Hyperspectral image classification using improved multi-scale block local binary pattern and bi-exponential edge-preserving smoother
作者:
Wan, Xiaoqing;Chen, Shuanghao
期刊:
European Journal of Remote Sensing ,2023年56(1) ISSN:1129-8596
通讯作者:
Wan, XQ
作者机构:
[Wan, Xiaoqing] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.;[Chen, Shuanghao] Zhengzhou Univ, Coll Comp & Engn, Zhengzhou, Peoples R China.
通讯机构:
[Wan, XQ ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
关键词:
Classification;hyperspectral image;multiple strategy fusion;multi-scale block local binary pattern;edge-preserving filtering
摘要:
In this paper, a multi-strategy fusion (MSF) framework, based on improved MBLBP and bi-exponential edge-preserving smoother (BEEPS), is proposed for hyperspectral image (HSI) classification. First, MBLBP operator is adopted to characterize the overall structural information of HSI, where the averaging strategy allocates same weights for the pixels in a local sub-region, so that the edges tend to be blurred due to being isotropic. To solve this question, the steering kernel is first introduced into MBLBP for learning the local structure prior of HSI. Then, a support vector machine classifier is used to calculate the soft classified probabilities of pixels. Furthermore, BEEPS is adopted to smooth the soft classified probabilities maps in the post-processing stage, and the purpose is to further improve classification accuracy of HSI by considering context-aware information for each class label. Experiments are performed on three real hyperspectral datasets, namely, Indian Pines, KSC, and Houston 2013, only 1%, 6, and 5 labeled samples are randomly selected for training, the overall accuracy(kappa) obtained by MSF is 99.47%(99.40), 99.52%(99.47), and 94.25%(93.78), respectively, which is far better than the contrast methods. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
语种:
英文
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VStyclone: Real-time Chinese voice style clone
作者:
Wu, Yichun;Zhao, Huihuang;Liang, Xiaoman;Sun, Yaqi
期刊:
Computers & Electrical Engineering ,2023年105:108534 ISSN:0045-7906
通讯作者:
Zhao, Huihuang(happyday.huihuang@gmail.com)
作者机构:
[Liang, Xiaoman; Zhao, Huihuang; Sun, Yaqi; Wu, Yichun] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Liang, Xiaoman; Zhao, Huihuang; Sun, Yaqi] Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
通讯机构:
[Huihuang Zhao] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang 421002, China
关键词:
VStyclone;Voice clone;Efficient tone extractor;Style synthesizer;Transformer;Vocoder
摘要:
This paper proposes a novel Chinese speech cloning model named VStyclone, which consists of three stages: multi-speaker training, target speaker encoding, and target speaker synthesis. In this work, we design an efficient tone extractor, which can reallocate resources to the sequences of log-mel spectrogram frames obtained from multiple speakers’ speech, thus allowing the network to learn multiple speakers’ features differently. This approach allows the network to focus more on the voice features of the target speaker and extract the target features accurately. To cluster the voices of the same speaker and sparse the voices of different speakers, we build an optimal softmax loss to optimize the model. Then, we develop a style synthesizer, which adopts the idea of transformer instead of recurrent neural network, so that the model can not only process text information in parallel, but also improve the model's ability to process long-distance contextual information. Meanwhile, we embed a style extraction module in the style synthesizer to dynamically capture style ranges in an unsupervised manner. In addition, the VStyclone model uses generative adversarial networks as the base generation model of the vocoder to improve the generation speed, which runs 1.2 times faster than the real-time generation speed on CPU, and finally the VStyclone model obtains the SOTA effect. © 2022
语种:
英文
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An efficient differential analysis method based on deep learning
作者:
Huang, Ying;Li, Lang;Guo, Ying;Ou, Yu;Huang, Xiantong
期刊:
Computer Networks ,2023年224:109622 ISSN:1389-1286
通讯作者:
Li, Lang(lilang@hynu.edu.cn)
作者机构:
[Li, Lang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
通讯机构:
[Li, L.] C;College of Computer Science and Technology, Hengyang Normal University, China
关键词:
Deep learning;Differential analysis;MLP;ResNet
摘要:
Differential analysis is a vital tool for evaluating the security of cryptography algorithms. There has been a growing interest in the differential distinguisher based on deep learning. Various neural network models have been created to increase the accuracy of distinguishing between ciphertext and random sequences. However, few studies have focused on differential analysis at the design stage of cryptographic algorithms. This paper presents an appropriate model for differential analysis of block ciphers. The model is similar to multilayer perceptron (MLP) models in simplicity and clarity. It also introduces a shortcut connection that enables one to learn more information about the differential analysis dataset. The model is used to predict the minimum number of active S-boxes (AS), linking differential analysis results to algorithm features. This model and two classical neural network models are compared under fair experimental conditions. The findings indicate that our model predicts the AS values with an accuracy of 97%. It can effectively predict the results of differential analysis. In addition, the differential analysis dataset is constructed for SPN structure cryptographic algorithms. It can be used for further differential analysis studies based on deep learning. © 2023 Elsevier B.V.
语种:
英文
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DBST: a lightweight block cipher based on dynamic S-box
作者:
Yan, Liuyan;Li, Lang;Guo, Ying
期刊:
计算机科学前沿(英文) ,2023年17(3):177-185 ISSN:2095-2228
通讯作者:
Li, Lang(lilang911@126.com)
作者机构:
[Guo, Ying; Li, Lang; Yan, Liuyan] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Guo, Ying; Li, Lang; Yan, Liuyan] Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.
通讯机构:
[Lang Li] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, China
关键词:
internet of things;5G;dynamic S-box;bit-slice technology;lightweight block cipher
摘要:
IoT devices have been widely used with the advent of 5G. These devices contain a large amount of private data during transmission. It is primely important for ensuring their security. Therefore, we proposed a lightweight block cipher based on dynamic S-box named DBST. It is introduced for devices with limited hardware resources and high throughput requirements. DBST is a 128-bit block cipher supporting 64-bit key, which is based on a new generalized Feistel variant structure. It retains the consistency and significantly boosts the diffusion of the traditional Feistel structure. The SubColumns of round function is implemented by combining bit-slice technology with subkeys. The S-box is dynamically associated with the key. It has been demonstrated that DBST has a good avalanche effect, low hardware area, and high throughput. Our S-box has been proven to have fewer differential features than RECTANGLE S-box. The security analysis of DBST reveals that it can against impossible differential attack, differential attack, linear attack, and other types of attacks. © 2023, Higher Education Press.
语种:
英文
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SepFE: Separable Fusion Enhanced Network for Retinal Vessel Segmentation
作者:
Wu, Yun;Jiao, Ge;Liu, Jiahao
期刊:
工程与科学中的计算机建模(英文) ,2023年136(3):2465-2485 ISSN:1526-1492
通讯作者:
Jiao, Ge(jiaoge@126.com)
作者机构:
[Wu, Yun; Liu, Jiahao; Jiao, Ge] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Jiao, Ge] Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
通讯机构:
[Jiao, G.] C;College of Computer Science and Technology, China
关键词:
depth-wise separable convolution;feature fusion;Retinal vessel segmentation;U-Net
摘要:
The accurate and automatic segmentation of retinal vessels from fundus images is critical for the early diagnosis and prevention of many eye diseases, such as diabetic retinopathy (DR). Existing retinal vessel segmentation approaches based on convolutional neural networks (CNNs) have achieved remarkable effectiveness. Here, we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net, which is one of the most popular architectures. In view of the excellent work of depth-wise separable convolution, we introduce it to replace the standard convolutional layer. The complexity of the proposed model is reduced by decreasing the number of parameters and calculations required for the model. To ensure performance while lowering redundant parameters, we integrate the pre-trained MobileNet V2 into the encoder. Then, a feature fusion residual module (FFRM) is designed to facilitate complementary strengths by enhancing the effective fusion between adjacent levels, which alleviates extraneous clutter introduced by direct fusion. Finally, we provide detailed comparisons between the proposed SepFE and U-Net in three retinal image mainstream datasets (DRIVE, STARE, and CHASEDB1). The results show that the number of SepFE parameters is only 3% of U-Net, the Flops are only 8% of U-Net, and better segmentation performance is obtained. The superiority of SepFE is further demonstrated through comparisons with other advanced methods. © 2023 Tech Science Press. All rights reserved.
语种:
英文
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Towards Robust Rain Removal with Unet++
作者:
Hu, Boxia;Sun, Yaqi;Yang, Yufei;Ouyang, Ze;Zhang, Feng
期刊:
计算机、材料和连续体(英文) ,2023年75(1):879-890 ISSN:1546-2218
通讯作者:
Hu, Boxia(happylife008@163.com)
作者机构:
[Yang, Yufei; Hu, Boxia] Hunan Univ, Sch Math, Changsha 410082, Peoples R China.;[Hu, Boxia] Hengyang Normal Univ, Coll Math & Stat, Hengyang 421002, Peoples R China.;[Ouyang, Ze; Sun, Yaqi; Zhang, Feng] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Yang, Yufei] Changsha Univ, Sch Math, Changsha 410022, Peoples R China.
通讯机构:
[Hu, B.] S;School of Mathematics, China
关键词:
Rain removal;edge optimization;robust;Unet plus plus
摘要:
Image deraining has become a hot topic in the field of computer vision. It is the process of removing rain streaks from an image to reconstruct a high-quality background. This study aims at improving the performance of image rain streak removal and reducing the disruptive effects caused by rain. To better fit the rain removal task, an innovative image deraining method is proposed, where a kernel prediction network with Unet++ is designed and used to filter rainy images, and rainy-day images are used to estimate the pixel-level kernel for rain removal. To minimize the gap between synthetic and real data and improve the performance in real rainy image handling, a loss function and an effective data optimization method are suggested. In contrast with other methods, the loss function consists of Structural Similarity Index loss, edge loss, and L1 loss, and it is adopted to improve performance. The proposed algorithm can improve the Peak Signal-to-Noise ratio by 1.3% when compared to conventional approaches. Experimental results indicate that the proposed method can achieve a better efficiency and preserve more image structure than several classical methods. © 2023 Tech Science Press. All rights reserved.
语种:
英文
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RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection
作者:
Zhang, Pan;Deng, Hongwei;Chen, Zhong
期刊:
计算机、材料和连续体(英文) ,2023年75(1):1411-1430 ISSN:1546-2218
通讯作者:
Deng, HW
作者机构:
[Zhang, Pan; Deng, Hongwei; Chen, Zhong] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
通讯机构:
[Deng, HW ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
关键词:
Attention mechanism;RT-YOLO;small target detection;YOLOv5s
摘要:
In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is added to strengthen the connection between space and channel and better retain important feature information. (2) The feature information is enriched by improving the multi-scale feature pyramid structure and strengthening the feature fusion at different scales. (3) A new loss function is created and the diagonal penalty term of the anchor frame is introduced to improve the speed of training and the accuracy of reasoning. The proposed model is called residual feature fusion triple attention YOLO (RT-YOLO). Experiments showed that the mean average precision (mAP) of RT-YOLO is increased from 57.2% to 60.8% on the vehicle detection in aerial image (VEDAI) dataset, and the mAP is also increased by 1.7% on the remote sensing object detection (RSOD) dataset. The results show that the RT-YOLO outperforms other mainstream models in UAV aerial image object detection. © 2023 Tech Science Press. All rights reserved.
语种:
英文
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CBA-GAN: Cartoonization style transformation based on the convolutional attention module
作者:
Zhang, Feng;Zhao, Huihuang* ;Li, Yuhua;Wu, Yichun;Sun, Xianfang
期刊:
Computers & Electrical Engineering ,2023年106:108575 ISSN:0045-7906
通讯作者:
Zhao, Huihuang
作者机构:
[Zhao, Huihuang; Sun, Xianfang; Wu, Yichun; Zhang, Feng] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Zhao, Huihuang; Sun, Xianfang] Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.;[Li, Yuhua] Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales.
通讯机构:
[Huihuang Zhao] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
关键词:
Attention;Cartoonization;Convolutional block attention;Edge detection;Generative adversarial networks
摘要:
Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video. © 2023 Elsevier Ltd
语种:
英文
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SCENERY: a lightweight block cipher based on Feistel structure
作者:
Feng, Jingya;Li, Lang*
期刊:
计算机科学前沿(英文) ,2022年16(3):1-10 ISSN:2095-2228
通讯作者:
Li, Lang
作者机构:
[Li, Lang; Feng, Jingya] Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.;[Li, Lang; Feng, Jingya] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Peoples R China.;[Li, Lang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
通讯机构:
[Li, Lang] H;Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.;Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Peoples R China.;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
关键词:
lightweight block cipher;feistel structure;cryptanalysis;internet of things
摘要:
In this paper, we propose a new lightweight block cipher called SCENERY. The main purpose of SCENERY design applies to hardware and software platforms. SCENERY is a 64-bit block cipher supporting 80-bit keys, and its data processing consists of 28 rounds. The round function of SCENERY consists of 8 4 × 4 S-boxes in parallel and a 32 × 32 binary matrix, and we can implement SCENERY with some basic logic instructions. The hardware implementation of SCENERY only requires 1438 GE based on 0.18 um CMOS technology, and the software implementation of encrypting or decrypting a block takes approximately 1516 clock cycles on 8-bit microcontrollers and 364 clock cycles on 64-bit processors. Compared with other encryption algorithms, the performance of SCENERY is well balanced for both hardware and software. By the security analyses, SCENERY can achieve enough security margin against known attacks, such as differential cryptanalysis, linear cryptanalysis, impossible differential cryptanalysis and related-key attacks. © 2022, Higher Education Press.
语种:
英文
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Side-channel analysis attacks based on deep learning network
作者:
Ou, Yu;Li, Lang
期刊:
计算机科学前沿(英文) ,2022年16(2):1-11 ISSN:2095-2228
通讯作者:
Li, L
作者机构:
[Ou, Yu; Li, Lang] Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.;[Ou, Yu; Li, Lang] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Peoples R China.;[Li, Lang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
通讯机构:
[Li, L ] H;Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.;Hunan Normal Univ, Coll Informat Sci & Engn, Changsha 410081, Peoples R China.;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
关键词:
side-channel analysis;template attack;machine learning;deep learning
摘要:
There has been a growing interest in the side-channel analysis (SCA) field based on deep learning (DL) technology. Various DL network or model has been developed to improve the efficiency of SCA. However, few studies have investigated the impact of the different models on attack results and the exact relationship between power consumption traces and intermediate values. Based on the convolutional neural network and the autoencoder, this paper proposes a Template Analysis Pre-trained DL Classification model named TAPDC which contains three sub-networks. The TAPDC model detects the periodicity of power trace, relating power to the intermediate values and mining the deeper features by the multi-layer convolutional net. We implement the TAPDC model and compare it with two classical models in a fair experiment. The evaluative results show that the TAPDC model with autoencoder and deep convolution feature extraction structure in SCA can more effectively extract information from power consumption trace. Also, Using the classifier layer, this model links power information to the probability of intermediate value. It completes the conversion from power trace to intermediate values and greatly improves the efficiency of the power attack. © 2022, Higher Education Press.
语种:
英文
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POFMakeup: A style transfer method for Peking Opera makeup
作者:
Zhang, Fachao;Liang, Xiaoman;Sun, Yaqi;Lin, Mugang;Xiang, Jin;...
期刊:
Computers & Electrical Engineering ,2022年104:108459 ISSN:0045-7906
通讯作者:
Liang, Xiaoman(liangxm@hynu.edu.cn)
作者机构:
[Zhang, Fachao; Liang, Xiaoman; Zhao, Huihuang; Sun, Yaqi; Xiang, Jin; Lin, Mugang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Liang, Xiaoman; Zhao, Huihuang; Sun, Yaqi; Lin, Mugang] Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.
通讯机构:
[Xiaoman Liang] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang 421002, China
关键词:
Face key point detection;Facial segmentation;Peking Opera;Style transfer
摘要:
When using standard neural network transfer methods for portrait style transfer, semantically correct transfers often cannot be guaranteed; the texture details in the style examples tend to be ignored. This paper proposes a style transfer method for Peking Opera faces called POFMakeup. This method can transfer the style of a portrait with a Peking Opera face to another portrait. This method uses two guides, namely, a position guide and an appearance guide. The position guide ensures semantic consistency in the transfer process, while the appearance guide ensures the appearance of the target subject can be preserved during transfer. The experimental results show that this method not only solves the problem of traditional portrait style transfer but also can quickly and perfectly realize the style transfer of the Peking Opera face. The proposed method showed a 17% improvement in structural similarity (SSIM) over conventional methods. © 2022 Elsevier Ltd
语种:
英文
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SQEIR: An epidemic virus spread analysis and prediction model
作者:
Wu, Yichun;Sun, Yaqi;Lin, Mugang
期刊:
Computers & Electrical Engineering ,2022年102:108230 ISSN:0045-7906
通讯作者:
Sun, Yaqi(happysyq@hynu.edu.cn)
作者机构:
[Sun, Yaqi; Wu, Yichun; Lin, Mugang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Sun, Yaqi; Lin, Mugang] Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.
通讯机构:
[Yaqi Sun] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
关键词:
Susceptible quarantined exposed infective;Infectious disease model;Mathematical model;COVID-19
摘要:
In 2019, a new strain of coronavirus pneumonia spread quickly worldwide. Viral propagation may be simulated using the Susceptible Infectious Removed (SIR) model. However, the SIR model fails to consider that separation of patients in the COVID-19 incubation stage entails difficulty and that these patients have high transmission potential. The model also ignores the positive effect of quarantine measures on the spread of the epidemic. To address the two flaws in the SIR model, this study proposes a new infectious disease model referred to as the Susceptible Quarantined Exposed Infective Removed (SQEIR) model. The proposed model uses the weighted least squares for the optimal estimation of important parameters in the infectious disease model. Based on these parameters, new differential equations were developed to describe the spread of the epidemic. The experimental results show that this model exhibits an accuracy 6.7% higher than that of traditional infectious disease models. © 2022
语种:
英文
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Sketch to portrait generation with generative adversarial networks and edge constraint
作者:
Liu, Qingyun;Zhao, Huihuang;Wang, Ying;Zhang, Feng;Ramasamy, Manimaran;...
期刊:
Computers & Electrical Engineering ,2021年95:107338- ISSN:0045-7906
通讯作者:
Zhao, Huihuang(happyday.huihuang@gmail.com)
作者机构:
[Ramasamy, Manimaran; Zhao, Huihuang; Wang, Ying; Liu, Qingyun; Zhang, Feng] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Zhao, Huihuang; Wang, Ying; Liu, Qingyun] Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.;[Qiao, Zhijun] Univ Texas Rio Grande Valley, Sch Math & Stat Sci, Brownsville, TX 78520 USA.
通讯机构:
[Zhao, H.] C;College of Computer Science and Technology, China
关键词:
Conditional GANs;Edge constraint;Laplace operator;Pix2Pix;Sketch to portrait
摘要:
A novel method for generating color portraits from sketch images using the edge constraint algorithm and generative adversarial networks (GANs) is proposed in this paper. In converting sketches into color portraits, the details of portrait output by GANs are often blurred and unrealistic. A new method with edge constraint is proposed in this work to address this issue. The image generated from generator network and its edge generated from the followed edge network are combined and provided to the discriminator for authenticity identification. Experiments show that the portrait output by the proposed method provides a more clear and realistic edge than a Pix2Pix model and has a better ability to generate color portraits from sketches compared with other common methods. The average structural similarity index measure (SSIM) value of the proposed method is 82.78%, while the values obtained by other methods and Pix2Pix are 42.99% and 78.60%, respectively. © 2021
语种:
英文
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Two classes of permutation trinomials with Niho exponents
作者:
Zheng, Lijing;Kan, Haibin* ;Peng, Jie;Tang, Deng
期刊:
Finite Fields and Their Applications ,2021年70:101790 ISSN:1071-5797
通讯作者:
Kan, Haibin
作者机构:
[Zheng, Lijing] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421001, Hunan, Peoples R China.;[Kan, Haibin] Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China.;[Tang, Deng; Kan, Haibin] Shanghai Engn Res Ctr Blockchain, Fudan Zhongan Joint Lab Blockchain & Informat Sec, Shanghai 200433, Peoples R China.;[Peng, Jie] Shanghai Normal Univ, Math & Sci Coll, Shanghai 200234, Peoples R China.
通讯机构:
[Kan, Haibin] F;[Kan, Haibin] S;Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China.;Shanghai Engn Res Ctr Blockchain, Fudan Zhongan Joint Lab Blockchain & Informat Sec, Shanghai 200433, Peoples R China.
关键词:
Finite field;Niho exponent;Permutation trinomial
摘要:
In this paper, we consider two classes of permutation trinomials with Niho-type exponents over the finite field F22m, where m is a positive integer. We transform the problem into investigating on some quartic equations (2k-th degree equations) over the subfield F2m in the first class (second class, respectively). We show that these equations have no solutions in F2m. Some sufficient conditions are established to characterize the coefficients in the two classes of permutation polynomials. The numerical result suggests that the sufficient conditions on the coefficients for the case of m odd in the first class, and in the second class, are also necessary. © 2020 Elsevier Inc.
语种:
英文
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Implementation of PRINCE with resource-efficient structures based on FPGAs
作者:
李浪;冯景亚;刘波涛;郭影;李秋萍
期刊:
信息与电子工程前沿(英文) ,2021年22(11):1505-1516 ISSN:2095-9184
通讯作者:
Feng, Jingya(fengjyk@126.com)
作者机构:
[李浪; 冯景亚; 刘波涛; 郭影; 李秋萍] Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang;421002, China;College of Information Science and Engineering, Hunan Normal University, Changsha;410081, China;College of Computer Science and Technology, Hengyang Normal University, Hengyang
通讯机构:
[Jingya Feng] H;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, China<&wdkj&>College of Information Science and Engineering, Hunan Normal University, Changsha, China
关键词:
Lightweight block cipher;Field-programmable gate array (FPGA);Low-cost;PRINCE;Embedded security
摘要:
In this era of pervasive computing, low-resource devices have been deployed in various fields. PRINCE is a lightweight block cipher designed for low latency, and is suitable for pervasive computing applications. In this paper, we propose new circuit structures for PRINCE components by sharing and simplifying logic circuits, to achieve the goal of using a smaller number of logic gates to obtain the same result. Based on the new circuit structures of components and the best sharing among components, we propose three new hardware architectures for PRINCE. The architectures are simulated and synthesized on different programmable gate array devices. The results on Virtex-6 show that compared with existing architectures, the resource consumption of the unrolled, low-cost, and two-cycle architectures is reduced by 73, 119, and 380 slices, respectively. The low-cost architecture costs only 137 slices. The unrolled architecture costs 409 slices and has a throughput of 5.34 Gb/s. To our knowledge, for the hardware implementation of PRINCE, the new low-cost architecture sets new area records, and the new unrolled architecture sets new throughput records. Therefore, the newly proposed architectures are more resource-efficient and suitable for lightweight, latency-critical applications.
语种:
英文
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Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition
作者:
Zhao, Hui-huang;Liu, Han
期刊:
Granular Computing ,2020年5(3):411-418 ISSN:2364-4966
通讯作者:
Liu, Han(LiuH48@cardiff.ac.uk)
作者机构:
[Zhao, Hui-huang] College of Computer Science and technology, Hengyang Normal University, Hengyang;421008, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang;[Liu, Han] School of Computer Science and Informatics, Cardiff University, Queen’s Buildings, 5 The Parade, Cardiff;CF24 3AA, United Kingdom
通讯机构:
[Liu, H.] S;School of Computer Science and Informatics, Queen’s Buildings, 5 The Parade, United Kingdom
关键词:
Classification;Classifiers fusion;Ensemble learning;Granular computing;Machine learning;Random forests
摘要:
Handwritten digits recognition has been treated as a multi-class classification problem in the machine learning context, where each of the ten digits (0–9) is viewed as a class and the machine learning task is essentially to train a classifier that can effectively discriminate the ten classes. In practice, it is very usual that the performance of a single classifier trained using a standard learning algorithm is varied on different datasets, which indicates that the same learning algorithm may train strong classifiers on some datasets but weak classifiers may be trained on other datasets. It is also possible that the same classifier shows different performance on different test sets, especially when considering the case that image instances can be highly diverse due to the different handwriting styles of different people on the same digits. To address the above issue, development of ensemble learning approaches have been very necessary to improve the overall performance and make the performance more stable on different datasets. In this paper, we propose a framework that involves CNN-based feature extraction from the MINST dataset and algebraic fusion of multiple classifiers trained on different feature sets, which are prepared through feature selection applied to the original feature set extracted using CNN. The experimental results show that the classifiers fusion can achieve the classification accuracy of ≥ 98%. © 2019, The Author(s).
语种:
英文
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Portrait style transfer using deep convolutional neural networks and facial segmentation
作者:
Zhao, Huihuang* ;Zheng, Jinghua;Wang, Yaonan;Yuan, Xiaofang;Li, Yuhua
期刊:
Computers & Electrical Engineering ,2020年85:106655- ISSN:0045-7906
通讯作者:
Zhao, Huihuang
作者机构:
[Zheng, Jinghua; Zhao, Huihuang] Hunan Prov Key Lab Intelligent Informat Proc & Ap, Changsha, Hunan, Peoples R China.;[Zheng, Jinghua; Zhao, Huihuang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.;[Yuan, Xiaofang; Wang, Yaonan] Hunan Univ, Coll Elect & Informat Engn, Changsha, Peoples R China.;[Li, Yuhua] Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales.
通讯机构:
[Zhao, Huihuang] H;Hunan Prov Key Lab Intelligent Informat Proc & Ap, Changsha, Hunan, Peoples R China.;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
关键词:
Deep convolutional neural networks;Portrait;Style transfer;Facial segmentation
摘要:
When standard neural style transfer approaches are used in portrait style transfer, they often inappropriately apply textures and colours in different regions of the style portraits to the content portraits, leading to unsatisfied transfer results. This paper presents a portrait style transfer method to transfer the style of one image to another. It first proposes a combined segmentation method for the portrait parts, which segments both the style portrait and the content portrait into masks of seven parts automatically, including background, face, eyes, nose, eyebrows, mouth and foreground. These masks are extracted to capture elements of the styles for objects in the style image and to preserve the structure in the content portrait. This paper then proposes an augmented deep Convolutional Neural Network (CNN) framework for portrait style transfer. The masks of seven parts are added into a trained deep convolutional neural network as feature maps in certain selected layers in the augmented deep CNN model. An improved loss function is proposed for the training of the portrait style transfer. Results on various images show that our method outperforms the state-of-the-art style transfer techniques. © 2020 Elsevier Ltd
语种:
英文
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A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration
作者:
Xuan, Zhanwei;Feng, Xiang* ;Yu, Jingwen;Ping, Pengyao;Zhao, Haochen;...
期刊:
Computational and Mathematical Methods in Medicine ,2019年2019(11):7614850:1-7614850:13 ISSN:1748-6718
通讯作者:
Feng, Xiang;Wang, Lei
作者机构:
[Feng, Xiang; Feng, X; Wang, Lei; Xuan, Zhanwei] Changsha Univ, Coll Comp Engn & Appl Math, Changsha 410001, Hunan, Peoples R China.;[Ping, Pengyao; Zhao, Haochen; Feng, Xiang; Zhu, Xianyou; Feng, X; Wang, Lei; Yu, Jingwen; Xuan, Zhanwei] Xiangtan Univ, Key Lab Intelligent Comp & Informat Proc, Xiangtan 411105, Peoples R China.;[Zhu, Xianyou] Hengyang Normal Univ, Dept Comp Sci, Hengyang 421008, Peoples R China.
通讯机构:
[Feng, X; Wang, L] C;[Feng, X; Wang, L] X;Changsha Univ, Coll Comp Engn & Appl Math, Changsha 410001, Hunan, Peoples R China.;Xiangtan Univ, Key Lab Intelligent Comp & Informat Proc, Xiangtan 411105, Peoples R China.
摘要:
A lot of research studies have shown that many complex human diseases are associated not only with microRNAs (miRNAs) but also with long noncoding RNAs (lncRNAs). However, most of the current existing studies focus on the prediction of disease-related miRNAs or lncRNAs, and to our knowledge, until now, there are few literature studies reported to pay attention to the study of impact of miRNA-lncRNA pairs on diseases, although more and more studies have shown that both lncRNAs and miRNAs play important roles in cell proliferation and differentiation during the recent years. The identification of disease-related genes provides great insight into the underlying pathogenesis of diseases at a system level. In this study, a novel model called PADLMHOOI was proposed to predict potential associations between diseases and lncRNA-miRNA pairs based on the higher-order orthogonal iteration, and in order to evaluate its prediction performance, the global and local LOOCV were implemented, respectively, and simulation results demonstrated that PADLMHOOI could achieve reliable AUCs of 0.9545 and 0.8874 in global and local LOOCV separately. Moreover, case studies further demonstrated the effectiveness of PADLMHOOI to infer unknown disease-related lncRNA-miRNA pairs. © 2019 Zhanwei Xuan et al.
语种:
英文
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Resource multi-objective mapping algorithm based on virtualized network functions: RMMA
作者:
Zou, Sai;Tang, Yuliang* ;Ni, Wei;Liu, Ren Ping;Wang, Lei
期刊:
Applied Soft Computing ,2018年66:220-231 ISSN:1568-4946
通讯作者:
Tang, Yuliang
作者机构:
[Zou, Sai] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.;[Zou, Sai; Tang, Yuliang] Xiamen Univ, Dept Commun Engn, Xiamen, Peoples R China.;[Ni, Wei] CSIRO, Div CSIRO Computat Informat CCI, Canberra, ACT, Australia.;[Liu, Ren Ping] Univ Technol Sydney, Fac Engn & IT, Sch Comp & Commun, Sydney, NSW, Australia.;[Wang, Lei] Xiangtan Univ, Dept Comp Sci & Technol, Xiangtan, Peoples R China.
通讯机构:
[Tang, Yuliang] X;Xiamen Univ, Dept Commun Engn, Xiamen, Peoples R China.
关键词:
Multi-objective optimization;Virtualization;Heterogeneous radio access networks;Dynamic differential evolution;Mapping
摘要:
Existing radio access network systems are static and rigid; they cannot easily satisfy the increasingly large volume of mobile traffic. A new multi-objective optimization approach was developed to leverage the complexity and scalability of radio resource allocation in large-scale radio access networks. A mathematical model of virtualized resource mapping in a heterogeneous radio access network is proposed in this study. We expanded the dynamic differential evolutionary algorithm by regulating the weight parameters of each objective with machine learning to solve the mathematical model. Our approach is evaluated comprehensively in terms of complexity and convergence, and simulations are conducted to verify the proposed approach and demonstrate that the unilateral value of our multi-objective optimization can mirror the results of single-objective optimizations. © 2018 Elsevier B.V.
语种:
英文
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Magpie:一种高安全的轻量级分组密码算法
作者:
李浪;李肯立;贺位位;邹祎;刘波涛
期刊:
电子学报 ,2017年45(10):2521-2527 ISSN:0372-2112
通讯作者:
Li, Lang(lilang911@126.com)
作者机构:
[贺位位; 邹祎; 刘波涛; Li, Lang] Department of Computer Science, Hengyang Normal University, Hengyang, Hunan, 421008, China;[Li, Lang; 李浪] College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China
通讯机构:
Department of Computer Science, Hengyang Normal University, Hengyang, Hunan, China
关键词:
轻量级密码;分组密码;FPGA实现
摘要:
论文提出了一种新的高安全轻量级密码算法,命名为Magpie.Magpie是基于SPN结构,分组长度为64位,密钥长度为96位,包含32轮运算.Magpie密码算法包括两个部分:运算部分和控制部分.运算部分,每轮运算包括五个基本运算模块:常数加,S盒变换,行移位,列混合,轮密钥加.控制部分,将密钥的第65位到96位作为Magpie加密算法的控制信号,其中密钥第65位到第80位作为S盒变换控制信号,第81位到第96位值作为列混合,行移位变换和每轮运算的控制信号.在Xilinx Virtex-5 FPGA上实现面积仅为10679 Slices,加密速率为6.4869Gb/s.
语种:
中文
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