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A Cloud-Based Real Time Weather Reporting System Using IoT 1

A Cloud-Based Real Time Weather Reporting System Using IoT

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1st International Conference on Recent Developments in Electronics and Communication Systems, RDECS 2022

作者： Mazumder, Aisheek Bharti, Gaurav Kumar Computer Science and Engineering Department Chandigarh University Punjab 140413 India Electrical Engineering Department Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9781643683607

Predicting weather has become very difficult due to constant changing in weather patterns over the months. IoT based plays a critical role in measuring environmental factors. IoT extends internet connectivity beyond mobile phones and desktops. In this paper, an IoT and cloud-based weather reporting system has been developed. The proposed work detects, record, and display various weather characteristics like temperature, humidity, and precipitation. The system uses sensors to detect and monitor weather factors, and the data is transferred to a cloud that can be accessed via the Internet. The system uses a microcontroller, sensors, a WiFi connection that sends data to a cloud-based server. The data is presented to the user using the cloud application. © 2023 The authors and IOS Press.

关键词： Internet of things

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Enhanced range-doppler algorithm for SAR image formation 20

Enhanced range-doppler algorithm for SAR image formation

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20th International Conference on Aerospace sciences and Aviation Technology, ASAT 2023

作者： Sakr, M. Amein, A.S. Ahmed, F.M. Amer, G.M. Youssef, A. Electrical Engineering Military Technical College Cairo Egypt Faculty of Information and Computer Science October 6 University Giza Egypt Electrical Engineering Faculty of Engineering MUST University Giza Egypt

A Synthetic Aperture Radar (SAR) system is a powerful source of information due to its capability to operate day and night and in all weather conditions. It is essential for military and civilian purposes. Pulse-focusing is employed by the SAR system for achieving both long-range detection and fine-range resolution. The Range-Doppler Algorithm (RDA) is the most popular pulse-focusing technique for creating images from coherent SAR data. Pulse compression techniques for SAR signal processing targets seek to improve map resolution, lower peak power, and increase the Signal-to-Noise Ratio (SNR) of the detected object. This paper demonstrates an enhanced RDA algorithm that uses a modified pulse compression technique based on mismatched filter optimization where Quadratic Constrained Quadratic Program (QCQP) is used. The performance evaluation of the proposed method is performed using visual assessment and the standard image quality metrics peak-side-lobe-ratio (PSLR), impulse-response-width (IRW), and integrated-side-lobe-ratio (ISLR). Simulated and real SAR raw data are used for performance evaluation. The experimental results demonstrate the proposed method's robustness, efficiency, and reliability in providing a higher-quality SAR image than the traditional RDA algorithm. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Synthetic aperture radar

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Edge AI-Driven Video Analytics: A Mobile Deep Learning Framework for Victim Detection in SAR Robotics 3

Edge AI-Driven Video Analytics: A Mobile Deep Learning Frame...

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3rd FORTEI-International Conference on electrical engineering, FORTEI-ICEE 2024

作者： Ihsan, Muhammad Putra, Karisma Trinanda Mustar, Muhamad Yusvin Setyawan, Aufa Faiz Chu, Hsueh-Ting Chen, Hsing-Chung Department of Electrical Engineering Universitas Muhammadiyah Yogyakarta Bantul Indonesia Department of Electrical Engineering Center of Artificial Intelligence and Robotics Studies Universitas Muhammadiyah Yogyakarta Bantul Indonesia Department of Computer Science and Information Engineering Asia University Taichung Taiwan

ISBN: (纸本)9798331542207

In post-disaster search and rescue (SAR) missions, it is crucial for robots to distinguish between actual victims and dummy objects, despite their similar characteristics. Edge video analytics have demonstrated exceptional performance in such missions, however, the trade-off between high-performing deep learning models and computational complexity often results in inadequate computational efficiency for SAR robotics. This study tackles this challenge by deploying a deep learning (DL) algorithm, i.e., MobileNet Single Shot Detector (SSD), specifically tailored for embedded systems, to perform object detection and classification tasks on lab-scale SAR robots. The proposed algorithm is developed in Python and implemented on a low-powered edge computing device. The effectiveness of the proposed algorithm in object localization is highlighted by its mAP score of 0.6247 and an average IoU of 0.9036. Experimental tests using a confusion matrix showed that the model accurately predicted 42 dummy objects and 43 victim dolls. The model achieved an overall accuracy of 89.47%, with a precision of 1.0, a recall of 0.8947, and an F1 Score of 0.9444. Real-time testing confirmed the model’s robust performance, maintaining high accuracy and a stable frame rate of 47 FPS. These findings corroborate the efficacy of the MobileNet SSD algorithm in reliably detecting and differentiating objects within embedded SAR robotic operations. © 2024 IEEE.

关键词： Mobile edge computing

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Exploring the Importance of Security in Mobile Health (mHealth) Data Management 1

Exploring the Importance of Security in Mobile Health (mHeal...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Kulkarni, Mukund Kashinath, Amruta Garagate Momin Irshad, Anjuman Sekhar, G Chandra Patil, P.B. Phatak, Pratiksha Institute of Management Kolhapur India Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India Krishna Vishwa Vidyapeeth Deemed to Be University Department of Anaesthesiology Karad India Jaywant College of Engineering and Polytechnic Department of Electrical Engineering Karad India

ISBN: (纸本)9798350360707

Clinical the healthcare and awareness towards it is now most prominent among the living beings in the world. The mobile health and healthcare are now increasing among the populations over the worlds. Thus, the focus of study is to know the importance of mobile healthcare and its data management, storage and security. The utilization of IoT gadgets and mHealth apps is only two examples of how digital medicine is becoming more and more common in daily medical practice. It has the ability to promote improved patient health outcomes, facilitate care coordination, and increase communication with lower costs. The security to healthcare helps to protect the health-related data from the hackers by providing high security to the devices and the applications. Thus, with time hacking will be getting more intense and will provide more damage so it is necessary to improve the security and data management through mHealth. © 2024 IEEE.

关键词： Electronic health record

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Numerical Simulation and Mathematical Modelling of Machine Learning Based Improved Crypto Currency Forecasting System 3rd

Numerical Simulation and Mathematical Modelling of Machine L...

引用

3rd International Conference on Communication, Networks and Computing, CNC 2022

作者： Sharma, Ravi Bhushan Sharma, Himani Goyal Iqbal, Md. Asif Department of Computer Science Engineering Chandigarh University Chandigarh India Department of Electrical Engineering Chandigarh University Gharuan Chandigarh India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9783031431449

The crypto currency market is a rapidly growing trading and investment industry that has attracted merchants, investors, and entrepreneurs on a global scale never seen before in this century. By providing comparison studies and insights from the pricing data of crypto currency marketplaces, it will aid in documenting the behavior and habits of such a lucratively demanding and rapidly growing sector. The bitcoin market is at one of its all-time highs in 2021. The introduction of new exchanges has made crypto currencies more accessible to the general public, enhancing their attractiveness. This, together with the launch of several genuine crypto initiatives by some of the founders, has resulted in a surge in crypto currency users and interest. Virtual currencies are growing in popularity, with corporations like Microsoft, Dell, and Tesla all accepting them. As the number of individuals using decentralised digital currencies grows, As new currencies gain popularity, it's more important than ever to properly educate the public about them so that they understand what they possess and how their money is being invested. The algorithm predicts that the high, low, and open prices will all be close to the same value on the same day. The coefficients indicate that the high price has the biggest influence on the closing price. Contrary to popular assumption, "volume" does not add to the data on the closing price provided by the other factors. We also observed that the "marketcap" argument offers no more information. This is odd because market cap is often determined by the price of a product. After removing the two previously mentioned factors, we found no discernible difference in adjusted R2. It's worth mentioning that the model was built using data from the same time period. At the same moment as the closing price, the variables we obtained are made public. We are hopeful that the model will be able to forecast the closing price based on the existing high and low values for

关键词： Machine learning

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Idempotency in Service Mesh: For Resiliency of Fog-Native Applications in Multi-Domain Edge-to-Cloud Ecosystems 15

Idempotency in Service Mesh: For Resiliency of Fog-Native Ap...

引用

15th International Conference on Cloud Computing and Services science, CLOSER 2025

作者： Whitaker, Matthew Volckaert, Bruno Al-Naday, Mays School of Computer Science and Electrical Engineering University of Essex United Kingdom Ghent University - imec IDLab Department of Information Technology Gent Belgium

ISBN: (纸本)9789897587474

Resilient operation of cloud-native applications is a critical requirement to service continuity, and to fostering trust in the cloud paradigm. So far, service meshes have been offering resiliency to a subset of failures. But, they fall short in achieving idempotency for HTTP POST requests. In fact, their current resiliency measures may escalate the impact of a POST request failure. Besides, the current tight control over failures - within central clouds - is being threatened by the growing distribution of applications across heterogeneous clouds. Namely, in moving towards a fog-native paradigm of applications. This renders achieving both idempotency and request satisfaction for POST microservices a non-trivial challenge. To address this challenge, we propose a novel, two-pattern, resiliency solution: Idempotency and Completer. The first is an idempotency management system that enables safe retries, following transient network/infrastructure failure. While the second is a FaaS-based completer system that enables automated resolution of microservice functional failures. This is realised through systematic integration and application of developer-defined error solvers. The proposed solution has been implemented as a fog-native service, and integrated over example service mesh Consul. The solution is evaluated experimentally, and results show considerable improvement in user satisfaction, including 100% request completion rate. The results further illustrate the scalability of the solution and benefit in closing the current gap in service mesh systems. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Hypertext systems

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Quantum Generative Adversarial Network for Autonomous Drone Navigation in Urban Wind Zones 6

Quantum Generative Adversarial Network for Autonomous Drone ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Swarnalatha, Anumula Archana, Nalluri Mukkapati, Naveen Selvakumar, Subramanian Tamir, Eneyachew Manikandaprabu, P. Department of Aeronautical and Aerospace Engineering KCG College of Technology Tamil Nadu Chennai600097 India Department of Computer Science and Engineering Vignan's Foundation for Science Technology and Research Andhra Pradesh 522213 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh 522502 India Department of Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Mechanical Engineering Sona College of Technology Tamil Nadu Salem636005 India

ISBN: (纸本)9798331522667

Major challenges are observed when autonomous drones fly in the complex, changeable environment such as wind zones regarding accurate path planning, avoiding the object, and completing tasks. Lack of flexibility in different situations reduces the reliability of conventional models in real time. To overcome these difficulties the Savitzky-Golay Denoising CSWin Quantum Generative Adversarial Network with Emperor Penguin Optimizer known as SGDCQGANNet-EPO was developed. The RGB data and GPS-estimated wind forces in this model undergo Savitzky-Golay Denoising;this model does not rely on global data, only RGB camera data and GPS data. Recently a CSWin transformer (CSWin-T) makes feature extraction and obstacles detection as well as wind angle, wind speed and wind position identification easier. Quantum Generative Adversarial Networks (QGANet) are then used for optimized path planning, and the model is enhanced by the Emperor Penguin Optimizer (EPO) to fine tune the generator and discriminator loss functions. More accurate compared to current approaches in accuracy and efficiency, the model achieves an accuracy of 99.28%, a low RMSE of 0.38, a recall of 99.25%, and EVA of 0.95. In Confidence using this new technique, the free roaming and self-organizing drone maneuvers in complex environments achieved remarkable results. © 2025 IEEE.

关键词： Generative adversarial networks

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Computational Wavefront Shaping with Epi-mode 3D Refractive Index Microscopy

Computational Wavefront Shaping with Epi-mode 3D Refractive ...

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2023 Computational Optical Sensing and Imaging, COSI 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Cai, Mingxuan Zhou, Kevin C. Xue, Yi Waller, Laura Department of Electrical Engineering and Computer Science University of California Berkeley United States Department of Biomedical Engineering University of California Davis United States

We present a wavefront shaping method that computes the optimal wavefront for random-access focusing through scattering in 3D, by using prior knowledge of the reconstructed 3D refractive index (RI), measured in epi-mo... 详细信息

ISBN: (纸本)9781957171289

关键词： Refractive index

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PneuTeleCNN: Deep Learning-assisted Framework for Pneumonia Identification in Web-enabled Telemedicine Systems 10

PneuTeleCNN: Deep Learning-assisted Framework for Pneumonia ...

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10th International Conference on Web Research, ICWR 2024

作者： Mahida, Nikunjkumar Parikh, Keertan Amit Darji, Krisha Gupta, Rajesh Tanwar, Sudeep Shahinzadeh, Hossein Mohammad-Hosseini, Melika Nirma University Institute of Technology Department of Computer Science and Engineering Ahmedabad India Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tehran Iran Najafabad Branch Islamic Azad University Department of Electrical Engineering Najafabad Iran

ISBN: (纸本)9798350394986

An important development can be observed in the current environment of rapid technological progress in the healthcare industry, which can be demonstrated by the emergence of telemedicine as a key paradigm. Extensive use of telemedical techniques has happened with a particular focus on Tele-Pneumonia examinations, which provide doctors with crucial resources in remote diagnostic situations. In response to the necessity of accurate pneumonia prediction, we present a novel architecture PneuTeleCNN that makes utilization of pretrained models such as DenseNet-121, Visual Geometry Group (VGG-16), MobileNet, and InceptionV3. This thorough approach of PneuTeleCNN meticulously evaluates how effectively the system detects pneumonia in web-based telemedicine environments, establishing the stage for improved remote healthcare solutions. These model's performance is rigorously evaluated using key performance indicators, including accuracy, F1-score, recall, and precision. In addition, the evaluation includes sophisticated measures like the confusion matrix and receiver operating characteristic (ROC) curve, which offer an intricate perspective on the model's performance. To help in the identification of the model that performs better in this scenario, a comparative study is made more more straightforward by presenting of each model's validation accuracy and validation loss graphs. This PneuTeleCNN framework underscores the commitment to ensuring the robustness and reliability of the proposed telemedical approach for pneumonia detection in remote healthcare scenarios. © 2024 IEEE.

关键词： Telemedicine

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2D Structure Pushing Nanocrystalline Common Mode Inductor First Resonant Frequency Above 10 MHz

2D Structure Pushing Nanocrystalline Common Mode Inductor Fi...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Zhang, Rongrong Zhao, Hui Jiang, Chaoqiang Wang, Shuo Long, Teng Liu, Kefu Qiu, Jian Fudan University The School of Information Science and Technology Shanghai China City University of Hong Kong The Department of Electrical Engineering Hong Kong University of Florida The Electrical and Computer Engineering GainesvilleFL United States University of Cambridge The Electrical Engineering Division Department of Engineering Cambridge United Kingdom

ISBN: (纸本)9798350376067

Nanocrystalline materials, known for their excellent magnetic properties, are widely used in the design of inductors and transformers, yet their application has been primarily limited to low-frequency scenarios due to their low resonance frequency. This paper introduces an innovative 2D nanocrystalline inductor design that not only capitalizes on the excellent magnetic characteristics of nanocrystalline materials but also effectively increases the resonance frequency to above 10 MHz, enabling the use of nanocrystalline materials in high-frequency power electronics. This design is not only suitable for mass production but also breakthrough in frequency limitations of nanocrystalline Materials. Experimental and simulation results confirm the superiority of the proposed design in enhancing high-frequency impedance, increasing high-frequency insertion loss, exhibiting improvements in EMI conduction frequency range (150 kHz - 30 MHz). The performance of the filter with the proposed 2D inductor is superior to the performance of EMI filters using other inductors within high frequency range, marking a significant breakthrough in magnetic device design. © 2024 IEEE.

关键词： Natural frequencies

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