scholarly journals Classification of Dynamic Cartographic Symbols applied to Augmented Reality (AR) Systems

2021 ◽  
Vol 4 ◽  
pp. 1-8
Author(s):  
Fabrício Rosa Amorim ◽  
Marcio Augusto Reolon Schmidt
Keyword(s):  
Augmented Reality ◽  
Spatial Information ◽  
Cognitive Effort ◽  
Outdoor Environment ◽  
Information Communication ◽  
Scientific Investigations ◽  
Visual Variables ◽  
Use Context ◽  
Dynamic Variables

Abstract. Paper maps were widely used during centuries; however, these maps do not change dynamically regarding its use context, the user behaviour and the change in the representations through time. Considering the research related to digital cartography, maps started to be seen both digitally and in a dynamic way due to the application of static and dynamic visual variables. During the process of navigation supported by maps, the comprehension of certain cartographic symbols can be a complex task for people. When using representations for virtual environment, specifically the Augmented Reality (AR) and Virtual Reality (VR), an advantage is the complementing of the information communication through virtual objects, which reduces the cognitive effort to decode all the representation as in the traditional maps. Until now, several scientific investigations about adjusting the cartographic design aimed to personal and vehicular navigation maps in AR are being developed. However, few studies investigate the application of dynamic symbols in AR built from the dynamic visual variables of Cartography. In this way, the aim on this research is to classify the symbols that use the dynamic variables. In addition, verify the presence of these variables in Augmented Reality systems in mobile devices that use AR to represent spatial information in the context of personal navigation in an outdoor environment.

scholarly journals Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

2021 ◽  
Vol 11 (13) ◽  
pp. 6047
Author(s):  
Soheil Rezaee ◽  
Abolghasem Sadeghi-Niaraki ◽  
Maryam Shakeri ◽  
Soo-Mi Choi
Keyword(s):  
Big Data ◽  
Augmented Reality ◽  
User Study ◽  
Spatial Information ◽  
Ease Of Use ◽  
Tourist Attraction ◽  
Gender And Education ◽  
Time Distance ◽  
Data Source ◽  
The Right

A lack of required data resources is one of the challenges of accepting the Augmented Reality (AR) to provide the right services to the users, whereas the amount of spatial information produced by people is increasing daily. This research aims to design a personalized AR that is based on a tourist system that retrieves the big data according to the users’ demographic contexts in order to enrich the AR data source in tourism. This research is conducted in two main steps. First, the type of the tourist attraction where the users interest is predicted according to the user demographic contexts, which include age, gender, and education level, by using a machine learning method. Second, the correct data for the user are extracted from the big data by considering time, distance, popularity, and the neighborhood of the tourist places, by using the VIKOR and SWAR decision making methods. By about 6%, the results show better performance of the decision tree by predicting the type of tourist attraction, when compared to the SVM method. In addition, the results of the user study of the system show the overall satisfaction of the participants in terms of the ease-of-use, which is about 55%, and in terms of the systems usefulness, about 56%.

scholarly journals Towards modeling of human skilling for electrical circuitry using augmented reality applications

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Ana Villanueva ◽  
Ziyi Liu ◽  
Yoshimasa Kitaguchi ◽  
Zhengzhe Zhu ◽  
Kylie Peppler ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Learning Outcomes ◽  
Cognitive Assessment ◽  
Teaching And Learning ◽  
Stem Learning ◽  
Educational Value ◽  
Learning Content ◽  
Hands On ◽  
Specific Learning

AbstractAugmented reality (AR) is a unique, hands-on tool to deliver information. However, its educational value has been mainly demonstrated empirically so far. In this paper, we present a modeling approach to provide users with mastery of a skill, using AR learning content to implement an educational curriculum. We illustrate the potential of this approach by applying this to an important but pervasively misunderstood area of STEM learning, electrical circuitry. Unlike previous cognitive assessment models, we break down the area into microskills—the smallest segmentation of this knowledge—and concrete learning outcomes for each. This model empowers the user to perform a variety of tasks that are conducive to the acquisition of the skill. We also provide a classification of microskills and how to design them in an AR environment. Our results demonstrated that aligning the AR technology to specific learning objectives paves the way for high quality assessment, teaching, and learning.

scholarly journals SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING NEURAL NETWORK ALGORITHM AND HIERARCHICAL SEGMENTATION

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 1-5
Author(s):  
D. Akbari ◽  
M. Moradizadeh ◽  
M. Akbari
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Washington Dc ◽  
Hierarchical Segmentation ◽  
Vector Machines ◽  
Neural Network Algorithm ◽  
New Framework

<p><strong>Abstract.</strong> This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Washington DC Mall hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms.</p>

scholarly journals Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels

TecnoLógicas ◽  
2019 ◽  
Vol 22 (46) ◽  
pp. 1-14 ◽  
Author(s):  
Jorge Luis Bacca ◽  
Henry Arguello
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Subspace Clustering ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Image Clustering ◽  
Spectral Signature ◽  
Spectral Image ◽  
Sparse Subspace Clustering

Spectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume that hyperspectral images lie in the union of multiple low-dimensional subspaces.  Using this, SSC groups spectral signatures in different subspaces, expressing each spectral signature as a sparse linear combination of all pixels, ensuring that the non-zero elements belong to the same class. Although these methods have shown good accuracy for unsupervised classification of hyperspectral images, the computational complexity becomes intractable as the number of pixels increases, i.e. when the spatial dimension of the image is large. For this reason, this paper proposes to reduce the number of pixels to be classified in the hyperspectral image, and later, the clustering results for the missing pixels are obtained by exploiting the spatial information. Specifically, this work proposes two methodologies to remove the pixels, the first one is based on spatial blue noise distribution which reduces the probability to remove cluster of neighboring pixels, and the second is a sub-sampling procedure that eliminates every two contiguous pixels, preserving the spatial structure of the scene. The performance of the proposed spectral image clustering framework is evaluated in three datasets showing that a similar accuracy is obtained when up to 50% of the pixels are removed, in addition, it is up to 7.9 times faster compared to the classification of the data sets without incomplete pixels.

scholarly journals On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements

2018 ◽  
Vol 8 (2) ◽  
pp. 20170039 ◽  
Author(s):  
Zhan Li ◽  
Michael Schaefer ◽  
Alan Strahler ◽  
Crystal Schaaf ◽  
David Jupp
Keyword(s):  
Laser Scanning ◽  
Spatial Information ◽  
Information Source ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Biochemical Properties ◽  
Dual Wavelength ◽  
Wavelength Scanning ◽  
Spatial Attributes

The Dual-Wavelength Echidna Lidar (DWEL), a full waveform terrestrial laser scanner (TLS), has been used to scan a variety of forested and agricultural environments. From these scanning campaigns, we summarize the benefits and challenges given by DWEL's novel coaxial dual-wavelength scanning technology, particularly for the three-dimensional (3D) classification of vegetation elements. Simultaneous scanning at both 1064 nm and 1548 nm by DWEL instruments provides a new spectral dimension to TLS data that joins the 3D spatial dimension of lidar as an information source. Our point cloud classification algorithm explores the utilization of both spectral and spatial attributes of individual points from DWEL scans and highlights the strengths and weaknesses of each attribute domain. The spectral and spatial attributes for vegetation element classification each perform better in different parts of vegetation (canopy interior, fine branches, coarse trunks, etc.) and under different vegetation conditions (dead or live, leaf-on or leaf-off, water content, etc.). These environmental characteristics of vegetation, convolved with the lidar instrument specifications and lidar data quality, result in the actual capabilities of spectral and spatial attributes to classify vegetation elements in 3D space. The spectral and spatial information domains thus complement each other in the classification process. The joint use of both not only enhances the classification accuracy but also reduces its variance across the multiple vegetation types we have examined, highlighting the value of the DWEL as a new source of 3D spectral information. Wider deployment of the DWEL instruments is in practice currently held back by challenges in instrument development and the demands of data processing required by coaxial dual- or multi-wavelength scanning. But the simultaneous 3D acquisition of both spectral and spatial features, offered by new multispectral scanning instruments such as the DWEL, opens doors to study biophysical and biochemical properties of forested and agricultural ecosystems at more detailed scales.

The use of virtual reality interfaces in the field of information security

2021 ◽  
pp. 118-127
Author(s):  
K.N. Zhernova
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
Information Security ◽  
Computer Security ◽  
Virtual And Augmented Reality

Technologies of virtual and augmented reality are gaining popularity. Virtual reality is used in many areas, including beginning to be used in the field of information and computer security. In addition, virtual reality interfaces are also susceptible to attacks. However, there are still few works on research in this area. This article provides an overview of existing solutions to computer security problems using virtual and augmented reality interfaces, as well as an overview and classification of the identified threats for these interfaces themselves.

Logic Tester for the Classification of Cyberterrorism Attacks

2019 ◽  
pp. 487-503
Author(s):  
N. Veerasamy ◽  
M.M. Grobler
Keyword(s):  
Communication Technologies ◽  
Cyber Attacks ◽  
Boolean Logic ◽  
Current Debate ◽  
Information Communication Technologies ◽  
Information Communication ◽  
International Understanding ◽  
Life Threatening

The merging of terrorism with the cyber domain introduces the potential for using computers and networked technologies in cyberspace to carry out extremist activities. Despite the current debate on whether cyberterrorism can be regarded as a real threat, this research will propose a method for classifying incidents as either cyberterrorism or cyber attacks. Although there have been no reported cases of Information Communication Technologies causing life-threatening situations or death, this research aims to show that cyberterrorism is not a negligible threat but instead a dangerous risk that should not be overlooked. This research will investigate the merging of terrorism with the cyber domain and present a multi-layered definition for cyberterrorism. This proposed definition is founded on the definition for traditional terrorism and incorporates elements of the international understanding of cyberterrorism. The research future presents a Logic Tester that uses Boolean logic to test the application of the multi-layered definition for cyberterrorism in terms of past international cyber incidents. The merit of the Logic Tester is presented through its application on a number of potential cyberterrorism scenarios, using the definition to classify these as either cyberterrorism or cyber attacks.

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