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40th National Radio Science Conference, NRSC 2023

作者： Naeem, Mohamed Aboul-Dahab, Mohamed Technology and Maritime Transport Computer Networks and Data Centre-Cairo Arab Academy for Science Cairo11799 Egypt Technology and Maritime Transport Department of Electronics and Communications Engineering. Arab Academy for Science Cairo11799 Egypt

ISBN: (纸本)9798350311440

Smart agriculture requires field sensing, especially video and photo transmissions, to detect severe issues in this field. Although the most suitable communication infrastructure is wireless transmission, the agriculture field suffers from severe diffraction and scattering of the wirelessly propagated waves. This paper proposes an optimization technique for improving wireless transmission in the agricultural environment by utilizing the most appropriate wireless impairment mitigation methods. The proposed technique adopts a joint wireless transmission optimization approach for both the client and the network sides. The amendment in the wireless transmission from the client-side perspective utilized the concurrent load balance over the heterogeneous wireless connections. While the refinement of the heterogeneous wireless network achieved better coverage by minimizing the number of propagating nodes by optimizing the propagating nodes' placement within the agricultural plot. The simulation, experiment, and results proved the effectiveness of the proposed solution for achieving better wireless video transmission performance. © 2023 IEEE.

关键词： Heterogeneous networks

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Machine Learning based Education System with Sentiment Analysis for Students 2

Machine Learning based Education System with Sentiment Analy...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Deepa, S. Revathi, N. Sivakami, K. Pareek, Piyush Kumar Nehru Institute of Engineering and Technology Dept. of Ece Surat India Nehru Institute of Engineering and Technology Dept. of Ece India Nehru Institute of Engineering and Technology Electronics Engineering Department Surat India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

After a deep questionnaire among students, faculty members and higher education experts. The demand for qualified engineers in the specific field as understandably gone down. The situation is grimmer for tier-2 and tier-3 colleges in India. The purpose of opting engineering course is not to get expertise in the particular course, instead to get a job in government sector with valid degree. The problem identified in the field of engineering education towards improving the quality in education is addressed with the help of exploratory data analysis. The dataset used in our experiment is collect from UCI machine Learning Repository having 33 variables and 1044 observations. The contribution made in the paper is to identify the vital attributes using single and multi-variant regression techniques. To perform prediction using Decision Tree, Random Forest, Support vector Machine and compare their performance in terms of classification accuracy and F-Score. In addition to that a convolution Neural Network (CNN) model is established in which, the vital attributes identified using regression techniques are provided as inputs and weights at each stage is estimated using Gradient Decent algorithm with step size 0.5. The classification accuracy from 63% is improved to 97% with the help of CNN model in 26664 iterations. The finding of the present research states that student having backlogs are less frequently opting for higher studies. © 2022 IEEE.

关键词： Support vector machines

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Comparative Analysis of Wireless Communication Technologies for IoT Applications 3rd

Comparative Analysis of Wireless Communication Technologies ...

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3rd International Conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies, ICRTAC-AIT 2020

作者： Shilpa, B. Radha, R. Movva, Pavani Department of Electronics and Communication IcfaiTech Hyderabad Shankarpally India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789811664472

The Internet of things (IoT) is no longer a new word, because it got to be necessarily portioned of our daily life since of its gigantic applications in every domain. In simple terms, IoT refers to things connecting to the Internet;here things do not mean only electronic devices, and it can be anything like physical, biological, and environmental objects. In present technological world, every second, 127 new devices are connected to Internet. But in the next few years, we can hope to see the IoT-linked devices touching hundreds of billions. There are growing wireless communication technologies are evolving to handle these large connected devices. We have gone through many research and survey papers which were explaining about the IoT technologies and their performance comparison. In this paper, we tried to facilitate the end user with efficient selection of the communication technology for their IoT application by investigating the available technologies and matching with the application requirements. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Real-Time Video Management System for Robotic Waste Sorting and Recycling Using IoT and Machine Learning 2

Real-Time Video Management System for Robotic Waste Sorting ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Raman, Ramakrishnan Meenakshi, R. Latha, R. Rawandale, Chandrashekhar J. Jayaprakash, S. Srinivasan, C. Maharashtra Pune India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Symbiosis Law School Uttar Pradesh Noida India Maharashtra Pune India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

The system automated robotic for waste sorting and recycling by identifying and categorizing various forms of waste. Traditional sorting of waste takes time and is prone to human mistakes. This solution uses IoT and machine learning to overcome these restrictions and improve waste management efficiency. The system begins with waste-collecting area sensors and cameras. The sensors record waste item weight, size, and color, while the cameras record images. The Raspberry Pi, a small image-processing single-board computer, processes sensor and camera data. Raspberry Pi transfers processed data to the cloud for analysis and decision-making. The Support vector machine (SVM) method classifies waste in the cloud using machine learning. A dataset of waste categories trains the SVM algorithm to discriminate recyclable from non-recyclable materials. The system controls robotic arms or conveyor belts to segregate waste after classification. The robotic arms effectively classify garbage into containers. Real-time video management ensures fast, precise waste sorting, enhancing recycling efficiency. The system can gather data from many garbage-collecting regions concurrently. The cloud architecture enables real-time data processing and analysis. SVM-based machine learning techniques improve waste categorization accuracy, improving trash sorting. Waste sorting and recycling using the real-time video management system is novel. IoT, machine learning, and cloud computing facilitate robotic waste segregation. The method might improve waste management and environmental conservation. Optimizing waste management operations, reducing manual labor, and promoting sustainable methods. © 2023 IEEE.

关键词： Cloud Internet of Things Recycling Sensors SVM Algorithm Waste Sorting

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STAR-RIS Assisted Ambient Backscatter Communication System With Index Modulation

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IEEE Transactions on Vehicular technology 2025年

作者： Yue, Ming Peng, Yuyang Ye, Runlong Lee, Juho Macau University of Science and Technology School of Computer Science and Engineering Macau China Samsung Electronics Samsung Research Seoul Korea Republic of

Ambient Backscatter Communication System (ABCS) is regarded as one of the core technologies in the Internet of Things (IoT) due to its ultra-low power consumption and low-cost advantages. However, it also faces multiple challenges in terms of communication reliability, coverage range, and Spectral Efficiency (SE), which constrain its application potential in dense networks and complex scenarios. In this paper, we apply Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) with pattern technology and Index Modulation (IM) into ABCS to improve the SE and Bit Error Rate (BER) performance as well as coverage range. Specifically, the proposed scheme enables extra information bits to be transmitted by allocating subsurfaces to different Backscatter Tags (BTs) and leveraging antenna combinations formed by active antennas of BTs. Meanwhile, the integration of STAR-RIS into ABCS not only significantly enhances signal strength but also achieves full communication coverage. Furthermore, the theoretical and simulation analysis of BER for the proposed scheme is investigated, which reveals the reliability of this scheme with high SE and comparable BER performance. © 1967-2012 IEEE.

关键词： Signal modulation

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Efficient Data Compression on Healthcare Images Using Improved Hadamard Transform Based Filter

Efficient Data Compression on Healthcare Images Using Impro...

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International Conference on Intelligent Systems and Networks, ICISN 2024

作者： Thi, Thu Nguyen Haridoss, G. Rubia, Jency Dilipkumar, S. Chowdhury, Subrata Nguyen, Anh Phuong Duc, Tan Tran Faculty of Electronic Engineering Hanoi University of Industry Hanoi10000 Viet Nam Department of Electronics and Communication Engineering M.A.M College of Engineering and Technology Tiruchirappalli India Department of Electronics and Communication Engineering Vel Tech University Chennai India Department of Analytics SCOPE Vit Vellore Vellore India Department of Computer Sciences and Engineering Sreenivasa Institute of Technology and Management Studies AP Chittoor India Foreign Language Specialized School University of Languages and International Studies Hanoi Viet Nam Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi Viet Nam

ISBN: (纸本)9789819755035

The Hadamard transform is an ancient method for efficient data compression, especially for medical image data. Thus, the proposed work consists of analyzing the filter performance using a novel Hadamard transform to apply data compression. The parametric evaluation has been employed for factors such as PSNR, compression time, and compression ratio. For the application purpose, the proposed modified Hadamard transform is applied for the encryption and decryption process and retrieves the original image. The experimental analysis reveals that the proposed Novel Hadamard Transform gives the best performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Compression ratio Data compression and decompression Hadamard transform Vector Merging Adder PSNR

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A Deep Learning Approach for Classification of Vitiligo and Scar Images

A Deep Learning Approach for Classification of Vitiligo and ...

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International Conference on Communications and Signal Processing

作者： E Rajesh C Madanagopal Reovwin John V Thanikaiselvan T Tamizharasi Rengarajan Amirtharajan School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Skin diseases such as vitiligo present significant challenges in accurate diagnosis due to their similarities with other conditions, often requiring time-consuming assessments by medical professionals. Leveraging advancements in deep learning and medical image processing, this research proposes a novel approach to efficiently and accurately classify these conditions. The proposed model comprises multiple convolutional layers for extracting intricate features from skin images, followed by max-pooling layers to downsample spatial dimensions while retaining essential information. Additionally, fully connected layers enable complex feature learning and classification. This architecture, with six convolutional layers progressively increasing in depth and spatial reduction through max-pooling, culminates in three fully connected layers for precise classification. This comprehensive design ensures effective classification between vitiligo and scar images. By utilising this architecture, our model achieves a high training accuracy of 93.53% and precision of 96.50%, demonstrating its effectiveness in discriminating between vitiligo and scar images. This approach holds promise for enhancing diagnostic accuracy and efficiency in clinical settings, contributing to improved patient outcomes and healthcare delivery.

关键词： Deep learning Training Analytical models Computational modeling Medical services computer architecture Feature extraction

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A Head Posture-based Interactive Method of Auxiliary Mobile Robot for Direction Recognition and Obstacle Avoidance 5

A Head Posture-based Interactive Method of Auxiliary Mobile ...

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5th International Academic Exchange Conference on Science and technology Innovation, IAECST 2023

作者： Zhang, Bosi Hu, Shiyan Wang, Qianlong Shenyang University of Technology School of Electrical Engineering Shenyang China University of Southampton School of Electronics and Computer Science Southampton United Kingdom China Petroleum Pipeline Engineering Corporation Shenyang Liaoning Province China Shenyang China

ISBN: (纸本)9798350357738

With the aging of China's population and the increasing number of disabled people, the demand for medical assistive technology has increased significantly. Patients with severe paralysis may face the challenge of moving and avoiding obstacles when using wheelchairs. In this study, we propose a novel interaction method based on head posture control of intelligent wheelchair. This method can accurately capture the user's direction intention and achieve accurate navigation, so it can complete the task of obstacle avoidance and ensure the safety and comfort of wheelchair operation. Firstly, the mpu6050 sensor is used to collect the head pose information, and then the Kalman filter is used for data fusion. Then, we integrate these data with fuzzy logic reasoning technology to determine the precise movement intention of the patient. Finally, an obstacle avoidance method of intelligent wheelchair based on an improved artificial potential field method is proposed, which uses an array of ultrasonic sensor to help users make appropriate motion decisions, to avoid obstacles. The experimental results show that the accuracy of head posture recognition direction is 90.23%. In the actual obstacle avoidance experiment, all subjects can complete the obstacle avoidance task accurately. Therefore, the interaction method can help users, especially those with severe paralysis, achieve independent control of wheelchairs, and effectively improve their self-care level and quality of life. This technology is expected to be applied in hospitals and nursing scenes in the future. © 2023 IEEE.

关键词： Wheelchairs

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Exploring the Influence of Digital Media on Secondary School Students' Daily Lives 7

Exploring the Influence of Digital Media on Secondary School...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Gogh, Elod Kovari, Attila Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Science University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The focus of this study is to investigate how digital media use affects the daily activities, emotional well-being, and general lifestyle of high school students in relation to education amidst concerns about a 'new terrain or venue' for learning in an era moving full swing towards digitization. The study, conducted with students from grades 9-11, seeks to identify overall trends and associations involving digital media interactions: considering both byproducts of using new technology as an educational tool- in addition discursive aspects allowing for different forms of distraction. In summary, the results showed that digital media use is common and often regarded as educationally beneficial or a positive form of relaxation;however, daily activities are it affected by substantial adverse impacts on everyday life and time management. Suggestions focus on supporting healthy digital media habits, including content-wise engagement and time management tools. © 2024 IEEE.

关键词： Daily Activities Digital Media Digital Media Impact

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Anomaly Detection of Smart City in IoT Cyberattacks based on Hybrid A-BiLSTM-CRF Method

Anomaly Detection of Smart City in IoT Cyberattacks based on...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Chinchawade, Amit Jaykumar Kalaiselvi, M. Kumar, T. Sathish Singh, Yogendra Srisathirapathy, S. Babu, G. Charles Sharad Institute of Technology College of Engineering Department of Electronics and Computer Engineering Maharashtra Yadrav India Vivekanandha College of Technology for Women Department of Cse Tamil Nadu Tiruchengode India S.A.Engineering College Department of Electrical and Electronics Engineering Chennai India Mausam Bhawan India Meteorological Department New Delhi India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Griet Department of Cse Hyderabad India

ISBN: (纸本)9798350313987

In recent years, the new Internet of Things (IoT) paradigm has gained widespread adoption, and as a result, smart cities have evolved. 'Smart cities' deploy their solutions in real time to enhance the convenience and happiness of urban dwellers. The network traffic of a smart city via IoT systems is rising dramatically, generating new cyber security issues, because these IoT devices are being connected to sensors that are directly connected to massive cloud servers. Researchers need to improve techniques for detecting compromised Internet of Things (IoT) devices in order to defend against these incursions. The proposed method employs the steps of preprocessing the data, selecting the features to use, and training the model. Data preprocessing in machine learning and statistics typically involves the use of normalization and standardization feature scaling techniques to convert the numerical values included in the dataset into the desired scales and ranges. Chi-square, Pearson's Correlation Coefficient, and mutual information are the three main metrics used in the feature selection process. A-BiLSTM-CRF is then used to train the models with the input features. When compared to the two most common alternatives, CRF and Bi-LSTM, the proposed method performs better. The proposed method was successful with a success rate of 97.32%. © 2023 IEEE.

关键词： Internet of Things (IoT) Pearson's Correlational Coefficient (PCC) Radio-Frequency Identification (RFID)

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