scholarly journals Seeking a Reference Frame for Cartographic Sonification

2018 ◽  
Author(s):  
Megen Brittell
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Visual Display ◽  
Data Cube ◽  
Geospatial Data ◽  
Data Sets ◽  
Extensive Experience ◽  
Map Design ◽  
Position Data ◽  
New Perspective

Sonification of geospatial data must situate data values in two (or three) dimensional space. The need to position data values in space distinguishes geospatial data from other multi-dimensional data sets. While cartographers have extensive experience preparing geospatial data for visual display, the use of sonification is less common. Beyond availability of tools or visual bias, an incomplete understanding of the implications of parameter mappings that cross conceptual data categories limits the application of sonification to geospatial data. To catalyze the use of audio in cartography, this paper explores existing examples of parameter mapping sonification through the framework of the geographic data cube. More widespread adoption of auditory displays would diversify map design techniques, enhance accessibility of geospatial data, and may also provide new perspective for application to non-geospatial data sets.

scholarly journals JL-GFDN: A Novel Gabor Filter-Based Deep Network Using Joint Spectral-Spatial Local Binary Pattern for Hyperspectral Image Classification

2020 ◽  
Vol 12 (12) ◽  
pp. 2016 ◽  
Author(s):  
Tao Zhang ◽  
Puzhao Zhang ◽  
Weilin Zhong ◽  
Zhen Yang ◽  
Fan Yang
Keyword(s):  
Local Binary Pattern ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Spectral Characteristics ◽  
Three Dimensional ◽  
Real Data ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Data Sets ◽  
Deep Network

The traditional local binary pattern (LBP, hereinafter we also call it a two-dimensional local binary pattern 2D-LBP) is unable to depict the spectral characteristics of a hyperspectral image (HSI). To cure this deficiency, this paper develops a joint spectral-spatial 2D-LBP feature (J2D-LBP) by averaging three different 2D-LBP features in a three-dimensional hyperspectral data cube. Subsequently, J2D-LBP is added into the Gabor filter-based deep network (GFDN), and then a novel classification method JL-GFDN is proposed. Different from the original GFDN framework, JL-GFDN further fuses the spectral and spatial features together for HSI classification. Three real data sets are adopted to evaluate the effectiveness of JL-GFDN, and the experimental results verify that (i) JL-GFDN has a better classification accuracy than the original GFDN; (ii) J2D-LBP is more effective in HSI classification in comparison with the traditional 2D-LBP.

Advanced Data Mining and Visualization Techniques with Probabilistic Principal Surfaces

2011 ◽  
pp. 244-264
Author(s):  
Antonino Staiano ◽  
Lara De Vinco ◽  
Giuseppe Longo ◽  
Roberto Tagliaferri
Keyword(s):  
Density Function ◽  
Latent Variables ◽  
Latent Variable ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Fixed Number ◽  
Data Sets ◽  
Real World Data ◽  
Variable Model ◽  
Principal Surfaces

Probabilistic Principal Surfaces (PPS) is a non linear latent variable model with very powerful visualization and classification capabilities which seem to be able to overcome most of the shortcomings of other neural tools. PPS builds a probability density function of a given set of patterns lying in a high-dimensional space which can be expressed in terms of a fixed number of latent variables lying in a latent Q-dimensional space. Usually, the Q-space is either two or three dimensional and thus the density function can be used to visualize the data within it. The case in which Q = 3 allows to project the patterns on a spherical manifold which turns out to be optimal when dealing with sparse data. PPS may also be arranged in ensembles to tackle complex classification tasks. As template cases we discuss the application of PPS to two real- world data sets from astronomy and genetics.

scholarly journals Mental Rotation in Human Infants

2008 ◽  
Vol 19 (11) ◽  
pp. 1063-1066 ◽  
Author(s):  
David S. Moore ◽  
Scott P. Johnson
Keyword(s):  
Mental Rotation ◽  
Sex Difference ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Familiar Object ◽  
Mirror Image ◽  
Human Infants ◽  
New Perspective ◽  
Three Dimensional Space

A sex difference on mental-rotation tasks has been demonstrated repeatedly, but not in children less than 4 years of age. To demonstrate mental rotation in human infants, we habituated 5-month-old infants to an object revolving through a 240° angle. In successive test trials, infants saw the habituation object or its mirror image revolving through a previously unseen 120° angle. Only the male infants appeared to recognize the familiar object from the new perspective, a feat requiring mental rotation. These data provide evidence for a sex difference in mental rotation of an object through three-dimensional space, consistently seen in adult populations.

scholarly journals Creating Immersive Virtual Environments Based on Open Geospatial Data and Game Engines

2021 ◽  
Author(s):  
Julian Keil ◽  
Dennis Edler ◽  
Thomas Schmitt ◽  
Frank Dickmann
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Geospatial Data ◽  
Game Engine ◽  
Advantages And Disadvantages ◽  
Elevation Data ◽  
Game Engines ◽  
3D Environments ◽  
Spatial Hazard ◽  
Game Developers

AbstractModern game engines like Unity allow users to create realistic 3D environments containing terrains as well as natural and artificial objects easily and swiftly. In addition, recent advances of game engine capabilities enable effortless implementation of virtual reality (VR) compatibility. 3D environments created with VR compatibility can be experienced from an egocentric and stereoscopic perspective that surpasses the immersion of the ‘classical’ screen-based perception of 3D environments. Not only game developers benefit from the possibilities provided by game engines. The ability to use geospatial data to shape virtual 3D environments opens a multitude of possibilities for geographic applications, such as construction planning, spatial hazard simulations or representation of historical places. The multi-perspective, multimodal reconstruction of three-dimensional space based on game engine technology today supports the possibility of linking different approaches of geographic work more closely. Free geospatial data that can be used for spatial reconstructions is provided by numerous national and regional official institutions. However, the file format of these data sources is not standardized and game engines only support a limited number of file formats. Therefore, format transformation is usually required to apply geospatial data to virtual 3D environments. This paper presents several workflows to apply digital elevation data and 3D city model data from OpenStreetMap and the Open.NRW initiative to Unity-based 3D environments. Advantages and disadvantages of different sources of geospatial data are discussed. In addition, implementation of VR compatibility is described. Finally, benefits of immersive VR implementation and characteristics of current VR hardware are discussed in the context of specific geographic application scenarios.

scholarly journals Distance Between Two Circles In Any Number Of Dimensions Is A Vector Ellipse

2021 ◽  
Author(s):  
Asli Pinar Tan
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Mathematical Basis ◽  
Multiple Dimensions ◽  
Special Cases ◽  
Position Data ◽  
Gravitational Pull ◽  
Elliptical Motion ◽  
The Universe ◽  
Moving Bodies

Based on measured astronomical position data of heavenly objects in the Solar System and other planetary systems, all bodies in space seem to move in some kind of elliptical motion with respect to each other, whereas objects follow parabolic escape orbits while moving away from Earth and bodies asserting a gravitational pull, and some comets move in near-hyperbolic orbits when they approach the Sun. In this article, it is first mathematically proven that the “distance between points on any two different circles in three-dimensional space” is equivalent to the “distance of points on a vector ellipse from another fixed or moving point, as in two-dimensional space.” Then, it is further mathematically demonstrated that “distance between points on any two different circles in any number of multiple dimensions” is equivalent to “distance of points on a vector ellipse from another fixed or moving point”. Finally, two special cases when the “distance between points on two different circles in multi-dimensional space” become mathematically equivalent to distances in “parabolic” or “near-hyperbolic” trajectories are investigated. Concepts of “vector ellipse”, “vector hyperbola”, and “vector parabola” are also mathematically defined. The mathematical basis derived in this Article is utilized in the book “Everyhing Is A Circle: A New Model For Orbits Of Bodies In The Universe” in asserting a new Circular Orbital Model for moving bodies in the Universe, leading to further insights in Astrophysics.

scholarly journals Dynamic Sensor Matching for Parallel Point Cloud Data Acquisition

2021 ◽  
Author(s):  
Simone Müller ◽  
Dieter Kranzlmüller
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Object Perception ◽  
Point Clouds ◽  
Sensor Data ◽  
Virtual Object ◽  
Data Sets ◽  
Cloud Data ◽  
Digital Twins

Based on depth perception of individual stereo cameras, spatial structures can be derived as point clouds. The quality of such three-dimensional data is technically restricted by sensor limitations, latency of recording, and insufficient object reconstructions caused by surface illustration. Additionally external physical effects like lighting conditions, material properties, and reflections can lead to deviations between real and virtual object perception. Such physical influences can be seen in rendered point clouds as geometrical imaging errors on surfaces and edges. We propose the simultaneous use of multiple and dynamically arranged cameras. The increased information density leads to more details in surrounding detection and object illustration. During a pre-processing phase the collected data are merged and prepared. Subsequently, a logical analysis part examines and allocates the captured images to three-dimensional space. For this purpose, it is necessary to create a new metadata set consisting of image and localisation data. The post-processing reworks and matches the locally assigned images. As a result, the dynamic moving images become comparable so that a more accurate point cloud can be generated. For evaluation and better comparability we decided to use synthetically generated data sets. Our approach builds the foundation for dynamic and real-time based generation of digital twins with the aid of real sensor data.

scholarly journals A New Perspective on Low-Cost MEMS-Based AHRS Determination

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1383
Author(s):  
Neda Navidi ◽  
Rene Landry
Keyword(s):  
Reference System ◽  
Dimensional Space ◽  
Low Cost ◽  
Three Dimensional ◽  
Navigation Systems ◽  
Adaptive Kalman Filter ◽  
Initial Attitude ◽  
New Perspective ◽  
And Control ◽  
Three Dimensional Space

Attitude and heading reference system (AHRS) is the term used to describe a rigid body’s angular orientation in three-dimensional space. This paper describes an AHRS determination and control system developed for navigation systems by integrating gyroscopes, accelerometers, and magnetometers signals from low-cost MEMS-based sensors in a complementary adaptive Kalman filter. AHRS estimation based on the iterative Kalman filtering process is required to be initialized first. A new method for AHRS initialization is proposed to improve the accuracy of the initial attitude estimates. Attitude estimates derived from the initialization and iterative adaptive filtering processes are compared with the orientation obtained from a high-end reference system. The improvement in the accuracy of the initial orientation as significant as 45% is obtained from the proposed method as compared with other selected techniques. Additionally, the computational process is reduced by 96%.

GPS Satellite Surveying — Practical Aspects

1984 ◽  
Vol 38 (3) ◽  
pp. 177-192 ◽  
Author(s):  
Larry D. Hothem ◽  
Clyde C. Goad ◽  
Benjamin W. Remondi
Keyword(s):  
Three Dimensional ◽  
Field Tests ◽  
Geodetic Survey ◽  
National Geodetic Survey ◽  
Extensive Experience ◽  
Global Positioning ◽  
Position Data ◽  
Survey System ◽  
And Storage ◽  
Processing Steps

No satellite-based survey system has gained as much attention and interest as the Global Positioning System (GPS). Establishing precise positions by use of the GPS is rapidly becoming a powerful and economical tool for surveyors. The extensive experience gained at the National Geodetic Survey (NGS), from field tests in March 1983 to operational projects performed later in 1983 and early 1984, has demonstrated that observations of the GPS satellite signals yield very accurate three-dimensional relative position data. Baselines of up to 100 km are routinely measured to accuracies of 1 cm to 5 cm rms. Differential ellipsoid height accuracies at the few centimeter level have been achieved; depending on accuracies of geoidal undulations, useful elevation differences can be computed. This paper focuses on the more mundane activities required to obtain such results. Although GPS satellite surveying employs sophisticated methodology and instrumentation, there are many practical aspects to be considered. These include requirements for ties to existing control, station selection criteria, field procedures, data handling and storage, and processing steps.

scholarly journals Possible Observational Evidence for the Existence of a Parallel Universe

Foundations ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 1-5
Author(s):  
Eugene Oks
Keyword(s):  
Flow Velocity ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Bulk Flow ◽  
Data Sets ◽  
Microwave Background ◽  
Detection Systems ◽  
Preferred Direction ◽  
Wide Range ◽  
Gravitational Pull

Many totally different kinds of astrophysical observations demonstrated that, in our universe, there exists a preferred direction. Specifically, from observations in a wide range of frequencies, the alignment of various preferred directions in different data sets was found. Moreover, the observed Cosmic Microwave Background (CMB) quadrupole, CMB octopole, radio and optical polarizations from distant sources also indicate the same preferred direction. While this hints at a gravitational pull from the “outside”, the observational data from the Plank satellite showed that the bulk flow velocity was relatively small: much smaller than was initially thought. In the present paper we propose a configuration where two three-dimensional universes (one of which is ours) are embedded in a four-dimensional space and rotate about their barycenter in such a way that the centrifugal force nearly (but not exactly) compensates their mutual gravitational pull. This would explain not only the existence of a preferred direction for each of the three-dimensional universes (the direction to the other universe), but also the fact that the bulk flow velocity, observed in our universe, is relatively small. We point out that this configuration could also explain the perplexing features of the Unidentified Aerial Phenomena (UAP), previously called Unidentified Flying Objects (UFOs), recorded by various detection systems—the features presented in the latest official report by the US Office of the Director of National Intelligence. Thus, the proposed configuration of the two rotating, parallel three-dimensional universes seems to explain both the variety of astrophysical observations and (perhaps) the observed features of the UAP.

Advanced Data Mining and Visualization Techniques with Probabilistic Principal Surfaces

2008 ◽  
pp. 2067-2087
Author(s):  
Antonino Staiano ◽  
Lara De Vinco ◽  
Giuseppe Longo ◽  
Roberto Tagliaferri
Keyword(s):  
Density Function ◽  
Latent Variables ◽  
Latent Variable ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Fixed Number ◽  
Data Sets ◽  
Real World Data ◽  
Variable Model ◽  
Principal Surfaces

Probabilistic Principal Surfaces (PPS) is a non linear latent variable model with very powerful visualization and classification capabilities which seem to be able to overcome most of the shortcomings of other neural tools. PPS builds a probability density function of a given set of patterns lying in a high-dimensional space which can be expressed in terms of a fixed number of latent variables lying in a latent Q-dimensional space. Usually, the Q-space is either two or three dimensional and thus the density function can be used to visualize the data within it. The case in which Q = 3 allows to project the patterns on a spherical manifold which turns out to be optimal when dealing with sparse data. PPS may also be arranged in ensembles to tackle complex classification tasks. As template cases we discuss the application of PPS to two real- world data sets from astronomy and genetics.

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