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GILV: An integrated GNSS/IMU positioning method enhanced by lane line and template matching VO 2

GILV: An integrated GNSS/IMU positioning method enhanced by ...

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2nd international conference on Frontiers of Applied Optics and computer engineering, AOCE 2025

作者： Wang, Rongchen Zeng, Qingxi Huang, Yuhua Ji, Xufang Shi, Yanwei Yuan, Liying College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Xuzhou Power Supply Branch of State Grid Jiangsu Electric Power Co. Ltd. Xuzhou221000 China

ISBN: (纸本)9781510689404

Ensuring precise and flexible positioning is a fundamental prerequisite for autonomous mobility of unmanned ground vehicles. In the context of Global Navigation Satellite System (GNSS) signals facing suppression, the traditional joint GNSS and IMU positioning is subject to positioning drift due to the cumulative error of the IMU. In this paper, we propose a low-cost integrated localization system called GILV that fuses inertial and visual data through an error state Kalman filter (ESKF) framework to achieve continuous localization under GNSS rejection. GILV utilizes a downward-facing camera to provide vehicle position constraints through a template matching algorithm that is used to smooth out IMU predictions and mitigate dispersion of localization errors. At the same time, the forward-facing camera detects lane lines and combines this information with high-precision maps to provide lateral position constraints without cumulative errors, ensuring that the Integrated Positioning System (IPS) accuracy is stabilized at the decimeter level without degradation due to cumulative IMU errors. The Integrated Positioning System (IPS) is realized on the ROS platform and has been verified through outdoor experiments. The results show that GILV consistently limits the position error to within 0.5 meters and maintains an average position error within 10 centimeters during a 100-second GNSS signal outage. This resilience greatly reduces the dependence of the positioning system on the quality of GNSS signals. © The Authors.

关键词： Global positioning system

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Building Resilient CICD Pipelines: A DevOps Security-First Framework

Building Resilient CICD Pipelines: A DevOps Security-First F...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Ashtagi, Rashmi Belani, Chirag Bhalerao, Prasanna Bhalerao, Yash Chawle, Atharva Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798331512965

Continuous Integration and Continuous Deployment are now integrated in automated systems that enhance code stability, and accelerate the deployment process as well as improving monitoring of the whole system in the software development field. This document deals with setting up of a strong CI/CD process involving AWS cloud services and DevOps. The infrastructure is built with the help of Terraform which ensures the scalability and repeatability applying the Infrastructure as Code (IaC) approach. A safe Virtual Private Cloud (VPC) is designed consisting of public and private subnets, load balancing for traffic distribution, and CloudFront CDN to improve geographical distribution. Jenkins is used in CI process to perform disk analysis, OWASP and Trivy vulnerability scanning, and making Docker images. The CD phase use ArgoCD for updates on Kubernetes and integrates Monitoring and observability using Prometheus and Grafana. Notifications are produced to JIRA and email communication for improvement. With the help of the offered cloud services and DevOps tool it is possible to consolidate the SDLC and bring effects in code safety, testing and utilization. This document also provides an overview of methods, instruments, and outcomes, demonstrating how the CI/CD automation influences the development and operation positively. © 2025 IEEE.

关键词： Observability

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Enhancing Energy Efficiency in IoT Networks with AI optimized LEACH Protocol and Digital Twin Simulation 1

Enhancing Energy Efficiency in IoT Networks with AI optimize...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Harinaiha, Adarsha Chaudhary, Pavan Faculty of Engineering and Technology Department of Mechanical Engineering Ramnagar District Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

The convergence of the Internet of Things (IoT) via artificial intelligence (AI) will undoubtedly revolutionize networking. IoT is a technological paradigm shift facilitating the incorporation of many applications, including smart homes and wearable gadgets, via automation, wireless sensors, and embedded systems. Furthermore, the production of AI enhances this phenomenon by delivering data-driven insights, streamlining procedures, and creating new avenues for development. The extensive implementation of devices in Internet of Things (IoT) networks has raised worries over escalating energy usage. Utilizing environmentally friendly techniques is crucial to maintain network service for nodes that sense with limited power supplies. One approach that substantiates this assertion is LEACH, an acronym for Low Energy Adaptive Clustering Hierarchy. The objective of this form of communication is to optimize the transfer of information to base sites by successfully configuring clusters within networks and executing real-time adjustments across cluster nodes. This work enhances the effectiveness of IoT systems by using a virtual twin simulation to optimize the protocol known as LEACH. Employ artificial intelligence analysis and virtual network models to evaluate power consumption and efficiency. Cache nodes are essential in this architecture since they receive information from the cluster's head & transmit it through the main hub. Utilizing simulation tools and artificial intelligence, we can enhance the reliability and energy efficiency of IoT devices. The results indicate an 84% decrease in inactive nodes & a 1.67-fold rise in node energy use relative to traditional methods. The present study suggests a prospective future trajectory for LEACH-based, AI-augmented, low-power IoT infrastructure. © 2025 IEEE.

关键词： Energy utilization

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Artificial Intelligence Algorithms for Robotic Harvesting of Agricultural Produce 1

Artificial Intelligence Algorithms for Robotic Harvesting of...

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1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Kolhalkar, Nilesh R. Pandit, Anupama A. Kedar, Shridhar Ashok Yedukondalu, G. MKSSS's Cummins College of Engg. For Women Department of Mechanical Engineering MH Pune India Department of Computer Science & Engg. Pune MH Pune India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Mechanical Engineering A.P. Vijayawada India

ISBN: (纸本)9798350355611

Robotic harvesting of fruits and vegetables is an advanced technology that leverages Robotics, Artificial Intelligence, and Machine Vision to harvest the fruits autonomously from plants or trees. This technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. AI algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. The accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. Soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. Machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations. © 2025 IEEE.

关键词： Machine vision

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Analysis of Sensor Technologies for Structural Health Monitoring in Civil engineering Applications 3

Analysis of Sensor Technologies for Structural Health Monito...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Agarwal, Trapty Gandhi, Ayush Mohapatra, Yogomaya Singh, Jagtej Bahri, Dimple Reddy, V. Venkata Ramesh Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Department of Civil Engineering Karnataka Bangalore India Presidency University Sponsored Research Karnataka Bangalore India

ISBN: (纸本)9798331542375

Civil engineering is considered complete with structural health monitoring SHM, a continuous process in which structures are tested for their health and integrity, ensuring they remain safe and reliable. Recent advancements in sensor technologies for SHM have improved the empirical side of structural performance. The advancement of sensor technologies for SHM is largely related to implementing wireless and smart sensors. It is easily fixable and provides real-time information on the strain, temperature, vibration, etc. Wireless and smart sensors can also be deployed in difficult-to-access or non-safe areas, allowing for an increased size of monitored structures. In addition, the emergence of sensor technologies has resulted in smart algorithms and data analytics methods suitable for SHM. Such algorithms process huge amounts of data and detect potential structural problems or anomalies before any expected maintenance intervention. © 2025 IEEE.

关键词： Structural Mechanics

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Dual-Plate Verification System for Vehicle Identifcation using IoT and Embedded Systems 3

Dual-Plate Verification System for Vehicle Identifcation usi...

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

作者： Sampath, A.V.S.S. Malagati, Nischal S. Kanapathi, V. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

This research uses a dual-plate verification system for improving vehicle security by using IoT and embedded systems. The system compares front and rear license plates to detect any anomaly, which signals unauthorized modifications. Using real-time image processing and IoT-based data communication, this solution gives us an accurate plate matching system, making sure of reliable vehicle identity verification. Its possible applications include traffic monitoring, parking management, and detecting any unlawful activity. This project is unique because it integrates the dual plate verification system with an Arduino IoT setup. For hassle-free text recognition, this system uses deep learning frameworks combined with a wireless mobile setup that serves as a camera. The system’s accuracy is shown through experiments, displaying its value in improving transportation security with automation. The number plates were detected, and the respective penalties for the vehicles were imposed accordingly. © 2025 IEEE.

关键词： Anomaly detection

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Augmenting Human Decision-Making: Exploring AI-Driven automation in IT Operations (AIOps) for Enhanced Efficiency and Innovation 4

Augmenting Human Decision-Making: Exploring AI-Driven Automa...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Lakshmi Kumar, Tukkisetty Vasantha Naresh, Tippa Ganesh, Chunduru Saran Varma, Chekuri Tarun Raju, K.V. Hema, Chindu Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798331523923

This article analyzes how artificial intelligence (AI) has changed the IT industry, ushering in an age of unmatched technical growth and new potential for human-AI communication. Through a detailed research, it explores the complex dynamics of AI integration in IT operations, examining the likelihood that AI may supplant people in the workforce and highlighting the complexities and challenges that accompany this paradigm change. By examining present AI technologies, such as machine learning, deep learning, natural language processing, and data science, and emphasizing both the benefits and imminent obstacles, this article discusses the complex environment of AI adoption in IT. It also digs thoroughly into the legal frameworks, social repercussions, and ethical challenges that must be overcome in order to govern the AI-driven revolution of the IT industry. Through this research, the article wants to peacefully cohabit human knowledge with AI talents, opening up new paths for creativity, productivity, and sustainable development in the digital age. © 2025 IEEE.

关键词： Adversarial machine learning

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MEIC: Re-thinking RTL Debug automation using LLMs 24

MEIC: Re-thinking RTL Debug Automation using LLMs

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

作者： Xu, Ke Sun, Jialin Hu, Yuchen Fang, Xinwei Shan, Weiwei Wang, Xi Jiang, Zhe National Center of Technology Innovation for EDA School of Integrated Circuits Southeast University China Department of Computer Science University of York United Kingdom

ISBN: (纸本)9798400710773

The deployment of Large Language Models (LLMs) for code debugging (e.g., C and Python) is widespread, benefiting from their ability to understand and interpret intricate concepts. However, in the semiconductor industry, utilising LLMs to debug Register Transfer Level (RTL) code is still insufficient, largely due to the underrepresentation of RTL-specific data in training sets. This work introduces a novel framework, Make Each Iteration Count (MEIC), which contrasts with traditional one-shot LLM-based debugging methods that heavily rely on prompt engineering, model tuning, and model training. MEIC utilises LLMs in an iterative process to overcome the limitation of LLMs in RTL code debugging, which is suitable for identifying and correcting both syntax and function errors, while effectively managing the uncertainties inherent in LLM operations. To evaluate our framework, we provide an open-source dataset comprising 178 common RTL programming errors. The experimental results demonstrate that the proposed debugging framework achieves fix rate of 93% for syntax errors and 78% for function errors, with up to 48x speedup in debugging processes when compared with experienced engineers. The Repo. of dataset and code: https://***/SEU-ACAL/reproduce-MEIC-ICCAD. © 2024 Copyright is held by the owner/author(s).

关键词： Program debugging

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Automated Intracerebral Hemorrhage Detection in CT Images Using InceptionResNetV2 24

Automated Intracerebral Hemorrhage Detection in CT Images Us...

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8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Liu, Ying Zhong, Suixi Hao, Mingjiao Huang, Junsheng School of Medicine Hainan Vocational University of Science and Technology Hainan Haikou China School of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

Intracerebral hemorrhage (ICH) is a common cerebrovascular disease with a high mortality rate, making accurate and timely identification crucial. This paper introduces a machine learning-based model for detecting ICH from brain CT images, aimed at improving the automation and accuracy of medical image diagnostics. The model leverages a InceptionResNetV2 as the feature extraction network, combined with global average pooling, dropout, and fully connected layers tailored for binary classification tasks. Experimental results show that the model achieves an accuracy of 0.9733 on the validation set and 0.9200 on the test set. The loss rates are 0.1644 and 0.1567, recall rates are 0.9864 and 0.9623, F1 scores are 0.9638 and 0.9273, and AUC values are 0.9972 and 0.9889 for the validation and test sets, respectively. These metrics indicate the model’s strong generalization capability. Additionally, a user interface was developed to predict the probability of ICH in brain CT images, providing an effective tool for automated detection. This tool holds promise in assisting clinicians with rapid and accurate diagnostic decisions. © 2024 Copyright held by the owner/author(s).

关键词： computerized tomography

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Design and PID Control of a Lower Limb Exoskeleton for Virtual Reality-Based Telerehabilitation 11

Design and PID Control of a Lower Limb Exoskeleton for Virtu...

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11th international conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Aktan, Mehmet Emin Falkowski, Piotr Zawalski, Krzysztof Jeznach, Kajetan Ömürlü, Vasfi Emre Akdoǧan, Erhan Bartin University Department of Mechatronics Engineering Bartin Turkey Health Institutes of Türkiye Istanbul Turkey Industrial Research Institute for Automation and Measurements Piap Lukasiewicz Research Network Warsaw Poland Yildiz Technical University Department of Mechatronics Engineering Istanbul Turkey

ISBN: (纸本)9798331509293

Rehabilitation is a critical process that enables people who have lost motor skills to regain their physical abilities. Exoskeletons offer innovative solutions to accelerate the functional recovery of patients and increase the effectiveness of treatment. The integration of virtual reality and biological signal processing technologies into such systems allows the user's movements to be detected in real time with high accuracy, enabling remote and personalised treatment. This study explains the working principle and control structure of a 4-degree-of-freedom lower limb exoskeleton robot developed for virtual reality-based telerehabilitation. The details of the electrical components of the system such as controller, input and output units, motor, sensor, virtual reality goggles, EEG and EMG devices and their interrelationships are explained. The position control performance for each joint using the PID control method is analysed. The results show that the exoskeleton can follow the given target trajectories with different error values depending on the period and joint. A maximum position error of 0.659 degrees was observed in the hip flexion-extension joint, while the average error for this joint was recorded as 0.385 degrees. © 2025 IEEE.

关键词： Functional neural stimulation

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