Trusted Monitoring Service (TMS)

2021 ◽  
pp. 87-95
Author(s):  
Xiaoqian Jiang ◽  
Miran Kim ◽  
Kristin Lauter ◽  
Tim Scott ◽  
Shayan Shams
Keyword(s):  
Monitoring Service

MackDroid - An Android based Application to monitor devices

2016 ◽  
Vol 9 ◽  
pp. 1
Author(s):  
Maaz Sirkhot ◽  
Ekta Sirwani ◽  
Aishwarya Kourani ◽  
Akshit Batheja ◽  
Kajal Jethanand Jewani
Keyword(s):  
Operating System ◽  
Open Source ◽  
Vital Role ◽  
Mobile Users ◽  
Android Application ◽  
Web Page ◽  
Technological World ◽  
Monitoring Service ◽  
User Friendly

In this technological world, smartphones can be considered as one of the most far-reaching inventions. It plays a vital role in connecting people socially. The number of mobile users using an Android based smartphone has increased rapidly since last few years resulting in organizations, cyber cell departments, government authorities feeling the need to monitor the activities on certain targeted devices in order to maintain proper functionality of their respective jobs. Also with the advent of smartphones, Android became one of the most popular and widely used Operating System. Its highlighting features are that it is user friendly, smartly designed, flexible, highly customizable and supports latest technologies like IoT. One of the features that makes it exclusive is that it is based on Linux and is Open Source for all the developers. This is the reason why our project Mackdroid is an Android based application that collects data from the remote device, stores it and displays on a PHP based web page. It is primarily a monitoring service that analyzes the contents and distributes it in various categories like Call Logs, Chats, Key logs, etc. Our project aims at developing an Android application that can be used to track, monitor, store and grab data from the device and store it on a server which can be accessed by the handler of the application.

scholarly journals The On-Line Integrated Mesoscale Chemistry Model BOLCHEM

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 192
Author(s):  
Rita Cesari ◽  
Tony Christian Landi ◽  
Massimo D’Isidoro ◽  
Mihaela Mircea ◽  
Felicita Russo ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Transport Model ◽  
Horizontal Resolution ◽  
Integration Step ◽  
Chemistry Transport Model ◽  
European Environmental Agency ◽  
On Line ◽  
One Year ◽  
Pm2.5 And Pm10 ◽  
Monitoring Service

This work presents the on-line coupled meteorology–chemistry transport model BOLCHEM, based on the hydrostatic meteorological BOLAM model, the gas chemistry module SAPRC90, and the aerosol dynamic module AERO3. It includes parameterizations to describe natural source emissions, dry and wet removal processes, as well as the transport and dispersion of air pollutants. The equations for different processes are solved on the same grid during the same integration step, by means of a time-split scheme. This paper describes the model and its performance at horizontal resolution of 0.2∘× 0.2∘ over Europe and 0.1∘× 0.1∘ in a nested configuration over Italy, for one year run (December 2009–November 2010). The model has been evaluated against the AIRBASE data of the European Environmental Agency. The basic statistics for higher resolution simulations of O3, NO2 and particulate matter concentrations (PM2.5 and PM10) have been compared with those from Copernicus Atmosphere Monitoring Service (CAMS) ensemble median. In summer, for O3 we found a correlation coefficient R of 0.72 and mean bias of 2.15 over European domain and a correlation coefficient R of 0.67 and mean bias of 2.36 over Italian domain. PM10 and PM2.5 are better reproduced in the winter, the latter with a correlation coefficient R of 0.66 and the mean bias MB of 0.35 over Italian domain.

scholarly journals Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences

2021 ◽  
Vol 13 (6) ◽  
pp. 1098
Author(s):  
Egor Prikaziuk ◽  
Peiqi Yang ◽  
Christiaan van der Tol
Keyword(s):  
Meteorological Data ◽  
Original Data ◽  
Google Earth ◽  
Target Area ◽  
Pixel Resolution ◽  
Point Of Interest ◽  
Homogeneity Assumption ◽  
Google Earth Engine ◽  
Level 1 ◽  
Monitoring Service

In this study, we demonstrate that the Google Earth Engine (GEE) dataset of Sentinel-3 Ocean and Land Color Instrument (OLCI) level-1 deviates from the original Copernicus Open Access Data Hub Service (DHUS) data by 10–20 W m−2 sr−1μμm−1 per pixel per band. We compared GEE and DHUS single pixel time series for the period from April 2016 to September 2020 and identified two sources of this discrepancy: the ground pixel position and reprojection. The ground pixel position of OLCI product can be determined in two ways: from geo-coordinates (DHUS) or from tie-point coordinates (GEE). We recommend using geo-coordinates for pixel extraction from the original data. When the Sentinel Application Platform (SNAP) Pixel Extraction Tool is used, an additional distance check has to be conducted to exclude pixels that lay further than 212 m from the point of interest. Even geo-coordinates-based pixel extraction requires the homogeneity of the target area at a 700 m diameter (49 ha) footprint (double of the pixel resolution). The GEE OLCI dataset can be safely used if the homogeneity assumption holds at 2700 m diameter (9-by-9 OLCI pixels) or if the uncertainty in the radiance of 10% is not critical for the application. Further analysis showed that the scaling factors reported in the GEE dataset description must not be used. Finally, observation geometry and meteorological data are not present in the GEE OLCI dataset, but they are crucial for most applications. Therefore, we propose to calculate angles and extraterrestrial solar fluxes and to use an alternative data source—the Copernicus Atmosphere Monitoring Service (CAMS) dataset—for meteodata.

scholarly journals Spatiotemporal Variability and Trends of Marine Heat Waves in the Red Sea over 38 Years

2021 ◽  
Vol 9 (8) ◽  
pp. 842
Author(s):  
Bayoumy Mohamed ◽  
Hazem Nagy ◽  
Omneya Ibrahim
Keyword(s):  
Red Sea ◽  
Heat Waves ◽  
Sea Surface ◽  
Spatiotemporal Variability ◽  
Environment Monitoring ◽  
Orthogonal Function ◽  
Sst Variability ◽  
Warming Rate ◽  
Marine Environment Monitoring ◽  
Monitoring Service

Marine heat waves (MHWs) can have catastrophic consequences for the socio-environmental system. Especially in the Red Sea, which has the world’s second longest coral reef system. Here, we investigate the sea surface temperature (SST) variability and trends, as well as the spatiotemporal characteristics of marine heat waves (MHWs) in the Red Sea, using high resolution daily gridded (1/20°) SST data obtained from the Copernicus Marine Environment Monitoring Service (CMEMS) for the period 1982–2019. Results show that the average warming rate was about 0.342 ± 0.047 °C/decade over the entire Red Sea over the whole study period. The Empirical Orthogonal Function (EOF) analysis reveals that the maximum variability is over the central part of the Red Sea, while the minimum variability is in the southernmost part of the Red Sea. Over the last two decades (2000–2019), we have discovered that the average MHW frequency and duration increased by 35% and 67%, respectively. The results illustrate that the MHW frequency and duration trends have increased by 1.17 counts/decade and 1.79 days/decade, respectively, over the study period. The highest annual MHW frequencies were detected in the years 2018, 2019, 2010, and 2017. A strong correlation (R = 0.89) was found between the annual MHW frequency and the annual mean SST.

scholarly journals Visibility Prediction over South Korea Based on Random Forest

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 552
Author(s):  
Bu-Yo Kim ◽  
Joo Wan Cha ◽  
Ki-Ho Chang ◽  
Chulkyu Lee
Keyword(s):  
Random Forest ◽  
South Korea ◽  
Correlation Coefficients ◽  
Predictive Performance ◽  
Air Pollutant ◽  
Economic Losses ◽  
Observation Data ◽  
Local Data ◽  
Weather Forecasts ◽  
Monitoring Service

In this study, the visibility of South Korea was predicted (VISRF) using a random forest (RF) model based on ground observation data from the Automated Synoptic Observing System (ASOS) and air pollutant data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) model. Visibility was predicted and evaluated using a training set for the period 2017–2018 and a test set for 2019. VISRF results were compared and analyzed using visibility data from the ASOS (VISASOS) and the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (VISLDAPS) operated by the Korea Meteorological Administration (KMA). Bias, root mean square error (RMSE), and correlation coefficients (R) for the VISASOS and VISLDAPS datasets were 3.67 km, 6.12 km, and 0.36, respectively, compared to 0.14 km, 2.84 km, and 0.81, respectively, for the VISASOS and VISRF datasets. Based on these comparisons, the applied RF model offers significantly better predictive performance and more accurate visibility data (VISRF) than the currently available VISLDAPS outputs. This modeling approach can be implemented by authorities to accurately estimate visibility and thereby reduce accidents, risks to public health, and economic losses, as well as inform on urban development policies and environmental regulations.

scholarly journals Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

2021 ◽  
Vol 18 (2) ◽  
pp. 813
Author(s):  
Ningrong Lei ◽  
Murtadha Kareem ◽  
Seung Ki Moon ◽  
Edward J. Ciaccio ◽  
U Rajendra Acharya ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Decision Support ◽  
Stroke Prevention ◽  
Stroke Risk ◽  
Universal Access ◽  
Healthcare Providers ◽  
High Quality ◽  
Physician Support ◽  
Monitoring Service

In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis.

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