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A New Fractal Coder for SAR Imagery with High Frequency Energy Matching

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A New Fractal Coder for SAR Imagery with High Frequency Ener...

High Accuracy Measurement of The Dispersion of CFBGs and Discussion on Related Issues

2008 11th IEEE International Conference on Communication technology(ICCT2008)

作者： chen Deyuan Luan Enjie Tu Guofang school.of information science and engineering Graduate university of Chinese Academy of sciences beijing.100049 china

A new fractal image compression algorithm based on high frequency energy （HFE） partitioning and matched domain block searching is proposed for SAR imagery *** proposed coder uses a new criterion to partition image and a novel method to search the matched domain block for each range block to code the low frequency component of the transformed SAR image by *** signal-to-noise ratio （PSNR）,encoding time and subjective visual assessment were used to evaluate the performance of the proposed *** experiment results show that the proposed coder outperforms the traditional fractal coders.

关键词： Synthetic aperture radar fractal image compression high frequency energy

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High Accuracy Measurement of The Dispersion of CFBGs and Dis...

Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN

第二届国际测量仪器与测试自动化学术会议

作者： chen Qingshan~(1 2) Lv Naiguang~2 Deng Wenyi~2 Jiang Feng~2 1.school.of electronic engineering beijing.university of post & telecommunication 2.school.of opto-electronic information & telecpmmunication engineering beijing.information science & technology university 100192 china

＜正＞CFBG（Chirped Fiber Bragg Grating）is a passive optical fiber devices with broad application prospect in the chromatic dispersion compensation of optical communication *** precision characterization of the dispersion property of a CFBG is the key to its utilization in this aspect. For this purpose,a low-coherence fiber interferometric system is developed and the principle is *** experiments are carried out and the acquired interferograms are manipulated to obtain the dispersion property of a CFBG. Especially,according to the measurement principle,the error of the system is analyzed,involving error source analysis,error transferring relationship derivation as well as error synthesizing. Based on all these,relative conclusion is drawn.

关键词： CFBG Low-coherence Dispersion Measurement Accuracy

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Global Energy Interconnection 2024年第1期7卷 1-13页

作者： hui Liu Yundan cheng Yanhui Xu Guanqun Sun Rusi chen Xiaodong Yu Institute of Electric Power Systems School of Electrical and Electronic EngineeringNorth China Electric Power UniversityBeijing 102206P.R.China Electric Power Research Institute of State Grid Hubei Electric Power Co. Ltd.Wuhan 430077P.R.China Central china Branch of State Grid Corporation of china Wuhan 430077P.R.China

The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation ***,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations *** paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ***,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization ***,the trained CNN model realizes the online localization of subsynchronous oscillation *** effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design *** results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.

关键词： Subsynchronous oscillation source localization Synchronous squeezing transform Enhanced short-time Fourier transform Convolutional neural networks

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无人飞行器在不确定扰动下的动态目标跟踪

engineering 2024年第4期35卷 74-85页

作者： Yanjie chen Yangning Wu Limin Lan Hang Zhong Zhiqiang Miao hui Zhang Yaonan Wang school.of Mechanical engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Computer science Aberystwyth UniversityAberystwyth SY233DBUK school.of Robotics Hunan UniversityChangsha 410082China College of Electrical and information engineering Hunan UniversityChangsha 410082China National engineering Research Center of Robot Visual Perception and Control technology Hunan UniversityChangsha 410082China

This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle Visual servoing Velocity observer Target tracking

Distinguishing Between Natural and GAN-Generated Face Images by Combining Global and Local Features

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Chinese Journal of Electronics 2022年第1期31卷 59-67页

作者： chen beijing.TAN Weijin WANG Yiting ZHAO Guoying engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology school.of Computer Nanjing University of Information Science and Technology Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment technology (CICAEET) Nanjing University of Information Science and Technology Warwick Manufacturing Group University of Warwick Center for Machine Vision and Signal Analysis University of Oulu

With the development of face image synthesis and generation technology based on generative adversarial networks(GANs), it has become a research hotspot to determine whether a given face image is natural or generated. However, the generalization capability of the existing algorithms is still to be improved. Therefore,this paper proposes a general algorithm. To do so, firstly,the learning on important local areas, containing many face key-points, is strengthened by combining the global and local features. Secondly, metric learning based on the Arc Face loss is applied to extract common and discriminative features. Finally, the extracted features are fed into the classification module to detect GAN-generated *** experiments are conducted on two publicly available natural datasets(Celeb A and FFHQ) and seven GANgenerated datasets. Experimental results demonstrate that the proposed algorithm achieves a better generalization performance with an average detection accuracy over 0.99 than the state-of-the-art algorithms. Moreover, the proposed algorithm is robust against additional attacks, such as Gaussian blur, and Gaussian noise addition.

关键词： Generated image Global feature Local features Generative adversarial network Metric learning

A Quaternion Two-Stream R-CNN Network for Pixel-Level Color Image Splicing Localization

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Chinese Journal of Electronics 2021年第6期30卷 1069-1079页

作者： chen beijing.JU Xingwang GAO Ye WANG Jinwei engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science &Technology school.of Computer Nanjing University of Information Science & Technology Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment technology (CICAEET) Nanjing University of Information Science & Technology

Recently, Zhou et al. designed a twostream faster Region-Convolutional neural networks(RCNN) model RGB-N for color image splicing localization in CVPR2018. However, the RGB-N locates spliced regions only at block-level and ignores the entirety and inherent correlation of three channels. Therefore, an improved quaternion two-stream R-CNN model is proposed to solve these drawbacks: a mask branch combining fully convolutional network and condition random field is added for locating spliced regions at pixel-level; quaternion representation of color images is used to process color spliced images in a holistical way. In addition, feature pyramid network based on quaternion residual network is considered to extract multi-scale features for color spliced images; attention region proposal network is combined with attention mechanism and is designed to pay more attention to the spliced regions; a high-pass filter designed for image splicing detection specifically is adopted to replace steganalysis rich model filter in the RGB-N to obtain noise input for the noise stream. Experimental results on a new synthetic dataset and three standard forgery datasets demonstrate that the proposed method is superior to the existing methods in the abilities of localization, generalization, and robustness.

关键词： Optical, image and video signal processing Computer vision and image processing techniques Neural nets Color image Quaternion Image splicing localization R-CNN ResNet

Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning

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World Journal of Gastroenterology 2024年第6期30卷 542-555页

作者： hui-Ting Ge Jian-Wu chen Li-Li Wang Tian-Xiu Zou Bin Zheng Yuan-Fen Liu Yun-Jing Xue Wei-Wen Lin Department of Radiology Fujian Medical University Union HospitalFuzhou 350001Fujian ProvinceChina Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors Fujian Medical UniversityFuzhou 350001Fujian ProvinceChina Digestive Hematological and Breast MalignanciesClinical Research Center for Radiology and Radiotherapy of Fujian ProvinceFuzhou 350001Fujian ProvinceChina Department of Radiation Oncology Fujian Medical University Union HospitalFuzhou 350001Fujian ProvinceChina Department of Diagnostic Radiology Fujian Medical University Union HospitalFuzhou 350001Fujian ProvinceChina school.of Electrical and Computer engineering University of OklahomaOklahomaOK 73019United States

BACKGROUND Lymphovascular invasion(LVI)and perineural invasion(PNI)are important prognostic factors for gastric cancer(GC)that indicate an increased risk of metastasis and poor *** preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment ***,prior models using conventional computed tomography(CT)images to predict LVI or PNI separately have had limited *** CT provides quantitative enhancement parameters that may better capture tumor *** hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC *** To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status *** This study used a retrospective dataset involving 257 GC patients(training cohort,n=172;validation cohort,n=85).First,several clinical indicators,including serum tumor markers,CT-TN stages and CT-detected extramural vein invasion(CT-EMVI),were extracted,as were quantitative spectral CT parameters from the delineated tumor ***,a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters.A logistic regression(LR)-based nomogram model was subsequently constructed to predict LVI/PNI status,and its performance was evaluated using the area under the receiver operating characteristic curve(AUC).RESULTS In both the training and validation cohorts,CT T3-4 stage,CT-N positive status,and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant(P<0.05).LR analysis of the training group showed preoperative CT-T stage,CT-EMVI,single-energy CT values of 70 keV of venous phase(VP-70 keV),and the ratio of standardized iodine co

关键词： Spectral computed tomography Gastric cancer Lymphovascular invasion Perineural invasion

Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

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china Communications 2024年第4期21卷 38-52页

作者： Jiang Fan Qin Junwei Liu Lei Tian hui school.of Communications and information engineering Xi’an University of Posts and TelecommunicationsXi’an 710121China State Key Laboratory of Networking and Switching technology Beijing University of Posts and TelecommunicationsBeijing 100876China

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy co

关键词： associative tasks cache-aided procedure double deep Q-network Internet of Medical Things(IoMT) multi-access edge computing(MEC)

Intelligent Sliding Mode Control and Application to Weapon Pointing Systems

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Intelligent Sliding Mode Control and Application to Weapon P...