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

共 16349 条记录，以下是241-250 订阅

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A Survey on Intelligent Attack Mitigation and Latency Optimization in High-Speed Edge Networks using Deep Learning and Bio-Inspired algorithms

A Survey on Intelligent Attack Mitigation and Latency Optimi...

引用

Trends in Material Science and Inventive Materials (ICTMIM), International conference on

作者： Gandham Kiran Kumar M. Thirumaran Department of Computer Science and Engineering Puducherry Technological University Puducherry India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

As the Internet connected devices and cyber– physical systems (CPS) are exponentially increasing, edge computing has become a crucial enabler to support real time processing in high-speed networks (HSNs). However, integrating edge computing into broadband infrastructure is a challenging problem and that includes mitigating security risks such as DDoS and time-delay attacks and keeping ultra-low latency. Explored for this survey are some recent technologies on how to detect attacks and optimize latency on the edge, focusing on forms of deep learning and bioinspired approaches. It analyzes the lack of effectiveness in traditional mitigation strategies and represents new means, based on the use of ensemble learning, dimensionality reduction and hybrid models. Distributed edge infrastructures are specially emphasized on real time implementation, computational overhead and security. This paper attempts to contribute valuable insight for researchers developing robust, scalable, and intelligent edge-based solutions in high-speed networking environments by consolidating recent methodologies and their outcomes.

关键词： Surveys Deep learning High-speed networks image edge detection Prevention and mitigation Focusing Real-time systems Security Optimization Edge computing

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LLM-Based Task Planning for Mobile Robots Using a Novel SLAM Method for Materials Distribution

LLM-Based Task Planning for Mobile Robots Using a Novel SLAM...

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International conference on Autonomous Robots and Agents, ICARA

作者： Aphilak Lonklang Pedro Alejandro Cordero Jara Haichen Xu Wentao Zhang Natabara Máté Gyöngyössy János Botzheim Department of Artificial Intelligence Faculty of Informatics ELTE Eötvös Loránd University Budapest Hungary

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This paper presents a novel autonomous mobile robot system for material distribution, integrating dynamic map generation, natural language processing (NLP), and efficient path-planning for real-world factory applications. The system begins with a top-view image capture of the environment using a fisheye-lens camera, followed by image correction and color-based object detection to identify stations and generate an occupancy map for navigation, eliminating the need for traditional SLAM techniques. User interaction is facilitated through an NLP module connected to a large language model (LLM) trained on predefined assembly manuals, enabling the robot to interpret requests and determine the required materials for specific assembly tasks. The global path-planning is managed by the Improved RRT* with Reduced Random Map Size (IRRT*-RRMS) algorithm, ensuring optimal and efficient motion in dynamic environments. Experimental validation using a TurtleBot3 Waffle Pi equipped with an OpenManipulator-X demonstrates the system's effectiveness, particularly in handling assembly model H, which includes bolts, nuts, and screws.

关键词： Simultaneous localization and mapping Navigation Heuristic algorithms Robot vision systems Object detection Fasteners Natural language processing Mobile robots Assembly Autonomous robots

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Face Recognition Attendance Framework using Quantum Edge processing

Face Recognition Attendance Framework using Quantum Edge Pro...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International conference

作者： S. Rajarajeswari Hassan Srinivas Ranjitha Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Through the use of a Streamlit web application, the script is a highly intelligent tool that combines cutting-edge quantum processing principles with traditional computer vision approaches to handle a variety of face detection and identification tasks. While other libraries handle computer vision jobs and numerical calculations, such as NumPy and OpenCV, Streamlit is essential to developing the web application interface. While Streamlit WebRTC allows real-time video streaming, PIL is utilized for image processing. The application is set up to have a personalized look and a centered layout. Users may upload photographs, choose detection modes, and modify face detection settings on its header and sidebar. The program detects faces in uploaded photographs by encircling them with bounding boxes using the Haar Cascade Classifier. This may be used to build a face dataset or train a face recognition algorithm. Another important feature is real-time face identification, in which the software uses the Haar Cascade Classifier to identify faces in a video stream. Bounding boxes are used to emphasize faces, and photos containing faces are retained for later study or training. The program may utilize taken photographs to train a model for face recognition. These photos are processed, turned into grayscale, and then the LBPH (Local Binary Patterns Histograms) technique is used to train the model. The trained model is then kept for use in upcoming facial recognition assignments. Moreover, the program may be set up to track attendance in real time by identifying faces in a webcam stream. Time stamps are recorded, attendance is logged using recognized faces, and the information is saved to a CSV file. All things considered, the script provides a complete solution that combines traditional and quantum methods for real-time face identification, picture processing, and attendance monitoring. This makes it an adaptable tool for a range of computer vision and machine learning application

关键词： Quantum computing Codes Face recognition image edge detection Streaming media Real-time systems Libraries Classification algorithms Face detection Monitoring

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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

作者： Pei Zhang Dong Wang Chanyue Wu Jing Yang Lei Kang Zongwen Bai Ying Li Qiang Shen School of Computer Science Northwestern Polytechnical University Xi’an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an University Yan’an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd Qingdao China Department of Computer Science Aberystwyth University UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques.

关键词： Training Transfer learning Self-supervised learning Transformers Diffusion models Real-time systems Decoding Computational efficiency Speech processing Hyperspectral imaging

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6th International conference on Smart Computing and Informatics, SCI 2024

6th International Conference on Smart Computing and Informat...

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6th International conference on Smart Computing and Informatics, SCI 2024

ISBN: (纸本)9789819619801

The proceedings contain 46 papers. The special focus in this conference is on Smart Computing and Informatics. The topics include: Resilient Domain Authentication Framework for Enhancing Digital Identity Security;traffic Sign Detection with Pattern Recognition Techniques Using image processing;exploring Advanced Techniques in Natural Language processing and Machine Learning for In-depth Analysis of Insurance Claims;Network Intrusion Detection with SMOTE-ENN and Deep Learning Techniques;leveraging Transfer Learning to Enhance Location Accuracy in Mapping Services: A Case Study of Google Maps;assessment of Enhanced Email Spam Detection System Through Machine Learning algorithms;machine Learning Methods for Predicting Traffic Congestion Forecasting;hybridization of Computational Intelligence Algorithm for Scheduling of Tasks and Balancing of Load in Cloud Network;MDSV: Mobs Detection by Enhanced Fused Feature Base Deep Neural Network from Surveillance Camera;ioT-Based Solution for Enhanced Tracking of Individuals Living with Dementia;a Novel Task Scheduling Algorithm in Heterogeneous Multi-cloud Environment;Evaluating the Integration and Usage of AI in Higher Education;evaluating the Connectional Benefits of Artificial Intelligence in the Digital Classroom;Influence of AI as an Aspect of Modern Education Era in Present World;Hilbert–Huang Transform Framework-Based Email and SMS Spam Detection;the Advancement and Utilization of Artificial Intelligence and Machine Learning in the Financial Industry and Its Impact on Macro and Microeconomics;analysis on the Cutting-Edge Approach to Assess Artificial Intelligence’s Educational Consequences in Contemporary Studies;data Analytics in Sales and Marketing: A Comprehensive Methodology for Business Analysts;Wireless Energy Transfer for UAV (Drone) Using Machine Learning.

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A Vision-Based System for Real-Time Monitoring of Water Quality in Natural and Artificial Reservoirs

A Vision-Based System for Real-Time Monitoring of Water Qual...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Shripad Bhatlawande Shrihari Mohole Yash Pahade Swati Shilaskar Aditya Munjale Vishwakarma Institute of technology Pune India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Manual sampling and laboratory testing are the mainstays of traditional water quality monitoring methods, which causes delays in data processing and decision-making. This study proposes an enhanced monitoring of water quality system that uses machine learning algorithms to classify and segment water samples in real-time, thereby overcoming these restrictions. Water samples are classified as “Clean” or “Polluted” having an accuracy of 83.99% using a CNN model. The CNN model, trained over 20 epochs, effectively learns patterns from the dataset to deliver precise results. Additionally, a U-Net model is implemented for image segmentation, generating masks that accurately highlight areas of interest within the water samples, with a segmentation accuracy of 68%. Streamlit's intuitive interface makes it simple for users to upload images and get immediate feedback on water quality. This system helps to improve the health of the environment monitoring with automating the method of water quality evaluation. It does this by providing a simpler, quicker, and scalable solution that enables prompt, data-driven actions.

关键词： image segmentation Accuracy Water quality Manuals Streaming media User interfaces Reservoirs Water pollution Real-time systems Testing

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Dcha: Distributed-Centralized Heterogeneous Architecture Enables Efficient Multi-Task processing for Smart Sensing

Dcha: Distributed-Centralized Heterogeneous Architecture Ena...

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Design, Automation and Test in Europe conference and Exhibition

作者： Erxiang Ren Cheng Qu Li Luo Yonghua Li Zheyu Liu xinghua Yang Qi Wei Fei Qiao School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China MakeSens AI Beijing China College of Science Beijing Forestry University Beijing China Department of Precision Instrument Tsinghua University Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The rapid development of artificial intelligence (AI) has accelerated the progression of IoT technology into the smart era. Integrating AI processing capabilities into IoT devices to create smart sensing systems holds significant promise. In this work, we propose a distributed-centralized heterogeneous architecture that enables efficient multitask processing for smart sensing. This architecture improves the operational efficiency of sensing systems and enhances the deployment scalability through collaborative computing across end, edge, and center nodes. Specifically, we partition the network in traditional centralized sensing systems into several parts and perform algorithm-hardware co-design for each part on its respective deployment platform. We developed a sample design to validate the proposed architecture. By implementing a lightweight image encoder, we achieved an 88x reduction in encoder parameters and up to 9873x energy gain, facilitating deployment on resource-constrained devices. Experimental results demonstrate that the proposed architecture effectively reduces overall energy consumption by 0.0573x to 0.0889x, while maintaining robust multitask inference capabilities. Moreover, energy consumption reductions of 2.88x to 3.22x on edge nodes and 6311.56x to 10037.23x on end nodes were observed.

关键词： Performance evaluation Energy consumption Soft sensors Scalability Collaboration Computer architecture Sensors Partitioning algorithms Internet of Things Artificial intelligence

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Bone Fracture Detection using CNN and VGG

Bone Fracture Detection using CNN and VGG

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Ambient Intelligence in Health Care (ICAIHC), International conference on

作者： Kasvi Soni Aanya Gupta Karishma Dhyani Sunil Kumar Department of IoT and IS Manipal University Jaipur Jaipur India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Technology has played a huge role in human evolution since time immemorial. Now it is not only limited to engineers and data scientists, but far beyond, to even medicine. With increasing load on medical professionals and healthcare systems, digitalization can come handy. The traditional X-ray imaging gives unclear images of bone components in tissue making it difficult for surgeons to diagnose. Using technology, accurate diagnosis can be achieved. This includes a series of steps comprising pre-processing, edge detection, feature extraction and machine learning classifications. Machine learning algorithms such as Convolutional Neural Networks(CNN) and Visual Geometry Group(VGG) were used on a dataset consisting of 9246 training, 506 test and 829 validation images with 0.95 and 0.85 accuracies respectively. The data loss for CNN was found to be 0.24 in comparison to 0.4 for VGG. So from statistical inference, CNN is better.

关键词： Visualization Accuracy image edge detection Medical services Machine learning Feature extraction Bones Convolutional neural networks Medical diagnostic imaging X-ray imaging

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DFI-YOLOv8 based defect detection method for fan blades 24

DFI-YOLOv8 based defect detection method for fan blades

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Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Bao Liu Naiqing Zhou Zaiying Wang College of Electrical & Control Engineering Xi'an University of Science and Technology Xi'an Shaanxi China

ISBN: (纸本)9798400713880

To achieve real-time and accurate detection of blade defects in wind turbines, a wind turbine blade defect detection algorithm based on the YOLOv8 network model is proposed DFI-YOLOv8. Firstly, in response to the fixed shape and size of convolution kernels in ordinary convolutions, and the defects of complex background, high noise, and strong interference on the surface of wind turbine blades, an expandable deformable convolution network DCN was added to the Backbone network, and a DCN_C2f module was constructed to enhance the feature extraction ability of the model under complex background conditions. At the same time, an EMA attention mechanism was introduced to highlight important feature information on the surface of wind turbine blades; Secondly, to address the issues of traditional detection models being bulky and difficult to deploy, a lightweight module called FasterNet is embedded in the Neck layer to reduce redundant computation and memory access, while more effectively extracting spatial features; Finally, to address the issue of varying sizes of defects in wind turbine blades during the detection process, which can lead to missed detections due to smaller defects, a hollow Inception SimAM convolution module is proposed to effectively improve the model's ability to recognize small targets. The experimental results show that the accuracy of DFI-YOLOv8 can reach 80.8%. Compared with the mainstream YOLO series algorithms, mAP50 has significantly improved, while the number of parameters has also been reduced to a certain extent. This fully demonstrates the superior performance and efficient accuracy of DFI-YOLOv8 in detecting defects in wind turbine blades.

关键词： Fan blades

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LoG Feature Extraction and Supervised SVM Classification for Accurate Lung Malignancy Detection and Classification in CT Scans

LoG Feature Extraction and Supervised SVM Classification for...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Dudekula. Subhan Boya. Kowshik D. Lokanath Chowdary K. Uma Maheswari Dept. of Electronics & Communication G. Pullaiah College of Engineering & Technology Kurnool A.P India Dept. of Electronics & Communication G. Pullaiah College of Engineering & Technology A.P India Dept. of Electronics & Communication G. Pullaiah College of Engineering and Technology Kurnool A.P India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Pulmonary cancer is still one of the most prevalent forms of carcinoma along with breast cancer is the realms of growing focus for fundamentals healthcare. This project focuses on the identification of lung neoplasm based on the study of CT scan images using these methods. In this case investigation begins with image preparation, which Median filtering is used to decrease noise. while maintaining important image data. mathematical morphological transformations are then used for picture segmentation. The goal is to separate tumor tissues from the remainder of the image for additional investigation. Area, perimeter, eccentricity and texture features from the segmented images are extracted for GLCM, which are then supplied to the SVM classifier. This task is best suited for the SVM because it is known that the SVM is good at working within high dimensional spaces and is proficient at separating cancerous from non cancerous tumors. First estimates prove that the proposed system is a lot better than traditional systems at pose estimation detection because there are less mistakes made by the operator. The incorporation of these advanced image processing techniques together with machine learning algorithms have great prospects for boosting the detection rate of lung cancer.

关键词： Support vector machines image segmentation Accuracy Filtering Lungs Computed tomography Lung cancer Feature extraction Telecommunication computing Tumors

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