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International Conference on ent Computing and Control Systems (ICICCS)

作者： Dinesh Besiahgari Manjunath R Kounte Shivananda S University of Cincinnati Electronics and Computer Engineering School of Electronics and Communication Engineering REVA University Bengaluru India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This paper outlines an IoT based smart system to improve road safety in developing countries by solving the problem of poorly maintained roads. The proposed solution uses ultrasonic sensors and Radio Frequency (RF) transmission system to detect potholes and humps and sends alarm signals to the drivers in real time through Blynk application. A GPS module is used to pin down the exact coordinates of these irregularities and the data is saved in a database which can be used by other drivers and road authority. Although this system presents a feasible and economical design, aspects such as the cost of implementation, robustness towards environmental factors and long term sustainability are analyzed in detail to determine the feasibility of the system. This work is intended to minimize vehicle break down and accidents resulting from road hazards and therefore provides a solution that can be scaled up for enhanced road safety.

关键词： Radio frequency Smart systems Sensor systems and applications Road safety Robustness Real-time systems Pins Internet of Things Sustainable development Vehicles

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An Intelligent System for Remote Monitoring of Patients Health and the Early Detection of Coronary Artery Disease

An Intelligent System for Remote Monitoring of Patients Heal...

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2022 International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2022

作者： Kawale, Sheetalrani R Prasad, K.D.V. Dekka, Satish Dankan Gowda, V. Tanguturi, Rama Chaithanya Poornima, Galiveeti Karnataka State Akkamahadevi Women's University Department of Computer Science Karnataka Vijayapura India Pune India Lendi Institute of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Pace Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole India School of Cse & Is Presidency University Karnataka Bangalore India

ISBN: (纸本)9781665454995

India is a country where number of death occur due to road accidents and thus because of the bad health condition of the driver's. To overcome this issue the remote patient monitoring system has to be deployed to find the conditions of patients and update their health information. A new system has been developed, that monitors the activities of the user their behavior, health monitoring and detection of stress this device helps in accessing all those conditions and provides better results for the healthier living of the user or patients. The ability of predicting the prevalence of diabetes and hypertension in the Indian women using the specific thresholds indices are examined. The cut off point for waist circumstances is >35% thresholds, 12% have more than WC threshold in hypertension and 13 % in case of diabetic patients. The body mass index cut point threshold value is 25.02 kg/m2 and 34% people have more than BMI cut-point in case of hypertension and 25% have more than BMI cut point in case of diabetes. In case of waist to height ratio cutoff only 1% have missed the cutoff in hypertension and 0% in case of diabetes, from these three parameters have the same ability in predicting the hypertension and diabetes in Indian women. The health monitoring system is proposed using decision tree. © 2022 IEEE.

关键词： Diseases

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Design of Arrow Shaped Microstrip Patch Antenna for X and Ku- Band Applications 1

Design of Arrow Shaped Microstrip Patch Antenna for X and Ku...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Yuvaraj, D. Nageswara Rao, M.V. Shanmuga priya, P. Naveneetha krishanan, S. Department of Computer Science Cihan University Kurdistan region Duhok Iraq Dept. of ECE GMR Institute of Technology RajamSrikakulam Andhra Pradesh India Department of Computer Science and Engineering M.I.E.T Engineering College Trichy India Department of Electronics and Communication Engineering SACS MAVMM Engineering College Madurai India

ISBN: (纸本)9781607685395

A small size printed patch antenna to cover applications of X and Ku band is suggested in this paper. The suggested antenna consists of an arrow shaped patch on front view of FR 4 substrate and a partial ground on the other side. Initial design started with a rectangular patch of dimensions 10.7 mm x 7 mm. Then it is modified into arrow shaped patch in order to improve the reflection coefficient at lower and higher *** measurement of the antenna is 12 x 15 x 1.6 mm3. The antenna parameters are optimized using FEM based HFSS software. The operating range of frequencies include from 6.75 GHz to 18.96 GHz. The proposed antenna displays a good omnidirectional radiation pattern and maximum gain of 7.41 dBi at 17 GHz. Simulated S11 parameters, VSWR, Gain and current distributions are offered in this paper and S11 simulated parameters are in good agreement with measured parameters. © The Electrochemical Society

关键词： Arrow shaped Ku band Printedmicrostrip antenna X band

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Design and Analysis of Terahertz Multi-Frequency Low-Profile Waveguide Antenna Based on Micro-Coaxial Feeding

Design and Analysis of Terahertz Multi-Frequency Low-Profile...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： LiBo Wang Shu Lin Tong Xu Xingqi Zhang Xinyue Zhang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electronic Engineering Maynooth University Ireland

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This paper presents a comprehensive theoretical analysis of electric field distribution in low-profile waveguide antennas operating in the Terahertz frequency range. We have developed a micro-coaxial-based, low-profile, multi-frequency waveguide antenna, integrating pin and gradient ridge structures to enhance its performance. The antenna dimensions are 4.7 mm ×13.3 mm × 0.9 mm. The reflection coefficient of this waveguide antenna is below -10 dB within the frequency ranges of 110–130 GHz, 148–156 GHz, and 175–182 GHz. It exhibits excellent directional radiation characteristics at the center frequencies of these three bands. With its compact size, lightweight, and low-profile features, this antenna is suitable for Terahertz high-speed communication applications, providing a practical approach for harnessing the electromagnetic spectrum resources in high-frequency domains.

关键词： Antenna theory Conferences Waveguide theory Reflector antennas Antenna feeds Reflection Reflection coefficient

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Frequency Domain Separation of DAS Multi-Source Aliased Signals

Frequency Domain Separation of DAS Multi-Source Aliased Sign...

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2023 Asia communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Gu, Huaxin Liu, Shuaiqi Yu, Feihong Xu, Deyu Chen, Xingwei Shao, Liyang Wu, Yu Zhang, Haifeng College of Innovation and Entrepreneurship Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology Chengdu China Research Institute of Tsinghua University Advanced Measurement and Control and Equipment R&d Center Chengdu China

ISBN: (纸本)9798350312614

In this paper, fast independent vector analysis (FastIVA) based on convolutional aliasing model is proposed to separate the aliased signals collected by distributed acoustic sensing (DAS) system, and the time-frequency entropy is used to judge the separation performance. The results show that the time-frequency entropy interval of the separated signals and the source signals correspond to each other, which means that the aliased signals are separated. This method is used to improve the accuracy of DAS system signal detection and recognition in complex real environment, and reduce the number of false alarm events. © 2023 IEEE.

关键词： convolutional aliasing model DAS FastIVA time-frequency entropy

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Traffic Classification Using CNN-LSTM Based on Multimedia communication System

Traffic Classification Using CNN-LSTM Based on Multimedia Co...

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Integrated Circuits and communication Systems (ICICACS), IEEE International Conference on

作者： Srinivasan Jagannathan Montater MuhsnHasan Panjagari Kavitha K. S. Vigneshwaran Neethu C Francis Department of Computer Applications Madanapalle Institute of Technology & Science Madanapalle India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Electronics and Communication Engineering CVR College of Engineering Hyderabad India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Network traffic classification is the process of determining and categorizing network data by identifying various kinds of services, applications, and threats. However, the challenge lies in recognizing and classifying the various class of network traffic among high volume of network traffic data. The classification with high network traffic data struggles from imbalanced data and noise. This research proposes an effective Convolution Neural Network - Long Short-Term Memory (CNN-LSTM) model to classify the network traffic based on multimedia communication system. Initially, the proposed CNN-LSTM is evaluated using Information Security centre for Excellence (ISCX) Virtual Private Network (VPN) and non-VPN traffic dataset to determine the model performance. After obtaining data, the min-max normalization is applied to transform numerical values in a standardized range followed by feature extraction using CNN to capture spatial relationships in network patterns. Finally, the proposed CNN-LSTM is performed to classify the network traffic effectively by utilizing CNN for spatial feature extraction and LSTM to learn sequential dependencies of data effectively in traffic flow. While compared to existing methods like Advanced Density-Based Spatial Clustering of Applications with Noise (A-DBSCAN), the proposed method obtains a high accuracy of 0.9876 using ISCX VPN-non-VPN traffic dataset.

关键词： Noise Telecommunication traffic Transforms Feature extraction Virtual private networks Numerical models Convolutional neural networks Multimedia communication Integrated circuit modeling Long short term memory

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Retraction Note: Exploring subcellular location anomalies: a novel quantum bioimaging paradigm

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Optical and Quantum electronics 2024年第11期56卷 1-2页

作者： Kumar, Kailash Abbas, Syed Hauider Gupta, Manish Waris, Saiyed Faiayaz Bordoloi, Dibyahash Kirubakaran, M. K. College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia Department of Computer Science and Engineering Integral University Lucknow India Department of Electronics and Communication Engineering GLA University Mathura India Department of Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi Guntur India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Artificial Intelligence and Data Science St. Joseph’s Institute of Technology Chennai India

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Overlay Space-Air-Ground Integrated Networks with SWIPT-Empowered Aerial communications

arXiv

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arXiv 2024年

作者： Verma, Anuradha Sharma, Pankaj K. Kumar, Pawan Kim, Dong In Department of Electronics and Communication Engineering National Institute of Technology Rourkela Rourkela769008 India Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

In this article, we consider overlay space-air-ground integrated networks (OSAGINs) where a low earth orbit (LEO) satellite communicates with ground users (GUs) with the assistance of an energyconstrained coexisting air-to-air (A2A) network. Particularly, a non-linear energy harvester with a hybrid SWIPT utilizing both power-splitting and time-switching energy harvesting (EH) techniques is employed at the aerial transmitter. Specifically, we take the random locations of the satellite, ground and aerial receivers to investigate the outage performance of both the satellite-to-ground and aerial networks leveraging the stochastic tools. By taking into account the Shadowed-Rician fading for satellite link, the Nakagami-m for ground link, and the Rician fading for aerial link, we derive analytical expressions for the outage probability of these networks. For a comprehensive analysis of aerial network, we consider both the perfect and imperfect successive interference cancellation (SIC) scenarios. Through our analysis, we illustrate that, unlike linear EH, the implementation of non-linear EH provides accurate figures for any target rate, underscoring the significance of using non-linear EH models. Additionally, the influence of key parameters is emphasized, providing guidelines for the practical design of an energy-efficient as well as spectrum-efficient future non-terrestrial networks. Monte Carlo simulations validate the accuracy of our theoretical developments. © 2024, CC BY.

关键词： Energy harvesting

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Real – Time communication System for Specially Abled Powered by AI

Real – Time Communication System for Specially Abled Powere...

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International Conference on computer and communications Technologies (ICCCT)

作者： T. Poornachandar T. Sathieskumar K. Thangadurai M. Dinesh Kumar V. Anuradha T. Jayasri Department of Computer Science and Engineering Annapoorana Engineering College Salem Tamilnadu India Department of Electonics and Instrumentaion Engg Sri Sairam Engineering College Chennai Tamilnadu India Department of Electronics and Communication Engineering Knowledge Institute of Technology Salem Tamilnadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Our project primarily focuses on developing a model to facilitate communication for deaf or dumb individuals, who often face significant challenges in interacting with others. Real-time communication is a critical aspect of life, enabling individuals to connect instantly from anywhere globally. Our system aims to recognize hand gestures, text and speech. It perform tasks such as Sign Language convert into text and speech and the text will be convert into speech with our desired language, ensuring it is received within the appropriate hearing frequency range. To achieve this, the proposed system will incorporate artificial intelligence by creating a model using Convolutional Neural Networks (CNNs) to recognize Indian Sign Language or human actions and convert them into text and audio formats. The corresponding messages will be sent via Global System for Mobile communication (GSM) module to the wearer's Android device, which will convert the text into speech. By utilizing an Automatic Speech Recognition (ASR), and Statistical Machine Translation (SMT) algorithm, facilitate multilingual speech translation. By using Real-time Protocol (RTP) for recording functions for the future use. This AI-Powered system also establishes speech and text processing via Bluetooth-enabled mobile connectivity for seamless interaction.

关键词： Hands GSM Sign language Translation Text recognition User centered design Real-time systems Convolutional neural networks Speech processing Text processing

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Plant Disease Prediction Using Convolutional Neural Networks

Plant Disease Prediction Using Convolutional Neural Networks

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： K. Sai Prasad K. Shekar P Chinnasamy Ajmeera Kiran Mohamed Junaid K A B Rachana Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Electronics and Communication Engineering R.M.K. Engineering College TamilNadu India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

This study demonstrates how to track the wellness of plants more accurately in general using deep learning along with the World Wide Web of Things. The key advantage of the article is that Convolution neural networks, an advanced neural network type, can forecast scenarios in plant environments by analyzing leaf pictures. In order to give farmers additional data, sensors that measure water in the soil, temperature and pressure are also used. The application informs the farmer on the agricultural industry. This study offers a workable technique that would enable farmers to remotely observe plant illnesses in their fields, saving them time and labor. In the agricultural sector, illnesses of leaves reduce the level of agricultural yield. As a consequence, in order to boost productivity in agriculture, leaf illness detection can be automated. Multiple leaf illnesses that impact detection sensitivity cause the majority of solutions fall short of objectives. This research establishes a model that encompasses image capture, extracting features, and image classification to build a computer vision framework. A neural networks classifiers called CNN is employed to categories Streaming images. According to experimental findings on jalapeno leaf rot being identified, the suggested technique has a higher identification rate than other existing methods. The categorization outcome tells whether the leaf is ill or not.

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