检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

18,612 篇 会议
9,213 篇 期刊文献
277 册 图书

馆藏范围

28,102 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

16,422 篇 工学
- 9,128 篇 计算机科学与技术...
- 7,131 篇 软件工程
- 5,466 篇 信息与通信工程
- 4,071 篇 电子科学与技术（可...
- 3,380 篇 电气工程
- 2,577 篇 控制科学与工程
- 1,784 篇 光学工程
- 1,561 篇 生物工程
- 1,330 篇 生物医学工程（可授...
- 1,290 篇 仪器科学与技术
- 1,189 篇 机械工程
- 1,153 篇 动力工程及工程热...
- 1,100 篇 化学工程与技术
- 1,062 篇 材料科学与工程（可...
- 720 篇 网络空间安全
- 660 篇 安全科学与工程
- 541 篇 交通运输工程
9,457 篇 理学
- 4,087 篇 数学
- 4,025 篇 物理学
- 1,807 篇 生物学
- 1,339 篇 统计学（可授理学、...
- 1,232 篇 化学
- 838 篇 系统科学
3,408 篇 管理学
- 2,390 篇 管理科学与工程(可...
- 1,147 篇 图书情报与档案管...
- 1,055 篇 工商管理
1,452 篇 医学
- 1,187 篇 临床医学
- 1,031 篇 基础医学(可授医学...
- 623 篇 公共卫生与预防医...
- 598 篇 药学(可授医学、理...
606 篇 法学
431 篇 经济学
315 篇 农学
169 篇 教育学
130 篇 文学
84 篇 军事学
24 篇 艺术学
3 篇 哲学
2 篇 历史学

主题

678 篇 accuracy
605 篇 deep learning
531 篇 internet of thin...
416 篇 feature extracti...
360 篇 real-time system...
334 篇 computer science
322 篇 wireless communi...
306 篇 wireless sensor ...
305 篇 support vector m...
300 篇 machine learning
296 篇 computational mo...
283 篇 training
274 篇 convolutional ne...
271 篇 optimization
269 篇 bandwidth
264 篇 throughput
236 篇 artificial intel...
227 篇 routing
226 篇 simulation
215 篇 reliability

机构

242 篇 iccub universita...
237 篇 department of ph...
232 篇 infn sezione di ...
225 篇 universiteit maa...
222 篇 physics and micr...
209 篇 university of ch...
166 篇 massachusetts in...
163 篇 department of el...
136 篇 department of el...
126 篇 warsaw
124 篇 horia hulubei na...
121 篇 school of comput...
121 篇 department of el...
121 篇 h.h. wills physi...
117 篇 université clerm...
115 篇 infn sezione di ...
115 篇 infn laboratori ...
114 篇 school of physic...
114 篇 infn sezione di ...
114 篇 institute of par...

作者

155 篇 beiter a.
147 篇 barter w.
136 篇 brundu d.
130 篇 bellee v.
122 篇 bobulska d.
121 篇 bowcock t.j.v.
120 篇 aliouche z.
112 篇 casse g.
110 篇 blake t.
83 篇 arzymatov k.
72 篇 borsato m.
71 篇 n. bondar
71 篇 m. artuso
70 篇 l. r. madhan moh...
70 篇 a. jawahery
70 篇 m. gandelman
70 篇 a. maevskiy
70 篇 t. gershon
70 篇 r. mountain
70 篇 f. maciuc

语言

25,918 篇 英文
1,712 篇 其他
442 篇 中文
27 篇 日文
6 篇 朝鲜文
4 篇 法文
3 篇 阿拉伯文
3 篇 德文
1 篇 荷兰文

检索条件"机构=Institute of Computer and Communication Engineering and Department of Electronic Engineering"

共 28102 条记录，以下是4781-4790 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G

A new symbol timing and carrier frequency offset estimation ...

引用

Emerging Research in Computational Science (ICERCS), International Conference on

作者： Gunasekaran Thangavel Sheeba Rajan S. Subbulekshmi Arun Anthonisamy Rohini Ganapathi Gowri Velankanni Department of Electronics and Communication Engineering University of Technology and Applied Sciences Muscat Sultanate of Oman School of Computing SRM Institute of Science and Technology Tiruchirapalli India Department of Electronics and Communication Engineering Prathyusha Engineering College Chennai India Department of Computer Science and Business System Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering St. Joseph’s Institute of Technology Chennai India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Trichy India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions.

关键词： Wireless communication Machine learning algorithms Heuristic algorithms Estimation Symbols Interference Prediction algorithms Frequency estimation Timing Reliability

来源：评论

学校读者我要写书评

暂无评论

An Effective Network communication algorithm for Dynamic Handover 3

An Effective Network Communication algorithm for Dynamic Han...

引用

3rd International Conference on Smart Technologies in Computing, Electrical and electronics, ICSTCEE 2022

作者： Sivasankaran, V. Maheswar, R. Sugumaran, S. Sountharrajan, S. Jayarajan, P. School of Electrical and Electronics Engineering VIT Bhopal University Bhopal India KPR Institute of Engineering and Technology Department of Electronics and Communication Enegineering Coimbatore India Sreenivasa Institute of Technology and Management Stuides Department of Electronics and Communication Enegineering Chittoor India Amrita School of Computing Chennai Amrita Viswa Department of Computer Science Engineering Vidyapeetham India Sri Krishna College of Technology Department of Electronics and Communication Enegineering Coimbator India

ISBN: (纸本)9781665456647

Today many devices like laptops and smartphones have access to both WIFI and LTE at the same time. The reliability and performance of wireless networks and the Internet is not perfect always. Sometimes users need to manually disconnect devices from slow or unreliable WIFI networks, in order to switch to LTE. when the connected WIFI network becomes unstable The Android operating system already includes a way to automatic switch to LTE,. In this research work, addressed the issues of switching between WIFI and LTE in Android. It is done by emulating various network conditions in order to provoke an Android phone to switch from WIFI to LTE. We show that the Android phone sometimes can stay connected to WIFI network networks even when there is very poor Internet access. The proposed algorithm shows the consistent in switching to LTE from poorly performing networks WIFI, however it also leads to much higher overhead in terms of data usage. © 2022 IEEE.

关键词： Android (operating system)

来源：评论

学校读者我要写书评

暂无评论

Thick Film Interdigitated Electrode Graphite and Carbon Nanotube on Flexible Substrate for Hydrogen Gas Detection

Thick Film Interdigitated Electrode Graphite and Carbon Nano...

引用

2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023

作者： Abu Bakar, Azlinda Nizar Hamidon, Mohd Yaacob, Mohd Hanif Paiman, Suriati Jaafar, Haslina Wan Jusoh, Wan Nursheila Universiti Putra Malaysia Funtional Device Laboratory Institute of Nanoscience and Nanotechnology Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Science Department of Physics Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Electrical and Electronic Selangor Serdang Malaysia Universiti Kuala Lumpur Section of Aerospace Malaysians Institute of Aviation Technology Selangor Dengkil Malaysia

ISBN: (纸本)9798350333312

The comparison on the performance of interdigitated electrode (IDE) graphite and carbon nanotube (CNT) using titanium dioxide-multiwall carbon nanotube (TiO2-MWCNT) composite as sensing materials to detect hydrogen gas is presented in this research work. Screen printing technique has been selected to deposited IDE graphite and CNT conductive pastes as the electrode thick film on a flexible substrate, which is continued by firing at 120°C each. TiO2-MWCNT paste was printed as a sensing layer on top of IDE, followed by firing at 350°C. FESEM was used to characterize the morphological analysis of the IDE film. The operating temperature was varied at 27°C, 50°C and 100°C and concentration varied from 100-1000 ppm for gas hydrogen. The gas sensor demonstrated as n-type gas characteristics where the response of the gas sensor produced increased current from the results. The results purpose when the sensor was exposed to hydrogen gas. Therefore, the ability of IDE graphite and CNT as an electrode to harvest the electron from sensing materials were successfully proven. The IDE CNT showed better response compare to IDE graphite, but both electrodes were able to sense 100 - 1000 ppm at the operating temperature of 27°C, 50°C and 100°C. The optimal of operating temperature for IDE graphite is 50°C and IDE CNT is at room temperature for the hydrogen gas detections in this research work. © 2023 IEEE.

关键词： Thick films

来源：评论

学校读者我要写书评

暂无评论

Signal Processing Techniques for Classifying Heartbeat Patterns in ECG Signals for Cardiac Disease Detection Using Graph Convolutional Networks

Signal Processing Techniques for Classifying Heartbeat Patte...

引用

Mobile Networks and Wireless communications (ICMNWC), IEEE International Conference on

作者： Laith H. Alzubaidi Priyadharshini. C Vasanthakumar G U Srinivas Samala S. Ananthi Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Bharathiyar Institute of Engineering for Women Salem India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering SR University Warangal India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In recent days, the information about electrical activity is obtained in the form of Electro-Cardo-Diograms (ECG) which is become a typical way of monitoring heart rhythm and function of patients. In existing, the Convolutional Neural Networks (CNN) is utilized for the effective detection of Cardiac Disease (CD). However, the CNN model may not extract the spatial features from the signals effectively. To overcome this issue, Graph Convolutional Network (GCN) is proposed to extract the spatial features from signal effectively. Initially, the data is taken from Massachusetts institute of Technology and Beth Israel Hospital (MIT-BIH) dataset. Then, min-max normalization is utilized to preprocess the data and the high frequency noise is removed with Butterworth Frequency Filter (BFF). After that, the preprocessed data is further processed into classification with proposed GCN model. In the classification, the convolutional layers are utilized to extract the spatial features by using graph-based structure. Finally, the fully connected layer with softmax activation function is utilized to classify the CD into normal or abnormal labels. From the results, the proposed GCN model established better results than existing CNN including precision (99.75%), recall (98.60%), F1-score (98.20%) and Area Under Curve (99.70%) respectively.

关键词： Wireless communication Convolution Heart beat Graph convolutional networks Noise Cardiac disease Electrocardiography Feature extraction High frequency Resilience

来源：评论

学校读者我要写书评

暂无评论

Proactive Network Optimization Using Deep Learning in Predicting IoT Traffic Dynamics

Proactive Network Optimization Using Deep Learning in Predic...

引用

Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： S. Anu Priya Niyati Bhat B. Rajesh Kanna S. Rajalakshmi R. Beaulah Jeyavathana Srimathi S Department of Computer Applications Vels Institute of Science Technology & Advanced Studies ((VISTAS) Chennai B.E Computer Engineering University of Mumbai Mumbai Department of Electronics and Communication Engineering Alagappa Chettiyar College of Engineering and Technology Karaikudi Department of Electronics and Communication Engineering RMK College of Engineering and Technology Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Problems have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. This study aims to provide a deep learning-based strategy for forecasting and analyzing IoT traffic patterns to solve this problem. The model uses recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture the complex temporal dependencies in IoT data streams. Following careful data gathering from various IoT sources, a thorough preprocessing phase is performed to account for missing values and outliers. Effective modeling is enabled by applying feature engineering approaches to distill meaningful information and by temporally aggregating the dataset. The heart of the study is the creation of a sequence-to-sequence architecture that allows for reliable forecasting of future IoT traffic trends. Mean Squared Error (MSE) and other performance metrics measure the quality of the proposed model's predictions. In addition to its predictive abilities, the model's analytical section reveals interesting facts about the fundamental habits underpinning IoT traffic. This dual purpose helps network managers better comprehend the ever-changing IoT ecosystem and allocate scarce resources. The results of this study have important implications for network optimization, providing a preventative strategy for dealing with the difficulties posed by the exponential growth of IoT data traffic. Regarding accuracy and interpretability, the suggested deep learning model significantly contributes to the development of intelligent network resource management in the IoT era.

关键词： Deep learning Analytical models Recurrent neural networks Ecosystems Predictive models Traffic control Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Development of Cancer Drug Targets and Potential Anti-Cancer Agents using Deep Learning and Screening Processes

Development of Cancer Drug Targets and Potential Anti-Cancer...

引用

Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： K S Balamurugan Ramesh T Sathish Kumar P J Surendran R Tamilvizhi T Department of Electronics and Communication Engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of Computer Science & Engineering R.M.K Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Cancer is one of the global health issues with various complications that lead to fatal. A novel approach is initiated using deep learning techniques. They help in the analysis of complex biological structures which helps to provide potential anti-cancer drugs. They help in the screening of emergent compounds through therapeutic potential. The first stage in the proposed system involves the development of comprehensive datasets that include multi-omics data and protein-protein interaction network structures. They proceeded with preprocessing and feature extraction that led to the contribution of cancer progression stages. The deep learning model involves learning from cancer-associated genes and pathways. They help in the prediction of protein targets that are decisive for cancer development. In the subsequent stage, the deep learning model helps in employing a screen for the collection of emerging compounds from diverse platforms ranging from synthetic libraries. Another stage involves the validation process that leads to in vivo and in vitro experiments. They are proceeded with cellular assays. Compound efficacy is obtained through xenograft and mouse models followed by molecular biology techniques such as protein profiling and gene analysis. Thus the system provides a cumulative approach in obtaining anti-cancer drug targets.

关键词： Deep learning Proteins Biological system modeling Precision medicine Cancer drugs Oncology Compounds Pharmaceutical industry Artificial intelligence Cancer

来源：评论

学校读者我要写书评

暂无评论

GCN-Integrated Multi-Agent Reinforcement Learning (MARL) with Modified Firefly Algorithm for Cross-Layer Optimization in Cognitive Sensor Networks 3

GCN-Integrated Multi-Agent Reinforcement Learning (MARL) wit...

引用

3rd International Conference on Integrated Circuits and communication Systems, ICICACS 2025

作者： Jakranpally, Karthik Babu, Nagabathula Ramesh Babu, Doddavarapu Sreenu Rakesh, K. Barve, Amit Shelke, Atmaram Valiant It Services 4000 Stanwick ave SacramentoCA95835 United States J.B. Institute of Engineering & Technology Department of Electronics and Communication Engineering Hyderabad India Bharatratna Indira Gandhi College of Engineering Department of Electronics and Communication Engineering Solapur India Malla Reddy Engineering College for Women Department of Electronics and Communication Engineering Hyderabad India Parul University Parul Institute of Technology Faculty of Engineering and Technology Department of Computer Science and Engineering P.O.Limda Gujarat India Pune India

ISBN: (纸本)9798331508456

CSN s are integrating cognitive abilities, making it challenging to achieve optimized cross-layer performance with limited resources. Non-coordinated techniques typically demonstrate lower proficiency in dynamic spectrum management and network synchronization. We introduce Graph Convolutional Networks (GCN) and Multi-Agent Reinforcement Learning (MARL) in our architectural solution, with the help of the specially designed Firefly Algorithm. The framework implements a distributed learning process, utilizing GCN for local topology perception and MARL for cooperative spectrum and optimal routing decisions. The results of experimental analysis show large gains compared to traditional methods. The proposed model provided a 94.2% recognition rate in spectrum hole identification, an 89.7% enhancement in network throughput, and a 78.5% diminution in end-to-end delay. The energy efficiency surpasses baseline techniques by 67.3%, while the raw packet delivery reliability remains at 92.8%. Integrating GCN with MARL and the improved Firefly Algorithm can significantly improve cross-layer optimization, offering a promising solution for the next generation of sensor networks. © 2025 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Home Connectivity with IoT-Enabled Smart Mirrors for Internet Browsing and Smart Home Integration

Revolutionizing Home Connectivity with IoT-Enabled Smart Mir...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： M. Manicka Prabha S. Jegadeesan S. D. Jayavathi G. Vinoth Rajkumar J. Nirmal Jothi R. Santhana Krishnan Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering JP College of Engineering Ayikudi Tenkasi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India

This research study explores the transformative potential of IoT-Enabled Smart Mirrors to redefine home connectivity by enabling seamless internet browsing and smart home integration. It introduces a novel model algorithm, IoT-ViSense (IVS), which leverages IoT sensor data fusion with vision-based Convolutional Neural Networks (CNN) for real-time environmental monitoring and security. A comprehensive simulation analysis of IoT-ViSense is conducted and compared against existing algorithms using pertinent simulation metrics. IoT-Enabled Smart Mirrors serve as multifunctional interfaces, integrating personalized weather updates, news, calendar schedules, and real-time compliments. They also incorporate features such as a built-in camera for capturing high-quality selfies, on-demand workout sessions facilitated by fitness experts, and the ability to browse the internet, make calls, and interact with other connected devices. The proposed IoT-ViSense (IVS) algorithm combines IoT sensor data with Vision-based CNN analysis to enhance environmental monitoring and security. It continuously processes data from IoT sensors and camera feeds, detecting anomalies and patterns in real-time. By fusing these data streams, IVS enhances security by identifying unauthorized access and suspicious activities while concurrently optimizing resource usage and ensuring comfortable living and working environments. The simulation analysis of IoT-ViSense (IVS) is compared with suitable existing algorithms, assessing its efficiency in terms of accuracy, response time, and resource optimization. Results reveal that IoT-ViSense (IVS) outperforms other algorithms, demonstrating its prowess in achieving a harmonious blend of smart home integration and real-time environmental monitoring.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Investigation of Traffic Radar Coverage Efficiency under Different Placement Strategies

Investigation of Traffic Radar Coverage Efficiency under Dif...

引用

International Conference on Internet of Things 2024, ICIoT 2024

作者： Yao, Shun Zhou, Jin Chieng, David Kwong, Chiew Foong Lee, Boon Giin Li, Jianqiang Chen, Yusen Next-Generation Internet of Everything Laboratory Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Computer Science University of Nottingham Ningbo China Ningbo China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Zhejiang Communication Investment Group Co. Ltd. Zhejiang China

This paper presents a comprehensive investigation of traffic radar coverage efficiency under different placement strategies in collaboration with Zhejiang communications Investment Group Company Limited (CICO). The overall goal is to maximise coverage while minimising blind spots and the number of radars, given the constraints of the road. The NSGA-II multi-objective optimisation algorithm is found to be capable of generating a set of optimal solutions for the proposed radar placement configurations. When aiming for 100% coverage, the results indicate that a multi-antenna radar configuration requires the lowest radar density. Overall, this study provides valuable insights to transportation infrastructure owners in determining the best radar placement strategies for road traffic monitoring. © The Institution of engineering & Technology 2024.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Development of Efficient Wireless Sensor Network for IoT Applications 3

Development of Efficient Wireless Sensor Network for IoT App...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Shankar, S. Deepika, G. Devi, G. Ramesh, S. Srivastava, Suraj Kumar, S Sandeep Sri Krishna Arts and Science College Department of Electronics and Communication Systems Tamil Nadu Coimbatore641008 India St. Peter's Engineering College Department of Electronics and Communication Engineering Telangana Hyderabad500100 India Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Dindigul624622 India Institute of Artificial Intelligence and Machine Learning Saveetha University Depattment of Computational Intelligence Tamil Nadu Chennai602105 India I.K. Gujral Punjab Technical University Department of Computer Science and Engineering Mohali Campus-1 Punjab Sahibzada Ajit Singh Nagar160055 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350322842

This research study provides an overview of the effort that went into developing reliable 'Wireless sensor networks' for IoT applications, including both theoretical study and practical testing. The report also includes the findings of an analysis of variance and regression analysis, which pinpoint the significance of such aspects as network architecture, traffic load, and ambient conditions on the performance of Wireless Sensor Network (WSN). Finally, study provides a discussion on the future research directions, as well as the design and implementation of a Wireless Sensor Network (WSN) for Internet of Things (IoT) applications. © 2023 IEEE.

关键词： Electric power utilization

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 475 476 477 478 479 480 481 482 483 484 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：