Privacy-Preserving IoT Cloud Data Processing Using SGX

2019 ◽  
Author(s):  
Pascal Gremaud ◽  
Arnaud Durand ◽  
Jacques Pasquier
Keyword(s):  
Data Processing ◽  
Privacy Preserving ◽  
Cloud Data ◽  
Cloud Data Processing

Flexible Access Control over Privacy-Preserving Cloud Data Processing

2020 ◽  
pp. 231-255
Author(s):  
Wenxiu Ding ◽  
Xinren Qian ◽  
Rui Hu ◽  
Zheng Yan ◽  
Robert H. Deng
Keyword(s):  
Access Control ◽  
Data Processing ◽  
Privacy Preserving ◽  
Cloud Data ◽  
Cloud Data Processing

Cloud Data Management Based on Cloud Computing Technology

2014 ◽  
Vol 543-547 ◽  
pp. 3573-3576
Author(s):  
Yuan Jun Zou
Keyword(s):  
Cloud Computing ◽  
Parallel Computing ◽  
Data Processing ◽  
Development Planning ◽  
Computer Data ◽  
Cloud Data ◽  
Computing Platform ◽  
Convergence Error ◽  
Cloud Computing Platform ◽  
Cloud Data Processing

Cloud computing, networking and other high-end computer data processing technology are the important contents of eleven-five development planning in China. They have developed rapidly in recent years in the field of engineering. In this paper, we combine parallel computing with the collaborative simulation principle, design a cloud computing platform, establish the mathematical model of cloud data processing and parallel computing algorithm, and verify the applicability of algorithm through the numerical simulation. Through numerical calculation, cloud computing platform can be divided into complex grids, and the transmission speed is fast, which is eight times than the finite difference method. The mesh is meticulous, which reaches millions. Convergence error is minimum, only 0.001. The calculation accuracy is up to 98.36%.

The Research of Point Cloud Data Processing Technology

2014 ◽  
Vol 628 ◽  
pp. 426-431
Author(s):  
Li Bo Zhou ◽  
Fu Lin Xu ◽  
Su Hua Liu
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Quality Data ◽  
Surface Generation ◽  
Point Cloud Data ◽  
Data Smoothing ◽  
Cloud Data ◽  
Data Point ◽  
Cloud Data Processing

Data processing is a key to reverse engineering, the results of which will directly affect the quality of the model reconstruction. Eliminate noise points are the first step in data processing, The method of using Coons surface to determine the noise in the data point is proposed. To reduce the amount of calculation and improve the surface generation efficiency, data point is reduced. According to the surrounding point coordinate information, the defect coordinates are interpolated. Data smoothing can improve the surface generation quality, data block can simplify the creation of the surface. Auto parts point cloud data is processed, and achieve the desired effect.

Point Cloud Data Processing Method of Cavity 3D Laser Scanner

2013 ◽  
Vol 33 (8) ◽  
pp. 0812003 ◽  
Author(s):  
陈凯 Chen Kai ◽  
张达 Zhang Da ◽  
张元生 Zhang Yuansheng
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Laser Scanner ◽  
Processing Method ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Processing Method ◽  
Cloud Data Processing

Triangulation-Based Point Cloud Data Processing Technology Research and Application

2014 ◽  
Vol 610 ◽  
pp. 729-733
Author(s):  
Ke He Wu ◽  
Wen Chao Cui ◽  
Bo Hao Cheng ◽  
Qian Yuan Zhang
Keyword(s):  
Data Processing ◽  
Data Storage ◽  
Point Cloud ◽  
Extraction Process ◽  
Programming Algorithm ◽  
Point Cloud Data ◽  
Boundary Extraction ◽  
Processing Technologies ◽  
Cloud Data ◽  
Cloud Data Processing

With the "Digital Earth" concept being put forward, people are starting to focus on geospatial information technology. Traditional manual building modeling process is gradually eliminated by history due to cumbersome and inefficient work. With massive data storage and processing technologies emerging and improving, people begin to explore building point cloud data measured by laser radar technology and to use point cloud data processing software for further building boundary extraction. In the model boundary extraction process, the use of prototype with the model fit is a good, clear and easy programming algorithm and triangulation algorithm.

scholarly journals Efficient point cloud data processing in shipbuilding: Reformative component extraction method and registration method

2014 ◽  
Vol 1 (3) ◽  
pp. 202-212 ◽  
Author(s):  
Jingyu Sun ◽  
Kazuo Hiekata ◽  
Hiroyuki Yamato ◽  
Norito Nakagaki ◽  
Akiyoshi Sugawara
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Accuracy Evaluation ◽  
Point Cloud Data ◽  
Continuous Part ◽  
Registration Method ◽  
Shipbuilding Industry ◽  
Seed Point ◽  
Cloud Data ◽  
Cloud Data Processing

Abstract To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.

scholarly journals FORTLS: An R Package for Processing TLS Data and Estimating Stand Variables in Forest Inventories

2020 ◽  
Vol 3 (1) ◽  
pp. 38
Author(s):  
Juan Alberto Molina-Valero ◽  
Maria José Ginzo Villamayor ◽  
Manuel Antonio Novo Pérez ◽  
Juan Gabriel Álvarez-González ◽  
Fernando Montes ◽  
...  
Keyword(s):  
Data Processing ◽  
Laser Scanning ◽  
Basal Area ◽  
R Package ◽  
Efficient Manner ◽  
Cloud Data ◽  
Forest Inventories ◽  
Cost Efficient ◽  
Inference Techniques ◽  
Cloud Data Processing

Terrestrial Laser Scanning (TLS) enables rapid, automatic, and detailed 3D representation of surfaces with an easily handled scanner device. TLS, therefore, shows great potential for use in Forest Inventories (FIs). However, the lack of well-established algorithms for TLS data processing hampers operational use of the scanner for FI purposes. Here, we present FORTLS, which is an R package specifically developed to automate TLS point cloud data processing for forestry purposes. The FORTLS package enables (i) detection of trees and estimation of their diameter at breast height (dbh), (ii) estimation of some stand variables (e.g., density, basal area, mean, and dominant height), (iii) computation of metrics related to important tree attributes estimated in FIs at stand level, and (iv) optimization of plot design for combining TLS data and field measured data. FORTLS can be used with single-scan TLS data, thus, improving data acquisition and shortening the processing time as well as increasing sample size in a cost-efficient manner. The package also includes several features for correcting occlusion problems in order to produce improved estimates of stand variables. These features of the FORTLS package will enable the operational use of TLS in FIs, in combination with inference techniques derived from model-based and model-assisted approaches.

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