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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Denis Ivanović Josip Job Marko Puntarić Marijan Herceg CX TTTech-Auto Osijek Croatia Department of Software Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia Department of Communications Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization’s network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-to-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

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Comparison of Machine Learning Techniques Based on Feature Reduction to Detect Diabetic Retinopathy 9

Comparison of Machine Learning Techniques Based on Feature R...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Singh, Devendra Joshi, Deepika Dobhal, Dinesh C. Pant, Janmejay Pant, Himanshu Graphic Era Hill University Computer Science and Engineering Department Nainital India Amrapali Group of Institutes Faculty of Technology and Computer Application Haldwani India Graphic Era Deemed to Be University Computer Application Department Uttarakhad Dehradun India Graphic Era Hill University School of Computing Department Nainital India

ISBN: (纸本)9798350397376

Diabetic retinopathy is considered as an eye disorder condition that can affect persons who are diagnosed positive against diabetes. This condition causes damage to the retina's blood vessels. Diabetic retinopathy detection at an early stage may help patients protect their vision. This disease is often asymptomatic in the early stages but has a significant impact in the latter stages. The timely identification and intervention of diabetic retinopathy (DR) can greatly mitigate the potential for visual impairment. It takes time, effort, and money for ophthalmologists to manually diagnose DR retina fundus images. So there is a need for a screening system that can detect the disease in its early stages. There are various machine learning-based approaches available to detect diabetic retinopathy. In our proposed work first, we have to select the optimal features then we perform the comparative analysis with the help of various machine learning algorithms. We have used Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machine (SVM), Naïve Bayes, and AdaBoost. In our experiment, we observed that Logistic Regression achieved the highest accuracy of 74.2 with optimal features. © 2023 IEEE.

关键词： Adaptive boosting

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AI-Powered Drone to Address Smart City Security Issues 37th

AI-Powered Drone to Address Smart City Security Issues

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Salama, Ramiz Al-Turjman, Fadi Culmone, Rosario Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Near East University Nicosia Mersin 10 Nicosia Turkey Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Turkey Computer Science Division School of Science and Technology University of Camerino Camerino62032 Italy

ISBN: (纸本)9783031286933

The idea of a dazzling metropolis has drawn interest from all across the world. New innovations like blockchain, IoT, artificial intelligence, robots, and many other things were added to it. Security is one of the top issues for people living in big cities, and everyone wants to feel completely secure when traveling around every single day. In this research, we will look into how CEOs in affluent cities use and value robots, especially in terms of security. To understand the robot security the board stream, some approaches and intricacies are used. Following that is a discussion of issues with urban security and the application of artificial intelligence to drones as a management tool. The use of cutting-edge technology, such as blockchain, to support smart urban community management is covered in the last part. The smart city idea and all of its benefits for local inspection are supported by robotic use. The idea of thriving urban areas is spreading around the world and is crucial in the context of developing economies. We examined how to leverage cutting-edge technologies in this project to make it feasible. Artificial intelligence, robotics breakthroughs, and blockchain technologies all have significant effects. This research highlights their significance to analysts and how they consider them when assessing prospects. This project will be very beneficial for analysts and experts in a relevant subject. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Drones

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Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

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computers, Materials & Continua 2022年第1期70卷 361-374页

作者： Mamdouh El Haj Assad Ibrahim Mahariq Raymond Ghandour Mohammad Alhuyi Nazari Thabet Abdeljawad Sustainable and Renewable Energy Engineering Department University of SharjahP.O.Box 27272SharjahUAE College of Engineering and Technology American University of theMiddle EastKuwait Faculty of New Sciences and Technologies University of TehranTehranIran Department of Mathematics and General Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Medical Research China Medical UniversityTaichung40402Taiwan Department of Computer Science and Information Engineering Asia UniversityTaichungTaiwan

Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their *** heat capacity of nanofluids,as one of the thermophysical properties,performs principal role in heat transfer of thermal mediums utilizing *** this regard,different studies have been carried out to investigate the influential factors on nanofluids specific ***,several regression models based on correlations or artificial intelligence have been developed for forecasting this property of *** the current review paper,influential parameters on the specific heat capacity of nanofluids are ***,the proposed models for their forecasting and modeling are *** to the reviewed works,concentration and properties of solid structures in addition to temperature affect specific heat capacity to large extent and must be considered as inputs for the ***,by using other effective factors,the accuracy and comprehensive of the models can be ***,some suggestions are offered for the upcoming works in the relevant topics.

关键词： Specific heat capacity nanofluid artificial neural network concentration

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Large-area Tomographic Tactile Skin with Air Pressure Sensing for Improved Force Estimation

arXiv

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arXiv 2025年

作者： Chen, Haofeng Himmel, Bedrich Kubik, Jiri Hoffmann, Matej Lee, Hyosang Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Eindhoven Netherlands

This paper presents a dual-channel tactile skin that integrates Electrical Impedance Tomography (EIT) with air pressure sensing to achieve accurate multi-contact force detection. The EIT layer provides spatial contact information, while the air pressure sensor delivers precise total force measurement. Our framework combines these complementary modalities through: deep learning-based EIT image reconstruction, contact area segmentation, and force allocation based on relative conductivity intensities from EIT. The experiments demonstrated 15.1% average force estimation error in single-contact scenarios and 20.1% in multi-contact scenarios without extensive calibration data requirements. This approach effectively addresses the challenge of simultaneously localizing and quantifying multiple contact forces without requiring complex external calibration setups, paving the way for practical and scalable soft robotic skin applications. © 2025, CC BY.

关键词： Image segmentation

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

A graph-based CNN-LSTM stock price prediction algorithm with...

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作者： Wu, Jimmy Ming-Tai Li, Zhongcui Herencsar, Norbert Vo, Bay Lin, Jerry Chun-Wei Shandong University of Science and Technology Qingdao China Department of Telecommunications Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Faculty of Information Technology Ho Chi Minh City University of Technology Ho Chi Minh Viet Nam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly. © 2021, The Author(s).

关键词： Long short-term memory

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Buffer-Based Adaptive Bitrate Algorithm for Enhanced Quality of Experience

Buffer-Based Adaptive Bitrate Algorithm for Enhanced Quality...

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Telecommunications Conference (ITC-Egypt), International

作者： Tarnim Nos Ahmed O. Elmeligy Mohamed S. Hassan Taha Landolsi Mahmoud H. Ismail Department of Electrical Engineering American University of Sharjah Sharjah UAE Department of Computer Science & Engineering American University of Sharjah Sharjah UAE Department of Electronics and Communications Engineering Faculty of Engineering Cairo University Giza Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Video streaming has become a popular method for delivering video content over the Internet, and the number of users and video content is increasing rapidly. The user's quality of experience (QoE) is a crucial factor in the success of video streaming services, as it quantifies the user's satisfaction. The QoE is affected by the video quality, starvation instants, and quality switching. To maximize the QoE, adaptive bitrate (ABR) algorithms select the video quality based on the network conditions and the client's buffer capacity. In this paper, a buffer-based ABR algorithm is proposed to maximize the user's QoE by minimizing the duration and frequency of starvation events and the time spent at the lowest quality level while maintaining smooth quality switching. A SimEvents-based scalable video coding SVC dynamic adaptive streaming over HTTP (DASH) system is developed to simulate the proposed algorithm. The performance of the proposed algorithm is compared to that of a baseline algorithm under different network conditions, and it is shown that the proposed algorithm outperforms the baseline algorithm in terms of buffer occupancy, quality switching and video quality.

关键词： Adaptive systems 5G mobile communication Heuristic algorithms Bit rate Switches Streaming media Quality assessment

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Empowering Aquarists Comprehensive Study on IOT-Enabled Smart Aquarium Systems for Remote Monitoring and Control

Empowering Aquarists Comprehensive Study on IOT-Enabled Smar...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Al-Jawahry, Hassan M. Patel, Neeraj Thakur, Gaurav Santhosh Kumar, C. Sapaev, B. Kumar, Rakesh Prashanthi, Bhupathi The Islamic University Najaf Iraq IES University IES Insititute of Technology and Management Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Uttaranchal University Uttaranchal Institute of Technology Department of Civil Engineering Dehradun248007 India Prince Shri Venkateshwara Padmavathy Engineering College CIVIL Chennai127 India Alfraganus University Faculty of Medicine Department of Pharmaceuticals and Chemistry Tashkent Uzbekistan GLA University Department of Computer Engineering & Applications Mathura India GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

Taking care of the aquarium's pet fish is a laborious procedure that requires careful setup and maintenance to avoid the fish having a frustrated and brief existence. A smart aquarium monitoring system is an autonomous system that does just that. Fish are a popular indoor pet in modern society. The aquarium hobbyist has been feeding the fish in the tanks;thus, an appropriate system for maintenance is required. Some of the problems faced include variations in water quality, feeding fish, controlling temperature and lighting, and challenges in manually determining an aquarium's characteristics. Therefore, it's crucial to routinely assess the physical attributes and raise the water quality. As a outcome, this proposal recommends a system with sensors and real-time operation. It keeps an eye on the food, pH of the water, and temperature. Additionally, it recognizes when fish are eating. An IoT-based system tracks the aquarium's condition and transmits it to the user's web application. Intelligent aquarium management has been merged with a temperature, pH, and turbidity sensor. This decreases the manual effort required to maintain the aquarium and guarantees that the fish are neither overfed nor underfed, and output is monitored by each feed using IoT. As a result, it's crucial to keep an eye on aquarium conditions and raise the micro aquarium tank's water quality. © 2023 IEEE.

关键词： Water quality

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A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

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computer Systems science & engineering 2024年第3期48卷 571-608页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Abdulatif Alabdulatif Ruba Abu Khurma Maribel Garcia-Arenas Pedro A.Castillo University Institute of Computing Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Uttaranchal UniversityUttarakhand248007India Department of Computer Science College of Computer Qassim UniversityBuraydah52571Saudi Arabia MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

As cloud computing usage grows,cloud data centers play an increasingly important *** maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance *** purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data *** aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management *** review revealed three task allocation research topics and seven performance management *** allocation research areas are resource allocation,load-Balancing,and *** management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network *** study proposes new techniques to enhance cloud computing work allocation and performance ***-comings in each approach can guide future *** research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and *** methodologies can steer future research to fill gaps in the literature.

关键词： Cloud computing data centre task allocation performance management resource utilization

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