NDSU Theses & Dissertations

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Research performed to achieve a formal degree from NDSU. Includes theses, dissertations, master's papers, and videos. The Libraries are currently undertaking a scanning project to include all bound student theses, dissertations, and masters papers.

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Adaptive Mesh Refinment Applications for Adviction-Diffusion Problems Using Amrex
(North Dakota State University, 2020) Kanumuru, Venkata
In this paper we implemented an adaptive mesh refinement in high performance computing environment to study a wide range of problems in engineering. They are nonlinear Fisher-Kolmogorov equation, heat equation, advection equation and poisons equation using traditional message passing interface (MPI). We used adaptive mesh refinement library called AMReX for computation. AMReX is a numerical library containing the functionality to write massively parallel, block-structured adaptive mesh refinement (AMR) applications. Our study includes examples to solve poisons equation in traditional MPI approach and compared the performance between the two methods.
Agent Based Modeling for Simulation of Microbial Community
(North Dakota State University, 2018) Tekeste, Noah Daniel
Agent based modeling uses interacting agents and a governing rule to understand a complex phenomenon. It is an important mode of inquiry in the field of life sciences. For this paper a Haploid Evolutionary Constructor (HEC) tool was used for modeling and simulation of two sample models. The model samples were analyzed under the same and varying level of specific and non-specific substrates. In the first part of each experiment, the survival rate of the models was examined based on the model’s inputs and outputs. However, since close association of microbes enhances the probability of Horizontal Gene Transfer (HGT) between organisms, HGT behavior was introduced within the populations in the second sets of experiments. This enabled them to adapt to dwindling resources in their environment and creation of new populations that are better suited for the ecosystem. Based on the obtained results, the behavior of the dominant population(s) is assessed.
Agent Communication and Negotiation in a Supply Chain
(North Dakota State University, 2013) Upadhyay, Rajat
Supply Chain Management is an essential management paradigm for almost every organization. Effective implementation of Supply Chain Management (SCM) can significantly increase an organization’s profit. In this paper, a simple SCM is implemented using multi-agent modeling. A typical SCM consists of the supplier, manufacturer, inventory, seller, and customer. A multi-agent system provides a natural solution for SCM because various entities involved in SCM can be represented as intelligent agents. In the present approach various agents representing various entities of SCM, negotiate with each other in order to achieve their goals. Apart from the entities associated with typical SCM, additional entities have been added to the proposed system. This system consists of six agents representing the SCM entities: Customer Agent, Seller Agent, Coordinator Agent, Inventory Agent, Manufacturer Agent, and Supplier Agent. The system also consists of other agents to initialize the system and the database.
Agent-Based Modeling to Simulate the Movement of a Flock of Birds
(North Dakota State University, 2013) Byrisetty, Naga Chaitanya
The most beautiful, mysterious movements of bird flocks have always amused the human and led his thinking towards what makes these complex movements possible. This paper discusses the simulation of such bird behavior based on Craig W. Reynolds’s work on bird flocking, satisfying three main objectives and trying to mimic the natural behavioral characteristics of the flock in various circumstances using Netlogo5.1.  Objective One: To develop working software that simulates movement for a flock of birds using agent-based modeling in a two-dimensional world.  Objective Two: To direct the flock towards forage grounds while sustaining the wind and obstacles affecting the flock’s propagation.  Objective Three: To introduce a predator into the world, study, and simulate the flock’s escape behavior.
Algorithms for Coverage Improvement in a Sensor Network
(North Dakota State University, 2011) Maddi, Sunil Reddy
Sensors are devices which have the ability to receive and respond to a signal. These sensors, when used as a group, form a sensor network. Sensors in a sensor network can communicate and transmit data. In the early stages of research on sensor networks, only static sensors were used to form a sensor network. As research advanced, a combination of static and mobile sensors was used to form a wireless sensor network instead of just static sensors. The primary advantage of this type of sensor network over a sensor network with static sensors is the ability of mobile sensors to move to a new location in the network to increase the overall area covered by the sensors. Some concerns in a wireless sensor network are coverage area, energy consumption of the sensors, the ratio of static and mobile sensors to be used in sensor network, and the deployment of sensors in a network. Major research in sensor networks is focused on addressing the issue of coverage area. The objective of this paper is to design, implement and analyze Most Overlapped First and Highest Coverage Gain algorithms that address the issue of coverage area in a wireless sensor network. Local Spiral Search was used as the base to develop these two algorithms in combination with the Utility Function. Both algorithms were tested, and the results were analyzed with coverage area and change in overlap area as the metrics. Results showed a significant gain in coverage area using both the algorithms, and there was a consistent change in overlap area for a varying number of sensors.
Analysis of Image Classification Deep Learning Algorithm
(North Dakota State University, 2023) Kanungo, Shivam
This study explores the use of TensorFlow 2 and Python for image classification problems. Image categorization is an important area in computer vision, with several real-world applications such as object identification/recognition, medical imaging, and autonomous driving. This work studies TensorFlow 2 and its image categorization capabilities. We also demonstrate how to construct an image classification model using Python and TensorFlow 2. This analysis of image classification neural network problems with the use of Convolutional Neural Network (CNN) on the German and the Chinese traffic sign datasets is an engineering task. Ultimately, this work provides step-by-step guidance for creating an image classification model using TensorFlow 2 and Python, while also showcasing its potential to tackle image classification issues across various domains.
Analysis of Power Line Communication Channel Model using Communication Techniques
(North Dakota State University, 2013) Singh, Varinder Pal
With the advent of technology, human dependency on power (electricity) and communication has grown beyond leaps and bounds. Many efforts have been made to continuously improve and increase the efficiency in both areas. Power Line Communication (PLC) is a technology where power lines or transmission lines are being used for communication purposes along with transmitting electrical energy. Because the power grid is already in place, the PLC has the obvious advantage of reducing communication infrastructure cost. However, the power grid is designed optimally for power delivery (not the data). The power transmission line generally appears as a harsh environment for the low-power high-frequency communication signals. In order to evaluate the performance of PLC, this paper simulates a practical multipath power line communication channel model and provides the Bit Error Rate (BER) vs signal-to-noise ratio (SNR) curves for orthogonal frequency division multiplexing.
Analyzing Access Logs Data using Stream Based Architecture
(North Dakota State University, 2018) Gautam, Nitendra
Within the past decades, the enterprise-level IT infrastructure in many businesses have grown from a few to thousands of servers, increasing the digital footprints they produce. These digital footprints include access logs that contain information about different events such as activity related to usage patterns, networks and any hostile activity affecting the network. Apache Hadoop has been one of the most standardized frameworks and is used by many Information Technology (IT) companies for analyzing these log files in distributed batch mode using MapReduce programming model. As these access logs include important information related to security and usage patterns, companies are now looking for an architecture that allows analyzing these logs in real time. To overcome the limitations of the MapReduce based architecture of Hadoop, this paper proposes a new and more efficient data processing architecture using Apache Spark, Kafka and other technologies that can handle both real-time and batch-based data.
Analyzing Student Learning Outcomes in Programming Course Using Individual Study Vs. Pair Programming
(North Dakota State University, 2014) Abeyratne, Keith
Pair programming has been common practice in the programming industry during last three decades, but only recently did it start to draw the attention as a teaching strategy. This paper investigates whether we should introduce pair programming at the beginning of the semester, instead later in the semester. To perform this investigation, we performed a control group empirical study wherein pair programming was used in the first half of the semester (in one section of introductory CS course). The control group (the other section of the same course) introduced pair programming in the second half of the semester. This study supported the implementation of specific assessment strategies to assess individual programming abilities during pair programming situations. Results found that students perceive pair programming as being beneficial and all of the subjects who used pair programming indicated that they would prefer using it again as opposed to working individually.
Analyzing Three Different Tuning Strategies for Random Forest Hyperparameters for Fraud Detection
(North Dakota State University, 2021) Sarao, Kulwinder Kaur
Technology is advancing rapidly, and more tasks are becoming online than ever. Along with the benefits comes the disadvantages of this great advancement. While online services relieve from the struggle of in person activities, it also puts you on the risk of getting deceived by the fraudsters. This paper aims to detect the fraudulent transactions made online from a bank using a synthetically produced dataset. A random forest model has been trained to predict the fraudulent transactions. To achieve the best performance, the hyperparameters of the model have been tuned using three different tuning methods. As it turns out, grid search proved to be the best tuning strategy in terms of the mean cv score, precision, recall, f1-score and accuracy. It only lacked in providing the best run time, where Bayesian Optimization scored well than the others.
Application for Capturing Potential Tardiness in Operating Room Suites
(North Dakota State University, 2012) Gronneberg, Bethlehem Abera
This paper describes the design, architecture and implementation of a Dashboard Application, a tool developed to capture the potential tardiness in Operating Room (OR) Suites. The paper is focused on developing an automated application which is intended to monitor and display the progress of multiple out-patients on the day of their elective surgery as they advance from admission to the pre-operative stages leading up to their final destination of the operating room. The application will display OR patients’ real-time location, the time they entered at that location and the time remaining for their scheduled surgery once they arrive at the admission office. The objective of this paper is to implement an automated application using real-time locating technology to provide timely and accurate information regarding patient preparedness for surgery. This information otherwise would have taken the traditional modes of communication (face-to-face and telephone) to check for patient preparedness.
An Application of Association Rule Mining to Unit Test Selection
(North Dakota State University, 2013) Gunderson, Karl Nils
Appropriate selection of unit tests during the software development process is vital when many unit tests exist. The developer may be unfamiliar with some tests and non-obvious relationships between application code and test code may exist. Poor test selection may lead to defects. This is especially true when the application is large and many developers are involved. By the application of association rule mining to the unit test selection process and by comparison with extant selection techniques, we will provide a quantitative analysis of the benefits of heuristic and its limit to development where process patterns are stable.
An Application of Simplicial Intercept Depth (SID) Method for Fitting Linear Models
(North Dakota State University, 2014) Sun, Zhongxing
This paper presents an application based on the Simplicial Intercept Depth method introduced by Liu (2004). We use this method to get the best linear fit of the phenotypic data for spot blotch resistant reaction of two different barley groups. The Simplicial Intercept Depth method is generalized by Simplicial Depth, also proposed by Liu in 1990. It provides a robust way for data analysis when outliers appear. In this paper, we use the Bootstrapping method, which is introduced by Bradley Efron (1979), to resample from the original dataset to get a distribution of the estimates. We also compare the SID with least squares regression and the Theil-type estimate which introduced by Shen (2009). The result shows that the SID is a robust method for estimating the coefficients of the linear regression model.
Application of the Kusumoto Cost-Metric to Evaluate the Cost-Effectiveness of Software Inspections
(North Dakota State University, 2012) Mandala, Narendar Reddy
Inspection and testing are two widely recommended techniques for software quality improvement with a common goal of defect detection and removal in software products. While testing cannot be conducted until software is implemented, inspections can be applied in early stages of software development. In this way inspection enables saving of testing cost and time. To manage the quality of their software, project managers need objective information to make a trade-off between the testing costs saved by performing inspections against the testing cost that would otherwise be spent if no inspections were performed. Project managers also need to decide on the number of inspectors that would make it a cost-effective inspection process. To accomplish these research goals, we have analyzed the cost invested in the inspection process against the cost saved from the inspection process by applying the Kusumoto cost-metrics on an inspection data set with varying number of inspection team size.
Applied Nonparametric Statistical Tests to Compare Evolutionary and Swarm Intelligence Approaches
(North Dakota State University, 2014) Amanchi, Srinivas Adithya
Recently, in many experimental studies, the statistical analysis of nonparametric comparisons has grown in the area of computational intelligence. The research refers to application of different techniques that are used to show comparison among the algorithms in an experimental study. Pairwise statistical technique perform individual comparison between two algorithms and multiple statistical technique perform comparison between more than two algorithms. Techniques include the Sign test, Wilcoxon signed ranks test, the multiple sign test, the Friedman test, the Friedman aligned ranks test and the Quade test. In this paper, we used these tests to analyze the results obtained in an experimental study comparing well-known algorithms and optimization functions. The analyses showed that the application of statistical tests helps to identify the algorithm that is significantly different than the remaining algorithms in a comparison. Different statistical analyses were conducted on the results of an experimental study obtained with varying dimension size.
Aspects of Job Satisfaction in Female Millennial Generation Retail Employees
(North Dakota State University, 2016) Frank, Victoria Wrae
Since young adult female employees represent a sizeable portion of the retail workplace, particularly in clothing, shoes and jewelry, this study focused on aspects of job satisfaction of female Millennial generation (born between 1980 and 2000) retail stores employees. In-depth qualitative interviews regarding the workers’ major values, concerns, and motivations were conducted during the Spring of 2016 in a Midwestern city in the United States of America. Participants indicated that camaraderie among coworkers and the team, and flexible scheduling practices were the most important values relating to job satisfaction. Work-life balance and maintaining a 40-hour work week or steady scheduled hours were also reported as important values relating to job satisfaction. The goal of the study was to gain female Millennial generation employees’ perspectives to help employers counteract high turnover rates in the retail industry.
Assessing Changes in Within Individual Variation Over Time for Nutritional Intake Data Using 24 Hour Recalls from the National Health and Examination Survey
(North Dakota State University, 2012) Brandt, Kyal Scott
Nutritional surveys often use 24 hour recalls to assess the nutritional intake of certain populations. The National Health and Examination Survey (NHANES) collects two 24-hour recalls for each individual in the study. This small sampling can lead to a great deal of variation due to day-to-day differences in an individual’s intake, making it difficult to assess “usual intake.” The ISU method is implemented in the PC-Side software package, breaking our observed variation into two components: within individual variation (WIV) and between-individual variation (BIV). In this paper, we will use the PC-Side software to get WIV estimates for several different age, gender, and nutrients from NHANES nutrition data. We will look at how WIV estimates change over time and using past WIV estimates to get a “usual intake” distribution and the calculated proportion below an estimated average requirement (EAR).
Assessing the Preparedness and Mitigation Research on Tornadoes: Clarifying Risk Perceptions and Identifying Contradictions
(North Dakota State University, 2012) Freeman, Pierre Albert
This paper examines literature from various disciplines contributing to the objective of saving lives and reducing damages from tornadoes. Specific topics include changes in tornado incidence, the genesis of tornadoes, and alterations in geographical distributions of tornadoes. I also review data on damages, casualties and deaths along with associated housing type vulnerability and atypical nocturnal tornado events. Literature associated with predictions including historical data and forecasting is addressed. Further data was presented regarding false alarms, warnings, watches and response behavior. Finally, mitigation issues regarding policy and planning, building practices and sheltering is reviewed. While reviewing the data, several contradictions were found regarding density, lead time expectations, vehicular use and actual increases in events and damages. Perception of risk may be dependent on factors of cultural geography and societal memory. Improved understanding of warning times, effective education, outreach and removing the human factor in tornadoes are points that need further study.
Assessment and Policy Recommendations for Domestic Illegal Firearms Trafficking
(North Dakota State University, 2012) Heley, Frank Anton
This report examined the nature of domestic illegal firearms trafficking, including key features and predictors of firearms trafficking, and rationalizes the defining of domestic illegal firearms trafficking into three levels that are indicative of the seriousness and features inherent to the particular level of trafficking. This report also examined the scope of the trafficking situations at their respective levels, the means already being utilized to address firearms trafficking problems, and their effectiveness. Finally, this report recognizes that the differing levels of trafficking may require varied and differing efforts be used in combating firearms trafficking and policy recommendations involving both legislative and enforcement efforts are detailed at the respective levels of trafficking.
Assessment and Recommendation of Requirement Management Tool Based on User Needs
(North Dakota State University, 2012) Mohpal, Aditi
Over recent years, requirement management has played a very vital role in materializing IT projects. Whether it is the healthcare sector, finance sector, banking sector, or any other industrial sector, requirement management is crucial for the successful completion of IT projects. Due to a tight time to market and severe competition, requirement management becomes one of the most critical activities for the software-development lifecycle process [1]. Requirements change all the time during the software-development lifecycle. Unmanaged changes lead to project overruns. Most companies are using requirement-management tools to manage requirements. However, many companies end up choosing a requirement-management tool that does not address the project’s needs and problems. In this research, requirement-management tools available on the market have been identified, and a “decision-making application” has been developed for systematic evaluation of requirement-management tools based on user needs so that a project team can identify and choose the correct requirement-management solution.

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Browsing NDSU Theses & Dissertations by browse.metadata.type "Master's paper"