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A Python-based study of data processing in OCT system 14

A Python-based study of data processing in OCT system

引用

Optics in Health Care and Biomedical Optics XIV 2024

作者： Zhang, Hongwei Yu, Bin He, Dongsheng Guo, Jian Chen, Kaihong Wu, Shulian Li, Zhifang Key Laboratory of Optoelectronic Science and Technology for Medicine Ministry of Education Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Provincial Key Lab of Photonic Technology Information Photonics Research Center College of Photonic and Electronic Engineering Fujian Normal University Fuzhou350007 China The Internet of Things and Artificial Intelligence College Fujian Polytechnic of Information Technology Fujian Fuzhou China

ISBN: (纸本)9781510682122

Optical Coherence Tomography(OCT) system is a non-contact imaging modality based on low-coherence optical interferometry, used for imaging turbid scattering media. They excel in rendering depth-resolved images of internal structures with micrometer-scale resolution. Previous OCT systems have some defects in image reconstruction, which are limited by complex signal processing and mathematical computation, slow image processing speed, difficult to realize real-time imaging, and high equipment cost. This paper proposes an OCT image reconstruction algorithm acceleration scheme based on the combination of FPGA (Field-Programmable Gate Array) and Python, aiming at accelerating and simplifying the image acquisition and processing of the OCT system through Python, so as to enhance the efficiency of medical diagnosis and biological *** Python as the upper computer control software, provide user-friendly graphical interface, output spectral waveform and then realize the Fourier transform, de-direct current and autocorrelation terms and other algorithmic steps to generate OCT images, to realize the real-time data transmission and *** not only has a powerful data visualization ability, but also has the advantages of simple operation, easy to develop the program to ensure that the system operates efficiently. © 2024 SPIE.

关键词： Python

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Polar-Ano: Surface Anomaly Detection via Deep Polarization Imaging and Data Synthesis with Physic-based Rendering

Polar-Ano: Surface Anomaly Detection via Deep Polarization I...

引用

international IEEE conference on signal-image Technologies and internet-based System

作者： Yaxu Xie Alain Pagani Didier Stricker Augmented Vision German Research Center for Artificial Intelligence Kaiserslautern Germany

In this paper, we propose a complete pipeline from generating polarization images via physics-based rendering to train and deploy an image anomaly detection and localization model for polarimetric industrial inspection. The method consists of two stages. We first compute the Polarimetric Priors with both determined and learning-based method. Then, the Polarimetric Priors are given to a self-supervised surface anomaly detection network to predict the anomalies score and anomalies masks. To train the network, we adapt and modify a physic-based rendering pipeline to generate photo-realistic data samples of polarized images on a large scale. Our experiments show the effectiveness of our proposed pipeline.

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Helicopter automatic aiming technology for sea targets based on deep learning

Helicopter automatic aiming technology for sea targets based...

引用

2023 international conference on image, signal Processing, and Pattern Recognition, ISPP 2023

作者： Yang, Zhang Chinese Flight Test Establishment China

ISBN: (纸本)9781510666351

As the main force of naval warfare, helicopters also require rapid identification and targeting of targets in addition to the detection of ship targets1. From the current public information, it is known that helicopters mostly use optical targeting systems for target search, and pilots need to manually adjust the optical targeting video screen to confirm the targeting2. In the complex background weather environment, the pilot confirms the target for a long time, which affects the combat efficiency. In recent years, the world pattern has changed, and various modern technologies have been applied to all aspects of the battlefield. Among them, automatic target recognition technology based on deep learning has become the most efficient application in the battlefield. In terms of business applications, Google, Facebook AI, and Tesla are all developing deep learning technologies. In this paper, the deep recognition model YOLO-V5 is used to train and learn the ship target set to meet the purpose of automatic aiming of helicopters for sea targets, and its accuracy and recall rate meet the requirements. © 2023 SPIE.

关键词： Helicopters

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Lightweight real-time detection model for multi-sheep abnormal behaviour based on yolov7-tiny

Lightweight real-time detection model for multi-sheep abnorm...

引用

IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Zhang, Haotian Ma, Yuan Wang, Xiaobo Mao, Rui Wang, Meili Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Minist Agr Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligen Yangling 712100 Shaanxi Peoples R China

ISBN: (纸本)9781665491907

animal. Animal behaviour recognition is a vital part of automated farming systems. Although image-based deep learning algorithms can accurately identify animal behaviour, the lack of data on animal abnormal behaviour makes the practical deployment of models of limited significance. At the same time, the ageing of farm monitoring equipment is also a key factor hindering automated farming. This paper constructs a sheep abnormal behaviour dataset ABSB to address these issues and proposes a lightweight real-time multi-sheep abnormal behaviour detection model YOLOv7-Lrab based on the YOLOv7-tiny network. The abnormal behaviour dataset includes four normal behaviours: standing, lying, eating and drinking, and three abnormal behaviours: lameness, attack and death. In the proposed YOLOv7-Lrab model, the small target detection layer, Coordinate attention module, SPD-Conv and Mobileone module are added compared to YOLOv7-tiny. The experimental results show that with a 7:3 ratio of training data to test data, 96.5% recognition accuracy and 95.5% recall can be achieved, and the model size is only 4.5MB with fps of 156. The model is compressed to a minimum without loss of accuracy, providing a new idea for deploying deep learning model in practical application scenarios.

关键词： Animals

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Proceedings of the 18th INDIAcom;2024 11th international conference on Computing for Sustainable Global Development, INDIACom 2024

Proceedings of the 18th INDIAcom;2024 11th International Con...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

ISBN: (纸本)9789380544519

The proceedings contain 310 papers. The topics discussed include: blockchain technology and artificial intelligence based integrated framework for sustainable supply chain management system;contribution of microbial mechanism and artificial intelligence in wine production;a study of various clustering algorithms for image segmentation;deep learning based energy-efficient task scheduling in cloud computing;enhancing e-commerce fashion sales through personalized recommendation systems;an investigation of the intelligent and secure child rescue system from borewell;design of a dual band microstrip patch antenna;ml-based blood pressure estimation using converted ppg signal from video;auditing of outsourced data in cloud computing: an overview;and interpretable machine learning models for credit risk assessment.

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A Review on Tuberculosis Pattern Detection based on Various Machine Learning Techniques 1

引用

international conference on internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Agrahari, Devansh Bhanushali, Akash Biradar, Karunesh Bhole, Chitra Department of Computer Engineering K.J. Somaiya Institute of Technology Eastern Express Highway Maharashtra Mumbai400022 India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

This paper examines various respiratory tuberculosis detection methods, including conventional and enhanced algorithms, across multiple datasets. It evaluates the impact of image enhancement on the preliminary processing of TB chest Xray images and examines various image preprocessing strategies. Three image enhancing methods—Unsharp Masking, High-Frequency Emphasis Filtering, and Contrast Limited Adaptive Histogram Equalization— were evaluated to improve lung segmentation and heat mapping accuracy. The deep feature enhancement network identified in this study significantly improves accuracy, achieving 82.9% Average Precision and 77.6% recall on the TBXllK dataset. The paper also explores an advanced visualization technique that ensures comprehensive analysis and a deeper understanding of the subject matter. The CBAMWDnet model demonstrated outstanding performance in TB identification across multiple datasets, increasing the likelihood of early evaluation and treatment, marking a significant milestone in TB diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

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DOA estimation based on SC-FDMA signal 2

DOA estimation based on SC-FDMA signal

引用

2nd international conference on signal image Processing and Communication, ICSIPC 2022

作者： Chen, Rui Song, Yaoliang Wang, Jinyu School of Electronic & Optical Engineering Nanjing University of Science & Technology China

ISBN: (纸本)9781510657694

Communication and sensing are the basic functions of IOV (internet of Vehicle), it is an efficient and economical way to realize radar sensing function by using communication signal. In this paper, it is proposed a target DOA estimation scheme based on a series of super-resolution algorithms in which the 5G upstream SC-FDMA signal is used as the radar probing signal and a uniform linear array is used to receive the target echo. Modeling analysis and simulation results show that the proposed scheme can accurately find the direction of radar target. © 2022 SPIE.

关键词： Radar

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Securing Medical Data in Cloud-based IoT Networks Using Deep Learning Techniques 4

Securing Medical Data in Cloud-Based IoT Networks Using Deep...

引用

4th international conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Saranya, K. Valarmathi, A. Tiruchirappalli Anna University Faculty of Information and Communication Engineering UCE-BIT Campus Chennai India Tiruchirappalli Anna University Department of Computer Applications UCE-BIT Campus Chennai India

ISBN: (纸本)9798331529635

The way sensitive medical data is created and sent to cloud-based platforms has been completely transformed by the growing use of internet of Things (IoT) technology in the healthcare industry. Even with these developments, protecting the privacy and accuracy of medical records is still quite difficult. The diagnosis of diseases using medical data has become increasingly important as medical technology has advanced. It is common for medical data to move via the branches of the network from one end to the other. As a result, a high degree of security is required. Problems arise when the image data is used without authorization. One of the finest methods for securing data in an image is encryption, which uses confusion and diffusion. To address this, our study suggests a novel approach to improve medical data security by incorporating confusion and diffusion method. This feature immediately addresses issues related to unauthorized access by guaranteeing the privacy of medical records both during transmission and storage. Identifying and reducing possible security risks is greatly aided by deep learning algorithms, particularly RNN for stroke and Deep Convolutional Neural Networks (Xception model) for skin cancer photos. Our study broadens its scope to include secure and predictive analytics on medical data in the suggested system. Several metrics, including accuracy, precision, recall, confusion metrics, AUC, PSNR, and MSE, are used in this study to assess prediction models. This all-encompassing strategy improves the security of medical data while simultaneously enhancing predictive analytics' potential to improve patient care and results. © 2024 IEEE.

关键词： Convolutional neural networks

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Equitable water distribution: Solution based on genetic algorithms

Equitable water distribution: Solution based on genetic algo...

引用

international IEEE conference on signal-image Technologies and internet-based System

作者： Amina Jandoubi Abdelaziz El Fazziki M. Taha Bennani Olfa Mosbahi LIPSIC Laboratory Faculty of Sciences of Tunis University of Tunis El Manar Tunis Tunisia Computer Science Dept LISI Laboratory Caddi Ayyad University of Marrakesh Marrakech Morocco LISI Laboratory INSAT University of Carthage Tunis Tunisia

This paper introduces an integrated and inclusive approach based on genetic algorithms (GAs) to solve the complex problem of equitably allocating irrigation water between crops. Our proposal considers the minimum and maximum thresholds for the water requirements of each crop while assuming priorities within the context of the internet of Things (IoT) based smart irrigation. To achieve a fair distribution of irrigation water the IoT platform, determines all parameters related to the irrigation, including the water requirements of each crop. The main objective of this work is to introduce an innovative and holistic approach to the equitable allocation of irrigation water, combining the power of genetic algorithms and IoT technology. By addressing the complexities of water distribution in agriculture, we aim to contribute to the sustainability of smart and equitable irrigation systems and the better management of precious water resources.

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Cross-modal meta-learning for WiFi-based human activity recognition 23

Cross-modal meta-learning for WiFi-based human activity reco...

引用

29th Annual international conference on Mobile Computing and Networking, MobiCom 2023

作者： Han, Bin Wang, Lei Lu, Xinxin Meng, Jie Zhou, Zhiyi Dalian University of Technology School of Software Dalian60004538 China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China

ISBN: (纸本)9781450399906

WiFi-based Human Activity Recognition (HAR) faces challenges in achieving widespread deployment due to its reliance on massive data and limited scalability. However, the emergence of Few-Shot Learning (FSL) provides opportunities to address this issue. In this paper, we propose a cross-modal meta-learning approach based on Model-Agnostic Meta-Learning (MAML) to enable few-shot WiFi-based HAR. The hypothesis is that models can learn "learning methods"from thousands of diverse image classification tasks and apply them to WiFi-based HAR. By solely leveraging public image and WiFi signal datasets, the proposed approach trains a model capable of recognizing previously unseen activities with only 5 samples per class, achieving an average accuracy of 88.5% over thousands of tests. © 2023 ACM.

关键词： Learning systems

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