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2nd international conference for Innovation in Technology, INOCON 2023

作者： Kambhampaty, Harish Rohan Battula, Naga Datha Saikiran Rentala, Salwesh Challa, Sai Charan Anuradha, K. Gokaraju Rangaraju Institute of Engineering and Technology Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350320923

In the age of automation, a growing number of devices require high dimensional data to increase the reliability of the machines to better predict and classify objects for autonomous decision-making. 3D object detection directly links to environmental interpretation and therefore builds the base for prediction and movement planning for autonomous systems. With greater computing and storage capacities at the edge level, higher-density data can be captured and analyzed, and hence better predictability of natural environments is achieved. Convolutional Neural Networks are becoming increasingly important for object detection and tracking in many autonomous systems due to the improvement of affordable edge-computing devices capable of working with higher-dimensional data, such as LiDAR sensors, in recent years. Although the capabilities of edge-computing devices are growing, the computational requirements for real-time applications are also rising. This paper proposes two types of optimizations to the networks: pruning and quantization. The proposed optimizations are applied to a base model trained on the KITTI dataset, which shows an average inference speed-up of up to 20% while minimizing the loss of performance. © 2023 ieee.

关键词： Object detection

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Enhancing Traffic Management with Deep Learning Based Vehicle Detection and Scheduling systems

Enhancing Traffic Management with Deep Learning Based Vehicl...

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2023 ieee international conference on Modelling, Simulation and Intelligent computing, MoSICom 2023

作者： Patil, Archita Raorane, Ashwini Kundale, Jyoti Ramrao Adik Institute of Technology Department of Electronics Engineering Navi Mumbai India Ramrao Adik Institute of Technology Department of Information Technology Navi Mumbai India

ISBN: (纸本)9798350393415

Traffic congestion is an inescapable issue in contemporary urban life, leading to time wastage and increased accident risks. This paper presents an innovative approach that harnesses machine learning and computer vision to monitor vehicle density on roads and optimize traffic signal timings at intersections. With the escalating number of vehicles on the roads, traffic congestion has become a critical problem. Queue lengths at intersections surge with rising traffic flows, rendering traditional traffic system inefficient. In response, this paper introduces a real-time traffic light control algorithm that adapts to traffic flow. utilizing computer vision and machine learning to discern traffic flow characteristics at signalized road intersections, employing state-of-The-Art real-time object detection using a deep Convolutional Neural Network known as YOLO. YOLOcan be implemented on embedded controllers through Transfer Learning, enabling deep neural network capabilities even on limited hardware resources. The proposed system comprises a camera system capturing real-time video feeds of roads, which are then processed by a machine learning model to detect vehicle density. The scheduling system dynamically adjusts traffic signal green times based on observed vehicle density. Furthermore, we conduct an in-depth analysis using various machine learning models to predict traffic patterns. This analysis encompasses a performance comparison of models like K-Nearest Neighbor, random forests, and decision trees to forecast traffic volume with respective accuary of 53.43%, 98.88%, 99.09%. The study also investigates the influence of parameters such as time of day, weather and temperature conditions. The primary objective of our system is to optimize traffic flow by minimizing waiting times at traffic signals, thereby reducing congestion and enhancing the overall commuting experience for drivers. © 2023 ieee.

关键词： Traffic congestion

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Identification of Paradigms of Heliacal Luminosity Through Onotological Mechanism and 5G Networks 4

Identification of Paradigms of Heliacal Luminosity Through O...

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4th ieee international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Santhakumar, D. Kumar, Neelam Sanjeev Arunprasath, R. Sekhar, G.G.Raja Jalal, Sarah Salah Al-Taee, Fay Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Srm Institute of Science & Technology Department of Computer Science & Engineering Chennai Vadapalani26 India Sri Krishna College of ENGG. Eee DEPT. Tamilnadu Arakkonam India Koneru Lakshmaiah Education Foundation Department of Electrical & Electronics Engineering Guntur Andhra Pradesh Vaddeswaram India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

This paper develops a new ontological-based approach to network slicing in 5G backhaul networks, targeting low- or ultra-low-latency services. Our approach supports a modified A∗ approach with management of congestion to be more efficient than Dijkstra's approach in real time with the help of a precomputed heuristic function. It embeds the network quality-of-service factors in a combined measure. The proposed approach is embedded in a slice module, which is, in turn, encapsulated by a route computation element that is a slice module used to compute the optimal paths of network slices. According to experiment results, our approach showed an increase in the order of magnitude of processing time reduction in comparison with the Dijkstra algorithm. Especially in the case of complex network topologies, the system dynamically changes the path to ensure the packet delay is smaller than the specified level, to satisfy the low-latency requirements. These measurements from the performed end-to-end tests for the Speed test client, which allows URLLC in 5G backhaul networks, give the system the capability to differentiate between no- and low-latency-bound traffic. © 2024 ieee.

关键词： 5G mobile communication systems

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Guest Editors' Introduction: Special Issue on 2021 Top Picks in Hardware and embedded Security

IEEE DESIGN & TEST

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ieee DESIGN & TEST 2022年第4期39卷 5-6页

作者： Devadas, Srini Rajendran, Jeyavijayan MIT Elect Engn & Comp Sci 77 Massachusetts Ave Cambridge MA 02139 USA Texas A&M Univ Elect & Comp Engn Kingsville TX 78363 USA

Hardware is the foundation of many systems ranging from embedded systems, and Internet of Things devices, to cyber–physical systems. The increasing design complexity of hardware continues to challenge our ability to provide robust security guarantees, thereby undermining the security of systems and resulting in security breaches and leakage of private information. The direct and indirect costs of addressing security vulnerabilities (e.g., root cause analysis, deploying fixes and mitigations, and risk of product recalls) not only damage the reputation of a company, but also prolong time-to-market deadlines, thereby squeezing the supply chain. To this end, researchers from academia and industry have been developing tools and techniques that can help identify and mitigate security issues in hardware, thereby building a bedrock for system security. One important task toward this ambitious goal is to identify the best set of attack and defense tools and techniques in hardware and embedded security, which typically spans many communities ranging from devices to circuits to architecture to CAD to cryptography. This special issue presents the articles selected during the third edition of the workshop “Top Picks in Hardware and embedded Security” (shortly, Top Picks) held virtually (due to COVID-19) on November 5, 2021, “co-located” with the ieee/ACM international conference on Computer- Aided Design.

关键词： Special issues and sections embedded systems Security Hardware Reverse engineering Cloud computing Field programmable gate arrays Context awareness

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Wearable Smart Health Monitoring Transmitter and Portable Receiver systems for Enhanced Wellness Insights 2

Wearable Smart Health Monitoring Transmitter and Portable Re...

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2nd international conference on Automation, computing and Renewable systems, ICACRS 2023

作者： Sivaprasad, T.C. Surendhar, R. Heiner, A.J. Electronics and Communication Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9798350340235

This study presents the development and evaluation of a smart wearable belt designed for comprehensive health monitoring and fall detection. The wearable integrates advanced sensors, including vibration and heartbeat, to give users real-time insights into their well-being. The vibration sensor, meticulously calibrated for precise fall detection, exhibited high sensitivity and responsiveness, distinguishing between daily activities and potential accidents. Simultaneously, the heartbeat sensor demonstrated accurate and consistent heart rate monitoring, contributing to proactive cardiovascular health management. The seamless integration of these sensors, supported by MQTT connectivity and secure data transmission, ensures a user-friendly experience. Cloud integration enhances data processing and analytics, empowering users with a comprehensive health dashboard. Results indicate the reliability of the fall detection system and the accuracy of heart rate monitoring. This intelligent wearable belt represents a promising advancement in health technology, offering a holistic solution for users seeking real-time health insights and proactive fall prevention. Ongoing research focuses on refining algorithms and incorporating user feedback for continuous improvement and broader applications in personalizedhealth care. © 2023 ieee.

关键词： Data analytics Patient monitoring Smart belt Wearable Sensor

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Integrating Third-Party Asset Monitoring applications in an Edge Architecture using the Asset Administration Shell 27

Integrating Third-Party Asset Monitoring Applications in an ...

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ieee 27th international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Platenius-Mohr, Marie Gruner, Sten ABB Corp Res Ctr Germany Ladenburg Germany

ISBN: (数字)9781665499965

ISBN: (纸本)9781665499965

As malfunctioning devices may lead to expensive plant downtimes or to severe safety risks, monitoring of industrial plants and their devices is a crucial task. There are many providers for device monitoring solutions on the market. However, integrating third-party monitoring approaches into real industrial systems, especially for time-critical data, is complicated due to mismatching APIs and different parties using different formats for specifying the involved assets and data. This leads to expensive, time-taking, and error-prone manual work. In this paper, we present an edge-computing-based approach to integrate monitoring functionality in an industrial IoT system. It addresses the presented challenges by combining edge computing with an Industry 4.0 information modeling and access format, the Asset Administration Shell.

关键词： Edge computing Industry 4.0 IoT Asset Monitoring Asset Administration Shell

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Machine Learning-Based Solution for SMS Spam Detection Problem 2

Machine Learning-Based Solution for SMS Spam Detection Probl...

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2nd international conference of Intelligent Methods, systems and applications, IMSA 2024

作者： Shdefat, Ahmed Younes Sedky, Nouran M. Bialy, Zeina H. El Ahmed, Shahd Fathi, Hanaa Abdelminaam, Diaa Salama College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait Applied Science Private University Applied Science Research Center Amman Jordan Misr International University Faculty of Computer Science Cairo Egypt Jadara University Jadara Research Center Irbid21110 Jordan Middle East University MEU Research Unit Amman11831 Jordan

ISBN: (纸本)9798350362633

The article describes a new method for image detection using machine learning, based on the development of sophisticated security systems tailored for examining digital media to identify and scrutinize altered images. Using deep learning models and advanced image processing techniques, the authors demonstrate high accuracy and efficiency i n detecting face modifications a nd malicious content within digital images. The authors illustrate the proposed technique by evaluating its performance on a unique dataset of unaltered and modified images. The method allows for improving detection accuracy by 93.99% and demonstrates robustness in real-time applications. The new method's effectiveness is confirmed by calculating precision and recall metrics. New research results develop advanced security solutions that can be used to enhance digital forensics, content authentication, and cybersecurity applications. Novelty and scientific contributions lie in integrating deep learning for real-time media analysis and enhancing data authenticity and integrity. © 2024 ieee.

关键词： Adversarial machine learning

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Agri Sage: A Mobile Application for Agricultural Disease Detection, E-Commerce, and real-time Information systems 17

Agri Sage: A Mobile Application for Agricultural Disease Det...

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17th international conference on Developments in esystems Engineering, DeSE 2024

作者： Aslam, Talha Ghareeb, Shatha Mustafina, Jamila Teesside University Middlesbrough United Kingdom Kazan Federal University Kazan Russia

ISBN: (纸本)9798350368697

This paper introduces Agri Sage, a mobile application aimed at addressing critical challenges in modern agriculture. Agri Sage integrates real-time plant disease detection using machine learning, an e-commerce platform for agricultural products, and weather updates alongside government pricing and subsidy information. Leveraging TensorFlow Lite for image analysis, the app diagnoses plant diseases even under suboptimal image conditions. The e-commerce platform allows farmers to connect with buyers and manage bulk transactions, while real-time weather and government data help farmers make informed decisions. This holistic approach enhances farm management, boosts productivity, and makes technology accessible to farmers in diverse environments. Extensive testing of Agri Sage demonstrated its efficacy in real-world applications, particularly in improving crop health and market access. © 2024 ieee.

关键词： Agribusiness

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Transparency of AI-XR systems: Insights from Experts 7

Transparency of AI-XR Systems: Insights from Experts

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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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Research on the Design and Application of Integrated Monitoring and Visualization systems in Intelligent Project Management 16

Research on the Design and Application of Integrated Monitor...

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16th ieee international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Wang, Feng Wang, Ji Cai, Hao Yang, Gang National Energy Group Northeast Electric Power Co. Ltd. Jilin Thermal Power Plant Jilin China

ISBN: (纸本)9798331505264

As the complexity and scale of project management continue to grow, traditional project management methods struggle to meet the demands for efficiency and transparency. Intelligent project management leverages modern technological tools to enhance these aspects by incorporating monitoring and visualization integration systems. These systems not only improve the efficiency of project management processes but also significantly enhance decision-making capabilities. This paper aims to explore the design concepts and practical applications of monitoring and visualization integration systems within the context of intelligent project management. Through case studies, the paper validates the effectiveness of these systems, demonstrating how they can facilitate better project outcomes, streamline workflows, and provide real-time insights into project performance. Ultimately, this research highlights the transformative potential of intelligent project management methodologies in navigating the complexities of contemporary project environments. © 2024 ieee.

关键词： Project management

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