207 research outputs found
Investigating Classical Be Stars & Their Surrounding Disks
Identify physical conditions of B-emission stars at certain positions over specific wavelengths. Classify stars based on colors and Identify the spectral type of a star and temperature based on its absorption lines. Summarize the changes and excess in flux over certain wavelengths of multiple B-emission stars while also looking at the flux when filtered over the V, B, U and K bands. Identify properties of the Be stars and evaluate how changing certain properties effect the stars and their surrounding disks
Development of a Low Field MRI-Based Approach for Observation of Water Penetration into Clay: Preliminary Results
Magnetic resonance imaging (MRI) are considered one of the most efficient and non-invasive methods of observing water content in permeable substances. MRI can visualize and quantify the movement of water in real time. In this study, MRI was used to observe the water penetration through clay. Furthermore, MRI can acquire three-dimensional data due to its radio-frequency signals from any orientation. The contrast of the images produced by MRI is a display of the fluid concentration. As such, any change in the contrast intensity is interpreted as a regional change in the concentration of fluid. This report summarizes the preliminary results from a series of experiments performed with an MRI. The primary goal of the study is to provide a non-destructive method to quantify the permeation of clay using different amounts of water to determine if the low-field MRI approach can be viable option when evaluating the development of storage containers. This investigation is motivated with the intent to develop better and more environmentally friendly containers used to store radioactive waste
Spatial modelling of air pollution for open smart cities
A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsHalf of the world’s population already lives in cities, and by 2050 two-thirds of the
world’s population are expected to further move into urban areas. This urban growth
leads to various environmental, social and economic challenges in cities, hampering
the Quality of Life (QoL). Although recent trends in technologies equip us with
various tools and techniques that can help in improving quality of life, air pollution
remains the ‘biggest environmental health risk’ for decades, impacting individuals’
quality of life and well-being according to World Health Organisation (WHO). Many
efforts have been made to measure air quality, but the sparse arrangement of
monitoring stations and the lack of data currently make it challenging to develop
systems that can capture within-city air pollution variations. To solve this, flexible
methods that allow air quality monitoring using easily accessible data sources at the
city level are desirable. The present thesis seeks to widen the current knowledge
concerning detailed air quality monitoring by developing approaches that can help
in tackling existing gaps in the literature. The thesis presents five contributions
which address the issues mentioned above. The first contribution is the choice of
a statistical method which can help in utilising existing open data and overcoming
challenges imposed by the bigness of data for detailed air pollution monitoring.
The second contribution concerns the development of optimisation method which
helps in identifying optimal locations for robust air pollution modelling in cities.
The third contribution of the thesis is also an optimisation method which helps
in initiating systematic volunteered geographic information (VGI) campaigns for
detailed air pollution monitoring by addressing sparsity and scarcity challenges
of air pollution data in cities. The fourth contribution is a study proposing the
involvement of housing companies as a stakeholder in the participatory framework
for air pollution data collection, which helps in overcoming certain gaps existing in
VGI-based approaches. Finally, the fifth contribution is an open-hardware system that
aids in collecting vehicular traffic data using WiFi signal strength. The developed
hardware can help in overcoming traffic data scarcity in cities, which limits detailed
air pollution monitoring. All the contributions are illustrated through case studies
in Muenster and Stuttgart. Overall, the thesis demonstrates the applicability of the developed approaches for enabling air pollution monitoring at the city-scale under
the broader framework of the open smart city and for urban health research
Neural Network Based Epileptic EEG Detection and Classification
Timely diagnosis is important for saving the life of epileptic patients. In past few years, a lot of treatment are available for epilepsy. These treatments involve use of medicines. But these are not effective in controlling frequency of seizure. There is need of removal of affected region using surgery. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is used before surgery for locating affected region. This manual process using EEG graphs is time consuming and requires deep expertise. In the present paper, a model has been proposed that preserves the true nature of EEG signal in form of textual one dimensional vector. The proposed model achieves a state of art performance for Bonn University dataset giving an average sensitivity, specificity of 81% and 81.4% respectively for classification among all five classes. Also for binary classification achieving 99.9%, 99.5% score value for specificity and sensitivity instead of 2D models used by other researchers. Thus developed system will significantly help neurosurgeons in increasing their performance
Understanding knee points in bicriteria problems and their implications as preferred solution principles
A knee point is almost always a preferred trade-off solution, if it exists in a bicriteria optimization problem. In this article, an attempt is made to improve understanding of a knee point and investigate the properties of a bicriteria problem that may exhibit a knee on its Pareto-optimal front. Past studies are reviewed and a couple of new definitions are suggested. Additionally, a knee region is defined for problems in which, instead of one, a set of knee-like solutions exists. Edge-knee solutions, which behave like knee solutions but lie near one of the extremes on the Pareto-optimal front, are also introduced. It is interesting that in many problem-solving tasks, despite the existence of a number of solution methodologies, only one or a few of them are commonly used. Here, it is argued that often such common solution principles are knee solutions to a bicriteria problem formed with two conflicting goals of the underlying problem-solving task. The argument is illustrated on a number of tasks, such as regression, sorting, clustering and a number of engineering designs
Telemedicine Solution using Django
The average person usually don’t have much information about diseases related to symptoms they have and which doctor to visit for that disease. This causes a lot of wastage of time and money because they have to search doctor by doctor to get the right doctor and get an appointment with that doctor. Also not all doctors treat all diseases, this means just knowing your disease is not enough. Through this telemedicine solution we have tried to mitigate the inefficiency and delays in the system. Patients can get a basic idea of the possible disease they might have and a list of doctors suited to cure this disease is given as output to the patient. Then the patient can connect with doctors on a website
EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence
Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly involve the use of anti-seizure drugs for controlling seizures. But in 70% of cases, these drugs are not effective, and surgery is the only solution when the condition worsens. So patients need to take care of themselves while having a seizure and be safe. Wearable electroencephalogram (EEG) devices have come up with the development in medical science and technology. These devices help in the analysis of brain electrical activities. EEG helps in locating the affected cortical region. The most important is that it can predict any seizure in advance on-site. This has resulted in a sudden increase in demand for effective and efficient seizure prediction and diagnosis systems. A novel approach to epileptic seizure prediction and diagnosis system "EpilNet" is proposed in the present paper. It is a one-dimensional (1D) convolution neural network. EpilNet gives the testing accuracy of 79.13% for five classes, leading to a significant increase of about 6-7% compared to related works. The developed Web API helps in bringing EpilNet into practical use. Thus, it is an integrated system for both patients and doctors. The system will help patients prevent injury or accidents and increase the efficiency of the treatment process by doctors in the hospitals
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