GPS: User Position Calculation Including Advanced Troposphere Delay Modeling

Objective of this project is to analyze and mitigate troposphere delays induced in GPS signals, which can result in very large position errors while estimating user position. The standard models currently present do not take into account all the various set of parameters or elements of the troposphere that can cause a significant delay. This project also includes study of troposphere propagation delays that improve the understanding of GPS signal propagation through the troposphere during irregular conditions. This characteristic is very important as it can play crucial role in real time surveying, navigation, precision farming and positioning for emergency services. Due to the tropical nature of the Indian climate the troposphere delay can be observed significantly in India sub-continent. In order to accurately estimate delay troposphere in real time conditions is taken into account, which are provided by the Indian meteorological department, by their automatic weather surveillance systems. GPS data for stations in India is obtained from CORS data for Bangalore, from where we obtain the observation and navigation files used in the calculations. Obtained data is processed and run through various algorithms like least squares satellite position calculation, error mitigation and ray tracing algorithms to mitigate troposphere and better estimate user position. Apart from these algorithms this project also includes a study on various concepts/formulas that help in using the forecasted real time data to be used in snell’s law to estimate delay as part of ray tracing techniques. All the code development in this project is done using MATLAB by math works and GUI is developed for an easier interface. For analysis purposes the data is analyzed with and without the advanced mitigation techniques to show the improvement in position estimation using advanced troposphere mitigation techniques.

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Mobile technologies for disease surveillance in humans and animals

Onderstepoort Journal of Veterinary Research ◽

10.4102/ojvr.v81i2.737 ◽

2014 ◽

Vol 81 (2) ◽

Cited By ~ 20

Author(s):

Mpoki Mwabukusi ◽

Esron D. Karimuribo ◽

Mark M. Rweyemamu ◽

Eric Beda

Keyword(s):

Developing Countries ◽

Human Health ◽

Real Time ◽

Disease Surveillance ◽

Open Data ◽

Weather Conditions ◽

Mobile Technologies ◽

Surveillance Systems ◽

Time Data ◽

Real Time Data

A paper-based disease reporting system has been associated with a number of challenges. These include difficulties to submit hard copies of the disease surveillance forms because of poor road infrastructure, weather conditions or challenging terrain, particularly in the developing countries. The system demands re-entry of the data at data processing and analysis points, thus making it prone to introduction of errors during this process. All these challenges contribute to delayed acquisition, processing and response to disease events occurring in remote hard to reach areas. Our study piloted the use of mobile phones in order to transmit near to real-time data from remote districts in Tanzania (Ngorongoro and Ngara), Burundi (Muyinga) and Zambia (Kazungula and Sesheke). Two technologies namely, digital and short messaging services were used to capture and transmit disease event data in the animal and human health sectors in the study areas based on a server–client model. Smart phones running the Android operating system (minimum required version: Android 1.6), and which supported open source application, Epicollect, as well as the Open Data Kit application, were used in the study. These phones allowed collection of geo-tagged data, with the opportunity of including static and moving images related to disease events. The project supported routine disease surveillance systems in the ministries responsible for animal and human health in Burundi, Tanzania and Zambia, as well as data collection for researchers at the Sokoine University of Agriculture, Tanzania. During the project implementation period between 2011 and 2013, a total number of 1651 diseases event-related forms were submitted, which allowed reporters to include GPS coordinates and photographs related to the events captured. It was concluded that the new technology-based surveillance system is useful in providing near to real-time data, with potential for enhancing timely response in rural remote areas of Africa. We recommended adoption of the proven technologies to improve disease surveillance, particularly in the developing countries.

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Big data analytics—lessons learnt from global E&P operators

The APPEA Journal ◽

10.1071/aj14044 ◽

2015 ◽

Vol 55 (2) ◽

pp. 409

Author(s):

Kevin Kalish

Keyword(s):

Real Time ◽

Predictive Models ◽

Big Data Analytics ◽

Surveillance Systems ◽

Well Performance ◽

Time Data ◽

Quality Of Data ◽

Real Time Data ◽

Exploration And Production ◽

Intelligent Wells

Exploration and production operators are striving to attain the hidden knowledge in their key asset: data. Data and real-time data from intelligent wells supplement historical interpretations and generated datasets. It is paramount to gain insight from these multiple datasets, which enable engineers and stakeholders to make faster and more accurate decisions under uncertainty. By combining the traditional deterministic and interpretive workflows with a data-driven probabilistic set of analyses, it is possible to predict events that result in poor reservoir or well performance or facility failures. By building predictive models based on cleansed historical data and by analysing them in real-time data streams, it is now feasible to optimise production. Controlling costs and ensuring efficient processes that impact positively on health, safety and environment and resource usage are key benefits that fall out of analytical methodologies. This extended abstract provides recent examples of global exploration and production operators using an analytics oilfield framework to: improve the quality of data by integrating relevant sources from multiple monitoring and surveillance systems across all geology, geophysics and reservoir engineering (GGRE) disciplines into a unified view; predict unplanned events so that mitigation can be planned in advance; use predictive models to avoid frequent and unnecessary preventive maintenance that interferes with production schedules, strains maintenance staff and increases costs; and, increase decision support across disparate upstream disciplines by using data mining to create accurate predictive and descriptive models.

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Implementing Real-Time Data Suicide Surveillance Systems

Crisis ◽

10.1027/0227-5910/a000829 ◽

2021 ◽

Vol 42 (5) ◽

pp. 321-327

Author(s):

Anna Baran ◽

Rebekka Gerstner ◽

Michiko Ueda ◽

Agnieszka Gmitrowicz

Keyword(s):

Real Time ◽

Surveillance Systems ◽

Time Data ◽

Real Time Data

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Ambulatory Assessment

European Psychologist ◽

10.1027/1016-9040.14.2.109 ◽

2009 ◽

Vol 14 (2) ◽

pp. 109-119 ◽

Cited By ~ 66

Author(s):

Ulrich W. Ebner-Priemer ◽

Timothy J. Trull

Keyword(s):

Real Time ◽

Clinical Psychology ◽

Real Life ◽

Ambulatory Assessment ◽

Computer Assisted ◽

Affective Valence ◽

Time Data ◽

Physiological Processes ◽

Context Specific ◽

Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

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