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3P tool: Parallel Point-Cloud Processing Tool with Customized Data Distribution Scheme using Local Binning Algorithm
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Sangyoon Oh | - |
| dc.contributor.author | SAHLE, EYASSU BERHANU | - |
| dc.date.accessioned | 2018-11-08T08:22:26Z | - |
| dc.date.available | 2018-11-08T08:22:26Z | - |
| dc.date.issued | 2016-08 | - |
| dc.identifier.other | 23097 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/13276 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :컴퓨터공학과,2016. 8 | - |
| dc.description.tableofcontents | 1.Introduction 1 2.Background and Related Works 4 2.1 Existing Data Distribution Schemes 4 2.2 Spatial Interpolation 6 2.2.1 IDW 6 2.2.2 Kriging 7 3.Proposed Data Distribution Scheme 9 3.1 Analysis of Proposed Data Distribution Scheme 14 3.2 Implementation of Proposed Data Distribution Scheme 16 4.System Model of 3P Tool 21 4.1 User Interface of 3P Tool 22 4.2 GIS Processing Tools 27 5.Evaluation 30 5.1 Experimental Environment 30 5.2 Study Sites and Data Description 31 5.3 Empirical Experiment Results 33 6.Conclusion and Future Work 42 REFERENCES 43 Appendix 46 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 3P tool: Parallel Point-Cloud Processing Tool with Customized Data Distribution Scheme using Local Binning Algorithm | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 컴퓨터공학과 | - |
| dc.date.awarded | 2016. 8 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 758603 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000023097 | - |
| dc.subject.keyword | data distribution | - |
| dc.subject.keyword | interpolation | - |
| dc.subject.keyword | parallel processing | - |
| dc.subject.keyword | point-cloud data | - |
| dc.subject.keyword | lidar | - |
| dc.description.alternativeAbstract | The emergence of light detection and ranging (LiDAR) technologies has resulted in collection of massive amount of 3-dimentsion (3D) point-cloud data. The traditional interpolation approaches require extensive computational resources and long runtime to create digital elevation model (DEM) from these LiDAR data. Recent advances in high-performance computing have enabled researchers to develop parallel approaches. Several parallel implementation methods have been proposed for processing massive amounts of point-cloud data acquired from the LiDAR technology. However, such parallelization strategies demand scheduling, load-balancing, heavy communication and I/O operations which is different from the traditional approaches. In this dissertation, we present a customized data distribution scheme for massive LIDAR data processing and also developed a parallel point-cloud processing (3P) tool to support our scheme. The customized data distribution scheme allocates dynamic grid size for processes in parallel environment based upon the characteristics of point-cloud data. This scheme could enhance the performance of the parallel processing tool by providing a better load balancing among processes. The 3P tool is implemented based on the message passing interface libraries and supports two popular interpolation algorithms: inverse distance weighting and kriging. 3P also provides an interactive graphical user interface mode that encapsulate the complexity of the parallelized interpolation methods. We evaluated the proposed scheme on our 3P tool on two computing environments(x86 and ARM) using various point-cloud datasets. The results demonstrate that the proposed data distribution scheme have better load balancing that the static approach and provides efficient performance speedup. | - |
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- Graduate School of Ajou University > Department of Computer Engineering > 3. Theses(Master)
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