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

共 13603 条记录，以下是181-190 订阅

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FPGA based Vehicle Collision Avoidance and Accident Warning using Sobel Operation and Manhattan Distance Metrics

FPGA based Vehicle Collision Avoidance and Accident Warning ...

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

作者： Vishal Pranao Amarnath Kumar Sanjana Bhattacharjee Harsh Kumar Rupam Mal Vaarshini Ravichandran K. Sivasankaran School of Electronics Engineering VIT University Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

A collision avoidance system in vehicles is a safety technology which is designed to prevent or reduce accidents caused due to any obstacles such as other vehicles or pedestrians. It utilizes cameras, or other sensors to monitor the vehicle's surroundings and alerting the driver to any potential danger. Advanced driver assistance systems (ADAS) and collision avoidance works hand in hand as this enhances overall vehicle safety, reduces any driver errors and also contributes to overall safety on road. This paper presents the design and implementation of a real-time collision avoidance system using Field-Programmable Gate Arrays (FPGAs). The proposed system processes the video input from a vehicle's camera and converts the video's frame to grayscale and applies Sobel edge detection on it which helps to calculate the distance between the two vehicles using Manhattan distance formula and hence effectively detects any potential collision. It utilizes FPGA's parallel processing capabilities, reconfigurability and low power to enhance the efficiency and speed of the collision detection and response in autonomous vehicles. The experiments were performed real-time on Altera Cyclone iv-E FPGA which demonstrated effectiveness of the system as the results indicated high accuracy and reliability in distance measurement and collision warning by analyzing parameters like dynamic color variation and background noise reduction for improving the safety and performance in autonomous vehicles.

关键词： image color analysis image edge detection Streaming media Parallel processing Gray-scale Real-time systems Collision avoidance Field programmable gate arrays Autonomous vehicles Vehicles

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EEG processing for Brain-Computer Interface using Wavelet-Decomposition Ant Colony Optimization and Machine Learning

EEG Processing for Brain-Computer Interface using Wavelet-De...

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Women in Data Science at Prince Sultan University (WiDS PSU), International conference of

作者： Saeed Mian Qaisar Muhammad Nadeem Alaa Eleyan Soraia Oueida College of Engineering and Technology American University of the Middle East Kuwait

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

The research in this paper leverages Electroencephalography (EEG) to develop a Brain-Computer Interface (BCI). By processing and analyzing EEG signals, the system translates brain activity into commands, enabling individuals with severe motor impairments to control devices and regain independence. The Berlin BCI Competition iv dataset is used in the study. EEG recordings of healthy subjects doing hand and foot motor imagery tasks are included in the collection. Discrete wavelet transformation (DWT) decomposes the EEG signals into sub-bands to obtain pertinent information. Feature selection is performed using the ant colony optimization algorithm (ACOA) to reduce dimensionality and improve classification accuracy. An evaluation of three robust classifiers-support vector machine (SVM), weighted k-nearest neighbors (Weighted k-NN), and wide neural networks (WNN)-is conducted to ascertain their capability in Motor imagery (MI) task classification. The results demonstrate that the SVM classifier in conjunction with DWT and ACO, achieves the highest average accuracy score of 85.36% while achieving a dimension reduction of 2.064-times. It makes it an appealing approach for the BCI systems.

关键词： Support vector machines Ant colony optimization Accuracy Transforms Motors Feature extraction Electroencephalography Brain-computer interfaces Discrete wavelet transforms Classification algorithms

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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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A Hybrid Approach for Detection and Reduction of different types of noises in gray scale images

A Hybrid Approach for Detection and Reduction of different t...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Amanpreet Kaur Sandhu University Institute of Computing Chandigarh University Gharuan Mohali Punjab India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

image denoising is a critical task in various fields such as digital photography, medical imaging, satellite or remote sensing, and, also preserving important image details while removing noise is essential. This paper introduces the Hybrid Adaptive Median-Gaussian Filter (HAMGF), a novel method designed to effectively mitigate both salt-and-pepper and Gaussian noise especially in grayscale images. The proposed HAMGF technique combines adaptive median filtering, which dynamically adjusts the window size to remove impulsive noise, with adaptive Gaussian filtering, which varies the smoothing strength based on local image variance to reduce Gaussian noise. However, the proposed hybrid technique is implemented on a variety of noisy images, demonstrating its ability to outperform traditional filtering methods that are typically specialized for single noise types. HAMGF not only excels in reducing noise but also in preserving critical image features or details, such as edges and fine textures, making it particularly suited for applications requiring high-fidelity image restoration. The results highlight the flexibility and robustness of HAMGF, showcasing its potential for broader adoption in noise reduction tasks. The proposed research work demonstrates an important advancement in the area of image processing which is offering a versatile as well as powerful tool for the enhancement of image quality across varied domains.

关键词： Smoothing methods Filtering Gaussian noise image edge detection Noise reduction Adaptive filters Filtering algorithms image restoration Remote sensing image denoising

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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...

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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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Robust Phase Retrieval from Quantized and Noisy Measurements

Robust Phase Retrieval from Quantized and Noisy Measurements

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Acoustics, Speech, and Signal processing Workshops (ICASSPW), IEEE International conference on

作者： Yihong Gao Luteng Zhu Zhuoyang An Mingjie Shao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China School of Information Science and Engineering Shandong University Qingdao China Computer School Beijing Information Science and Technology University Beijing China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The phase retrieval (PR) problem involves recovering a signal from amplitude-only measurements and has been extensively studied in various physical measurement and signal processing systems. Recently, there has been increasing interest in the quantized phase retrieval (QPR) problem, where the observed amplitude is quantized to binary values. The existing studies have focused on the noiseless case. In this paper, we address a robust QPR problem that accounts for both quantization and noise. We formulate the robust QPR problem using maximum-likelihood estimation (MLE), which results in a non-convex and challenging optimization problem due to the complexity of its likelihood function. To obtain a high-quality solution, we develop an expectation-maximization (EM) algorithm combined with the Wirtinger flow, allowing us to convert the problem into a more manageable form at each iteration. Our method is implemented on both randomly generated synthetic data and image reconstruction tasks. The results demonstrate that our approach significantly enhances signal recovery performance under the combined effects of quantization and noise. Additionally, the proposed method exhibits better runtime efficiency compared to existing approaches.

关键词： Maximum likelihood estimation Phase measurement Quantization (signal) Runtime Noise Signal processing algorithms Noise measurement Speech processing Optimization Synthetic data

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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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Prediction of Cervical Cancer Staging via Diffusion-Weighted MR Radiomic Features Analysis

Prediction of Cervical Cancer Staging via Diffusion-Weighted...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International conference for

作者： Zarina Ramlia Sri Herwiningsih Mohd Saiful Asmal Mohd Amiruddin Zaharudin Haron Muhammad Khalis Abdul Karim Department of Physics Universiti Putra Malaysia Selangor Malaysia Department of Physics Universitas Brawijaya Malang Indonesia Department of Radiology Institut Kanser Negara Putrajaya Malaysia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Contemporary technology has made it possible to diagnose diseases with greater precision, enabling doctors to determine tumor stages by measuring and analyzing features in medical images. This study aimed evaluate radiomic features of DWI-MRI cervical and to classify tumors based on their stages using several machine learning models. The study involved 60 patients from the National Cancer Institute in Putrajaya using which 852 radiomic were derived from each patient. The patients were divided into two groups based on the stage of cervical cancer: Group one is defined by stages I and II, while group two is labelled by stages III and iv. A semi-automatic process was used to segment the image data and extract various features such as shape, wavelength, and both first and second-order statistics (GLSZM) from the segmented images. In our study, we used three different model algorithms: Logistic Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT). We conducted a detailed analysis to compare their performance. The SVM algorithm stood out as the top performer, achieving the highest accuracy among the models tested. We evaluated its effectiveness using multiple performance metrics, which confirmed its superiority in processing features extracted from enhanced images. Notably, the SVM classifier demonstrated excellent results, with an accuracy of 0.77, and precision of 0.63. It also showed a robust area under the ROC curve of 96%, highlighting its effectiveness in classification tasks. Furthermore, this research emphasizes the significance of image enhancement as the useful preprocessing step which increases the classification accuracy.

关键词： Support vector machines image segmentation Technological innovation Accuracy Feature extraction Decision trees image enhancement Cervical cancer Radiomics Tumors

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Research on Collaborative Filtering Algorithm of E-commerce Based on Flink

Research on Collaborative Filtering Algorithm of E-commerce ...

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International conference on Intelligent Control and Information processing (ICICIP)

作者： Haohao Zheng Xiaonan Luo Xiaoshu Zhu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

In online shopping, a person’s interest in a product is closely related to whether they will purchase it Analyzing people’s interest in various products on time, along with product recommendations, can increase purchase rates. However, in many cases, We only know user implicit feedback, such as browsing or favoriting items, and cannot directly access user explicit feedback, such as product ratings. Moreover, users have limited exposure to products, resulting in a sparse user-item matrix. The traditional Alternating Least Squares (ALS) algorithm can partially address the is of of matrix sparsity but its accuracy in recommending products based on implicit feedback alone is insufficient. This paper proposes an improved ALS algorithm that converts implicit feedback into explicit ratings. We calculate the browsing frequency of a product based on user browsing counts. For a given product, we represent user preference by suimming the browsing frequencies of all products with browsing frequencies less than or equal to that of the product. Then, by multiplying the preference level by the product’s maximum rating, we derive a product rating, achieving a transformation from implicit feedback to explicit scores. Additionally, traditional collaborative filtering algorithms often use offline data, which may not align with user current preferences. To improve the algorithm’s accuracy, we also utilize Flink for real-time data processing, allowing us to capture the most recent feedback data. This study tests the approach using the UserBehavior dataset from the publicly available Tianchi dataset. Experimental results indicate improvements in both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

关键词： Accuracy Collaborative filtering Prediction algorithms Real-time systems Electronic commerce Sparse matrices User preference Matrix converters Root mean square Intelligent control

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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...

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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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