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Correction to: Underwater image enhancement using an edge-preserving filtering Retinex algorithm

Multimedia Tools and Applications 2020年第25期79卷 17279-17279页

作者： Peixian Zhuang Xinghao Ding Jiangsu Key Laboratory of Meteorological Observation and Information Processing Jiangsu Technology and Engineering Center of Meteorological Sensor Network School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University Xiamen China

In the original publication, Equations were incorrectly presented. The original article has been corrected.

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Scale Adaptive Block Target Tracking Based on Multi-layer Convolution Features and Kernel Correlation Filter

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Ting Zhang Dong Hu Jing Zhang Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy.

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Joint DBF and SAO Parallel Filtering Based on Multithread Load Balancing

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Hao Ma Dong Hu Yi Li Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China

This paper presents a joint parallel loop filtering algorithm based on multi-thread load balancing in HEVC decoding, which implements the parallel processing of deblocking filtering (DBF) and sample adaptive compensation (SAO). Because of the diversity of video, the texture of different regions in an image is also different, which leads to various CTU partition methods. Therefore, the number of the boundary to be filtered is greatly different, resulting the computation load among multiple threads unbalanced in parallel processing. To solve this problem, an area division scheme is proposed, which divides the image into multiple areas, and the number of boundaries to be filtered in each area is similar. Then, the mapping relationship table is used to allocate these areas to multiple threads for parallel processing, so as to achieve the load balancing among the filtering threads. Finally, the cache technology is used to combine DBF and SAO to reduce the delay between them and improve the overall parallelism of the loop filter. Experimental results show that the performance of the proposed load balancing joint filtering algorithm is 8.15% higher than the previous scheme.

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A Siamese network Tracking Algorithm Based on Hierarchical Attention Mechanism

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Hu Zhang Dong Hu Yingcan Qiu Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Nanjing University of Posts and Telecommunications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing 210003 China

A siamese network tracking algorithm based on hierarchical attention mechanism is proposed in this paper. In order to obtain more robust target tracking results, different layer features are fused effectively. In the process of extracting features, attention mechanism is used to recalibrate the feature map, and AdaBoost algorithm is used to weight the target feature map, which improves the reliability of the response map. Besides, the Inception module is also introduced which not only increases the width of the network and the adaptability of the siamese network to the scale, but also reduces the parameters and improves the speed of network training. Experimental results show that this method can effectively solve the impact of background clutter and improve the accuracy of tracking.

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Computation Offloading and Resource Allocation for MEC in C-RAN: A Deep Reinforcement Learning Approach 19

Computation Offloading and Resource Allocation for MEC in C-...

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19th IEEE International Conference on Communication technology, ICCT 2019

作者： Jin, Xiaoyan Zhang, Jun Sun, Xinghua Zhang, Ping Cai, Shu Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Key Laboratory of Computer Application Technology Anhui Polytechnic University Wuhu241000 China

ISBN: (纸本)9781728105352

Mobile edge computing (MEC) technology has become a promising example for cloud radio access networks (CRAN) to provide close-range services, thereby reducing service delays and saving energy consumption. In this paper, we consider a multi-user MEC system and solve the problem of the computation offloading strategies and resource allocation policies. We set the total cost of delays and energy consumption as our optimization goal. However, getting an optimal strategy in a dynamic environment is challenging. Reinforcement learning (RL) aims at long-term cumulative rewards, which are essential for time-varying dynamic systems. Therefore, we propose an optimization framework based on deep RL to solve these problems. The deep neural network (DNN) is used to estimate the value function of the critics, thereby reducing the state space complexity of the optimization target. The actor part uses another DNN to represent a parametritis stochastic strategy and improve the strategy with the help of critics. Compared with other schemes, the simulation results show that the scheme significantly reduces the total cost. © 2019 IEEE.

关键词： Reinforcement learning

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Load Feedback-Based Resource Scheduling and Dynamic Migration-Based Data Locality for Virtual Hadoop Clusters in OpenStack-Based Clouds

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Tsinghua Science and technology 2017年第2期22卷 149-159页

作者： Dan Tao Zhaowen Lin Bingxu Wang School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing 210003China Network and Information Center Institute of Network TechnologyScience and Technology on Information Transmission and Dissemination in Communication Networks LaboratoryNational Engineering Laboratory for Mobile Network SecurityBeijing University of Posts and TelecommunicationsBeijing 100876China

With cloud computing technology becoming more mature, it is essential to combine the big data processing tool Hadoop with the Infrastructure as a Service（Iaa S） cloud platform. In this study, we first propose a new Dynamic Hadoop Cluster on Iaa S（DHCI） architecture, which includes four key modules： monitoring,scheduling, Virtual Machine（VM） management, and VM migration modules. The load of both physical hosts and VMs is collected by the monitoring module and can be used to design resource scheduling and data locality solutions. Second, we present a simple load feedback-based resource scheduling scheme. The resource allocation can be avoided on overburdened physical hosts or the strong scalability of virtual cluster can be achieved by fluctuating the number of VMs. To improve the flexibility, we adopt the separated deployment of the computation and storage VMs in the DHCI architecture, which negatively impacts the data locality. Third, we reuse the method of VM migration and propose a dynamic migration-based data locality scheme using parallel computing entropy. We migrate the computation nodes to different host（s） or rack（s） where the corresponding storage nodes are deployed to satisfy the requirement of data locality. We evaluate our solutions in a realistic scenario based on Open *** experimental results demonstrate the effectiveness of our solutions that contribute to balance the workload and performance improvement, even under heavy-loaded cloud system conditions.

关键词： Hadoop resource scheduling data locality Infrastructure as a Service（Iaas） OpenStack

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Study on the Segmentation Method of Multi-Phase CT Liver Tumor Based on Dual Channel U-Nets

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Changchang Xu Dong Hu YaoDong Zhang Yuwei Pang Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China The first clinical medical college of Nanjing Medical University Nanjing University of Posts and Telecommunications Nanjing 210003 China

As a common malignant tumor disease, hepatocellular carcinoma is the most common cancers in the world. The incidence of hepatocellular carcinoma in China is higher than that in the world. Therefore, it is very important for doctors to separate liver and tumor from CT images by means of computer-aided diagnosis and treatment. In this paper, a multiscale DC-CUNets network liver tumor segmentation method is proposed to enhance the fusion of multi-phase image features in CT, the scale of liver tumors, and the optimization of network training process. (1) A multistage CT liver tumor segmentation method based on two-channel cascaded U-Nets (DC-CUNets) is proposed. The liver was segmented using the first-order U-Net, and then the segmented area of interest of the liver was input into the second-order U-Net network to segment liver tumors. We designed two-channel U-Nets to learn the image characteristics of CT images in arterial and venous phases respectively, and to achieve two-channel feature fusion through feature cascade to improve the overall accuracy of liver tumor segmentation.(2) A multistage CT liver tumor segmentation method based on multiscale DC-CUNets was proposed. For the scale problem of liver tumors, we designed a two-layer multiscale void convolution module to obtain image features at different scales for large, medium and small tumors, and fuse the multiscale features at the output of the module. We have replaced the convolution layer of the fourth module in the second-order two-channel liver tumor segmentation U-Nets by the two-layer multiscale cavity convolution module to implement multiscale DC-CUNets.

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Outsourced data modification algorithm with assistance of multi-assistants in cloud computing

Outsourced data modification algorithm with assistance of mu...

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作者： Shen, Jian Shen, Jun Li, Xiong Wei, Fushan Li, Jiguo Department of Jiangsu Engineering Center of Network Monitoring Jiangsu210044 China Technology and Engineering Center of Meteorological Sensor Network Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing Jiangsu210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Hunan University of Science and Technology Hunan410082 China PLA Information Engineering University Henan450002 China Hohai University Nanjing210098 China

The rapid development of cloud storage has caused a wave of research craze. To improve cloud user experience, a large number of schemes are proposed with various practical performances, like long-term correct ensurence and dynamic support. In most works, however, authors seem to completely ignore that data owner alone could not have enough energy to discover and correct all inappropriate data outsourced. Others considered it and gave more than one user both read and write permissions, leading to chaotic management of multiusers. In this paper, we propose a novel algorithm, where data owner and authenticated assistants form a team to support data dynamics. Assistants are in charge of detecting problems of data and discussing a corresponding modification suggestion, while data owner is responsible for the implementation of the modification. Additionally, our algorithm supports identity authentication, malicious assistant revocation, and lazy update. Sufficient numerical analysis validates the performance of our algorithm. Copyright © 2017 Inderscience Enterprises Ltd.

关键词： Cloud storage

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Layered Compression Scheme for Efficient Data Collection of sensory Data

Layered Compression Scheme for Efficient Data Collection of ...

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International Conference on Computer Communication and networks

作者： Siguang Chen Shujun Zhang Bin Xu Jing Hu Vijayakumar Varadarajan Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China Information Center Jiangsu China Tobacco Industry Co. Ltd. Nanjing China School of Computing Science and Engineering VIT University Chennai India

Although the existing compressed sensing (CS) based spatio-temporal data compression schemes can significantly decrease communication consumption for data collection, they ignore the data correlation among different clusters over spatial dimension. Actually, the discovery and utilization of spatial correlation among different clusters can further increase the compression rate (or improve the recovery effect). In this paper, we propose a layered compression scheme for efficient data collection of sensory data (LCS-EDC). In the proposed scheme, first, we design a multi-layer network architecture to support the exploration of spatio-temporal correlations, especially for exploring the spatial correlation among different clusters. And then, we construct the specific projection methods respectively for exploring the temporal correlation in sensory nodes, spatial correlation (intra-cluster) in cluster heads and spatial correlation (inter-cluster) in processing nodes. Meanwhile, the detailed solving method is developed to recover original data and achieve the approximate data collection in sink node. Finally, simulation results indicate that the proposed layered compression scheme has better recovery performance as compared with traditional clustered compression schemes (i.e., achieving efficient data collection with high quality).

关键词： Compressed sensing data correlation multi-layer architecture data collection recovery error

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Intra/Inter-frame Joint WPP Coding Algorithm on Multicore Platform

Intra/Inter-frame Joint WPP Coding Algorithm on Multicore Pl...

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2018 3rd International Conference on Electrical, Automation and Mechanical engineering(EAME 2018)

作者： Chuanwei Yin Dong Hu Tao Gu Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications

Although wavefront parallel processing(WPP) proposed in the HEVC standard and various inter frame WPP algorithms can achieved comparatively high parallelism, their scalability for its parallelism is still very limited due to various dependencies introduced in spatial and temporal prediction in HEVC. In this paper, through pixel correlation analysis, establishment of CTU node model, multi-core resource allocation strategy, we proposed an intra/inter-frame joint WPP coding algorithm for multi-core platform which can significantly improve the parallelism, while achieved good results in bit rate, PSNR, and acceleration ratio. Experiments on standard HD test sequence show that the proposed algorithm can lead to up to 2x, 1.3x speed up compared with the original WPP and IWF parallelism.

关键词： video coding HEVC multicore platform high parallelism WPP

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