A New Approach for Modeling Spatio-Temporal Events in an Earthquake Rescue Scenario

2009 ◽  
Vol 9 (3) ◽  
pp. 513-520 ◽  
Author(s):  
A.R. Vafaeinezh ◽  
A.A. Alesheikh ◽  
A.A. Roshanneja ◽  
R. Shad
Keyword(s):  
New Approach ◽  
Spatio Temporal ◽  
Temporal Events

scholarly journals A New Approach for Approximate Solution of ADE: Physical-Based Modeling of Carriers in Doping Region

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 458
Author(s):  
Leobardo Hernandez-Gonzalez ◽  
Jazmin Ramirez-Hernandez ◽  
Oswaldo Ulises Juarez-Sandoval ◽  
Miguel Angel Olivares-Robles ◽  
Ramon Blanco Sanchez ◽  
...  
Keyword(s):  
Differential Equation ◽  
Approximate Solution ◽  
Analytic Solution ◽  
Order Differential Equation ◽  
Electric Circuit ◽  
New Approach ◽  
In Doping ◽  
Electric Behavior ◽  
Spatio Temporal ◽  
Physical Phenomena

The electric behavior in semiconductor devices is the result of the electric carriers’ injection and evacuation in the low doping region, N-. The carrier’s dynamic is determined by the ambipolar diffusion equation (ADE), which involves the main physical phenomena in the low doping region. The ADE does not have a direct analytic solution since it is a spatio-temporal second-order differential equation. The numerical solution is the most used, but is inadequate to be integrated into commercial electric circuit simulators. In this paper, an empiric approximation is proposed as the solution of the ADE. The proposed solution was validated using the final equations that were implemented in a simulator; the results were compared with the experimental results in each phase, obtaining a similarity in the current waveforms. Finally, an advantage of the proposed methodology is that the final expressions obtained can be easily implemented in commercial simulators.

scholarly journals Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification

2021 ◽  
Vol 13 (13) ◽  
pp. 2604
Author(s):  
Patrick Osei Darko ◽  
Margaret Kalacska ◽  
J. Pablo Arroyo-Mora ◽  
Matthew E. Fagan
Keyword(s):  
Statistical Physics ◽  
Near Infrared ◽  
Information Gain ◽  
Hyperspectral Imagery ◽  
Full Range ◽  
Surface Reflectance ◽  
Mangrove Species ◽  
New Approach ◽  
Spatio Temporal ◽  
Shortwave Infrared

Hyperspectral remote sensing across multiple spatio-temporal scales allows for mapping and monitoring mangrove habitats to support urgent conservation efforts. The use of hyperspectral imagery for assessing mangroves is less common than for terrestrial forest ecosystems. In this study, two well-known measures in statistical physics, Mean Information Gain (MIG) and Marginal Entropy (ME), have been adapted to high spatial resolution (2.5 m) full range (Visible-Shortwave-Infrared) airborne hyperspectral imagery. These two spectral complexity metrics describe the spatial heterogeneity and the aspatial heterogeneity of the reflectance. In this study, we compare MIG and ME with surface reflectance for mapping mangrove extent and species composition in the Sierpe mangroves in Costa Rica. The highest accuracy for separating mangroves from forest was achieved with visible-near infrared (VNIR) reflectance (98.8% overall accuracy), following by shortwave infrared (SWIR) MIG and ME (98%). Our results also show that MIG and ME can discriminate dominant mangrove species with higher accuracy than surface reflectance alone (e.g., MIG–VNIR = 93.6% vs. VNIR Reflectance = 89.7%).

scholarly journals Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

2017 ◽  
Vol 21 (6) ◽  
pp. 2923-2951 ◽  
Author(s):  
Laurie Caillouet ◽  
Jean-Philippe Vidal ◽  
Eric Sauquet ◽  
Alexandre Devers ◽  
Benjamin Graff
Keyword(s):  
Climate Change ◽  
Low Flow ◽  
Reference Network ◽  
Large Set ◽  
Historical Events ◽  
Variable Threshold ◽  
Matching Procedure ◽  
Spatio Temporal ◽  
Temporal Events ◽  
Rich Countries

Abstract. The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989–1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

scholarly journals Spatio-Temporal Deforestation Measurement Using Automatic Clustering

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Irene Erlyn Wina Rachmawan ◽  
Ali Ridho Barakbah ◽  
Tri Harsono
Keyword(s):  
Experimental Study ◽  
Satellite Images ◽  
New Approach ◽  
Deforestation Rate ◽  
Automatic Interpretation ◽  
Automatic Clustering ◽  
Landsat 7 ◽  
Spatio Temporal ◽  
Temporal Measurement ◽  
World Records

Deforestation is one of the crucial issues in Indonesia. In 2012, deforestation rate in Indonesia reached 0.84 million hectares, exceeding Brazil. According to the 2009 Guinness World Records, Indonesia's deforestation rate was 1.8 million hectares per year between 2000 and 2005. An interesting view is the fact that Indonesia government denied the deforestation rate in those years and said that the rate was only 1.08 million hectares per year in 2000 and 2005. The different problem is on the technique how to deal with the deforestation rate. In this paper, we proposed a new approach for automatically identifying the deforestation area and measuring the deforestation rate. This approach involves differential image processing for detecting Spatio-temporal nature changes of deforestation. It consists series of important features extracted from multiband satellite images which are considered as the dataset of the research. These data are proceeded through the following stages: (1) Automatic clustering for multiband satellite images, (2) Reinforcement Programming to optimize K-Means clustering, (3) Automatic interpretation for deforestation areas, and (4) Deforestation measurement adjusting with elevation of the satellite. For experimental study, we applied our proposed approach to analyze and measure the deforestation in Mendawai, South Borneo. We utilized Landsat 7 to obtain the multiband images for that area from the year 2001 to 2013. Our proposed approach is able to identify the deforestation area and measure the rate. The experiment with our proposed approach made a temporal measurement for the area and showed the increasing deforestation size of the area 1.80 hectares during those years.

scholarly journals Sensing as a Service for the Internet of Things

2016 ◽  
Vol 4 (3) ◽  
pp. 28
Author(s):  
Omar Subhi Aldabbas
Keyword(s):  
Internet Of Things ◽  
Cost Effective ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Heterogeneous Services ◽  
Coordination Languages ◽  
Spatio Temporal ◽  
Temporal Events ◽  
New Applications ◽  
Common Application

Internet of Things (IoT) is a ubiquitous embedded ecosystem known for its capability to perform common application functions through coordinating resources, which are distributed on-object or on-network domains. As new applications evolve, the challenge is in the analysis and usage of multimodal data streamed by diverse kinds of sensors. This paper presents a new service-centric approach for data collection and retrieval. This approach considers objects as highly decentralized, composite and cost effective services. Such services can be constructed from objects located within close geographical proximity to retrieve spatio-temporal events from the gathered sensor data. To achieve this, we advocate Coordination languages and models to fuse multimodal, heterogeneous services through interfacing with every service to achieve the network objective according to the data they gather and analyze. In this paper we give an application scenario that illustrates the implementation of the coordination models to provision successful collaboration among IoT objects to retrieve information. The proposed solution reduced the communication delay before service composition by up to 43% and improved the target detection accuracy by up to 70%, while maintaining energy consumption 20% lower than its best rivals in the literature.

The Digital Earth Viewer: A new visualization approach for geospatial time series data

2021 ◽  
Author(s):  
Valentin Buck ◽  
Flemming Stäbler ◽  
Everardo Gonzalez ◽  
Jens Greinert
Keyword(s):  
Time Series Data ◽  
Environmental Data ◽  
Series Data ◽  
Multiple Sources ◽  
Data Types ◽  
New Approach ◽  
Digital Earth ◽  
Spatio Temporal ◽  
Diverse Data

<p>The study of the earth’s systems depends on a large amount of observations from homogeneous sources, which are usually scattered around time and space and are tightly intercorrelated to each other. The understanding of said systems depends on the ability to access diverse data types and contextualize them in a global setting suitable for their exploration. While the collection of environmental data has seen an enormous increase over the last couple of decades, the development of software solutions necessary to integrate observations across disciplines seems to be lagging behind. To deal with this issue, we developed the Digital Earth Viewer: a new program to access, combine, and display geospatial data from multiple sources over time.</p><p>Choosing a new approach, the software displays space in true 3D and treats time and time ranges as true dimensions. This allows users to navigate observations across spatio-temporal scales and combine data sources with each other as well as with meta-properties such as quality flags. In this way, the Digital Earth Viewer supports the generation of insight from data and the identification of observational gaps across compartments.</p><p>Developed as a hybrid application, it may be used both in-situ as a local installation to explore and contextualize new data, as well as in a hosted context to present curated data to a wider audience.</p><p>In this work, we present this software to the community, show its strengths and weaknesses, give insight into the development process and talk about extending and adapting the software to custom usecases.</p>

Alzheimer's Electroencephalogram Event Scalp and Source Localization

2016 ◽  
pp. 33-49
Author(s):  
Pedro Miguel Rodrigues ◽  
João Paulo Teixeira ◽  
Diamantino R. S. Freitas
Keyword(s):  
Source Localization ◽  
The Elderly ◽  
Clinical Settings ◽  
New Approach ◽  
Brain Anomalies ◽  
Available Technology ◽  
Temporal Events ◽  
Near Future ◽  
Electroencephalogram Eeg ◽  
Irreversible Impairment

Alzheimer's disease is the most common cause of dementia which causes a progressive and irreversible impairment of several cognitive functions. The aging population has been increasing significantly in recent decades and this disease affects mainly the elderly. Its diagnostic accuracy is relatively low and there is not a biomarker able to detect AD without invasive tests. Despite the progress in better understanding the disease there remains no prospect of cure at least in the near future. The electroencephalogram (EEG) test is a widely available technology in clinical settings. It may help diagnosis of brain disorders, once it can be used in patients who have cognitive impairment involving a general decrease in overall brain function or in patients with a located deficit. This study is a new approach to improve the scalp localization and the detection of brain anomalies (EEG temporal events) sources associated with AD by using the EEG.

Detection of Alzheimer’s Disease Electroencephalogram Temporal Events

2013 ◽  
Vol 2 (3) ◽  
pp. 44-61 ◽  
Author(s):  
Pedro Miguel Rodrigues ◽  
Diamantino Rui Freitas ◽  
João Paulo Teixeira
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
The Elderly ◽  
Clinical Settings ◽  
Brain Disorder ◽  
New Approach ◽  
Self Organized ◽  
Available Technology ◽  
Temporal Events ◽  
Electroencephalogram Eeg

Alzheimer’s Disease (AD) is a chronic progressive and irreversible neurodegenerative brain disorder. The aging population has been increasing significantly in recent decades. Therefore, AD will continue to increase because the disease affects mainly the elderly. Its diagnostic accuracy is relatively low, and there is not a biomarker able to detect AD without invasive tests. The electroencephalogram (EEG) test is a widely available technology in clinical settings. It may help diagnosis of brain disorders, once it can be used in patients who have cognitive impairment involving a general decrease in overall brain function or in patients with a located deficit. This study is a new approach to detect EEG temporal events in order to improve the AD diagnosis. For that, K-means and Self-Organized Maps were used, and the results suggested that there are sequences of EEG energy variation that appear more frequently in AD patients than in healthy subjects.

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