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SSRN 2024年

作者： Huang, Chengquan Lei, Huan Chen, Yang Cai, Jianghai Qin, Xiaosu Peng, Jialei Zhou, Lihua Zheng, Lan Engineering Training Center Guizhou Minzu University Guiyang550025 China School of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University Guiyang550025 China

The fuzzy clustering algorithms based on interval type-2 are effective methods for data clustering and image segmentation with some potential advantages in dealing with higher-order uncertainty. However, the fuzzy clustering algorithms based on interval type-2 have limited accuracy in noisy image segmentation. Therefore, we propose a new noisy image segmentation algorithm based on weighted local information for interval type-2 enhanced possibilistic fuzzy C-means clustering. Firstly, a new possibilistic fuzzy weighted local information factor, which amalgamates the mutually guided image filtering, is devised to control the relationship between mutually guided image filtering and the original image utilizing the absolute difference image between the original image and mutually guided image filtering. Additionally, two weighted sum functions are also introduced into the new possibilistic fuzzy weighted local information factor to calculate the greyscale differences between the current pixel and its neighborhood pixels. Secondly, the distance of the objective function is corrected using mutually guided image filtering to obtain the local features and global information of the image. Finally, this paper introduces the new possibilistic fuzzy weighted local information factor into the interval type-2 possibilistic fuzzy C-means clustering algorithm and proposes a new interval type-2 enhanced possibilistic fuzzy C-means clustering algorithm with local information and mutually guided image filtering constraints to improve noisy image segmentation results. Through extensive experiments on noisy synthetic images and noisy real images, the proposed algorithm can achieve higher performance and more accurate segmentation results compared with several existing algorithms. © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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RIESZ TYPE THEOREMS FOR κ-PLURIHARMONIC MAPPINGS, INVARIANT HARMONIC QUASIREGULAR MAPPINGS AND HARMONIC QUASIREGULAR MAPPINGS

arXiv

引用

arXiv 2023年

作者： Chen, Shaolin Huang, Manzi College of Mathematics and Statistics Hengyang Normal University Hunan Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application 421002 China Key Laboratory of High Performance Computing and Stochastic Information Processing College of Mathematics and Statistics Hunan Normal University Hunan Changsha410081 China

The main purpose of this paper is to develop some methods to improve and generalize the main results in a recent paper by Liu and Zhu (Adv. Math., 2023, i.e., [19]). The paper consists of two parts. In the first part, we discuss the Riesz type theorem in the setting of n-dimensional complex spaces for all n ≥ 1. In this part, we first introduce the family of κ-pluriharmonic mappings of the n-dimensional complex unit ball. Then we establish two Riesz type theorems for these mappings, which are the n-dimensional versions of Theorems 1.1 and 1.2 in [19], respectively. Furthermore, even when n = 1, our first result shows that the assumption of the real parts of the mappings not being negative (or being negative) in [19, Theorem 1.1] is redundant;and our second result illustrates that the assumption of "quasiconformality" on the mappings in [19, Theorem 1.2] can be replaced by the weaker one of "quasiregularity". In the second part, we investigate the Riesz type theorem in the setting of n-dimensional real spaces for all n ≥ 2. In this part, first, we prove a Riesz type theorem for invariant harmonic quasiregular mappings of the unit n-dimensional real ball. Our result indicates that (i) the range of the parameter p discussed in [19, Theorem 1.3] can be changed from (1, 2) to (1, ∞);(ii) the assumption of the first coordinate functions of the mappings being non-zero in [19, Theorem 1.3] is redundant. In this way, we complete the discussions carried out in [19, Theorems 1.3 and 1.4]. Second, we obtain a Riesz type theorem for harmonic K-quasiregular mappings of the unit n-dimensional real ball. Our result demonstrates that the range of the parameter p discussed in [15, Theorem 2.1] can be changed from (1, 2) to (1, ∞).MSC Codes 30H10, 30C62 Copyright © 2023, The Authors. All rights reserved.

关键词： Harmonic analysis

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Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation

arXiv

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arXiv 2024年

作者： Lan, Zhibin Niu, Liqiang Meng, Fandong Zhou, Jie Zhang, Min Su, Jinsong School of Informatics Xiamen University China Pattern Recognition Center WeChat AI Tencent Inc China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Xiamen University Ministry of Culture and Tourism China Institute of Computer Science and Technology Soochow University China

In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image containing translations in target language. In this regard, conventional cascaded methods suffer from issues such as error propagation, massive parameters, and difficulties in deployment and retaining visual characteristics of the input image. Thus, constructing end-to-end models has become an option, which, however, faces two main challenges: 1) the huge modeling burden, as it is required to simultaneously learn alignment across languages and preserve the visual characteristics of the input image;2) the difficulties of directly predicting excessively lengthy pixel sequences. In this paper, we propose Translatotron-V(ision), an end-to-end IIMT model consisting of four modules. In addition to an image encoder, and an image decoder, our model contains a target text decoder and an image tokenizer. Among them, the target text decoder is used to alleviate the language alignment burden, and the image tokenizer converts long sequences of pixels into shorter sequences of visual tokens, preventing the model from focusing on low-level visual features. Besides, we present a two-stage training framework for our model to assist the model in learning alignment across modalities and languages. Finally, we propose a location-aware evaluation metric called Structure-BLEU to assess the translation quality of the generated images. Experimental results demonstrate that our model achieves competitive performance compared to cascaded models with only 70.9% of parameters, and significantly outperforms the pixel-level end-to-end IIMT model. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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Fractal and multifractal analyses of bipartite networks

引用

Scientific reports 2017年第1期7卷 45588页

作者： Jin-Long Liu Jian Wang Zu-Guo Yu Xian-Hua Xie Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan 411105 China. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia.

Bipartite networks have attracted considerable interest in various fields. Fractality and multifractality of unipartite (classical) networks have been studied in recent years, but there is no work to study these properties of bipartite networks. In this paper, we try to unfold the self-similarity structure of bipartite networks by performing the fractal and multifractal analyses for a variety of real-world bipartite network data sets and models. First, we find the fractality in some bipartite networks, including the CiteULike, Netflix, MovieLens (ml-20m), Delicious data sets and (u, v)-flower model. Meanwhile, we observe the shifted power-law or exponential behavior in other several networks. We then focus on the multifractal properties of bipartite networks. Our results indicate that the multifractality exists in those bipartite networks possessing fractality. To capture the inherent attribute of bipartite network with two types different nodes, we give the different weights for the nodes of different classes, and show the existence of multifractality in these node-weighted bipartite networks. In addition, for the data sets with ratings, we modify the two existing algorithms for fractal and multifractal analyses of edge-weighted unipartite networks to study the self-similarity of the corresponding edge-weighted bipartite networks. The results show that our modified algorithms are feasible and can effectively uncover the self-similarity structure of these edge-weighted bipartite networks and their corresponding node-weighted versions.

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A novel feature extracting method for dynamic gesture recognition based on support vector machine

A novel feature extracting method for dynamic gesture recogn...

引用

International Conference on information and Automation (ICIA)

作者： Yuanrong Xu Qianqian Wang Xiao Bai Yen-Lun Chen Xinyu Wu Shenzhen Key Lab for Computer Vision and Pattern Recognition Chinese Academy of Sciences University of Science and Technology of China Dept. Mechanical and Automation Engineering The Chinese University of Hong Kong Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences

In this paper, we propose a method based on the SVM algorithm to recognize dynamic hand gestures. The information of motion trajectory is captured by a leap motion in three-dimension space. A new methodology of feature extracting is proposed to guarantee the length of samples being the same. The elements of feature vectors are ranged according to two different criteria: one is the amplitude of the variation of orientation angles, and the other criterion is the order of the appearance of features. Experimental results show that this method can classify the dynamic hand gestures effectively.

关键词： Vectors Trajectory Feature extraction Equations Mathematical model Hidden Markov models Automation

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Region stability analysis for switched discrete-time recurrent neural network with multiple equilibria

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Neurocomputing 2017年 249卷 182-190页

作者： Gang Bao Zhigang Zeng Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control China Three Gorges University Yichang 443002 China School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China

This paper investigates a kind of switched discrete-time neural network. Such neural network is composed of multiple sub-networks and switched different sub-networks according to the states of neural network. There is no common equilibrium for all of sub-networks, i.e., multiple equilibria coexist. Firstly, a bounded condition is presented for the switched discrete-time neural network. And then sufficient conditions are derived to ensure region stability of the equilibrium points of such neural network by mathematical analysis and nonsingular M-matrix theory. Four examples are presented to verify the validity of our results.

关键词： Switched neural network Region stability Multiple equilibria

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A Noise Extraction Method for Cryo-EM Single-Particle Denoising

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Journal on Big Data 2022年第1期4卷 61-76页

作者： Huanrong Tang Sihan Wang Jianquan Ouyang Tianming Liu Key Laboratory of Intelligent Computing Information Processing Ministry of EducationSchool of Computer Science and School of Cyberspace ScienceXiangtan UniversityXiangtan411100China Department of Computer Science The University of GeorgiaGeorgia30301USA

Cryo-Electron Microscopy(cryo-EM)has become a powerful method to study the structure and function of biological ***,in clustering tasks based on the projection angle of particles in cryoEM,the noise considerably affects the clustering *** denoising algorithms are ineffective due to the extremely low signal-to-noise ratio(SNR)of cryo-EM images and the complexity of noise *** noise of a single particle greatly influences the orientation estimation of the subsequent clustering task,and the result of the clustering task directly affects the accuracy of the 3D *** this paper,we propose a construction method of cryo-EM denoising dataset that uses U-Net to extract noise blocks from cryoEM images,superimpose the noise block with the projected pure particles to construct our simulated *** we adopt a supervised generative adversarial network(GAN)with perceptual loss to train on our simulated dataset and denoise the real cryo-EM single *** method can solve the problem of poor denoising performance caused by assuming that the noise of the Gaussian distribution does not conform to the noise distribution of cryo-EM,and it can retain the useful information of particles to a great *** compared traditional image filtering methods and the classic deep learning denoising algorithm DnCNN on the simulated and real *** results show that the method based on deep learning has more advantages than traditional image denoising *** is worth mentioning that our method achieves a competitive peak signal to noise ratio(PSNR)and structural similarity(SSIM).Moreover,visualization results,indicate that our method can retain the structure information and orientation information of particles to a greater extent compared with other state-of-the-art image denoising *** means that our denoising task can provide considerable help for subsequent cryo-EM clustering tasks.

关键词： Cryo-EM noise extraction denoising GAN

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Blockchain-Assisted Computation Offloading Collaboration: A Hierarchical Game to Fortify IoT Security and Resilience

SSRN

引用

SSRN 2023年

作者： Zhou, Zitao Ye, Fang Li, Yingsong Geng, Xiaoyu Zhang, Sitong Key Laboratory of Advanced Marine Communication and Information Technology College of Information and Communication Engineering Harbin Engineering University Harbin China National Key Laboratory of Underwater Acoustic Technology College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education College of Electronic and Information Engineering Anhui University Hefei China

With the rapid development of the Internet of Things (IoT), computation offloading collaboration has become an important solution to address the data processing challenges faced by resource-constrained devices. However, security and privacy issues have become a major concern in the computation offloading collaboration process. This paper proposes a blockchain-assisted computation offloading collaboration scheme, Blockchain-Assisted Computation Offloading Collaboration (BACOC). The scheme uses blockchain technology to assist computation offloading collaboration under IoT conditions to fortify security and resilience during the computation offloading process. At the same time, a hierarchical game model based on Stackelberg game theory and contract theory is adopted to determine the optimal service pricing strategy. Experimental and simulation results demonstrate the effectiveness and feasibility of the BACOC scheme. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded ...

引用

2022 International Conference on Radar Systems, RADAR 2022

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

ISBN: (纸本)9781839537776

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Compressed sensing

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Algebraic geometry codes with complementary duals exceed the asymptotic Gilbert-Varshamov bound

arXiv

引用

arXiv 2017年

作者： Jin, Lingfei Xing, Chaoping School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Physical and Mathematical Sciences Nanyang Technological University

It was shown by Massey that linear complementary dual (LCD for short) codes are asymptotically good. In 2004, Sendrier proved that LCD codes meet the asymptotic Gilbert-Varshamov (GV for short) bound. Until now, the GV bound still remains to be the best asymptotical lower bound for LCD codes. In this paper, we show that an algebraic geometry code over a finite field of even characteristic is equivalent to an LCD code and consequently there exists a family of LCD codes that are equivalent to algebraic geometry codes and exceed the asymptotical GV bound. Copyright © 2017, The Authors. All rights reserved.

关键词： Geometry

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