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A Novel Intelligent Task Offloading Scheme for Multi-Controller Environment in Software Defined Internet of Vehicles

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IEEE Internet of Things Journal 2025年

作者： Hussain, Mir Wajahat Sangaiah, Arun Kumar Reddy, K. Hemant Kumar Roy, Diptendu Sinha Alenazi, Mohammed J.F. Javvaji, Pavan Kumar Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Anekal562106 India International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Punjab Mohali140413 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Meghalaya 793003 India College of Computer and Information Sciences Department of Computer Engineering King Saud University Riyadh11451 Saudi Arabia

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing platform meets these stringent computation requirements, but it has a significant latency that fog computing effectively handles. Software Defined Network (SDN) has become the de facto standard for next-generation networking due to its unique and flexible features, which handle the network pre-requisites in an agile manner. Task Offloading (TO) is a crucial issue in Software Defined-IoV (SDIoV), particularly when the vehicle's resources are insufficient. Fog nodes schedule offloaded tasks;however, neglecting computational and network details during task scheduling can lead to longer completion times, thereby restricting the functionality of SD-IoV due to its time-sensitive nature. Therefore, this paper introduces two proactive Intelligent Task Offloading (ITO' and ITO) schemes. These schemes schedule tasks onto fog nodes with enhanced computational capabilities, dynamically enable the network through the ovs-ofctl SDN utility, and consider vehicle mobility during task scheduling. Experiments with Mininet show that the proposed (ITO' and ITO) schemes improve performance in terms of CPU availability, bandwidth, and throughput by 5.2 times, 50% and (30.7-13.9)% more than the existing scheme. They also reduce average packet loss, delay, Round Trip Time (RTT), and offloaded node time selection by (45.35-21.3)%, (50-33.3)%, (53.9-9.9)% and (43.2-26.06)% which demonstrates the efficacy of the proposed scheme. © 2025 IEEE.

关键词： Fog computing

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Generic Hardware Realization of K Nearest Neighbors on FPGA

Generic Hardware Realization of K Nearest Neighbors on FPGA

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2022 International Conference on Microelectronics, ICM 2022

作者： Yacoub, Mohammed H. Ismail, Samar M. Said, Lobna A. Madian, Ahmed H. Radwan, Ahmed G. Giza Egypt School of Engineering and Computer Science Egypt National Centre for Radiation Research and Technology Radiation Engineering Department Cairo11787 Egypt Cairo University Faculty of Engineering Engineering Mathematics and Physics Department Giza12613 Egypt Nile University School of Engineering and Applied Sciences Giza Egypt

ISBN: (纸本)9781665493246

K Nearest Neighbors (KNN) algorithm is a straight-forward yet powerful Machine Learning (ML) tool widely used in classification, clustering, and regression applications. In this work, KNN is applied, with three distance metrics, to classify different datasets, experimentally testing each distance metric effect on the classification performance. A static K is applied for the whole dataset optimally chosen based on a 5-fold cross-validation. A reconfigurable hardware realization on field programmable gate array (FPGA) of each distance metric applying selection sort algorithm is proposed. The FPGA realization reaches a throughput up to 4.44 Gbit/sec while only occupying 1% of the Genesys 2 Kintex-7 board area. The algorithm managed to classify all the tested datasets with above 90% accuracy. © 2022 IEEE.

关键词： Machine learning

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Advances in lithium niobate photonics:development status and perspectives

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Advanced Photonics 2022年第3期4卷 40-82页

作者： Guanyu Chen Nanxi Li Jun Da Ng Hong-Lin Lin Yanyan Zhou Yuan Hsing Fu Lennon Yao Ting Lee Yu Yu Ai-Qun Liu Aaron J.Danner National University of Singapore Department of Electrical and Computer EngineeringSingapore A*STAR(Agency for Science Technology and Research)Institute of MicroelectronicsSingapore Huazhong University of Science and Technology School of Optical and Electronic InformationWuhan National Laboratory for OptoelectronicsWuhanChina Nanyang Technological University Quantum Science and Engineering CentreSingapore

Lithium niobate(LN)has experienced significant developments during past decades due to its versatile properties,especially its large electro-optic(EO)*** example,bulk LN-based modulators with high speeds and a superior linearity are widely used in typical fiber-optic communication ***,with everincreasing demands for signal transmission capacity,the high power and large size of bulk LN-based devices pose great challenges,especially when one of its counterparts,integrated silicon photonics,has experienced dramatic developments in recent *** long ago,high-quality thin-film LN on insulator(LNOI)became commercially available,which has paved the way for integrated LN photonics and opened a hot research area of LN photonics *** allows a large refractive index contrast,thus light can be confined within a more compact *** with other properties of LN,such as nonlinear/acousto-optic/pyroelectric effects,various kinds of high-performance integrated LN devices can be demonstrated.A comprehensive summary of advances in LN photonics is *** LN photonics has experienced several decades of development,our review includes some of the typical bulk LN devices as well as recently developed thin film LN *** this way,readers may be inspired by a complete picture of the evolution of this *** first introduce the basic material properties of LN and several key processing technologies for fabricating photonics *** that,various kinds of functional devices based on different effects are ***,we give a short summary and perspective of LN *** hope this review can give readers more insight into recent advances in LN photonics and contribute to the further development of LN related research.

关键词： lithium niobate etching photonics integrated optics nanotechnology devices.

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Metasurface Antenna for Gain and Bandwidth Enhancement in 28 GHz Radio-over-Fiber System

Metasurface Antenna for Gain and Bandwidth Enhancement in 28...

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International Topical Meeting on Microwave Photonics (MWP)

作者： Asif Bilal Pham Tien Dat Marco A. Antoniades Atsushi Kanno Keizo Inagaki Tetsuya Kawanishi Stavros Iezekiel Department of Electrical amd Computer Engineering and EMPHASIS Research Centre University of Cyprus Nicosia Cyprus National Institute of Information and Communications Technology Tokyo Japan Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada Nagoya Institute of Technology and National Institute of Information and Communications Technology Tokyo Japan Waseda University and National Institute of Information and Communications Technology Tokyo Japan

ISBN: (数字)9798350375398

ISBN: (纸本)9798350375404

A dual layer metasurface antenna designed for 28 GHz gain and bandwidth enhancement has been integrated with a radio-over-fiber system to demonstrate successful transmission of a 16-QAM 5G NR signal. Measured results for the magnitude of the reflection coefficient and gain of the fabricated metasurface antenna are presented. Good matching along with gain and bandwidth enhancement has been achieved for the 28 GHz mm-wave band. Subsequently a 5G radio-overfiber demonstration is used to evaluate the impact of the improved antenna on EVM (error vector magnitude) performance. Inclusion of a metasurface superstrate reduces the measured EVM from 3.73% to 2.68%. By leveraging the strengths of both metasurfaces and radio-over-fiber, network operators can achieve high-capacity, reliable, and cost-effective communication infrastructures.

关键词： Antenna measurements Microwave antennas 5G mobile communication Transmitting antennas Bandwidth Metasurfaces Reflector antennas Vectors Reflection coefficient Reliability

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Improving Brain MRI Image Segmentation Quality: A Hybrid Technique for Intensity Inhomogeneity Correction 34

Improving Brain MRI Image Segmentation Quality: A Hybrid Tec...

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34th Conference of Open Innovations Association FRUCT, FRUCT 2023

作者： Aziz, Samah Abdel Hawbani, Ammar Wang, Xingfu Abdelhamid, Talaat Maolood, Ismail Y. Alsamhi, Saeed Ismail, A.S. School of Computer Science and Technology University of Science and Technology of China Hefei23002 China Zagazig University Zagazig44519 Egypt Menoufia University Faculty of Electronic Engineering Menoufia Egypt -System Information Ministry of Higher Education and Scientific Research Kurdistan Region Erbil Iraq National University of Ireland Insight Centre for Data Analytics Galway Ireland

ISBN: (纸本)9789526524603

Intensity inhomogeneity is a significant issue in magnetic resonance imaging (MRI), where the presence of bias field causes distortions in pixel values, resulting in inconsistent and erroneous intensities across the image. This artifact not only hampers accurate diagnosis by radiologists but also negatively impacts the performance of computer-aided diagnosis algorithms, particularly in tasks like segmentation. In our proposed approach, we use a hybrid technique called KIFCM, which integrates K-means and Fuzzy C-means to enhance brain tumor segmentation. K-means provides computational efficiency, while Fuzzy C-means improves accuracy by detecting missed tumor cells. We employ a bias correction method based on the level set framework, removing noise with a median filter and applying the hybrid KIFCM technique for optimal segmentation. Our method effectively addresses intensity variation challenges, ensuring precise brain tumor region segmentation. We compare our results with DFCM and MFFLs, and the comparison shows the efficiency of our proposed method by highlighting the superior quality and accuracy of 81% achieved, requiring less computational time. Consequently, our results demonstrate KIFCM's potential to boost both accuracy and speed in MRI-based brain tumor detection through computer-aided diagnosis. © 2023 FRUCT Oy.

关键词： Magnetic resonance imaging

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Automatic Humor Identification with Novel Features from Berger's Typology and Ensemble Models

SSRN

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SSRN 2023年

作者： Mahajan, Rutal Zaveri, Mukesh A. Department of Computer Science and Engineering S.N.Patel Institute of Technology and Research Centre Umrakh Gujarat India Department of Computer Science and Engineering Sardar Vallabhbhai Patel National Institute of Technology Gujarat Surat India

Humor identification in text is essential for natural language understanding, impacting human-computer interactions and emotion recognition. This study introduces a novel approach to automatic humor identification in text using ensemble learning techniques. The objective is to classify text into two categories: humor or not humor. This research delves into humor detection in customer reviews, focusing on specific domains, and leverages diverse text features, including novel features derived from Berger's typology. State-of-the-art machine learning models, such as Support Vector Machine, Multilayer Perceptron, and Naïve Bayes, are employed. The study conducts an in-depth analysis of the Yelp review dataset, evaluating the performance of various ensemble learning methods. The proposed approach demonstrates exceptional results, achieving an 83.32% F-score with the Support Vector Machine classifier and an 85.68% accuracy using the stacking-based ensemble learning method with Random Forest on the Yelp Review dataset. Furthermore, this research includes an ablation study to assess the effectiveness of the novel Berger’s typology based features in the humor identification process. This research not only contributes to the advancement of humor identification in text but also highlights the effectiveness of ensemble learning techniques, especially when incorporating novel features inspired by Berger's typology. © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Ultra-flexible semitransparent organic photovoltaics

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npj Flexible Electronics 2023年第1期7卷 274-282页

作者： Hanbee Lee Soyeong Jeong Jae-Hyun Kim Yong-Ryun Jo Hyeong Ju Eun Byoungwook Park Sung Cheol Yoon Jong H.Kim Seung-Hoon Lee Sungjun Park Department of Electrical and Computer Engineering Ajou UniversitySuwon 16499Republic of Korea Department of Chemistry and Centre for Processable Electronics Imperial College LondonWhite City W120BZUK Department of Intelligence Semiconductor Engineering Ajou UniversitySuwon 16499Republic of Korea Advanced Photonics Research Institute(APRI) Gwangju Institute of Science and Technology(GIST)Gwangju 61005Republic of Korea Division of Molecular Science and Technology Ajou UniversitySuwon 16499Republic of Korea Division of Advanced Materials Korea Research Institute of Chemical Technology(KRICT)Daejeon 34114Republic of Korea Division of Advanced materials Engineering Kongju National University1223-24 Cheonan-daeroSeobuk-guCheonan-siChungcheongnam-doRepublic of Korea

Ultra-flexible organic photovoltaics(OPVs)are promising candidates for next-generation power sources owing to their low weight,transparency,and ***,obtaining ultra-flexibility under extreme repetitive mechanical stress while maintaining optical transparency remains challenging because of the intrinsic brittleness of transparent ***,we introduce straindurable ultra-flexible semitransparent OPVs with a thickness below 2μ*** conformal surface coverage of nanoscale thin metal electrodes(<10 nm)is achieved,resulting in extremely low flexural rigidity and high strain ***-depth optical and electrical analyses on ultrathin metal electrodes showed that the devices maintain over 73%of their initial efficiency after 1000 cycles of repetitive compression and release at 66%compressive strain,and the average visible light transmittances remain higher than 30%.To our knowledge,this is the first systematical study on mechanical behaviors of strain-durable ultra-flexible ST-OPVs through precise adjustment of each ultrathin electrode thickness toward the emergence of next-generation flexible power sources.

关键词： transparency durability rigidity

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A Novel Deep Learning Approach for Tissue Microstructure Estimation Using Spherical Mean Technique

A Novel Deep Learning Approach for Tissue Microstructure Est...

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IEEE International Symposium on Biomedical Imaging

作者： Yiang Pan Yuanjing Feng Jianzhong He Jianan Cui Lei Xie Zan Chen Qingrun Zeng Jiawei Zhang Yan Yu Konstantin K. Kukanov Ye Wu Institution of Information Processing and Automation Zhejiang University of Technology Hangzhou China Polenov Neurosurgery Institute The branch of Almazov National Medical Research Centre School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Multi-shell diffusion magnetic resonance imaging (dMRI) models connect tissue organization with the observed dMRI signals to infer tissue microstructure. Given the highly non-linear nature of these models, several deep network based approaches have been proposed to improve the efficiency and accuracy of tissue microstructure estimation. However, these approaches rely on dMRI data with dense q-space sampling, which can lead to high computational costs. In this work, we propose a novel deep learning framework, termed DeepSMT, which utilizes the spherical mean technique (SMT) to directly estimate NODDI and DKI microstructure. The network comprises an inter-voxel block and a multi-shell feature block that can capture the feature relationship of the average signal between inter-voxels and integrate features from each shell. We validate the effectiveness of our method using the Human Connectome Project dataset. Overall, the DeepSMT has better performance compared to other methods in estimating tissue microstructure.

关键词： Deep learning Training Accuracy Costs Magnetic resonance imaging Computational modeling Estimation Organizations Data models Microstructure

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NIR-Responsive Methotrexate-Modified Iron Selenide Nanorods for Synergistic Magnetic Hyperthermic, Photothermal, and Chemodynamic Therapy

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ACS Applied Materials and Interfaces 2024年第20期16卷 25622-25636页

作者： Thirumurugan, Senthilkumar Muthiah, Kayalvizhi Samuvel Lin, Yu-Chien Dhawan, Udesh Liu, Wai-Ching Wang, An-Ni Liu, Xinke Hsiao, Michael Tseng, Ching-Li Chung, Ren-Jei No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom Faculty of Science and Technology Technological and Higher Education Institute of Hong Kong Hong Kong New Territories999077 Hong Kong Scrona AG Grubenstrasse 9 Zürich8045 Switzerland College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Genomics Research Center Academia Sinica Taipei115 Taiwan Department and Graduate Institute of Veterinary Medicine School of Veterinary Medicine National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Research Center of Biomedical Device College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Cell Therapy and Regenerative Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Taipei10608 Taiwan

Breast cancer is a malignant tumor with a high mortality rate among women. Therefore, it is necessary to develop novel therapies to effectively treat this disease. In this study, iron selenide nanorods (FeSe2 NRs) were designed for use in magnetic hyperthermic, photothermal, and chemodynamic therapy (MHT/PTT/CDT) for breast cancer. To illustrate their efficacy, FeSe2 NRs were modified with the chemotherapeutic agent methotrexate (MTX). MTX-modified FeSe2 (FeSe2-MTX) exhibited excellent controlled drug release properties. Fe2+ released from FeSe2 NRs induced the release of •OH from H2O2 via a Fenton/Fenton-like reaction, enhancing the efficacy of CDT. Under alternating magnetic field (AMF) stimulation and 808 nm laser irradiation, FeSe2-MTX exerted potent hyperthermic and photothermal effects by suppressing tumor growth in a breast cancer nude mouse model. In addition, FeSe2 NRs can be used for magnetic resonance imaging in vivo by incorporating their superparamagnetic characteristics into a single nanomaterial. Overall, we presented a novel technique for the precise delivery of functional nanosystems to tumors that can enhance the efficacy of breast cancer treatment. © 2024 The Authors. Published by American Chemical Society

关键词： Magnetic resonance imaging

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60 GHz mmWave Metasurface Superstrate for Gain and Bandwidth Improvement 3

60 GHz mmWave Metasurface Superstrate for Gain and Bandwidth...

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3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

作者： Bilal, Asif Quddious, Abdul Kanno, Atsushi Kawanishi, Tetsuya Antoniades, Marco A. Iezekiel, Stavros University of Cyprus Emphasis Research Centre Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus University of Cyprus Kios Research Center for Excellence Nicosia Cyprus National Institute of Information and Communications Technology Tokyo Japan Waseda University Tokyo Japan Ryerson University Department of Electrical Computer & Biomedical Engineering TorontoON Canada

ISBN: (纸本)9789463968058

A multi-layer structure designed for the 60 GHz mm-wave frequency band is presented. The design consists of an antenna element on the substrate and a split ring resonator (SRR) metasurface on the superstrate. With the addition of the metasurface superstrate layer, an improved performance of the antenna in terms of gain and bandwidth is achieved. These enhancements are based on the negative index metamaterial (NIM) properties of the designed metasurface sheet. In this proposed design the bandwidth is improved from 3 GHz to 4.5 GHz, while the gain of the antenna is increased from 7.87 dBi to 13.87 dBi at 60 GHz. © 2022 URSI.

关键词： Millimeter waves

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