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Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization

Tsinghua Science and technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi Liu Shigen Shen Xiaodong Liu School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Information Engineering Huzhou UniversityHuzhou 313000China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

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Automatic text inpainting and quality elevation in video sequences

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Multimedia Tools and Applications 2024年 1-34页

作者： Palivela, Lakshmi Harika Dharmalingam, Vivekanandan Gayathri, D. Bala School of Computer Science and Engineering Vellore Institute of Technology Chennai Vandalur-Kelambakkam Road Chennai600127 India Department of Information Technology Madras Institute of Technology Anna University Chennai India Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute Of Science and Technology Chennai India

Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text containing text strokes with their hazy backgrounds. Text in the real world uses a variety of font kinds, some of which are difficult to localize due to their chaotic shapes, varied shading degrees, and orientation *** text erasing may include the subtasks of text detection as well as text inpainting. Both subtasks require a large amount of data to be successful;but, existing approaches were limited by insufficient real-world data for scene-text elimination. Eventhough the existing works produced considerable performance improvement in scene text removal, they often leave many text remains like text strokes, thus producinglow-quality visual outcomes. Therefore, this paper proposes an automatic text inpainting and video quality elevation model by using the Improved Convolutional Network-based ***, the video samples are collected from the diverse datasets and then converted into frames. Next, the frames are deblurred using an enhanced Convolutional Neural Network (CNN) model that has three convolutional layers for accurately localizing the texts in frames. Subsequently, the texts are detected by utilizing the CLARA-based VGG-16 network. Afterward, the text strokes are removed using a convolutional Encoder and decoder network to eliminate the presence of text on complex backgrounds and textures. Here, the coordinates of text in the deblurred frames are used to crop out the text stroke regions. So, the texts are in-painted, and then, the text in-painted regions are pasted back to their original positions in the frames. Furthermore, the video quality is elevated with the help of the DenseNet-centric Enhancement network. The experimental outcomes demonstrate that the proposed model effectively removed scene texts and enhanced the video qu

关键词： Convolutional neural networks

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PIDSNeRF: pose interpolation depth supervision neural radiance fields for view synthesis from challenging input

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Multimedia Tools and Applications 2024年 1-21页

作者： Wang, Jianxin Shao, Haijian Deng, Xing Jiang, Yingtao School of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China Department of Electrical and Computer Engineering University of Nevada Las VegasNV89154 United States

Recently, Neural Radiance Fields(NeRF) have shown remarkable performance in the task of novel view synthesis through multi-view. The present study introduces an advanced optimization framework, termed Pose Interpolation Depth Supervision Neural Radiance Fields (PIDSNeRF), designed to address the challenges encountered by NeRF in novel view synthesis. These challenges manifest as artifacts, texture details loss, and geometric inconsistencies, particularly in scenes characterized by different lighting conditions, Textureless regions, and sparse images as inputs. The principle of PIDSNeRF involves interpolating virtual camera positions in the 3D space domain based on known cameras, Subsequently, the sparse point clouds formed during camera pose estimation is re-projected onto these virtual poses, employing an inverse angle weighting strategy, thereby generating depth-supervised rays. Furthermore, we propose a depth diffusion method that spreads depth-supervised rays along the pixel plane, forming Gaussian functions with different variances based on the diffusion distances. Finally, depth supervision during volume rendering is achieved by optimizing the Kullback-Leibler(KL) divergence between the weight distribution of sampled points along each ray and the probability density function of the corresponding Gaussian function along that ray. The above process enables a more comprehensive optimization of multi-resolution grid features that align with the sampled points along the rays. The experiments on the DTU, LLFF, and DL3DV-10K datasets demonstrate, PIDSNeRF can synthesize complete novel views within minutes, and the various metrics of the synthesized images achieve the best performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Probability density function

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Cloud‐based video streaming services:Trends,challenges,and opportunities

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CAAI Transactions on Intelligence technology 2024年第2期9卷 265-285页

作者： Tajinder Kumar Purushottam Sharma Jaswinder Tanwar Hisham Alsghier Shashi Bhushan Hesham Alhumyani Vivek Sharma Ahmed I.Alutaibi CSE Department JMIETIYamunanagarHaryanaIndia Department of Information Technology ASETAmity University Uttar PradeshNoidaIndia Tata Communications Limited New DelhiIndia Department of Information Technology College of Computer and Information SciencesMajmaah UniversityMajmaahSaudi Arabia Amity School of Engineering and Technology Amity UniversityPatnaBiharIndia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityMajmaahSaudi Arabia

Cloud computing has drastically changed the delivery and consumption of live streaming *** designs,challenges,and possible uses of cloud computing for live streaming are studied.A comprehensive overview of the technical and business issues surrounding cloudbased live streaming is provided,including the benefits of cloud computing,the various live streaming architectures,and the challenges that live streaming service providers face in delivering high‐quality,real‐time *** different techniques used to improve the performance of video streaming,such as adaptive bit‐rate streaming,multicast distribution,and edge computing are discussed and the necessity of low‐latency and high‐quality video transmission in cloud‐based live streaming is *** such as improving user experience and live streaming service performance using cutting‐edge technology,like artificial intelligence and machine learning are *** addition,the legal and regulatory implications of cloud‐based live streaming,including issues with network neutrality,data privacy,and content moderation are *** future of cloud computing for live streaming is examined in the section that follows,and it looks at the most likely new developments in terms of trends and *** technology vendors,live streaming service providers,and regulators,the findings have major policy‐relevant *** on how stakeholders should address these concerns and take advantage of the potential presented by this rapidly evolving sector,as well as insights into the key challenges and opportunities associated with cloud‐based live streaming are provided.

关键词： cloud computing video analysis video coding

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Energy-Efficient Dynamic Configurable Datapath Architecture for IoT Devices

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Journal of Communications and Information Networks 2024年第3期9卷 251-261页

作者： Ruizhe Zhang Junhui Liu Han Wang Li Lu Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

This paper introduces a novel RISC-V processor architecture designed for ultra-low-power and energy-efficient applications,particularly for Internet of things(IoT)*** architecture enables runtime dynamic reconfiguration of the datapath,allowing efficient balancing between computational performance and power *** is achieved through interchangeable components and clock gating mechanisms,which help the processor adapt to varying workloads.A prototype of the architecture was implemented on a Xilinx Artix 7 field programmable gate array(FPGA).Experimental results show significant improvements in power efficiency and *** mini configuration achieves an impressive reduction in power consumption,using only 36%of the baseline ***,the full configuration boosts performance by 8%over the *** flexible and adaptable nature of this architecture makes it highly suitable for a wide range of low-power IoT applications,providing an effective solution to meet the growing demands for energy efficiency in modern IoT devices.

关键词： dynamic reconfiguration Internet of things(IoT) power efficiency RISC-V

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ViT-SENet-Tom: machine learning-based novel hybrid squeeze–excitation network and vision transformer framework for tomato fruits classification

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Neural Computing and Applications 2025年第9期37卷 6583-6600页

作者： Swapno, S M Masfequier Rahman Nobel, S. M. Nuruzzaman Islam, Md Babul Bhattacharya, Pronaya Mattar, Ebrahim A. Department of CSE Bangladesh University of Business and Technology Dhaka1216 Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India Robotics - Cybernetic College of Engineering University of Bahrain Manama Bahrain

Tomatoes are essential fruits in numerous nations for their vast demand. It is very important to maintain the freshness of tomatoes. One of the primary challenges in the recent culinary landscape is accurately identifying healthy tomatoes while effectively eliminating damaged or rejected ones. Existing approaches employ various strategies for categorizing tomato fruit, but they often suffer from inaccuracies, slow detection, and suboptimal performance. Thus, motivated by this gap, in this paper, we propose a novel machine learning (ML) framework, ViT-SENet-Tom, which is a hybrid vision transformer (ViT) model with squeeze and excitation (SENet) block network for fast, accurate, and efficient tomato fruit classification. The framework works on three tomato classes, respectively, the ripe, unripe, and reject. In developing the proposed model, we utilized advanced and newly designed layers and functions. This integration created a more complex and sophisticated neural network, significantly enhancing efficiency and contributing to the model’s novelty. Our chosen dataset was small initially, but we implemented augmentation techniques to increase its size. This approach made our system more reliable, efficient, and effective. The hybrid ViT-SENet framework employs encoders and self-attention networks with squeeze and excitation channel functions to allow precise, robust, fast, and efficient tomato classification. In simulation, the framework achieves a training accuracy of 99.87% and validation accuracy of 93.87%, indicating the precise classification of tomatoes. Besides, this work tests accuracy using fivefold cross-validation. The highest accuracy seen at fold-5 is 99.90%. These testing results demonstrate the efficacy of the proposed framework in real-deployment scenarios. The implementation has the potential to provide enhanced and more sustainable food security and safety in future. © The Author(s) 2025.

关键词： Fruits

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Securing STAR-FC-RIS empowered integrated sensing and multiuser communications against target eavesdropping

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Science China(Information Sciences) 2024年第11期67卷 335-346页

作者： Shuying LIN Yulong ZOU Fu XIAO Bin LI School of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications School of Computer Science Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications

This paper investigates a simultaneous-transmitting-and-reflecting fully-connected reconfigurable intelligent surface(STAR-FC-RIS) empowered integrated sensing and multiuser communications(ISAMC) network, where a dual-functional radar-communication base station detects a malicious radar target nearby and communicates with multiple legitimate users on the other side of the STAR-RIS. We utilize an integrated architecture that combines the fully-connected(FC)-RIS, an emerging type of beyond-diagonal(BD)-RIS, with the time-switching(TS)-STAR-RIS to enhance both the sensing and communications at the cost of possible target intercepting and propose the simultaneous-transmitting-and-reflecting fully-connected RIS(STAR-FCR) schemes to strike a balance between sensing and communications performance. Thereafter, observing the security-reliability performance tradeoff of the downlink ISAMC, we conduct closed-form analyses to compare COPs of round-robin scheduling(RS) and multiuser scheduling(MS) with the aid of a TS-based STAR-FC-RIS. Furthermore, we derive closed-form expressions of the sensing outage probability, communications outage probability(COP), and communications intercept probability, where an average of the three probabilities is exploited to obtain an optimized time allocation(OTA) of the *** results verify that the STAR-FCR-MS scheme outperforms the STAR-FCR-RS scheme in terms of sensing reliability and communications security. Moreover, an OTA remarkably enhances the overall performance of the STAR-FCR schemes of ISAMC systems.

关键词： simultaneous-transmitting-and-reflecting reconfigurable intelligent surface (RIS) fully-connected beyond-diagonal RIS integrated sensing and communications multiuser scheduling security

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A novel transfer deep learning method for detection and classification of plant leaf disease

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12407-12424页

作者： Kaur, Prabhjot Harnal, Shilpi Gautam, Vinay Singh, Mukund Pratap Singh, Santar Pal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Rashtrakavi Ramdhari Singh Dinkar College of Engineering Begusarai India

The major cause of plant mortality and devastation, particularly among trees is plant diseases. This problem, however, may be handled and treated effectively through early detection. Crop/plant diseases must be identified and prevented as soon as possible to improve yield. Plant disease classification and identification by plant protection professionals through eye observation is a time-consuming and error-prone process. Previously, many researchers used hand-crafted techniques for the detection and classification of diseases. Also, due to a lack of knowledge experts and improper laboratory facilities, classification at an early stage is not possible. In terms of precision agriculture applications, developing effective, rapid, and highly successful computer-aided disease detection systems have become a necessity. A deep learning-based convolutional neural network (CNN) technique is used successfully in numerous computer vision tasks such as picture classification, object detection, segmentation, and image analysis for finding the best results. The existing models had been worked on a mixed crop approach and single class dataset with average accuracy. In this paper, the Modified InceptionResNet-V2 (MIR-V2) which is a form of the CNN model is used in conjunction with a transfer learning approach to recognize illnesses in images of tomato leaves. The proposed model is trained on a public dataset and self-collected dataset that includes seven different tomato leaf disease classifications with one healthy leaf. For the evaluation of model performance, different parameters such as dropout, learning rate, batch size, count of epochs, and accuracy are used. The disease classification accuracy rate for the applied models is 98.92% and the F1 score is 97.94%. The experimental results support the suggested approach’s validity and find it effective in detecting tomato leaf disease. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer

关键词： Classification (of information)

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Rule and Encryption based Secure Routing for Internet of Vehicle Networks 2

Rule and Encryption based Secure Routing for Internet of Veh...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Anand, M. Muthurajkumar, S. Selvi, M. Thangaramya, K. Svn, Santhosh Kumar Kannan, A. Anna University Madras Institute of Technolog Department of Computer Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350365269

The Internet of Vehicles (IoV) has become one challenging communication technology in the current internet world. IoV enables real-time data exchange between vehicles, road infrastructures, and mobile communication devices and hence it helps in information exchange to vehicle drivers and the passengers. In IoV, Cybersecurity threats including Denial-of-Service (DoS) attacks are present and it compromises the security in communication among the vehicles and the passengers. Therefore, it is needed to develop a robust security mechanism that can protect the data communicated through routing in IoV. The inclusion of Encryption and Signature verification techniques in the routing algorithm will enhance the security. This paper proposes a Rule with Encryption-Oriented Secure Routing Protocol (REO-SRP) is proposed using clustering and RSA encryption with DSA Digital signature verification with rules for routing. Here, routing is performed after encryption of data and analysis of other vehicles using rules for reliable and secured data delivery. © 2024 IEEE.

关键词： Vehicles

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A security-enhanced equipment predictive maintenance solution for the ETO manufacturing

A security-enhanced equipment predictive maintenance solutio...

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作者： Cao, Xiangyu Jing, Zhengjun Zhao, Xiaorong Xu, Xiaolong School of Mechanical Engineering Jiangsu University of Technology Jiangsu Changzhou China School of Computer Engineering Jiangsu University of Technology Jiangsu Changzhou China

With the rapid advancement of intelligent manufacturing, ensuring equipment safety has become a crucial prerequisite for enterprise production. In the engineer-to-order (ETO) production mode, characterized by diverse equipment types and frequent adjustments in production lines, equipment maintenance has become increasingly complex. Traditional maintenance plans are no longer adequate to meet the evolving demands of equipment maintenance. This paper proposes a security-enhanced predictive maintenance scheme specifically designed for ETO-type production equipment. The scheme utilizes industrial Internet of Things (IIoT) technology to monitor machines and equipment, constructs prediction models using machine learning methods, and reinforces the security of the prediction system through adoption of a decentralized architecture with blockchain distributed storage. In this experiment, six supervised learning models were compared, and it was found that the model based on the random forest algorithm achieved an outstanding accuracy rate of 98.88%. Furthermore, the average total response time for generating predictions within the system is 2.0 s, demonstrating a performance suitable for practical equipment maintenance applications. © 2024 John Wiley & Sons Ltd.

关键词： Blockchain

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