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SSRN 2024年

作者： Lou, Cuijuan Zhou, Quan Li, Ranyang Liu, Wufeng Li, Yan Yuchi, Ming Ding, Mingyue Key Laboratory of Grain Information Processing and Control Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China Department of Automation School of Automation Wuhan University of Technology Wuhan430070 China Department of Biomedical Engineering School of Life Science and Technology Key Laboratory of Molecular Biophysics of the Ministry of Education Medical Ultrasound Laboratory Huazhong University of Science and Technology Wuhan430074 China

Noise suppression and resolution improvement are important issues in high-echo target ultrasound imaging detection. To address this problem, this paper proposes a phased array focused ultrasound imaging method based on an adaptive spectral broadening filter. The proposed method first standardizes the received signals after fixed-point transmission by subtracting the mean of the received aperture signals and dividing by the standard deviation of the received aperture signals. Then, spectralbroadening was achieved through covariance and temporal autocorrelation coefficient calculations. Finally, the variance factor proposed for the first time is calculated as a weight from standardized received data after fixed-point transmission focusing to obtain the final scanline data. To evaluate the performance of the proposed method in high-echo target ultrasound imaging detection, experimental phantom images and two views of the interventricular septum including the parasternal long-axisviews and the apical four-chamber views, are reconstructed. In three scenarios, some finer structures of the targets in the images by the proposed method can be more easily identified compared to delay and sum (DAS), square-neighborhood robust signal mean-to-standard-deviation factor (SN-RSMSF), and coherence factor beamforming (CF). For the experimental phantom data, the image resolution measured by -20dB mainlobe width of the proposed method can reach 14.2 percents of DAS, 18.3 percents of SN-RSMSF and 18.3 percents of CF. The proposed method also outperforms the other three methods in terms of noise suppression. The proposed method can be used to detect diseases that involve high-echo targets such as stones, heart valves, and calcifications. © 2024, The Authors. All rights reserved.

关键词： Ultrasonic imaging

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86-Gb/s optical chaos communication over 100-km fiber transmission using semiconductor lasers

86-Gb/s optical chaos communication over 100-km fiber transm...

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49th European Conference on Optical Communications (ECOC 2023)

作者： X. Mao X. Fan L. Wang S. Fu Y. Wang A. Wang Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province Taiyuan 030024 People's Republic of China School of Information Engineering Guangdong University of Technology Guangzhou 510006 People's Republic of China

An optical-chaos secure communication of 86-Gb/s 16-ary QAM signal over 100-km fiber transmission is experimentally demonstrated under the 20%-overhead SD-FEC BER threshold of 2.0×10-2 by using wideband chaos syn...

ISBN: (数字)9781839539268

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Fast single image super-resolution using sparse Gaussian process regression

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Signal processing 2017年 134卷 52-62页

作者： Haijun Wang Xinbo Gao Kaibing Zhang Jie Li State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi'an 710071 China School of Mathematics and Statistics Henan University of Science and Technology Luoyang 471023 China College of Electrics and Information Xi'an Polytechnic University Xi'an 710048 China Video and Image Processing System Laboratory School of Electronic Engineering Xidian University Xi'an 710071 China

Gaussian process (GP) regression is a popular statistical kernel method for learning the relationship hidden in data. However, the extensive calculation of kernel matrix hinders the further applications in many computer vision tasks such as super-resolution (SR). While active-sampling using active learning can extract a small informative subset from a large training set to overcome the bottleneck of GP regression based SR, there is still room for improving the SR quality as well as efficiency. In this paper, we target a nearly real-time GP-based SR, termed as SpGPR, by integrating the active-sampling and traditional sparse GP. The proposed framework is based on the statistics that the model projection vector is approximately sparse. To be more specific, we first train a full GP model based on an informative subset obtained by active sampling from the original training dataset. And then we propose to employ the sparse GP to further approximate the full GP model by seeking a sparse projection vector, which can significantly accelerate the prediction efficiency while getting higher reconstruction quality. The proposed method is fundamentally coarse-to-fine. Extensive experimental results indicate that the proposed method is superior to other state-of-art competitors and is promising for real-time SR application.

关键词： Super-resolution Gaussian process regression Active learning Sparse

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Single Sensor information Fusion for Local Fault Prediction of Large Rotating Machinery

Single Sensor Information Fusion for Local Fault Prediction ...

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International Conference on Electrical and control Engineering (ICECE)

作者： Sun Jianghong Zuo Yunbo Xu Xiaoli School of ElectroMechanical Engineering Beijing Information Science and Technology University Beijing China School of Mechanical & Vehicular Engineering Beijing Institute of Technology Beijing China Beijing Key Laboratory Measurement and Control of Mechanical and Electrical System Beijing China

Data process of large rotating machinery is in line with basic features of information fusion. A framework of fault diagnosis and prediction based on sensor information fusion is built. An improved extracting method of features is used to deal with the information fusion of single sensor, which raises the calculation efficiency and precision. The local fault prediction process is presented, and the fault deterioration trend is judged on the basis of dynamic weighted method. Actual example of Beijing Yanshan Petrochemical Co. shows the correction of conclusion.

关键词： Machinery Feature extraction Fault diagnosis Vibrations Accuracy Turbines Monitoring

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Synchronization of interconnected embedded systems via timer interrupts

Synchronization of interconnected embedded systems via timer...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Fangyuan Li Jiahu Qin Shaoshuai Mou Yu Kang Department of Automation University of Science and Technology of China Hefei China School of Aeronautics and Astronautics Purdue University West Lafayette Indiana United States State Key Laboratory of Fire Science University of Science and Technology of China Hefei China Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing China

ISBN: (纸本)9781509035502

Some applications of the interconnected embedded systems such as sensor networks rely on all nodes in the network to execute certain tasks simultaneously. To meet this demand for simultaneity, a multi-timer model based fully distributed task synchronization algorithm is proposed in this paper. In this multi-timer model, each node containing an embedded system is characterized by a timer. The microcontrollers (MCUs) within the interconnected embedded systems are switched to the assigned tasks by timer interrupts. Each timer decides when to trigger interrupts by only using the information from its neighbors. Task synchronization is realized by using the proposed synchronization algorithm. Some simulation examples are presented in the end to verify the effectiveness of the proposed synchronization algorithm.

关键词： Embedded systems Synchronization Clocks Nickel Switches Algorithm design and analysis Time-frequency analysis

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A nonlocal interferometric phase filtering strategy for practical InSAR system

A nonlocal interferometric phase filtering strategy for prac...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xue Lin Dongsheng Fang Fangfang Li Ke Wang Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China School of Information Science and Engineering University of Jinan Jinan China Institute of Electronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Information Science and Engineering Henan University of Technology Zhengzhou China

Nonlocal interferometric phase filtering methods achieve excellent performance in both noise reduction and texture preservation, even in the case of complicated topography and low coherence. The main limitation of the nonlocal methods is the computational burden. This paper proposed a nonlocal phase filtering strategy for the practical InSAR system, which combine the nonlocal algorithm with the traditional method to improve the efficiency.

关键词： Filtering Coherence Noise reduction Frequency estimation Estimation Algorithm design and analysis Surfaces

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

arXiv

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arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

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Solving online for time-varying pth root via ZD from real domain to complex domain

Solving online for time-varying pth root via ZD from real do...

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International Conference on information Science and Technology (ICIST)

作者： Yunong Zhang Deyang Zhang Zhengli Xiao Hongzhou Tan Zhen Li Key Laboratory of Autonomous System and Networked Control Ministry of Education Guangzhou China SYSU-CMU Shunde International Joint Research Institute Shunde China School of Information Science and Technology Sun Yat-sen University Guangzhou China

In this paper, continuous-time Zhang dynamics (CTZD) models and discrete-time Zhang dynamics (DTZD) models are proposed to solve in real time for the time-varying pth root, from real domain to complex domain. In addition, the convergence properties of the proposed Zhang dynamics (ZD) models are discussed and proved. Furthermore, exploiting different parameters in the proposed ZD models is investigated in order to achieve superior convergence and better accuracy. Computer-simulation and experiment results further substantiate the efficacy of the proposed ZD models. Moreover, the superiority of DTZD models is verified by comparing with Newton-Raphson iteration (NRI).

关键词： Computational modeling Polynomials Force Lead Convergence Logic gates

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Learning from pseudo lesion: A self-supervised framework for covid-19 diagnosis

arXiv

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arXiv 2021年

作者： Li, Zhongliang Jin, Zhihao Li, Xuechen Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo COVID-19 images. The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data. The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID- 19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis and the accuracy of the proposed method outperformed the supervised model pretrained on large scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively. © 2021, CC BY-NC-SA.

关键词： Coronavirus

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ZG control for 2-output tracking of 3-input nonlinear system with GD used additionally twice more

ZG control for 2-output tracking of 3-input nonlinear system...

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International Conference on information and Automation (ICIA)

作者： Yunong Zhang Ming Liu Yinyan Zhang Zeqi Chen Hongzhou Tan Key Laboratory of Autonomous System and Networked Control Ministry of Education Guangzhou China SYSU-CMU Shunde International Joint Research Institute Shunde China School of Information Science and Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781467391054

In this paper, the Zhang-gradient (ZG) method, which is a combination of Zhang dynamics (ZD) and gradient dynamics (GD) methods, is proposed for solving the tracking control problem of the multiple-input multiple-output (MIMO) system. Different from the traditional ZG method, GD is used additionally twice more in this paper to get through the derivation procedure. Moreover, each GD parameter is tunable for each GD design formula instead of being traditionally assigned the same value, which is the key point in this research for making simulations successful. Besides, simulation verifications further illustrate that the proposed controller group based on the ZG method achieves not only satisfactory tracking accuracy but also rapid tracking rate.

关键词： Trajectory Nonlinear systems MIMO Accuracy Automation Linear systems

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