检索结果-内蒙古大学图书馆

A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors

引用

IEEE Access 2025年 13卷 83148-83170页

作者： Obiri, Nahshon Mokua Laerhoven, Kristof Van University of Siegen Department of Electrical Engineering and Computer Science Germany

This paper presents a comprehensive dataset of LoRaWAN technology path loss measurements collected in an indoor office environment, focusing on quantifying the effects of environmental factors on signal propagation. Utilizing a network of six strategically placed LoRaWAN end devices (EDs) and a single indoor gateway (GW) at the University of Siegen's Hölderlinstraße Campus in the City of Siegen, Germany, we systematically measured signal strength indicators such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR) under various environmental conditions, including temperature, relative humidity, carbon dioxide (CO2) concentration, barometric pressure, and particulate matter levels (PM2.5). Our empirical analysis confirms that transient phenomena such as reflections, scattering, interference, occupancy patterns (induced by environmental parameter variations), and furniture rearrangements can alter signal attenuation by as much as 10.58 dB, highlighting the dynamic nature of indoor propagation. As an example of how this dataset can be utilized, we tested and evaluated a refined Log-Distance Path Loss and Shadowing Model that integrates both structural obstructions (Multiple Walls) and Environmental Parameters (LDPLSM-MW-EP). Compared to a baseline model that considers only Multiple Walls (LDPLSMMW), the enhanced approach reduced the root mean square error (RMSE) from 10.58 dB to 8.04 dB and increased the coefficient of determination (R2) from 0.6917 to 0.8222. By capturing the extra effects of environmental conditions and occupancy dynamics, this improved model provides valuable insights for optimizing power usage and prolonging device battery life, enhancing network reliability in indoor Internet of Things (IoT) deployments, among other applications. The dataset, accessible upon reasonable request, offers a solid foundation for future research and development in indoor wireless communication. © 2013 IEEE.

关键词： Green computing

来源：评论

学校读者我要写书评

暂无评论

Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

引用

engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

来源：评论

学校读者我要写书评

暂无评论

Optimal trained ensemble of classification model for satellite image classification

引用

Multimedia Tools and Applications 2025年第7期84卷 3745-3766页

作者： Singh, Sarabjeet Kundra, Harish Kundra, Sheetal Pratima, P. Venkata Devi, M. V. Anjana Kumar, Sushil Hassan, Mariya Department of Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh160019 India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Artificial Intelligence and Data Science Guru Nanak Institute of Technology Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Computer Science and Engineering Central University of Haryana Jant-Pali Mahendergarh123031 India

Satellite image classification is the most significant remote sensing method for computerized analysis and pattern detection of satellite data. This method relies on the image's diversity structures and necessitates a thorough validation of the training data using the chosen classification algorithm. The classifying of remotely sensed images refers to the process of classifying pixels into a fixed set of distinct classes based on their data values. The pixel is categorized into that class if it complies with a specific set of requirements. A new SIC scheme is suggested in this work, where, Contrast limited adaptive histogram equalization (CLAHE) is deployed for pre-processing the image. Further, statistical features, Vegetation Indices (VI) and Improved Hybrid Vegetation Index (IHVI) are extracted from the preprocessed image. Then, we deploy 2 phase classification process for classifying the image. In the first phase, Bi-Directional Gated Recurrent Unit (BI-GRU), Convolutional Neural Network (CNN) and Deep Max Out (DMO) are used. In 2nd phase, optimized RNN is used for determining the final classification result. Also, the training of the model is done by the new Chaotic Opposition Behavior Improvised Coot Algorithm (COBI-CA) optimization via tuning the optimal weights. The COOT method has been enhanced and is now the suggested algorithm. Lastly, we conduct an analysis to compare the suggested Satellite Image Classification (SIC) model's performance to the traditional approaches in terms of statistical analysis, precision, accuracy, and error measure. In comparison to other conventional methods, the accuracy of the suggested model is 97%. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Human Gait Recognition Based on Sequential Deep Learning and Best Features Selection

引用

computers, Materials & Continua 2023年第6期75卷 5123-5140页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Usman Tariq Ye Jin Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxilaPakistan Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj11942Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Gait recognition is an active research area that uses a walking theme to identify the subject *** Gait Recognition(HGR)is performed without any cooperation from the ***,in practice,it remains a challenging task under diverse walking sequences due to the covariant factors such as normal walking and walking with wearing a ***,over the years,have worked on successfully identifying subjects using different techniques,but there is still room for improvement in accuracy due to these covariant *** paper proposes an automated model-free framework for human gait recognition in this *** are a few critical steps in the proposed ***,optical flow-based motion region esti-mation and dynamic coordinates-based cropping are *** second step involves training a fine-tuned pre-trained MobileNetV2 model on both original and optical flow cropped frames;the training has been conducted using static *** third step proposed a fusion technique known as normal distribution serially *** the fourth step,a better optimization algorithm is applied to select the best features,which are then classified using a Bi-Layered neural *** publicly available datasets,CASIA A,CASIA B,and CASIA C,were used in the experimental process and obtained average accuracies of 99.6%,91.6%,and 95.02%,*** proposed framework has achieved improved accuracy compared to the other methods.

关键词： Human gait recognition optical flow deep learning features fusion feature selection

来源：评论

学校读者我要写书评

暂无评论

Marine Life Ecosystem Analysis Based on Climate Change Detection Using Deep Learning Algorithms

引用

Remote Sensing in Earth Systems sciences 2025年第2期8卷 545-554页

作者： Rebecca, B. Sandhya, A. Pokkuluri, Kiran Sree Kanishka, Gunipati Shaik, Khasimbee Krishna, B. Murali Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India Computer Science and Engineering (Cyber Security) Madanapalle Institute of Technology & Science Madanapalle India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The surrounding environmental and climatic conditions have a significant impact on the utilisation of ecosystem services for recreational purposes. Climate change poses a threat to future natural leisure opportunities because of this reliance. The way society functions is being challenged by climate change and significant adaptation will probably be needed to deal with future changes in weather patterns. Machine learning (ML) methods have advanced to the point that they are now being offered as a tool to support climate studies, in addition to being the source of advances in other fields of study. This research proposes a novel technique in marine life ecosystem–based climate change detection using deep learning model. Input is collected as climate change detection–based marine life ecosystem and processed for noise removal with normalisation. Then this data has been segmented and classified using watershed semantic fuzzy component analysis with quantile shrinkage convolutional regression model. The experimental analysis has been carried out in terms of training accuracy, specificity, recall, AUC and NSE. Suggested technique achieved training accuracy 98%, specificity 96%, NSE 45%, recall 95% and AUC 97%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change detection Convolutional regression Deep learning Fuzzy component analysis Marine life ecosystem

来源：评论

学校读者我要写书评

暂无评论

Clinical deep model to analyse medical multivariate time-series data for health diagnosis

引用

Cyber-Physical Systems 2025年第2期11卷 139-164页

作者： Almahadeen, Layth Vijay, Richa Shabaz, Mohammad Soni, Mukesh Singh, Pavitar Parkash Patel, Pavan Byeon, Haewon Department of Financial and Administrative Sciences Al- Balqa’Applied University Al-Salt Jordan Department of computer science Amity University UP Noida India Department of Computer Science Engineering Model Institute of Engineering and Technology Jammu Jammu and Kashmir India Department of Computer Science Engineering University Centre for Research & Development Chandigarh University Mohali India Department of Management Lovely Professional University Phagwara India Department of Computer Science Engineering Ahmedabad Institute of Technology Ahmedabad India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features;feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Blockchain Integration in IoT:Applications,Opportunities,and Challenges

引用

computers, Materials & Continua 2025年第5期83卷 1561-1605页

作者： Mozhgan Gholami Ali Ghaffari Nahideh Derakhshanfard Nadir iBRAHIMO GLU Ali Asghar Pourhaji Kazem Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabriz***Iran Department of Computer Engineering Faculty of Engineering and Natural ScienceIstinye UniversityIstanbul34396Türkiye Department of Computer Science Khazar UniversityBakuAZ1096Azerbaijan MSDC Department Huawei R&D Center˙Istanbul34768Türkiye

The Internet has been enhanced recently by blockchain and Internet of Things(IoT)*** Internet of Things is a network of various sensor-equipped *** gradually integrates the Internet,sensors,and cloud *** is based on encryption algorithms,which are shared database technologies on the *** technology has grown significantly because of its features,such as flexibility,support for integration,anonymity,decentralization,and independent *** nodes in the blockchain network are used to verify online ***,this integration creates scalability,interoperability,and security *** the last decade,several advancements in blockchain technology have drawn attention fromresearch communities and *** technology helps IoT networks become more reliable and enhance security and *** also removes single points of failure and lowers the *** recent years,there has been an increasing amount of literature on IoT and blockchain technology *** paper extensively examines the current state of blockchain technologies,focusing specifically on their integration into the Internet of ***,it highlights the benefits,drawbacks,and opportunities of recent studies on security issues based on blockchain solutions into *** survey examined various research papers fromdifferent types of ***,a review of the other IoT applications has been included,focusing on the security requirements and challenges in IoT-based *** research directions are gathered for the effective integration of Blockchain and IoT.

关键词： Internet ofThings blockchain cybersecurity privacy security

来源：评论

学校读者我要写书评

暂无评论

MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

引用

computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

来源：评论

学校读者我要写书评

暂无评论

Deep features and metaheuristics guided optimization-based method for breast cancer diagnosis

引用

Multimedia Tools and Applications 2024年 1-25页

作者： Asad, Emon Mollah, Ayatullah Faruk Basu, Subhadip Chakraborti, Tapabrata Department of Computer Science and Engineering Aliah University Kolkata700160 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India The Alan Turing Institute and University College London London United Kingdom

Breast cancer is one of the most prevalent cancer types and the second leading cause of death among women. But fortunately, early diagnosis and treatment of breast cancer reduces mortality rates and improves the quality of life significantly. In this work, a computer-aided three-stage pipeline for the diagnosis of breast cancer from mammogram images has been proposed. Firstly, an attention-aided VGG16 network has been applied for deep feature extraction from mammogram images. Then, the extracted features are passed to the adaptive beta hill climbing-aided whale optimization algorithm for the most informative feature selection. Lastly, the features from stage 2 are again evaluated by the grey wolf optimizer combined with the adaptive beta hill climbing algorithm for the selection of the best subsets of features and subsequent identification of cancers using the k-nearest neighbours classifier. The proposed three-stage method has achieved an average accuracy of 97.63% and has reduced 81.78% of the original features. Considering the fact that well annotated imaging data is a costly resource for modern data intensive deep networks, this work shows that a fairly lightweight off the shelf network like VGG-16 can be coupled with a robust feature selection policy to outperform several recent approaches which employ more data intensive learning strategies with larger networks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mammography

来源：评论

学校读者我要写书评

暂无评论

Security experimental framework of trajectory planning for autonomous vehicles

引用

International Journal of Intelligent Networks 2024年第1期5卷 315-324页

作者： Al-sheyab, Sujoud Al-shara, Zakarea Al-khaleel, Osama Department of Computer Engineering Jordan University of Science and Technology Irbid Jordan Department of Software Engineering Jordan University of Science and Technology Irbid Jordan

In the contemporary landscape, autonomous vehicles (AVs) have emerged as a prominent technological advancement globally. Despite their widespread adoption, significant hurdles remain, with security standing out as a critical concern. The potential for attacks within AV networks, exemplified by the Trajectory Privacy Attack on Autonomous Driving (T-PAAD), underscores the urgency for robust security measures. Unfortunately, existing simulations for preemptively assessing the T-PAAD attack's impact are scarce. This paper introduces the Security Experimental Framework for Autonomous Vehicles (SEFAV), designed to address this gap by providing a versatile platform for simulating security scenarios in AV environments. SEFAV is cross-platform and compatible with different operating systems such as Windows and Linux, enhancing accessibility for researchers and practitioners. Our primary focus lies in showcasing the T-PAAD attack within our framework, highlighting its efficacy in evaluating and fortifying AV security. © 2024 The Authors

关键词： Vehicle routing

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：