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Investigation on FBG based optical sensor for pressure and temperature measurement in civil application

Optoelectronics Letters 2024年第9期20卷 531-536页

作者： Somesh Nandi Chethana K T Srinivas Preeta Sharan Department of Artificial Intelligence and Machine Learning Rashtreeya Vidyalaya College of EngineeringBangalore560059India Department of Electronics and Communications Jyothy Institute of TechnologyBangalore 560082India Department of Electrical Communication Engineering Indian Institute of ScienceBangalore 560012India Department of Electronics and Communications the Oxford College of EngineeringBangaloreIndia

Optical fiber Bragg grating(FBG)sensors have advanced significantly in the last several *** use of innovative FBG in temperature and pressure measurement is examined in this *** benefits of FBGs,such as their compact size,low weight,resilience to corrosion,immunity to electromagnetic interference,distributed sensing,and remote monitoring,have brought attention to the growing research in this field of structural health monitoring of civil infrastructures.

关键词： immunity civil application

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ID-like Prompt learning for Few-Shot Out-of-Distribution Detection

ID-like Prompt Learning for Few-Shot Out-of-Distribution Det...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yichen Bai Zongbo Han Bing Cao Xiaoheng Jiang Qinghua Hu Changqing Zhang College of Intelligence and Computing Tianjin University Tianjin Key Lab of Machine Learning School of Computer and Artificial Intelligence Zhengzhou University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples, especially discovering challenging outliers from auxiliary outliers dataset to improve OOD detection. However, they may still face limitations in effectively distinguishing between the most challenging OOD samples that are much like in-distribution (ID) data, i.e., ID-like samples. To this end, we propose a novel OOD detection framework that discovers ID-like outliers using CLIP [32]from the vicinity space of the ID samples, thus helping to identify these most challenging OOD samples. Then a prompt learning framework is proposed that utilizes the identified ID-like outliers to further leverage the capabilities of CLIP for OOD detection. Benefiting from the powerful CLIP, we only need a small number of ID samples to learn the prompts of the model without exposing other auxiliary outlier datasets. By focusing on the most challenging ID-like OOD samples and elegantly exploiting the capabilities of CLIP, our method achieves superior few-shot learning performance on various real-world image datasets (e.g., in 4-shot OOD detection on the ImageNet-1k dataset, our method reduces the average FPR95 by 12.16% and improves the average AUROC by 2.76%, compared to state-of-the-art methods). Code is available at https://***/ycfate/ID-like

关键词： Training learning systems computer vision Focusing Feature extraction Data models Pattern recognition

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Point Cloud Registration of non-rigid objects in sparse 3D Scans with applications in Mixed Reality

arXiv

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arXiv 2022年

作者： Jha, Manorama Artificial Intelligence and Machine Learning School of Computer Science and Mathematics

Point Cloud Registration is the problem of aligning the corresponding points of two 3D point clouds referring to the same object. The challenges include dealing with noise and partial match of real-world 3D scans. For non-rigid objects, there is an additional challenge of accounting for deformations in the object shape that happen to the object in between the two 3D scans. In this project, we study the problem of non-rigid point cloud registration for use cases in the Augmented/Mixed Reality domain. We focus on a special class of non-rigid deformations that happen in rigid objects with parts that move relative to one another in joints, for example, robots with hands and machines with hinges. We propose an efficient and robust point-cloud registration workflow for such objects and evaluate it on real-world data collected using the Microsoft Hololens 2, a leading Mixed Reality device. © 2022, CC BY.

关键词： Mixed reality

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Spinach Classification and Its Health Benefits Suggestion using Deep learning

Spinach Classification and Its Health Benefits Suggestion us...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Kirubasri, G. Sreesubha, S. Vidyasree, G. Anitha Elavarasi, S. Aanandha Saravanan, K. Computer Science and Engineering Sona College of Technology TamilNadu Salem India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Computer Science and Engineering Sri Sai Ranganathan Engineering College TamilNadu Coimbatore India Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522988

Spinach is a popular green leafy vegetable known for its numerous health benefits. Accurate classification of spinach varieties is critical to ensure consistent quality and nutritional value. In recent years, deep learning techniques have shown remarkable success in classification. The proposed Deep learning based Spinach Classification and Health benefit suggestion System (DL-SCHS) uses pre-trained VGG16 architecture for classification. The dataset of 2,500 images of 25 different spinach varieties, with each variety containing 100 images are used for training DL-SCHS. Our proposed system consists of three stages includes Pre-processing of the data, Training of the model, and Evaluation. During pre-processing, images are resized into 224 x 224 pixels and random transformations are applied for image augmentation. Canny filter is used for performing feature extraction. Training of the VGG16 model on the pre-processed data is implemented using the categorical cross-entropy loss function and the Adam optimizer. The experimental results showed that the VGG16 model achieved a higher accuracy of 94% on the testing set. © 2024 IEEE.

关键词： Convolutional neural networks

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Localization for V2X communication with noisy distance measurement

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International Journal of Intelligent Networks 2023年第1期4卷 355-360页

作者： Javed, Iram Tang, Xianlun Saleem, Muhammad Asim Javed, Ashir Zia, Muhammad Azam Shoukat, Ijaz Ali School of Computer Science and Technology Chongqing University of Posts and Telecommunications China Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Aging Research Center Karolinska Institutet Stockholm171 65 Sweden Department of Computer Science University of Agriculture Faisalabad Pakistan Riphah College of Computing Riphah International University Faisalabad Campus Pakistan

Mobile sensor network localization is a growing research topic after IEEE 802.15.4 specified the procedure of low-rate wireless personal area networks (LR-WPANs), which further helps localize vehicles in the automobile industry. This paper presents a new localization scheme based on flying anchors deployed in vehicular infrastructure. The mobile anchor nodes follow a random C-shaped trajectory. A global positioning system (GPS) is attached to each anchor node, transmitting beacons with ID and location to all other vehicles in a network. Distance calculation is facilitated through link quality induction, employing the centroid method to compute localization error. Mobile anchor localization, particularly when employing a C-shaped trajectory commonly adopted by various topologies, consistently yields optimal positioning outcomes. However, this approach can be susceptible to the impact of noisy measurements, potentially reducing overall localization performance. To overcome this problem, we proposed a framework based on extended Kalman filtering (EKF), which is used to refine the coordinates of the vehicles. To compute the lower bounding of the vehicular node, an analytical framework is also proposed to enhance the localization error accuracy. Simulation results show that the EKF framework provides better positioning accuracy compared to the existing C-shaped solution, irrespective of noise statistics, topology selection, and anchor node density. With the help of the Extended Kalman Filter (EKF) framework, we achieved a comprehensive localization error of 0.99 m, accompanied by a standard deviation of 0.47. © 2023 The Authors

关键词： Extended Kalman filters

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Enhancing the Prediction Accuracy of Brain Tumor Detection by using Fuzzy C-Means Segmentation and Support Vector machine 5

Enhancing the Prediction Accuracy of Brain Tumor Detection b...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Revathi, M. Kalaiarasi, P. Jeevitha, S. Akilandasowmya, G. St. Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India Kalasalingam University Department of Machine Learning Krishnankoil India Jerusalem College of Engineering Information Technology Chennai India Sri Sairam Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331518097

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector machine. The results shows 98% percentage of accuracy. © 2024 IEEE.

关键词： Support vector machines

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Breast Cancer Detection Using Ultrasound Images Based and Convolutional Neural Network

Breast Cancer Detection Using Ultrasound Images Based and Co...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Meher Langote Kunal Hiwase Radha Wande Abhay Tale Harsh Kalekar Swapnil Gundewar Faculty of Engineering and Technology Sawangi Artificial Intelligence and Data Science Maharashtra India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Sawangi Maharashtra India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Breast cancer forms one of the most common types of cancer among women worldwide; therefore, it highlights the importance of having rapid and timely diagnosis methods. Here, we will survey the use of convolutional neural networks (CNNs) for mutually exclusive cancer detection in ultrasound images using detectors. A dataset of benign and malignant breast tumors visualized via ultrasound is taken as the base for the training and testing of the CNN model. The CNN architecture is arranged to select valuable features from the ultrasound pictures only, but not malignant lesions. The performance and the evaluation of the CNN model are done through metrics like accuracy, precision, recall, and F1 score. The findings confirm the efficiency of using the CNN here, as it shows the extent and solidity of reading textures with excellent precision and quick performance in breast cancer screening. This investigation is the advancement of computer-aided diagnostic tools for breast cancer screening. As medical practice sees these benefits, the efficiency of healthcare delivery and patient outcomes are bound to be optimized.

关键词： Training Surveys Visualization Ultrasonic imaging Accuracy Breast cancer Sensors Convolutional neural networks Reliability Testing

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Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation Graph Convolutional Networks and Structural MRI Data 5

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (AD) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve AD diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of AD. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how AD progresses. © 2024 IEEE.

关键词： Convolutional neural networks

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Detecting the Eye Movement to Control the Cursor Movement of the system 4

Detecting the Eye Movement to Control the Cursor Movement of...

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4th IEEE International Conference on Data Engineering and Communication Systems, ICDECS 2024

作者： Padma, R. Yadav, Suchitra T Keerthana, R.J. Tejeshwari, U.S. Kenche, Amulya Ramaiah University of Applied Sciences Computer Science and Engineering Bengaluru India Ramaiah University of Applied Sciences Information Science and Engineering Bengaluru India Ramaiah University of Applied Sciences Artifical Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350393354

A novel approach to cursor control via real-time eye movement that employs a synergistic combination of OpenCV and machine learning approaches. The ability to operate a cursor in a natural and intuitive manner using eye tracking has the potential to revolutionize human-computer interaction, particularly for people with motor limitations or those looking for more natural and efficient interfaces. The suggested method makes use of OpenCV for robust eye detection and tracking, laying the groundwork for precise gaze estimation. Convolutional Neural Networks (CNNs) are trained to recognize different gaze patterns, enabling precise and dependable cursor control. Over time, the machine learning component adapts the system to individual users, optimizing efficiency and improving user experience. This advances human-computer interaction by presenting a unique and practical cursor solution based on real-time eye movement. The combination of OpenCV, deep learning, and machine learning creates a strong and adaptable framework that paves the way for more intuitive and inclusive computing interfaces in a variety of applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Defending Against Poisoning Attacks in Federated learning with Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第7期5卷 3743-3756页

作者： Dong, Nanqing Wang, Zhipeng Sun, Jiahao Kampffmeyer, Michael Knottenbelt, William Xing, Eric Shanghai Artificial Intelligence Laboratory Shanghai200232 China Imperial College London Department of Computing LondonSW7 2AZ United Kingdom FLock.io LondonWC2H 9JQ United Kingdom UiT the Arctic University of Norway Department of Physics and Technology Tromso9019 Norway Carnegie Mellon University Machine Learning Department School of Computer Science PittsburghPA15213 United States Mohamed Bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates

In the era of deep learning, federated learning (FL) presents a promising approach that allows multiinstitutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy. However, most existing FL approaches rely on a centralized server for global model aggregation, leading to a single point of failure. This makes the system vulnerable to malicious attacks when dealing with dishonest clients. In this work, we address this problem by proposing a secure and reliable FL system based on blockchain and distributed ledger technology. Our system incorporates a peer-to-peer voting mechanism and a reward-and-slash mechanism, which are powered by on-chain smart contracts, to detect and deter malicious behaviors. Both theoretical and empirical analyses are presented to demonstrate the effectiveness of the proposed approach, showing that our framework is robust against malicious client-side behaviors. © 2020 IEEE.

关键词： Smart contract

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