检索结果-内蒙古大学图书馆

Remotely mind-controlled metasurface via brainwaves

eLight 2022年第1期2卷 121-131页

作者： Ruichao Zhu Jiafu Wang Tianshuo Qiu Yajuan Han Xinmin Fu Yuzhi Shi Xingsi Liu Tonghao Liu Zhongtao Zhang Zuntian Chu Cheng‑Wei Qiu Shaobo Qu Shaanxi Key Laboratory of Artificially‑Structured Functional Materials and Devices Air Force Engineering UniversityXi’an 710051ShaanxiChina National Key Laboratory of Science and Technology on Micro/Nano Fabrication Department of Micro/Nano ElectronicsShanghai Jiao Tong UniversityShanghai 200240China Department of Electrical and Computer Engineering Faculty of EngineeringNational University of Singapore4 Engineering Drive 3Singapore 117583Singapore

The power of controlling objects with mind has captivated a popular fascination to human *** possible path is to employ brain signal collecting technologies together with emerging programmable metasurfaces(PM),whose functions or operating modes can be switched or customized via on-site programming or pre-defined ***,most of existing PMs are wire-connected to users,manually-controlled and not ***,we propose the concept of remotely mind-controlled metasurface(RMCM)via *** than DC voltage from power supply or AC voltages from signal generators,the metasurface is controlled by brainwaves collected in real time and transmitted wirelessly from the *** an example,we demonstrated a RMCM whose scattering pattern can be altered dynamically according to the user’s brain waves via *** attention intensity information is extracted as the control signal and a mapping between attention intensity and scattering pattern of the metasurface is *** such a framework,we experimentally demonstrated and verified a prototype of such metasurface system which can be remotely controlled by the user to modify its scattering *** work paves a new way to intelligent metasurfaces and may find applications in health monitoring,5G/6G communications,smart sensors,etc.

关键词： Brainwave Mind-controlled Reprogrammable metasurface Intelligent metasurface

来源：评论

学校读者我要写书评

暂无评论

Analysis of Distance Measures for WiFi-based Indoor Positioning in Different Settings 2

Analysis of Distance Measures for WiFi-based Indoor Position...

引用

2nd International Conference on Innovative Research in Applied Science, engineering and technology, IRASET 2022

作者： Duong-Bao, Ninh He, Jing Thi, Luong Nguyen Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Dalat University Faculty of Information Technology Dalat Viet Nam Dalat University Department of Electronics and Telecommunications Dalat Viet Nam

ISBN: (纸本)9781665422093

Recently, indoor positioning systems based on wireless technologies such as WiFi fingerprinting become more popular. The nearest neighbor-based algorithms using Euclidean distance are very common and used in many fingerprinting systems. Thus, the distance measure is very important and it affects much to the tracking result. In this paper, we present an analytical study of using different distance measures for the weighted K-nearest neighbor algorithm to determine the position of a user. We implement five distance measures and compare the positioning results of each measure to find out the best one. To check the robustness of the measures, we change some settings when creating the radio map in the offline phase such as the number of access points or the distance between two reference points. From the experiments, it is shown that the Chi-Squared distance outperforms other distance measures since it achieves the mean error of 1.13 meters in a simple test case and 1.20 meters in a more complicated test case. Even when we change the settings, Chi-Squared distance remains the best positioning result. © 2022 IEEE.

关键词： Indoor positioning systems

来源：评论

学校读者我要写书评

暂无评论

A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

引用

International Electrical engineering Congress (iEECON)

作者： Prayoot Akkaraekthalin Tanee Somwong Nonchanutt Chudpooti Nuttawut Hirun Suwaluck. Meesomklin Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents a new IOT system for monitoring and controlling plant cultivation in greenhouse system. The system aims to regulate and monitor the environmental conditions suitable for plant cultivation in real-time. The system utilizes sensors to measure environmental conditions, such as soil moisture sensor, light intensity sensor, temperature sensor, and air humidity sensor. The Node MCU ESP32 is used for processing and controlling these operations. The system is designed to send notifications through Line application and allows farmers to monitor the system's performance and plant growth in real-time via a smartphone application. It can utilize the Blynk IoT application on a smartphone for remotely monitoring and controlling the environmental conditions and provides historical data. This experiment demonstrates that the designed system can successfully cultivate plants and promote better growth compared to traditional cultivation methods with a 13.38% improvement. Additionally, it provides convenience and ease of management for plant cultivation.

关键词： Temperature sensors Temperature measurement Smart agriculture System performance Green products Sensor systems Real-time systems Sensors Internet of Things Monitoring

来源：评论

学校读者我要写书评

暂无评论

Internet of things based intelligent accident avoidance system for adverse weather and road conditions

引用

Journal of Reliable Intelligent Environments 2021年第4期7卷 299-313页

作者： Onesimu, J. Andrew Kadam, Abhishikt Sagayam, K. Martin Elngar, Ahmed A. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India Faculty of Computers and Artificial Intelligence Beni-Suef University Beni Suef City62511 Egypt

Study shows that road accidents cause nearly 6,000 people to die and more than 400,000 people injured in the United States every year. Adverse weather and road conditions are some of the major reasons that contribute to 22% of accidents. People get injured and sometimes even lose their life due to road accidents that cause physical and mental instability. Regardless of the expertise of a good driver, at some point, an intelligent transportation system is necessary for the vehicle to make an immediate decision to avoid accidents. Road and weather-related mishap are those which occur due to adverse conditions like fog, winds, snow, rain, slick pavement, sleet, etc. Such accidents, though completely unavoidable, but can be reduced to some extent if proper measures are taken. Vehicle velocity, vehicle size, vehicle weight, momentum are a few of the reasons for a vehicle to go out of control. An intelligent accident avoidance system can predict the safe speed of a vehicle according to its size, weight, and momentum in different weather and road conditions. It can reduce the likelihood of accidents related to weather and road conditions. In this paper, we propose an Internet of Things (IoT) based intelligent accident avoidance system for adverse weather and road conditions. The proposed system comprises of an IoT system that perceives the environment for different weather and road conditions and a machine learning-based intelligent system that learns the adverse conditions that influence an accident to predict and suggest safe speed to the driver. The proposed system is experimented with real-time datasets and simulated using the Blynk application. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Analysis of SARIMA Models for Forecasting Electricity Demand

Analysis of SARIMA Models for Forecasting Electricity Demand

引用

International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Ahmet Aksöz Saadin Oyucu Emre Biçer Ramazan Bayındır Mobilers Team Sivas Cumhuriyet University Sivas Türkiye Department of Computer Engineering Faculty of Engineering Adıyaman Türkiye Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas University of Science and Technology Sivas Türkiye Faculty of Technology Electrical and Electronics Engineering Department Gazi University Ankara Türkiye

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

This article presents an in-depth evaluation of electricity consumption predictions using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Leveraging historical electricity consumption data, the SARIMA model demonstrates a commendable ability to forecast future consumption patterns. Our analysis reveals a strong alignment between the model’s predictions and actual consumption data, affirming the efficacy of time series modeling in capturing complex energy consumption dynamics. Notably, while the model excels in predicting near-term consumption trends, uncertainties widen for long-term forecasts, prompting critical reflections on the model’s evolving accuracy and reliability over time. For future research endeavors, we recommend comparing the performance of diverse time series models to discern optimal modeling approaches. Further optimization of model parameters stands as a paramount endeavor to refine prediction accuracy and mitigate uncertainties. Specifically, efforts to identify and address potential overfitting or underfitting tendencies within the model are advised. Additionally, leveraging supplementary data sources and integrating seasonal factors could bolster the reliability of future predictions, expanding the model’s predictive scope and ensuring more robust and precise forecasts.

关键词： Analytical models Uncertainty Accuracy Electricity Soft sensors Time series analysis Predictive models

来源：评论

学校读者我要写书评

暂无评论

A Multi-Resolution Deep Learning Approach for IoT-Integrated Biomedical Signal Processing using CNN-LSTM

A Multi-Resolution Deep Learning Approach for IoT-Integrated...

引用

Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： K. Priyadharshini Ajisha Mathias R. Santhana Krishnan N Kanthimathi J. Relin Francis Raj P. Stella Rose Malar Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The rapid advancement of Internet of Things (IoT) technology and deep learning has enabled real-time health monitoring for elderly individuals and patients. This work proposes an IoT-based health monitoring system integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to analyze physiological signals and detect anomalies effectively. A network of biomedical sensors, including ECG, EMG, EEG, heart rate, blood pressure, SpO2, temperature, accelerometer, gyroscope, and respiratory rate sensors, continuously collects health data. These sensors transmit real-time physiological signals using wireless communication protocols such as Wi-Fi, Bluetooth, LoRa, and NB-IoT, ensuring seamless data acquisition and remote monitoring. To enhance signal quality, the preprocessing stage employs Wavelet Transform (WT) for denoising biomedical signals, mitigating motion artifacts and environmental noise. Discrete Wavelet Transform (DWT) is applied to extract meaningful features while preserving critical health information. Additional preprocessing steps, including feature normalization, segmentation, and feature extraction, improve deep learning model efficiency. The proposed CNN-LSTM hybrid model leverages CNN for spatial feature extraction and LSTM for capturing temporal dependencies in time-series biomedical data. The architecture includes multiple convolutional layers with ReLU activation, batch normalization, max-pooling, bidirectional LSTM layers, dropout regularization, and an attention mechanism. Model training utilizes labeled medical datasets with an Adam optimizer and binary cross-entropy loss function. Performance evaluation considers accuracy, precision, recall, F1-score, and AUC-ROC metrics. Results demonstrate improved anomaly detection for conditions such as arrhythmias, respiratory irregularities, and early stroke indicators. Deployment in real-time health monitoring applications facilitates continuous patient assessment, fall detecti

关键词： Deep learning Medical services Feature extraction Real-time systems Convolutional neural networks Biosensors Biomedical monitoring Monitoring Medical diagnostic imaging Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Enhancing Smart City Mobility Using Software Defined Networks

Enhancing Smart City Mobility Using Software Defined Network...

引用

International Conference on Mechatronics (ICOM)

作者： Zeinab E. Ahmed Aisha A. Hashim Rashid A. Saeed Mamoon M. Saeed Department of Computer Engineering University of Gezira Sudan Department of Electrical and Computer Engineering International Islamic University Malaysia Malaysia School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Smart cities face challenges in mobility management, such as scalability, latency, resource allocation, security, and energy efficiency, which traditional methods struggle to address effectively. Software Defined Networking (SDN) is a rapidly evolving field that provides centralized control and programmability of network devices. SDN-based MM can advance sustainable urban development by centralizing control, adapting to changes, optimizing resource allocation, reducing energy consumption, and improving urban mobility. This paper investigates SDN's role in enhancing urban sustainability and analyzes its implications for sustainable cities. The paper categorizes the use of SDN technology to improve various aspects of smart cities into three categories: Emergency Services and Traffic Management, Security and DDoS Defense, and Network Performance. Results showed a 20% reduction in average travel times and a 33% improvement in emergency response times. Future research should focus on advanced real-time traffic management algorithms, integrating emerging technologies like 6G, and artificial intelligence to improve SDN-based systems' scalability and efficiency in smart cities.

关键词： Energy consumption Smart cities Scalability Transforms Emergency services Energy efficiency Security

来源：评论

学校读者我要写书评

暂无评论

Hall effect diameter sensor integration in DIY filament extruder

引用

Procedia computer Science 2022年 207卷 1437-1445页

作者： Zmuda Trzebiatowski Piotr Walczak Marcin Bazych Lukasz Janisiak Aleksandra Królikowski Tomasz Banucha Sebastian Blarzejewski Andrzej Faculty of Mechanical Engineering Koszalin University of Technology Koszalin 75-453 Poland Faculty of Electronics and Computer Science Koszalin University of Technology Koszalin 75-453 Poland

The paper presents the concept and modernization of filament extruder for use in FDM technology, through the implementation of the filament diameter sensor. Issues related to the process of designing solutions and their adaptation to the existing device were described, and the decision to develop their own design was justified. The issues, advantages and disadvantages of the solutions used from the perspective of reducing the costs of producing materials on site were also raised.

关键词： Fused Deposition Modeling 3D FDM Filament Extruder Hall sensor Implementation DIY

来源：评论

学校读者我要写书评

暂无评论

Deterministic Networking (DetNet): Architecture, Protocols, and Advanced Applications

Deterministic Networking (DetNet): Architecture, Protocols, ...

引用

International Conference on Mechatronics (ICOM)

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

This paper provides a thorough overview of Deterministic Networking (DetN et), focusing on establishing predictable data routes in Layer 2 and Layer 3 networks to address network issues like congestion, latency, and packet loss. It discusses DetNet's principles, architecture, and Operations, Administration, and Maintenance (OAM) protocols, crucial for fault detection and network monitoring. DetN et ensures bounded latency and minimal data loss, catering to industries' need for reliable flows over multi-hop paths. It integrates Time Sensitive Networking (TSN) and MPLS, using Resource Allocation (RA), Service Protection (SP), and Explicit Routing (ER) as core technologies, within a structured architecture of controller, application, and network planes. OAM is pivotal for efficient DetN et management, offering tools for fault detection, continuity checks, and maintenance activities to maintain Service Level Objectives (SLOs). The paper delves into advanced DetN et applications, highlighting enhanced protocols, health care frameworks, service function chain management, and industrial networking solutions, emphasizing their potential to improve reliability and network efficiency across diverse applications.

关键词： Industries Multiprotocol label switching Job shop scheduling Service function chaining Fault detection Packet loss Medical services

来源：评论

学校读者我要写书评

暂无评论

A Comparative Configuration and Decision of Main Restrictions of a Renewable Energy Powered Campus Micro Grid: A Case Study

SSRN

引用

SSRN 2023年

作者： Güğül, Gül Nihal Oğuz, Cansu Selcuk University Faculty of Technology Computer Engineering Department Konya Turkey Selcuk University Institute of Science Electrics and Electronics Engineering Department Konya Turkey

Higher education institutes are representative of small cities in terms of characteristics. Availability of building level data enables categorizing the models according to load profile instead of developing one model for whole communities, which is a gap in *** this study, hourly electricity consumption of 34 buildings is measured in Selcuk University to develop optimum Zero energy campus (ZEC) model. To the best of our knowledge, this is the first such dataset from Turkey and the first study to develop ZEC model using building level data. ZEC models developed until now used data of whole campus. In this study, campus and building level ZEC models are compared using PV/Wind/Biomass/Hydropower for the first *** is found as a good option for high annual electricity consumption (AEC) buildings whereas using more than two type of renewable sources was better for low AEC buildings. Required system size was found to be smaller at campus level design. However, renewable factor (RF) was higher at building level design which decreased LCOE. Therefore, the main limitation of ZEC is found as electricity sell price. If the electricity sell price was the same or more then purchase price, campus level design would be cost effective. © 2023, The Authors. All rights reserved.

关键词： Wind turbines

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：