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9th International conference on advanced Computing and Communication systems, ICACCS 2023

作者： Satyanarayana, P. Bhoomika, K. Mukesh, D. Srujana, P. Bai, R. Manasa Sriramam, Yadavalli S S V. R. Siddhartha Engineering College Department of ECE Vijayawada India S.R.K.R. Engineering College Department of ECE A.P Bhimavaram India

ISBN: (纸本)9798350397376

Mobile Adhoc Networks (MANETs) are mainly composed of minimum power gadgets dispersed over remote locations. Clusters of sensors are arranged. Each cluster identifies a crucial node called a cluster head (CH). To communicate the acquired data to a base station (BS), each CH gathers it from its sensor node. sensors with non-replaceable batteries have been utilized. For MANET, energy consumption is a critical problem. We suggest an improvement strategy to lower energy consumption and improve network lifetime. The energy dissipation during network communications has been reduced by improving the energy balance in clusters across all sensor nodes. A cluster head selection method is the foundation of the enhanced approach. Additionally, an updated TDMA schedule has been put into place. Results from MATLAB 2015a simulations and mathematical analysis demonstrate the feasibility of the suggested strategy. MANET's energy usage has been lowered by about 60%, and the network life cycle has been extended by 73%. © 2023 IEEE.

关键词： Energy utilization

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基于自解耦三明治结构的横向运动栅场效应晶体管MEMS微力传感器

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Engineering 2023年第2期21卷 61-74,M0004页

作者：高文迪乔智霞韩香广王小章 Adnan Shakoor 刘存朗卢德江杨萍赵立波王永录王久洪蒋庄德孙东 State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesOverseas Expertise Introduction Center for Micro/Nano Manufacturing and Nano Measurement Technologies Discipline InnovationXi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and SystemSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150006China Eleventh Research Institute Sixth Academy of China Aerospace Science and Technology Co.Xi’an 710100China Department of Control and Instrumentation Engineering King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia Department of Biomedical Engineering City University of Hong KongHong Kong 999077China

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵... 详细信息

本文介绍了一种基于横向可移动栅极场效应晶体管(LMGFET)的新型微型力传感器的开发。提出了一种精确的电气模型,用于小型LMGFET器件的性能评估,与以前的模型相比,其精度有所提高。采用了一种新型三明治结构,该结构由一个金交叉解耦栅阵列层和两个软光阻层SU-8组成。通过所提出的双差分传感结构,LMGFET横向工作在垂直干扰下的输出电流被大大消除,所提出传感器的相对输出误差从4.53%(传统差分结构)降低到0.01%。提出了一种实用的传感器制造工艺,并对其进行了模拟。基于LMGFET的力传感器的灵敏度为4.65μA·nN^(-1),可与垂直可移动栅极场效应晶体管(VMGFET)器件相媲美,但非线性度提高了0.78%,测量范围扩大了±5.10μN。这些分析为LMGFET器件的电气和结构参数提供了全面的设计优化,并证明了所提出的传感器在生物医学显微操作应用中具有良好的力传感潜力。

关键词：力传感器场效应晶体管横向运动三明治结构电气模型显微操作非线性度双差分

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Coronavirus Disease Outbreak Prediction and Analysis Using Machine Learning and Classical Time Series Forecasting Models

Coronavirus Disease Outbreak Prediction and Analysis Using M...

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2024 International conference on Artificial Intelligence and Quantum Computation-Based sensor applications, ICAIQSA 2024

作者： Rabby, Habibor Rahman Hasan, Mahadi Jahan, Israt Jahan, Rumana Siddiky, Rafiuddin Campbellsville University Department of Computer Science KY United States Washington University of Science and Technology Department of Information Technology VA United States Military Institute of Science and Technology Industrial and Production Engineering Dhaka Bangladesh Technology Wilmington University Department of Information Systems DE United States

ISBN: (纸本)9798331517953

This study explores the efficacy of machine learning and classical time series forecasting models in predicting COVID-19 case counts, addressing a critical global health challenge. We compared four models - Holt-Winters, ARIMA, Facebook Prophet, and XGBoost - across eight countries, forecasting both confirmed and death cases over a 14-day period. Our findings reveal that Facebook Prophet with holiday impact and XGBoost consistently outperform other models in predicting new cases, with XGBoost excelling in forecasting death cases, followed by ARIMA and Facebook Prophet. Notably, XGBoost with a mean absolute error (MAE) of 1.42, mean squared error (MSE) of 4.06, and root mean squared error (RMSE) of 2.01, demonstrated the best performance for death prediction. These results underscore the potential of advanced analytical techniques in pandemic management, offering valuable insights for public health officials and policymakers. By providing accurate short-term projections, these models can facilitate proactive decision-making, resource allocation, and intervention strategies, ultimately contributing to more effective COVID-19 mitigation efforts worldwide. This re- search not only demonstrates the practical application of machine learning in epidemiological forecasting but also highlights the importance of model selection in achieving optimal predictive performance for different aspects of the pandemic. © 2024 IEEE.

关键词： COVID-19

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DyDom: Detecting Malicious Domains with Spatial-Temporal Analysis on Dynamic Graphs 23

DyDom: Detecting Malicious Domains with Spatial-Temporal Ana...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Yixin Luo, xi Wang, Liming Xu, Zhen Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University China

ISBN: (纸本)9781665494571

Domain Name System (DNS) has been widely abused by cybercriminals as the critical infrastructure to supply their activities. Thus malicious domains detection is a crucial assignment to combat and mitigate cybercrimes. Existing studies usually recognize malicious domains with blacklists or handcrafted local domain features. However, blacklists and expert-knowledge-based features can be bypassed by attackers with orchestrated techniques, which calls for new detection methods. In this paper, we propose a novel system named DyDom to detect malicious domains intelligently. The key idea of our system is to employ spatial-temporal analysis on domains' behaviors and their temporal variations to catch deep associations among them. In DyDom, we first analyze interactive behaviors among domains, clients and resolve IPs, then generate discrete-time dynamic graphs to model the temporal variations of domains. Further, a classifier based on graph convolutional network (GCN) and gated recurrent unit (GRU) is employed to detect malicious domains by analyzing spatial-temporal associations. As far as we know, DyDom is the first effort to model DNS scene with dynamic graphs and discover malicious domains using spatial-temporal analysis. We develop a prototype of DyDom and evaluate it in real-world data collected from an educational network. The experiment results reveal the effectiveness of our system. © 2021 IEEE.

关键词： Analytical models Smart cities Scalability Prototypes Logic gates Feature extraction Behavioral sciences

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The EE-COV-SSI Method for the Exact Identification of Model Order in Modal Analysis 10

The EE-COV-SSI Method for the Exact Identification of Model ...

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10th International conference on Industrial Engineering and applications, ICIEA 2023

作者： Zhang, Yafei Wang, Youming School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781643684086

The covariance-driven stochastic subspace modal parameter identification method has been widely used in the field of engineering structures. Effective determination of the model order of the structural system is the key to applying this method to identify the modal parameters. It is particularly difficult to determine the model order for unstable systems affected by noise disturbances and computational errors. In order to effectively determine the model order, an exponential eigenvalue entropy incremental covariance-driven stochastic subspace identification (EE-COV-SSI) algorithm is proposed. The condition number of the state matrix is used to determine the degree of perturbation of the response signal to the system stability. Meanwhile, the identification accuracy of the modal parameters is reflected by calculating the modal frequency coefficient of variation. Finally, the method is applied to the modal analysis of a four-story frame structure. The results show that the method can accurately identify the model order and improve the identification accuracy of the modal parameters. © 2023 The authors and IOS Press.

关键词： Modal analysis

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Research on Recommendation System Based on Neural Network and Data Mining 3

Research on Recommendation System Based on Neural Network an...

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3rd IEEE International conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Lu, Zhuo Xi 'An Jiaotong University Xi'an China

ISBN: (纸本)9798350317022

Recommendation systems are an important part of today's digital age and play a key role in helping users discover and access information, products or services of interest. With the continuous development of the Internet and the increasing amount of data, the traditional recommendation system can no longer meet the needs of users. Therefore, this study explores a recommendation system based on neural networks and data mining to improve the accuracy and personalization of recommendations. We will delve into how neural networks and data mining techniques can be used to build smarter and more effective recommendation systems, and explore their applications in different fields. In the aspect of neural network, this study discusses the application of advanced models such as LSTM in recommendation system. These models can better capture the complex relationships between users and items, thus improving the accuracy of recommendations. Based on online reviews, this paper studies the impact of online reviews on users' consumption behavior and the design of recommendation algorithms. Firstly, the classical algorithms of neural network and data mining are introduced. Secondly, the demand features mined from user behavior are added to the research of user consumption behavior, which fills the research gap. Finally, the feature is integrated into the neural network recommendation algorithm for feature extraction to achieve personalized recommendation for users. Finally, we verify the superiority of the model through experiments. Compared with the existing methods, our model achieves higher accuracy in the relevant indicators. © 2023 IEEE.

关键词： behavior analysis data mining neural networks recommendation systems

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A PM2.5 Concentration Prediction in High-Cost and Low-Cost Wireless sensor Networks Using Neural Networks 16

A PM2.5 Concentration Prediction in High-Cost and Low-Cost W...

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16th International conference on advanced Technologies, systems and Services in Telecommunications, TELSIKS 2023

作者： Markovic, Marko Z. Neskovic, Dorde A. Kasca, Lara A. Markovic, Goran B. Drajic, Dejan D. University of Belgrade The School of Electrical Engineering Belgrade11120 Serbia Vlatacom Institute Belgrade11070 Serbia University of Belgrade School of Electrical Engineering Belgrade11120 Serbia The Vlatacom Institute Belgrade11070 Serbia University of Belgrade School of Electrical Engineering Belgrade11200 Serbia University of Belgrade The Innovation Centre of School of Electrical Engineering Belgrade11120 Serbia

ISBN: (纸本)9798350347029

Due to its increasing impact on human health, air pollution is becoming a progressively important topic in modern society. Particulate matter with a diameter of 2.5 μm is cited as one of the main air pollutants. Thus, prediction of concentration of these particles presents a very important research topic. Therefore, in this paper, we observed a Deep Learning based spatial prediction of this concentration realized by using the installed high-cost sensors and/or low-cost sensors, which are simulated. Based on the obtained analysis results, a proposal was made to employ the low-cost sensors, completely or partially, with distributed Deep Learning Neural Networks, instead of the currently used high-cost sensors in the wireless sensor network for PM2.5 concentration measuring. It is shown that in this way we can lower complexity, datasets and time for training without the loss (or even with the gain) in the prediction quality. © 2023 IEEE.

关键词： Wireless sensor networks

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BioScatter: Low-Power Sweat Sensing with Backscatter 23

BioScatter: Low-Power Sweat Sensing with Backscatter

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21st Annual International conference on Mobile systems, applications and Services, MobiSys 2023

作者： Jiao, Wenli Li, Yanlin xi, xiangdong Wang, Ju Fang, Dingyi Chen, xiaojiang Northwest University Xi'an China

ISBN: (纸本)9798400701108

Sweat contains a wealth of physiologically relevant information and has been used to detect underlying diseases or the sub-health state. However, existing sweat sensors suffer from high energy consumption due to the need for energy-hungry components (i.e., ADC and DAC) and active radio front-ends, making them unable to support continuous and long-term *** paper introduces BioScatter, a backscatter-based accurate and ultra-low-power sweat sensing wearable sensor that does not need any energy-hungry ADC, DAC, and active radios. The key to eliminating DAC is a novel low-power voltage sweeping circuit design that can perform as well as a 12-bit DAC. To eliminate the ADC, we borrow backscatter technology that can directly transmit the measured analog sensing values to the reader, thus avoiding digital sampling. Extensive results show that BioScatter has a low-power consumption of 313.5 μW and achieves more than 98.5% sensing accuracy for detecting five concentration levels of three types of important bio-fluid in sweat. © 2023 Owner/Author(s).

关键词： Internet of things

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Design of an Intelligent Voice-Controlled Home System Based on Raspberry Pi 25

Design of an Intelligent Voice-Controlled Home System Based ...

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25th IEEE International conferences on High Performance Computing and Communications, 9th International conference on Data Science and systems, 21st IEEE International conference on Smart City and 9th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Kaiyi Li, Jiaying Yu, Jiabin Hua, Yuhao xiao, Binggang Wang, Wu College of Information Engineering China Jiliang University Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province Hangzhou China School of Computer Science and Technology Xidian University Xi'an China School of Psychological and Cognitive Sciences Peking University Multisensory Integration Laboratory Beijing China

ISBN: (纸本)9798350330014

A complete intelligent voice-controlled home system should include voice capture, voice recognition, authentication, voice result processing, and other processes. Based on the intelligent voice interaction model of Raspberry Pi 3b+ and Alibaba Cloud platform, this paper realized a high recognition accurate, stable, and efficient intelligent voice-controlled home system. We used the Raspberry Pi to control the hardware interface, collected voice data through an external microphone, and performed command control with external sensors and controllable devices in the hardware part;The software was based on Alibaba Cloud intelligent voice interaction platform to realize voice data recognition. The system constructed can achieve the expected function after repeatedly testing the controlled device. The test results show that the recognition accuracy and recognition efficiency of the system's voice control instructions are both high in a relatively quiet environment. © 2023 IEEE.

关键词： Speech recognition

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Data Gathering Trials for the Development of Military Imaging systems 20

Data Gathering Trials for the Development of Military Imagin...

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conference on Electro-Optical and Infrared systems - Technology and applications XX

作者： Niebla, Maria Hickman, Duncan L. Koh, Eunjin ChanyongLee Kim, Hoseong Lim, Chaehyeon Kim, Sunghwan Tektonex Ltd Long View Argyll Rd Kilcreggan Argyll & Bute Scotland Agcy Def Dev 160 Bugyuseong Dero 488beon Gil Daejeon South Korea

ISBN: (数字)9781510667044

ISBN: (纸本)9781510667037

Data gathering trials remain an important part of the development and testing of imaging sensor systems. However, the role of trials has evolved to reflect emergent technology, engineering methodologies, operational requirements, and project constraints such as schedule and cost. The changing nature of data gathering trials is reviewed. Although trial programmes are still used for product acceptance, there has been an increased demand for trial data to support the design process. In this paper, the emphasis is on air-to-ground image-based military systems where the timely availability of relevant image data is critical to the development of advanced image processing software which, in turn, underpins the performance of the latest imaging systems. Other factors which affect the nature of trial programmes are also considered. These include the widespread availability of synthetic image generators and the use of low-cost drones as either targets or sensor platforms. Furthermore, the increasing use of AI data processing techniques demands a larger and more diverse image data set for training and evaluation purposes. Against this background of changing requirements, trial planning has become increasingly important. Although the great flexibility of low-cost commercial drones has resulted in them becoming a preferred solution for camera platforms, they present unique challenges, ranging from logistics through to image truthing of target locations. These issues are discussed, and recommendations made based on experience gained through multiple trial programmes.

关键词： Trials Programmes Imaging System Evaluation Field Testing Drones UAV Ground Truth Image Truth Data Air to Ground Targeting

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