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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 48226 条记录，以下是1551-1560 订阅

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Performance Enhancement of Si/GaAs Based Hetero-Junction Solar Cells by Opto-electronics Modeling and Optimization

Performance Enhancement of Si/GaAs Based Hetero-Junction Sol...

引用

2024 international conference on Recent advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Roy, Arijit Bardhan Valiji, Navaid Hussain Rasulji Mohammad, Rehan Giridhar, P. Mondal, Pratik SRM Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350354539

When it comes to renewable energy sources, solar photovoltaic (PV) technology is the most popular and extensively utilized. Thus, one of the key challenges in acknowledging photovoltaic technology as an archaic source of energy is raising the solar cell's efficiency. Because of this, one of the best methods to achieve high efficiency through enhancing open circuit voltage and electron hole pair creation throughout a wide spectrum is through hetero-junction solar cells with suitable band gap tuning. This article focuses on the design and analysis of Si-GaAs based hetero-junction solar cells using PC1D software to optimize opto-electronics parameters such as material thickness and doping profile, resulting in an efficiency of over 20%. © 2024 IEEE.

关键词： Gallium arsenide

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Real-Time 3D Bounding Box Estimation with RCNN-Resnet101 and Adaptive Projection Matrices 4

Real-Time 3D Bounding Box Estimation with RCNN-Resnet101 and...

引用

4th international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kumar, Utsav Parihar, Pratik Vaithiyanathan, Dhandapani Manigandan, Muniraj National Institute of Technology Delhi Department of Electronics and Communication Engineering Delhi110036 India Vellore Institute of Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798350343670

In recent years, there has been a surge in demand for precise object detection and 3D bounding box estimation across various applications in computer vision, ranging from autonomous driving to robotics and augmented reality. This paper introduces a method specifically designed for real-time 3D bounding box estimation using a monocular camera. In contrast to conventional approaches that directly estimate 3D bounding boxes, our method begins by calculating accurate 2D bounding boxes. Subsequently, it employs a combination of mathematical modelling to predict 3D bounding boxes. The primary goal of this research is to deliver a practical and efficient solution for achieving accurate 3D bounding box estimation, capitalizing on the widespread availability of monocular camera hardware. Within our study, we independently generate the Projection Matrix, ensuring the robustness and applicability of our method across diverse datasets. This underscores the effectiveness of our approach in accurately estimating 3D object properties. Notably, our methodology demonstrates satisfactory results on both the Kitti and Cityscapes datasets, further emphasizing its proficiency in accurately estimating 3D object properties. © 2024 IEEE.

关键词： 2D to 3D Bounding Box Autonomous Navigation Depth Estimation Object Detection Single Shot Detection

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Design of Household Intelligent Trash Can Control System Based on MCU 3

Design of Household Intelligent Trash Can Control System Bas...

引用

3rd international conference on Control and Intelligent robotics, ICCIR 2023

作者： Li, Huixiu Zhao, Lina Zhang, Liying Liu, Ping Dalian University of Science School of Traffic and Electrical Engineering Dalian116052 China

ISBN: (纸本)9781510671867

Since the trash can is not sanitary and fails to do the garbage classification, a household intelligent trash can based on SCM is designed. Firstly, the hardware of the intelligent trash can is designed, whose function includes garbage classification, voice tracking, UV disinfection, and other hardware circuit design. In this design, a speech recognition module is used to classify trash cans, UV disinfection lamp components are employed to achieve the disinfection and sterilization of garbage viruses, and the tracking function is used to realize the free movement of trash cans, providing great convenience for the intelligent trash cans. Secondly, combined with software design, the system functions are further improved to make the trash can more intelligent and practical. © 2023 SPIE.

关键词： Software design

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Detection of Thyroid Diseases Using Deep Learning 2

Detection of Thyroid Diseases Using Deep Learning

引用

2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Archana, P. Suhana Anjum, S. Keerthana, S.M. Srusti, P.D. Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (纸本)9798350383867

Thyroid diseases, including hypothyroidism, hyperthyroidism, thyroiditis, nodules, cancer, and euthyroid disease, are serious health problems that require accurate and timely diagnosis and treatment. This article presents a new method that specifically uses deep learning in convolutional neural networks (CNN) to successfully classify thyroid disease based on X-ray and ultrasound images. Our method, which uses CNN principles and data such as x-ray and ultrasound containing multiple thyroid images, is effective in classifying diseases. Thanks to a careful pre-processing, optimization and elimination process, our CNN model achieved an accuracy of 97.92%, demonstrating its potential as an important diagnostic tool. Early diagnosis and treatment of thyroid disease. © 2024 IEEE.

关键词： Ultrasonic imaging

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Line Fitting-Based Corner-Like Detector for 2D Laser Scanners Data∗ 10

Line Fitting-Based Corner-Like Detector for 2D Laser Scanner...

引用

10th international conference on Automation, robotics, and Applications, ICARA 2024

作者： Sousa, Ricardo B. Sobreira, Heber Miguel Silva, Manuel F. Moreira, Antonio Paulo University of Porto Faculty of Engineering Porto4200-465 Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal ISEP Polytechnic of Porto R. Dr. António Bernardinode Almeida Porto4249-015 Portugal

ISBN: (纸本)9798350394245

The extraction of geometric information from the environment may be of interest to localisation and mapping algorithms. Existent literature on extracting geometric features from 2D laser data focuses mainly on detecting lines. Regarding corners, most methodologies use the intersection of line segment features. This paper presents a feature extraction algorithm for corner-like points in the 2D laser scan. The proposed methodol-ogy defines arrival and departure neighbourhoods around each scan point and performs local line fitting evaluated in multiple distance-based scales. Then, a set of indicators based on line fitting error, the angle between arrival and departure lines, and consecutive observation of the same keypoint across different scales determine the existence of a corner-like feature. The experiments evaluated the corner-like features regarding their relative position and observability, achieving standard deviations on the relative position lower than the sensor noise and visibility ratios higher than 75% with very low false positives rates. © 2024 IEEE.

关键词： Feature extraction

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An Efficient Prediction System for Heart Diseases Using Machine Learning Algorithms 2

An Efficient Prediction System for Heart Diseases Using Mach...

引用

2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Archana, P. Shashikala, S.V. Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University BG Nagara Mandya India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

One of the most severe and demanding conditions that the arena is currently facing is heart illness. Forecasting cardiovascular illness is one among the most challenging pursuits in the world of medical facts analysis. Machine learning classification algorithms has been shown to be useful in making predictions and options that are solely dependent on substantial amounts of data. This study offers a technique to predict and classify if a person has coronary heart disease and to inform the affected individual about their risk through records or a diagnosis. The prediction version is built using a variety of options and numerous additional classification techniques. This is accomplished by comparing the accuracy of many algorithms to the outputs of each version independently, then choosing the method with the highest forecast accuracy. This paper's main goal is to use data analytics to predict whether or not contamination will persist for the foreseeable future. © 2024 IEEE.

关键词： Prediction models

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Cross-Lingual Transfer Learning in RNNs for Enhancing Linguistic Diversity in Natural Language Processing

Cross-Lingual Transfer Learning in RNNs for Enhancing Lingui...

引用

2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Tiwari, Shikha Babu, Ch. Meher Shanker, P. Shahnaz, K.V. Roy, Vandana Kashyap, Ramgopal Amity University Raipur India Potti SriRamulu Chalavadi Mallikarkuna Rao College of Engineering and Technology Vijayawada India Cvr College of Engineering Dept. of Cse Hyderabad India Veltech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Dept. of Ece Chennai India Ggits DoEC Jabalpur India Amity University Chhattisgarh India

ISBN: (纸本)9798350388800

In addition to removing biases, these findings reveal a comprehensive method for improving cross-lingual transfer learning in NLP, which in turn makes language more diverse. This approach was created as a result of this effort. In order to construct the system utilizing these methodologies, five fundamental procedures are followed. One approach that falls under this category is MTI, which stands for Multilingual Embedding Adaptation and is used for Machine Translation-based Initialization. Ethical Bias Mitigation, Linguistic Feature Alignment, and Recurrent Neural Network Adaptation are some supplementary methods. Developing each strategy to address the wide variety of issues that may arise during cross-lingual transfer learning required considerable deliberation. Starting with translated data, the MTI algorithm aligns the source and target language embeddings, the RNA algorithm aligns RNN structures, the LFA algorithm aligns linguistic characteristics, and the EBM method lowers biases fairly. The desired outcomes are attained by employing all these techniques. The outcomes of ablation research demonstrated the significance of each approach and their interdependence in ensuring the system's functionality. The framework provides a comprehensive strategy for increasing the linguistic diversity of natural language processing (NLP) models to accommodate individuals from varying cultural backgrounds and language abilities. Issues of social justice and equity receive substantial consideration within the framework. © 2024 IEEE.

关键词： Contrastive Learning

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Research on Vehicle Lane Change Prediction Considering Driver’s Behavior 3

Research on Vehicle Lane Change Prediction Considering Drive...

引用

3rd international conference on Control and Intelligent robotics, ICCIR 2023

作者： Lijiale, Su Jiangtian, Li School of Automotive Engineering Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781510671867

Accurate prediction of vehicle lane-change behaviour is beneficial for traffic efficiency and safety. This paper incorporates driver behavioural information indicators into the prediction of lane change behaviour. A driving simulator was used to collect driver behavioural characteristics and vehicle operating parameters during lane keeping and lane changing, and through theoretical analysis and data testing, seven indicators were obtained that could be used to predict lane changing behaviour: target area gaze time, head level turning angle, vehicle speed, lane drift angle, distance to the vehicle in front, and time to collision. Using the experimentally collected variable data, a logistic prediction model was developed and validated to have satisfactory prediction accuracy. © 2023 SPIE.

关键词： Eye movements

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An Improved YOLOv8 Algorithm for Oriented Object Detection in Remote Sensing Images 6

An Improved YOLOv8 Algorithm for Oriented Object Detection i...

引用

6th international conference on robotics and Computer Vision, ICRCV 2024

作者： Yu, Jie Huang, Wei Shen, Wei School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

Remote sensing target detection is faced with the problems of random target direction, difficult detection of small targets, and dense arrangement. In order to solve the above problems, we propose a lightweight rotating remote sensing target detection algorithm based on YOLOv8, namely Slim-YOLOR. Firstly, we reduce the number of feature map channels in the feature fusion module, and incorporate a large kernel convolutional spatial attention (LSA) module to increase the model sensing field. Secondly, dynamic upsampling is used to reduce the information loss in the process of small target feature reconstruction and enhance the detection ability of dense small targets. Finally, the shared convolution is used to interact with the classification and regression tasks, and the deformable convolution is combined to enhance the localization of the oriented target. Experimental results on the DIOR-R dataset demonstrate that compared to YOLOv8, Slim-YOLOR reduces the number of parameters by 39.5% while increasing the mAP@O.5:0.95 by 1.6%. Additionally, Slim-YOLOR shows clear advantages over other classical algorithms. © 2024 IEEE.

关键词： Optical remote sensing

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A Comprehensive Approach to Identify Stroke based on Real-time Medical Data 1

A Comprehensive Approach to Identify Stroke based on Real-ti...

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1st IEEE international conference on Cognitive robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Maragatharajan, M. Shanmugapriya, S. Sudhan, M.B. Deeptha, R. Paramasivam, Sankar Muthu Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kothrikalan India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu Srivilliputhur India Srm Institute of Science and Technology Department of Computer Science and Engineering RamapuramTamilnadu Chennai India Srm Institute of Science and Technology Department of Information Technology RamapuramTamilnadu Chennai India Palo Alto Networks Inc Distinguished Software Engineer 3000 Tannery Way Santa ClaraCA United States

ISBN: (纸本)9798350372748

Strokes or cardiovascular disorders continue to be a major global health concern. Proactive intervention and timely detection greatly reduce the morbidity and death related to strokes. The goal of this research is to build a prediction model using machine learning techniques to assess a person's risk of stroke based on a variety of sociodemographic and health characteristics. The dataset used in this study consists of a large collection of medical records. It contains important details including age, gender, BMI, smoking habits, heart disease, hypertension, and the type of residential property. Using this information, a Decision Tree Classifier is trained to predict the likelihood of strokes, enabling early detection and customized treatment plans. A Decision Tree Classifier with a predetermined maximum depth is used in the proposed methodology's rigorous data preprocessing, which includes handling missing values, encoding categorical features, and developing models. Both the training and testing datasets are used to evaluate the model's performance using evaluation measures including accuracy, precision, and recall. The model's insights provide important characteristics that influence the prediction of stroke risk, assisting medical professionals in focused risk assessment and preventive intervention. The proposed method has the potential to improve healthcare practices by facilitating prompt interventions and personalized treatment for stroke risk individuals. The proposed model has resulted in 86.78% accuracy for this dataset by using the traditional machine learning algorithms. This work highlights the value of predictive modelling in the healthcare industry by demonstrating how it may improve early detection techniques and lessen the impact of strokes on public health. © 2024 IEEE.

关键词： Forecasting

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