A Service Oriented Architecture Approach for Global Positioning System Quality of Service Monitoring

This research focuses on the development of a Service Oriented Architecture (SOA) for monitoring the Global Positioning System (GPS) Standard Positioning Service (SPS) in near real time utilizing a Mobile Crowd Sensing (MCS) technique. A unique approach to developing the MCS SOA was developed that utilized both the Depart- ment of Defense Architecture Framework (DoDAF) and the SOA Modeling Language (SoaML) guidance. The combination of these two frameworks resulted in generation of all the architecture products required to evaluate the SOA through the use of Model Based System Engineering (MBSE) techniques. Ultimately this research provides a feasibility analysis for utilization of mobile distributed sensors to provide situational awareness of the GPS Quality of Service (QoS). First this research provides justification for development of a new monitoring architecture and defines the scope of the SOA. Then an exploration of current SOA, MBSE, and Geospatial System Information (GIS) research was conducted. Next a Discrete Event Simulation (DES) of the MCS participant interactions was developed and simulated within AGI\u27s Systems Toolkit. The architecture performance analysis was executed using a GIS software package known as ArcMap. Finally, this research concludes with a suitability analysis of the proposed architecture for detecting sources of GPS interference within an Area of Interest (AoI)

Multi-Architecture Monte-Carlo (MC) Simulation of Soft Coarse-Grained Polymeric Materials: SOft coarse grained Monte-carlo Acceleration (SOMA)

Multi-component polymer systems are important for the development of new materials because of their ability to phase-separate or self-assemble into nano-structures. The Single-Chain-in-Mean-Field (SCMF) algorithm in conjunction with a soft, coarse-grained polymer model is an established technique to investigate these soft-matter systems. Here we present an im- plementation of this method: SOft coarse grained Monte-carlo Accelera- tion (SOMA). It is suitable to simulate large system sizes with up to billions of particles, yet versatile enough to study properties of different kinds of molecular architectures and interactions. We achieve efficiency of the simulations commissioning accelerators like GPUs on both workstations as well as supercomputers. The implementa- tion remains flexible and maintainable because of the implementation of the scientific programming language enhanced by OpenACC pragmas for the accelerators. We present implementation details and features of the program package, investigate the scalability of our implementation SOMA, and discuss two applications, which cover system sizes that are difficult to reach with other, common particle-based simulation methods

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017

Effective Use Methods for Continuous Sensor Data Streams in Manufacturing Quality Control

This work outlines an approach for managing sensor data streams of continuous numerical data in product manufacturing settings, emphasizing statistical process control, low computational and memory overhead, and saving information necessary to reduce the impact of nonconformance to quality specifications. While there is extensive literature, knowledge, and documentation about standard data sources and databases, the high volume and velocity of sensor data streams often makes traditional analysis unfeasible. To that end, an overview of data stream fundamentals is essential. An analysis of commonly used stream preprocessing and load shedding methods follows, succeeded by a discussion of aggregation procedures. Stream storage and querying systems are the next topics. Further, existing machine learning techniques for data streams are presented, with a focus on regression. Finally, the work describes a novel methodology for managing sensor data streams in which data stream management systems save and record aggregate data from small time intervals, and individual measurements from the stream that are nonconforming. The aggregates shall be continually entered into control charts and regressed on. To conserve memory, old data shall be periodically reaggregated at higher levels to reduce memory consumption

Evaluating traffic safety network screening: an initial framework utilizing the hierarchical Bayesian philosophy

Highway crashes result in over 40,000 deaths per year (500,000 worldwide). Their impact on the national economy is estimated at more than 230 billion dollars. Highway safety is the top priority of the United States Department of Transportation (US DOT). Funds dedicated to the problem are expected to increase substantially.;Highway safety is a multidisciplinary issue. An important tool is the safety improvement candidate location (SICL) list. SICL lists list high crash locations for potential mitigation. SICL lists are developed using crash data. Crash frequency, rate, or loss is used to rank the worst locations. Classical statistical techniques are applied. In some cases, simple frequency analyses are used to draw attention to problem locations.;Simple ranked lists suffer from methodological and practical limitations. Chief among these is the inability to identify sites with promise , sites where mitigation has the best chance of success. Agencies representing engineering and enforcement generally examine top sites prior to resource dedication. This is resource intensive and efforts of different safety interests are often not well coordinated.;For over 20 years, empirical Bayesian (EB) has been proposed to address these limitations. EB identifies sites where mitigation might be most effective, increases estimate confidence, and provides information on relative site safety. EB is being widely implemented at the national level. State and local agencies continue SILL development based on long-standing procedures.;EB allows decision makers to more reliably estimate the crash reduction potential at specific sites. However, EB requires development of safety performance functions for road type classes. The technique also requires a priori development of accident modification factors. These requirements add significant expense.;Powerful computers and advanced statistical sampling techniques allow hierarchical Bayesian statistics to be applied to highway safety. Hierarchical Bayesian eliminates the need for a priori functions and factors. This approach can readily incorporate additional information. It can also explicitly identify important relationships between causal factors and safety performance. The approach uses data to define results, based on an analyst-specified level of uncertainty. This dissertation discusses SICL list development and evaluates the potential of Bayesian statistics to improve their utility

Application of ecological, geological and oceanographic ERTS-1 imagery to Delaware's coastal resources management

The author has identified the following significant results. Data from twelve successful ERTS-1 passes over Delaware Bay have been analyzed with special emphasis on coastal vegetation, land use, current circulation, water turbidity and pollution dispersion. Secchi depth, suspended sediment concentration and transmissivity as measured from helicopters and boats were correlated with ERTS-1 image radiance. Multispectral signatures of acid disposal plumes, sediment plumes and slick were investigated. Ten vegetative cover and water discrimination classes were selected for mapping: (1) forest-land; (2) Phragmites communis; (3) Spartina patens and Distichlis spicata; (4) Spartina alterniflora; (5) cropland; (6) plowed cropland; (7) sand and bare sandy soil; (8) bare mud; (9) deep water; and (10) sediment-laden and shallow water. Canonical analysis predicted good classification accuracies for most categories. The actual classification accuracies were very close to the predicted values with 8 of 10 categories classified with greater than 90% accuracy indicating that representative training sets had been selected

Predicting Grain Yield by Utilizing Multispectral Images and Convolutional Neural Network

According to the UN’s sustainability goals, hunger should have been eradicated by 2030, but the number is going the wrong way, with around 800 million people who suffer from undernourishment in 2022. Therefore, there is a pressing need to pro- duce more food with fewer resources while minimizing crop losses. A combination of remote sensing and deep learning can contribute to more efficient phenotyping of plants. Aerial photography is an excellent alternative to traditional manual or mech- anical plant health assessments. By applying remote sensing technologies, drones are now economically feasible to capture multispectral images of fields. The use of deep learning algorithms, such as Convolutional Neural Networks (CNNs), allows for the simultaneous analysis of large areas, significantly saving time. This research examines multispectral unmanned aerial vehicle imagery to extract essential features for wheat yield prediction in wheat breeding programs in NMBU at Ås. The thesis dataset comprises two fields with crops, each with eight multispectral images for each field, with five bands each (red, green, blue, near-infrared, and red-edge). In this thesis, two simple CNN models with different architectures were utilized. The different combinations of datasets were trained using the two CNN architectures. The most effective model was the model, which included additional variables, such as days to heading and fertilization level, has enhanced the model’s predictive accuracy. This thesis employs a CNN to predict grain yield by utilizing all spectra recorded for each plot. The results suggest that this approach is satisfactory when predicting wheat grain yield. Unlike similar studies, this thesis takes a different approach by utilizing the entire plot as a multispectral image, which allows for extracting all spectra recorded for each plot. Previous studies have used the median value of the plots to make predic- tions and have not incorporated CNN as part of their methodology. The resultant CNN model achieved an R2 score of 0.885

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018