Internet Technology Letters最新文献_第2页

Enhanced channel prediction in large‐scale 5G MIMO‐OFDM systems using pyramidal dilation attention convolutional neural network 利用金字塔扩张注意卷积神经网络增强大规模 5G MIMO-OFDM 系统的信道预测能力

Internet Technology Letters Pub Date : 2024-05-07 DOI: 10.1002/itl2.532

C. R. Rathish, Balakrishnan Manojkumar, Lakshmanaperumal Thanga Mariappan, Panchapakesan Ashok, Udayakumar Arun Kumar, Krishnan Balan

{"title":"Enhanced channel prediction in large‐scale 5G MIMO‐OFDM systems using pyramidal dilation attention convolutional neural network","authors":"C. R. Rathish, Balakrishnan Manojkumar, Lakshmanaperumal Thanga Mariappan, Panchapakesan Ashok, Udayakumar Arun Kumar, Krishnan Balan","doi":"10.1002/itl2.532","DOIUrl":"https://doi.org/10.1002/itl2.532","url":null,"abstract":"In order to enhance communication while minimizing complexity in 5G and beyond, MIMO‐OFDM systems need accurate channel prediction. In order to enhance channel prediction, decrease Error Vector Magnitude, Peak Power, and Adjacent Channel Leakage Ratio, this study employs the Pyramidal Dilation Attention Convolutional Neural Network (PDACNN). Simplified clipping with filtering (SCF) reduces PAPR data, and this technique employs a PDACNN trained with the reduced data. By combining attention techniques with pyramidal dilated convolutions, the suggested PDACNN architecture is able to extract OFDM channel parameters across several scales. Attention approaches enhance channel prediction by allowing the model to dynamically concentrate on essential information. The primary objective is to make use of the network's ability to comprehend intricate spatial–temporal connections in OFDM channel data. The goal of these techniques is to make channel forecasts more accurate and resilient while decreasing concerns about EVM, Peak Power, and ACLR. To confirm the effectiveness of the suggested CP‐LSMIMO‐OFDM‐PDACNN, we measure its spectral efficiency, peak‐to‐average power ratio, bit error rate (BER), signal‐to‐noise ratio (SNR), and throughput. Throughput gains of 23.76%, 30.45%, and 18.97% are achieved via CP‐LSMIMO‐OFDM‐PDACNN, while bit error rates of 20.67%, 12.78%, and 19.56% are reduced. PAPRs of 21.66%, 23.09%, and 25.11% are also decreased.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141003710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safety protection using artificial intelligence internet of things for preschool education 利用人工智能物联网为学前教育提供安全保护

Internet Technology Letters Pub Date : 2024-05-05 DOI: 10.1002/itl2.537

Yun Tan, Shuangyuan Mo

{"title":"Safety protection using artificial intelligence internet of things for preschool education","authors":"Yun Tan, Shuangyuan Mo","doi":"10.1002/itl2.537","DOIUrl":"https://doi.org/10.1002/itl2.537","url":null,"abstract":"With the rapid development of social economy and information technology, safety protection in daily life has become more and more important. Although the awareness of safety has increased, the children's safety is still not paid enough attention. Children still may suffer accidental injuries, especially in developing countries. Children spend most of time at school in a day. Thus, it has become an emergent challenge to guarantee children's safety at school. In order handle this issue, this paper designs an Artificial Intelligence Internet of Things (AIoT) safety protection system for preschool education. The AIoT safety protection system consists of three parts: camera, Raspberry Pi, and monitoring computer. The camera captures the images of classroom scene during preschool education. The Raspberry Pi analyzes the images from camera to determine the unsafe behaviors of children, in which a YOLOv8 model is deployed. The monitoring computer receives the alarms from Raspberry Pi. The camera, Raspberry Pi, and monitoring computer are connected using wireless sensor network. The experiments show the behavior recognition model can correctly identify most of dangerous behaviors of children in classroom. The simulation result demonstrates the AIoT safety protection system can find the dangerous behaviors in time.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141011659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance evaluation of a new Kalman filter based peer‐to‐peer tracking scheme for indoor environment 基于卡尔曼滤波器的新型室内环境点对点跟踪方案的性能评估

Internet Technology Letters Pub Date : 2024-05-05 DOI: 10.1002/itl2.529

S. Chattaraj, Amartya Chakraborty, Biplab Das

引用次数: 0

Air‐ground integrated assisted proactive eavesdropping 空地一体化辅助主动窃听

Internet Technology Letters Pub Date : 2024-05-05 DOI: 10.1002/itl2.536

Xianming Wang, Heng Zhang, Yan Ren, Feiran Xu, Chenglong Gong

引用次数: 0

Computer intelligent network security and preventive measures of internet of things devices 计算机智能网络安全与物联网设备的预防措施

IF 0.9

Internet Technology Letters Pub Date : 2024-04-24 DOI: 10.1002/itl2.519

Jianfeng Ye, Li Li, Kaiyan Zheng

{"title":"Computer intelligent network security and preventive measures of internet of things devices","authors":"Jianfeng Ye, Li Li, Kaiyan Zheng","doi":"10.1002/itl2.519","DOIUrl":"10.1002/itl2.519","url":null,"abstract":"The paper focused on researching and analyzing computer intelligence network security and preventive measures in the context of the IoT, aiming to improve the security coefficient of the IoT network and reduce IoT network security accidents through computer intelligence technology. Through experiments, we obtained data that demonstrated the effectiveness of computer intelligence in improving IoT security. In several groups of experiments, the maximum number of information leaks in the IoT network using computer intelligence within a month was 10 times smaller than the maximum number in traditional IoT networks, and the minimum number was 8 times smaller. This shows that computer intelligence can prevent information leakage in the IoT. Similarly, in several groups of experiments, the maximum number of data thefts in a month in the IoT network using computer intelligence was 15 times smaller than the maximum number in traditional IoT networks, and the minimum number was 16 times smaller. This demonstrates that computer intelligence can prevent data theft in the IoT. These findings confirm that computer intelligence can improve the security of the IoT network.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140662751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault monitoring method for misalignment replacement operation error of electricity acquisition system based on internet of things engineering evaluation 基于物联网工程评估的电力采集系统错位更换操作错误的故障监测方法

IF 0.9

Internet Technology Letters Pub Date : 2024-04-02 DOI: 10.1002/itl2.521

Yinghui Lu, Jiyang Zhu

{"title":"Fault monitoring method for misalignment replacement operation error of electricity acquisition system based on internet of things engineering evaluation","authors":"Yinghui Lu, Jiyang Zhu","doi":"10.1002/itl2.521","DOIUrl":"10.1002/itl2.521","url":null,"abstract":"With the rapid development of Internet of Things (IoT) technology, electricity collection systems have been widely used in various fields. It can connect various items to the internet and achieve remote monitoring and maintenance of devices. During the operation of the electricity collection system, there may be issues such as misalignment due to various reasons, which can lead to errors in data collection and affect the accuracy and stability of the system. How to timely monitor the operational errors of the system and replace and repair them has become an urgent problem to be solved. The fault location algorithm can accurately diagnose the cause of the fault and provide corresponding repair suggestions, thereby reducing maintenance costs and optimizing the efficiency of the electricity collection system. This article would analyze the monitoring method for the misalignment replacement operation error of the power acquisition system based on IoT engineering analysis, and used fault location algorithms to locate its misalignment. The research results indicated that, under the same other conditions, the total satisfaction score of the X system was 253 points, and the total satisfaction score of the Y system was 141 points. The score of the X system was much higher than that of the Y system. The results indicated that IoT engineering analysis could optimize the monitoring method for operational errors caused by inaccurate replacement of electricity acquisition systems, and there was a positive relationship between the two.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140755401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A semantic big data analysis method based on enhanced neural networks in IoT 基于增强型神经网络的物联网语义大数据分析方法

IF 0.9

Internet Technology Letters Pub Date : 2024-03-28 DOI: 10.1002/itl2.524

Chongke Wang

引用次数: 0

Semantic sensor data annotation method for industrial scene efficiency optimization to enable digital economy 面向工业场景效率优化的语义传感器数据标注方法，助力数字经济发展

IF 0.9

Internet Technology Letters Pub Date : 2024-03-14 DOI: 10.1002/itl2.508

Na Tao, Tao Zhang

引用次数: 0

Wearing sensor data integration for promoting the performance skills of music in IoT 整合穿戴式传感器数据，提升物联网音乐表演技能

IF 0.9

Internet Technology Letters Pub Date : 2024-03-14 DOI: 10.1002/itl2.517

Xiaochan Li, Yi Shi, Daohua Pan

引用次数: 0

Multimodal information fusion method in emotion recognition in the background of artificial intelligence 人工智能背景下情绪识别中的多模态信息融合方法

IF 0.9

Internet Technology Letters Pub Date : 2024-03-12 DOI: 10.1002/itl2.520

Zhen Dai, Hongxiao Fei, Chunyan Lian

{"title":"Multimodal information fusion method in emotion recognition in the background of artificial intelligence","authors":"Zhen Dai, Hongxiao Fei, Chunyan Lian","doi":"10.1002/itl2.520","DOIUrl":"10.1002/itl2.520","url":null,"abstract":"Recent advances in Semantic IoT data integration have highlighted the importance of multimodal fusion in emotion recognition systems. Human emotions, formed through innate learning and communication, are often revealed through speech and facial expressions. In response, this study proposes a hidden Markov model-based multimodal fusion emotion detection system, combining speech recognition with facial expressions to enhance emotion recognition rates. The integration of such emotion recognition systems with Semantic IoT data can offer unprecedented insights into human behavior and sentiment analysis, contributing to the advancement of data integration techniques in the context of the Internet of Things. Experimental findings indicate that in single-modal emotion detection, speech recognition achieves a 76% accuracy rate, while facial expression recognition achieves 78%. However, when state information fusion is applied, the recognition rate increases to 95%, surpassing the national average by 19% and 17% for speech and facial expressions, respectively. This demonstrates the effectiveness of multimodal fusion in emotion recognition, leading to higher recognition rates and reduced workload compared to single-modal approaches.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0