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Midwest Symposium on Circuits and systems (MWSCAS)

作者： Abdullah H Al-Shabili Hashan K Weerasooriya Harshana Weligampola Prateek Chennuri Pamela Abshire Stanley Chan School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Electrical and Computer Engineering and Institute of System Research University of Maryland College Park MD USA

Real-time mapping of the surroundings plays a pivotal role in ensuring the success of autonomous vehicles. Among the technologies showing promise for depth mapping is Single Photon Light Detection and Ranging (SPL) which offers impressive long-range capabilities, superior depth resolution, and energy-efficient laser sources. However, the adoption of high-resolution, high frame-rate SPL systems results in significant data volumes, often reaching several Gbit/s. This creates a throughput bottleneck that impedes real-time applications and necessitates the development of effective compression schemes. This paper addresses the challenges associated with SPL compression and provides a comprehensive overview of recent advancements in this domain. The review encompasses various compression methods, including algorithmic and/or hardware-based approaches, spatial and/or temporal compression techniques. We also discuss future directions, such as integrating deep learning and fusion techniques with other sensing modalities.

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DENSE-Guided Deep Motion Networks Accounted by Large Rotations to Improve Myocardial Strain Analysis from Routine Cine MRI

DENSE-Guided Deep Motion Networks Accounted by Large Rotatio...

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IEEE International Symposium on Biomedical Imaging

作者： Pengcheng Lei Jiarui Xing Faming Fang Frederick H. Epstein Miaomiao Zhang Department of Electrical and Computer Engineering University of Virginia USA School of Computer Science and Technology East China Normal University Shanghai China Department of Biomedical Engineering University of Virginia Health System USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Myocardial strain imaging provides a valuable tool for detecting subclinical left ventricular (LV) dysfunction and adding prognostic value in assessing various types of heart disease. Recent studies have utilized highly accurate strain-dedicated techniques, such as displacement encoding with stimulated echoes (DENSE), to train a deep learning (DL) framework to predict the myocardial displacements/deformations from routine cine balanced steady state free precession (bSSFP) images. However, these methods have shown limited performance in capturing the large rotational motion of the myocardium associated with twist and torsion over time, which are important aspects of myocardial mechanics. To address this gap, this paper introduces a novel DENSE-guided DL network that explicitly accounts for large rotational motion to further improve strain analysis of standard cine bSSFP images. Specifically, our proposed network includes two key components: (i) a time-series rotation estimation network employing a 3D convolutional encoder-decoder architecture to model the large rotational dynamics of the LV myocardium over time, and (ii) a radial motion prediction network based on deformable image registration. The output of these two sub-networks was integrated and refined through a fusion network to predict the final myocardial displacements, supervised by DENSE ground truth. Experimental results show that our method improves the accuracy of myocardial strain with effectively captured large rotations.

关键词： Deep learning Solid modeling Accuracy Three-dimensional displays Magnetic resonance imaging Myocardium Predictive models Steady-state Standards Strain

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Sensing Metric-Based Hybrid Underlay/Overlay Transmission Framework for CR-IoT Networks for Smart City Applications 7

Sensing Metric-Based Hybrid Underlay/Overlay Transmission Fr...

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7th IET Smart Cities Symposium, SCS 2023

作者： Bala, Indu Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results. © The Institution of engineering & Technology 2023.

关键词： Smart city

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A Comprehensive Study on Drones and Big Data for Supply Chain Optimization Using a Novel Approach 1

A Comprehensive Study on Drones and Big Data for Supply Chai...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dankan Gowda, V. Palanikkumar, D. Suganthi, N. Malleswari, A.S. Thapa, Sanjog Tanguturi, Rama Chaithanya Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Dr Ngp Institute of Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Dayananda Sagar College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Lamar University Department of Information System and Analysis BeaumontTX United States Woxsen University School of Business Hyderabad India

ISBN: (纸本)9798350367720

This research paper aims to provide qualitative and quantitative insights regarding the application of drones and big data techniques in the SCM field through a new scientific method. The proposed method exploits the opportunities of drones for real-time data capturing and big data technologies to enrich decision making in supply chain. The study is pertinent to modern tendencies in supply chain systems and proves the efficiency of the new method through the precise calculations and contrastive analysis. The following are some of the findings that suggest efficiency, cost decrease, and outstanding performance of the supply chain. The studies being offered in this research will prove beneficial for practitioners and researchers who endeavor to advance the concepts and practice of logistics and supply chain management. © 2024 IEEE.

关键词： Big Data Cost Reduction Data Analytics Decision-making Drones Efficiency Improvement Logistics Novel Approach Real-time Data Collection Supply Chain Optimization

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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Synergy of machine learning and Mie-tronics: tailoring resonant response of meta-atom on demand 28

Synergy of machine learning and Mie-tronics: tailoring reson...

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Integrated Optics: Devices, Materials, and Technologies XXVIII 2024

作者： George, Renee C. Sedeh, Hooman Barati Lai, Fangxing Li, Hao Li, Wenhao Gao, Jiannan Sun, Jingbo Xiao, Shumin Kuznetsov, Arseniy Litchinitser, Natalia M. Department of Electrical and Computer Engineering Duke University DurhamNC United States Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen Shenzhen518055 China School of Materials Science and Engineering Tsinghua University Beijing100084 China A*STAR Institute Singapore Singapore

ISBN: (纸本)9781510670389

Optical metasurfaces are engineered 2D electromagnetic structures enabling flat optical elements with properties not readily found in nature. Their unit cells, meta-atoms, usually are represented by a set of electric and magnetic multipoles. All-dielectric-based metasurfaces have recently attracted significant attention owing to their virtually lossless transmission properties at optical frequencies. A majority of reported dielectric metamaterials are composed of relatively simple meta-atoms such as spheres, cubes, and cylinders, whose electromagnetic response is dominated by the electric dipole. However, magnetic dipoles and higher-order multipoles may enable new optical properties and functionalities, including directional scattering, beam steering, and new frequency generation. Despite impressive progress in the field of optical metamaterials and nanofabrication technologies, engineering meta-atoms that support such higher-order resonances is still challenging. Here, we demonstrate that designed titanium dioxide meta-atoms can enable dominant magnetic dipole response. We apply a machine-learning model to predict a meta-atom shape with a strong magnetic dipole resonant mode at the operating wavelength of 750 nm. Using finite-element-based numerical simulations implemented in COMSOL Multiphysics, we found that the optimized meta-atom is robust against experimental variations and conditions such as a non-perfectly collimated incident beam, nanofabrication inaccuracies, and an added substrate. The meta-atoms have been fabricated using two approaches, focused ion beam lithography and an electron beam lithography followed by reactive ion etching, and characterized using white light spectroscopy. To the best of our knowledge, this is the first experimental realization of a machine-learning-based optimization of a magnetic dipole mode at optical frequencies. © 2024 SPIE.

关键词： Atoms

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Impact of Image Enhancement for Osteoporosis Detection Based on Deep Learning Algorithm 2

Impact of Image Enhancement for Osteoporosis Detection Based...

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2nd International Conference on computer system, Information Technology, and electrical engineering, COSITE 2023

作者： Iman, Riza Dianil Roslidar, Roslidar Muchtar, Kahlil Amalia, Amalia Syaryadhi, Mohammad Mufti, Alfatirta Maulina, Novi Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Graduate School of System Designs Tokyo Metropolitan University Tokyo Japan Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (纸本)9798350343069

Osteoporosis can be defined as a degenerative disease with reduced bone mass and changes in bone architecture that can lead to bone fragility and the risk of fractures. This abnormality can be indicated by the bone density which in visual can be determined using X-Ray images. However, X-Ray images are susceptible to noise, while in image analysis image contrast affects deep learning abilities. Hence, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is used as a contrast enhancement technique in X-Ray images. This study aims to build a deep learning model using the CLAHE-enhanced image dataset with the ResNet-50 and ResNet-101 architectures. The model was built using two different datasets, namely the original image dataset and the CLAHE-enhanced image dataset. The result shows that the highest performance is given by the ResNet-101 model using the CLAHE-enhanced image dataset with an accuracy rate of 96%, precision of 95%, specificity of 95%, recall of 97% and an F1-score of 96%, respectively. By using the CLAHE algorithm, the resulting image has high contrast and looks better at displaying features in the image so as to produce better model performance. © 2023 IEEE.

关键词： Image enhancement

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Prototype Guided Personalized Federated Intrusion Detection system 10

Prototype Guided Personalized Federated Intrusion Detection ...

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10th IEEE Smart World Congress, SWC 2024

作者： Cheng, Long Yan, Huiru Zhou, Hanlin Wang, Ying Tang, Haichuan Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Ai Lab of Crrc Academy Beijing China

ISBN: (纸本)9798331520861

Preventing network attacks and protecting user privacy are consistently hot research topics in the Internet of Things (IoT) and edge computing fields. Recent advancements in Federated Learning (FL) have shown promise in addressing these challenges. FL allows various clients to collaboratively build an intrusion detection system (IDS) without sharing their private data. However, most existing methods train a single intrusion detection model for all clients and assume that the training and test data distribution are identical, leading to unsatisfactory detection accuracy and generalization abilities in practice. To overcome these challenges, this paper introduces a prototype-guided personalized FL approach named PG-FedIDS. We propose two novel mechanisms within this method. Firstly, we utilize class prototypes as auxiliary information carriers to generate personalized models for each client, rather than generating a single global model as in previous works. Secondly, we propose a prototype-guided ensemble learning strategy, which can leverage the global knowledge in prototypes to enhance detection accuracy and generalization abilities for each client. We conduct extensive experiments on two benchmark datasets with different evaluation test settings. The results demonstrate that our PG-FedIDS achieves promising detection accuracy and consistently outperforms other FL baselines. © 2024 IEEE.

关键词： Federated learning

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Visualization Analysis and Trends in Indonesian Internet of Things Research Using Bibliometrics 1

Visualization Analysis and Trends in Indonesian Internet of ...

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1st International Conference on Information system and Information Technology, ICISIT 2022

作者： Maulana, Fairuz Iqbal Pangestu, Gusti Febriantono, M. Aldiki Arifuddin, Rahman Widartha, Vandha Pradwiyasma Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

The Internet of Things (IoT) has emerged as a critical issue and area of research for a variety of communities, academics, and industries worldwide. Due to the lack of research in IoT and understanding the direction of IoT development in the future. This research presents bibliometric analysis and analysis of science mapping on IoT. Data was taken from the Scopus database based on Indonesian state affiliation from 2012-2020. A total of 1415 documents (Conference proceedings, journals, book series, and books) were retrieved, which were then processed online via Scopus Web and by VOSviewer software to perform advanced bibliometric analysis and science mapping analysis. The method is divided into five stages: keyword selection, initial search results, search result refinement, initial compilation, and data analysis. Scopus's list of the most frequently published and indexed articles, the most published papers are in the subject area of computer Science (29.0%) with 938 documents, engineering (20.7%) with 668 documents, Physics and Astronomy (9.5%) with 308 documents. The data processed and visualized on this topic offer exploratory information about the current status and trends in the IoT scientific literature and provide insight for established and novice researchers in understanding this research topic. © 2022 IEEE.

关键词： Mapping

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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