bluuebunny/crossref_metadata_2025 huggingface dataset:
A Disaster Management Specific Mobility Model for Flying Ad-hoc Network
Authors: Mukherjee Amartya , Dey Nilanjan , Kausar Noreen , Ashour Amira S. , Taiar Redha , Hassanien Aboul Ella
Published: June 2016

The extended Mobile Ad-hoc Network architecture is a paramount research domain due to a wide enhancement of smart phone and open source Unmanned Aerial Vehicle (UAV) technology. The novelty of the current work is to design a disaster aware mobility modeling for a Flying Ad-hoc network infrastructure, where the UAV group is considered as nodes of such ecosystem. This can perform a collaborative task of a message relay, where the mobility modeling under a “Post Disaster” is the main subject of interest, which is proposed with a multi-UAV prototype test bed. The impact of various parameters like UAV node attitude, geometric dilution precision of satellite, Global Positioning System visibility, and real life atmospheric upon the mobility model is analyzed. The results are mapped with the realistic disaster situation. A cluster based mobility model using the map oriented navigation of nodes is emulated with the prototype test bed.
Stabilization of Mechanical Systems with Backlash by PI Loop Shaping
Authors: Azar Ahmad Taher , Serrano Fernando E.
Published: June 2016

Backlash is one of several discontinuities found in different kinds of systems, it can be found in actuators of different types, such as mechanical and hydraulic, giving way to unwanted effects in the system behavior. PI loop shaping control design implementing a describing function to find the limit cycle oscillations and the appropriate control gain is developed. Therefore a frequency domain approach is implemented for the control of nonlinear system of any kind such as robotics, mechatronics, other kind of mechanisms, electrical motors etc. Finally, in order to corroborate the theoretical background explained in this article, the stabilization of a cart-pendulum system with the proposed control strategy is shown.
Quality Evaluation of Group-Buy Websites
Authors: Yu Zhuoxi , Wu Yanqing , Zhao Zhiwen
Published: June 2016

Group buying mode has been one of the hottest business models in the Chinese Internet area in recent years. Though the market competition is intense, the demand of group purchase is still on the rise. Nowadays, the research on comprehensive evaluation of group buying websites in the country is still tiny. The paper used DEA combined with AHP to evaluate popular group buying websites. According to the characteristics of group buying websites, the authors evaluate the quality of group buying websites from four aspects. There are running efficiency, profit ability, popularity and customer satisfaction. Then they get the comprehensive evaluation results of each website which based on those four aspects. Some suggestions of website building for group purchasing enterprises are proposed.
E-Government Project Evaluation
Authors: Yao Jianrong , Liu Jin
Published: June 2016

Due to the large amount of investment in e-government projects, a series of e-government project performance evaluation index systems with strong maneuverability and synthesis are come up with to pilot sound development of e-government. It is one's responsibility to reconsider giant projects and update knowledge about it. This paper is mainly talking about the construction of e-government projects performance evaluation from the view of financial, user, internal process and learning and growth which are the core elements of the balanced scorecard. Using AHP to calculate the weight of each index and figure out the final score of a project. After analyzing the weaknesses of AHP, the method of minimum range and GA is put forward to prefect some weight values and make the appraisal result more impersonal and convincing.
Application of Modified OPTICS Algorithm in E-Commerce Sites Classification and Evaluation
Authors: Yu Zhuoxi , Jin YuJia , Parmar Milan , Wang Limin
Published: June 2016

In the era of the development in network economy, e-commerce sites' operational efficiency is in relation to the development of enterprises. Thus, how to evaluate e-commerce sites have become a hot topic. Due to the evaluation index of e-commerce sites have the characteristics of high dimension and data inhomogeneity, the new method combines PCA with the improved OPTICS algorithm to classify and evaluate the e-commerce demonstration enterprise websites. Firstly, using PCA to reduce the dimension of high-dimensional data. Secondly, for the limitation of OPTICS algorithm in dealing with sparse points, then using the improved OPTICS algorithm in clustering low-dimensional data to evaluate the effect of e-commerce sites and make suggestions.
North Coast geohazards - 2016 seismology update
Authors: Brillon C
Published: June 2016

Within British Columbia's north coast, specifically between Prince Rupert and Bella Bella, from the western coastline to the eastern extent of the Coast Mountains, there has been minimal research to understand earthquake hazards (Brillon, 2016). A more detailed
understanding of where and how regional strain energy is being accumulated in the crust and the likelihood of damaging earthquakes in this area is necessary to estimate ground shaking and other earthquake-related hazards. To improve the understanding of seismic hazard in the area, five monitoring
stations consisting of seismic and GPS instrumentation were installed in August 2014. In addition, a soft soil seismograph to measure effects of local site conditions was installed in Kitimat and improvements to older GPS and seismic stations in the region were carried out.
During the first winter of the deployment a number of the stations suffered significant weather-related damages that resulted in long periods of station downtime. By June 2015 all the stations were repaired and reinforced to minimize damages the following winter. Aside from the stations located in
Kitimat, the background noise levels are within the Petersen high and low noise models (Peterson, 1993). The low seismic noise levels consistently allow earthquakes offshore Haida Gwaii with magnitudes less than M2.0 to be used in analysis. In addition to these events 145 earthquakes within the
study area have been located. Although a number of earthquakes have been located in the north coast, the locations do not identify any new seismically active areas.
Covenant and the consolidated Christian community: The covenantal roots of theologico-political federalism in Samuel Rutherford’s Respublica Christiana
Authors: Raath Andries , De Freitas Shaun
Published: June 2016

The celebrated author on the mind of Samuel Rutherford, John Coffey, chose the Calvinist tradition as the appropriate context for interpreting Rutherford’s covenantal perspectives. The authors question this assumption and produce evidence to the effect that the Bullinger– Vermigli approach to theologico-political federalism in Rutherford’s views proves to be a more cogent paradigm for understanding Rutherford’s thoughts. It is concluded that Rutherford utilised the insights of both Bullinger and Vermigli in structuring the ideal form of government in the Christian state as well as the relationships between ecclesiastical and political offices in the state, a theory that gained in importance in later political theories.
Feline panleukopaenia virus in captive non-domestic felids in South Africa
Authors: Lane Emily P. , Brettschneider Helene , Caldwell Peter , Oosthuizen Almero , Dalton Desiré L. , Du Plessis Liza , Steyl Johan , Kotze Antoinette
Published: June 2016

An outbreak of feline panleukopaenia virus (FPLV) infection was diagnosed by pathology, electron microscopy and polymerase chain reaction (PCR) in vaccinated captive-bred subadult cheetahs in South Africa. Subsequent to this disease outbreak, 12 cases of FPLV diagnosed on histology were confirmed by PCR in captive African black-footed cat, caracal, cheetah, lion, ocelot and serval. Phylogenetic analyses of the viral capsid protein gene on PCR-positive samples, vaccine and National Center for Biotechnology Information (NCBI) reference strains identified a previously unknown strain of FPLV, present since at least 2006, that differs from both the inactivated and the modified live vaccine strains. A previously described South African strain from domestic cats and cheetahs was identified in a serval. Surveys of FPLV strains in South African felids are needed to determine the geographical and host species distribution of this virus. Since non-domestic species may be reservoirs of parvoviruses, and since these viruses readily change host specificity, the risks of FPLV transmission between captive-bred and free-ranging carnivores and domestic cats and dogs warrant further research.Keywords: feline panleukopaenia; parvovirus; felid; cheetah; vaccination
Histological underestimation of a 9-gauge stereotactic vacuum-assisted breast biopsy system compared with surgical excision at a tertiary hospital in South Africa
Authors: Pieters Michael , Otto Susan , Joubert Gina
Published: June 2016

Background: Breast cancer is a major cause of morbidity and mortality worldwide. Certain lesions encountered on mammography require histological assessment of biopsy samples to identify benign versus malignant disease. Stereotactic vacuum-assisted breast biopsy (SVAB) is a useful technique, especially for non-palpable microcalcific lesions, and was introduced at our institution in 2011.Objectives: To determine whether the histological underestimation from 9-gauge SVABs performed at our institution is within acceptable limits.Method: In this cross-sectional study, 9-gauge stereotactic biopsy histology results and breast imaging and reporting data system (BI-RADS) findings of 158 lesions (from 153 patients) were analysed and the histological findings compared with surgical excision histology results (54 lesions) to determine histological underestimation (upgrade rates).Results: One out of eight cases of ductal carcinoma in situ (DCIS) was underestimated, yielding a DCIS underestimation rate of 12.5%.Conclusion: The DCIS underestimation obtained from the present study in our institution was on a par with other authors’ findings and was therefore within acceptable limits. Atypical ductal hyperplasia underestimation could not be reliably obtained with the small study population.Keywords: Stereotactic vacuum assisted breast biopsy; histological underestimation; histological upgrade
Cubulated groups: thickness, relative hyperbolicity, and simplicial boundaries
Authors: Behrstock Jason , Hagen Mark F.
Published: June 2016

Let

G

be a group acting geometrically on a CAT(0) cube complex

\mathbf X

. We prove first that

G

is hyperbolic relative to the collection

\mathbb P

of subgroups if and only if the simplicial boundary

\partial_{\triangle} \mathbf X

is the disjoint union of a nonempty discrete set, together with a pairwise-disjoint collection of subcomplexes corresponding, in the appropriate sense, to elements of

\mathbb P

. As a special case of this result is a new proof, in the cubical case, of a Theorem of Hruska and Kleiner regarding Tits boundaries of relatively hyperbolic CAT(0) spaces. Second, we relate the existence of cut-points in asymptotic cones of a cube complex

\mathbf X

to boundedness of the 1-skeleton of

\partial_{\triangle} \mathbf X

. We deduce characterizations of thickness and strong algebraic thickness of a group

G

acting properly and cocompactly on the CAT(0) cube complex

\mathbf X

in terms of the structure of, and nature of the

G

-action on,

\partial_{\triangle} \mathbf X

. Finally, we construct, for each

n\geq 0, k\geq 2

, infinitely many quasi-isometry types of group

G

such that

G

is strongly algebraically thick of order

n

, has polynomial divergence of order

n+1

, and acts properly and cocompactly on a

k

-dimensional CAT(0) cube complex.
A short proof of Handel and Mosher's alternative for subgroups of Out$(F_N)$
Authors: Horbez Camille
Published: June 2016

We give a short proof of a theorem of Handel and Mosher [21] stating that any finitely generated subgroup of Out

(F_N)

either contains a fully irreducible automorphism, or virtually fixes the conjugacy class of a proper free factor of

F_N

, and we extend their result to non finitely generated subgroups of Out

(F_N)

.
Irrational $l^2$ invariants arising from the lamplighter group
Authors: Grabowski Łukasz
Published: June 2016

We show that the Novikov–Shubin invariant of an element of the integral group ring of the lamplighter group

\mathbf Z_2 \wr \mathbf Z

can be irrational. This disproves a conjecture of Lott and Lück. Furthermore we show that every positive real number is equal to the Novikov–Shubin invariant of some element of the real group ring of

\mathbf Z_2 \wr \mathbf Z

. Finally we show that the

l^2

-Betti number of a matrix over the integral group ring of the group

\mathbf Z_p \wr \mathbf Z

, where

p

is a natural number greater than

1

, can be irrational. As such the groups

\mathbf Z_p \wr \mathbf Z

become the simplest known examples which give rise to irrational

l^2

-Betti numbers.
Photovoltaic Cell: Optimum Photon Utilisation
Authors: Caruana Liam , Nommensen Thomas , Dinh Toan , Tran Dennis , McCormick Robert
Published: June 2016

In the 21st century, global energy consumption has increased exponentially and hence, sustainable energy sources are essential to accommodate for this. Advancements within photovoltaics, in regards to light trapping, has demonstrated to be a promising field of dramatically improving the efficiency of solar cells. This improvement is done by using different nanostructures, which enables solar cells to use the light spectrum emitted more efficiently. The purpose of this meta study is to investigate irreversible entropic losses related to light trapping. In this respect, the observation is aimed at how nanostructures on a silicon substrate captures high energy incident photons. Furthermore, different types of nanostructures are then investigated and compared, using the étendue ratio during light trapping. It is predicted that étendue mismatching is a parasitic entropy generation variable, and that the matching has an effect on the open circuit voltage of the solar cell. Although solar cells do have their limiting efficiencies, according to the Shockley-Queisser theory and Yablonovitch limit, with careful engineering and manufacturing practices, these irreversible entropic losses could be minimized. Further research in energy losses, due to entropy generation, may guide nanostructures and photonics in exceeding past these limits.Keywords: Photovoltaic cell; Shockley-Queisser; Solar cell nanostructures; Solar cell intrinsic and extrinsic losses; entropy; étendue; light trapping; Shockley Queisser; Geometry; Meta-study
Integrated Gasification Combined Cycle from coal
Authors: Giouzelis Kieran , Chou Jacky , Yeung Jeremy
Published: June 2016

An integrated gasification combined cycle (IGCC) is a technology that uses a high pressure gasifier to turn coal, a carbon based fuels into pressurized gas, this is also known as synthesis gas or syngas. The IGCC system consist of 4 main structures; air compression and separation unit, gasifier, combustion and steam turbine and heat recovery generator.A meta-analysis was conducted to investigate possible relationships between the efficiency and types of gasifiers used in the integrated gasification combined cycle in terms of the key thermodynamic laws. Through this analysis correlations were established between varying coal compositions, types of gasification systems and thermal efficiency. It was found that the updraft gasifier had the highest efficiency across most reports, thus making this procedure the most efficient with today’s current knowledge in terms of the thermodynamic principles associated with coal-fired power plants. It was also established that coal with lower moisture content will generally allow a system to be more efficient.
Survival and settling of larval Macoma balthica in a large-scale mesocosm experiment at different fCO2 levels
Authors: Jansson Anna , Lischka Silke , Boxhammer Tim , Schulz Kai G. , Norkko Joanna
Published: June 2016

Abstract. Anthropogenic carbon dioxide (CO2) emissions are causing severe changes in the global inorganic carbon balance of the oceans. Associated ocean acidification is expected to pose a major threat to marine ecosystems worldwide, and it is also expected to be amplified in the Baltic Sea where the system is already exposed to relatively large natural seasonal and diel pH fluctuations. We studied the responses of larvae of the benthic key species Macoma balthica to a range of future CO2 scenarios using six  ∼  55 m3 mesocosms encompassing the entire pelagic community. The mesocosms were deployed in the northern Baltic Sea in June 2012. We focused on the survival, growth and subsequent settlement process of Macoma balthica when exposed to different levels of future CO2. The size and time to settlement of M. balthica increased along the CO2 gradient, suggesting a developmental delay. With ongoing climate change, both the frequency and extent of regularly occurring high CO2 conditions are likely to increase, and a permanent pH decrease will likely occur. The strong impact of increasing CO2 levels on early-stage bivalves is alarming as these stages are crucial for sustaining viable populations, and a failure in their recruitment would ultimately lead to negative effects on the population.
REAL-TIME ENVIRONMENTAL SENSORS TO IMPROVE HEALTH IN THE SENSING CITY
Authors: Marek L. , Campbell M. , Epton M. , Storer M. , Kingham S.
Published: June 2016

Abstract. The opportunity of an emerging smart city in post-disaster Christchurch has been explored as a way to improve the quality of life of people suffering Chronic Obstructive Pulmonary Disease (COPD), which is a progressive disease that affects respiratory function. It affects 1 in 15 New Zealanders and is the 4th largest cause of death, with significant costs to the health system. While, cigarette smoking is the leading cause of COPD, long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust can also cause and exacerbate it. Currently, we do know little what happens to the patients with COPD after they leave a doctor’s care. By learning more about patients’ movements in space and time, we can better understand the impacts of both the environment and personal mobility on the disease. This research is studying patients with COPD by using GPS-enabled smartphones, combined with the data about their spatiotemporal movements and information about their actual usage of medication in near real-time. We measure environmental data in the city, including air pollution, humidity and temperature and how this may subsequently be associated with COPD symptoms. In addition to the existing air quality monitoring network, to improve the spatial scale of our analysis, we deployed a series of low-cost Internet of Things (IoT) air quality sensors as well. The study demonstrates how health devices, smartphones and IoT sensors are becoming a part of a new health data ecosystem and how their usage could provide information about high-risk health hotspots, which, in the longer term, could lead to improvement in the quality of life for patients with COPD.
NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONS
Authors: Dierenbach K. O. , Weinmann M. , Jutzi B.
Published: June 2016

Abstract. This work describes an iterative algorithm for estimating optimal viewpoints, so called next-best-views (NBVs). The goal is to incrementally construct a topological network from the scene during the consecutive acquisition of several views. Our approach is a hybrid method between a surface-based and a volumetric approach with a continuous model space. Hence, a new scan taken from an optimal position should either cover as much as possible from the unknown object surface in one single scan, or densify the existing data and close possible gaps. Based on the point density, we recover the essential and structural information of a scene based on the Growing Neural Gas (GNG) algorithm. From the created graph representation of topological relations, the density of the point cloud at each network node is estimated by approximating the volume of Voronoi cells. The NBV Finder selects a network node as NBV, which has the lowest point density. Our NBV method is self-terminating when all regions reach a predefined minimum point density or the change of the GNG error is zero. For evaluation, we use a Buddha statue with a rather simple surface geometry but still some concave parts and the Stanford Dragon with a more complex object surface containing occluded and concave parts. We demonstrate that our NBV method outperforms a “naive random” approach relying on uniformly distributed sensor positions in terms of efficiency, i.e. our proposed method reaches a desired minimum point density up to 20% faster with less scans.
EXTERIOR ORIENTATION ESTIMATION OF OBLIQUE AERIAL IMAGERY USING VANISHING POINTS
Authors: Verykokou Styliani , Ioannidis Charalabos
Published: June 2016

Abstract. In this paper, a methodology for the calculation of rough exterior orientation (EO) parameters of multiple large-scale overlapping oblique aerial images, in the case that GPS/INS information is not available (e.g., for old datasets), is presented. It consists of five main steps; (a) the determination of the overlapping image pairs and the single image in which four ground control points have to be measured; (b) the computation of the transformation parameters from every image to the coordinate reference system; (c) the rough estimation of the camera interior orientation parameters; (d) the estimation of the true horizon line and the nadir point of each image; (e) the calculation of the rough EO parameters of each image. A developed software suite implementing the proposed methodology is tested using a set of UAV multi-perspective oblique aerial images. Several tests are performed for the assessment of the errors and show that the estimated EO parameters can be used either as initial approximations for a bundle adjustment procedure or as rough georeferencing information for several applications, like 3D modelling, even by non-photogrammetrists, because of the minimal user intervention needed. Finally, comparisons with a commercial software are made, in terms of automation and correctness of the computed EO parameters.
AUTOMATIC KAPPA ANGLE ESTIMATION FOR AIR PHOTOS BASED ON PHASE ONLY CORRELATION
Authors: Xiong Z. , Stanley D. , Xin Y.
Published: June 2016

Abstract. The approximate value of exterior orientation parameters is needed for air photo bundle adjustment. Usually the air borne GPS/IMU can provide the initial value for the camera position and attitude angle. However, in some cases, the camera’s attitude angle is not available due to lack of IMU or other reasons. In this case, the kappa angle needs to be estimated for each photo before bundle adjustment. The kappa angle can be obtained from the Ground Control Points (GCPs) in the photo. Unfortunately it is not the case that enough GCPs are always available. In order to overcome this problem, an algorithm is developed to automatically estimate the kappa angle for air photos based on phase only correlation technique. This function has been embedded in PCI software. Extensive experiments show that this algorithm is fast, reliable, and stable.
DETECTION OF GEOMETRIC KEYPOINTS AND ITS APPLICATION TO POINT CLOUD COARSE REGISTRATION
Authors: Bueno M. , Martínez-Sánchez J. , González-Jorge H. , Lorenzo H.
Published: June 2016

Abstract. Acquisition of large scale scenes, frequently, involves the storage of large amount of data, and also, the placement of several scan positions to obtain a complete object. This leads to a situation with a different coordinate system in each scan position. Thus, a preprocessing of it to obtain a common reference frame is usually needed before analysing it. Automatic point cloud registration without locating artificial markers is a challenging field of study. The registration of millions or billions of points is a demanding task. Subsampling the original data usually solves the situation, at the cost of reducing the precision of the final registration. In this work, a study of the subsampling via the detection of keypoints and its capability to apply in coarse alignment is performed. The keypoints obtained are based on geometric features of each individual point, and are extracted using the Difference of Gaussians approach over 3D data. The descriptors include features as eigenentropy, change of curvature and planarity. Experiments demonstrate that the coarse alignment, obtained through these keypoints outperforms the coarse registration root mean squared error of an operator by 3 - 5 cm. The applicability of these keypoints is tested and verified in five different case studies.
AN APPROACH TO AUTOMATIC DETECTION AND HAZARD RISK ASSESSMENT OF LARGE PROTRUDING ROCKS IN DENSELY FORESTED HILLY REGION
Authors: Chhatkuli S. , Kawamura K. , Manno K. , Satoh T. , Tachibana K.
Published: June 2016

Abstract. Rock-fall along highways or railways presents one of the major threats to transportation and human safety. So far, the only feasible way to detect the locations of such protruding rocks located in the densely forested hilly region is by physically visiting the site and assessing the situation. Highways or railways are stretched to hundreds of kilometres; hence, this traditional approach of determining rock-fall risk zones is not practical to assess the safety throughout the highways or railways. In this research, we have utilized a state-of-the-art airborne LiDAR technology and derived a workflow to automatically detect protruding rocks in densely forested hilly regions and analysed the level of hazard risks they pose. Moreover, we also performed a 3D dynamic simulation of rock-fall to envisage the event. We validated that our proposed technique could automatically detect most of the large protruding rocks in the densely forested hilly region. Automatic extraction of protruding rocks and proper risk zoning could be used to identify the most crucial place that needs the proper protection measures. Hence, the proposed technique would provide an invaluable support for the management and planning of highways and railways safety, especially in the forested hilly region.
EVALUATION OF METHODS FOR COREGISTRATION AND FUSION OF RPAS-BASED 3D POINT CLOUDS AND THERMAL INFRARED IMAGES
Authors: Hoegner L. , Tuttas S. , Xu Y. , Eder K. , Stilla U.
Published: June 2016

Abstract. This paper discusses the automatic coregistration and fusion of 3d point clouds generated from aerial image sequences and corresponding thermal infrared (TIR) images. Both RGB and TIR images have been taken from a RPAS platform with a predefined flight path where every RGB image has a corresponding TIR image taken from the same position and with the same orientation with respect to the accuracy of the RPAS system and the inertial measurement unit. To remove remaining differences in the exterior orientation, different strategies for coregistering RGB and TIR images are discussed: (i) coregistration based on 2D line segments for every single TIR image and the corresponding RGB image. This method implies a mainly planar scene to avoid mismatches; (ii) coregistration of both the dense 3D point clouds from RGB images and from TIR images by coregistering 2D image projections of both point clouds; (iii) coregistration based on 2D line segments in every single TIR image and 3D line segments extracted from intersections of planes fitted in the segmented dense 3D point cloud; (iv) coregistration of both the dense 3D point clouds from RGB images and from TIR images using both ICP and an adapted version based on corresponding segmented planes; (v) coregistration of both image sets based on point features. The quality is measured by comparing the differences of the back projection of homologous points in both corrected RGB and TIR images.
CLASSIFICATION OF LIDAR DATA FOR GENERATING A HIGH-PRECISION ROADWAY MAP
Authors: Jeong J. , Lee I.
Published: June 2016

Abstract. Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.
ANALYSIS OF THE POSSIBILITIES OF USING LOW-COST SCANNING SYSTEM IN 3D MODELING
Authors: Kedzierski M. , Wierzbickia D. , Fryskowska A. , Chlebowska B.
Published: June 2016

Abstract. The laser scanning technique is still a very popular and fast growing method of obtaining information on modeling 3D objects. The use of low-cost miniature scanners creates new opportunities for small objects of 3D modeling based on point clouds acquired from the scan. The same, the development of accuracy and methods of automatic processing of this data type is noticeable. The article presents methods of collecting raw datasets in the form of a point-cloud using a low-cost ground-based laser scanner FabScan. As part of the research work 3D scanner from an open source FabLab project was constructed. In addition, the results for the analysis of the geometry of the point clouds obtained by using a low-cost laser scanner were presented. Also, some analysis of collecting data of different structures (made of various materials such as: glass, wood, paper, gum, plastic, plaster, ceramics, stoneware clay etc. and of different shapes: oval and similar to oval and prism shaped) have been done. The article presents two methods used for analysis: the first one - visual (general comparison between the 3D model and the real object) and the second one - comparative method (comparison between measurements on models and scanned objects using the mean error of a single sample of observations). The analysis showed, that the low-budget ground-based laser scanner FabScan has difficulties with collecting data of non-oval objects. Items built of glass painted black also caused problems for the scanner. In addition, the more details scanned object contains, the lower the accuracy of the collected point-cloud is. Nevertheless, the accuracy of collected data (using oval-straight shaped objects) is satisfactory. The accuracy, in this case, fluctuates between ± 0,4 mm and ± 1,0 mm whereas when using more detailed objects or a rectangular shaped prism the accuracy is much more lower, between 2,9 mm and ± 9,0 mm. Finally, the publication presents the possibility (for the future expansion of research) of modernization FabScan by the implementation of a larger amount of camera-laser units. This will enable spots the registration , that are less visible.
AUTOMATED MOSAICKING OF MULTIPLE 3D POINT CLOUDS GENERATED FROM A DEPTH CAMERA
Authors: Kim H. , Yoon W. , Kim T.
Published: June 2016

Abstract. In this paper, we propose a method for automated mosaicking of multiple 3D point clouds generated from a depth camera. A depth camera generates depth data by using ToF (Time of Flight) method and intensity data by using intensity of returned signal. The depth camera used in this paper was a SR4000 from MESA Imaging. This camera generates a depth map and intensity map of 176 x 44 pixels. Generated depth map saves physical depth data with mm of precision. Generated intensity map contains texture data with many noises. We used texture maps for extracting tiepoints and depth maps for assigning z coordinates to tiepoints and point cloud mosaicking. There are four steps in the proposed mosaicking method. In the first step, we acquired multiple 3D point clouds by rotating depth camera and capturing data per rotation. In the second step, we estimated 3D-3D transformation relationships between subsequent point clouds. For this, 2D tiepoints were extracted automatically from the corresponding two intensity maps. They were converted into 3D tiepoints using depth maps. We used a 3D similarity transformation model for estimating the 3D-3D transformation relationships. In the third step, we converted local 3D-3D transformations into a global transformation for all point clouds with respect to a reference one. In the last step, the extent of single depth map mosaic was calculated and depth values per mosaic pixel were determined by a ray tracing method. For experiments, 8 depth maps and intensity maps were used. After the four steps, an output mosaicked depth map of 454x144 was generated. It is expected that the proposed method would be useful for developing an effective 3D indoor mapping method in future.
SINGLE IMAGE CAMERA CALIBRATION IN CLOSE RANGE PHOTOGRAMMETRY FOR SOLDER JOINT ANALYSIS
Authors: Heinemann D. , Knabner S. , Baumgarten D.
Published: June 2016

Abstract. Printed Circuit Boards (PCB) play an important role in the manufacturing of electronic devices. To ensure a correct function of the PCBs a certain amount of solder paste is needed during the placement of components. The aim of the current research is to develop an real-time, closed-loop solution for the analysis of the printing process where solder is printed onto PCBs. Close range photogrammetry allows for determination of the solder volume and a subsequent correction if necessary. Photogrammetry is an image based method for three dimensional reconstruction from two dimensional image data of an object. A precise camera calibration is indispensable for an accurate reconstruction. In our certain application it is not possible to use calibration methods with two dimensional calibration targets. Therefore a special calibration target was developed and manufactured, which allows for single image camera calibration.
POINT CLOUD ORIENTED SHOULDER LINE EXTRACTION IN LOESS HILLY AREA
Authors: Min Li , Xin Yang , Liyang Xiong
Published: June 2016

Abstract. Shoulder line is the significant line in hilly area of Loess Plateau in China, dividing the surface into positive and negative terrain (P-N terrains). Due to the point cloud vegetation removal methods of P-N terrains are different, there is an imperative need for shoulder line extraction. In this paper, we proposed an automatic shoulder line extraction method based on point cloud. The workflow is as below: (i) ground points were selected by using a grid filter in order to remove most of noisy points. (ii) Based on DEM interpolated by those ground points, slope was mapped and classified into two classes (P-N terrains), using Natural Break Classified method. (iii) The common boundary between two slopes is extracted as shoulder line candidate. (iv) Adjust the filter gird size and repeat step i-iii until the shoulder line candidate matches its real location. (v) Generate shoulder line of the whole area. Test area locates in Madigou, Jingbian County of Shaanxi Province, China. A total of 600 million points are acquired in the test area of 0.23km2, using Riegl VZ400 3D Laser Scanner in August 2014. Due to the limit Granted computing performance, the test area is divided into 60 blocks and 13 of them around the shoulder line were selected for filter grid size optimizing. The experiment result shows that the optimal filter grid size varies in diverse sample area, and a power function relation exists between filter grid size and point density. The optimal grid size was determined by above relation and shoulder lines of 60 blocks were then extracted. Comparing with the manual interpretation results, the accuracy of the whole result reaches 85%. This method can be applied to shoulder line extraction in hilly area, which is crucial for point cloud denoising and high accuracy DEM generation.
TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION
Authors: Li N. , Liu C. , Pfeifer N. , Yin J. F. , Liao Z.Y. , Zhou Y.
Published: June 2016

Abstract. Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.
POINT CLOUD REFINEMENT WITH A TARGET-FREE INTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM
Authors: Nouiraa H. , Deschaud J. E. , Goulettea F.
Published: June 2016

Abstract. LIDAR sensors are widely used in mobile mapping systems. The mobile mapping platforms allow to have fast acquisition in cities for example, which would take much longer with static mapping systems. The LIDAR sensors provide reliable and precise 3D information, which can be used in various applications: mapping of the environment; localization of objects; detection of changes. Also, with the recent developments, multi-beam LIDAR sensors have appeared, and are able to provide a high amount of data with a high level of detail. A mono-beam LIDAR sensor mounted on a mobile platform will have an extrinsic calibration to be done, so the data acquired and registered in the sensor reference frame can be represented in the body reference frame, modeling the mobile system. For a multibeam LIDAR sensor, we can separate its calibration into two distinct parts: on one hand, we have an extrinsic calibration, in common with mono-beam LIDAR sensors, which gives the transformation between the sensor cartesian reference frame and the body reference frame. On the other hand, there is an intrinsic calibration, which gives the relations between the beams of the multi-beam sensor. This calibration depends on a model given by the constructor, but the model can be non optimal, which would bring errors and noise into the acquired point clouds. In the litterature, some optimizations of the calibration parameters are proposed, but need a specific routine or environment, which can be constraining and time-consuming. In this article, we present an automatic method for improving the intrinsic calibration of a multi-beam LIDAR sensor, the Velodyne HDL-32E. The proposed approach does not need any calibration target, and only uses information from the acquired point clouds, which makes it simple and fast to use. Also, a corrected model for the Velodyne sensor is proposed. An energy function which penalizes points far from local planar surfaces is used to optimize the different proposed parameters for the corrected model, and we are able to give a confidence value for the calibration parameters found. Optimization results on both synthetic and real data are presented.
MULTIPARAMETER CORRECTION INTENSITY OF TERRESTRIAL LASER SCANNING DATA AS AN INPUT FOR ROCK SURFACE MODELLING
Authors: Paleček V. , Kubíček P.
Published: June 2016

Abstract. A large increase in the creation of 3D models of objects all around us can be observed in the last few years; thanks to the help of the rapid development of new advanced technologies for spatial data collection and robust software tools. A new commercially available airborne laser scanning data in Czech Republic, provided in the form of the Digital terrain model of the fifth generation as irregularly spaced points, enable locating the majority of rock formations. However, the positional and height accuracy of this type of landforms can reach huge errors in some cases. Therefore, it is necessary to start mapping using terrestrial laser scanning with the possibility of adding a point cloud data derived from ground or aerial photogrammetry. Intensity correction and noise removal is usually based on the distance between measured objects and the laser scanner, the incidence angle of the beam or on the radiometric and topographic characteristics of measured objects. This contribution represents the major undesirable effects that affect the quality of acquisition and processing of laser scanning data. Likewise there is introduced solutions to some of these problems.
COMPARISON OF SEMI AUTOMATIC DTM FROM IMAGE MATCHING WITH DTM FROM LIDAR
Authors: Rahmayudi Aji , Rizaldy Aldino
Published: June 2016

Abstract. Nowadays DTM LIDAR was used extensively for generating contour line in Topographic Map. This method is very superior compared to traditionally stereomodel compilation from aerial images that consume large resource of human operator and very time consuming. Since the improvement of computer vision and digital image processing, it is possible to generate point cloud DSM from aerial images using image matching algorithm. It is also possible to classify point cloud DSM to DTM using the same technique with LIDAR classification and producing DTM which is comparable to DTM LIDAR. This research will study the accuracy difference of both DTMs and the result of DTM in several different condition including urban area and forest area, flat terrain and mountainous terrain, also time calculation for mass production Topographic Map. From statistical data, both methods are able to produce 1:5.000 Topographic Map scale.
FIRST PRISMATIC BUILDING MODEL RECONSTRUCTION FROM TOMOSAR POINT CLOUDS
Authors: Sun Y. , Shahzad M. , Zhu X.
Published: June 2016

Abstract. This paper demonstrates for the first time the potential of explicitly modelling the individual roof surfaces to reconstruct 3-D prismatic building models using spaceborne tomographic synthetic aperture radar (TomoSAR) point clouds. The proposed approach is modular and works as follows: it first extracts the buildings via DSM generation and cutting-off the ground terrain. The DSM is smoothed using BM3D denoising method proposed in (Dabov et al., 2007) and a gradient map of the smoothed DSM is generated based on height jumps. Watershed segmentation is then adopted to oversegment the DSM into different regions. Subsequently, height and polygon complexity constrained merging is employed to refine (i.e., to reduce) the retrieved number of roof segments. Coarse outline of each roof segment is then reconstructed and later refined using quadtree based regularization plus zig-zag line simplification scheme. Finally, height is associated to each refined roof segment to obtain the 3-D prismatic model of the building. The proposed approach is illustrated and validated over a large building (convention center) in the city of Las Vegas using TomoSAR point clouds generated from a stack of 25 images using Tomo-GENESIS software developed at DLR.
DIGITAL TERRAIN MODELS FROM MOBILE LASER SCANNING DATA IN MORAVIAN KARST
Authors: Tyagur N. , Hollaus M.
Published: June 2016

Abstract. During the last ten years, mobile laser scanning (MLS) systems have become a very popular and efficient technology for capturing reality in 3D. A 3D laser scanner mounted on the top of a moving vehicle (e.g. car) allows the high precision capturing of the environment in a fast way. Mostly this technology is used in cities for capturing roads and buildings facades to create 3D city models. In our work, we used an MLS system in Moravian Karst, which is a protected nature reserve in the Eastern Part of the Czech Republic, with a steep rocky terrain covered by forests. For the 3D data collection, the Riegl VMX 450, mounted on a car, was used with integrated IMU/GNSS equipment, which provides low noise, rich and very dense 3D point clouds. The aim of this work is to create a digital terrain model (DTM) from several MLS data sets acquired in the neighbourhood of a road. The total length of two covered areas is 3.9 and 6.1 km respectively, with an average width of 100 m. For the DTM generation, a fully automatic, robust, hierarchic approach was applied. The derivation of the DTM is based on combinations of hierarchical interpolation and robust filtering for different resolution levels. For the generation of the final DTMs, different interpolation algorithms are applied to the classified terrain points. The used parameters were determined by explorative analysis. All MLS data sets were processed with one parameter set. As a result, a high precise DTM was derived with high spatial resolution of 0.25 x 0.25 m. The quality of the DTMs was checked by geodetic measurements and visual comparison with raw point clouds. The high quality of the derived DTM can be used for analysing terrain changes and morphological structures. Finally, the derived DTM was compared with the DTM of the Czech Republic (DMR 4G) with a resolution of 5 x 5 m, which was created from airborne laser scanning data. The vertical accuracy of the derived DTMs is around 0.10 m.
A WEIGHTED CLOSED-FORM SOLUTION FOR RGB-D DATA REGISTRATION
Authors: Vestena K. M. , Dos Santos D. R. , Oilveira Jr. E. M. , Pavan N. L. , Khoshelham K.
Published: June 2016

Abstract. Existing 3D indoor mapping of RGB-D data are prominently point-based and feature-based methods. In most cases iterative closest point (ICP) and its variants are generally used for pairwise registration process. Considering that the ICP algorithm requires an relatively accurate initial transformation and high overlap a weighted closed-form solution for RGB-D data registration is proposed. In this solution, we weighted and normalized the 3D points based on the theoretical random errors and the dual-number quaternions are used to represent the 3D rigid body motion. Basically, dual-number quaternions provide a closed-form solution by minimizing a cost function. The most important advantage of the closed-form solution is that it provides the optimal transformation in one-step, it does not need to calculate good initial estimates and expressively decreases the demand for computer resources in contrast to the iterative method. Basically, first our method exploits RGB information. We employed a scale invariant feature transformation (SIFT) for extracting, detecting, and matching features. It is able to detect and describe local features that are invariant to scaling and rotation. To detect and filter outliers, we used random sample consensus (RANSAC) algorithm, jointly with an statistical dispersion called interquartile range (IQR). After, a new RGB-D loop-closure solution is implemented based on the volumetric information between pair of point clouds and the dispersion of the random errors. The loop-closure consists to recognize when the sensor revisits some region. Finally, a globally consistent map is created to minimize the registration errors via a graph-based optimization. The effectiveness of the proposed method is demonstrated with a Kinect dataset. The experimental results show that the proposed method can properly map the indoor environment with an absolute accuracy around 1.5% of the travel of a trajectory.