Engineering Commons^™

Comparing Importance Of Knowledge And Professional Skill Areas For Engineering Programming Utilizing A Two Group Delphi Survey, John F. Hutton

Theses and Dissertations

All engineering careers require some level of programming proficiency. However, beginning programming classes are challenging for many students. Difficulties have been well-documented and contribute to high drop-out rates which prevent students from pursuing engineering. While many approaches have been tried to improve the performance of students and reduce the dropout rate, continued work is needed. This research seeks to re-examine what items are critical for programming education and how those might inform what is taught in introductory programming classes (CS1). Following trends coming from accreditation and academic boards on the importance of professional skills, we desire to rank knowledge and …

Design, Development And Evaluation Of The Ruggedized Edge Computing Node (Recon), Sahil Girin Patel

Theses and Dissertations

The increased quality and quantity of sensors provide an ever-increasing capability to collect large quantities of high-quality data in the field. Research devoted to translating that data is progressing rapidly; however, translating field data into usable information can require high performance computing capabilities. While high performance computing (HPC) resources are available in centralized facilities, bandwidth, latency, security and other limitations inherent to edge location in field sensor applications may prevent HPC resources from being used in a timely fashion necessary for potential United States Army Corps of Engineers (USACE) field applications. To address these limitations, the design requirements for RECON …

Assessment Of Simulated And Real-World Autonomy Performance With Small-Scale Unmanned Ground Vehicles, William Peyton Johnson

Theses and Dissertations

Off-road autonomy is a challenging topic that requires robust systems to both understand and navigate complex environments. While on-road autonomy has seen a major expansion in recent years in the consumer space, off-road systems are mostly relegated to niche applications. However, these applications can provide safety and navigation to dangerous areas that are the most suited for autonomy tasks. Traversability analysis is at the core of many of the algorithms employed in these topics. In this thesis, a Clearpath Robotics Jackal vehicle is equipped with a 3D Ouster laser scanner to define and traverse off-road environments. The Mississippi State University …

Modeling, Verification, And Simulation Of A Uav Swarm Consensus Protocol, Rohit Martin Menghani

Theses and Dissertations

Unmanned Aerial Vehicles (UAVs), particularly electrically powered multi-rotors, are becoming increasingly popular in the entertainment, transportation, logistics, and military sectors. One of the main drawbacks presented by these vehicles at the time of writing is the limited range achieved as a consequence of the limits of battery technology. One common method used to overcome such limitations, is the use of multiple vehicles in cooperation to achieve a certain goal. This application of UAVs is called swarming, where multiple agents can coordinate their actions to fly in a certain formation, to access a certain challenging area, or to fly further. As …

Towards Orchestration In The Cloud-Fog Continuum, Xavier Jesus Merino Aguilera

Theses and Dissertations

The proliferation of the Internet-of-Things has raised demand for computing, storage, and network resources. The cloud model is ill-equipped to handle the volume and variety of data travelling to and from the cloud’s core as more data is generated and consumed at the network’s edge. Some applications necessitate low-latency connectivity and geographical awareness, highlighting the cloud’s centralization shortcomings. By localizing resources, minimizing bandwidth utilization, and lowering latency, the fog and edge layers are proposed to circumvent these limitations. At these layers, resource orchestration is crucial because poor resource management has an impact on service delivery. The aim of this study …

Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug

Theses and Dissertations

Modern multi-tasking computer systems run numerous applications simultaneously. These applications must share hardware resources including the Central Processing Unit (CPU) and memory while maximizing each application’s performance. Tasks executing in this shared environment leave residue which should not reveal information. This dissertation applies machine learning and statistical analysis to evaluate task residue as footprints which can be correlated to identify tasks. The concept of privilege strata, drawn from an analogy with physical geology, organizes the investigation into the User, Operating System, and Hardware privilege strata. In the User Stratum, an adversary perspective is taken to build an interrogator program that …

Preserving Users’ Privacy In Iot Systems Through Network-Based Access Control, Ahmed Khalid A Alhazmi

Theses and Dissertations

Privacy issues have plagued the rapid proliferation of the Internet of Things (IoT) devices. Resource-constrained IoT devices often obscure transparency for end-users. A lack of transparency and control complicates user trust in IoT. Additionally, a growing history of misuse and abuse exists in IoT. Notably, a smart TV has periodically scanned and collected users’ private information without consent, while power companies have adjusted the temperature of smart thermostats during heat waves. Due to a hybrid of distributed ecosystems within IoT, users cannot easily implement traditional access control over their devices as data flows within different nodes for storage and processing. …

Automotive Sensor Fusion Systems For Traffic Aware Adaptive Cruise Control, Jonah T. Gandy

Theses and Dissertations

The autonomous driving (AD) industry is advancing at a rapid pace. New sensing technology for tracking vehicles, controlling vehicle behavior, and communicating with infrastructure are being added to commercial vehicles. These new automotive technologies reduce on road fatalities, improve ride quality, and improve vehicle fuel economy. This research explores two types of automotive sensor fusion systems: a novel radar/camera sensor fusion system using a long shortterm memory (LSTM) neural network (NN) to perform data fusion improving tracking capabilities in a simulated environment and a traditional radar/camera sensor fusion system that is deployed in Mississippi State’s entry in the EcoCAR Mobility …

The Effects Of Ecological Simulation For Ground Vehicle Mobility Forecasting, Christopher R. Hudson

Theses and Dissertations

Unmanned ground vehicles (UGV) are being explored for use in military domains. Military UGVs operate in complex off-road environments. Vehicle mobility forecasting plays an important role in understanding how and where a vehicle can operate. Traditional mobility forecasting has been done using an analytical model known as the NATO Reference Mobility Model (NRMM). There has been a push to extend the forecasting capabilities of NRMM by integrating more simulation methods. Simulation enables the repeated testing of UGVs in scenarios that would be difficult or dangerous to study in real world testing. To accurately capture UGV performance in simulation, the operating …

Efficient And Scalable Algorithms For Estimating Structural Properties Of Large Graphs, Shiju Li

Theses and Dissertations

Large complex networks and graphs are everywhere. Examples include social networks, web graphs, communication networks, power grids, and transportation networks. The ongoing rapid expansion of such networks has triggered a tremendous amount of attention in various disciplines. Estimating the structural and topological properties of the large complex networks has been at the heart of the understanding of the networks. However, such estimations often become technically challenging. This dissertation is focused on how to estimate three non-trivial structural quantities on large networks efficiently and scalably by transcending the current state-of-the-art algorithms. The structural quantities to estimate are (1) a function of …

Detecting Cybersecurity And Behavioral Anomalies In Smart Grid Substation, Anfal Yahya Hathah

Theses and Dissertations

Due to the increasing number of cyberattacks on smart grid networks globally in the last decade, maintaining a stable electric power supply has become increasingly challenging. The shift from traditional means of measurement instrumentation to smarter devices in electrical substations is experiencing increasing incidents of intrusion. Defense against those intrusions is now a global trending research topic and is attracting governments’ attention. Various techniques have been developed to mitigate the effect of cyberattacks on national power grid systems. The state estimation technique has proven its capability in detecting random false data-injection attacks (FDIAs). However, attackers may have considerable knowledge of …

A Component-Based Analysis For Online Proctoring, Salma Roshdy Ali

Theses and Dissertations

The switch to online learning due to the COVID-19 revealed flaws in the existing learning methods, especially with online proctored assessments. Hence, online proctoring using computers was needed for a fair evaluation. Many studies develop cheating detection systems using several approaches. However, to the best of our knowledge, none of the existing studies investigated the impact of their system components in detecting cheating behaviors. Combining system components, even if they do not significantly improve the system performance in cheating detection, can cause an overload on the system. Therefore, our goal is to investigate the system components’ impact, individually and combined, …

Managing Radio Frequency Interference In Vehicular Multi-Antenna Transceivers, Jakob W. Kunzler

Theses and Dissertations

Radio frequency interference is an ever growing problem in the wireless community. This dissertation presents methods to reduce interference for vehicular multi-antenna devices. This document is organized into two parts: the main chapters and the appendices. The main chapters present research conducted primarily by the author. These deserve the reader's primary attention. The appendices showcase contributions made by the author serving in a supporting role to projects led by others and/or do not fit the vehicular theme. These should receive secondary attention. The main chapter contributions are summarized as follows. A device was created that provides over 105 dB of …

Exploiting The Iot Through Network-Based Covert Channels, Kyle S. Harris

Theses and Dissertations

Information leaks are a top concern to industry and government leaders. The IoT is a technology capable of sensing real-world events. A method for exfiltrating data from these devices is by covert channel. This research designs a novel IoT CTC without the need for inter-packet delays to encode data. Instead, it encodes data within preexisting network information, namely ports or addresses. Additionally, the CTC can be implemented in two different modes: Stealth and Bandwidth. Performance is measured using throughput and detectability. The Stealth methods mimic legitimate traffic captures while the Bandwidth methods forgo this approach for maximum throughput. Detection results …

Evaluating The Use Of Boot Image Encryption On Talos Ii Architecture, Calvin M. Muramoto

Theses and Dissertations

Sensitive devices operating in unprotected environments are vulnerable to hardware attacks like reverse engineering and side channel analysis. This represents a security concern because the root of trust can be invalidated through boot firmware manipulation. For example, boot data is rarely encrypted and typically travels across an accessible bus like the LPC bus, allowing data to be easily intercepted and possibly manipulated during system startup. The ash chip storing the boot data can also be removed from these devices and examined to reveal detailed boot information. This paper details an implementation of encrypting a section of the boot image and …

Securing Infiniband Networks With End-Point Encryption, Noah B. Diamond

Theses and Dissertations

The NVIDIA-Mellanox Bluefield-2 is a 100 Gbps high-performance network interface which offers hardware offload and acceleration features that can operate directly on network traffic without routine involvement from the ARM CPU. This allows the ARM multi-core CPU to orchestrate the hardware to perform operations on both Ethernet and RDMA traffic at high rates rather than processing all the traffic directly. A testbed called TNAP was created for performance testing and a MiTM verification process called MiTMVMP is used to ensure proper network configuration. The hardware accelerators of the Bluefield-2 support a throughput of nearly 86 Gbps when using IPsec to …

Uav Payload Identification With Acoustic Emissions And Cell Phone Devices, Hunter G. Doster

Theses and Dissertations

The growing presence of Unmanned Aerial Vehicle (UAV) brings new threats to the civilian and military front. In response, the Department of Defense (DoD) is developing many drone detection systems. Current systems use Radio Detection and Ranging (RADAR), Light Detection and Ranging (LiDAR), and Radio Frequency (RF). Although useful, these technologies are becoming easier to spoof every year, and some are limited to line of sight. Acoustic emissions are a unique quality all drones emit. Acoustics are difficult to spoof and do not require line of sight for detection. This research expands the research field of study by creating HurtzHunter, …

Implementation And Characterization Of Ahr On A Xilinx Fpga, Andrew J. Dittrich

Theses and Dissertations

A new version of the Adaptive-Hybrid Redundancy (AHR) architecture was developed to be implemented and tested in hardware using Commercial-Off-The-Shelf (COTS) Field-Programmable Gate Arrays (FPGAs). The AHR architecture was developed to mitigate the effects that the Single Event Upset (SEU) and Single Event Transient (SET) radiation effects have on processors and was tested on a Microprocessor without Interlocked Pipeline Stages (MIPS) architecture. The AHR MIPS architecture was implemented in hardware using two Xilinx FPGAs. A Universal Asynchronous Receiver Transmitter (UART) based serial communication network was added to the AHR MIPS design to enable inter-board communication between the two FPGAs. The …

Considerations Using Iterative Closest Point In Presence Of Occlusions In Automated Aerial Refueling, Joel M. Miller

Theses and Dissertations

The United States Air Force is researching vision-based AAR and different methods for this actualization. Previous work has established a computer vision based pipeline with ICP. This work focuses on how ICP can become resilient to boom occlusion by minimizing errors and discusses the limitations of ICP in the face of occlusions. Specifically, we look at various filtering techniques to remove non-salient points. To register point clouds while maintaining real time interactivity, this work also presents a method for downsampling high resolution camera calibrations to preserve real-time processing and significantly decrease the vision pipeline latency.

Evaluating Neural Network Decoder Performance For Quantum Error Correction Using Various Data Generation Models, Brett M. Martin

Theses and Dissertations

Neural networks have been shown in the past to perform quantum error correction (QEC) decoding with greater accuracy and efficiency than algorithmic decoders. Because the qubits in a quantum computer are volatile and only usable on the order of milliseconds before they decohere, a means of fast quantum error correction is necessary in order to correct data qubit errors within the time budget of a quantum algorithm. Algorithmic decoders are good at resolving errors on logical qubits with only a few data qubits, but are less efficient in systems containing more data qubits. With neural network decoders, practical quantum computation …

Camera And Lidar Fusion For Point Cloud Semantic Segmentation, Ali Abdelkader

Theses and Dissertations

Perception is a fundamental component of any autonomous driving system. Semantic segmentation is the perception task of assigning semantic class labels to sensor inputs. While autonomous driving systems are currently equipped with a suite of sensors, much focus in the literature has been on semantic segmentation of camera images only. Research in the fusion of different sensor modalities for semantic segmentation has not been investigated as much. Deep learning models based on transformer architectures have proven successful in many tasks in computer vision and natural language processing. This work explores the use of deep learning transformers to fuse information from …

Extractive Text Summarization On Single Documents Using Deep Learning, Shehab Mostafa Abdel-Salam Mohamed

Theses and Dissertations

The task of summarization can be categorized into two methods, extractive and abstractive summarization. Extractive approach selects highly meaningful sentences to form a summary while the abstractive approach interprets the original document and generates the summary in its own words. The task of generating a summary, whether extractive or abstractive, has been studied with different approaches such as statistical-based, graph-based, and deep-learning based approaches. Deep learning has achieved promising performance in comparison with the classical approaches and with the evolution of neural networks such as the attention network or commonly known as the Transformer architecture, there are potential areas for …

Adding Temporal Information To Lidar Semantic Segmentation For Autonomous Vehicles, Mohammed Anany

Theses and Dissertations

Semantic segmentation is an essential technique to achieve scene understanding for various domains and applications. Particularly, it is of crucial importance in autonomous driving applications. Autonomous vehicles usually rely on cameras and light detection and ranging (LiDAR) sensors to gain contextual information from the environment. Semantic segmentation has been employed to process images and point clouds that were captured from cameras and LiDAR sensors respectively. One important research direction to consider is investigating the impact of utilizing temporal information in the domain of semantic segmentation. Many contributions exist in the field with regards to utilizing temporal information for semantic segmentation …

Continual Learning From Stationary And Non-Stationary Data, Lukasz Korycki

Theses and Dissertations

Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals.

Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty …

Changeset-Based Retrieval Of Source Code Artifacts For Bug Localization, Agnieszka Ciborowska

Theses and Dissertations

Modern software development is extremely collaborative and agile, with unprecedented speed and scale of activity. Popular trends like continuous delivery and continuous deployment aim at building, fixing, and releasing software with greater speed and frequency. Bug localization, which aims to automatically localize bug reports to relevant software artifacts, has the potential to improve software developer efficiency by reducing the time spent on debugging and examining code. To date, this problem has been primarily addressed by applying information retrieval techniques based on static code elements, which are intrinsically unable to reflect how software evolves over time. Furthermore, as prior approaches frequently …

Improving Feature Learning Capability And Interpretability Of Unsupervised Neural Networks, Chathurika S. Wickramasinghe Brahmana

Theses and Dissertations

The motivation for this dissertation is two-prong. Firstly, the current state of machine learning imposes the need for unsupervised Machine Learning (ML). Secondly, once such models are developed, a deeper understanding of ML models is necessary for humans to adapt and use such models.

Real-world systems generate massive amounts of unlabeled data at rapid speed, limiting the usability of state-of-the-art supervised machine learning approaches. Further, the manual labeling process is expensive, time-consuming, and requires the expertise of the data. Therefore, the existing supervised learning algorithms are unable to take advantage of the abundance of real-world unlabeled data. Thus, relying on …

Machine Learning (Ml) - Assisted Tools For Enhancing Security And Privacy Of Edge Devices, Santosh Kumar Nukavarapu

Theses and Dissertations

The rapid growth of edge-based IoT devices, their use cases, and autonomous communication has created new challenges with privacy and security. Side-channel attacks are one of the examples of security and privacy vulnerabilities that can cause inference at Internet-Service Provider (ISP) and local Wi-Fi networks. Such an attack would leak user’s sensitive information such as home occupancy, medical activity, and daily routines. Another example is that these devices have weak authentication and low encryption standards, making them an easy target for malware-based attacks such as denial of service or launching other network attacks using these infected devices. This thesis dissertation …

Learning Robot Motion From Creative Human Demonstration, Charles C. Dietzel

Theses and Dissertations

This thesis presents a learning from demonstration framework that enables a robot to learn and perform creative motions from human demonstrations in real-time. In order to satisfy all of the functional requirements for the framework, the developed technique is comprised of two modular components, which integrate together to provide the desired functionality. The first component, called Dancing from Demonstration (DfD), is a kinesthetic learning from demonstration technique. This technique is capable of playing back newly learned motions in real-time, as well as combining multiple learned motions together in a configurable way, either to reduce trajectory error or to generate entirely …