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Hybrid tri-memristor hyperchaotic map and application in Wasserstein generative adversarial nets

Science China(Technological Sciences) 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213159China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic *** investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations,*** the HTMH mapping structure with hybrid parallel/cascade operation as an example,this map possesses a spatial invariant set whose stability is closely related to the initial states of the *** distributions and bifurcation behaviours dependent on the control parameters are explored with numerical ***,the memristor initial offset-boosting mechanism is theoretically demonstrated,and memristor initial offset-boosting behaviours are numerically *** results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite *** addition,an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high ***,the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs)to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

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Free form deformation and symmetry constraint‐based multimodal brain image registration using generative adversarial nets

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1492-1506页

作者： Xingxing Zhu Mingyue Ding Xuming Zhang Department of Biomedical Engineering School of Life Science and TechnologyMinistry of Education Key Laboratory of Molecular BiophysicsHuazhong University of Science and TechnologyWuhanChina

Multi‐modal brain image registration has been widely applied to functional localisation,neurosurgery and computational *** existing registration methods based on the dense deformation fields involve too many parameters,which is not conducive to the exploration of correct spatial correspondence between the float and reference ***,the unidirectional registration may involve the deformation folding,which will result in the change of topology during *** address these issues,this work has presented an unsupervised image registration method using the free form deformation(FFD)and the symmetry constraint‐based generative adversarial networks(FSGAN).The FSGAN utilises the principle component analysis network‐based structural representations of the reference and float images as the inputs and uses the generator to learn the FFD model parameters,thereby producing two deformation ***,the FSGAN uses two discriminators to decide whether the bilateral registration have been realised ***,the symmetry constraint is utilised to construct the loss function,thereby avoiding the deformation *** on BrainWeb,high grade gliomas,IXI and LPBA40 show that compared with state‐of‐the‐art methods,the FSGAN provides superior performance in terms of visual comparisons and such quantitative indexes as dice value,target registration error and computational efficiency.

关键词： Free‐form deformation generative adversarial nets Multi‐modal brain image registration Structural representation Symmetry constraint

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Triple discriminator generative adversarial network for zero-shot image classification

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Science China(Information Sciences) 2021年第2期64卷 5-18页

作者： Zhong JI Jiangtao YAN Qiang WANG Yanwei PANG Xuelong LI School of Electrical and Information Engineering Tianjin University Center for OPTical IMagery Analysis and Learning Northwestern Polytechnical University

One key challenge in zero-shot classification(ZSC) is the exploration of knowledge hidden in unseen classes. generative methods such as generative adversarial networks(GANs) are typically employed to generate the visual information of unseen classes. However, the majority of these methods exploit global semantic features while neglecting the discriminative differences of local semantic features when synthesizing images, which may lead to sub-optimal results. In fact, local semantic information can provide more discriminative knowledge than global information can. To this end, this paper presents a new triple discriminator GAN for ZSC called TDGAN, which incorporates a text-reconstruction network into a dual discriminator GAN(D2GAN), allowing to realize cross-modal mapping from text descriptions to their visual representations. The text-reconstruction network focuses on key text descriptions for aligning semantic relationships to enable synthetic visual features to effectively represent images. Sharma-Mittal entropy is exploited in the loss function to make the distribution of synthetic classes be as close as possible to the distribution of real classes. The results of extensive experiments over the Caltech-UCSD Birds-2011 and North America Birds datasets demonstrate that the proposed TDGAN method consistently yields competitive performance compared to several state-of-the-art ZSC methods.

关键词： zero-shot classification generative adversarial nets text reconstruction Sharma-Mittal entropy

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UFC-Net with Fully-Connected Layers and Hadamard Identity Skip Connection for Image Inpainting

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Computers, Materials & Continua 2021年第9期68卷 3447-3463页

作者： Chung-Il Kim Jehyeok Rew Yongjang Cho Eenjun Hwang Korea Electronics Technology Institute Seongnam13488Korea School of Electrical Engineering Korea UniversitySeoul02841Korea

Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding *** its performance has been improved significantly using diverse convolutional neural network(CNN)-based models,these models have difficulty filling in some erased areas due to the kernel size of the *** the kernel size is too narrow for the blank area,the models cannot consider the entire surrounding area,only partial areas or none at *** issue leads to typical problems of inpainting,such as pixel reconstruction failure and unintended *** alleviate this,in this paper,we propose a novel inpainting model called UFC-net that reinforces two components in *** first component is the latent networks in the middle of U-net to consider the entire surrounding *** second component is the Hadamard identity skip connection to improve the attention of the inpainting model on the blank areas and reduce computational *** performed extensive comparisons with other inpainting models using the Places2 dataset to evaluate the effectiveness of the proposed *** report some of the results.

关键词： Image processing computer vision image inpainting image restoration generative adversarial nets

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generative adversarial networks enhanced location privacy in 5G networks

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Science China(Information Sciences) 2020年第12期63卷 41-52页

作者： Youyang QU Jingwen ZHANG Ruidong LI Xiaoning ZHANG Xuemeng ZHAI Shui YU School of Information Technology Deakin University School of Computer Science University of Technology Sydney National Institute of Information and Communications Technology (NICT) School of Information and Communication Engineering University of Electronic Science and Technology of China Center of AI and Big Data Southeast Digital Economic Development Institute

5G networks, as the up-to-date communication platforms, are experiencing fast booming. Meanwhile, increasing volumes of sensitive data, especially location information, are being generated and shared using 5G networks for various purposes ceaselessly. Location and trajectory information in the published data has always been and will keep courting risks and attacks by malicious adversaries. Therefore, there are still privacy leakage threats by simply sharing the original data, especially data with location information, due to the short cover range of 5G signal tower. To better address these issues, we proposed a generative adversarial networks(GAN) enhanced location privacy protection model to cloak the location and even trajectory information. We use posterior sampling to generate a subset of data, which is proved complying with differential privacy requirements from the end device side. After that, a data augmentation algorithm modified from classic GAN is devised to generate a series of privacy-preserving full-sized synthetic data from the central server side. With the synthetic data generated from a real-world dataset, we demonstrate the superiority of the proposed model in terms of location privacy protection, data utility, and prediction accuracy.

关键词： 5G privacy preservation generative adversarial nets differential privacy

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