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

共 36766 条记录，以下是91-100 订阅

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A Paradigm Shift in real-time Detection: The YOLOv7 Approach to Trainable Freebie Optimization 3

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Vinitha, A. Jeevitha, S. Kumar, N.B. Mahesh Sathish Kumar, L. Saravanan, D. Karpagam Academy of Higher Education Department of Computer Applications Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil Tamilnadu Virudhunagar India Hindusthan Institute of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset. © 2025 ieee.

关键词： Contrastive Learning

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Enhancing Cybersecurity in Medical Cyber-Physical systems Using Blockchain and Deep Learning

Enhancing Cybersecurity in Medical Cyber-Physical Systems Us...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Pimple, Jagdish Sharma, Avinash Madhyanchal Professional University Department of Computer Sci. & Engineering Bhopal India

ISBN: (纸本)9798331529833

Medical Cyber-Physical systems (MCPS) are essential in modern healthcare, enabling real-time patient monitoring, diagnosis, and treatment. However, their integration of digital and physical elements exposes them to cyber-attacks, threatening both patient safety and data integrity. This paper shows a novel cybersecurity framework that combines blockchain technology and deep learning-based intrusion detection to protect MCPS. The proposed model uses a deep DNN-based intrusion detection system (IDS) to categorize medical data as either normal or malicious. Blockchain technology is incorporated to ensure secure, tamper-proof, and decentralized storage of patient information. To improve detection accuracy and reduce false positives, feature fusion techniques, processed through convolutional neural networks (CNNs), are applied. Ethereum-based blockchain ensures that medical data remains confidential and resistant to unauthorized alterations. The system's effectiveness is validated using a healthcare dataset, showing a marked improvement in detection accuracy, precision, and recall over traditional methods. This integrated solution strengthens the safety and reliability of MCPS, protecting patient data while maintaining efficient data shared between medical devices and healthcare networks. Future research will focus on real-time applications and scaling the model for larger healthcare systems. © 2025 ieee.

关键词： Cybersecurity

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Securing the internet of things communication and data transmission by using Blockchain based Architecture 3

Securing the internet of things communication and data trans...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Sheetal Kumari, Vasantha Badiger, Veena S Presidency College Department of Computer Applications Bengaluru India

ISBN: (纸本)9798331515911

Due to the rapid growth of technology usage of Internet of Things devices has revolutionized in multiple domains and areas, but still there are few challenges to address like security and performance issues. Most of the traditional approaches are not sufficient in solving those issues related to IoT ecosystems. This paper aims to present a block chain technology-based solution to the problems of security challenges and improvement in integrity, confidentiality, and availability for IoT systems. The features of Block chain in the sense of an immutable nature help secure the IoT system to share data with others, enhance authentication, and transparency. The integration of block chain with IoT devices can also ensure data provenance thus creating an environment for trust with users and stakeholders. To build this blockchain-based IoT system, the proposed architecture ensures a framework of not only ensuring the security of the IoT ecosystem but also sustainability. Future work will be based on designing prototypes and applications within real-world environments to validate the effectiveness of these proposed solutions in various IoT scenarios. © 2025 ieee.

关键词： Blockchain Technology challenges IoT architecture security

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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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IoT based Thermal Management and State of Charge Estimation of LFP battery 3

IoT based Thermal Management and State of Charge Estimation ...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Anand, Deepthi Paraag, V. Naik, Sahana Kavana, T.M. Vasudha, Karanam Shubha Rao, K. Bnm Institute of Technology Dept. of Eee Bangalore India

ISBN: (纸本)9798331515911

Efficient storage of electricity is pivotal in modern applications, necessitating the use of batteries. However, enclosing batteries in compact spaces, such as power plants or electric vehicles, often leads to excessive heat generation, diminishing efficiency and longevity. Heat generation primarily stems from the charging and discharging processes. A liquid cooling system, powered by a motor connected to the battery system, circulates fluid externally around the battery layers to manage temperature, optimizing the battery's performance within the ideal range of 25°C to 35°C. Thermal Modelling of the battery is implemented in systems to understand how temperature changes affect their performance, efficiency, and reliability. IoT-based Battery Thermal Management System (IoT-BTMS) is introduced resulting in a sophisticated solution utilizing sensors, communication technologies, and data analytics. This system monitors and regulates battery temperature in real-time, based on sensed data a relay will enable cooling system if temperature values exceed the limit values or when the system becomes unstable, the cooling system continues until the system stabilizes and temperatures fall within limits. © 2025 ieee.

关键词： Cooling systems

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User-Centric Evaluation Methods for Digital Twin applications in Extended reality 7

User-Centric Evaluation Methods for Digital Twin Application...

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7th ieee international conference on Artificial Intelligence and eXtended and Virtual reality, AIxVR 2025

作者： Vona, Francesco Warsinke, Maximilian Kojić, Tanja Voigt-Antons, Jan-Niklas Möller, Sebastian Hochschule Hamm-Lippstadt Immersive Reality Lab Lippstadt Germany Technische Universität Berlin Quality and Usability Lab Berlin Germany

ISBN: (纸本)9798331521578

The integration of Digital Twins (DTs) with Extended reality (XR) technologies, such as Virtual reality (VR) and Augmented reality (AR), is transforming industries by enabling more immersive, interactive experiences and enhancing real-time decision-making. User-centered evaluations are crucial for aligning XR-enhanced DT systems with user expectations, enhancing acceptance and utility in real-world settings. This paper proposes a user-centric evaluation method for XR-enhanced DT applications to assess usability, cognitive load, and user experience. By employing a range of assessment tools, including questionnaires and observational studies across various use cases-such as virtual tourism, city planning, and industrial maintenance-this method provides a structured approach to capturing the user's perspective. © 2025 ieee.

关键词： Digital Twin Extended reality User-centric evaluation

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Transparency of AI-XR systems: Insights from Experts 7

Transparency of AI-XR Systems: Insights from Experts

引用

7th ieee international conference on Artificial Intelligence and eXtended and Virtual reality, AIxVR 2025

作者： Maathuis, Clara Datcu, Dragos Open University of the Netherlands Netherlands Netherlands

ISBN: (纸本)9798331521578

Recent advancements in the field of Extended reality (XR) have increasingly integrated AI (Artificial Intelligence) technologies to enhance user experience and interactions. These are now crucial in XR applications in tasks like real-time object recognition, responsive environment generation, and adaptive intelligent behavior creation. Nevertheless, AI-XR systems need to adhere to social, legal, and ethical standards, norms, and values to ensure that their development, deployment, and use is done in a responsible and trustworthy manner. This assures building trust, preventing misusing sensitive information, and safeguarding user privacy and autonomy. To this end, transparency of AI-XR systems is fundamental for building and assuring trust. Accordingly, this research aims to understand and reflect on the perspectives that experts have in relation to building transparent XI-XR systems. To do so, a workshop with 14 field experts is conducted and the findings serve as a design framework with a rich palette of technical, socio-technical, and human-centered instruments that could be of use to researchers and practitioners in this domain. © 2025 ieee.

关键词： AI-XR explainability explainable AI explainable AI-XR transparency transparent AI-XR

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Enhancing the Security of Healthcare Data in Cloud computing Environments 2

Enhancing the Security of Healthcare Data in Cloud Computing...

引用

2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sajwan, Himani Kumari, Anjali Chauhan, Garima Kumar, Raj School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (纸本)9798331530389

Cloud computing has completely evolved the way health care data is managed, but it also introduces critical security vulnerabilities that can compromise the privacy of patient information and threaten regulatory compliance. Herein, we present one of the most severe challenges: securing EHRs in a cloud environment with access to data while maintaining HIPAA compliance. We introduce a novel hybrid framework combining homomorphic encryption with blockchain-based access control and dynamic audit logging. The framework was used with a three-phase experimental approach and was deployed over five major healthcare institutions while being analyzed for 2.3 million patient records over 18 months. Results showed a high success rate of 99.97% against denial-of-service attacks while showing reductions in latency time about data retrieval up to 3 4 % compared to traditional methods of encryption. Security-incident-based variation was reduced up to 78%, and 99.99% data integrity was achieved during concurrent access scenarios. Cost analysis shows that overheads in security management amount to a reduction of 4 5 % against conventional systems. Our results show that the proposed framework is far better for healthcare data security, along with improved operational efficiency and regulatory compliance. This work contributes to the literature by providing a scalable and cost-efficient solution addressing the most complex security requirements of modern health care systems in cloud environments. The fact that the framework is implemented successfully in real life seems to reflect its effectiveness for mass-scale implementation in health organizations. © 2025 ieee.

关键词： Cloud security

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Autonomous Vehicle Navigation systems: Machine Learning for real-time Traffic Prediction

Autonomous Vehicle Navigation Systems: Machine Learning for ...

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

作者： Praveen, R.V.S. Hundekari, Sheela Parida, Prasanta Mittal, Tanusha Sehgal, Archana Bhavana, Munugapati LTIMindtree Limited Telangana Hyderabad India School of Computer Applications Pimpri Chinchwad University Pune India School of Rural Management KIIT University Odisha Bhubaneswar India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

By making transportation safer and more efficient, autonomous vehicle navigation systems are transforming the transportation industry. Autonomous cars are able to make route selections based on accurate, up-to-theminute traffic predictions, which help them avoid congestion and maximize their trip time. The use of machine learning algorithms to reliably forecast traffic conditions in real time for use in autonomous vehicle navigation is the focus of this research. Deep learning, support vector machines, and random forests are just a few examples of the machine learning models that can accurately forecast traffic conditions by mining massive datasets collected from GPS data, traffic sensors, and past trends. The suggested method incorporates these models into an autonomous vehicle navigation system that operates in real-time, enabling adaptive modifications according to the flow of traffic. We also look at how things like construction, road accidents, and weather affect the precision of traffic predictions. Based on the outcome of decreased trip time, increased safety and efficiency, results demonstrate how navigation systems supplemented with machine learning, far surpass conventional routing approaches. The study suggests that further research needs to be conducted on hybrid algorithms for better traffic prediction, and multi modal data integration to further optimize autonomous vehicle navigation and in general, machine learning presents great promise in this area. © 2025 ieee.

关键词： Traffic congestion

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AI-Powered Augmented reality for Satellite Assembly, Integration and Test 7

AI-Powered Augmented Reality for Satellite Assembly, Integra...

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7th ieee international conference on Artificial Intelligence and eXtended and Virtual reality, AIxVR 2025

作者： Patrício, Álvaro Valente, João Dehban, Atabak Cadilha, Inês Reis, Daniel Ventura, Rodrigo Institute for Systems and Robotics University of Lisbon Lisbon Portugal Lusospace Lisbon Portugal

ISBN: (纸本)9798331521578

The integration of Artificial Intelligence (AI) and Augmented reality (AR) is set to transform satellite Assem-bly, Integration, and Testing processes by enhancing precision, minimizing human error, and improving operational efficiency in cleanroom environments. This paper presents a technical description of the European Space Agency's (ESA) project 'AI for AR in Satellite AIT' which combines real-time computer vision and AR systems to assist technicians during satellite assembly. Leveraging Microsoft HoloLens 2 as the AR interface, the system delivers context-aware instructions and real-time feedback, tackling the complexities of object recognition and 6D pose estimation in AIT workflows. All AI models demonstrated over 70% accuracy, with the detection model exceeding 95% accuracy, indicating a high level of performance and reliability. A key contribution of this work lies in the effective use of synthetic data for training AI models in AR applications, addressing the significant challenges of obtaining real-world datasets in the highly dynamic satellite environments, as well as the creation of the Segmented Anything Model for Automatic Labelling (SAMAL), which facilitates the automatic annotation of real data, achieving speeds up to 20 times faster than manual human annotation. The findings demonstrate the efficacy of AI-driven AR systems in automating critical satellite assembly tasks, setting a foundation for future innovations in the space industry. © 2025 ieee.

关键词： Tropics

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