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Incorporating Onboard Visual Sensing and Geographic Information for Lane-Level Vehicle Localization

IEEE Sensors Journal 2025年第12期25卷 21867-21877页

作者： Lin, Huei-Yung Li, Jun-Yi Su, Ming-Wei National Taipei University of Technology Department of Computer Science and Information Engineering No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan National Chung Cheng University Department of Electrical Engineering 168 University Road Chiayi621 Taiwan

With the recent advances in deep learning and artificial intelligence, vehicular technologies are progressing rapidly toward fully autonomous driving. Critical components of advanced driver assistance systems (ADAS) have been extensively investigated in the past decade. In this paper, we address the challenge of precise vehicle localization using visual perception and geographic information. The proposed method incorporates drivable area detection, lane line detection, and image-map matching to achieve lane-level localization accuracy. It takes advantage of digital maps and images from the onboard cameras to simultaneously optimize vehicle localization and lane detection. A confidence estimation is proposed to generate virtual lane lines for the lane marking identification. Our networks are pre-trained on the Cityscapes, CULane and TuSimple datasets, and fine-tuned with the newly proposed TRoad and TLane. In the experiments, the performance evaluated using MAE and RMSE have demonstrated a significant improvement over the conventional GPS/GNSS-based vehicle localization techniques. Source code and datasets are available at https://***/Wally0924/Enhancing-Vehicle-Localization-Using-Lane-Detection-and-Geographic-Information. © 2001-2012 IEEE.

关键词： Advanced driver assistance systems

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AI-driven multimodal colorimetric analytics for biomedical and behavioral health diagnostics

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Computational and Structural Biotechnology Journal 2025年 27卷 2219-2232页

作者： Hagos, Desta Haileselassie Aryal, Saurav Keshari Ymele-Leki, Patrick Burge, Legand L. Howard University Department of Electrical Engineering and Computer Science 2400 Sixth Street NW Washington DCDC20059 United States Howard University Department of Chemical Engineering 2400 Sixth Street NW Washington DCDC20059 United States

The exponential growth of multi-scale biomedical and behavioral data introduces both challenges and opportunities for [Figure presented]-driven analytics. Effectively managing the complexity and variability of these data sources requires advanced computational techniques for accurate interpretation and robust decision-making. Integrating [Figure presented] with colorimetric biosensing and multimodal data fusion offers scalable solutions that can improve diagnostic accuracy, enable early disease detection, and support personalized medicine. This work explores mobile-based colorimetry, an [Figure presented]-driven approach that uses image processing and [Figure presented] to detect colorimetric changes in chemical and biological solutions. We propose a modular conceptual framework that integrates mobile-based colorimetry with multimodal biomedical data, such as clinical, imaging, and environmental datasets, to develop scalable, low-cost tools for predictive modeling, real-time health monitoring, and personalized diagnostics. We review recent advancements in [Figure presented]-enabled colorimetric analysis and multimodal data fusion for healthcare applications, emphasizing innovations in [Figure presented]-assisted biosensors, [Figure presented]-driven biomedical imaging, and multimodal fusion techniques. In addition, we highlight the need for robust data management systems and interpretable AI/ML models to ensure security, privacy, and reliability in biomedical and behavioral research. This work also highlights practical directions for improving diagnostic accuracy and accessibility, particularly in resource-limited settings. © 2025 The Author(s)

关键词： Personalized medicine

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AutoColor: learned light power control for multi-color holograms 5

AutoColor: learned light power control for multi-color holog...

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Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) V 2024

作者： Zhan, Yicheng Kavakll, Koray Urey, Hakan Sun, Qi Akşit, Kaan University College London Department of Computer Science London United Kingdom Koç University Department of Electrical and Electronics Engineering Istanbul Turkey New York University Department of Computer Science and Engineering New YorkNY United States

ISBN: (纸本)9781510670860

Multi-color holograms rely on simultaneous illumination from multiple light sources. These multi-color holograms could utilize light sources better than conventional single-color holograms and can improve the dynamic range of holographic displays. In this letter, we introduce AutoColor, the first learned method for estimating the optimal light source powers required for illuminating multi-color holograms. For this purpose, we establish the first multi-color hologram dataset using synthetic images and their depth information. We generate these synthetic images using a trending pipeline combining generative, large language, and monocular depth estimation models. Finally, we train our learned model using our dataset and experimentally demonstrate that AutoColor significantly decreases the number of steps required to optimize multi-color holograms from > 1000 to 70 iteration steps without compromising image quality. © 2024 SPIE.

关键词： Holograms

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Towards migration from IEC 61131-3 to IEC 61499 in process industry: Redesign of visualisation 32

Towards migration from IEC 61131-3 to IEC 61499 in process i...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Kothalawala, Hiruni Jhunjhunwala, Pranay Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Lulea Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798350399714

This work focuses on reengineering an HMI implemented in a third-party legacy tool to an IEC 61499 implementation. We propose a method to re-engineer the view for a process system and gather relevant information for the migration of the controller to IEC 6499. The proposed methodology has been tested and demonstrated using a lab-scale 'Water process system' that is currently controlled using an IEC 61131-3 compliant PLC with a view and HMI developed on a legacy tool. It is intended to migrate to the IEC 61499-compliant EcoStruxure Automation Expert (EAE) tool. © 2023 IEEE.

关键词： Process control

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Optimal Placement of Solar Power Plant by Using AVOA Algorithm

Optimal Placement of Solar Power Plant by Using AVOA Algorit...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Klington, A. George Karthikeyan, K. St. Mother Theresa Engineering College Department of Computer Science Engineering Tuticorin India St. Mother Theresa Engineering College Department of Electrical and Electronics Engineering Tuticorin India

ISBN: (纸本)9798331521394

Infrastructure development and its design are more complex because of its constraint on budget and efficiency. By remembering these difficulties, a modified electrical structure is necessary for the nation. The power system structure should not depend on the conventional system. This main criterion is being in our engineer's mind. But, strengthening loads and their necessaries do not accept them to sleep. To face complex structures and constraints-based infrastructure facilities, optimal places for renewable systems are necessary. Here, the optimal placement of solar power plant is taken as the primary concern and its related analysis is made. Even though many algorithms are coming from all over the world. The part of those algorithms is not analyzed in many environments. In this paper, an AVOA algorithm is used for our analysis. This part of our research makes us excited with its results. The IEEE 57 system is used for the analysis. © 2025 IEEE.

关键词： African vultures optimization algorithm CPF MATPOWER PSO PV cell voltage stability

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Enhanced Self-Deployment in IoT Sensor Networks through Leveraging 3D-Voronoi Diagrams with an Advanced Genetic Algorithm

Enhanced Self-Deployment in IoT Sensor Networks through Leve...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Andreas, Andreou Mavromoustakis, Constandinos X. Markakis, Evangelos Bourdena, Athina Mastorakis, George University of Nicosia Department of Computer Science Nicosia Cyprus Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete Greece Hellenic Mediterranean University Department of Management Science and Technology Crete Greece

ISBN: (纸本)9798350351255

Smart spaces integrate advanced technologies like the Internet of Things (IoT), Machine Learning, and Artificial Intelligence (AI) to enhance automation and control within various environments. Effective deployment of IoT nodes is crucial for maximizing coverage, minimizing costs, and ensuring network stability in these spaces. This paper presents a novel approach combining 3D Voronoi diagrams with a modified Genetic Algorithm (GA) to optimize IoT node placement in three-dimensional environments. The proposed method starts with node placement using a homogeneous Poisson Point Process (PPP) and partitions the space into Voronoi cells, followed by iterative adjustments using the modified GA. The method achieves a 15% improvement in coverage ratio, a 10% reduction in deployment effort, and a 20% increase in network stability compared to existing algorithms, with results statistically significant at 5%. Moreover, optimising sensor placements indirectly enhances network security by reducing redundant data paths and strengthening network resilience. This study provides a scalable, efficient solution for IoT network deployment in complex environments, addressing key challenges in smart space optimization and paving the way for more secure and robust IoT infrastructures. © 2024 IEEE.

关键词： Genetic algorithms

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Adaptive Multi-Path Video Streaming Using AI-Driven Edge Computing for Enhanced Quality of Experience (QoE) 2

Adaptive Multi-Path Video Streaming Using AI-Driven Edge Com...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi University Department of Electrical and Computer Engineering MS38677 United States University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA70504 United States

ISBN: (纸本)9798331529529

With the increasing demand for high-quality video streaming services, there is a need to enhance the Quality of Experience (QoE) for users, especially in environments with variable network conditions. This research paper proposes a novel approach leveraging AI-driven edge computing to implement adaptive multi-path video streaming. The proposed method uses machine learning algorithms to dynamically select the optimal streaming paths and adjust video quality in real-time based on network conditions and user preferences. By utilizing edge computing resources, this approach aims to reduce latency, improve bandwidth utilization, and provide a more reliable and high-quality video streaming experience. © 2024 IEEE.

关键词： Video streaming

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3D CNN with Attention Modules for Enhanced COVID-19 and Pneumonia Screening from CT Scans 2

3D CNN with Attention Modules for Enhanced COVID-19 and Pneu...

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2nd International Conference on Power, electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Rahman, Taifur Faruk, Md. Farukuzzaman Khansur, Md. Nasif Srizon, Azmain Yakin Hasan, S. M. Mahedy Al Mamun, Md. Hossain, Md. Faruk Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electrical & Electronic Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

During the COVID-19 outbreak, deep-learning techniques have been extensively studied for computer-assisted diagnosis. However, distinguishing COVID-19 from other types of pneumonia remains challenging. This research describes an automated chest CT imaging technique for COVID-19 pneumonia screening. CT scans provide valuable three-dimensional information, aiding in detecting COVID-19 symptoms. The limited availability of comprehensive 3D datasets has slowed the use of attention-based models in this field. To address this, we propose a novel 3D Convolutional Neural Network (CNN) with a residual block incorporating self-attention and spatial attention modules. Our primary goal is accurately classifying COVID-19 and pneumonia cases based on CT scans. Integrating attention modules enhances spatial correlation analysis across CT slices, enabling dynamic re-calibration of the CNN output to identify complex patterns. Training our 3D CNN on CT scan images from the CC-CCII dataset achieved an impressive accuracy rate of 99.42%. Subsequently, we used the CNN's prediction probabilities to construct feature vectors for each sample. By training various machine learning classifiers independently and combining them through soft voting, we achieved a remarkable 98.66% accuracy. This study highlights how our approach can improve pulmonary patient care and diagnostic processes, leveraging state-of-the-art deep learning techniques for accurate and efficient COVID-19 pneumonia screening. © 2024 IEEE.

关键词： COVID-19

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Automatic Segmentation Labeling for Autonomous Driving Datasets

Automatic Segmentation Labeling for Autonomous Driving Datas...

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2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023

作者： Lee, Jinsun Lee, Si Woo Hong, HyeongKeun Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Department of Computer Science and Engineering Suwon Korea Republic of

ISBN: (纸本)9798350344318

In this paper, we propose an algorithm that automatically labels datasets for the development and testing of autonomous vehicles. Traditionally, it is commonly used hand-labeled datasets, which is costly and time-consuming. In this work, we address these problems by automatically labeling based on YOLOPv2 model and validate it on an autonomous driving algorithm. © 2023 IEEE.

关键词： Autonomous vehicles

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FSD-Net: a fuzzy semi-supervised distillation network for noise-resistant classification of medical images

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Multimedia Tools and Applications 2024年第36期83卷 83661-83676页

作者： Du, Xiangtong Shen, Ao Wang, Ximing Li, Zongsheng Deng, Hai School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Information Science and Engineering Hohai University Changzhou213022 China Department of Radiology First Affiliated Hospital of Soochou University Suzhou215006 China Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States

Deep Learning (DL)-based models have been successfully applied for medical image classifications. However, the performance of traditional medical image classifiers is limited by insufficient training samples and inaccurate annotations due to labeling noise. Numerous methods were proposed to address the issues, to no avail. To fill this gap, we propose a Fuzzy Semi-supervised Distillation Network (FSD-Net) to improve the noise-resistant classification performance. Firstly, with the aid of semi-supervised training based on the pseudo-labels, the problem of insufficient training samples can be alleviated by using unlabeled data to a certain extent. Then, a Confidence Filtering model (CF-model) is designed to improve the quality of the pseudo-labels and filter the noisy labeling data by combining the soft labels generated from the classification model and the improved Soft Fuzzy C-Means (SFCM) model. Compared with the setting thresholds, the data with low confidence are reclassified as unlabeled to participate in training, while the labeled and unlabeled data (generating pseudo-labels) with high confidences are added into the mixed dataset. Finally, the mixed data are fed to train a multi-model online distillation module consisting of the SFCM and two other student models. The weighted average value of the three student models is used as teacher knowledge to train the students and obtain the final classification results. Experimental results on multiple medical datasets show that the proposed FSD-Net can solve the problems of insufficient labeled samples and labeling noise simultaneously to some extent. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Distillation

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