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arXiv 2020年

作者： Baurzhanovna, Kondybayeva Almagul National University of Science and Technology MISiS

Subject. This work is dedicated to the questions of the contemporary medical image visualization, the architecture design of the cloud server systems and the using of methods for the .DICOM data representation for the distributed smart healthcare industry ***. In modern medicine and biology, in the pace of research along with the objective need for a constant increase, there is a sharp necessity of the three-dimensional data representation [1]: requiring high-performance methods of the three-dimensional visualization, processing, decomposition, reconstruction and analysis. In many ways, the reason for the sharp growth is associated with the development of the computer technologies (from quantity into quality): the quantitative characteristics of the devices and computers used, the emergence of three-dimensional technologies for processing, visualization and research of data, and, on this basis, the creation of new three-dimensional methods for the human-machine *** methodology. This paper proposes a method for visualization using direct volumetric rendering based on the cubic spline interpolation on a ray emission technology. Modifications of these algorithms, based on a block decomposition, are proposed and still investigated. The server architecture is proposed as a cloud hypervisors server system for the grid processing of the data ***: Data Science for Smart Healthcare, healthcare, internet of things, computer science, interactive visualization, distributed, web-server, cloud, parallel, systems, scientific research, cloud technologies, communications, information, cloud healthcare systems, web-services, web-technologies, information systems, experimental research, DICOM, CT, computed tomography, medical imaging, volume render 2020 MSC: 68-00, 97U99Scope of application. The work aims to investigate the possible contemporary interactive cloud based solutions in the fields of the applied medicine for the smart Healthcare a

关键词： Web services

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Learning Hierarchical Graph for Occluded Pedestrian Detection 20

Learning Hierarchical Graph for Occluded Pedestrian Detectio...

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28th ACM International Conference on Multimedia (MM)

作者： Li, Gang Li, Jian Zhang, Shanshan Yang, Jian Nanjing Univ Sci & Technol Nanjing Peoples R China Tencent Youtu Lab Shenzhen Peoples R China Nanjing Univ Sci & Technol PCA Lab Key Lab Intelligent Percept & Syst High Dimens In Minist Educ Nanjing Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Image & Video Understanding Socia Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781450379885

Although pedestrian detection has made significant progress with the help of deep convolution neural networks, it is still a challenging problem to detect occluded pedestrians since the occluded ones can not provide sufficient information for classification and regression. In this paper, we propose a novel Hierarchical Graph Pedestrian Detector (HGPD), which integrates semantic and spatial relation information to construct two graphs named intra-proposal graph and inter-proposal graph, without relying on extra cues w.r.t visible regions. In order to capture the occlusion patterns and enhance features from visible regions, the intra-proposal graph considers body parts as nodes and assigns corresponding edge weights based on semantic relations between body parts. On the other hand, the inter-proposal graph adopts spatial relations between neighbouring proposals to provide additional proposal-wise context information for each proposal, which alleviates the lack of information caused by occlusion. We conduct extensive experiments on standard benchmarks of CityPersons and Caltech to demonstrate the effectiveness of our method. On CityPersons, our approach outperforms the baseline method by a large margin of 5.24pp on the heavy occlusion set, and surpasses all previous methods;on Caltech, we establish a new state of the art of 3.78% MR. Code is available at https://***/ligang-cs/PedestrianDetection-HGPD.

关键词： computer vision object detection occluded pedestrian detection graph neural networks

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DENOISING CONVOLUTIONAL AUTOENCODER BASED B-MODE ULTRASOUND TONGUE image FEATURE EXTRACTION 44

DENOISING CONVOLUTIONAL AUTOENCODER BASED B-MODE ULTRASOUND ...

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44th IEEE International Conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Li, Bo Xu, Kele Feng, Dawei Mi, Haibo Wang, Huaimin Zhu, Jian Beijing Univ Posts & Telecommun Automat Dept Beijing 100876 Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Natl Univ Def Technol Changsha Hunan Peoples R China Univ Michigan Dept Linguist Ann Arbor MI 48109 USA

ISBN: (纸本)9781479981311

B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the recent success of unsupervised deep learning approach, we explore unsupervised convolutional network architecture for the feature extraction in the ultrasound tongue image, which can be helpful for the clinical linguist and phonetics. By quantitative comparison between different unsupervised feature extraction approaches, the denoising convolutional autoencoder (DCAE)-based method outperforms the other feature extraction methods on the reconstruction task and the 2010 silent speech interface challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the state-of-the-art value of 6.45% using Discrete cosine transform as the feature extractor. Our codes are available at https://***/DeePBluE666/Source-code1.

关键词： B-mode ultrasound tongue imaging unsupervised learning feature extraction convolutional autoencoder denoising convolutional autoencoder

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Nonnegative Constrained Graph Based Canonical Correlation Analysis for Multi-view Feature Learning

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NEURAL processing LETTERS 2019年第2期50卷 1215-1240页

作者： Tan, Huibin Zhang, Xiang Lan, Long Huang, Xuhui Luo, Zhigang Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Inst Quantum Informat State Key Lab High Performance Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Dept Comp Sci Coll Comp Changsha Hunan Peoples R China

Understanding and analyzing multi-view data is a fundamental research topic of feature learning for a wide range of practical applications such as image classification. Canonical correlation analysis (CCA) is a popular unsupervised method of analyzing multi-view data, which captures common subspace of two groups of variable sets by maximizing the correlations between them. However, traditional CCA ignores the underlying geometric structure within dataset, which shows great power in describing data distribution, and thus fails in some tasks such as classification. To handle this limitation, this paper proposes an improved CCA variant of Nonnegative Constrained Graph regularized CCA (NCGCCA). Specifically, we improve CCA to NCGCCA with the following two contributions. Firstly, we develop a nonnegative constrained graph based self-representation to explore the underlying group-wise structure within dataset. Secondly, based on the proposed informative representation, we offer a graph embedding schema to incorporate the underlying structure into CCA. Experiments of image classification on four face datasets including Yale, ORL, UMIST, and YaleB demonstrate the efficacy of NCGCCA compared with existing baseline CCA methods.

关键词： Canonical correlation analysis Face recognition Sparse projection Cross-view correlation

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distributed parallel Super-Block-Based Star Detection and Centroid Calculation

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IEEE SENSORS JOURNAL 2018年第19期18卷 8096-8107页

作者： Wang, Zhen Jiang, Jie Zhang, Guangjun Beijing Univ Aeronaut & Astronaut Sch Instrumentat Sci & Optoelect Engn Minist Educ Key Lab Precis Optomechatron Technol Beijing 100191 Peoples R China

Due to the inadequacies of the speed of star detection and centroid calculation (SDCC), the applications of high-resolution and high-frame-rate image sensors are greatly limited in star trackers, restricting the performance of the star trackers. To resolve this problem, a novel distributed parallel super-block-based SDCC method is proposed in this paper. In contrast to the previous SDCC methods, the proposed method divides the star images into several sub-images and processes these sub-images in parallel. Then, by using a super-block-based method and a distributed shared memory-based method to process the stars in sub-images (sub-stars) and in adjacent sub-images (boundary stars), respectively, the position information of all the stars are detected during the parallel scan of the sub-images. The proposed method exhibits a speed that is up to 4 x M times faster than the previous SDCC methods in processing the star images that are divided into M sub-images. Experimental results demonstrate that the proposed method is correct and effective. It is applicable to the use of most high-performance image sensors and resolves the performance limitation of star trackers to achieve better attitude accuracy and attitude update rate.

关键词： distributed parallel star detection centroid location star tracker distributed shared memory image sensors application

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Forgery Detection For High-Resolution Digital images Using FCM And PBFOAAlgorithm

Forgery Detection For High-Resolution Digital Images Using F...

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International Conference on parallel distributed and Grid Computing (PDGC)

作者： Satinder Jeet Kaur Nidhi Bhatla Sachdeva Engineering College for Girls Gharuan(Mohali) India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

image forgery detection is the area of research in the field of biometric and forensics. Digital pictures are the resource of data. In the present world of technology, image processing software tools have developed to generate and modify digital images from one location to another. With the current technology, it is simple to establish image forgery by addition and subtraction of the components from the pictures that lead to image interfering. Copy-move image forgery is created by copying and pasting the element in a similar image. Hence, copy-move forgery has become an area of research in the image forensic unit. Various methods have been implemented to detect digital image forgery. Some issues still required to resolve like time complexity, fake, and blurred image. In existing research, the block and feature-based approach used to remove a forged area from the image using SIFT and RANSAC algorithm. The forgery dataset of the 80 pictures collected to achieve accuracy of up to 95%. In the research work, the PBFOA method has been implemented to optimize and extract the features using the component analysis method. FCM is used for image segmentation in the input image. PBFOA is based on an optimization process to select valuable features based on the calculation of the fitness function. In this method, two steps are used to re-verify the instance, features (i) Slower and faster condition. BFOA steps are described in detail in this research paper. Initial steps, Spread the feature set in the whole system. In the rapid condition selected and to eliminate the valuable features one at a time, then reproduction phase is implemented with the help of the fitness function to recover the feature values and detect the forgery information in the uploaded image. The simulation setup using MATLAB 2016a version and improve the accuracy rate and image quality parameter. Performance analysis depends on the proposed metrics FAR, FRR, ACC, Precision, Recall, and compared with the exist

关键词： Forgery Feature extraction Digital images Databases image recognition Handheld computers Grid computing

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PSO-PS:Parameter Synchronization with Particle Swarm Optimization for distributed Training of Deep Neural Networks

PSO-PS:Parameter Synchronization with Particle Swarm Optimiz...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qing Ye Yuxuan Han Yanan Sun Jiancheng Lv College of Computer Science Sichuan University Chengdu China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Parameter updating is an important stage in parallelism-based distributed deep learning. Synchronous methods are widely used in distributed training the Deep Neural Networks (DNNs). To reduce the communication and synchronization overhead of synchronous methods, decreasing the synchronization frequency (e.g., every n mini-batches) is a straightforward approach. However, it often suffers from poor convergence. In this paper, we propose a new algorithm of integrating Particle Swarm Optimization (PSO) into the distributed training process of DNNs to automatically compute new parameters. In the proposed algorithm, a computing work is encoded by a particle, the weights of DNNs and the training loss are modeled by the particle attributes. At each synchronization stage, the weights are updated by PSO from the sub weights gathered from all workers, instead of averaging the weights or the gradients. To verify the performance of the proposed algorithm, the experiments are performed on two commonly used image classification benchmarks: MNIST and CIFAR10, and compared with the peer competitors at multiple different synchronization configurations. The experimental results demonstrate the competitiveness of the proposed algorithm.

关键词： Training Synchronization parallel processing Neural networks Computational modeling Mathematical model Data models

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CNN-based Monocular Decentralized SLAM on embedded FPGA

CNN-based Monocular Decentralized SLAM on embedded FPGA

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IEEE International Symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Jincheng Yu Feng Gao Jianfei Cao Chao Yu Zhaoliang Zhang Zhengfeng Huang Yu Wang Huazhong Yang Electronic Engineering Department Tsinghua University Beijing China School of Microelectronics Hefei University of Technology Hefei China

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Decentralized visual simultaneous localization and mapping (DSLAM) can share locations and environmental information between robots, which is an essential task for many multi-robot applications. The visual odometry (VO) is a basic component to estimate the 6-DoF absolute pose for robot applications. Decentralized place recognition (DPR) is a fundamental element to produce candidate place matches for sharing information among different robots. The goal of this paper is to build a CNN-based real-time DSLAM system on embedded FPGA platforms. Because of the high precision requirement of VO, the existing quantization methods can not be directly applied. We improve the fixed-point fine-tune method for the CNN-based monocular VO, which enables VO can be deployed on the fixed-point FPGA accelerator. We also explore the influence of the DPR frequency on the DSLAM results, and find out a proper DPR frequency to balance the accuracy and speed. A cross-component pipeline scheduling method is proposed to improve DPR frequency and further improve the final accuracy of DSLAM under the same hardware resource constraints.

关键词： Robots Field programmable gate arrays Cameras image reconstruction Quantization (signal) Hardware Feature extraction

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Cloud-based difference algorithm using big GPR data for roadbed damage detection

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第23期32卷 e5545-e5545页

作者： Xu, Xianlei Gao, Wenru Zhang, Di Li, Taotao Qiao, Xu State Key Lab Water Resources Protect & Utilizat Beijing Peoples R China China Univ Min & Technol Beijing State Key Lab Coal Resources & Safe Min Beijing 100083 Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing Peoples R China

Ground-penetrating radar (GPR) technology is being widely used for urban roadbed damage detection. In this paper, we present the development of a cloud-based difference detection method using big GPR data for roadbed damage detection. Unlike many processing methods relying on standalone servers, the proposed method can achieve high accuracy and processing efficiency by distributing the computation over a server cluster. The method includes a difference detection algorithm for GPR image enhancement and damage extraction, the Hadoop distributed File System, and MapReduce distributed parallel computing framework for big GPR data parallel processing and interpretation. Moreover, to automatize roadbed damage classification, a criterion with seven grades is established based on the wave group shape, amplitude, phase, and attenuation characteristics. We validate the proposed method through a detection experiment that was conducted on the Fourth Ring Road in Beijing, China. The detection revealed 187 damage from the efficient processing of big GPR data, thus suggesting the ability of the proposed method to provide timely support for road safety in cities.

关键词： cloud computing difference detection GPR parallel computation roadbed damage detection

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An improved MCB localization algorithm based on received signal strength indicator 20

An improved MCB localization algorithm based on received sig...

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20th International Conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Qiao, Yan Chunyue, Zhou Baitong, Zhong Hui, Tian School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Laboratory of Communication Engineering Beijing Jiaotong University Beijing China Institute of Image Communication and Network Engineering Uniview Hangzhou Zhejiang Province China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)9781728126166

An improved Monte Carlo Localization Boxed (MCB) localization Algorithm based on received signal strength indicator (RSSI) for mobile wireless sensor networks node localization is proposed. Aiming at the characteristics of instantaneity, mobility and complexity of mobile node location, this algorithm combines RSSI ranging model with MCL algorithm which has high positioning accuracy, good performance and wide application. It establishes anchor node sampling box by using the actual distance of signal propagation obtained, and effectively reduces the sampling range. The simulation results show that this algorithm improves the sampling efficiency, shortens the sampling time, and increases the positioning accuracy compared to the MCL algorithm. It is a low-cost, low-power and low-complexity localization algorithm without additional hardware and communication overhead. © 2019 IEEE.

关键词： Monte Carlo methods

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