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Lightweight AI for Drones: A Survey

Lightweight AI for Drones: A Survey

引用

international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Moez Krichen Mohamed S. Abdalzaher Mostafa Shaaban Raafat Aburukba Al-Baha University Al-Baha Saudi Arabia ReDCAD Laboratory University of Sfax Sfax Tunisia Electrical Engineering Department American University of Sharjah Sharjah UAE Seismology Department National Research Institute of Astronomy and Geophysics Cairo Egypt Computer Science and Engineering Department American University of Sharjah Sharjah UAE

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

this paper explores the transformative potential of Lightweight AI techniques in enhancing the capabilities of Unmanned Aerial Vehicles (UAVs). As the demand for real-time processing and efficient data management grows, traditional AI models face significant limitations due to their high computational and resource requirements. We discuss key Lightweight AI techniques, including Tiny Machine Learning (TinyML), model compression methods, edge computing, transfer learning, and federated learning, which collectively enable more efficient UAV operations. the paper further examines various applications of Lightweight AI in drones, highlighting its role in pandemic response, object detection, vegetation segmentation, forest-fire detection, real-time path planning, and autonomous landing. Each application demonstrates how lightweight models can improve decision-making and operational efficiency in dynamic environments. We also address the challenges faced in implementing these technologies, such as resource constraints and the need for robust real-time processing. Finally, we outline future directions for research and development in Lightweight AI, emphasizing the necessity for continued innovation to fully leverage UAV potential across diverse sectors. this comprehensive analysis underscores the critical role of Lightweight AI in advancing UAV technology and enhancing their functionalities in various practical applications.

关键词： Surveys Tiny machine learning Computational modeling Transfer learning Vegetation mapping Object detection Autonomous aerial vehicles real-time systems Artificial intelligence Drones

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Decision systems Optimization for Cybersecurity Resilience 12th

Decision Systems Optimization for Cybersecurity Resilience

引用

12th international conference on Frontiers of Intelligent computing: theory and applications, FICTA 2024

作者： Ikhar, Sharayu Mondal, Dipannita Khubalkar, Deepti Yadav, Ranjeev Deshpande, Pallavi Patil, Sonali Vrushabhasagar Yashika Journal Publications Pvt. Ltd Maharashtra Wardha India Artificial Intelligence and Data Science Department Dr. Y. Patil College of Engineering and Innovation Telegaon410507 India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India EnTC VIIT Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601424

the increasing danger in the online environment highlights the necessity for strong and durable cybersecurity solutions. this study aims to improve Intrusion Detection systems (IDS) by introducing a new hybrid method that merges Variational Autoencoders (VAEs) and Ant Colony Optimization (ACO) to enhance anomaly detection. the main goal is to enhance decision-making systems in IDS to attain a high level of accuracy, with a proven success rate of 99.3%. the Variational Autoencoder (VAE) functions as a tool for extracting features and reducing dimensionality by capturing latent representations of typical network behavior. the model can detect anomalous activities more accurately by using VAE to identify subtle patterns and deviations in network traffic. the Ant Colony Optimization (ACO) algorithm is incorporated into the decision-making process of the IDS to complement the VAE. Ant Colony Optimization (ACO) is used to optimize the parameters of Intrusion Detection systems (IDS) and adjust the decision boundaries to improve the detection performance. the combined interaction of VAE and ACO forms a robust framework that can accurately detect anomalies and adjust in real-time to changing cyber threats. the hybrid model proposed was assessed with real-world cybersecurity datasets, showing an impressive accuracy of 99.3%. the experimental results highlight the VAE-ACO hybrid’s effectiveness in achieving high detection accuracy while remaining efficient in terms of computational resources. Combining Variational Autoencoders with Ant Colony Optimization shows great potential for enhancing decision systems in Intrusion Detection systems. the 99.3% accuracy demonstrates the capability of this hybrid approach to greatly enhance cybersecurity resilience against advanced and evolving cyber threats. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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Robotic Precision Fitness: Accurate Pose Training for Elderly Rehabilitation 25th

Robotic Precision Fitness: Accurate Pose Training for Elderl...

引用

25th international conference on Intelligent Data Engineering and Automated Learning

作者： Rincon, J. A. Marco-Detchart, C. Univ Burgos Dept Digitalizac Escuela Politen Super Ctra Oron 28 Burgos 09200 Spain Univ Politec Valencia UPV Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031777370;9783031777387

this study presents a novel robotic assistant tailored for elderly rehabilitation. the system combines state-of-the-art artificial intelligence, computer vision, and robotics to provide customized exercise routines and instant feedback. the core of the assistant is a Grove AI V2 vision system, driven by a dual-core Arm Cortex-M55 processor and an Arm Ethos-U55 neural network component. A custom-built robotic torso fitted with 12 servo motors supports the demonstration of a wide variety of upper body exercises. A crucial element in the experiments is the use of Restricted Radial Equivalence Functions (RREFs) and Radial Similarity Measures (RSMs) to precisely match human poses with the robot's demonstrations. this method ensures an accurate evaluation of exercise performance, enhancing the efficacy of physical therapy for the elderly. Using edge computing technologies, the system provides real-time monitoring and personalised care while addressing important privacy issues. the assistant is additionally characterized by a small-sized design that includes a 124x64 pixel OLED display, capable of displaying bio-signal monitoring and an animated face to engage users. this research advances AI-assisted elderly care, aiming to increase independence, physical health, and overall quality of life for aging populations while offering support to healthcare systems and caregivers.

关键词： Edge computing Elderly Rehabilitation Pose Estimation Robotic Assistance

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Target-Aware Implementation of real Expressions 25

Target-Aware Implementation of Real Expressions

引用

30th ACM international conference on Architectural Support for Programming Languages and Operating systems, ASPLOS 2025

作者： Saiki, Brett Brough, Jackson Regehr, Jonas Ponce, Jesus Pradeep, Varun Akhileshwaran, Aditya Tatlock, Zachary Panchekha, Pavel University of Washington SeattleWA United States University of Utah Salt Lake CityUT United States

ISBN: (纸本)9798400706981

New low-precision accelerators, vector instruction sets, and library functions make maximizing accuracy and performance of numerical code increasingly challenging. Two lines of work - -traditional compilers and numerical compilers - -attack this problem from opposite directions. Traditional compiler backends optimize for specific target environments but are limited in their ability to balance performance and accuracy. Numerical compilers trade off accuracy and performance, or even improve both, but ignore the target environment. We join aspects of both to produce Chassis, a target-aware numerical compiler. Chassis compiles mathematical expressions to operators from a target description, which lists the real expressions each operator approximates and estimates its cost and accuracy. Chassis then uses an iterative improvement loop to optimize for speed and accuracy. Specifically, a new instruction selection modulo equivalence algorithm efficiently searches for faster target-specific programs, while a new cost-opportunity heuristic supports iterative improvement. We demonstrate Chassis capabilities on 9 different targets, including hardware ISAs, math libraries, and programming languages. Chassis finds better accuracy and performance trade-offs than both Clang (by 3.5×) or Herbie (by up to 2.0×) by leveraging low-precision accelerators, accuracy-optimized numerical helper functions, and library subcomponents. © 2025 ACM.

关键词： Program compilers

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Intrusion Detection at the Edge computing: A Deep Learning Approach Using the UNSW-NB15 Dataset

Intrusion Detection at the Edge Computing: A Deep Learning A...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Vipin Kumar Vivek Kumar Abhay Pratap Singh Bhadauria Jay Dixit Amit Kumar Department of Computer Science Gurukula Kangri (Deemed to be University) Haridwar India Department of Computer Engineering & Applications GLA University Mathura Department of Computer Engineering & Applications GLA University Mathura UP

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. the findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. this study investigates the application of edge computing, Internet of things (IoT), and deep learning to improve the performance of Intrusion Detection systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. the results demonstrate better performance, showing their suitability for real-time security applications. these findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments.

关键词： Deep learning Image edge detection Computational modeling Intrusion detection real-time systems Internet of things Security Servers Long short term memory Edge computing

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2021 IEEE 23rd international conference on High Performance computing and Communications, 7th international conference on Data Science and systems, 19th international conference on Smart City and 7th international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

2021 IEEE 23rd International Conference on High Performance ...

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

ISBN: (纸本)9781665494571

the proceedings contain 343 papers. the topics discussed include: three-level compact caching for search engines based on solid state drives;a fine-grained volley gesture recognition method with direction independence;universal adversarial attack against 3d object tracking;efficient hardware redo logging for secure persistent memory;a cost-efficient metadata scheme for high-performance deduplication systems;high speed true random number generator controlled by logistic map;characterization and implication of edge WebAssembly runtimes;frend for edge servers: reduce server number! keeping service quality!;on-the-fly servers placement for online multiplayer games in the fog;advanced architecture design of high-radix router based on chiplet integration and IP reusability;embrace the conflicts: exploring the integration of single port memory in systolic array-based accelerators;visual sensitivity aware rate adaptation for video streaming via deep reinforcement learning;semi-supervised federated learning with non-IID data: algorithm and system design;on-demand intelligent routing algorithms for the deterministic networks;and distributed service placement in ultra-dense edge computing: a game-theoretical approach.

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Comparative Analysis of Edge-based and Cloud-based Intrusion Detection systems: A Systematic Literature Review

Comparative Analysis of Edge-based and Cloud-based Intrusion...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Sahar Ebadinezhad Ali Hussein Abdulrazzaq Alsaroah Department of Computer Information Systems Computer Information Systems Research and Technology Center (CISRTC) Near East University Nicosia Turkey Department of Computer Information Near East University Nicosia Turkey

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this research study intends to explore the benefits, drawbacks, and synergies existing between the edge and cloud-based intrusion detection systems. While edge-based IDS detects threats instantly in applications relying on latency, cloud-based IDS does centralized management to handle large data repositories that help to counter wide-scale attacks. Hybrid IDS architectures bring together local responsiveness with global intelligence by tying together both edge and cloud components. It comprehensively reviews edge-based and cloud-based security breach finding methods and lays the foundation for further cybersecurity research and applications.

关键词： Image edge detection Intrusion detection Computer architecture real-time systems Informatics Computer security Mobile computing Systematic literature review

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the Possibilities of Using Digital Twins to Predict the Bifurcation of Traffic Flow

The Possibilities of Using Digital Twins to Predict the Bifu...

引用

international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Alexandra. V Volosova Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

the article discusses the possibilities of using digital twins to predict the bifurcation of the traffic flow. the use of digital twins at different levels of a nonlinear dynamic system makes it possible to stabilize its lifecycle. the system's abnormal states can be predicted by digital twins based on AI algorithms. But the possibilities of such analytics are limited by the large amount of data and calculations based on a traditional element base. A possible solution of the problem is to use neuromorphic computing devices in combination with a conventional element base. the analogue calculations nature allows for additional capabilities for real-time data analysis. that is especially important for making predictions of the behavior of quantities, which can lead to undesirable consequences for the state of the system. the purpose of this article is to explore the possibilities of preventing critical states of the system, such as bifurcation, using digital twins.

关键词： Sufficient conditions Tensors Uncertainty Neuromorphic engineering Bifurcation Markov processes Prediction algorithms Mathematical models real-time systems Digital twins

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Integrating computing and Analysis in the Development of Interactive Quizzes for Sign Language Learning

Integrating Computing and Analysis in the Development of Int...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Arifa Ashrafi Viktor S. Mokhnachev Mohammed H. Uvaysov Evgenii P. Medvedev Department of Infocognitive Technologies Moscow Polytechnic University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

this paper outlines the innovative use of integrating computing and data analysis in developing the interactive quiz platform for sign language learning. With an increasing demand for effective educational strategies, the aim is to enhance the traditional learning methods by using modern technologies to create engaging, adaptive experiences for hearing impaired students. the interactive quizzes incorporate multimedia elements, including video demonstrations and real-time feedback, to foster a deeper understanding of sign language. By utilizing data analytics, it is possible to monitor learner progress, adaptation in real-time, and provide personalized learning pathways that address individual needs. Usability tests and pilot studies evaluate the efficacy of these quizzes in enhancing memory, comprehension, and practical usage of sign language among diverse learners. the primary findings suggest that the integration of computing and analysis significantly improves learner engagement and outcomes, showing the scientific novelty of this study. this research highlights the transformative potential of interactive educational technologies in making sign language learning more accessible and effective for a varied demographic as a multilingual sign language platform is aimed to develop. through these innovations, it is supposed to significantly impact the improvement of language education.

关键词： Sign language Technological innovation Power engineering Data analysis Auditory system Streaming media real-time systems Multilingual Usability Research and development

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Nanophotonic Edge computing System For Ultra-Low Latency Human-Machine Interface

Nanophotonic Edge Computing System For Ultra-Low Latency Hum...

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IEEE international conference on Micro Electro Mechanical systems

作者： Zhihao Ren Zixuan Zhang Yangyang Zhuge Zian Xiao Siyu Xu Jingkai Zhou Chengkuo Lee Department of Electrical & Computer Engineering National University of Singapore Singapore National Centre for Advanced Integrated Photonics (NCAIP) Singapore

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

this paper introduces a nanophotonic edge computing (NEC) system built on a bilayer AlN/Si waveguide platform, optimized for AI processing in wearable sensors. Utilizing AlN electro-optic micro-ring resonators (EOMRRs) for feature extraction and Si thermo-optic Mach-Zehnder interferometers (TOMZIs) for neural network computations, the NEC chip demonstrates high classification accuracy for gesture (96.77%) and gait (98.31%) analysis, ultra-low latency (<100 ps), and minimal energy consumption (<0.34 pJ). this breakthrough exemplifies the potential of nanophotonic technology to significantly enhance real-time, low-power, and high-accuracy AIoT applications, pushing the boundaries of edge computing capabilities.

关键词： Energy consumption Accuracy Human-machine systems Neural networks Virtual reality real-time systems Safety Low latency communication Monitoring Edge computing

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