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Performance Comparison of YOLOv8 and YOLOv10 for Traffic Light Detection on a Jetson Nano Board

Performance Comparison of YOLOv8 and YOLOv10 for Traffic Lig...

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international conference on applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Ballout, Hadi Berta, Riccardo Dabbous, Ali Fresta, Matteo Lazzaroni, Luca Pighetti, Alessandro Bellotti, Francesco University of Genoa Via Opera Pia 11a Genoa16145 Italy

ISBN: (纸本)9783031840999

Detecting traffic lights is crucial for the development of advanced driver-assistance systems (ADAS). Accurate traffic light detection allows for timely responses to changing traffic conditions, thereby enhancing road safety and reducing accidents. However, achieving high detection accuracy with small model size and fast inference times poses significant challenges, particularly for deployment on resource-constrained devices. In this study, we perform a detailed performance comparison of YOLOv8 and YOLOv10 nano models for traffic light detection (TLD), specifically targeting deployment on an NVIDIA Jetson Nano board. The evaluation focuses on key metrics including mean average precision (mAP), inference speed, and computational efficiency under real-time constraints. YOLOv8 demonstrated slightly superior mAP, indicating better detection accuracy. In contrast, YOLOv10 exhibited faster inference speeds due to its architectural optimizations. This comparison underscores the trade-offs between model complexity and deployment feasibility in embedded systems, providing insights for selecting the appropriate model for TLD applications in resource-constrained environments. Further research is needed to explore additional datasets, particularly those containing traffic lights at night, and to apply quantized models on smaller edge devices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Advanced driver assistance systems

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AI-Driven Dynamic Workload Balancing for real-time applications on Cloud Infrastructure

AI-Driven Dynamic Workload Balancing for Real-Time Applicati...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Saravanakumar Baskaran Sathyanarayanan Sayiram Jagadeesh Vedula Rajkumar Muthusamy Nasmin Jiwani T. Kiruthiga Microsoft Corporation Seattle USA IBM San Jose USA Digital Transformation Lead Houston USA NTT Data Irving USA Deptartment of Information Technology University of The Cumberlands Williamsburg USA Department of ECE Vetri Vinayaha College of Engineering and Technology Trichy India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The specification provides a new, resource-efficient method of Dynamic Workload Balancing in AI-driven real-time applications over Cloud Infrastructure. The real-time application keeps processing data at high speeds, and it is too difficult to get or make this type of arrangement using our traditional cloud setups as the system employs artificial intelligence methods to responsively reallocate resources between virtual machines by the required Quality of Service (QoS) levels and capacity requirements. It efficiently schedules jobs to available resources and delivers results promptly, which is necessary for real-time applications like video processing or stock trading. It uses machine learning models based on historical data to predict the resources required for upcoming tasks. These predictions are subsequently utilized to smartly assign resources across VMs, factoring in network latencies and inter-dependence. It allows the system to modify queues in real-time according to changes in workload patterns, performing a dynamic load balancing.

关键词： Cloud computing Quality of service Machine learning Streaming media Dynamic scheduling real-time systems Virtual machines time factors Integrated circuit modeling Load modeling

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Personalized Deep Brain Stimulation Parameter Optimization using Deep Learning with Dimensionality Reduction and Data Augmentation 1

Personalized Deep Brain Stimulation Parameter Optimization u...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Parimala, S. Iram, Faiza Kishore, Gudipudi Sivaraman, R. Srimathi, S. Jegajothi, B. Srm Institute of Science and Technology Department of Computer Science and Applications Vadapalani India Sr University School of Cs&ai Hasanparthy India University College of Arts and Science Krishna University Department of Biosciences and Biotechnology Machilipatnam India Dwaraka Doss Goverdhan Doss Vaishnav College Department of Mathematics Arumbakkam Chennai India Saveetha School of Engineering Simats Department of Bio Technology Chennai India Srs Tech Solutions Chennai India

ISBN: (纸本)9798331529246

Deep Brain Stimulation (DBS) is a recognized treatment for managing motor symptoms associated with various movement disorders. However, traditional DBS settings often rely on fixed parameters that do not address individual patient needs, leading to suboptimal outcomes and increased clinical visits. This research introduces a novel hybrid approach that employs Convolutional Neural Networks (CNN) combined with Reinforcement Learning (RL) to dynamically optimize DBS parameters using real-time patient feedback. The proposed model significantly enhances symptom management, achieving an 85% improvement compared to the 65% improvement seen with conventional DBS settings. Additionally, the time to reach optimal stimulation settings was reduced from twelve hours to five, and annual clinical visits decreased from five to two. These findings underscore the potential of integrating machine learning techniques into DBS therapy, paving the way for more personalized and effective treatment strategies for movement disorders, ultimately revolutionizing patient-centered care in this field. © 2025 ieee.

关键词： Deep reinforcement learning

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Comprehensive Comparative Analysis of Food Classification Using YOLOv8n, VGG19, and InceptionV3

Comprehensive Comparative Analysis of Food Classification Us...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： Mohit Sapat Sarthak Wadegaonkar Sarthak Patankar Pratibha Mahajan Department of Computer Science Engineering Vishwakarma University Pune India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Food classification plays a vital role in areas such as food recognition, nutritional tracking, and culinary exploration. This study evaluates the effectiveness of three deep learning models—YOLOv8n, VGG19, and InceptionV3—utilizing the In-dian Food Classification Dataset and Food-11. Metrics like accuracy, precision, recall, and F1 score indicate that InceptionV3 consistently delivers the highest accuracy, whereas YOLOv8n stands out for its real-time efficiency, making it well-suited for mobile or embedded systems. VGG19 provides a balanced performance but is limited by its larger model size and higher computational requirements. Statistical analysis (p ¡ 0.05) shows significant differences among the models, with confusion matrices revealing difficulties in classifying visually similar foods. The study suggests using InceptionV3 for tasks requiring precision, YOLOv8n for applications where speed is critical, and VGG19 for scenarios needing a balanced approach, while also proposing future research in cross-cultural food recognition, AI-driven food interaction, and personalized nutrition systems.

关键词： Deep learning Training Measurement Analytical models Accuracy embedded systems Statistical analysis Computational modeling real-time systems Informatics

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Modular Robotic Controllers with Ad-Hoc ESP32 Wireless Mesh Network and embedded AES Security 2nd

Modular Robotic Controllers with Ad-Hoc ESP32 Wireless Mesh ...

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2nd international conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Fang, Yisu Hafizh, Hadyan Ateeq, Muhammad Tan, Andrew Huey Ping School of Internet of Things Xi’an Jiaotong-Liverpool University Taicang China School of Engineering and Technology Sunway University Bandar Sunway Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Taicang China

ISBN: (纸本)9789819639489

This paper presents an innovative decentralized robotic control system utilizing an ESP32-based wireless mesh network (WMN). Building upon the "painlessMesh" library for network management, this study enables JSON-formatted data exchange in peer-to-peer and broadcast modes. A key contribution is our implementation of a security layer incorporating ECDH key exchange and AES128 encryption, which we thoroughly analyze across various node configurations. The WMN is seamlessly integrated with the Robot Operating System (ROS) via a custom serial bridge, combining the mesh network’s real-time, distributed control capabilities with ROS’s advanced computational resources. This architecture creates a flexible, scalable platform for sophisticated robotic applications. Our study rigorously evaluates system performance, balancing network topology, security, and responsiveness. The results provide critical insights for deploying secure, decentralized mesh networks in real-world robotic applications, particularly in areas such as swarm robotics and robot-infrastructure coordination, while maintaining the cost-effectiveness and energy efficiency of ESP32-based nodes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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28th international Symposium on Database Engineered applications, IDEAS 2024

28th International Symposium on Database Engineered Applicat...

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28th international Symposium on Database Engineered applications, IDEAS 2024

ISBN: (纸本)9783031834714

The proceedings contain 26 papers. The special focus in this conference is on Database Engineered applications. The topics include: Enhanced Classification of embedded System Vulnerabilities Using Ensemble Embedding and BiLSTM Networks;predicting Dyskinetic Events Through Verified Multivariate time Series Classification;R2-LGBM: Sales Informative Prediction System in E-commerce Application Using Ensemble Classifier;comparing Related Languages with a Fuzzy Morphism Matching Algorithm;Enhancing LLM Code Generation Using Natural Language Processing in the Context of Machine Learning;LaSSI: Logical, Structural, and Semantic Text Interpretation;clustEm4Ano: Clustering Text Embeddings of Nominal Textual Attributes for Microdata Anonymization;about Relationships in Data Lakes;from Theory to Practice of Multidimensional Big Data Analytics over Big Healthcare Data: A real-Life Case Study;a Database Engineered System for Big Data Analytics on Tornado Climatology;siren Federate: Bridging the Gap Between Document and Relational Data systems for Efficient Exploratory Graph Analysis;outlier-Weighted Traffic Flow Prediction Using Online Autoencoders;Speculative Query Support for RDBMS with Flexible Query Order and Benefit Verification;made to Measure: Towards Approximability of Query Evaluation Engines;DGGS-Based Continuous Trajectory Similarity Comparison;predictive Modeling of Key Performance Indicators for Greenhouse Gas Emission Reduction Using Machine Learning;validation Without Rules: A Data Integration Case Study;fairness in Group Recommender systems Using Variational Autoencoders;a Supervised Contrastive Learning Framework for Aspect-Based Recommendations;multi-attribute Bias Mitigation in Recommender systems;UpLIF: An Updatable Self-tuning Learned Index Framework;scheduling of Intermittent Query Processing;monitoring Environmental Factors for Optimal Health and Welfare of Broiler Chickens Using Sensor Technology in Southwest Nigeria;river Levels Affecting Firefighter Inte

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Designing Energy-Efficient embedded systems with CNNs for real-time Control in Autonomous Robotics

Designing Energy-Efficient Embedded Systems with CNNs for Re...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Kumar, Sanjay Sharma, Priti Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798331542375

This research proposes an energy-aware paradigm for the implementation of deep learning using CNN for real-time control in autonomous robotics for embedded systems. The overall power efficiency of the proposed system is achieved through efficient control of power consumption, latency, and runtime while has minor degradation in terms of accuracy through a number of approaches including model pruning, quantization, and adaptive CNN scaling. Studies show that power consumption has been cut down to as low as 47.3% thereby making the optimized system's power consumption in obstacle avoidance tasks as low as 2.8 W, the base system consuming up to 5.0 W power The delay was also lowered to 47.1% in key tasks from 20 ms, thus ensuring more immediate decisions in real time activities. Also, the system up time was enhanced by 40% to perform operations including object identification and sensors integration. Accuracy stayed over 92% in all tasks;the fully optimized CNN design was 0.6% less accurate than an ideal 95.0%, with a 92.4% score. These results endorse that much energy savings and performance improvement are realizable whilst incurring moderate costs on the precision of RTAS, which makes this approach perfect for battery operated, constraint embedded systems in 'smart' robotics. © 2025 ieee.

关键词： Energy utilization

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Natural Language Processing in Geographic Information systems 3

Natural Language Processing in Geographic Information System...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kushmitha, S. Kathrine, Jaspher W. Abisha, M.B. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331527549

The integration of Geographic Information systems (GIS) with Natural Language Processing (NLP) has transformed how spatial data is accessed and utilized. By enabling GIS interfaces to interpret natural language queries, NLP enhances user experience, democratizes geospatial data, and improves query accuracy. This paper reviews state-of-the-art methodologies for geo-parsing, geo-coding, and spatial query interpretation, addressing challenges like geographic ambiguity, imprecise spatial relationships, and scalability limitations. Key contributions include a systematic literature review identifying research gaps such as insufficient context-awareness and a lack of annotated datasets, alongside case studies demonstrating the benefits of NLP integration in applications like disaster management and urban planning. Future opportunities are explored in leveraging deep learning, multimodal data integration, and adaptive user interfaces to further enhance GIS accessibility and usability. Future opportunities are explored in leveraging deep learning for spatial reasoning, integrating multimodal data sources for enhanced decision-making, and developing adaptive user interfaces tailored for diverse audiences. Furthermore, the paper highlights the potential of NLP in generating geo-descriptions for distributed sensor networks, supporting accessibility for impaired users, and enhancing real-time data analysis in smart city initiatives. Addressing these gaps and building scalable frameworks will pave the way for GIS systems that are not only powerful but also inclusive, fostering global accessibility and broader adoption of geospatial technologies. © 2025 ieee.

关键词： Structured Query Language

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TAP-CAM: A Tunable Approximate Matching Engine based on Ferroelectric Content Addressable Memory 24

TAP-CAM: A Tunable Approximate Matching Engine based on Ferr...

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Ni, Chenyu Chen, Sijie Liu, Che-Kai Liu, Liu Imani, Mohsen Kämpfe, Thomas Ni, Kai Niemier, Michael Hu, Xiaobo Sharon Zhuo, Cheng Yin, Xunzhao Zhejiang University Hangzhou China Georgia Institute of Technology GA United States University of Notre Dame IN United States University of California IrvineCA United States Fraunhofer IPMS Dresden Germany Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China

ISBN: (纸本)9798400710773

Pattern search is crucial in numerous analytic applications for retrieving data entries akin to the query. Content Addressable Memories (CAMs), an in-memory computing fabric, directly compare input queries with stored entries through embedded comparison logic, facilitating fast parallel pattern search in memory. While conventional CAM designs offer exact match functionality, they are inadequate for meeting the approximate search needs of emerging data-intensive applications. Some recent CAM designs propose approximate matching functions, but they face limitations such as excessively large cell area or the inability to precisely control the degree of approximation. In this paper, we propose TAP-CAM, a novel ferroelectric field effect transistor (FeFET) based ternary CAM (TCAM) capable of both exact and tunable approximate matching. TAP-CAM employs a compact 2FeFET-2R cell structure as the entry storage unit, and similarities in Hamming distances between input queries and stored entries are measured using an evaluation transistor associated with the matchline of CAM array. The operation, robustness and performance of the proposed design at array level have been discussed and evaluated, respectively. We conduct a case study of K-nearest neighbor (KNN) search to benchmark the proposed TAP-CAM at application level. Results demonstrate that compared to 16T CMOS CAM with exact match functionality, TAP-CAM achieves a 16.95× energy improvement, along with a 3.06% accuracy enhancement. Compared to 2FeFET TCAM with approximate match functionality, TAP-CAM achieves a 6.78× energy improvement. © 2024 Copyright is held by the owner/author(s).

关键词： Ferroelectric RAM

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Efficient Resource Management for real-time AI systems in the Cloud using Reinforcement Learning

Efficient Resource Management for Real-Time AI Systems in th...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Jagadeesh Vedula Saravanakumar Baskaran Sathyanarayanan Sayiram Rajkumar Muthusamy Ketan Gupta N. Yuvaraj Digital Transformation Houston Texas USA Microsoft Corporation Seattle Washington USA IBM San Jose CA USA NTT Data Irving Texas USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The advent of artificial intelligence (AI) has driven an emergence in applications demanding online responses to immense amounts of data. Typically, the cloud-based deployment of these applications requires resource management to be optimized for high performance while meeting cost efficiency. However, conventional static resource allocation methods may only partially apply to the context of real-time AI applications as they are dynamic, unpredictable, and require more efficient & adaptive ways of allocating resources. To address this challenge, and as a solution in this research, we introduce an RL-based method for online resource management of cloud-based AI systems. Reinforcement learning (RL) is a machine-learning algorithm that allows systems to learn how best to perform tasks in changing environments based on feedback from those interactions with the environment. The research utilizes reinforcement learning to enable dynamic resource allocation, ensuring that resources are provisioned on demand rather than relying on static allocation methods defined at design time.

关键词： Cloud computing Costs System performance Reinforcement learning Dynamic scheduling Load management real-time systems Planning Resource management Artificial intelligence

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