorithm: Good, Robust, and Predictable Performance

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6658-6672页

作者： Zhang, Jinbei Chen, Chunpeng Cai, Kechao Lui, John C.S Sun Yat-sen University School of Electronic and Communication Engineering Shenzhen China The Chinese University of Hong Kong Department of Computer Science & Engineering N.T. Shatin Hong Kong

This paper proposes a replacement algorithm for file caching in mobile edge computing (MEC) networks. While there are numerous schemes for file replacement, it remains a challenge to achieve good, robust, and predictable performance simultaneously. To address this challenge, we introduce a general scheme called Incremental Least-Recently-Used (iLRU), which builds on the classic Least-Recently-Used (LRU) algorithm. iLRU initially caches only a "portion" of the file upon the first request and incrementally caches more when there are more requests for the file. In this regard, the request frequency can be inferred from the cached size without incurring additional overhead, where a larger cached size represents a higher request frequency. We derive the theoretical hit ratio of iLRU based on the Time-to-Live (TTL) analysis. With the Time-to-Live (TTL) analysis, we can theoretically derive the hit ratio and properties of iLRU and notably show that iLRU allocates more cache space to popular files, resulting in a higher hit ratio than LRU. Simulation results demonstrate the superior performance of iLRU and validate the accuracy of the theoretical hit ratio. Furthermore, we conduct simulations over various real-world traces to show that iLRU outperforms existing schemes across various real-world traces, defenestrating the robustness of iLRU. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Fine Tuned DBN Model for Food Ingredient Recognition: Introduction to Self-improved Tasmanian Devil Optimization Algorithm

Fine Tuned DBN Model for Food Ingredient Recognition: Introd...

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2023 IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2023

作者： Madival, Sharanabasappa A. Jawaligi, Shivkumar S. Sharnbasva University Faculty of Engineering & Technology Department of Computer Science and Engineering Kalburgi India Sharnbasva University Faculty of Engineering & Technology Department of Electronic and Communication Engineering Kalburgi India

ISBN: (纸本)9798350398465

Food recognition has captivated much significance for health-related appliances. The traditional schemes mainly concern on the classification of food depending on its names and disregards the basic ingredient compositions. However, owing to shortage of datasets with labels of ingredient, the issues on food ingredient recognition (FIR) is often unnoticed. This work develops a new FIR approach, where, textual features, SIFT and deep features are derived. Then, the DBN classifier is deployed for the recognition of food ingredients. To get a precise recognition, tuning the weights of DBN is done via Improved TDO (ITDO) model. © 2023 IEEE.

关键词： FIR filters

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Virtual Trial Room for Online Shopping 10

Virtual Trial Room for Online Shopping

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Kavin, A.M. Jithesh, T. Kalaiarasi, G. Selvi, M. Yogitha, R. Suresh Babu, T.N. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Melmaruvathur India

ISBN: (纸本)9798350353068

Virtual Trial Room - An Innovative Solution It provides a customized and immersive shopping experience those who save online have to attempt. With improvement Popularity of e-commerce, consumer base the problem is that you cannot get dressed in advance it is smooth to shop for Allows builders to test with this problem simply Clothing within the virtual *** normally takes a lot of time to try on things in a store. However, in situations like Online Shopping, it might not even be able to try on clothing. The popularity of online purchasing has greatly increased nowadays especially after pandemic breakdowns. When buying things like clothing, it's important to understand how the clothing will look on the buyer. This is the main cause of the decline in online clothing ***, a virtual trial room for online shopping would be a tremendous luxury for online retailers that could offer a large selection to users. This would allow people to see how clothes fit them individually. This would be a fantastic tool for Online shopping marketers to expand their market. By establishing a virtual trial room setting, we hope to improve accessibility and time efficiency for trying on clothing. In this project, we provide an Open-CV-based virtual trial room application for online shopping. The Virtual trial Room is an innovative solution that offers a personalized and immersive shopping experience to online shoppers. With the increasing popularity of e-commerce, customers face the challenge of not being able to try on clothes before purchasing them. The Virtual trial Room addresses this issue by allowing customers to virtually try on clothes in a digital environment © 2024 IEEE.

关键词： Sales

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Creating an Adaptive Robot for Personalized Health Monitoring and Assistances

Creating an Adaptive Robot for Personalized Health Monitorin...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Ponnuru, Sudhakiran Sai Pranathi, T. Nayak, Gnanesh H Pandi, Maruthu Rishitha Ambati, Sai Kode, Siddeshwar S New Horizon College of Engineering Department of Electronics & Communication Engineering Bangalore India New Horizon College of Engineering Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9798331529833

Medical robots are a groundbreaking advancement in healthcare, utilizing robotics and automation to improve precision, efficiency, and accessibility in medical procedures. They are utilized in various applications, including minimally invasive surgeries, rehabilitation, diagnostics, and patient care. Surgical robots, such as the da Vinci Surgical System, are notable examples because they allow physicians to do intricate procedures with greater dexterity and a lower chance of human error. Rehabilitation robots support patient mobility recovery by customizing therapy to meet individual needs and progress. Diagnostic robots utilize imaging technologies to assist in early disease detection and accurate diagnostics, often incorporating artificial intelligence (AI) for image analysis and pattern recognition. The emergence of autonomous and semi-autonomous robots in medical environments promises to alleviate the workload of healthcare professionals, streamline processes, and enhance response times in critical care scenarios. However, the integration of robotics into healthcare also poses challenges, such as ethical dilemmas, regulatory obstacles, and concerns regarding data privacy and patient safety. The future of medical robotics will rely on overcoming these issues while promoting innovation. © 2025 IEEE.

关键词： Medical robotics

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