Developing Computational Models to Detect Radiation in Urban Environments

The main objective of this project is to detect, characterize, and locate radioactive sources in urban environments using computational models based on machine learning and statistical techniques. The project explores multiple approaches such as signal processing methods, and neural networks. Unnatural radiation sources, such as Uranium or Plutonium, can present a risk to the population if they remain undetected by radiological search and response teams. Moreover, the computational model being developed must be capable of identifying the type of radiation source, classifying it as innocuous (i.e., isotopes used in medical and industrial settings) or harmful (nuclear weapons). The project is currently supported by the Pacific Northwest National Laboratory (PNNL), in collaboration with the Department of Mathematics at Embry-Riddle

Computational Models to Detect Radiation in Urban Environments

Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This study presents a computational model to detect radioactive sources in urban environments, which uses signal processing techniques to identify radiation signatures. Moreover, the model uses artificial neural networks to identify types of radiation sources, classifying them as innocuous or harmful, and discerning between weapons-grade material and radioactive isotopes used in medical/industrial settings

Implementation of Machine Learning Methods for Ionospheric Scintillation Data Analysis

The ionosphere is a region in the Earth’s upper atmosphere, where atoms are ionized due to solar radiation. The behavior of the ionosphere depends on time and location, and it is highly influenced by solar activity. The ionization process creates layers of free electrons at different altitudes, which can cause fluctuations in electromagnetic waves crossing the region. The effect of ionospheric events on radio signals can be measured using Global Navigation Satellite Systems (GNSS) receivers, in terms of ionospheric scintillation and Total Electron Content (TEC). The GNSS team at the Space Physics Research Lab (SPRL) studies ionospheric events using multi-frequency GNSS receivers (NovAtel GPStation-6) capable measuring high and low rate scintillation data as well as TEC values from three different GNSS systems (GPS, GALILEO, and GLONASS). The purpose of this project is to develop a machine learning algorithm, using recurrent neural networks, to detect ionospheric events in low-rate scintillation data. Recurrent neural networks are often used for time-series applications, including forecasting and prediction. The model is being trained using data collected by the GNSS receivers in multiple locations (including Daytona Beach), with a focus on high-latitude data from the Canadian High Artic Ionospheric Network (CHAIN). The machine learning model will be integrated with the Embry-Riddle Ionospheric Scintillation Algorithm (EISA), an existing model capable of processing ionospheric data. EISA was developed by the GNSS team at SPRL. The updated model will allow the team to automate the process of ionospheric event detection, which is currently done manually. Upon this implementation, EISA will become an end-to-end model for ionospheric data collection, processing, and modelling

Investigation into GNSS Ionospheric Scintillation from Thunderstorms in Daytona Beach, FL

The Global Navigation Satellite Systems (GNSS) has a wide variety of applications in today’s world, spanning multiple diverse industries. GNSS aids in providing data related to tracking and navigation. This network of vital information requires support, maintenance, and security. Several factors, such as space weather, are thought to have an impact on signals received from GNSS. This project is an ERAU Space Physics Research Lab (SPRL) initiative to better understand the effect that thunderstorms can have on these communications. The project will concentrate on mid-latitude regions within the ionosphere and analyze variables such as total electron content (TEC) in locating fluctuations of radio signals concurrent with thunderstorm periods in Daytona Beach. These fluctuations are also commonly known as ionospheric scintillation. The project builds upon the work of SPRL students from 2018 that utilized ERAU receivers to begin finding unique events of this phenomenon through various algorithms. The 2021 project will look to expand the task by finding and understanding more noteworthy events using recent developments such as the Embry-Riddle Ionospheric Scintillation Algorithm (EISA), with the added challenge of pinpointing lightning data in conjunction with scintillation appearances

Brazil Research Trip: Evaluating the success of Embraer and aviation industry in Brazil

The project is a research trip to Brazil with 9 Embry-Riddle Aeronautical University students during Spring Break 2018. The group evaluated the success of aviation and aerospace industry in Brazil through multiple company visits during the trip. The group visited Embraer S.A for an aircraft factory tour, GOL Transportes Aéreos for airline operations tour, and São Paulo – Guarulhos International Airport for an airport management tour. Through these company and facilities visits, the group examined what conditions in Brazil allowed companies like Embraer S.A to become the third largest airframe manufacturer in the world, such as society, economy, and politics of Brazil. The research also includes a group of students conducting a same research while staying in Daytona Beach. Both the travelling group to Brazil and Daytona group will be evaluated with a written exam to demonstrate whether travelling produces a better result. The research exposes students to a different perspective of aviation and aerospace industry and promotes globalization on campus through travelling

Investigation into Geomagnetic storms and ionospheric scintillation

Understanding how space weather phenomenon affects daily life has been a main focus of space weather studies. In particular, identifying the relationship between solar activities, ionospheric irregularities and consequently ionospheric scintillation has inspired numerous research efforts. Geomagnetic storms fueled by solar activities cause ionospheric irregularities. Ionospheric scintillation occurs when radio signals travel through these irregularities and experience rapid fluctuations in radio signal phase and amplitude. Such fluctuations have great consequences in radio wave based technology such as the Global Position system(GPS) as it causes a loss of lock. Therefore, through the implantation of two GPS Receivers, continuous data was obtained on phase and amplitude of radio signals from the Global Navigation Satellite Systems(GNSS). This data was then thoroughly analyzed to identify scintillation signatures. On January 31st, 2019, scintillation signatures that correlated to a G1 minor geomagnetic storm were observed. In this paper, the method of analysis is adapted from the aforementioned case study to identify past geomagnetic events that possibly correlated with observed scintillation. Through this study, it is hoped that a correlation between geomagnetic storms and ionospheric scintillation in the mid-latitude region will be highlighted

Flight Optimization

The use of optimization tools in business is a key ingredient in streamlining operations. In the airline industry, optimization is a very important concept as it saves airlines time and money. The purpose of this research is to develop an algorithm for flight optimization. The optimization framework will consist of three sections which include valid paths selection, optimizer development, and machine learning implementation. The first segment identifies all possible alternatives for a given flight leg. Once a leg has been identified, the script filters down all viable alternatives using available constraints which are stored in a Structural Query Language ( SQL) relational database. The optimization portion will utilize the Dijkstra’s algorithm to identify the most profitable routes. Customer satisfaction is at the core of every business model. Keeping this in mind, the optimizer will implement machine learning to observe current trends and predict future behavior, thus, improving overall customer experience. This research will serve as a preliminary model for the development of an optimizer for OneSky (a Private Charter conglomerate)