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

共 12592 条记录，以下是101-110 订阅

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Digital Twin-Driven Bearing-Fault Detection in Induction Motor and Drives using Graph Sampling and Aggregation Network

Digital Twin-Driven Bearing-Fault Detection in Induction Mot...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Haraprasad Badajena Suryanarayan Majhi Bivash Chakraborty Mamata Jenamani Aurobinda Routray Ronit Dutta Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Industrial and Systems Engineering Indian Institute of Technology Kharagpur India Dept. of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Bearing fault diagnosis is crucial for ensuring the reliability and safety of industrial systems, particularly in preventing operational failures and maintaining product quality. Traditional signal processing methods and deep learning algorithms, while useful, often overlook the complex structural relationships within sensor data, limiting their diagnostic effectiveness. To address this, we present a novel Digital Twin-Driven Fault Diagnosis Framework that integrates graph-based learning techniques with advanced signal analysis. Our approach employs XGBoost and GraphSAGE embeddings to capture both spatial and temporal correlations within the current signals. The raw sensor data is then processed using a sliding window technique and time-frequency domain features are extracted then transformed into a graph structure that represents the intricate relationship in the signal. GraphSAGE is then applied to these graph structures, generating embeddings that enhance fault detection accuracy. Additionally, XGBoost is utilized for classification, improving the overall robustness of the system. The proposed method, deployed on edge devices, delivers real-time diagnostics, providing a scalable and efficient solution for industrial applications. Experimental results using real-world datasets demonstrate that our method significantly outperforms state-of-the-art algorithms, improving detection accuracy and Area under the curve (AUC) scores up to 97% and 99%, respectively.

关键词： Fault diagnosis Time-frequency analysis Accuracy Correlation Fault detection image edge detection Signal processing algorithms Feature extraction Real-time systems Speech processing

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Automated Pomegranate Leaf Disease Identification: An image processing Perspectives

Automated Pomegranate Leaf Disease Identification: An Image ...

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International conference on Communication systems and Network Technologies (CSNT)

作者： B Meghana Janani J S Lalitha Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Agriculture is a predominant occupation in India, and to date, automated plant disease detection has been applied primarily to crops such as paddy, wheat, and various vegetables (e.g., tomato, cucumber, and potato). In this work, a novel approach is proposed for the automated detection and labeling of pomegranate leaf diseases by employing Support Vector Machine (SVM) for classification and K-Means Clustering for image segmentation. The study focuses on four specific plant diseases—Bacterial Blight, Alternaria Alternata, Cercospora Leaf Spot, and Anthracnose—as well as the detection of healthy leaves when no affected areas are present. To enhance the visibility of the leaf images, contrast enhancement is performed as a preliminary step. Subsequently, K-Means Clustering is applied to segment the input image in the Lab color space. A Multiclass SVM is then utilized to classify the segmented regions based on identified affected portions of the leaf. Finally, the Region of Interest (ROI) is determined to calculate and display the total affected area, facilitating precise disease-type detection. Experimental results indicate that the proposed method achieves an accuracy of 0.96, a precision of 0.95455, a recall of 0.9333, an F1 score of 0.978, and a kappa statistic of 0.947, thereby demonstrating the efficacy of the proposed system for automated pomegranate leaf disease detection.

关键词： Support vector machines image segmentation Plant diseases Machine learning algorithms image color analysis Clustering algorithms Crops Machine learning Labeling Diseases

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Sprinter Performance Evaluation System Based on Improved image Segmentation Method

Sprinter Performance Evaluation System Based on Improved Ima...

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Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Haili Meng Chongqing Vocational Institute of Engineering Chongqing China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This project attempts to build a sprinter performance analysis system using optimized image segmentation technology, aiming to solve the limitations of traditional evaluation methods in dynamic blur and complex backgrounds. By introducing the improved AO-GrabCut algorithm, the image segmentation process is optimized, the separation quality of athletes and backgrounds is improved, and dynamic images of athletes moving at high speed on the track are collected through high-resolution cameras. The speed and stride of athletes are calculated in real time using the optical flow method to screen key attributes of the action. The experimental results show that the system can accurately capture the performance indicators of athletes such as starting time, acceleration time, and sprint time. The accuracy and recall rate have reached a high level, and it is outstanding in complex and changing situations. This tool provides an effective auxiliary means for the training and competition analysis of sprinters, and provides strong data support for sports science research.

关键词： Performance evaluation Training image segmentation Accuracy Heuristic algorithms Stability analysis Real-time systems Performance analysis Optimization Sports

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Data processing, analysis and Machine Learning of Electrical Atomic Force Microscopy (AFM) in Data Cube mode: conduction of a nanomaterial for Lithium-ion battery electrodes

Data processing, analysis and Machine Learning of Electrical...

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International image processing, Applications and systems conference (IPAS)

作者： Rosine Coq Germanicus Peter De Wolf Petr Klapetek Unicaen Ensicaen CNRS CRISMAT UMR6508 Normandie Univ Caen France Bruker Nano Surfaces Santa Barbara CA USA Czech Metrology Institute Brno Czech Republic

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

With the increasing integration of functional systems, nanoscale characterization has become crucial not only for material investigation but also for advancing the understanding of local behavior and optimizing performances. This paper details data processing techniques and machine learning methodologies for the analysis and prediction of advanced electrical Atomic Force Microscopy datasets. The datasets are acquired using a hyperspectral Data Cube approach, which combines both mapping and spectroscopy acquisitions. In the context of decarbonization and electrification, current challenges are related to energy storage and transport. A cathode material used in Lithium-ion battery electrodes is investigated for its electrical transport properties. To comprehensively characterize the electrical properties, the conductive mode of Atomic Force Microscopy is employed using the Data Cube method, which enables current-voltage spectroscopy at each pixel of the acquisition. The analysis of experimental datasets reveals localized electrical behavior of the material’s components as a function of applied voltage. Furthermore, machine learning algorithms are employed to predict current maps and cluster distinct regions based on their electrical characteristics.

关键词： Lithium-ion batteries Atomic measurements Atomic force microscopy Spectroscopy Microscopy Force Conductivity Nanoscale devices Cathodes Hyperspectral imaging

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Garbage Detection System Using image processing

Garbage Detection System Using Image Processing

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Disruptive Technologies (ICDT), International conference on

作者： Aditya Pandey Praveen Kumar Rai Aditya Singh Aman Patwa Computer Science Engineering GL Bajaj Institute of Technology & Management Gr. Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This research presents a brand new development system for garbage detection using advanced techniques in image processing. This system is primarily to identify and then classify the waste in real time, thus tackling waste management problems. This has been done using Convolutional Neural Networks (CNNs) and image preprocessing to achieve high accuracy in the detection of different waste types such as plastics, metals, and organics. The experimental results prove the model's robustness across diverse environments that make it amenable to large-scale deployment. The work is meant to serve municipalities, industries, and environmental organizations for automating the waste segregation process [1], [2]. The paper deliberates about some image processing techniques on edge detection, segmentation, and feature extraction since these form the basis for waste identification and classification types that rely on them. Integration of such methods into machine learning and deep learning algorithms, especially using convolutional neural networks (CNN), makes the process more accurate and robust in detection. Also discussed in the review are the pre-processing methods, such as noise reduction and contrast enhancement, which improve the quality of input data to achieve better detection results.

关键词： Waste management Accuracy Reviews Noise reduction Metals Organizations Real-time systems Robustness Convolutional neural networks Plastics

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Face Mask Detection in Real Time using Machine Learning algorithms

Face Mask Detection in Real Time using Machine Learning Algo...

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Electronics and Renewable systems (ICEARS), International conference on

作者： Kavitha R Tanishq Sultania Padhmashri V Shreya A Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made it easier to identify if an individual is wearing a face mask or not. HAAR-CASCADE and ResNet10 algorithms are applied for initial face recognition and then Mobilenet V2 and VGG16 algorithm are employed for recognizing face masks in gatherings. Amongst the stages involved in accomplishing the AI training are the pre-processing augmenting data, training, testing, and segmenting images. During each step, the CNN algorithms are utilized to generate an image segmentation of the input dataset highlighting those wearing masks and those without mask. The model is then trained with dataset, Mobilenet V2 yields an accuracy of 96.6% and VGG16 yield an accuracy of 99.42% in detecting individuals with and without wearing masks. VGG16’s deep architecture, feature extraction and transfer learning abilities enable it to be highly precise and effective. However, VGG16 is highly resource intensive and needs extensive computation time.

关键词： COVID-19 Training Face masks image segmentation Accuracy Computer viruses Face recognition Transfer learning Static VAr compensators Testing

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Automated Immune Cell Classification Using image processing Techniques

Automated Immune Cell Classification Using Image Processing ...

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

作者： B. Sathiyaprasad Arva Hari Vardhan Reddy Usama Abdurrahman A. Annie Micheal Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

~ Advances in immunological research are essential for elucidating immune responses and developing targeted therapeutic approaches. This study proposes an automated method for immune cell classification leveraging machine learning and image processing techniques. A novel framework is introduced, combining high-resolution imaging with computational algorithms to accurately classify various immune cell types from biological samples. The methodology incorporated pre-processing steps, like normalization and noise reduction. Then, deep learning models, such as CNNs and SVMs, will be applied for feature extraction and classification. These models were trained on annotated datasets derived from flow cytometry and microscopy, demonstrating better accuracy and reliability. Results demonstrate an improvement in processing speed and accuracy of classification over traditional approaches, which underlines the importance of automation in immunological diagnostics. The data obtained can be used in high-throughput immunotherapy and disease diagnostic screening; it allows for the identification of immune cell populations within a short period and therefore improves research efficiency. The work provides a basis for real-time analysis and incorporation of multiscale datasets that will help to better understand immunity and its associated disorders.

关键词： Deep learning Accuracy Immunology Computational modeling Biological system modeling Real-time systems Vectors Classification algorithms Streams Immune system

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Radon-Fourier Transform for Timing Correction in Uncoupled Digital Radar Networks

Radon-Fourier Transform for Timing Correction in Uncoupled D...

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German Microwave conference

作者： Julian Aguilar Lukas Paulus David Werbunat Alexander Grathwohl Christian Waldschmidt Institut of Microwave Engineering Ulm University Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

Uncoupled radar networks offer many desired features such as large virtual apertures and flexibility in sensor placement without costly radio frequency (RF) cables. However, they also introduce novel challenges, especially the recovery of coherency between individual radar sensors. The correction of timing errors due to sampling frequency offsets (SFO) between a transmitter’s digital-to-analog converter (DAC) and receiver’s analog-to-digital converter (ADC) is essential for the coherent processing of uncoupled digital radar network measurements. systems with different error estimation and correction approaches have been proposed recently, which utilize resampling-based algorithms. However, they are costly to implement, i.e. on an FPGA, and require additional signal processing steps. The herein presented Radon-Fourier Transform (RFT)-based algorithm is capable of directly calculating the corrected radar image without any resampling, negating the downside of additional signal processing steps required after resampling. It is shown that the RFT approach can almost completely restore erroneous radar data, and even outperforms the resampling-based algorithm. In this work, an experimental orthogonal frequency-division multiplexing (OFDM) demonstrator is utilized to verify the concepts, which are applicable to other modulation types typically used for digital radar.

关键词： OFDM Computational modeling Signal processing algorithms Radar Transforms Radar imaging Data models image restoration Frequency measurement Frequency division multiplexing

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An Overview on Millimeterwave FMCW Radar Techniques for Precise Ranging and Material Imaging

An Overview on Millimeterwave FMCW Radar Techniques for Prec...

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International Workshop on Mobile Terahertz systems (IWMTS)

作者： Kristof Dausien Francesca Schenkel Jochen Altholz Lukas Piotrowsky Nils Pohl Jan Barowski Institute for Microwave Systems Ruhr University Bochum Bochum Germany Institute for Integrated Systems Ruhr University Bochum Bochum Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

This scientific paper is an outcome of the MARIE project (for more information, see ***) [M04]. This paper provides an overview, on recent developments as well as future challenges and prospects in measurement methods and algorithms for the determination of an object’s geometrical shape, its position, and its electromagnetic properties using FMCW radar sensors. The current research integrates radar imaging techniques with material characterization algorithms to evaluate the dielectric constant and losses, leveraging ultrawideband systems to capture dispersive features and enable µm-precise localization. Unlike single antenna synthetic aperture radar (SAR) methods, multiple-input multiple-output (MIMO) radar enhances imaging by capturing data from multiple channels and varying incident angles, enriching the information obtained. In conjunction with sensor calibration methods and radar-based material characterization, these imaging techniques provide a mobile platform for spatially resolved, precise reflection measurements in magnitude and phase that are used to derive the electromagnetic properties and, subsequently, to provide a semantically annotated material image. Due to the short wavelength and large apertures near-field wave propagation effects must be incorporated into the signal processing to achieve µm-precise ranging and imaging. Furthermore, the impact of environmental influences on the measurement accuracy must be investigated.

关键词： image sensors Accuracy Wavelength measurement Signal processing algorithms Radar imaging Sensor phenomena and characterization Radar antennas Distance measurement Ultra wideband radar Synthetic aperture radar

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DNA Computing: A Breakthrough in image Cryptography - A Comprehensive Review

DNA Computing: A Breakthrough in Image Cryptography - A Comp...

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International CSI Computer conference, CSICC

作者： Hesam Noorpoor Asadollah Shahbahrami Department of Computer Engineering University of Guilan Rasht Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Deoxyribonucleic acid-DNA computing, an interdisciplinary field at the frontier between molecular biology and computer science, has become a revolutionary field. DNA computation provides a new approach to data computation and cryptography. The inherent properties of DNA include high parallelism, excellent data density, and stability; it can hence be considered one of the ideal candidates in cryptographic applications, especially in image encryption. This review provides recent advances in DNA-based cryptographic algorithms, their fusion with chaotic systems, and also their applications to practical scenarios that enhance data security. Different algorithms are studied that exploit the encoding capability of DNA, such as substitution, transposition, and hybrid models including crossover and mutation. It also discusses DNA for secure image transmission in applications like healthcare, industrial IoT, and cloud environments. The latter presents scalability challenges and problems regarding key generation along with certain implementation constraints, while at the same time pointing future research toward a perspective of opening up for stronger cryptography.

关键词： Reviews Scalability DNA Medical services Parallel processing Stability analysis Encryption Molecular biology Computer security DNA computing

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