scholarly journals INDOOR MOBILE POSITIONING BASED ON LIDAR DATA AND CODED SEQUENCE PATTERN

2016 ◽  
Vol XLII-2/W2 ◽  
pp. 145-146
Author(s):  
Z. Wang ◽  
B. Dong ◽  
D. Chen
Keyword(s):  
Lidar Data ◽  
3D Mapping ◽  
Sequence Pattern ◽  
Hough Transformation ◽  
Mobile Positioning ◽  
Sequence Patterns ◽  
Rotation Parameters ◽  
Sift Features ◽  
Point Data ◽  
Automatic Matching

This paper proposed a coded sequence pattern for automatic matching of LiDAR point data, the methods including SIFT features, Otsu segmentation and Fast Hough transformation for the identification, positioning and interpret of the coded sequence patterns, the POSIT model for fast computing the translation and rotation parameters of LiDAR point data, so as to achieve fast matching of LiDAR point data and automatic 3D mapping of indoor shafts and tunnels.

TITAN to Google Earth: Workflow for Data Processing for Mobile Terrestrial LIDAR

2017 ◽  
Author(s):  
Michael Martin
Keyword(s):  
Data Processing ◽  
Spatial Information ◽  
New Technology ◽  
Google Earth ◽  
Lidar Data ◽  
Terrestrial Lidar ◽  
Urban Modelling ◽  
Feature Based ◽  
Point Data ◽  
Object Models

Terrestrial LIDAR scanners are pushing the boundaries of accurate urban modelling. Automation and the usability of tools used in feature abstraction and, to a lesser degree, presentation have become the chief concerns with this new technology. To broaden the use and impact of LIDAR in the geomatics, LiDAR datasets must be converted to feature-based representations without loss of precision. One approach, taken here, is to simultaneously examine the overall path that data takes through an organization and the operatordriven tasks carried out on the data as it is transformed from a raw point cloud to final product. We present a review of the current practices in LiDAR data processing and a foundation for future efforts to optimize. We examine alternative LIDAR processing workflows with two key questions in mind: computational efficiency - whether the process can be done using the tools at all - and tool complexity - what operator skill level is needed at each step. Using these workflows the usability of the specific software tools and the required knowledge to effectively carry out the procedures using the tools are examined. Preliminary results have yielded workflows that successfully translate LIDAR to 3D object models, highly decimated point representations of street data represented in Google Earth, and large volume point data flythroughs in ESRI ArcScene. We are documenting the pragmatic limits on each of these workflows and tools for endusers. Terrestrial LIDAR brings with it new innovations for spatial visualizations, but also questions of viability. The technology has proved valuable for specialized applications for experts, but can it be useful as a tool for proliferating 3d spatial information by and to non-experts. This study illustrates the issues associated with preparing 3d LIDAR data for presentation in mainstream visualization environments.

A Study of Push-Based Sequence Authorization Mechanism for Grid Security

2011 ◽  
Vol 63-64 ◽  
pp. 59-63
Author(s):  
You Chan Zhu ◽  
Juan Hua Bai
Keyword(s):  
Grid Computing ◽  
Grid Security ◽  
Sequence Pattern ◽  
The Core ◽  
Sequence Patterns ◽  
Core Issues

Grid security is one of the core issues of grid computing, and the authorization issue of grid security is a hot topic of current research. This paper describes three sequence patterns of the authorization, and focuses on the architecture of push sequence model and application example-Community Authorization Service (CAS). It analyzes and compares the push sequence pattern and pull sequence pattern. Authorization mechanism based on push sequence pattern solves the scalability issue of GSI quite well in some extent and improves the flexibility of the system.

scholarly journals Skim Siswa dalam Menyelesaikan Soal Pola Barisan

2020 ◽  
Vol 4 (1) ◽  
pp. 23-39
Author(s):  
Giesta Dike ◽  
Tri Nova Hasti Yunianta
Keyword(s):  
Qualitative Study ◽  
Sampling Techniques ◽  
Sequence Pattern ◽  
Purposive Sampling ◽  
Triangulation Method ◽  
Sequence Patterns ◽  
The Difference ◽  
Pattern Problem ◽  
Descriptive Qualitative

The aim of this descriptive qualitative study is to find out the types of students' schemes in solving the problem of sequence patterns. The subjects of this study were 9 students of VIIIB grade at SMP Negeri 1 Sumowono by using purposive sampling. Using purposive sampling techniques that are chosen with consideration and with specific objectives. The validity of the data in this study is guaranteed by carrying out the Triangulation Method. Method triangulation is done by observation, tests, and interviews which are supported by documentation in the form of recordings and photographs. The results of this study showed that there were 13 schemes appeared when solving the sequence pattern problem involving; scheme of dividing the term into 2 with the same number, scheme of multiplying the different multiplication plus one, scheme of adding , scheme of decreasing U1 to U2 then multiplied by 2 and plus 1, scheme of the difference patterns, scheme of multiplying 2 numbers and adding the numbers, scheme of multiplying , scheme of adding , scheme of multiplying 3, scheme of adding , scheme of adding , scheme of adding menambahkan  and scheme of adding 1 and the different number.   Keywords: Scheme, sequence of numbers, the configuration pattern of objects.

scholarly journals Top-k sequence pattern mining with non-overlapping condition

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1703-1710
Author(s):  
Xin Chai ◽  
Dan Yang ◽  
Jingyu Liu ◽  
Yan Li ◽  
Youxi Wu
Keyword(s):  
Data Structure ◽  
Pattern Matching ◽  
Pattern Mining ◽  
Experimental Results ◽  
Effective Algorithm ◽  
Effective Strategy ◽  
Sequence Pattern ◽  
Pattern Length ◽  
Sequence Patterns

Pattern mining has been widely applied in many fields. Users often mine a large number of patterns. However, most of these are difficult to apply in real applications. Top-k pattern mining, which involves finding the most frequent k patterns, is an effective strategy, because the more frequently a pattern occurs, the more likely they are to be important for users. However, top-k mining can only mine short patterns in mining applications with the Apriori property. It is well-known that short patterns contain less information than long patterns. In this paper, we focus on mining top-k sequence patterns of each pattern length. We propose an effective algorithm, named NOSTOPK (non-overlapping sequence pattern mining for top-k). The algorithm calculates the support of a pattern using a Nettree data structure, which has been introduced to tackle various types of pattern matching and sequence pattern mining issues. We find the top k patterns of length len, and calculate the supports of the corresponding k x |?| super-patterns of length len + 1 to discover the new top k super-patterns with len + 1. Experimental results demonstrate that the algorithm achieves a better performance than comparable algorithms.

scholarly journals Automatic and Visual Processing Method of Non-Contact Monitoring for Circular Stormwater Sewage Tunnels Based on LiDAR Data

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1599 ◽  
Author(s):  
Xiongyao Xie ◽  
Mingrui Zhao ◽  
Jiamin He ◽  
Biao Zhou
Keyword(s):  
Visual Processing ◽  
Point Cloud ◽  
Single Point ◽  
Three Dimensional ◽  
Point Clouds ◽  
Processing Method ◽  
Lidar Data ◽  
Health State ◽  
Short Period ◽  
Point Data

The application of Light Detection And Ranging (LiDAR) technology has become increasingly extensive in tunnel structure monitoring. The proposed processing method aims to carry out non-contact monitoring for circular stormwater sewage tunnels and provides an efficient workflow. This allows the automatic processing of raw point data and the acquisition of visualization results to analyze the health state of a tunnel within a short period of time. The proposed processing method employs a series of algorithms to extract the point cloud of a single tunnel segment without obvious noise by main three steps: axis acquisition, segment extraction, and denoising. The tunnel axis is extracted by fitting boundaries of the tunnel point cloud projection in the plane. With the guidance of the axis, the entire preprocessed tunnel point cloud is segmented by equal division to get a section of the tunnel point cloud which corresponds to a single tunnel segment. Then, the noise in every single point cloud segment is removed by clustering the algorithm twice, based on the distance and intensity. Finally, clean point clouds of tunnel segments are processed by an effective deformation extraction processor to determine the ovality and to get a three-dimensional visual deformation nephogram. The proposed method can significantly improve the efficiency of LiDAR data processing and extend the application of LiDAR technology in circular stormwater sewage tunnel monitoring.

An Application of HVG Method to Studying Variations of LDoS Flow Packet Sequence Pattern

2013 ◽  
Vol 791-793 ◽  
pp. 1347-1351
Author(s):  
Zhi Jian Huang ◽  
Wei Peng ◽  
Yong Jun Wang ◽  
Yuan Wang
Keyword(s):  
Time Series ◽  
Denial Of Service ◽  
One Dimension ◽  
Sequence Pattern ◽  
End To End Delay ◽  
Pattern Similarity ◽  
Sequence Patterns ◽  
Analyze Time Series ◽  
Low Rate ◽  
Horizontal Visibility Graph

By mapping one dimension time series into two dimension graph, Horizontal Visibility Graph (short as HVG) method provides a new way to analyze time series structural properties, it has been used in economics, biology, sociology and other areas related. In this paper, we apply it to investigate the Low-rate Denial-of-Service (LDoS) flow packet sequence pattern variations. We establish the Packet Sequence Model and put new metrics to measure the pattern similarity. Using the data carried out on PlanetLab, we find out that the packet sequence time series of the receiver has a bigger HVG degree than the sender, indicating that the packet sequence patterns get more irregular under the impacts of end-to-end delay.

scholarly journals A MIN-CUT BASED FILTER FOR AIRBORNE LIDAR DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 395-401 ◽  
Author(s):  
Serkan Ural ◽  
Jie Shan
Keyword(s):  
Energy Function ◽  
Spatial Coherence ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Global Properties ◽  
Level Information ◽  
Point Data ◽  
Smoothness Term ◽  
Data Collection Technique ◽  
Min Cut

LiDAR (Light Detection and Ranging) is a routinely employed technology as a 3-D data collection technique for topographic mapping. Conventional workflows for analyzing LiDAR data require the ground to be determined prior to extracting other features of interest. Filtering the terrain points is one of the fundamental processes to acquire higher-level information from unstructured LiDAR point data. There are many ground-filtering algorithms in literature, spanning several broad categories regarding their strategies. Most of the earlier algorithms examine only the local characteristics of the points or grids, such as the slope, and elevation discontinuities. Since considering only the local properties restricts the filtering performance due to the complexity of the terrain and the features, some recent methods utilize global properties of the terrain as well. This paper presents a new ground filtering method, Min-cut Based Filtering (MBF), which takes both local and global properties of the points into account. MBF considers ground filtering as a labeling task. First, an energy function is designed on a graph, where LiDAR points are considered as the nodes on the graph that are connected to each other as well as to two auxiliary nodes representing ground and off-ground labels. The graph is constructed such that the data costs are assigned to the edges connecting the points to the auxiliary nodes, and the smoothness costs to the edges between points. Data and smoothness terms of the energy function are formulated using point elevations and approximate ground information. The data term conducts the likelihood of the points being ground or off-ground while the smoothness term enforces spatial coherence between neighboring points. The energy function is optimized by finding the minimum-cut on the graph via the alpha-expansion algorithm. The resulting graph-cut provides the labeling of the point cloud as ground and off-ground points. Evaluation of the proposed method on the ISPRS test dataset for ground filtering demonstrates that the results are comparable with most current existing methods. An overall average filtering accuracy for the 15 ISPRS test areas is 91.3%.

scholarly journals A MIN-CUT BASED FILTER FOR AIRBORNE LIDAR DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 395-401 ◽  
Author(s):  
Serkan Ural ◽  
Jie Shan
Keyword(s):  
Energy Function ◽  
Spatial Coherence ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Global Properties ◽  
Level Information ◽  
Point Data ◽  
Smoothness Term ◽  
Data Collection Technique ◽  
Min Cut

LiDAR (Light Detection and Ranging) is a routinely employed technology as a 3-D data collection technique for topographic mapping. Conventional workflows for analyzing LiDAR data require the ground to be determined prior to extracting other features of interest. Filtering the terrain points is one of the fundamental processes to acquire higher-level information from unstructured LiDAR point data. There are many ground-filtering algorithms in literature, spanning several broad categories regarding their strategies. Most of the earlier algorithms examine only the local characteristics of the points or grids, such as the slope, and elevation discontinuities. Since considering only the local properties restricts the filtering performance due to the complexity of the terrain and the features, some recent methods utilize global properties of the terrain as well. This paper presents a new ground filtering method, Min-cut Based Filtering (MBF), which takes both local and global properties of the points into account. MBF considers ground filtering as a labeling task. First, an energy function is designed on a graph, where LiDAR points are considered as the nodes on the graph that are connected to each other as well as to two auxiliary nodes representing ground and off-ground labels. The graph is constructed such that the data costs are assigned to the edges connecting the points to the auxiliary nodes, and the smoothness costs to the edges between points. Data and smoothness terms of the energy function are formulated using point elevations and approximate ground information. The data term conducts the likelihood of the points being ground or off-ground while the smoothness term enforces spatial coherence between neighboring points. The energy function is optimized by finding the minimum-cut on the graph via the alpha-expansion algorithm. The resulting graph-cut provides the labeling of the point cloud as ground and off-ground points. Evaluation of the proposed method on the ISPRS test dataset for ground filtering demonstrates that the results are comparable with most current existing methods. An overall average filtering accuracy for the 15 ISPRS test areas is 91.3%.

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