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Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

computer Modeling in engineering & sciences 2023年第6期135卷 1863-1900页

作者： Qiqi Zhang Shaojin Geng Xingjuan Cai School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China College of Electronics and Information Engineering Tongji UniversityShanghai201804China

Cloud computing technology is favored by users because of its strong computing power and convenient *** the same time,scheduling performance has an extremely efficient impact on promoting carbon ***,scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak ***,the scheduling problem has promising application prospects under themulti-cloud *** paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between *** paper reviews the concepts,types,objectives,advantages,challenges,and research status of task scheduling in the multi-cloud *** scheduling strategies proposed in the existing related references are analyzed,discussed,and summarized,including research motivation,optimization algorithm,and related ***,the research status of the two kinds of task scheduling is compared,and several future important research directions of multi-cloud task scheduling are proposed.

关键词： Cloud computing task scheduling workflow review multi-cloud environment

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An Electrical Impedance Imaging System Towards Edge Intelligence for Non-Destructive Testing of Concrete

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Tsinghua science and Technology 2024年第3期29卷 883-896页

作者： Abolfazl Roshanpanah Mahdi Abbasi Hani Attar Ayman Amer Mohammad R.Khosravi Ahmed A.Solyman Department of Electrical Engineering Islamic Azad UniversityScience and Research BranceTehran ***Iran Department of Computer Engineering Bu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13110Jordan Department of Computer Engineering Persian Gulf UniversityBushehr ***Iran Department of Electrical and Electronics Engineering Faculty of Engineering and ArchitectureNişantaşıUniversityIstanbul 34398Turkey

In the construction industry,to prevent accidents,non-destructive tests are necessary and *** impedance tomography is a new technology in non-invasive imaging in which the image of the inner part of conductive bodies is reconstructed by the arrays of external electrodes that are connected on the periphery of the *** equipment is cheap,fast,and edge *** this imaging method,the image of electrical conductivity distribution(or its opposite;electrical impedance)of the internal parts of the target object is *** image reconstruction process is performed by injecting a precise electric current to the peripheral boundaries of the object,measuring the peripheral voltages induced from it and processing the collected *** an electrical impedance tomography system,the voltages measured in the peripheral boundaries have a non-linear equation with the electrical conductivity *** paper presents a cheap Electrical Impedance Tomography(EIT)instrument for detecting impurities in the concrete.A voltage-controlled current source,a micro-controller,a set of multiplexers,a set of electrodes,and a personal computer constitute the structure of the *** conducted tests on concrete with impurities show that the designed EIT system can reveal impurities with a good accuracy in a reasonable time.

关键词： non-destructive testing system concrete electrical impedance tomography

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FOD-YOLO NET: Fasteners fault and object detection in railway tracks using deep yolo network1

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8123-8137页

作者： Brintha, K. Jawhar, S. Joseph Department of Computer Science and Engineering Arunachala College of Engineering for Women Manavilai Tamil Nadu Nagercoil India Department of Electrical and Electronics Engineering Arunachala College of Engineering for Women Manavilai Tamil Nadu Nagercoil India

Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research. A rapid advancement in deep learning has promoted new possibilities for research in this field. In this work, a novel deep learning-based FOD-YOLO net is proposed for detecting the fasteners faults and objects in the railway tracks. There are two basic components in the deep learning-based YOLOv8: the backbone and the head. YOLOv8 utilizes an improved version of the CSPDarknet53 network for detecting objects on the railway track. The head of YOLOv8 consists of EfficientNet with various convolutional layers with squeeze and excitation blocks for detecting any defect in the track fasteners. These layers are liable for detecting the objectness scores, bounding boxes and class probabilities structured with fully connected layers for the objects and faults in tracks. Based on the results from the Yolo network, the alert message is sent to the loco pilot to avoid accidents using fuzzy logic. The experimental fallouts of proposed FOD-YOLO net achieve higher accuracy and yields better evaluation results with 98.14% accuracy, 98.84% precision and 95.94% recall. From the experimental results, the FOD-YOLO net improves the overall accuracy range by 5.44%, 4.72%, 0.73%, and 13.18% better than Fast RCNN, YOLOv5s-VF, YOLO-GD, and 2D-SSA + Deep network respectively. © 2024 – IOS Press. All rights reserved.

关键词： Object detection

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Gaze-Assisted Autism Spectrum Disorder Identification: A Fusion of Machine Learning and Deep Learning Approaches for Preemptive Identification 5

Gaze-Assisted Autism Spectrum Disorder Identification: A Fus...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Simon, Judy Kapileswar, N. Datchinamoorthi, M. Muthukumar, S. Keerthana Devi, G. SRM Institute of Science and Technology Dept of Electronics and Communication Engineering Chennai India SRM Institute of Science and Technology Dept of Electronics and Computer Engineering Chennai India

ISBN: (纸本)9798350395235

This research proposes an all-encompassing method for diagnosing Autism Spectrum Disorder (ASD) by leveraging eye-tracking data and advanced machine learning techniques. In this study, gaze-tracking data was collected from individuals diagnosed with Autism Spectrum Disorder and those not exhibiting ASD characteristics as they participated in various tasks and stimuli. Principal Component Analysis was employed to streamline the eye-tracking data, retaining crucial information while enhancing computational efficiency. These refined features were then integrated into Convolutional Neural Networks as the primary modelling approach. CNNs excel in recognizing relevant spatial and temporal patterns within the visual data, leading to intricate feature extraction and classification. Additionally, we explored the efficacy of transfer learning by enhancing pre-trained CNN models with our eye-tracking dataset. This approach enables us to leverage knowledge from large-scale visual tasks, potentially enhancing the model's performance in ASD classification. The research findings highlight the success of this multifaceted approach in accurately categorizing individuals with ASD. The synergy of ML, CNN, and transfer learning models significantly enhances the accuracy of ASD diagnosis, offering a promising avenue for early identification and intervention. Consequently, this research has the potential to aid in the creation of accurate noninvasive instruments for diagnosing ASD, thereby accelerating timely assistance and treatment for individuals impacted by the condition. © 2024 IEEE.

关键词： Image classification

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RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

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computers, Materials & Continua 2024年第12期81卷 4833-4851页

作者： Weiwei Hu Kiran Sree Pokkuluri Rajesh Arunachalam Bander A.Jabr Yasser A.Ali Preethi Palanisamy School of Information Engineering Technology&MediaUniversity of HenanKaifeng475004China Department of Computer Science and Engineering Shri Vishnu Engineering College forWomenBhimavaram534202Andhra PradheshIndia Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.

关键词： Sensor node localization received signal strength indicator 3D wireless sensor network deep neural network average localization error and hybrid T cell immune with lotus effect optimization algorithm

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ISAC-Fi: Enabling Full-Fledged Monostatic Sensing over Wi-Fi Communication

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 139-153页

作者： Chen, Zhe Hu, Chao Zheng, Tianyue Cao, Hangcheng Yang, Yanbing Chu, Yen Jiang, Hongbo Luo, Jun Fudan University Institute of Space Internet School of Computer Science Shanghai200437 China Sichuan University College of Computer Science Chengdu610017 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore Hunan University College of Computer Science and Electronics Engineering Changsha410012 China University of Electronic Science and Technology of China Chengdu610054 China

Although Wi-Fi communications have been exploited for sensing purpose for over a decade, the bistatic or multistatic nature of Wi-Fi still poses multiple challenges, hampering real-life deployment of integrated sensing and communication (ISAC) within the Wi-Fi framework. In this article, we aim to redesign Wi-Fi so that monostatic sensing (mimicking radar) can be achieved over the multistatic communication infrastructure. Specifically, we propose, design, and implement ISAC-Fi as an ISAC-ready Wi-Fi prototype. We first present a novel self-interference cancellation scheme, in order to extract reflected (radio frequency) signals for sensing purpose in the face of transmissions. We then subtly revise the existing Wi-Fi framework so as to seamlessly operate monostatic sensing under the Wi-Fi communication standard. Finally, we offer two ISAC-Fi designs: 1) a Universal Software Radio Peripheral (USRP)-based design emulates a totally redesigned ISAC-Fi device and 2) another plug-and-play design allows for backward compatibility by attaching an extra module to an arbitrary Wi-Fi device. We perform extensive experiments to validate the efficacy of ISAC-Fi and also to demonstrate its superiority over existing Wi-Fi sensing proposals. © 2024 IEEE.

关键词： Wi-Fi

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Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G Communication Environment

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Tsinghua science and Technology 2024年第6期29卷 1728-1751页

作者： Ashish Bagwari Jaganathan Logeshwaran M.Raja P.Devisivasankari Jyotshana Bagwari Vikas Rathi Asma Mohammed Elbashir Saad Department of Electronics and Communication Engineering Uttarakhand Technical UniversityDehradun 248007India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore 641202India Department of Computer Science and Engineering CMR Institute of TechnologyKarnataka 560037India Department of Information Science and Engineering CMR Institute of TechnologyKarnataka 560037India Advanced and Innovative Research Laboratory Dehradun 248001India Department of Electronics and Communication Engineering Graphic Era(Deemed to be University)Dehradun 248007India Department of Physics College of Science and HumanitiesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 16273Saudi Arabia

Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.

关键词： hybrid energy storage system 4G digital growth 5G downloading Subscriber Identity Module(SIM) generation network energy efficiency Artificial Intelligence(Al)automation

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A Low-Complexity Diversity-Preserving Universal Bit-Flipping Enhanced Hard Decision Decoder for Arbitrary Linear Codes

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular Technology 2024年 5卷 1-22页

作者： Bere, Praveen Sai Khan, Mohammed Zafar Ali Hanzo, Lajos Department of Electrical Engineering Indian Institute of Technology Hyderabad Hyderabad India School of Electronics and Computer Science University of Southampton Southampton United Kingdom

V2X (Vehicle-to-everything) communication relies on short messages for short-range transmissions over a fading wireless channel, yet requires high reliability and low latency. Hard-decision decoding sacrifices the preservation of diversity order, leading to pronounced performance degradation in fading channels. By contrast, soft-decision decoding retains diversity order, albeit at the cost of increased computational complexity. We introduce a novel enhanced hard-decision decoder termed as the Diversity Flip decoder (DFD) designed for preserving the diversity order. Moreover, it exhibits ‘universal’ applicability to all linear block codes. For a $\mathscr {C}(n,k)$ code having a minimum distance ${d_{\min }}$, the proposed decoder incurs a worst-case complexity order of $2^{({d_{\min }}-1)}-1$. Notably, for codes having low ${d_{\min }}$, this complexity represents a significant reduction compared to the popular soft and hard decision decoding algorithms. Due to its capability of maintaining diversity at a low complexity, it is eminently suitable for applications such as V2X (Vehicle-to-everything), IoT (Internet of Things), mMTC (Massive Machine type Communications), URLLC (Ultra-Reliable Low Latency Communications) and WBAN (Wireless Body Area Networks) for efficient decoding with favorable performance characteristics. The simulation results provided for various known codes and decoding algorithms validate the performance versus complexity benefits of the proposed decoder. Authors

关键词： Vehicle to vehicle communications

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Deep Learning Based Tuberculosis Classification from Chest X-ray Images 15

Deep Learning Based Tuberculosis Classification from Chest X...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kavitha, S. Mahalakshmi, A. Prakash, P. Thambi Raj, V. Computer Science and Engineering Kongu Engineering College Erode India Computer Science and Engineering Sri Eshwar college of Engineering Coimbatore India Medical Electronics Velalar College of Engineering and Technology Erode India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370249

Tuberculosis (TB) is a serious health issue that kills a lot of people these days. TB is completely curable if it is discovered early enough. One way of diagnosing tuberculosis (TB) early on is to undergo a chest X-ray (CXR), which can show if a person has active TB. Chest x-ray images (CXR) can be analyzed to diagnose the deadly TB disease more accurately owing to deep learning (DL), a significant area of artificial intelligence. To automate tuberculosis (TB) identification, this study used Convolutional Neural Networks (CNNs), a form of deep learning models designed specifically for image processing. This architecture needs a lot of datasets to learn features, which makes the learning process take longer. Transfer Learning (TL), which uses a large number of pre-trained models to perform disease classification, is suggested as a solution to this issue. In this work, four different CNN architecturesEfficientNetB0, AlexNet, GoogleNet, and Xception-are trained and evaluated. The results demonstrated that GoogleNet could diagnose tuberculosis with an astounding 97% accuracy, even after extended training periods. By comparison, AlexNet trained faster due to its simpler architecture, but having a little lower accuracy of 96%. © 2024 IEEE.

关键词： Deep learning Accuracy Tuberculosis Computational modeling Transfer learning Predictive models Feature extraction Data models X-ray imaging Medical diagnostic imaging

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Advanced Breast Cancer Detection Using Spatial Attention and Neural Architecture Search (SANAS-Net)

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SN computer science 2025年第1期6卷 1-12页

作者： Souza, Melwin D. Ananth Prabhu, G. Kumara, Varuna Department of Computer Science & Engineering Sahyadri College of Engineering & Management Mangalore India Department of Electronics and Communication Engineering Moodlakatte Institute of Technology Kundapura India

This paper introduces a novel spatial attention neural architecture search network (SANAS-Net), which incorporates a spatial attention mechanism to enhance the model’s ability to focus on critical regions within mammograms. By utilizing multi-head attention within transformer blocks, the model captures diverse spatial relationships and interactions between features. Positional embeddings are incorporated into the transformer blocks, further enriching the model’s capacity to understand global spatial context. Extensive experiments were conducted to validate the proposed methodology. SANAS-Net was shown to successfully identify attentive networks that demonstrate high performance in distinguishing malignant from benign breast cancer cases. The model achieved a test accuracy of 89.95%, significantly surpassing the performance of previously proposed algorithms for breast cancer detection using mammography images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Breast cancer detection Deep learning Mammography Neural architecture search Spatial attention

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