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NetraAI-The 3rd Eye: AI-Empowered Surveillance 6

NetraAI-The 3rd Eye: AI-Empowered Surveillance

引用

6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Dave, Harsh K. Pathak, Sparsh J. Sutaria, Kamal Parul University CSE PIET Vadodara India Parul University PIET Department of CSE Vadodara India

ISBN: (纸本)9798331506520

This research paper explores the development and implementation of 'NetraAI - The 3rd Eye,' an AI-powered surveillance system aimed at enhancing public safety and security measures. The study investigates the technical architecture, real-time functionalities, and ethical considerations surrounding the deployment of NetraAI. It examines its impact on object detection, people tracking, vehicle recognition, and proactive threat detection in diverse surveillance scenarios. The findings highlight the system's efficacy, ethical deployment, and potential contributions to the field of AI-driven surveillance. © 2025 ieee.

关键词： Security systems

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Research on LiDAR Based on embedded Microcontroller 2

Research on LiDAR Based on Embedded Microcontroller

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2nd ieee international conference on Image Processing and Computer applications, ICIPCA 2024

作者： Su, Hang Zhang, Min Harbin Institute of Technology Weihai China

ISBN: (纸本)9798350360240

With the rise of intelligent robots, driverless cars and other emerging technologies, the application of lidar is becoming more and more widely used, and intelligent unmanned devices have become a very critical problem for the surrounding environment perception. This paper designs a small two-dimensional lidar detection system developed by using embedded microcontroller, consumer lidar module, industrial capacitive touch LCD screen and MATLAB host programming. This paper analyzes and discusses the principles of the hardware circuit and software part used. This paper introduces the program design idea and develop ent process in the form of overall flow chart and division program block diagram. Finally, the drawings are designed. The housing was made for the system. We completed the production and commissioning of the finished device of the lidar detection system. At the same time, the lidar system and the innovation points of the design, the future improvement ideas and development prospects are discussed. The radar detection has the real-time detection function of the plane 360 environment, and has the advantages of intuitive operation and display, compact size, low power consumption and low cost. © 2024 ieee.

关键词： MATLAB

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RTLB_Sched: real time Load Balancing Scheduler for CPU-GPU Heterogeneous systems

RTLB_Sched: Real Time Load Balancing Scheduler for CPU-GPU H...

引用

2023 international conference on Smart computing and Application, ICSCA 2023

作者： Rahmani, Taha Abdelaziz Belalem, Ghalem Mahmoudi, Sidi Ahmed Ahmed Ben Bella Oran 1 University Lio Laboratory Dept. Computer Science Oran Algeria University of Mons Faculty of Engineering Dept. Computer Science Mons Belgium

ISBN: (纸本)9798350347050

Heterogeneous computing systems consisting of Central Processing Units CPUs and Graphical Processing Units GPUs are found everywhere, from mobile phones, laptops to cloud clusters, due to their low cost/performance ratio and their impressive peak computational performance. One of the main and most complex problems in these systems is the load imbalance. This imbalance is caused by the large computing power difference between its computing nodes. We present RTLB_Sched, a real time Load Balancing Scheduler that minimizes the load difference between the CPU and GPU nodes using machine learning. We define a new metric called load difference using execution time prediction. We describe the method and provide mathematical formulas to calculate this metric. Experiments of scheduling multiple OpenCL applications on a heterogeneous system show that RTLB_Sched outperforms the Device Suitability and Round Robin approaches in load balancing. © 2023 ieee.

关键词： Machine learning

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Foveated Fluid Animation in Virtual reality 31

Foveated Fluid Animation in Virtual Reality

引用

ieee conference on Virtual reality and 3D User Interfaces (VR)

作者： Wang, Yue Zhang, Yan Yang, Xuanhui Wang, Hui Liu, Dongxu Yang, Xubo Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350374025;9798350374032

Large-scale fluid simulation is widely useful in various Virtual reality (VR) applications. While physics-based fluid animation holds the promise of generating highly realistic fluid details, it often imposes significant computational demands, particularly when simulating high-resolution fluid for VR. In this paper, we propose a novel foveated fluid simulation method that enhances both the visual quality and computational efficiency of physics-based fluid simulation in VR. To leverage the natural foveation feature of human vision, we divide the visible domain of the fluid simulation into foveal, peripheral, and boundary regions. Our foveated fluid system dynamically allocates computational resources, striking a balance between simulation accuracy and computational efficiency. We implement this approach using a multi-scale method. To evaluate the effectiveness of our approach, we have conducted subjective studies. Our findings show a significant reduction in computational resource requirements, resulting in a speedup of up to 2.27 times. It is crucial to note that our method preserves the visual quality of fluid animations at a level that is perceptually identical to full-resolution outcomes. Additionally, we investigate the impact of various metrics, including particle radius and viewing distance, on the visual effects of fluid animations. Our work provides new techniques and evaluations tailored to facilitate real-time foveated fluid simulation in VR, which can enhance the efficiency and realism of fluids in VR applications.

关键词： computing methodologies-Computer graphics-Graphics systems and interfaces-Virtual reality computing methodologies-Computer graphics-Animation-Physical simulation

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MARS: safely instrumenting runtime monitors in real-time resource-constrained distributed systems 22

MARS: safely instrumenting runtime monitors in real-time res...

引用

22nd ieee international conference on Industrial Informatics, INDIN 2024

作者： Nandi, Giann Spilere Pereira, David Proença, José Tovar, Eduardo CISTER Polytechnic Institute of Porto Porto Portugal

ISBN: (纸本)9798331527471

Advancements in the energy efficiency and computational power of embedded devices allow developers to equip resource-constrained systems with a greater number of features and more complex behavior. As complexity of a system grows, so does the difficulty in demonstrating its overall correctness. Formal methods have been successfully applied in a variety of verification and validation scenarios, but their wide adoption in the industry and academia is still lackluster. Among the explanations listed in the literature for the low adoption of these techniques are the perceived difficulty of getting into formal practices and how formal tools are not usually aimed at practical use cases. Striving to address these issues, we present MARS, an open-source domain-specific language for the safe instrumentation of runtime verification monitors into real-time resource-constrained distributed systems. Our main objective with MARS is to ease the integration of runtime verification monitors in distributed applications while also providing developers with evidence of their correct instrumentation in the context of systems where dependability and temporal requirements need to be respected even under extreme resource constraints. We present the language syntax, the set of tools embedded into its compiler, its functionalities, and a use case to exemplify its use in a practical distributed application. © 2024 ieee.

关键词： Program compilers

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Performance Modelling of IoT in Smart Agriculture 1

Performance Modelling of IoT in Smart Agriculture

引用

1st ieee international conference on Advances in Electronics, Communication, computing and Intelligent Information systems, ICAECIS 2023

作者： Gamal, Abdelrahman S. Mohamed, Hoda K. Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt

ISBN: (纸本)9798350348057

Nowadays, IoT is being used in several applications, such as smart cities, health care and innovating agriculture and other applications. Moreover, the evolution of IoT technologies such as LoRaWAN, SIGFOX, ZigBee, and others drives the industry to convert their traditional systems to IoT systems due to the need to achieve better performance. One of the most challenging applications in an IoT system is smart agriculture due to the need for high energy and water consumption to reach the desired crop yield. Several related works in different countries tried to solve this challenge for a better water consumption in irrigation process to get the maximum crop yield production. In this paper, we used artificial intelligence and deep learning to predict the crop yield and the needed parameters of the soil to achieve the best performance with minimum power consumption. Also, we used convolutional neural networks to detect the crop deficit needed for real-time monitoring. The performed simulation has shown that the proposed model outperforms other related works and will positively increase both saving resources and the performance. © 2023 ieee.

关键词： Crops

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Deep Learning Techniques for Intrusion Detection systems in Healthcare Environments 14

Deep Learning Techniques for Intrusion Detection Systems in ...

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Alam, Mahfooz Ashraf, Zubair Singh, Priya Pandey, Bishwajeet Rehman, Kaleemur Aldasheva, Laura Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida201306 India College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida201306 India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (纸本)9798331531935

Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. This paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. The goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems. © 2025 ieee.

关键词： Network intrusion

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Lightweight security Mechanism for IoT applications using Substitution-Box 4

Lightweight security Mechanism for IoT Applications using Su...

引用

4th ieee international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Hanif, Fozia Shams, Rehan Waheed, Urooj University of Karachi Department of Mathematics Karachi Pakistan Sir Syed University of Engineering and Technology Karachi Department of Telecommunication Pakistan DHA Suffa University Karachi Department of Computer Science Pakistan

ISBN: (纸本)9798331518882

In recent times the use of small computing devices for gathering information from the real world is increasing day by day. The devices like wireless sensors, RIFD tags, embedded devices and IoT devices are required to transmit huge amounts of confidential data for monitoring and controlling. IoT applications have now become an important factor across various industries. That is why the security of IoT device is always been an important issue, because they transmit private and sensitive data. For handling the security issue cryptography is one the way for handling the security issues of IoT devices but sometimes cryptographic algorithms are cracked and as well as very costly. Lightweight cryptography requires less memory and less computational cost. Substitution box is a very useful concept in generating the lightweight cryptographic algorithm that provide robust security in the process of encryption. This study produces lightweight cryptographic encryption and decryption algorithm with the help of substitution boxes. The provided scheme not only provides the encryption procedure but also generating a flexible key structure which enhances the security for IoT devices where lightweight image encryption is crucial. The provided technique will be beneficial for those places where confidential public data has to be secured. The aim of this paper is to recommend effective original and straightforward techniques of encryption and decryption by using the substitution box. Experimental results show the efficiency of the suggested technique in terms of execution time and throughput. © 2025 ieee.

关键词： Encryption algorithms

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Building the Future of IoT: Cloud Platforms, Integration Challenges, and Emerging applications

Building the Future of IoT: Cloud Platforms, Integration Cha...

引用

2024 international conference on Computer and applications, ICCA 2024

作者： Krishnappa, Manjunatha Sughaturu Jayabalan, Deepak Harve, Bindu Mohan Jayaram, Vivekananda Bidkar, Darshan Mohan Veerapaneni, Prema Kumar Gejjegondanahalli, Vedamurthy Yogeshappa Oracle America Inc CA United States Meta Inc. CA United States CA United States Jp Morgan Chase Bank Na TX United States WA United States NJ United States TX United States

ISBN: (纸本)9798350367560

The integration of cloud computing with the Internet of Things (IoT) has fostered scalable, efficient, and cost-effective solutions to meet the substantial data demands of modern IoT applications. Cloud-based IoT platforms provide the critical infrastructure needed to manage, process, and analyze vast amounts of data generated by IoT devices, supporting a range of applications from smart cities to healthcare and industrial automation. This paper presents an in-depth review of cloud-based IoT platform architectures, focusing on essential features like scalability, flexibility, and interoperability. It also delves into the roles of edge and fog computing, which are key to reducing latency and enabling real-time responses in data- sensitive applications. Further, the paper investigates the primary challenges cloud-based IoT systems face, including security risks, data privacy, and regulatory compliance, and assesses current strategies to mitigate these challenges. Case studies of real-world applications demonstrate successful implementations across diverse sectors. The paper also explores future research directions, emphasizing the integration of artificial intelligence and machine learning for predictive analytics, the impact of 5G technology in enhancing IoT performance, and the potential of quantum computing to strengthen data security. This study provides a comprehensive foundation for understanding cloud-based IoT platforms and their transformative potential in driving digital advancements across industries. © 2024 ieee.

关键词： Cloud platforms

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Scalable Federated Learning with System Heterogeneity 43

Scalable Federated Learning with System Heterogeneity

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43rd ieee international conference on Distributed computing systems (ICDCS)

作者： Ilhan, Fatih Su, Gong Wang, Qingyang Liu, Ling Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA IBM TJ Watson Reesarch Ctr Yorktown Hts NY USA Louisiana State Univ Dept Comp Sci & Engn Baton Rouge LA USA

ISBN: (纸本)9798350339864

Federated learning (FL) is a distributed learning framework that inherently provides data privacy and parallel computation capability over a set of participating devices (clients). In real-life applications, these clients can have a great variety in terms of resources (storage, RAM, CPU/GPU speed, network speed, etc.). However, most previous FL studies do not consider this scenario with system heterogeneity and assume that all clients can operate on the same full-size deep neural network (DNN) model. In this work, we demonstrate a scalable FL approach, ScaleFL, which tackles system heterogeneity through hierarchically downscaling the DNN model for clients with limited resources. ScaleFL utilizes early exits to form multi-exit DNN models by injecting early exit networks into the given DNN. During FL, the model is adaptively split along depth (exits) and width (hidden dimensions) based on the resource budget of each participating client. A proof-of-concept demonstration is provided with interactive features, demonstrating the system flow on image classification and NLP benchmark workloads.

关键词： federated learning heterogeneous systems neural networks

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