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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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Novel Differentiated Integrity Protection for Enhancing Performance of Beyond 5G systems 9

Novel Differentiated Integrity Protection for Enhancing Perf...

引用

9th ieee international conference on Electronics, computing and Communication Technologies, CONECCT 2023

作者： Rajavelsamy, R. Bharath Kumar, R. Ganesha Aswin, S. Lee, Duckey Das, Debabrata Bangalore India Communications Standards Lab Samsung Research Samsung Seoul Korea Republic of Bangalore India

ISBN: (纸本)9798350334395

Secure communication is vital for any wireless technology. However, there is always a tradeoff between security and network performance. Diverse CPS applications in real-time Internet of Things (IoT) with stringent throughput and latency requirements are posing exceptional challenges in terms of system design. Integrity protection (IPr) of the user traffic in 5G and beyond systems introduce a performance overhead, specifically for CPS applications that requires URLLC. In this paper, we explore the tradeoff between security and performance in 5G and beyond wireless networks. We focus on the impact of integrity protection (IPr) on the user plane traffic and its effect on URLLC metrics such as age of information (AoI), throughput, packet overhead, and latency. To address this challenge, we propose a novel mechanism - Differentiated IPr PDU Session (DIPS), which selectively applies IPr only to sensitive user plane traffic. Our experimental results on a 5G system test setup show promising results, including a reduced average AoI, an increase of 70% in throughput, and a reduction of 43% in latency when transferring 2GB of data. © 2023 ieee.

关键词： 5G mobile communication systems

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Eco-Friendly Next-Gen Air Conditioning with IoTBased embedded Monitoring and Control for Reduced Energy Consumption 3

Eco-Friendly Next-Gen Air Conditioning with IoTBased Embedde...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dusarlapudi, Kalyan Jamuna Rani, G. Sekhar, Pavuluri Soma Aekanth, P. Jyothi, B. Koteshwara Rao, K. Koneru Lakshmaiah Educational Foundation Vijayawada India Department of Mechanical Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Electrical and Electronics Engineering Koneru Lakshmaiah Educational Foundation Vijayawada India

ISBN: (纸本)9798331527549

The increasing demand for energy-efficient solutions in air conditioning has prompted the development of smart monitoring systems that leverage the Internet of Things (IoT). This study introduces an IoT-enabled embedded system for intelligent monitoring and control of air conditioning units, aiming to reduce energy consumption, enhance operational efficiency, and support preventive maintenance. The system utilizes an ESP32 microcontroller to process data from integrated sensors that monitor temperature, humidity, voltage, current, sound, and vibration. These sensors track real-time operational parameters, with data sent to a cloud platform for remote monitoring. Critical thresholds trigger automated safety responses, such as powering off the unit via relay activation, thereby preventing damage and optimizing energy use. This smart AC management system provides users with mobile and web access, promoting convenient, efficient energy management while contributing to reduced carbon emissions. The prototype demonstrates significant potential for deployment in both residential and industrial HVAC applications, aligning with global sustainability goals by curbing unnecessary energy consumption and enhancing device longevity. © 2025 ieee.

关键词： Preventive maintenance

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Cell Temperature Estimation In Lithium Ion Cell for Electric Two-Wheeler Application In Indian Conditions 2

Cell Temperature Estimation In Lithium Ion Cell for Electric...

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2nd ieee international conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Pandey, Bhaskar Vashist, Devendra Manav Rachna International Institute of Research and Studies Department of Mechanical Engineering Faridabad India Manav Rachna International Institute of Research and Studies Department of Automobile and Aeronautical Engineering Faridabad India

ISBN: (纸本)9798350390179

Green mobility is the thrust around the world to make transportation environmentally sustainable. Electric vehicles (EVs) are emerging as the front-runner in green technology-based transportation systems. Lithium-based battery technologies are backing the emergence of electric vehicles. High energy density and longer cycle life are the prominent characteristics of Li-ion cells, making them wellsuited for EV applications. However, the optimum performance of the battery pack is highly subjected to the operating temperature. India is emerging as a hub of electric vehicles, especially for electric two-wheelers and e-rickshaws. The temperature in the summer of India reaches up to 50°C or even more in some cases, which is beyond the optimum operating range of Li-ion cells. It becomes necessary to estimate the cell temperature so that safe operation can be ensured. This paper aims to assess the temperature rise of the 18650 Li -ion cell under the real-world drive cycle specifically developed for electric scooter applications for the city of Lucknow, India. The thermal model based on Joule's heating is simulated using Matlab Simulink. A comparison of simulated cell temperature rise is done for real-world drive cycle and standard drive cycles such as the IDC and WLTC. The comparison of results indicates the need to incorporate the real driving behavior during the thermal modeling of the cell as the standard drive cycles underestimate the temperature rise by many folds. © 2024 ieee.

关键词： Battery Pack

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Object Detection in real-time Surveillance Using Deep Learning-Based YOLO Framework

Object Detection in Real-Time Surveillance Using Deep Learni...

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2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kukreti, Sanjeev Praveen, R.V.S. Bansal, Saloni Raju, Hemanth Singh, Navdeep Begum, Rowsonara Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India LTIMindtree Limited Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

The continual growth of the computer vision and artificial intelligence fields overtime have provided the basis for more efficient and accurate surveillance. This paper examines the use of YOLO, a deep learning model that is used in the detection of objects, for use in real-time surveillance systems. The added benefits of identifying and labeling multiple objects in a single pass forward makes YOLO ideal for real-time surveillance. Unlike other approaches to object detection such as region-based methods or methods with multiple stages of detection, YOLO cast object detection as a regression problem where boxes and class probabilities are predicted jointly. This leads to a far improved performance when compared with their slower counterparts without the sacrifice of precision. In this work, the YOLO model is finetuned on a large database of surveillance videos to improve object detection at low lighting and in the presence of occlusion and crowding. The developed system has a high detection speed and accuracy;the application areas will include uses such as security inspection, traffic regulation, and anomaly detection. This is done to affirm that using YOLO is more efficient and faster than the other object detection algorithms in real-time surveillance tasks. The results indicate directions for further improvements to guarantee public safety and improve efficient surveillance systems. © 2025 ieee.

关键词： Deep learning

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Secure Protocol With Mutual Authentication For Telecare Medical Information System Using ECC For IOT Enabled WBAN Devices

Secure Protocol With Mutual Authentication For Telecare Medi...

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2024 ieee international conference on Intelligent systems and Advanced applications, ICISAA 2024

作者： Anushaa, S.S. Naundhini, S. Chaudhary, Dharminder Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Chennai India

ISBN: (纸本)9798331539948

Telecare Medical Information System (TMIS) is a technology that has been on rise in the recent times with the onset of the COVID-19 pandemic. TMIS can be implemented in wireless body area networks (WBAN) in the form of devices worn by the patient and is convenient and time saving for both patients and doctors. The data that is generated is transferred through an insecure channel and thus, there is a need to protect confidential patient data. There is a necessity to provide secure authentication and access control to ensure only authorized users can access the data even in a device with limited storage and computational power. In this paper, Elliptic Curve Cryptography and the three-factor authentication method are combined to propose an enhanced protocol that is immune to the flaws in previously proposed ones. It uses a trusted party that helps in the registration process, ensuring that only authorized parties can read the data. A cloud-assisted TMIS is implemented by using the cloud as a storage medium and securing it, as well as the transfer of data. The Automated Validation of Internet Security Protocols and applications (AVISPA) tool is used to validate the proposed protocol and prove its security. © 2024 ieee.

关键词： Medical information systems

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Experimental Evaluation of Artificial Intelligence Assisted E-Commerce Personalization and Recommendation Techniques 4

Experimental Evaluation of Artificial Intelligence Assisted ...

引用

4th international conference on Soft computing for Security applications, ICSCSA 2024

作者： Logapriya, T. Helen, Frederick Ruby Lavaraju, B. Saveetha School of Law SIMATS Chennai77 India

ISBN: (纸本)9798331515720

In the rapidly evolving field of e-commerce, personalized recommendations have become essential for enhancing user experience and driving sales. Traditional recommendation systems, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), have been widely used but often face challenges in handling sparse data, cold-start problems, and the complexity of high-dimensional datasets. This paper presents the Hybrid Ensembled Model (HEM), which integrates a Cascaded AutoEncoder with an Artificial Neural Network (ANN) to improve the accuracy and relevance of personalized recommendations. The proposed model was evaluated using the Amazon Product Reviews dataset, achieving an impressive accuracy of 96.39%, significantly outperforming traditional models, which ranged from 89% to 94%. The HEM model demonstrated superior performance across key metrics, including precision, recall, F1-score, and Mean Average Precision (MAP). This study highlights the potential of hybrid models in addressing the limitations of conventional recommendation systems, providing a robust solution for e-commerce platforms. The results indicate that the HEM model not only enhances recommendation accuracy but also effectively handles sparse data and cold-start problems, making it a versatile tool for real-world applications. © 2024 ieee.

关键词： Recommender systems

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INCL: An Effective Deep Learning Model to Identify Road Potholes by Using Image Based Neural Classification Logic

INCL: An Effective Deep Learning Model to Identify Road Poth...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Shasikala, Y. Kantha, M.Rajitha Dinesh, M. Nagamani, K. Bonde, A. Afanehsuha AdityaUniversity Departmentof Computer Applications Surampalem India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh Prakasam District India Dept of Vlsi Microelectronics Chennai India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Prakasam India AndhraPradesh Prakasam District India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan Universiry of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

This paper presents a deep learning-based approach for detecting road potholes using image-based neural classification logic. The methodology involves collecting road images from publicly available datasets, particularly from Kaggle, and augmenting the dataset to improve model performance. Preprocessing steps such as resizing, standardizing, and annotating the images were employed to ensure consistency. A pre-trained Faster R-CNN model was fine-tuned for pothole detection, using convolutional neural networks (CNNs) to extract features and classify the road images into pothole and non-pothole categories. The model was trained using transfer learning and tested under varying road and environmental conditions, including day, night, dry, and wet scenarios. The model achieved an accuracy of 96.5% in dry daylight conditions, with a slightly lower performance of 85.4% during wet nighttime conditions. Additionally, precision and recall were evaluated, with the model achieving a precision of 95.3% and a recall of 97.6% for daytime conditions. The system was implemented in a real-time environment, allowing for real-time pothole detection and reporting through a mobile or web application. Future work will aim to improve performance under challenging conditions and enhance real-time deployment efficiency. © 2024 ieee.

关键词： Convolutional neural networks

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Machine Learning to Predict Risk Management applications Performance 5

Machine Learning to Predict Risk Management Applications Per...

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5th ieee international conference on Autonomic computing and Self-Organizing systems Companion, ACSOS-C 2024

作者： De Giorgi, Laura Ardagna, Danilo Politecnico Di Milano Tinvention s.r.l. Department of Electronics Information and Bioengineering Italy Politecnico Di Milano Department of Electronics Information and Bioengineering Italy

ISBN: (纸本)9798350389760

Machine Learning is increasingly crucial for predicting application performance, offering a black-box approach that does not require a deep understanding of the application internal workings. This method enables accurate predictions without delving into complex system models. Our study utilized ML to forecast the execution time of an industrial application dealing with risk measures as part of the Solvency II regulations for insurance companies. By conducting a comparative analysis of multiple models, XGBoost was identified as the most effective, achieving a Mean Absolute Percentage Error of 18%. The results demonstrated robust accuracy for intermediate durations, though limitations were observed for shorter and significantly longer times due to data scarcity. Overall, this study highlights the significant potential of ML in improving prediction accuracy for complex industrial applications, offering valuable insights for resource allocation and performance management. © 2024 ieee.

关键词： Adversarial machine learning

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Post-Quantum Cryptography for embedded systems

Post-Quantum Cryptography for Embedded Systems

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ieee Mexican international conference on Computer Science (ENC)

作者： Koziel, Brian Kermani, Mehran Mozaffari Azarderakhsh, Reza Univ Jean Monnet CNRS Lab Hubert Curien F-42000 St Etienne France CINVESTAV Unidad Guadalajara Zapopan 45019 Mexico CINVESTAV Unidad Tamaulipas Ciudad Victoria 87130 Tamaulipas Mexico

ISBN: (数字)9781665473477

ISBN: (纸本)9781665473477

Over the years, important breakthroughs have been made in protecting data at rest or in-transit. The emergence of the Internet of Things (IoT) and Cyber Physical systems (CPS) has pose new challenges for protecting data in systems and devices tightly close to humans. embedded systems with constrained computing capabilities are a central part in IoT and CPS, susceptible to different security attacks and vulnerabilities. Lightweight cryptography is the term used for grouping the different cryptographic algorithms and techniques tailored to observe the requirements for data security in such constrained applications. Nowadays, the potential emergence of quantum computing threatens these security solutions widely in use. Thus, novel cryptographic algorithms deemed to resist quantum attack models are being standardized by the National Institute of Standards and Technology (NIST) in the Post-Quantum Cryptography (PQC) Standardization process. In this paper, we present a study on how the advent of quantum computing might have a deep impact on the security of constrained applications in the IoT and CPS domains. We focused on the NIST PQC finalists, from the perspective of their use for securing embedded systems.

关键词： NIST Cryptography Computer science Internet of Things embedded systems Computational modeling Art

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An embedded 3D Object Detection in real-time Using Deep Learning Techniques 4

An Embedded 3D Object Detection in Real-Time Using Deep Lear...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Aravinda, K. New Horizon College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350300888

The field of computer vision is currently experiencing significant growth in the area of real-time 3D object identification. This research aims to efficiently and precisely identify and locate objects in a three-dimensional space. The demand for efficient detection approaches has escalated due to the growing prevalence of sensors such as LiDAR and depth cameras in various applications, including autonomous vehicles and augmented reality. This study explores the most recent developments in utilizing deep learning algorithms for real-time 3D object recognition. It is worth mentioning that algorithms such as PointRCNN, VoteN et, and 3D-CNN s have exhibited the capability to effectively handle large and sparse point cloud data. Nevertheless, there are ongoing challenges that continue to exist in this field, including the issue of limited availability of data and the high computational demands required for real-time processing. By utilizing optimal neural network designs and integrating multi-modal sensor input, the systems presented demonstrate the potential to overcome these problems. This study provides novel research insights on the prospective trends by highlighting the potential of end-to-end learning and domain adaptability. © 2023 ieee.

关键词： Object detection

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