scholarly journals Positioning Locality Based on Cognitive Directions and Context in Indoor Landmark Reference System

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Yankun Wang ◽  
Weixi Wang ◽  
Xiaoming Li ◽  
Shengjun Tang ◽  
You Li
Keyword(s):  
Membership Function ◽  
Reference System ◽  
Spatial Information ◽  
Hot Spot ◽  
Joint Probability ◽  
Weak Sense ◽  
Human Interaction ◽  
Object A ◽  
Relative Direction ◽  
Direction Relations

<p><strong>Abstract.</strong> Geographic information sciences (GIS) have been entering an era of information explosion. The data-related geographic can be divided into many classes, according to their sources and format, such as raster dataset, shape file, textual information, and voice. Locality description, which is a common form of voice, conveys considerable spatial information and can be derived from our daily communication. The issue of dealing with the locality description information is a research hot spot of next-generation GIS for many scholars.</p><p>Locality description reflects direct or indirect human interaction with environment directly. As an external expression of cognition, the uncertainty that is associated with locality description is inevitable. Locality description generally contains spatial relationships (i.e., topological, distance, and direction relations) and reference objects (ROs). Any feature with a name can be regarded as an RO. Topological relations, which convey rough information-related locality and can be refined by distance or direction relations, are seldom used directly in locality description positioning. The distance and direction relations are usually combined to describe locality, which conveys many clues to position locality.</p><p>Humans have a weak sense of direction indoors, and relative directions are used frequently in locality description. For example, locality description indoors can be given as follows: “Object A is in front of me, and object B is on my left. Context is an unavoidable topic of Locality description. The locality description is complex, either explicitly or implicitly, especially in a landmark reference system (i.e., a reference system where people can describe his locality with one or several landmarks), in which the nearest landmark can be selected easily to describe locality. On the basis of this context, the locality description (“Object A is in front of me, and object B is on my left”) stated above in an indoor landmark reference system (ILRS) implies that objects A and B are near the individual. Hence, the meaning of “Object A is in front of me, and object B is on my left” in ILRS is the same as that of “Object A is in front of me, object B is on my left, and they are all near to me”.</p><p>This paper introduces a novel method of positioning localities indoors by using locality description in ILRS. To achieve positioning of localities with directions description and context in ILRS, we propose a joint probability function that consists of qualitative distance (i.e., near relation) and relative direction membership function. The qualitative distance membership function that considers both minimum Euclidean distance and the stolen area is based on fuzzy set. For consistency with cognition, some definitions are provided during the calculation of relative direction, which can also reduce the number of points to be explored from an algorithmic point of view.</p>

scholarly journals Spatial Pattern and Hotspots of Urban Rail Public Transport to Public Access Using Geospatial Techniques in Selangor, Malaysia

2021 ◽  
Vol 6 (1) ◽  
pp. 234-244
Author(s):  
Mohd Sahrul Syukri BIN Yahya ◽  
Edie Ezwan Mohd Safian ◽  
Burhaida Burhan
Keyword(s):  
Open Access ◽  
Spatial Pattern ◽  
Public Transport ◽  
Public Transportation ◽  
Spatial Information ◽  
Hot Spot ◽  
Developed Countries ◽  
Transportation Systems ◽  
Gis Mapping ◽  
Urban Public Transport

Currently, the trends in urban public transport have been changing over the years in developing countries for mobilization and accessibility development. Urban public transportation systems are the most popular in Selangor State, including big cities such as the Klang Valley Region. Objective measures of spatial pattern and hotspots have been used to understand how urban public transport development relate to open access. This method relies on specific spatial information and available web-based tool that shows the pattern primarily based on given vicinity and statistics connectivity. To date, several studies have finished tested in developed countries. In this study, we use Geographic Information Systems to analyse and consider hotspots identification precisely and efficaciously. Therefore, in this paper, we focus on two types of point sample evaluations – Gi* hot spot and point density analysis evaluation as statistical operations. Public rail transport was evaluated as a validation to describe the percentage of distribution of open access. The final result, GIS mapping capabilities to show that GIS's technology offers to the variation of urban public transport relate to public services, is to create maps and spatial interpretations.

Improved Locality Preserving Projection for Hyperspectral Image Classification in Probabilistic Framework

2021 ◽  
Author(s):  
Reza Seifi Majdar ◽  
Hassan Ghassemian
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Probability Distribution Function ◽  
Spatial Information ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Spectral Features ◽  
Probabilistic Framework ◽  
Locality Preserving ◽  
Training Samples

Unlabeled samples and transformation matrix are two main parts of unsupervised and semi-supervised feature extraction (FE) algorithms. In this manuscript, a semi-supervised FE method, locality preserving projection in the probabilistic framework (LPPPF), to find a sufficient number of reliable and unmixed unlabeled samples from all classes and constructing an optimal projection matrix is proposed. The LPPPF has two main steps. In the first step, a number of reliable unlabeled samples are selected based on the training samples, spectral features, and spatial information in the probabilistic framework. In this way, the spectral and spatial probability distribution function is calculated for each unlabeled sample. Therefore, the spectral features and spatial information are integrated together with a joint probability distribution function. Finally, a sufficient number of unlabeled samples with the highest joint probability distribution are selected. In the second step, the selected unlabeled samples are applied to construct the transformation matrix based on the spectral and spatial information of the unlabeled samples. The adjacency graph is improved by using new weights based on spectral and spatial information. This method is evaluated on three data sets: Indian Pines, Pavia University, and Kennedy Space Center (KSC) and compared with some recent and well-known supervised, semi-supervised, and unsupervised FE methods. Various experiments demonstrate the efficiency of the LPPPF in comparison with the other FE methods. LPPPF has also considerable performance with limited training samples.

scholarly journals USING REMOTE SENSING IN STUDY OF EL NIÑO EXTREME DIFFERENCES BETWEEN 1997/1998 AND 2015/2016

2021 ◽  
Vol 4 (17) ◽  
pp. 83-94
Author(s):  
Ricky Anak Kemarau ◽  
Oliver Valentine Eboy
Keyword(s):  
Remote Sensing ◽  
El Niño ◽  
Land Surface ◽  
Spatial Information ◽  
El Nino ◽  
Hot Spot ◽  
Remote Sensing Data ◽  
El Niño Events ◽  
The Impact ◽  
El Nino Events

The years 1997/1998 and 2015/2016 saw the occurrence of El Niño occur among the worst in human history. Until now there is still a lack of research in studying the degree of El Niño's strength impact on climate and weather, especially in the tropic region. The objective of this study is to study the effectiveness of remote sensing technology in identifying the differences between the 1997/1998 and 2015/2016 El Niño events. This study uses six satellite data and temperature data from the Malaysia Meteorology Department (MMD). The first step of remote sensing data will be through pre-processing, converting digital Numbers (DN) to Land Surface Temperature (LST). The results of the study found that there was a change in the pattern of LST columns during the 1997/1998 and 2015/2016 El Niño events. Spatial patterns change based on Oceanic Niño Index (ONI) values. The results of this study are important because of the importance of spatial information to those responsible for preparing measures to overcome and reduce the impact of El Niño on the population. at the developing country level, including Malaysia, there is still a lack of information technology infrastructure in channeling useful information to the community. Through the information, this spatial information provides critical hot spot information that needs more attention.

EEG-based emotion recognition with deep convolutional neural networks

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mehmet Akif Ozdemir ◽  
Murside Degirmenci ◽  
Elif Izci ◽  
Aydin Akan
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Convolutional Neural Networks ◽  
Spatial Information ◽  
Emotional State ◽  
Human Interaction ◽  
Emotional States ◽  
Eeg Signals ◽  
Deep Convolutional Neural Networks ◽  
Spectral Topology

AbstractThe emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed to communicate brain signals with external systems and make predictions over emotional states. This paper proposes a novel method for emotion recognition based on deep convolutional neural networks (CNNs) that are used to classify Valence, Arousal, Dominance, and Liking emotional states. Hence, a novel approach is proposed for emotion recognition with time series of multi-channel EEG signals from a Database for Emotion Analysis and Using Physiological Signals (DEAP). We propose a new approach to emotional state estimation utilizing CNN-based classification of multi-spectral topology images obtained from EEG signals. In contrast to most of the EEG-based approaches that eliminate spatial information of EEG signals, converting EEG signals into a sequence of multi-spectral topology images, temporal, spectral, and spatial information of EEG signals are preserved. The deep recurrent convolutional network is trained to learn important representations from a sequence of three-channel topographical images. We have achieved test accuracy of 90.62% for negative and positive Valence, 86.13% for high and low Arousal, 88.48% for high and low Dominance, and finally 86.23% for like–unlike. The evaluations of this method on emotion recognition problem revealed significant improvements in the classification accuracy when compared with other studies using deep neural networks (DNNs) and one-dimensional CNNs.

scholarly journals On the Efficiency of Haptic Based Object Identification: Determining Where to Grasp to Get the Most Distinguishing Information

2021 ◽  
Vol 8 ◽  
Author(s):  
Yu Xia ◽  
Alireza Mohammadi ◽  
Ying Tan ◽  
Bernard Chen ◽  
Peter Choong ◽  
...  
Keyword(s):  
Spatial Information ◽  
Information Gain ◽  
Object Identification ◽  
Robotic Hand ◽  
Haptic Information ◽  
Data Set ◽  
Identification Process ◽  
Object A ◽  
Haptic Object ◽  
Physical Information

Haptic perception is one of the key modalities in obtaining physical information of objects and in object identification. Most existing literature focused on improving the accuracy of identification algorithms with less attention paid to the efficiency. This work aims to investigate the efficiency of haptic object identification to reduce the number of grasps required to correctly identify an object out of a given object set. Thus, in a case where multiple grasps are required to characterise an object, the proposed algorithm seeks to determine where the next grasp should be on the object to obtain the most amount of distinguishing information. As such, the paper proposes the construction of the object description that preserves the association of the spatial information and the haptic information on the object. A clustering technique is employed both to construct the description of the object in a data set and for the identification process. An information gain (IG) based method is then employed to determine which pose would yield the most distinguishing information among the remaining possible candidates in the object set to improve the efficiency of the identification process. This proposed algorithm is validated experimentally. A Reflex TakkTile robotic hand with integrated joint displacement and tactile sensors is used to perform both the data collection for the dataset and the object identification procedure. The proposed IG approach was found to require a significantly lower number of grasps to identify the objects compared to a baseline approach where the decision was made by random choice of grasps.

scholarly journals Participatory GIS in design of the Wroclaw University of Science and Technology campus web map and spatial analysis of campus area quality

2018 ◽  
Vol 29 ◽  
pp. 00025
Author(s):  
Jan Blachowski ◽  
Jakub Łuczak ◽  
Paulina Zagrodnik
Keyword(s):  
Data Collection ◽  
Spatial Information ◽  
Hot Spot ◽  
Local Planning ◽  
Web Based ◽  
Participatory Gis ◽  
Web Map ◽  
Functional Aspects ◽  
Bicycle Infrastructure ◽  
Collection Methods

Public participation geographic information system (GIS) and participatory mapping data collection methods are means that enhance capacity in generating, managing, and communicating spatial information in various fields ranging from local planning to environmental management. In this study these methods have been used in two ways. The first one, to gather information on the additional functionality of campus web map expected by its potential users, i.e. students, staff and visitors, through web based survey. The second, to collect geographically referenced information on campus areas that are liked and disliked in a geo-survey carried out with ArcGIS Online GeoForm Application. The results of the first survey were used to map facilities such as: bicycle infrastructure, building entrances, wheelchair accessible infrastructure and benches. The results of the second one, to analyse the most and the least attractive parts of the campus with heat and hot spot analyses in GIS. In addition, the answers have been studied with regard to the visual and functional aspects of campus area raised in the survey. The thematic layers developed in the results of field mapping and geoprocessing of geosurvey data were included in the campus web map project. The paper describes the applied methodology of data collection, processing, analysis, interpretation and geovisualisation.

scholarly journals GIS-Based Optimum Geospatial Characterization for Seismic Site Effect Assessment in an Inland Urban Area, South Korea

2020 ◽  
Vol 10 (21) ◽  
pp. 7443
Author(s):  
Han-Saem Kim ◽  
Chang-Guk Sun ◽  
Mingi Kim ◽  
Hyung-Ik Cho ◽  
Moon-Gyo Lee
Keyword(s):  
Remote Sensing ◽  
South Korea ◽  
Urban Area ◽  
Spatial Information ◽  
Site Response ◽  
Hot Spot ◽  
Spatial Uncertainty ◽  
Detection Methods ◽  
Seismic Zonation ◽  
Seismic Site Effects

Soil and rock characteristics are primarily affected by geological, geotechnical, and terrain variation with spatial uncertainty. Earthquake-induced hazards are also strongly influenced by site-specific seismic site effects associated with subsurface strata and soil stiffness. For reliable mapping of soil and seismic zonation, qualification and normalization of spatial uncertainties is required; this can be achieved by interactive refinement of a geospatial database with remote sensing-based and geotechnical information. In this study, geotechnical spatial information and zonation were developed while verifying database integrity, spatial clustering, optimization of geospatial interpolation, and mapping site response characteristics. This framework was applied to Daejeon, South Korea, to consider spatially biased terrain, geological, and geotechnical properties in an inland urban area. For developing the spatially best-matched geometry with remote sensing data at high spatial resolution, the hybrid model blended with two outlier detection methods was proposed and applied for geotechnical datasets. A multiscale grid subdivided by hot spot-based clusters was generated using the optimized geospatial interpolation model. A principal component analysis-based unified zonation map identified vulnerable districts in the central old downtown area based on the integration of the optimized geoprocessing framework. Performance of the geospatial mapping and seismic zonation was discussed with digital elevation model, geological map.

Integrating Geographic Information Systems for Transportation and Air Quality Models for Microscale Analysis

1996 ◽  
Vol 1551 (1) ◽  
pp. 133-140 ◽  
Author(s):  
Shauna Hallmark ◽  
Wende O'Neill
Keyword(s):  
Air Quality ◽  
Spatial Data ◽  
Impact Analysis ◽  
Spatial Information ◽  
Hot Spot ◽  
Geographic Information ◽  
Quality Analysis ◽  
Quality Models ◽  
Mapping Tool ◽  
Common Signal

The inherently spatial nature of transportation-related air quality analysis makes the geographic information system (GIS) particularly well suited to enhancing microscale air quality analysis. GIS provides several features ideal for the type of analysis necessary to determine transportation-related air quality impacts. It is an excellent storage, manipulation, modeling, and mapping tool for spatial data. Spatial information such as street coordinates and accompanying attributes can be exported and manipulated as input to air quality models such as CALINE3 and CAL3QHC. Output from air quality models in the form of pollution concentrations at specified receptor locations can be input to GIS for hot-spot identification, estimation of contributions of off-road mobile sources, and impact analysis. GIS tools applied to air quality analysis include contour generation, classification, thematic analysis, point-in-polygon analysis, and polygon overlay. Several case studies demonstrating these capabilities using TRANSCAD, a transportation-based GIS package, are presented for microscale air quality analysis. Incompatibilities exist between current air quality models and most GIS. Differences in coordinate systems and distance metrics necessitate additional manipulation of data transferred between models and GIS. Other incompatibilities are that street segments are represented as centerlines in most planning applications of GIS and as a series of links in CAL3QHC and CALINE3, and that signalization parameters are represented differently from many common signal-analysis packages, which may necessitate additional data collection.

scholarly journals Reasoning about Topological and Cardinal Direction Relations Between 2-Dimensional Spatial Objects

2014 ◽  
Vol 51 ◽  
pp. 493-532 ◽  
Author(s):  
A. G. Cohn ◽  
S. Li ◽  
W. Liu ◽  
J. Renz
Keyword(s):  
Spatial Reasoning ◽  
Spatial Information ◽  
Qualitative Spatial Reasoning ◽  
Topological Information ◽  
Directional Information ◽  
Spatial Objects ◽  
Cardinal Direction ◽  
Direction Relations ◽  
Consistency Problem ◽  
Cardinal Direction Relations

Increasing the expressiveness of qualitative spatial calculi is an essential step towards meeting the requirements of applications. This can be achieved by combining existing calculi in a way that we can express spatial information using relations from multiple calculi. The great challenge is to develop reasoning algorithms that are correct and complete when reasoning over the combined information. Previous work has mainly studied cases where the interaction between the combined calculi was small, or where one of the two calculi was very simple. In this paper we tackle the important combination of topological and directional information for extended spatial objects. We combine some of the best known calculi in qualitative spatial reasoning, the RCC8 algebra for representing topological information, and the Rectangle Algebra (RA) and the Cardinal Direction Calculus (CDC) for directional information. We consider two different interpretations of the RCC8 algebra, one uses a weak connectedness relation, the other uses a strong connectedness relation. In both interpretations, we show that reasoning with topological and directional information is decidable and remains in NP. Our computational complexity results unveil the significant differences between RA and CDC, and that between weak and strong RCC8 models. Take the combination of basic RCC8 and basic CDC constraints as an example: we show that the consistency problem is in P only when we use the strong RCC8 algebra and explicitly know the corresponding basic RA constraints.

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