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Communication-efficient decentralised algorithms for seismic tomography with sensor networks

INTERNATIONAL JOURNAL OF parallel EMERGENT AND distributed SYSTEMS 2020年第5期35卷 550-570页

作者： Zhao, Liang Univ South Carolina Upstate Div Math & Comp Sci Spartanburg SC 29303 USA

We consider the decentralised consensus optimisation problem arising from in-situ seismic tomography in large-scale sensor networks. Unlike traditional seismic imaging performed in a centralised location, each node in this setting privately holds an objective function and partial data. The goal of each node is to obtain the optimal solution of the whole seismic image, while communicating only with its immediate neighbours. We present a fast decentralised gradient descent method and prove that this novel method can reach optimal convergence rate ofO(1/k(2))wherekis the number of communication/iteration rounds. A gossip-based asynchronous version is also proposed which is preferable when there is a divergence on the processing speed of the nodes. Extensive numerical experiments on synthetic and real-world sensor network seismic data demonstrate that the proposed algorithms significantly outperform existing methods. [GRAPHICS] .

关键词： Big data decentralised computing in-network processing seismic tomography sensor network

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Single High-Value Region Detection Based on image Equal-Segmentation

Single High-Value Region Detection Based on Image Equal-Segm...

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International conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC

作者： Jinlong Chen Lei Huang Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China School of Computer Information and Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728184487

ISBN: (纸本)9781728184494

With the development of computer hardware equipment and deep neural network (DNN) technology, computer vision has received more and more attention from AI developers and researchers. In the field of digital image processing, people try to use some calculation methods and techniques to identify objects in images. In the process of finding the rules, this method is called target detection, which is also a research hotspot in the field of computer vision. High-value area detection is based on target detection technology. On the basis of image detection of digital images, some methods of image are used to segment more concerned areas in the target. In this paper, the method of equally segmenting the image takes the image containing the detected object as input and performs detection to achieve the purpose of detecting high-value areas. The steps of the algorithm are divided into high-value area estimation, image equal segmentation, image data attenuation, algorithm acceleration, target fusion and classification.

关键词： image segmentation Computer vision Digital images Neural networks Object detection Classification algorithms Object recognition

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Big Data processing Technologies in distributed Information Systems 10

Big Data Processing Technologies in Distributed Information ...

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10th International conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) / 9th International conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH)

作者： Shakhovska, Nataliya Boyko, Nataliya Zasoba, Yevgen Benova, Eleonora Lviv Polytech Natl Univ 12 Bandera St UA-79000 Lvov Ukraine Comenius Univ Fac Management Odbojarov 10 Bratislava Slovakia

The analysis of Big data technologies was provided. An example of MapReduce paradigm application, uploading of big volumes of data, processing and analyzing of unstructured information and its distribution into the clustered database was provided. The article summarizes the concept of "big data". Examples of methods for working with arrays of unstructured data are given. The parallel system Resilient distributed Datasets (RDD) is organized. The class of basic database operations was realized: database con-nection, table creation, getting in line id, returning all elements of the database, update, delete and create the line. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Big data Web application Modeling processing analytics

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Towards a distributed Computation Offloading Architecture for Cloud Robotics 15

Towards a Distributed Computation Offloading Architecture fo...

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15th IEEE International Wireless Communications and Mobile Computing conference (IEEE IWCMC)

作者： Chaari, Rihab Cheikhrouhou, Omar Koubaa, Anis Youssef, Habib Hmam, Habib Univ Mannouba Prince Res Lab Manouba Tunisia Taif Univ Coll CIT At Taif Saudi Arabia Univ Sfax Comp & Embedded Syst Lab Sfax Tunisia Prince Sultan Univ Riyadh Saudi Arabia ISEP CISTER INESC TEC Porto Portugal Gaitech Robot Shanghai Peoples R China Univ Sousse Prince Res Lab Sousse Tunisia Univ Moncton Fac Engn Moncton NB E1A 3E9 Canada

ISBN: (纸本)9781538677476

Cloud robotics is incessantly gaining ground, especially with the rapid expansion of wireless networks and Internet resources. In particular, computation offloading is emerging as a new trend, enabling robots with more powerful computation resources. It helps them to overcome the hardware and software limitations by leveraging parallel computing capabilities and the availability of large amounts of resources in the cloud. However, the performance gain of computation offloading in cloud robotics is still an ongoing research problem because of the conflicting factors that affect the performance. In this paper, we investigate this issue and we design a distributed cloud robotic architecture for computation offloading based on Kafka middleware as messaging broker. We experimentally validated our solution and tested its performance using image processing algorithms. Experimental results show a significant reduction in robot CPU load, as expected, with an increase in robot communication delays.

关键词： cloud robotics processing offloading distributed computing

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MotionNet architecture with Randomized Emission training strategy for adaptive bit rate video compression

MotionNet architecture with Randomized Emission training str...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Sangeeta Preeti Gulia Maharshi Dayanand University Rohtak India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

The traditional methods of video compression are hand designed and fundamentally based on frame prediction strategies. Even though they are working efficiently but significant improvements have been observed by some deep learning based enhancements. In the way, to explore pure deep learning based approaches, the commendable performance motivated further research in the same domain. Adaptive bit rate representation of the videos is preferred over fixed bit representation providing flexibility for adjusting video quality with varying bandwidth in addition to efficient bit utilization. The proposed pure deep learning video compression architecture comprises of frame autoencoder and motion prediction network to propagate motion information for reconstruction of predicted frames. The network was trained with both fixed and random emission steps independently. The experimental results reveal that randomized emission training strategy gives better results for adaptive bit-rate video compression when compared in SSIM, PSNR, EPE and TPF performance parameters.

关键词： image coding Streaming media Deep learning Video compression image reconstruction Training Adaptation models

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SELECTING OPTIMAL PROPOSAL NUMBER FOR image-BASED OBJECT DETECTION 44

SELECTING OPTIMAL PROPOSAL NUMBER FOR IMAGE-BASED OBJECT DET...

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

作者： Guan, Wenjie Zhou, Xiaoqun Li, Ge Zou, Yuexian Peking Univ Sch ECE ADSPLAB Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781479981311

In order to balance the detection time and accuracy, the state-of-the-art region-based detectors use a fixed number of proposals to obtain detection results in the inference phase. However, in surveillance scenes, object population varies in different images, causing the fixed proposal number becomes an undeterminable hyper-parameter, which needs to be correspondingly adjusted to maintain high recall. To solve this problem, we propose two image-level optimal proposal number selection methods called linear proposal number (LPN) selection method and adaptive proposal number (APN) selection method respectively, both aiming at selecting an optimal proposal number for each image to adapt both the images with sparsely and densely distributed objects. In LPN selection method, we introduce a linear weighting hyper-parameter to formulate the relationship between the actual object number and proposals' scores to obtain the optimal proposal number. To avoid setting the hyper-parameter manually, we further propose another APN selection method where the optimal proposal number of each image is selected by exploring the distribution of the proposals' scores. Results obtained from the UA-DETRAC car dataset and self-built bird dataset (BSBDV 2017) show that our proposed methods can largely improve the detection performance in terms of detection time and accuracy without any re-training process.

关键词： Object detection Optimal proposal number Linear proposal number selection Adaptive proposal number selection Surveillance scenes

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Multi-coil magnetic resonance imaging reconstruction with a Markov random field prior

Multi-coil magnetic resonance imaging reconstruction with a ...

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conference on Medical Imaging: image processing

作者： Panic, Marko Aelterman, Jan Crnojevic, Vladimir Pizurica, Aleksandra Univ Novi Sad BioSense Inst Novi Sad Serbia Univ Ghent Dept Telecommun & Informat Proc TELIN IPI Imec Ghent Belgium

ISBN: (纸本)9781510625464

Recent improvements in magnetic resonance image (MRI) reconstruction from partial data have been reported using spatial context modelling with Markov random field (MRF) priors. However, these algorithms have been developed only for magnitude images from single-coil measurements. In practice, most of the MRI images today are acquired using multi-coil data. In this paper, we extend our recent approach for MRI reconstruction with MRF priors to deal with multi-coil data i.e., to be applicable in parallel MRI (pMRI) settings. Instead of reconstructing images from different coils independently and subsequently combining them into the final image, we recover MRI image by processing jointly the undersampled measurements from all coils together with their estimated sensitivity maps. The proposed method incorporates a Bayesian formulation of the spatial context into the reconstruction problem. To solve the resulting problem, we derive an efficient algorithm based on the alternating direction method of multipliers (ADMM). Experimental results demonstrate the effectiveness of the proposed approach in comparison to some well-adopted methods for accelerated pMRI reconstruction from undersampled data.

关键词： parallel MRI (pMRI) Markov random field (MRF) alternating direction method of multipliers (ADMM)

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Developing the parallelization methods for Finding the All-Pairs Shortest Paths in distributed Memory Architecture 38

Developing the Parallelization Methods for Finding the All-P...

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IEEE 38th International Performance Computing and Communications conference (IPCCC)

作者： Alghamdi, Mohammed He, Ligang Zhou, Yujue Li, Junyu Univ Warwick Dept Comp Sci Coventry W Midlands England King Khalid Univ Dept Informat Syst Abha Saudi Arabia

ISBN: (纸本)9781728110257

The All-Pairs Shortest Paths (APSP) is a fundamental graph problem aiming to find the shortest path between any two nodes in a graph. In this paper, a new method is presented to solve the APSP problem for big graphs on distributed systems. In this method, a graph is partitioned judiciously and then processed in parallel. In particular, the graph is first pre-processed to prepare the partition in the computation stages. After the graph is partitioned into smaller sub-graphs, a traditional shortest path algorithm, such as the Floyd-Warshall algorithm or the Dijkstra's algorithm, can be used to find the APSP in each sub-graph. Finally, through the common nodes between the sub-graphs, the local results in each sub-graph are combined to establish the APSP for the entire graph. Our method is implemented with MPI. Two different communication patterns among partitions (and processes) are proposed to achieve the parallelization and the combination of the local results. We then conducted extensive experiments on a high performance cluster. The experimental results show that comparing with the existing solution, our method is able to accelerate the solving of the APSP problem significantly.

关键词： All-Pairs Shortest Path Graph partition parallel processing distributed memory architecture

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An Efficient Hybrid Approach for Brain Tumor Detection in MR images using Hadoop-MapReduce

An Efficient Hybrid Approach for Brain Tumor Detection in MR...

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IEEE International conference on Green Computing and Communications

作者： Prabhjot Kaur Chahal Shreelekha Pandey Couer Science Engineering Deren Thr insiue of Engineering nd TechnoogyCouer Science Engineering Deren Thr insiue of Engineering nd Technoogy

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176482

The sheer expanding of brain tumor MR image in terms of volume demands swift and accurate processing mechanism. To segregate tumor from brain MR effectively an efficient computer aided detection system is required. The research to maximize the classification accuracy rate is improving at fast pace in field of medical image processing. However, somehow setbacks the execution time of the overall system. The objective of the research is now slanting to be proficient enough in both the performance parameters. In this manuscript, the focus is to develop a framework using fusion based approach to balances the accuracy and time simultaneously. The hybrid fuzzy c-means and k-means (FKM) methodology is implemented using Hadoop MapReduce platform to increase the scalability of clusters. Experimentation is verified using DICOM images of variable size datasets processed through multi-node Hadoop platform. The size of the images is taken is slots of 200, 400 and 600 images. The accuracy rate core fusion approach (FKM) to be 96% considering the maximum number of images. The execution time decreases as the number of nodes increases due to the parallel and distributed image data. The system without hadoop framework takes 30% more time as compare to the system with single nodes using 1000 images.

关键词： Tumors image segmentation Brain modeling Time factors Task analysis distributed databases Scalability Brain modeling distributed databases task analysis Scalability Time Factors image segmentation Tumours execution time images accuracy rate Brain Neoplasms characteristic parameter

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Analysis of Object Classification and Detection methods to Mitigate Power Utilization 2

Analysis of Object Classification and Detection Methods to M...

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2nd International conference on Intelligent Communication and Computational Techniques (ICCT)

作者： Talokar, Shreyas Anil Narde, Gajrajsingh Chaudhari, Mohit GH Raisoni Coll Engn Dept Elect Engn Nagpur 440016 Maharashtra India Raymond Luxury Cottons Ltd Dept Elect Engn Amravati 444601 Maharashtra India

ISBN: (纸本)9781728117119

Now a day's computing technology being grows rapidly so that object detection or identification is crucial in industry. The object detection is a technology related to computer perception and image processing for detecting samples of items to certain shapes in digital images and videos. The ultimate aim of object detection is segmenting the area of notified from a moving spot and to detecting of its motion, place and blockage. The Pattern recognition and classification introduced the pace for recognition of an object in sequence of images. This paper describes the different object detection, object classification methods including the analysis and comparative investigation of various methods and techniques useful for tracking the objects. Along with these the paper gives short idea about energy saving in industry by having application if image or video processing.

关键词： object detection object classification objects tracking visual monitoring system

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