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IEEE International Conference on Multimedia and Expo (ICME)

作者： Risheng Liu Shichao Cheng Xin Fan Zhongxuan Luo Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Department of Computing The Hong Kong Polytechnic University DUT-RU International School of Information & Software Engineering Dalian University of Technology School of Mathematical Sciences Dalian University of Technology Dalian University of Technology Dalian Liaoning CN

ISBN: (纸本)9781509060689

Blind image deblurring is one of the main phases in most media analysis tasks. Many existing works aim to simultaneously estimate the latent image and the blur kernel under a MAP framework. However, it has been demonstrated that such joint estimation strategies may lead to the undesired trivial solution. In this paper, we propose a learnable nonlinear dynamical system to formulate the image propagation so that the blur kernel estimation can be efficiently controlled by both cues and training data. Our analysis also indicates that the proposed dynamical system is feasible on image modeling socialities. Experimental results on different benchmark image sets evaluate the effectiveness of our proposed approach.

关键词： Kernel Estimation Deconvolution Image restoration Adaptive systems Nonlinear dynamical systems Training data

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Bi-dimensional Variational Mode Decomposition for Surface Texture Analysis

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Procedia CIRP 2022年 114卷 36-41页

作者： Zhuowei Li Yuanping Xu Tukun Li Yajing Shi Xiangqian Jiang Yanlong Cao Wenhan Zeng Zhijie Xu Chaolong Zhang Jian Huang School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Huddersfield HD1 3DH UK Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province College of Mechanical Engineering Zhejiang University Hangzhou 310027 China

In surface metrology, filtration is one of the key operations which is used to decompose the components of the surface and extract a scale-limited surface for further assessment. Although many types of filters have been proposed and some of them have been standardized in ISO16610 serials, the classic surface topography filters often inherit the mode mixing and boundary distortion problems that may lead to surface texture verification failures. This study proposes a new filter algorithm for the surface texture analysis, and it is based on the extended bi-dimensional variational mode decomposition (BVMD), named EBVMD. BVMD is widely used in the field of image processing and it is probably first-time to be used in the field of surface texture analysis. It consists of three steps. Firstly, a coarse-grained parallel genetic algorithm is applied in the devised model to select of optimal penalty factor and decomposition number of the bi-dimensional variational mode, and the best set of modes is gained. Secondly, the instantaneous wavelength value of each mode is obtained by calculating the corresponding isotropic monogenic signals. Finally, the scale-limited surface is extracted by the mean instantaneous wavelength. Experiments are conducted to verify the feasibility of the proposed filter on areal surface texture feature extraction with the mode aliasing and shape distortion phenomenon remedies. The experimental results show that the mean square error of roughness reconstructed by the proposed filter compared with the benchmarking values is 6.37×10 -5 , and owing to high flexibility in practice, the devised EBVMD provides a promising solution to achieve a high accuracy and efficiency filter.

关键词： Surface feature Filter Surface morphology evaluation 2D-VMD Monogenic signal PGA

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Image Fusion Algorithm Based On Orientation Information Motivated Pulse Coupled Neural Networks

Image Fusion Algorithm Based On Orientation Information Moti...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.6

作者： Xiaobo Qu Changwei Hu Jingwen Yan Department of Communication Engineering Xiamen University Xiamen Fujian China Department of Software Engineering Xiamen University Xiamen Fujian China Key Laboratory of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou Guangdong China

Pulse Coupled Neural Networks (PCNN) is a visual cortex-inspired neural networks and characterized by the global coupling and pulse synchronization of neurons. It has been proven suitable for image processing and successfully employed in image fusion. However, in most PCNN-based fusion algorithms, only single pixel value is input to motivate PCNN neuron. This is not effective enough because humans are often sensitive to features, not only pixel value. In this paper, novel orientation information is considered as features to motivate PCNN. Visual observation and objective performance evaluation criteria demonstrate that the proposed algorithm outperforms typical wavelet-based, lapacian pyramid transform-based and PCNN-based fusion algorithms.

关键词： Image fusion Pulse Coupled Neural Networks Orientation information Wavelet transform Image Processing

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Budgeted Persuasion on User Opinions via Varying Susceptibility

Budgeted Persuasion on User Opinions via Varying Susceptibil...

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IEEE International Conference on Performance, computing and communications (IPCCC)

作者： Wenyi Tang Xinrui Xu Guangchun Luo Zaobo He Kaiming Zhan School of Computer Science and Engineering University of Electronic Science and Technology of China School of Information and Software Engineering University of Electronic Science and Technology of China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Miami University Sichuan Jiuyuan Yinhai Software Co. Ltd

ISBN: (数字)9781728198293

ISBN: (纸本)9781728198309

Nowadays, the social network becomes an indispensable part of people's daily life, meanwhile offers an unprecedentedly convenient access for purposive individuals to influence the opinions of network users. Current studies present a subtle persuasion approach that finds a number of key users meanwhile varies their susceptibility extent to impact the public opinion. Such persuasion is significantly critical for public security, as it could facilitate both the spreading and dispelling of malicious rumors. However, the major body of these studies enclose impractical assumptions, such that persuaders have an unlimited budget, or the costs of varying different users' susceptibilities are the same, thus rendering these works unsuitable for realistic scenarios. Therefore, this work originally proposes a more practical and generalized problem of persuasion, where varying the susceptibilities of different users holds different costs. The analysis of its non-convexity, non-submodularity and complexity shows that solving the proposed problem is nontrivial, thus inspiring us to provide an intuitive greedy algorithm. Furthermore, we design an accelerated algorithm based on the community property, which reduces the time consumption more than one order of magnitude. The acceleration is based on the intuition that the impact of a user within a proper community could be a good estimation of the impact in the whole network, while the computation of the former one is much more efficient. The relationship between two algorithms is fully analyzed, which shows the community-based algorithm can degenerate to the intuitive greedy algorithm under a specific setting. Finally, comprehensive evaluations on real-world datasets show the superiority of proposed algorithms on both effectiveness and efficiency.

关键词： Greedy algorithms Social networking (online) Heuristic algorithms Search problems Rendering (computer graphics) Acceleration Security

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Evolutionary Design of Relatively Large Combinational Circuits with an Extended Stepwise Dimension Reduction

Evolutionary Design of Relatively Large Combinational Circui...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Zhifang Li Wenjian Luo Lihua Yue Xufa Wang School of Computer Science and Technology University of Science and Technology Hefei Anhui China Anhui Key Laboratory of Software in Computing and Communication University of Science and Technology Hefei Anhui China University of Science and Technology of China Hefei Anhui CN

In this paper, an eXtended Stepwise Dimension Reduction approach (XSDR) to evolutionary design of relatively large combinational logic circuits is proposed. In our previous work, a Stepwise Dimension Reduction approach (SDR) is introduced. The SDR divides a circuit into several layers. The layers are evolved one after another. However, some layers are difficult to be evolved. The XSDR improves the SDR by decomposing the original truth table of a layer to two truth tables. The new truth tables after decomposing are easy to be evolved. The proposed method has been tested with multipliers and the circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library. The experimental results demonstrate that the XSDR extensively improves the performance of the SDR in terms of the number of fitness evaluations and the computational time.

关键词： Combinational circuits Hardware Circuit testing Evolutionary computation Electronic circuits Computer science Laboratories Assembly Microelectronics Benchmark testing

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JOINT DETECTION AND CLINICAL SCORE PREDICTION IN PARKINSON'S DISEASE VIA MULTI-MODAL SPARSE LEARNING

JOINT DETECTION AND CLINICAL SCORE PREDICTION IN PARKINSON'S...

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International Symposium on Biomedical Imaging

作者： Haijun Lei Jian Zhang Zhang Yang Ee-leng Tan Baiying Lei Qiuming Luo College of Computer Science and Software Engineering Shenzhen University Key Laboratory of Service Computing and Applications Guangdong Province Key Laboratory of Popular High Performance Computers School of Information Engineering Shenzhen University School of Electric and Electronic Engineering Nanyang Technological University School of Biomedical Engineering Shenzhen University Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound

ISBN: (纸本)9781509011735

In this study, a novel feature selection framework is proposed to simultaneously perform classification and clinical scores prediction of Parkinson's disease (PD) via multi-modal neuroimaging data. Specifically, a new feature selection model is devised to capture discriminative features to train support vector regression model for clinical scores (e.g., sleep scores and olfactory scores) prediction and support vector classification model for class label identification. Our method is evaluated on a public dataset of 208 subjects including 56 normal controls (NC), 123 PD and 29 scans without evidence of dopamine deficit (SWEDD) via a 10-fold cross-validation method. The experimental results demonstrate that multimodal data can effectively improve the performance in disease status identification and clinical scores prediction compared to one single modality. Our proposed method also outperforms the related methods.

关键词： Parkinson's disease Feature selection Classification Prediction Multi-modal data

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Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond

arXiv

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

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Investigation on ULA Fitting Promoting Low Coupling Sparse Arrays

Investigation on ULA Fitting Promoting Low Coupling Sparse A...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yichang Yao Wanlu Shi Yingsong Li Zhixiang Huang Guohui Yang Kuang Zhang College of Information and Communication Engineering Harbin Engineering University Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

A low coupling sparse array (LCSA) is presented, analyzed and discussed on the basis of the recent proposed uniform linear array (ULA) fitting scheme with close-expressions, and its performance with different uniform subarrays in the basic layer is investigated via parameter estimation like direction-of-arrival(DOA). The results gives a superior performance for the presented LCSA.

关键词： Couplings Mutual coupling Direction-of-arrival estimation Fitting Estimation Computational electromagnetics

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Design of MEMS Step Impedance Microstrip Filter in Ku Band

Design of MEMS Step Impedance Microstrip Filter in Ku Band

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Tianyu Bai Guohui Yang Kuang Zhang Yingsong Li Tao Jiang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Harbin Engineering University Harbin China

According to the miniaturization characteristics of step impedance resonators, a step impedance bandpass filter with a center frequency of 16.55 GHz and a bandwidth of 500MHz is designed. The effects of parameter changes such as substrate resistivity and the shape of metal via etching on the performance of the filter are studied. Finally, the filter is fabricated and its performance is tested under MEMS technology.

关键词： Band-pass filters Micromechanical devices Microstrip filters Shape Resonator filters Metals Conductivity

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Interactive reflection simulation via physical shading model and fast environment mapping

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ICIC Express Letters, Part B: Applications 2016年第2期7卷 351-356页

作者： Xu, Jie Fu, Jinhua School of Software Zhengzhou University of Light Industry China School of Computer and Communication Engineering Zhengzhou University of Light Industry No. 5 Dongfeng Road Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute Zhengzhou China

In order to enhance realistic reflection effects on some objects and realize faster rendering to meet interactive requirements, we present a novel method to produce interactive reflection simulation using physical shading model and fast environment mapping. The physical shading model could efficiently express ambient lighting, specular lighting and diffuse lighting. Also the model is based on the microfacet distribution function, which can describe physics-based characteristics. Fast environment mapping is realized by Spherical Harmonics (SH) transformation, and the rendering equation can be rewritten as a simple dot product of SH coefficients, which fits for GPU (Graphics Processing Unit) computation. The results show that we can realize faster rendering and acquire realistic reflection effects such as glossy and mapping scene on the surface. © 2016 ICIC International.

关键词： Lighting

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