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IEEE International Workshop on VLSI Design and Video Technology, IWVDVT

作者： Cheng-Yi Xiong Jin-Wen Tian Jian Liu Institute of Pattem Recognition & Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China College of Electronic Information Engineering South Central University of Nationalities Wuhan China

ISBN: (纸本)0780390059

An efficient generic architecture for two-dimensional discrete wavelet transform (2-D DWT) with line-based method is proposed with using lifting scheme, in which the parallelism of four subbands transform in lifting-based 2-D DWT is exploited. The proposed architecture is designed to generate 4 subbands coefficients concurrently per clock cycle that :an perform a 1-level decomposition of a N/spl times/N image in approximately N/sup 2//4 working clock cycles, which has faster throughput rate but requires less hardware cost compared 10 the designs reported in previous literature.

关键词： Discrete wavelet transforms Parallel architectures Two dimensional displays Clocks image coding Throughput Hardware Costs Low pass filters Matrix converters

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A fast VLSI architecture for two-dimensional discrete wavelet transform based on lifting scheme [image compression applications]

A fast VLSI architecture for two-dimensional discrete wavele...

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International Conference on Solid-State and Integrated Circuit Technology

作者： Chengyi Xiong Jinwen Tian Jian Liu College of Electronic Information Engineering South-Center University of Science and Technology Wuhan China Institute of Pattern Recognition & Artification Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)078038511X

This paper proposes a novel fast architecture for a 2D discrete wavelet transform by using a lifting scheme, Parallel and embedded decimation techniques are employed to optimize the architecture, which is mainly composed of two horizontal filter modules and one vertical filter module, working in parallel and pipeline fashion with 100% hardware utilization. The architecture is designed to generate two outputs in one working clock cycle, with every two subbands coefficients alternately. The total time for computing J levels of decomposition for an N/spl times/N image is approximately 2N/sup 2/(1-4/sup -J/)/3 clock cycles. In comparison with the other devices reported in previous literature, the design has many advantages including lower hardware complexity and area and power efficiency. The design is also fast, regular and simple, as well as well suited for VLSI implementation.

关键词： Discrete wavelet transforms Very large scale integration Filters Computer architecture Wavelet transforms Hardware Clocks image coding Matrix converters Pipelines

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Supervised Enhanced Soft Subspace Clustering (SESSC) for TSK Fuzzy Classifiers

arXiv

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arXiv 2020年

作者： Cui, Yuqi Wang, Huidong Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Management Science and Engineering Shandong University of Finance and Economics Jinan250014 China

Fuzzy c-means based clustering algorithms are frequently used for Takagi-Sugeno-Kang (TSK) fuzzy classifier antecedent parameter estimation. One rule is initialized from each cluster. However, most of these clustering algorithms are unsupervised, which waste valuable label information in the training data. This paper proposes a supervised enhanced soft subspace clustering (SESSC) algorithm, which considers simultaneously the within-cluster compactness, between-cluster separation, and label information in clustering. It can effectively deal with high-dimensional data, be used as a classifier alone, or be integrated into a TSK fuzzy classifier to further improve its performance. Experiments on nine UCI datasets from various application domains demonstrated that SESSC based initialization outperformed other clustering approaches, especially when the number of rules is small. Copyright © 2020, The Authors. All rights reserved.

关键词： Fuzzy clustering

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image denoising algorithm based on noise detection

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Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence 2008年第3期21卷 298-302页

作者： Jin, Liang-Hai Li, De-Hua State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong Univ. of Sci. and Technol. Wuhan 430074 China

An efficient image denoising algorithm is introduced. Firstly, image pixels are classified into noisy pixels and noise-free pixels by four directional operators. Then an adaptive weighted median filter is designed to remove and restore the detected noisy pixels and keep the noise-free ones unchanged. Experimental results indicate that the proposed algorithm preserves image details well while removing impulsive noise efficiently, and its filtering performance is significantly superior to the classical median filter and some other typical and recently developed improved median filters.

关键词： Median filters

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An improved reference point sampling method on Pareto optimal front

An improved reference point sampling method on Pareto optima...

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Congress on Evolutionary Computation

作者： Cheng He Linqiang Pan Hang Xu Ye Tian Xingyi Zhang Laboratory of Image Information Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China Huazhong University of Science and Technology Wuhan Hubei CN Department of Computer Science Xiamen University Xiamen Fujian China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui China

ISBN: (纸本)9781509006243

In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.

关键词： Sampling methods Shape Convergence Evolutionary computation Optimization Euclidean distance

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Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016

arXiv

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arXiv 2020年

作者： Wu, Dongrui Xu, Yifan Lu, Bao-Liang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications - motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks - are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain computer interface

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A support value transform-based multi focus image fusion method

A support value transform-based multi focus image fusion met...

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International Conference on Signal processing Proceedings (ICSP)

作者： Sheng Zheng Jian Liu Guangxi Zhu Jinwen Tian Electronic & Information Engineering Department Huazhong University of Science and Technology Wuhan China College of Electrical Engineering & Information Technology China Three Gorges University Yichang China State Edu Commission Key Laboratory for Image Processing & Intelligent Control HUST Wuhan China Chinese Key laboratory of optic & electronics HUST Wuhan China

The image fusion is an important approach to produce a single complete image which preserves all relevant information from different sensors. In this paper, we proposed a support value transform-based multi focus image fusion method, where the fused saliency features are represented by support values. Based on the mapped least squares support vector machine, the support value transform is developed as a multi-scale analysis tool. The fusing results on the multi focus images demonstrate that the proposed image fusion method is effective and efficient

关键词： Focusing image fusion Support vector machines Discrete wavelet transforms Optical sensors image sensors Sensor fusion Support vector machine classification image processing Least squares methods

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A Geometric Approach to Second-Order Consensus of Heterogeneous Networked Systems

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IEEE Transactions on Cybernetics 2018年 2018 Jan 9页

作者： Su, Housheng Ye, Yanyan Chen, Xia He, Haibo School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China. China China Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI 02881 USA. United States

This paper investigates second-order consensus of networked systems with heterogeneous intrinsic nonlinear dynamics via a geometrical method, in which the nonlinear dynamics are governed by both velocity and position. First, two necessary conditions are deduced for the existence of consensus solutions by analyzing the inherent nonlinear dynamics of isolated nodes. Then a closed invariant set is constructed via geometrical methods. The nonempty of the set implies the existence of consensus solution. Assuming that the primary and the second dimension about the above invariant set are the same linear subspace, the system can be divided into two simple subsystems through a linear transformation. In addition, some sufficient conditions are proposed for reaching the global consensus based on matrix theory and Lyapunov method. Finally, numerical simulation results are provided to illustrate the validity of theoretical analysis. IEEE

关键词： Multi agent systems

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Performance of combined artificial neural networks for forecasting landslide displacement

Performance of combined artificial neural networks for forec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Cheng Lian Zhigang Zeng Wei Yao Huiming Tang School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan Hubei China School of Computer Science South-Central University for Nationalities Wuhan China Faculty of Engineering China University of Geo-sciences Wuhan China

An efficient and accurate method for landslide displacement prediction is very important to reduce the casualties and property losses caused by this type of natural hazard. In recent years, many kinds of artificial neural networks (ANNs) have been widely applied to landslide displacement prediction. But we can't know which type of ANN is the best until we have calculated the prediction error. An improper choice of ANN may result in bad prediction results. In this paper, we use a neural networks combination prediction method based on the discounted MSFE (mean squared forecast error) to reduce the risk of selecting the types of ANNs. Four popular ANNs, radial basis function neural network (RBFNN), support vector regression (SVR), least squares support vector machine (LSSVM) and extreme learning machine (ELM), are selected as candidate neural networks. The performance of our model is verified through two case studies in Baishuihe landslide and Bazimen landslide. Experimental results reveal that the combining neural networks can improve the generalization abilities of ANNs.

关键词： Terrain factors Artificial neural networks Monitoring Predictive models Kernel Training

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Attractor flow analysis for recurrent neural network with back-to-back memristors

Attractor flow analysis for recurrent neural network with ba...

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IEEE Symposium on Foundations of Computational Intelligence, FOCI

作者： Gang Bao Zhigang Zeng School of Automation Huazhong University of Science and Technology Wuhan Hubei China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Wuhan China college of Electrical Engineering and New Energy China Three Gorges University Yichang China

ISBN: (纸本)9781479944903

Memristor is a nonlinear resistor with the character of memory and is proved to be suitable for simulating synapse of neuron. This paper introduces two memristors in series with the same polarity (back-to-back) as simulator for neuron's synapse and presents the model of recurrent neural networks with such back-to-back memristors. By analysis techniques and fixed point theory, some sufficient conditions are obtained for recurrent neural network having single attractor flow and multiple attractors flow. At last, simulation with numeric examples is presented to illustrate our results.

关键词： Memristors Stability analysis Recurrent neural networks Resistance Numerical models

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