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

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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WSN Location Algorithm Based on Simulated Annealing Co-linearity DV-Hop

WSN Location Algorithm Based on Simulated Annealing Co-linea...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Weiguo Sun Linghua Zhang College of Telecommunication & Information Engineering Nanjing University of Posts and Telecommunications Nanjing Jiangsu China Jiangsu Provincial Engineering Research Center of Telecommunications and Network Technology Nanjing University of Posts and Telecommunications Nanjing Jiangsu China

Aiming at high error ration in the uneven distribution of network topology technology and the sensitive react to the distance error ration using the least squares method in the algorithm, an optimized positioning algorithm based on simulated annealing is put forward. The optimized positioning algorithm is improved firstly by the method of setting the threshold to realize the selective topological relation and avoid the ranging error. Secondly, the least squares method is replaced by the simulated annealing algorithm. The experiment result is proved that the optimized positioning algorithm has lower error ration than the traditional algorithm.

关键词： Simulated annealing Distance measurement Error analysis Linear programming Coordinate measuring machines Communications technology

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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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Computing Simulation of Interactions Between α + β Protein and Janus Nanoparticle 18th

Computing Simulation of Interactions Between α + β Protein...

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18th International Conference on Computational Science, ICCS 2018

作者： Guo, Xinlu Zhao, Xiaofeng Fang, Shuguang Bian, Yunqiang Kang, Wenbin Jiangsu Research and Development Center of Application Engineering Technology of Wireless Sensor System and School of Internet of Things Wuxi Vocational Institute of Commerce Wuxi214153 China Taihu University of Wuxi Wuxi214000 China Shandong Provincial Key Laboratory of Biophysics Institute of Biophysics Dezhou University Dezhou253023 China Bio-X Research Center and Department of Mathmatics and Physics Hubei University of Medicine Shiyan442000 China

ISBN: (纸本)9783319937120

Janus nanoparticles have surfaces with two or more distinct physical properties, allowing different types of chemical properties to occur on the same particle and thus making possible many unique applications. It is necessary to investigate the interaction between proteins and Janus nanoparticles (NPs), which are two typical building blocks for making bio-nano-objects. Here we computed the phase diagrams for an α + β protein(GB1) and Janus NP using coarse-grained model and molecular dynamics simulations, and studied how the secondary structures of proteins, the binding interface and kinetics are affected by the nearby NP. Two phases were identified for the system. In the folded phase, the formation of β-sheets are always enhanced by the presence of NPs, while the formation of α-helices are not sensitive to NPs. The underlying mechanism of the phenomenon was attributed to the geometry and flexibility of the β-sheets. The knowledge gained in this study is useful for understanding the interactions between proteins and Janus NP which may facilitate designing new bio-nanomaterials or devices. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Nanoparticles

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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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A Novel Multi-level Wavelet Image Coding Algorithm by Full Sub-band Compressed Sensing

A Novel Multi-level Wavelet Image Coding Algorithm by Full S...

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

作者： Weiping Qiu Dong Hu Yongyang He 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

The performance of wavelet transform based image compressed sensing coding algorithms severely rely on the level of wavelet transform. To this end, this paper investigated the combination of mutil-level wavelet full sub-band in compressed sensing framework. Firstly, we constructed a full sub-band coefficient sparse vector of mutil-level discrete wavelet transform. Secondly, we designed a weight matrix to improve measurement matrix. Finally, sparsity vector was processed by compressed sensing to get the measured value. Compared with the exiting algorithms, the experimental results of the proposed algorithm show that the PSNRs of reconstructed images is improve up to 1～2 dB under the same objective quality.

关键词： image coding wavelet transform compressed sensing full sub-band coefficient sparsity vector

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A Fast Quality Scalable Video Coding Method Based on Compressed Sensing

A Fast Quality Scalable Video Coding Method Based on Compres...

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

作者： Min Sun Dong Hu Jianyu Ding 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

This paper presents a fast quality scalable video coding method based on compressed sensing(CS). The proposed method obtained the coding scheme of the enhancement MJU by using the interlayer and spatial correlation and kept the base layer's coding scheme unchanged. And the part in the enhancement layer which needed to be fine quantified was combined with the compressed sensing theory selectively which based on the sparsity of the signal and the complexity of the reconstruction. In order to satisfy the coding syntax structure of the reference software, the measurement value got by compressed sensing was complemented by 0 s and the flag bit was set to distinguish the special sub-blocks coded by CS. Experimental results show that the proposed algorithm can effectively improve the efficiency of scalable video coding and reduce the computational complexity.

关键词： video coding compressed sensing quality scalable video coding

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Enhancement of DV-Hop by Weighted Hop Distance

Enhancement of DV-Hop by Weighted Hop Distance

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Qihong Tao Linghua Zhang Department of Information Network NJUPT Jiangsu Provincial Engineering Research Center of Telecommunications and Network Technology

ISBN: (纸本)9781509001668

Positioning scheme is one of the fundamental issue in wireless sensor network(WSN),especially for the situation needing the accurate position of sensed *** approaches have been performed to identify the inaccurate node ***-Hop(Distance Vector-hop) is a well-known localization algorithm [1,2].In wireless sensor network with conventional algorithm for DV-Hop localization,ordinary nodes use average hop distance value estimated by the nearest beacon node to locate ***,hop distance value estimated by only one beacon node may not reflect the true situation *** the purpose of further improving the position accuracy,in view of the analysis of DV-HOP algorithm,this paper presents a strengthen method of weighted distance: ordinary nodes reference estimated average hop distance node and operate on weighted numbers of hop from multiple beacon nodes to make network average hop distance estimate more accurate and improve positioning *** from simulation show that the proposed WDV-Hop algorithm has been greatly improved by 10%-15% in terms of the accuracy of positioning compared to the conventional DV-Hop.

关键词： DV-Hop Weighted hop distance positioning accuracy Matlab simulation

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A New BDI Forecasting Model based on Support Vector Machine

A New BDI Forecasting Model based on Support Vector Machine

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2016 IEEE Information technology,networking,Electronic and Automation Control Conference(ITNEC 2016)

作者： Jianmin Bao Lin Pan Yuanfa Xie Key Lab of Broadband Wireless Communication and Sensor Network Technology (Jiangsu Provincial Engineering Research Center of Telecommunications and Network Technology) Ministry of Education Baer Institute of Excellence Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781467391955

BDI(Dry Baltic Index) is a barometer of the shipping market, which is very important and difficult to predict precisely. The traditional forecasting method is not enough to predict precisely for it with higher complexity. The support vector machine has strong nonlinear function approximation and strong generalization ability. In this paper, a new BDI forecasting model based on support vector machine combined with CFS(Correlation-based Feature Selection) is presented and we first introduce the macroeconomic fundamental indicators of BDI. Firstly, we studied and researched the main macroeconomic fundamental indicators of BDI, investigate the inherent law and external influence of freight index fluctuation, and then put forward the BDI forecasting model based on support vector machine and demonstrate the construction principle of the forecasting model. Finally, in order to evaluate the predictive power of support vector machines, we compare their performance with the neural network model. The results show that the SVM model has a better performance in both the trend and the forecast precision, which can provide a powerful tool for the shipping market operators and investors to grasp the market trend and avoid the price risk.

关键词： BDI support vector machine feature selection CFS modeling

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