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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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A New Extrapolation Solution for MU-MISO Symbol-Level Precoding 16

A New Extrapolation Solution for MU-MISO Symbol-Level Precod...

引用

16th International conference on Wireless Communications and Signal processing, WCSP 2024

作者： Liang, Mu Li, Ang Hu, xiaoyan Masouros, Christos School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China University College London Department of Electronic and Electrical Engineering London United Kingdom

ISBN: (纸本)9798350390643

Constructive interference (CI) precoding, which converts the harmful multi-user interference into beneficial signals, is a promising and efficient interference management scheme in multi-antenna communication systems. However, CI-based symbol-level precoding (SLP) experiences high computational complexity as the number of symbol slots increases within a transmission block, rendering it unaffordable in practical communication systems. In this paper, we propose a symbol-level extrapolation (SLE) strategy to extrapolate the precoding matrix by leveraging the relationship between different symbol slots within in a transmission block, during which the channel state information (CSI) remains constant, where we design a closed-form iterative algorithm based on SLE both for PSK and QAM modulation. In order to further reduce the computational complexity, a sub-optimal closed-form solution based on SLE is further developed. Extensive simulation results illustrate that the proposed algorithms can dramatically reduce the computational complexity and time complexity with only marginal performance loss, compared with the conventional SLP design methods. © 2024 IEEE.

关键词： Extrapolation

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MedSeg: A Medical image Analyzer

MedSeg: A Medical Image Analyzer

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Control Instrumentation System conference (CISCON) - Guiding Tomorrow - Emerging Trends in Control, Instrumentation, and systems Engineering

作者： Mehta, Shlok Pancholi, Keyur Pandey, Sakshi Dhage, Dr Sudhir Sardar Patel Inst Technol Comp Engn Dept Mumbai Maharashtra India

ISBN: (纸本)9798350375480;9798350375497

This research paper aims to address the critical need for efficient and accurate identification of chest diseases using chest X-rays through a combination of advanced image processing techniques and machine learning algorithms. With the growing prevalence of respiratory and cardiovascular conditions worldwide, timely and precise diagnosis is paramount for effective patient care. The study begins with a comprehensive review of existing methodologies and technologies employed in the identification of chest diseases from X-ray images. It critically evaluates the strengths and limitations of current approaches, highlighting the challenges faced in achieving high accuracy, speed, and scalability. To address these issues, the project aims to develop an AI-powered system for medical image analysis. In response to these challenges, our research proposes a novel approach that integrates Inception V3 model and imagenet. We leverage a large dataset of annotated chest X-rays to train a deep neural network capable of recognizing subtle patterns indicative of various diseases, including pneumonia, pneumothorax, lung and cardiac abnormalities. The model is optimized to provide not only accurate diagnoses but also to minimize false positives and negatives. In conclusion, this research contributes to the ongoing efforts in utilizing chest X-ray images for disease identification, presenting a robust and efficient methodology that could revolutionize the current diagnostic landscape. The findings hold promise for the development of automated systems capable of assisting healthcare professionals in the accurate and timely detection of chest diseases, ultimately contributing to enhanced patient care and management.

关键词： Machine learning Medical image analysis Chest X-Rays prediction imageNET InceptionV3 Disease prediction

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Cancer Sight: Illuminating the Hidden-Advancing Breast Cancer Detection with Machine Learning-Based image processing Techniques

Cancer Sight: Illuminating the Hidden-Advancing Breast Cance...

引用

2023 International conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sirisati, Ranga Swamy Kumar, C. Srinivasa Venuthurumilli, Pradeep Ranjith, J. Rao, Kanusu Srinivasa Ghatkesar Hyderabad Telangana Kondapur India Vignan's Institute of Management and Technology for Women Department of Cse Ghatkesar Hyderabad Telangana Kondapur India Mallareddy Engineering College for Women Department of Computer Science & Engineering Maisammagida Hyderabad500090 India Vignan's Institute of Management and Technology for Women Department of Information Technology Ghatkesar Hyderabad Telangana Kondapur India Yogi Vemana University Department of Computer Science and Technology Kadapa India

ISBN: (纸本)9798350313987

Breast cancer is a significant global health concern, with early detection being critical for successful treatment and improved patient outcomes. In recent years, machine learning-based image processing techniques have emerged as powerful tools in the field of medical imaging, particularly in breast cancer detection and diagnosis. This research paper explores the application of machine learning algorithms to enhance the accuracy and efficiency of breast cancer detection using various medical imaging modalities, such as mammography, ultrasound, and magnetic resonance imaging (MRI). The study begins by reviewing the current state of breast cancer detection methodologies and highlighting their limitations. It then delves into the utilization of machine learning algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), and deep learning models, for the automated analysis and interpretation of breast cancer images. Various preprocessing steps, feature extraction techniques, and data augmentation methods are discussed to optimize the performance of these algorithms. Furthermore, the paper examines the integration of machine learning models with radiomics, genomics, and clinical data to create comprehensive breast cancer diagnostic systems. These integrated systems aim to provide more accurate risk assessment, personalized treatment recommendations, and improved patient management. The results of several case studies and clinical trials are presented to demonstrate the effectiveness of machine learning-based image processing techniques in breast cancer detection. These studies illustrate how these techniques can improve sensitivity, specificity, and overall diagnostic accuracy compared to traditional methods. This research underscores the promising role of machine learning-based image processing techniques in advancing breast cancer detection. It highlights the potential for early diagnosis and improved patient care, paving the way for mor

关键词： Breast cancer Convolutional neural networks (CNNs) image processing Machine learning Magnetic resonance imaging (MRI) Support vector machines (SVMs)

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Application of Machine Vision Technology in 3D Printing Quality Inspection System 4

Application of Machine Vision Technology in 3D Printing Qual...

引用

4th International Academic Exchange conference on Science and Technology Innovation, IAECST 2022

作者： Zhang, Ying Zhang, Yi Song, xinhao Li, Yongxing Xijing University Department of Robot Engineering Xi'an China Xijing University Department of Mechanical Engineering Shaanxi Xi'an China

ISBN: (纸本)9798350320008

3D printing has the characteristics of rapid prototyping, but this technology by layer-by-layer printing to construct objects, the prevalence of low precision molding problems. Machine vision technology is easy to realize information integration because of its automation and intelligence. It is of great significance to improve the accuracy of 3D printing products by analyzing the size, location, distribution and type of printing defects by using the processing algorithms related to machine vision technology. This paper summarizes the image processing algorithm of 3D printing quality detection system based on machine vision. Through the fusion and upgrading of traditional processing algorithms, combined with the image acquisition characteristics and reconstruction requirements of 3D printing process, the accuracy of image acquisition and preprocessing, feature point extraction and image matching is improved. © 2022 IEEE.

关键词： 3D printing

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Quantifying Stellar Energy Exchange: image Analysis with OpenCV and Machine Learning 5

Quantifying Stellar Energy Exchange: Image Analysis with Ope...

引用

5th IEEE International conference for Emerging Technology, INCET 2024

作者： Venkteswarulu, B. Kanagala, Chaitanya Snehitha Kakumanu, Naga Venkata Sai Mohana Raghavarapu, Laxmi Narasimha Charan Sai Peravali, Himavath Lakshmi Visveswar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350361155

Stellar energy exchange, fundamental to the dynamics of celestial systems, remains a captivating yet challenging area of study in astronomy. This paper introduces an innovative methodology for quantifying stellar energy exchange through sophisticated image analysis techniques, harnessing the power of OpenCV and machine learning. Our approach integrates OpenCV's robust image processing capabilities with state-of-the-art machine learning algorithms to automate the intricate task of analyzing astronomical images and predicting energy exchange rates within stellar constellations. The methodology begins with the preprocessing of astronomical images using OpenCV, where radiometric and atmospheric corrections are applied to ensure accurate representations of stellar luminance. Subsequently, features relevant to energy exchange dynamics are extracted from the processed images, employing advanced computer vision techniques. These features encapsulate intricate spatial, spectral, and radiometric characteristics of stars, providing rich representations for subsequent analysis. Machine learning algorithms are then employed to predict the energy exchange rates of stars based on the extracted features. Leveraging the power of convolutional neural networks and ensemble learning techniques, our model learns intricate patterns within the data and delivers accurate estimations of energy exchange dynamics. Through extensive experimentation on real astronomical datasets, our approach demonstrates exceptional performance, achieving precise predictions of energy exchange rates across diverse stellar systems. © 2024 IEEE.

关键词： Spectrum analysis

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Split Computing based Quantized PoseNet Model for Distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

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15th International conference on Communication systems and Networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, Internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep Neural Network (DNN) models on them. To address this, we introduce Split Computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called Distributed Artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. The QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. The result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling image edge detection Computer architecture Bandwidth Inference algorithms Partitioning algorithms

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Research on Traditional Art image Reconstruction Method Based on Computer Vision Analysis 2

Research on Traditional Art Image Reconstruction Method Base...

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2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Supriya, M. Anvesh, Kuncham Muruganandham, B. Manasa Krishnan, K. Muthu, R. Murugan, K. Kpr Institute of Engineering and Technology Department of Ece India

ISBN: (纸本)9798350369960

In the framework of the digital era, the technology of image processing is one of the technologies that is being used increasingly often in all aspects of modern life. image correction may be handled using algorithms based on computer vision, which can increase the correction's influence on the image as a whole. The conventional method of identifying structural systems collects data via the use of wired or wireless measuring systems. The process of establishing sensors and the equipment used for data collection is laborious and takes a lot of time. The process has two stages: the first is the extraction of the line drawing, and the second is the rendering of the line drawing. An edge detection approach that integrates low-level and middle-level visual information is used to extract the line drawing in the process of on-line drawing extraction. This method is used to extract the drawing. The edges are then simplified and linked in accordance with the computer vision theory. Additionally, the image processing technology is used as the research object in order to develop a scientific model for the distorted image. These steps are based on the findings of traditional edge detection. image restoration can be helped along by filtering samples of the image and getting rid of any noise that is there. The fundamentals of computer vision and the technologies that are connected to it are broken down in detail, and the behavioral patterns of visual interaction are investigated. The image of Chinese traditional art serves both the role of art communication and as a medium and symbol for cultural communication. At the same time, it carries on the legacy of a fine traditional culture that has been passed down from generation to generation in China. The image processing technology that is based on computer vision algorithms provides a bigger number of benefits, in addition to a better degree of accuracy. © 2023 IEEE.

关键词： Computer Vision feedback image reconstruction Traditional art Visual analysis

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image Based Classification of Methods-Time Measurement Operations in Assembly Using Recurrent Neuronal Networks 6th

Image Based Classification of Methods-Time Measurement Opera...

引用

6th International conference on System-Integrated Intelligence (SysInt)

作者： Rueckert, Patrick Birgy, Katrin Tracht, Kirsten Univ Bremen Bremen Inst Mech Engn BIME Badgasteiner Str 1 D-28359 Bremen Germany

ISBN: (纸本)9783031162817;9783031162800

image based classification enables the acquisition and transfer of data from manual assembly workstations into a digital environment. Based on the Methods-Time Measurement method, assembly processes are transformed into short, discrete basic operations that are recognised by means of image processing and used as input data for a multilayer neural network. A recurrent neural network algorithm is investigated for its applicability in combination with the sensor data. The five basic MTM operations reaching, grasping, bringing, releasing, and positioning are classified and additional influencing factors, as well as the implementation of an object recognition, are investigated. The following paper addresses the question of the extent to which manual assembly processes can be reliably derived from visual sensor data and classified by machine learning algorithms.

关键词： Neuronal networks Assembly image processing

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Digital image processing for the Diagnosis of the Cold Inducible Urticaria by Photoplethysmography-Based Methods

Digital image Processing for the Diagnosis of the Cold Induc...

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2023 Wave Electronics and its Application in Information and Telecommunication systems, WECONF 2023

作者： Guryleva, Anastasia Fomina, Daria Volkov, Mikhail Serdoteckova, Sofia Danilycheva, Inna Russian Academy of Sciences Acousto-Optic Spectroscopy Lab Scientific and Technological Center of Unique Instrumentation Moscow Russia Moscow State Scientific and Practical Center for Allergology and Immunology City Clinical Hospital No 52 Ministry of Healthcare of Moscow Russia Sechenov University Department of Clinical Immunology and Allergology Moscow Russia ITMO University Faculty of Physics and Technology Saint-Petersburg Russia NRC Institute of Immunology FMBA of Russia Moscow Russia

ISBN: (纸本)9798350348293

The objectivity and accuracy of the diagnosis of the inducible urticaria can be improved through the analysis of microcirculation parameters. Photoplethysmography is an easily implemented non-invasive method able to collect valuable diagnostic data on the state and function of the vessels. Existing descriptions of photoplethysmography image processing algorithms are limited to the basic principles and general mathematical expressions. At the same time, the algorithm options can vary significantly and affect the data quality. In this paper, we study the optimal characteristics of the algorithms for photoplethysmography-based skin inspection for the diagnosis of the cold inducible urticaria. Presented approach provides automatic, fast and repeatable processing under various input data. © 2023 IEEE.

关键词： image processing

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Experimental research on the colour interpolation algorithm of the full colour image 3

Experimental research on the colour interpolation algorithm ...

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3rd International conference on Computers, Information processing and Advanced Education, CIPAE 2022

作者： Tang, Haoran Wang, Zhuoyang Yang, Jianhui Zhou, Sichen Leeds College Southwest Jiaotong University Chengdu611756 China International College Macao University of Science and Technology China College of Computer and Engineering Tianshi College Tianjin301723 China Xi'An Jiaotong University Faculty of Electronic and Information Engineering Xi'an710049 China

ISBN: (纸本)9781665468121

With the rapid development of multimedia technology, images have become the main data form of people's daily communication and information expression. In order to ensure fast data transmission, much effort has been made to compress the three-channel color image into the 2D matrix and then recover the image from the 2D matrix through interpolation algorithms. However, there usually exists different degrees of edge blurring or noise generation when reconstructing images, which significantly affects the accuracy of reconstruction. More importantly, we find that different interpolation algorithms may have drastic performance differences when dealing with different scene images. All of these aspects inspire us to explore the application boundary of interpolation algorithms. In this paper, we compare the representative bilinear interpolation, cok interpolation and Hibbard interpolation from the degree of edge blurring, the amount of noise generated and the processing speed. With extensive experiments, both visual and quantitative analysis shows an interesting but meaningful result. Three algorithms obtain a similar accuracy for the image with a single color or simple lines, while the Hibbard algorithm could restore the most realistic image when the image structure is complex. © 2022 IEEE.

关键词： image reconstruction

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