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Synchronization of Switched Neural Networks with Communication Delays via the Event-Triggered Control

IEEE Transactions on Neural Networks and Learning Systems 2017年第10期28卷 2334-2343页

作者： Wen, Shiping Zeng, Zhigang Chen, Michael Z. Q. Huang, Tingwen Guangdong Province Key Lab of Digital Manufacturing Equipment Key Laboratory of Image Processing Intelligent Control of Education Ministry of China School of Automation Guangdong HUST Industrial Technology Research Institute Huazhong University of Science Technology Wuhan Hubei430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong Texas A M University at Qatar DohaTX5825 United States

This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results. © 2012 IEEE.

关键词： Synchronization

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Visual perception preserving decolorization method

International Journal of Signal Processing, Image Processing...

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International Journal of Signal processing, image processing and Pattern Recognition 2016年第7期9卷 65-78页

作者： Chen, Jie Li, Xin Zhu, Xiuchang Wang, Jin Key Lab of Image Processing and Image Communication of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210003 China College of Information Engineering Yangzhou University Yangzhou225009 China

This paper presents a decolorization method using gradient and saliency as the maintained features in the conversion to preserve the local and global visual perception. First, we construct a linear parametric mapping function of RGB color channels. Then, we calculate the feature value of each pixel in the color image and the parameterized grayscale image, the feature value integrates the pixel gradient and region saliency. Finally, we search for the parameters which can get the minimum of the total differences between the feature values of color and grayscale images, and substitute into the linear parametric function to get the decolorization result. To enhance the efficiency of getting the best parameters, we properly relax the strict computation formulas of the gradient and saliency to construct a linear least square problem, and obtain the optimal parameters by solving optimization. Experimental results show that our method using the discrete searching strategy can maintain the contrasts meanwhile avoid the excessive enlargement of the contrasts during the color-to-gray conversion, this property guarantees the preserving of the visual perception. Our method using the linear least square strategy can reduce the computation time and frequently get the similar results with our discrete searching method. © 2016 SERSC.

关键词： Pixels

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Analyzing Occupational Stress Based On Inner Composition Alignment Algorithm

Analyzing Occupational Stress Based On Inner Composition Ali...

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International Congress on image and Signal processing, BioMedical Engineering and Informatics

作者： Zhen Shi Shitong Wang Jun Wang Jiafei Dai Fengzhen Hou Jin Li Image Processing and Image Communications Key Lab Nanjing Univ. of Posts & Telecomm China Nanjing Brain Hospital China School of Science China Pharmaceutical University Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China

ISBN: (纸本)9781509037117

To distinguish different level of occupational stress of students and nurses, this paper applied inner composition alignment (IOTA) algorithm to analyse differences between the students' Electroencephalogram (EEG) and the nurses' EEG and to explore the influence of occupational stress on EEG. First, we selected nine cases of students EEG data and the same quantity of nurses EEG data from General Hospital of Nanjing Military Region, then, constructed two different brain networks by utilizing IOTA method and analyzed the networks characteristics which include IOTA coefficient, Clustering coefficient and Average degree. In order to verify the accuracy of the results, we used SPSS to analyse the significant difference hypothesis testing. The IOTA coefficients are inconsistent between students' EEG and nurses' EEG. Compared with the students, nurses' brain network is more complex. The level of occupational stress of nurses is higher than students. The IOTA algorithm can significantly distinguish occupational stress between students and nurses, and all results demonstrated the validity of the algorithm.

关键词： Brain Network Occupational Stress EEG IOTA Complex Network

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Compressive Network Coding for Approximate Sensor Data Gathering

Compressive Network Coding for Approximate Sensor Data Gathe...

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54th Annual IEEE Global Telecommunications Conference (GLOBECOM)

作者： Luo, Chong Sun, Jun Wu, Feng Shanghai Jiao Tong Univ Inst Image Commun & Informat Proc Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Digital Media Processing & Commu Shanghai 200030 Peoples R China Microsoft Res Beijing Peoples R China

ISBN: (纸本)9781424492688

This paper presents a joint source and network coding scheme, called compressive network coding (CNC), for approximate data gathering in wireless sensor networks. Injecting the concept of compressive sensing into network coding avoids the all-or-nothing effect of network decoding, allowing CNC to achieve graceful degradation in data precisions when the energy budget is not sufficient. Based on the observation that sensor links are highly unreliable, we formulate the energy constrained multi-source, multi-hop, and multi-path transmission as a network utility maximization (NUM) problem. A practical distributed algorithm is developed to achieve the optimal utility. We carry out simulations over real sensor data. Results show that CNC consistently outperforms conventional network coding, achieving an average gain over 3.7 dB in data reconstruction PSNR. Furthermore, CNC achieves over 10 dB gain for five sensors (out of 53) whose readings contain abrupt changes.

关键词： CNC Computer numerical control NUM problem Network coding Peer to peer computing Relays Temperature measurement Temperature sensors Wireless sensor networks approximate sensor data gathering approximation theory compressive network coding data precisions data reconstruction PSNR distributed algorithm distributed algorithms energy budget gain 10 dB joint source coding scheme multihop transmission multipath transmission network coding network utility maximization problem optimisation sensor links wireless sensor networks wireless sensor networks

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A QoE Friendly Rate Adaptation Method for DASH

A QoE Friendly Rate Adaptation Method for DASH

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Cao, Yuming You, Xiaoquan Wang, Jia Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai 200030 Peoples R China Chengdu Tech Univ Dept Commun Engn Chengdu Peoples R China Shanghai Key Lab Multi Media processing&Transmiss Shanghai Peoples R China

ISBN: (纸本)9781479916542

MPEG-DASH introduced open standards into HTTP streaming, which was a huge step forward. The scope of the MPEG-DASH standard was limited to the Media Presentation Description (MPD) format of the manifest files as well as the segmentation standards for the server, but did not include the client application as part of their standards. Particularly a good representation switching algorithm at DASH client should adapt to time varying bandwidth with better QoE (Quality of Experience). In this paper, we propose a QoE friendly rate adaptation method which can achieve less switching times and more gradual quality change. To achieve these purposes, we propose a fixed-interval buffer model, which keeps the representation level unchanged whenever the media buffer size is within the preset interval. We also design a step-wise switch-up method to prevent buffer overflow and a switch-down method based on intermediate quality level to prevent buffer underflow. A quick boot algorithm is introduced to fully improve the performance of the proposed scheme. Experimental results show that the proposed algorithm can achieve more satisfactory results compared with existing ones.

关键词： Congestion control Networking and QoS Rate adaptation Dynamic Adaptive Streaming over HTTP DASH Quality of Experience (QoE)

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A Deep Learning Approach to Generating Flattened CBCT Volume Across Dental Arch From 2D Panoramic X-ray for 3D Oral Cavity Reconstruction

A Deep Learning Approach to Generating Flattened CBCT Volume...

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International Technical Conference o Circuits/Systems, Computers and communications (ITC-CSCC)

作者： Anusree P S Wonsang You Department of Information and Communications Engineering Artificial Intelligence and Image Processing Laboratory (AIIP Lab) Sun Moon University Asan Chungnam South Korea

Panoramic radiography is the most popular imaging modality in dentistry. On the other hand, cone-beam computed tomography (CBCT) is usually utilized to review three-dimensional dental structures in high resolution, however, it is less utilized in dental clinics due to its high cost and radiation exposure. An automated method for reconstructing 3D oral cavity structure from panoramic X-ray will be beneficial for patients to reduce expenses for dental care without radiation exposure. As an initial step to the 3D shape reconstruction of the oral cavity, a flattened CBCT 3D volume across the dental arch can be generated from a 2D panoramic X-ray image using a deep learning method. Existing deep learning models are almost based on the encoder-decoder architecture, but their performance has not been well elucidated. In this paper, we evaluated the applicability of attention U-net for reconstructing the flattened CBCT 3D volume from a panoramic X-ray. In our quantitative and qualitative evaluations, the attention U-net outperformed the auto-encoder and led to enhanced perceptual quality.

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Study of Double SMO Algorithm Based on Attributes Reduction

Study of Double SMO Algorithm Based on Attributes Reduction

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6th International Symposium on Neural Networks

作者： Chen, Chen Hong, Liu Song, Haigang Chen, Xueguang Hou, TieMin Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Peoples R China Bas Res Serv Ministry Sci Beijing 100862 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Processing & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9783642015090

To solve the classification problem in data mining, this paper proposes double SMO algorithm based on attributes reduction. Firstly attributes reduction deletes irrelevant attributes (or dimensions) to reduce data amount, consequently the total calculation is reduced, the training speed is fastened and Classification mode is easy to understand. Secondly applying SMO algorithm on the sampling dataset to get the approximate separating hyperplane, and then we obtain all the Support vectors of original dataset. Finally again use SMO algorithm on the support vectors to get the final separating hyperplane. It is shown in the experiments that the algorithm reduces the memory space, effectively avoids the noise points' effect on the final separating hyperplane and the precision of the algorithm is better than Decision Tree, Bayesian and Neural Network.

关键词： Data mining Support vector machine Attribute reduction Training algorithm

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Denoising Diffusion Tensor images with Shearlet

Denoising Diffusion Tensor Images with Shearlet

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2012 IEEE 11th International Conference on Signal processing(ICSP 2012)

作者： Xiangfen Zhang Bao-Liang Lu Yan Ma Xiaozhong Xu Fangfang Wei Wenjie Xu Institute of Intelligent Computing & Image Processing College of Mechanical and Electronic EngineeringShanghai Normal University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and EngineeringShanghai Jiaotong University MOE-Microsoft Key Lab for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University

ISBN: (纸本)9781467321969

Diffusion tensor imaging （DTI） is known to be the best non-invasive imaging modality in providing anatomical information as white-matter fiber bundles. However, the Gaussian noise introduced into the diffusion tensor images can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Gaussian noise, many denoising methods have been presented. In this paper, a shearlet based denosing strategy is introduced. To evaluate the efficiency of the proposed shearlet based denoising method in accounting for the Gaussian noise introduced into the images, the peak to peak signal-to-noise ratio （PSNR）, signal-to-mean squared error ratio （SMSE） and edge keeping index （Beta） metrics are adopted. The experiment results acquired from both the synthetic and real data indicate the good performance of our proposed filter.

关键词： diffusion tensor imaging shearlet transform wavelet transform PSNR SMSE denoising

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The statistic modeling of eye movement viewing S3D images 14th

The statistic modeling of eye movement viewing S3D images

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14th International Forum of Digital TV and Wireless Multimedia Communication, IFTC 2017

作者： Zhang, Chi Zhou, Jun Zhu, Shoucheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai200240 China Shanghai Yanan High School Shanghai200336 China

ISBN: (纸本)9789811081071

Nowadays, more and more families are willing to buy 3D TV to improve their watching experience. Stereo perception produced by watching 3D images or videos brings strong immersive watching experience to users. However, accumulated vision fatigue confuses users a lot after watching 3D TV for a long time. When watching 3D images, controlled by past recognition experience and visual attention mechanism, gaze point of two eyes is changing among different objects which have different depth of field. The eye movement in this changing process is called vergence. Vergence can be defined as movement of our eyes in opposite directions to locate the area of interest on the fovea and accommodation as alteration of the lens to obtain and maintain the area of interest focused on the fovea. So the more frequently the vergence process occurs, the more uncomfortable we feel. We expect to obtain several eye movement patterns, which can be considered as some typical visual attention patterns, by building a top-down recognition and visual attention model and then applying some clustering methods to find them. So we use an eye tracker to record eye movement data and then model it as a bayesian network model. The generative model is based on beta process and we build an Autoregression-HMM model to describe the relationship between latent eye movement patterns and eye movement data. To uncover parameters which represent different eye movement patterns in this model, we use MCMC method to calculate them with iterative computations. In this work, some different latent patterns existed in the sequential eye movement data can be revealed. After analyzing these patterns, we are able to find out some similarities and differences of visual attention models between different people watching the same image or between different images viewed by the same one. These conclusions can help to improve quality of 3D image thus lessening the users’ vision fatigue when watching 3D TV. This work also will con

关键词： Eye movements

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Speech Separation Based on Robust Independent Component Analysis

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Chinese Journal of Biomedical Engineering(English Edition) 2013年第4期22卷 169-177页

作者： YAO Wen-po WU Min LIU Tie-bing WANG Jun SHEN Qian Nanjing General Hospital of Nanjing Military Command Image Processing and Image Communications Key Laboratory Nanjing University of Posts & Telecommunications

In this paper, we applied RobustICA to speech separation and made a comprehensive comparison to FastICA according to the separation results. Through a series of speech signal separation test, RobustICA reduced the separation time consumed by FastICA with higher stability, and speeches separated by RobustICA were proved to having lower separation errors. In the 14 groups of speech separation tests, separation time consumed by RobustICA was 3.185 s less than FastICA by nearly 68%. Separation errors of FastICA had a float between 0.004 and 0.02, while the errors of RobustlCA remained around 0.003. Furthermore, compared to FastICA, RobustlCA showed better separation robustness. Experimental results showed that RohustICA was successful to apply to the speech signal separation, and showed superiority to FastlCA in speech separation.

关键词： RobustlCA speech separation FastICA kurtosis optimal step size

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