scholarly journals Participator, A Participatory Urban Design Instrument

2019 ◽  
Author(s):  
Kas Oosterhuis ◽  
Arwin Hidding
Keyword(s):  
Point Cloud ◽  
Spatial Arrangement ◽  
Reference Points ◽  
Complex Data ◽  
Form Complex ◽  
Working Space ◽  
Nomadic Life ◽  
Urban Strategy ◽  
Set Of Points ◽  
New Babylon

A point cloud of reference points forms the programmable basis of a new method of urban and architectural modeling. Points in space from the smallest identifiable units that are informed to communicate with each other to form complex data structures. The data are visualized as spatial voxels [3d pixels] as to represent spaces and volumes that maintain their mutual relationships under varying circumstances. The subsequent steps in the development from point cloud to the multimodal urban strategy are driven by variable local and global parameters. Step by step new and more detailed actors are introduced in the serious design game. Values feeding the voxel units may be fixed, variables based on experience, or randomly generated. The target value may be fixed or kept open. Using lines or curves and groups of points from the original large along the X, Y and Z-axes organized crystalline set of points are selected to form the shape of actual working space. The concept of radical multimodality at the level of the smallest grain requires that at each stage in the design game individual units are addressed as to adopt a unique function during a unique amount of time. Each unit may be a home, a workplace, a workshop, a shop, a lounge area, a school, a garden or just an empty voxel anytime and anywhere in the selected working space. The concept of multimodality [MANIC, K Oosterhuis, 2018] is taken to its extreme as to stimulate the development of diversity over time and in its spatial arrangement. The programmable framework for urban multimodality acknowledges the rise and shine of the new international citizen, who travels the world, lives nowhere and everywhere, inhabits places and spaces for ultrashort, shorter or longer periods of time, lives her/his life as a new nomad [New Babylon, Constant Nieuwenhuys, 1958]. The new nomad lives on her/his own or in groups of like-minded people, effectuated by setting preferences and choices being made via the ubiquitous multimodality app, which organizes the unfolding of her / his life. In the serious design game nomadic life is facilitated by real time activation of a complex set of programmable monads. Playing and further developing the design journey was executed in 4 workshop sessions with different professional stakeholders, architects, engineers, entrepreneurs and project developers.

scholarly journals Automated placement of stereotactic injections using a laser scan of the skull

2014 ◽  
Author(s):  
Margaret Henderson ◽  
Vadim Pinskiy ◽  
Alexander Tolpygo ◽  
Stephen Savoia ◽  
Pascal Grange ◽  
...  
Keyword(s):  
Individual Variation ◽  
Point Cloud ◽  
Software Tool ◽  
Reference Points ◽  
Common Method ◽  
Matching Algorithm ◽  
Scan Registration ◽  
Automated Placement

Stereotactic targeting is a commonly used technique for performing injections in the brains of mice and other animals. The most common method for targeting stereoscopic injections uses the skull indentations bregma and lambda as reference points and is limited in its precision by factors such as skull curvature and individual variation, as well as an incomplete correspondence between skull landmarks and brain locations. In this software tool, a 3D laser scan of the mouse skull is taken in vitro and registered onto a reference skull using a point cloud matching algorithm, and the parameters of the transformation are used to position a glass pipette to place tracer injections. The software was capable of registering sample skulls with less than 100 micron error, and was able to target an injection in a mouse with error of roughly 500 microns. These results indicate that using skull scan registration has the potential to be widely applicable in automating stereotactic targeting of tracer injections.

Engineering cooperative patterns in multi-species bacterial colonies

2019 ◽  
Author(s):  
A. I. Curatolo ◽  
N. Zhou ◽  
Y. Zhao ◽  
C. Liu ◽  
A. Daerr ◽  
...  
Keyword(s):  
Population Level ◽  
Spatial Arrangement ◽  
Cell Types ◽  
Living Systems ◽  
Cellular Interactions ◽  
Cellular Motility ◽  
Periodic Patterns ◽  
Form Complex ◽  
Theoretical Approaches

Self-organization is a hallmark of all living systems [1]. In particular, coordinated cellular behavior, commonly orchestrated at the population level through reciprocal interactions among different cell species [2–4], regulates the spatial arrangement of specialized cell types to generate tissue patterning and form complex body layouts [5, 6]. The overwhelming complexity of living systems, however, makes deciphering the underlying mechanisms difficult and limits our knowledge of basic pattern-forming mechanism in vivo [7, 8]. A successful strategy is then to work with synthetic, engineered systems, in which cellular interactions can be more easily tailored and studied [9–13]. Here, we demonstrate a simple mechanism through which different populations of cells can self-organize in periodic patterns. Programmed population interactions are shown to lead to coordinated out-ofphase spatial oscillations of two engineered populations of Escherichia coli. Using a combination of experimental and theoretical approaches, we show how such patterns arise autonomously from reciprocal density-dependent activation of cellular motility between the two species, without the need of any preexisting positional or orientational cues. Moreover, by re-designing the interaction, the original out-of-phase spatial oscillation rhythm of the two populations can be accordingly turned into in-phase oscillations. The robustness and versatility of the underlying pattern-formation process suggest that it could both be generically encountered in nature, for instance in the complex bacterial ecosystems found in biofilms [14–16], and used to promote the mixing or demixing of active particles in a controlled way.

A Low Arithmetic-Degree Algorithm for Computing Proximity Graphs

2018 ◽  
Vol 28 (03) ◽  
pp. 227-253
Author(s):  
Fabrizio d’Amore ◽  
Paolo G. Franciosa
Keyword(s):  
Minimum Spanning Tree ◽  
Optimal Time ◽  
Complex Data ◽  
Double Precision ◽  
Asymptotically Optimal ◽  
Neighborhood Graph ◽  
Proximity Graphs ◽  
Set Of Points ◽  
Region Approach ◽  
Relative Neighborhood Graph

In this paper, we study the problem of designing robust algorithms for computing the minimum spanning tree, the nearest neighbor graph, and the relative neighborhood graph of a set of points in the plane, under the Euclidean metric. We use the term “robust” to denote an algorithm that can properly handle degenerate configurations of the input (such as co-circularities and collinearities) and that is not affected by errors in the flow of control due to round-off approximations. Existing asymptotically optimal algorithms that compute such graphs are either suboptimal in terms of the arithmetic precision required for the implementation, or cannot handle degeneracies, or are based on complex data structures. We present a unified approach to the robust computation of the above graphs. The approach is a variant of the general region approach for the computation of proximity graphs based on Yao graphs, first introduced in Ref. 43 (A. C.-C. Yao, On constructing minimum spanning trees in [Formula: see text]-dimensional spaces and related problems, SIAM J. Comput. 11(4) (1982) 721–736). We show that a sparse supergraph of these geometric graphs can be computed in asymptotically optimal time and space, and requiring only double precision arithmetic, which is proved to be optimal. The arithmetic complexity of the approach is measured by using the notion of degree, introduced in Ref. 31 (G. Liotta, F. P. Preparata and R. Tamassia, Robust proximity queries: An illustration of degree-driven algorithm design, SIAM J. Comput. 28(3) (1998) 864–889) and Ref. 3 (J. D. Boissonnat and F. P. Preparata, Robust plane sweep for intersecting segments, SIAM J. Comput. 29(5) (2000) 1401–1421). As a side effect of our results, we solve a question left open by Katajainen27 (J. Katajainen, The region approach for computing relative neighborhood graphs in the [Formula: see text] metric, Computing 40 (1987) 147–161) about the existence of a subquadratic algorithm, based on the region approach, that computes the relative neighborhood graph of a set of points [Formula: see text] in the plane under the [Formula: see text] metric.

scholarly journals Automated 3D-Objectdocumentation on the Base of an Image Set

2011 ◽  
Vol 6 ◽  
pp. 370-375
Author(s):  
Sebastian Vetter ◽  
Gunnar Siedler
Keyword(s):  
Point Cloud ◽  
Search Algorithm ◽  
Three Dimensional ◽  
Point Clouds ◽  
Spatial Dimension ◽  
High Accuracy ◽  
Relative Orientation ◽  
Reference Points ◽  
Surface Model ◽  
Object Surface

Digital stereo-photogrammetry allows users an automatic evaluation of the spatial dimension and the surface texture of objects. The integration of image analysis techniques simplifies the automation of evaluation of large image sets and offers a high accuracy [1]. Due to the substantial similarities of stereoscopic image pairs, correlation techniques provide measurements of subpixel precision for corresponding image points. With the help of an automated point search algorithm in image sets identical points are used to associate pairs of images to stereo models and group them. The found identical points in all images are basis for calculation of the relative orientation of each stereo model as well as defining the relation of neighboured stereo models. By using proper filter strategies incorrect points are removed and the relative orientation of the stereo model can be made automatically. With the help of 3D-reference points or distances at the object or a defined distance of camera basis the stereo model is orientated absolute. An adapted expansion- and matching algorithm offers the possibility to scan the object surface automatically. The result is a three dimensional point cloud; the scan resolution depends on image quality. With the integration of the iterative closest point- algorithm (ICP) these partial point clouds are fitted to a total point cloud. In this way, 3D-reference points are not necessary. With the help of the implemented triangulation algorithm a digital surface models (DSM) can be created. The texturing can be made automatically by the usage of the images that were used for scanning the object surface. It is possible to texture the surface model directly or to generate orthophotos automatically. By using of calibrated digital SLR cameras with full frame sensor a high accuracy can be reached. A big advantage is the possibility to control the accuracy and quality of the 3d-objectdocumentation with the resolution of the images. The procedure described here is implemented in software Metigo 3D.

scholarly journals A New Method for UAV Lidar Precision Testing Used for the Evaluation of an Affordable DJI ZENMUSE L1 Scanner

2021 ◽  
Vol 13 (23) ◽  
pp. 4811
Author(s):  
Rudolf Urban ◽  
Martin Štroner ◽  
Lenka Línková
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Reference Points ◽  
New Method ◽  
Color Information ◽  
Dense Point ◽  
Aerial Vehicle ◽  
Systematic Shift ◽  
Precision Testing ◽  
Better Than

Lately, affordable unmanned aerial vehicle (UAV)-lidar systems have started to appear on the market, highlighting the need for methods facilitating proper verification of their accuracy. However, the dense point cloud produced by such systems makes the identification of individual points that could be used as reference points difficult. In this paper, we propose such a method utilizing accurately georeferenced targets covered with high-reflectivity foil, which can be easily extracted from the cloud; their centers can be determined and used for the calculation of the systematic shift of the lidar point cloud. Subsequently, the lidar point cloud is cleaned of such systematic shift and compared with a dense SfM point cloud, thus yielding the residual accuracy. We successfully applied this method to the evaluation of an affordable DJI ZENMUSE L1 scanner mounted on the UAV DJI Matrice 300 and found that the accuracies of this system (3.5 cm in all directions after removal of the global georeferencing error) are better than manufacturer-declared values (10/5 cm horizontal/vertical). However, evaluation of the color information revealed a relatively high (approx. 0.2 m) systematic shift.

The Study of Point Cloud Data Automatic Merging Technology in 3D Measuring

2011 ◽  
Vol 109 ◽  
pp. 621-625
Author(s):  
Fa Su ◽  
Jun Ting Cheng
Keyword(s):  
Reference Point ◽  
Point Cloud ◽  
Reference Points ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Icp Algorithm ◽  
Cloud Data ◽  
Merging Process

The measuring of the 3D point cloud data in automatic merging process, it is affixed to the surface of an object to be tested through a reference point on the position, to realize the multi point cloud data automatic seamless splicing.The paper presents the monotone limited algorithm in the acquisition of reference point, this algorithm can effectively and accurately to the reference point for judging and extraction.and puts forward an algorithm which integrates with three reference points orientation principle and ICP algorithm. The algorithm not only realizes the merging, but also raises the efficiency to dozens of times based on raising reorientation precision.

Karma2

2011 ◽  
pp. 380-403
Author(s):  
Yogesh L. Simmhan ◽  
Beth Plale ◽  
Dennis Gannon
Keyword(s):  
Performance Evaluation ◽  
Workflow Engine ◽  
Work Flow ◽  
Complex Data ◽  
Form Complex ◽  
Workflow Execution ◽  
Loosely Coupled ◽  
The World ◽  
Data Products

The increasing ability for the sciences to sense the world around us is resulting in a growing need for datadriven e-Science applications that are under the control of workflows composed of services on the Grid. The focus of our work is on provenance collection for these workflows that are necessary to validate the work-flow and to determine quality of generated data products. The challenge we address is to record uniform and usable provenance metadata that meets the domain needs while minimizing the modification burden on the service authors and the performance overhead on the workflow engine and the services. The framework is based on generating discrete provenance activities during the lifecycle of a workflow execution that can be aggregated to form complex data and process provenance graphs that can span across workflows. The implementation uses a loosely coupled publish-subscribe architecture for propagating these activities, and the capabilities of the system satisfy the needs of detailed provenance collection. A performance evaluation of a prototype finds a minimal performance overhead (in the range of 1% for an eight-service workflow using 271 data products).

Abstract 102: Coregistration of Serial Angiograms Using Point Cloud Matching

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Fabien Scalzo ◽  
Noah Stier ◽  
Jingyu Liu ◽  
Weike Bi ◽  
David S Liebeskind
Keyword(s):  
Point Cloud ◽  
Endovascular Procedures ◽  
Care Center ◽  
Stroke Care ◽  
Patient Specific ◽  
Post Intervention ◽  
Matching Algorithm ◽  
Before And After ◽  
Real Time Visualization ◽  
Set Of Points

Introduction: Digital subtraction angiography (DSA) is the gold standard to assess reperfusion during endovascular procedures. Visualization and quantification of changes between successive runs is challenged by patient motion and variations in acquisition parameters such as zoom and pose of the x-ray receptor. Automated coregistration of successive DSA sequences would allow for the visualization of serial changes in perfusion during the procedure. The objective of this study is to develop a fully automated framework for the coregistration of patient-specific DSA acquired at different time points during an endovascular reperfusion procedure. Methods: The dataset was established retrospectively from patients admitted at a stroke care center and diagnosed with acute ischemic stroke. Included patients underwent a clot retrieval procedure. Biplane DSA was performed before and after endovascular reperfusion intervention. A neurologist manually coregistered the anterior-posterior (AP) view of successive DSA sequences from each patient using anatomical reference landmarks. The developed computer vision framework processed each DSA to extract the vasculature using a vessel detector, followed by resampling of the responses. The resulting set of points in the pre- and post-intervention DSA were then matched using a point cloud matching algorithm. In this study, we provide an experimental analysis with the conventional RANSAC algorithm. Evaluation was performed by measuring the error between the estimated affine transform, that relates the pre- to the post-intervention DSA, and the groundtruth established manually. Results: A total of 20 patients were included in the analysis. Mean age was 66.8 (range 34-91). Distribution of the TICI scores was as follows: TICI 0(3), TICI 1(0), TICI 2a(5), TICI 2b(10), TICI 3(2). Overall coregistration error was as follows: angle (8.6+- 3.1 degrees), shift (24.8 +- 19 mm), respectively. Conclusions: RANSAC point cloud matching algorithm can be used to accurately coregister serial angiograms during endovascular procedures. This could lead to near real-time visualization and quantification of revascularization.

scholarly journals Interactive model of magnetic field reconstruction stand for mobile robot navigation algorithms debugging which use magnetometer data

ACTA IMEKO ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 47
Author(s):  
Stanislaw Goll ◽  
Alexander Borisov
Keyword(s):  
Magnetic Field ◽  
Mobile Robot ◽  
Electrical Equipment ◽  
Reference Points ◽  
Mobile Robot Navigation ◽  
Navigation Systems ◽  
Temperature Drift ◽  
Simulink Model ◽  
Working Space ◽  
Magnetometer Data

An important addition to inertial navigation systems is the magnetometer. Areas with magnetic field anomalies serve to determine the reference points. However, magnetometers can be influenced by both the robot’s configuration and its electrical equipment. Compensation for the robot’s self-influence on the readings of the magnetometers is carried out by computer tools. In order to obtain the initial data, live experiments are required in a natural environment. To simplify data acquisition concerning the behaviour of the magnetometric systems of a mobile robot, a special facility that allows for the local compensation of the Earth’s magnetic field is used, and an artificial magnetic field that varies according to a predetermined algorithm is created. Using this facility, we can also simulate the magnetic field that will be present in the intended environment of the application of the robot. The facility features are a working space that is sufficient to place the mobile robot; a coil temperature drift correction; uniformity of the frequency response in operating frequency range; compensation for the power supply interference and similar disturbances; sensitivity equalisation of control channels; compensation for the misalignment of the sensor’s and coil system’s coordinate systems. An interactive Simulink model is designed and evaluated. The automated stand is created as an experimental facility, its parameters proving the proposed model’s adequacy.

Human Point Cloud Inpainting for Industrial Human-Robot Collaboration Using Deep Generative Model

2020 ◽  
Author(s):  
Le Wang ◽  
Shengquan Xie ◽  
Wenjun Xu ◽  
Bitao Yao ◽  
Jia Cui ◽  
...  
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Industrial Robot ◽  
Generative Model ◽  
Depth Image ◽  
Depth Data ◽  
Working Space ◽  
Arbitrary Position ◽  
Human Robot Collaboration ◽  
Model Algorithm

Abstract In complex industrial human-robot collaboration (HRC) environment, obstacles in the shared working space will occlude the operator, and the industrial robot will threaten the safety of the operator if it is unable to get the complete human spatial point cloud. This paper proposes a real-time human point cloud inpainting method based on the deep generative model. The method can recover the human point cloud occluded by obstacles in the shared working space to ensure the safety of the operator. The method proposed in this paper can be mainly divided into three parts: (i) real-time obstacles detection. This process can detect obstacle locations in real time and generate the image of obstacles. (ii) the application of the deep generative model algorithm. It is a complete convolutional neural network (CNN) structure and introduces advanced generative adversarial loss. The model can generate the missing depth data of operators at arbitrary position in the human depth image. (iii) spatial mapping of the depth image. The depth image will be mapped to point cloud by coordinate system conversion. The effectiveness of the method is verified by filling hole of the human point cloud occluded by obstacles in industrial HRC environment. The experiment results show that the proposed method can accurately generate the occluded human point cloud in real time and ensure the safety of the operator.

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