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OpenCV and its applications in Artificial Intelligent systems 5

OpenCV and its Applications in Artificial Intelligent System...

引用

5th international conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Odeh, Ayman Odeh, Nada College Of Engineering Al Ain University Department of Software Engineering Al Ain United Arab Emirates College Of Information Technology UAEU Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350354690

This paper explores the utilization of OpenCV (Open-Source Computer Vision Library) in artificial intelligence (AI) systems, elucidating its pivotal role in advancing various applications across diverse domains. OpenCV, renowned for its comprehensive functionalities and real-time processing capabilities, has become instrumental in tasks such as object detection (OD), facial recognition (FR), and image processing (IP). However, despite its widespread adoption, there remains a need for a thorough examination of its integration within AI frameworks and the optimization of its functionalities. Through a comprehensive analysis, this study delineates the advantages, limitations, and potential areas for improvement of OpenCV in AI applications. Case studies exemplifying its efficacy in OD, FR, and autonomous systems further elucidate its practical implications. By delving into these aspects, this paper not only underscores the significance of OpenCV in AI but also provides valuable insights for researchers, developers, and practitioners aiming to leverage its capabilities in their endeavors. This study collectively underscores the significant role of OpenCV in enhancing the capabilities of AI systems. © 2024 ieee.

关键词： Adversarial machine learning

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Practically Fast Finite-time Stability in the Mean Square of Stochastic Nonlinear systems with Dead-Zone

Practically Fast Finite-Time Stability in the Mean Square of...

引用

2024 international conference on Fuzzy Theory and Its applications, iFUZZY 2024

作者： Yuan, Yixuan Xie, Liping School of Automation Southeast University Nanjing China

ISBN: (纸本)9798350352788

In this paper, we address the fast finite-time stability problem for a class of uncertain stochastic nonlinear systems. The dead-zone phenomenon is a general and complex challenge in the field of nonlinear systems, which is frequently encountered in real control systems, especially in critical components such as actuators and sensors. In conjunction with the proposed backstepping approach, an adaptive fuzzy control design strategy is established and a corresponding dead-zone controller is constructed. Second, the approximation capability of fuzzy logic systems is utilized for the simultaneous handling of stochastic nonlinear systems with completely unknown nonlinearities as well as disturbance terms. Finally, a numerical simulation is provided to verify the correctness of the theoretical analysis. © 2024 ieee.

关键词： Dead zones

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Is It Running? Unveiling 6G-Driven System-of-systems Testbeds using Visual Metaphors 18

Is It Running? Unveiling 6G-Driven System-of-Systems Testbed...

引用

18th Annual ieee international systems conference (SysCon)

作者： Priyanta, Irfan Fachrudin Krieger, Cedrik Freytag, Julia Teper, Harun Schulte, Philipp Wietfeld, Christian Chen, Jian-Jia Roidl, Moritz TU Dortmund Univ Chair Mat Handling & Warehousing D-44227 Dortmund Germany TU Dortmund Univ Commun Networks Inst D-44227 Dortmund Germany Fraunhofer Inst Mat Flow & Logist D-44227 Dortmund Germany TU Dortmund Univ Design Automat Embedded Syst D-44227 Dortmund Germany

ISBN: (纸本)9798350358810;9798350358803

The rapidly evolving new concepts of the 6G technology raise the challenge of assessing how well 6G-enabled system-of-systems architectures perform in real-life situations beyond theories and simulations. To facilitate this, a new Visual Metaphor layer is presented in this paper to add the visualization feature for a system-of-systems Digital Twin (DT). Visual metaphor plays a crucial role in assisting network engineers to comprehend complex 6G concepts through intuitive visual representations. Powered by an immersive Augmented reality (AR) based on laser projection system, the metaphor framework seamlessly projects the simulated environments into reality and vice versa. Thus, it bridges the gap between theoretical development and practical implementation. Furthermore, it features a plug-andplay solution to accommodate the validation of integrated systems and applications easily. To validate the performance and show the flexibility of the visual metaphor layer, we perform three case studies from the application contexts of teleoperation, platooning, and intralogistics. The results show that the visual metaphor can visualize internal network states, such as communication links, as well as virtual objects and other environmental features. The metaphor can accomplish these diverse and modular tasks while preserving an overall system response time of 55 ms in large-scale scenarios with up to 40 robots.

关键词： System-of-systems Architecture Digital Twin 6G Testbed Mobile Robots Augmented reality

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Lightweight real-time C&C Detection using Deep Learning for Zombie DNS Queries 15

Lightweight Real-Time C&C Detection using Deep Learning for ...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vidhun, K. Kannimoola, Jinesh M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350370249

The effective coordination of attacks between malicious actors and compromised devices via Command and Control (C&C) communication channels presents a substantial cybersecurity threat. These channels often exploit legitimate protocols like the Domain Name System (DNS), making their detection challenging amidst regular network traffic. This paper introduces a unique approach to identifying C&C communication concealed within DNS queries in real-time. Our system employs deep learning techniques for accurate and efficient anomaly detection while conserving resources. Utilizing a pre-trained deep learning model trained on the CIRA-CIC-DoHBrw-2020 dataset, we distinguish between legitimate DNS traffic, encrypted DNS over HTTPS (DoH) traffic, and malicious C&C communication masquerading as DNS queries. To ensure lightweight operation suitable for resource-constrained systems, we implement the entire workflow, from data capture to model inference, as a Rust binary. This methodology provides a novel combination of real-time detection for prompt response to C&C activity, lightweight operation for widespread deployment, and high accuracy leveraging deep learning capabilities. By proactively identifying and mitigating C&C communication linked to zombie DNS botnets, our proposed system shows potential for significantly bolstering network security. © 2024 ieee.

关键词： Advanced Persistent Threats (APT) Command and Control (C2) Cyberattacks DNS Tunneling real-world applications

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Vision-Guided Pick and Place systems Using Raspberry Pi and YOLO 2

Vision-Guided Pick and Place Systems Using Raspberry Pi and ...

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2nd international conference on Networking, embedded and Wireless systems, ICNEWS 2024

作者： Hemanth Kumar, G. Kumar Raja, D.R. Suresh, Sharon Kottamala, Rishi Harsith, Macha Reva University School of ECE Bangalore India Reva University School of CSE Bangalore India

ISBN: (纸本)9798350367461

Vision-guided pick and place systems have revolutionized various industrial and robotic applications by enabling efficient automation and enhancing productivity. This paper introduces a vision-guided pick and place system merging Raspberry Pi and the YOLO (You Only Look Once) algorithm. By integrating Raspberry Pi's affordability and versatility with YOLO's real-time object detection, the system achieves accurate and prompt identification of objects for manipulation. The architecture incorporates Raspberry Pi with cameras and robotic arms, enabling seamless object detection and manipulation. Implementation details encompass software development and hardware setup to ensure smooth integration and optimal performance. Experimental validation confirms robust object detection capabilities, even under challenging conditions. real-time performance analysis underscores the system's suitability for dynamic industrial environments necessitating swift decision-making and precise manipulation. This approach offers a cost-effective, scalable solution for industrial automation, promising heightened productivity, reduced manual intervention, and enhanced operational efficiency across manufacturing and logistics sectors. © 2024 ieee.

关键词： Robotic arms

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timestamp Accuracy of 10BASE-T1L for TSN applications 3

Timestamp Accuracy of 10BASE-T1L for TSN Applications

引用

3rd ieee international conference on Signal, Control and Communication, SCC 2023

作者： Brunner, Kilian Miranda, Luis Da Silva Ostertag, Martin Zhaw Zurich University of Applied Sciences School of Engineering Institute of Embedded Systems Winterthur Switzerland

ISBN: (纸本)9798350326390

time Sensitive Networking (TSN) is a technology for deterministic networking that constantly gains momentum. Precise time synchronization is a prerequisite for TSN systems. This paper presents results of research on timestamping accuracy with 10BASE-T1L PHYs on their MII and RMII interfaces. Various sources for constant and dynamic time error superimpose in real systems, resulting in time stamp inaccuracies of several hundred nanoseconds in the worst case. Asymmetry and measured link delay may vary between successive link establishments. Measurements with existing 10BASE-T1L PHY components from two different vendors show that timestamping on the MAC-PHY interface with components available today is not sufficient to meet the requirements of ieee 802.1AS-2020 (gPTP). 10BASE-T1L is a very useful technology, but system designers should be aware of shown limitations in this paper. © 2023 ieee.

关键词： real time systems

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Suspicious Activity Monitoring System Using YOLOv5 15

Suspicious Activity Monitoring System Using YOLOv5

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kushwaha, Piyush Murmu, Mahendra Kumar National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350370249

In today's digital age, effective surveillance and anomaly detection systems are crucial. YOLOv5, a cutting-edge real-time object detection model that stands out for its remarkable speed and accuracy, making it ideal for these applications. This paper outlines the essential libraries and dependencies for the project, highlighting the importance of setting up the correct environment. It details the process of importing datasets in the YOLO format and provides insights on effective dataset preparation and curation. The exploratory data analysis (EDA) section covers visualizing sample images with bounding boxes, analyzing class distribution, and assessing image size and resolution for YOLOv5 compatibility. The core discusses applying the YOLOv5 algorithm, including model loading, training, and fine-tuning, along with evaluating model performance. Finally, practical applications of the trained model, such as real-time and batch inference, are explored, demonstrating its utility in detecting suspicious activities for security and surveillance. This paper serves as a comprehensive resource for implementing YOLOv5, ensuring the successful deployment of a robust object detection system. © 2024 ieee.

关键词： Anomaly Detection Computer Vision Deep Learning Exploratory Data Analysis (EDA) Security systems YOLOv5

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Adaptive Neuro-Fuzzy systems for real-time Noise Reduction in Urban Soundscapes 2

Adaptive Neuro-Fuzzy Systems for Real-Time Noise Reduction i...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Himabindu, Modi Revathi, V. Yadav, Kanchan Gupta, Manish Pratap, Bhishm Salh, Hussein Badi Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India Gla University Department of Electrical Engineering Mathura India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

The exponential growth of urbanization has intensified the need for effective noise reduction techniques in urban soundscapes. This paper introduces an innovative approach employing Adaptive Neuro-Fuzzy systems (ANFS) for real-time noise reduction. The proposed system integrates the adaptability and learning capabilities of neural networks with the robustness and interpretability of fuzzy logic. A comprehensive dataset comprising various urban soundscapes was used to train and validate the model. The ANFS architecture was meticulously designed to adjust itself according to the fluctuating acoustic characteristics of urban settings, achieving an unprecedented level of noise reduction efficiency. The system's performance was evaluated against traditional noise reduction methods, demonstrating significant improvements in terms of Signal-to-Noise Ratio (SNR), perceptual noise reduction, and computational efficiency. The simulation results showed promising resultsindicating its potential for practical applications. © 2024 ieee.

关键词： Noise abatement

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Accelerating Traditional Object Detection Methods on Sophisticated embedded systems 4th

Accelerating Traditional Object Detection Methods on Sophist...

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4th international conference on Intelligent systems and Pattern Recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual Processing Units VPUs, Tensor Processing Units TPUs, Neural Processing Units NPUs or Graphics Processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection Machine Learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra X1 Jetson Nano

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An Effective real-time Surveillance System for Fire and Smoke Detection Using CNN 9th

An Effective Real-Time Surveillance System for Fire and Smok...

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9th international conference on Pattern Recognition and Machine Intelligence (PReMI)

作者： Niranjan Natesha, B. V. Rashmi, M. Guddeti, Ram Mohana Reddy Natl Inst Technol Karnataka Mangalore India

ISBN: (纸本)9783031126994;9783031127007

Fire disasters are the most dangerous and lethal events that can cause social, economic, life losses. Early detection of fire or smoke is crucial to facilitate intervention in time to avoid large-scale damage. Hence, the effective utilization of embedded devices enhances the performance of the overall surveillance system. We proposed an efficient, low-cost, real-time, memory-optimized method for surveillance systems using the YOLOv4-tiny model for early fire and smoke detection. The proposed method provides the implementation of fire and smoke detection systems for real-world applications where the model could run on low-cost hardware like 1.44 GHz processor devices. The experimental results show that the developed surveillance system can detect fire and smoke in real-time.

关键词： embedded devices fire and smoke detection surveillance system

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