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

共 16444 条记录，以下是1431-1440 订阅

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Compressed Plane-Wave Compounding for Efficient Imaging on Portable Ultrasound Devices

Compressed Plane-Wave Compounding for Efficient Imaging on P...

引用

IEEE Biomedical Circuits and systems (BIOCAS)

作者： Deepanshu Trivedi Junjun Huan Arjuna Madanayake Swarup Bhunia Soumyajit Mandal Electrical & Computer Engineering Florida International University Miami FL Electrical & Computer Engineering University of Florida Gainesville FL Electrical & Computer Engineering Stony Brook University Stony Brook NY

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

Sub-Nyquist sampling, also known as compressive sensing (CS), leverages the sparsity of ultrasound signals to reduce the sampling rate required for accurate ultrasound image reconstruction. Such under-sampling can reduce the computational complexity of the reconstruction algorithm while also relaxing on-board memory requirements. This paper demonstrates up to $10 \times$ reduction in total sample count by applying CS algorithms to ultrasound imaging using coherent plane-wave compounding (CPWC), and up to $57 \times$ reduction for single plane waves by combining CS with convolution beamforming. The resulting compression facilitates processing of the acquired samples directly on portable ultrasound devices, thus eliminating the need to transmit the data to a remote cloud for processing. The proposed compressed CPWC imaging approach is suitable for efficient target localization during focused ultrasound (FUS) neuromodulation. A portable ultrasound system for FUS neuromodulation, based on a system-on-chip (SoC) module and equipped with a dual-mode wearable probe, is also presented.

关键词： Location awareness Ultrasonic imaging Reconstruction algorithms System-on-chip Sensors Neuromodulation Spatial resolution Probes image reconstruction Biomedical imaging

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Method of Multi-agent Path Planning for Lunar Robots Swarm Based on Improved CBS Algorithm 43

Method of Multi-agent Path Planning for Lunar Robots Swarm B...

引用

43rd Chinese Control conference, CCC 2024

作者： Feng, Haolong Wu, Songtai Liu, Shengyang Song, Ting Han, Fei Shanghai Aerospace Control Technology Institute Shanghai201109 China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai201109 China Northwestern Polytechnical University Xi'an710072 China

ISBN: (纸本)9789887581581

As lunar missions become increasingly complex, the mission terrain will vary depending on different operating areas. In the future, there may be multiple lunar rovers working together, a more flexible and efficient algorithm to generate mission paths for lunar rover clusters is required. To address the path planning problem of lunar rover groups in large-scale and complex lunar terrain, this paper constructs a gridded map based on lunar image information and the actual lunar surface image is woven into a ring map. Path planning algorithms are modeled on this map, and the actual paths are projected onto the original lunar surface and into missions for implementing multi-lunar rover path planning tasks based on the conflict based search(CBS) algorithm. As the key to CBS algorithm is to resolve conflicts among agents, this paper combines the Deep Q Network with the CBS algorithm to propose a CBS algorithm based on a DQN conflict choice strategy to solve the multi-agent path planning problem for lunar rover clusters. The DQN can learn the policy of an agent, enabling it to better select paths. By integrating DQN with the CBS algorithm, path planning can become more intelligent and efficient. Finally, the simulation verifies that the CBS algorithm can effectively solve the multi-agent path planning problem and the improved CBS algorithm proposed has a certain degree of improvement in terms of solution efficiency. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Lunar missions

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Research on Adaptive Clarification Method for Complex Weather images in Traffic Scenes 6

Research on Adaptive Clarification Method for Complex Weathe...

引用

6th International conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Shi, xiaolin Zhu, Huiqin Yang, xinqian School of Electronic Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Aiming at the problem of single application scenario of different weather image clarification algorithms in transportation scenarios, an adaptive clarification method for complex weather images is designed. Firstly, different types of complex weather images are collected, and a multi-category weather image dataset FRSO containing fog, rain, snow and other (sunny and cloudy) weather types is obtained by manual screening. Then, a Multi-scale Texture Attention (MTA) module is proposed based on the unique texture structure of the weather images and combined with Transfer Learning (TL), a lightweight weather recognition model WeatherNet-MTA-TL is constructed;Finally, the corresponding clarity algorithm is invoked for processing to realize the function of adaptive defogging, deraining and desnowing of complex weather images. The experimental results show that the weather recognition accuracy of WeatherNet-MTA-TL reaches 97.11%, which is higher than that of MobileNetV2-TL and EfficientNet-TL models by 1.44% and 0.67% respectively, and lower than that of ResNet101-TL model by 0.45%, and the number of its parameters is only 5.42M, which accounts for the percentage of 93.00%, 69.58% and 12.17% of MobileNetV2-T, EfficientNet-TL and ResNet101-TL models respectively, which has a smaller number of parameters while maintaining a higher recognition accuracy. The method proposed in this paper is able to clarify weather images in real time on the basis of automatic recognition of weather images with high accuracy, which meets the practical application requirements of vehicle detection, lane line detection, and other transportation scenarios. © 2023 ACM.

关键词： Deep learning

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Stochastic Computing Based on Volatile Ovonic Threshold Switching Devices 15

Stochastic Computing Based on Volatile Ovonic Threshold Swit...

引用

15th IEEE International conference on ASIC, ASICON 2023

作者： Chai, Z. Zhang, W. Zhang, J.F. Liverpool John Moores University School of Engineering United Kingdom Xi'An Jiaotong University State Key Laboratory for Mechanical Behavior of Materials School of Materials Science and Engineering Xi'an China

ISBN: (纸本)9798350312980

Ovonic Threshold Switching (OTS) selector is essential in the 1S (selector) -1R (resistive switching device) crossbar memory array to suppress the sneak current paths. OTS exhibits inherent stochastic characteristics in its switching process and can be used for implementing true random number generators (TRNGs). Stochastic computing (SC) can be further designed and realized by exploiting the probabilistic switching behavior in the OTS. The stochastic bit streams generated by OTS are demonstrated with good computation accuracy in both multiplication operation and edge detection circuit for image processing. Moreover, the distribution of random bit in the stochastic streams generated by OTS has been statistically studied and linked to the defect de/localization behavior in the chalcogenide material. Weibull distribution of the delay time supports the origin of such probabilistic switching, facilitates further optimization of the operation condition, and lays the foundation for device modelling and circuit design. Considering its other advantages such as simple structure, fast speed, and volatile nature, OTS is a promising material for implementing SC in a wide range of novel applications, such as image processors, neural networks, control systems and reliability analysis. © 2023 IEEE.

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2022 International conference on Intelligent systems and Computer Vision, ISCV 2022

2022 International Conference on Intelligent Systems and Com...

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5th International conference on Intelligent systems and Computer Vision, ISCV 2022

ISBN: (纸本)9781665495585

The proceedings contain 71 papers. The topics discussed include: a new sampling strategy to improve the performance of mobile robot path planning algorithms;machine learning based methods for Arabic duplicate question detection;robust traffic signs classification using deep convolutional neural network;image-based visual servoing techniques for robot control;SASHA: a shift-add segmented hybrid approximated multiplier for image processing;graph based method for Arabic text summarization;face information forensics analysis based on facial aging: a survey;advanced financial data processing and labeling methods for machine learning;and recognition system of human activities based on time-frequency features of accelerometer data.

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Radar Active Jamming Signal Identification Method Based on Time-frequency image Features

Radar Active Jamming Signal Identification Method Based on T...

引用

image processing and Media Computing (ICIPMC), International conference on

作者： Wanbing Hao xiaoyi Feng xiaoyue Jiang School of Electronics and Information Northwestern Polytechnical University Xi’an China

In this paper, the Choi-Williams method is used to describe five typical jamming signals, and the grayscale images of the time-frequency two-dimensional distribution of the jamming signals are obtained. The contour information is maximally enhanced by median filtering and image segmentation processing algorithms for the grayscale images. The effective time-frequency domain feature parameter set is extracted and the dimensionality reduction is done. SVM is used to identify typical radar active jamming signals and provide a priori information for radar anti-jamming. This paper verifies the adopted method by MATLAB simulation experiments and analyzes the performance and feasibility of the adopted method.

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Research on 3D-BTC Signal Denoise Method of Aero-Engine Blade Based on ICOA Optimized VMD

Research on 3D-BTC Signal Denoise Method of Aero-Engine Blad...

引用

IEEE International conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Fei Dai xiaodong Zhang Bochao Fan Chengxiong Tu School of Mechanical Engineering Xi'an Jiaotong University Xi'an Shaan Xi China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

Turbine is an important part of aero-engine, and the appearance of turbine blade crack affects the normal operation of aero-engine. It is very important to diagnose the blade crack of turbine, but due to the interference of many factors, the picked fault signal carries a lot of noise, which affects the subsequent signal processing. Therefore, improved coati optimization algorithm-variational mode decomposition-correlation coefficient(ICOA-VMD-CC) denoise algorithm is designed in this paper. Firstly, the ICOA is used to make original COA have better parameter optimization ability, then it is also used to optimize VMD parameters, and CC is used for Intrinsic mode function(IMF) screening to reconstruct signals. The results show that the optimization ability of ICOA in single-peak and multi-peak functions is greatly improved compared with other algorithms. At the same time, ICOA-VMD-CC algorithm has a good noise reduction effect on three-dimensional tip clearance signals of cracked blades. Compared with other algorithms, ICOA-VMD-CC algorithm removes the noise of irrelevant components and retains the effective peak data, achieving a good noise reduction effect.

关键词： Blades Noise Noise reduction Signal processing algorithms Interference Noise measurement Aircraft propulsion Intelligent systems Optimization Turbines

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A New Efficient Split & Merge Algorithm for Embedded systems

A New Efficient Split & Merge Algorithm for Embedded Systems

引用

IEEE International conference on image processing

作者： Nathan Maurice Julien Sopena Lionel Lacassagne LIP6 Sorbonne University Paris

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

This article presents a new image segmentation algorithm based on a Split & Merge approach. By nature, the execution time of Split & Merge algorithms is data-dependent, as their halting conditions are tied to the homogeneity of each region. While previous algorithms made the Split step less sensitive to input data, the execution time of the more complex Merge step remains highly sensitive to image content. This paper tackles the sensitivity and performance problems from a system and architecture perspective. Memory reallocations due to array fusions are eliminated with the introduction of a TTA (Three Table Array) structure in the Merge step. As iterating over entries in this structure causes a loss of memory locality, we propose two new mechanisms that implement a software cache to mitigate this. An experimental study on an embedded system (Nvidia Jetson Xavier NX) has shown our Merge algorithm to be 10.6 times faster than the state-of-the-art Split & Merge algorithm for $960 \times 720$ images. Moreover, the execution time of our algorithm is also more resistant to image characteristics.

关键词： Resistance image segmentation Embedded systems Sensitivity Software algorithms Streaming media Prediction algorithms

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Edge detection algorithm of low SNR image based on mixed filtering

Edge detection algorithm of low SNR image based on mixed fil...

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2021 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2021

作者： Qi, Tang Yi-Xuan, Sun Ke-xin, Zhou Wen-Tian, Wang Xi'An University of Posts Telecommunication Xi'an China

ISBN: (纸本)9781665429184

Aiming at the available edge detection algorithms can not effectively extract the edge information for low SNR image, a new edge detection algorithm for low SNR image based on mixed filter is proposed in this paper. Which using a mixed filter to achieve the effects of edge protection and denoising, and then acquires the image edge by high effective differential operators. Experimental results show that under the interference of different concentrations of salt-And-pepper noise, the proposed algorithm can not only filter out noise better, but also retain more edge information and improve edge connectivity than the available edge detection algorithms. © 2021 IEEE.

关键词： Edge detection

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Vision Transformer based ResNet Model for Pneumonia Prediction 4

Vision Transformer based ResNet Model for Pneumonia Predicti...

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4th International conference on Electronics and Sustainable Communication systems, ICESC 2023

作者： Sowmya, Thatikonda Sai Narasimhulu, Thumma Sunitha, Gurram Manikanta, Thupakula Venkatesh, Thirupathi Sree Vidyanikethan Engineering College Department of CSE A.P Tirupati India Mohan Babu University School of Computing Department of Ai & Ml A.P Tirupati India

ISBN: (纸本)9798350300093

This research study aims to develop a pneumonia detection system using vision transformers. Pneumonia is a very serious respiratory illness that may result in severe health issues, and early detection is essential for effective treatment. Deep learning-based computer vision algorithms have yielded encouraging results in medical image analysis in recent years, and vision transformers have emerged as a potent tool for processing visual data. The proposed system uses a vision transformer to process chest x-ray images and extract visual traits, which can be utilized for classification. The proposed model 'Vit_base_resnet50_224_in21k' is trained on a vast and diverse dataset of annotated chest x-ray images to understand the patterns and characteristics of pneumonia. The system's performance is estimated using standard evaluation parameters - accuracy, loss, specificity, sensitivity, F1-score and ROC curve. The proposed model has achieved a performance accuracy of 92.96% for pneumonia detection. The results demonstrate the potential of vision transformers in chest x-ray image analysis and contribute to the development of more accurate and efficient tools for pneumonia detection. This system has the potential to assist healthcare professionals in making faster and more accurate diagnosis, which can ultimately improve outcomes and save lives. © 2023 IEEE.

关键词： image analysis

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