Engineering Commons^™

Investigating Autonomous Ground Vehicles For Weed Elimination, Abraham Mitchell

Computer Science and Computer Engineering Undergraduate Honors Theses

The management of weeds in crop fields is a continuous agricultural problem. The use of herbicides is the most common solution, but herbicidal resistance decreases effectiveness, and the use of herbicides has been found to have severe adverse effects on human health and the environment. The use of autonomous drone systems for weed elimination is an emerging solution, but challenges in GPS-based localization and navigation can impact the effectiveness of these systems. The goal of this thesis is to evaluate techniques for minimizing localization errors of drones as they attempt to eliminate weeds. A simulation environment was created to model …

Understanding The Limits Of Deep Packet Inspection For Network Traffic Classification, Herman Ramey

Open Access Theses & Dissertations

We present our human network application labeling system that contributes a new level of distinction between the network traffic that should be labeled from the network traffic that should not be labeled. This distinction improves the label accuracy of the training data set produced from the human labeled data and will subsequently improve the performance of supervised machine learning classifiers used for network traffic classification. This system also allows for the human network user to label traffic, with little effort, in a manner consistent with normal network usage, i.e., no need for a contrived experiment. Lastly, we use human supplied …

An Edge Computing System With Amd Xilinx Fpga Ai Customer Platform For Advanced Driver Assistance System, Tsun Kuang Chi, Tsung Yi Chen, Yu Chen Lin, Ting Lan Lin, Jun Ting Zhang, Cheng Lin Lu, Shih Lun Chen, Kuo Chen Li, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and …

Particle Swarm Optimization For Training Quadrotor Pid Controller, Eric Xavier Rodriguez

Theses and Dissertations

The objective of this research is to establish a fundamental approach to tuning PID (Proportional-Integral-Derivative) parameters for a simulated quadrotor drone. Implementing a PID controller for autonomous flight provides a straightforward and efficient method for monitoring and correcting robotic movement based on the robot's current state. However, applying a PID approach to a quadrotor's flight controller poses challenges, such as assigning multiple parameters to control an inherently under-actuated system. This includes the need to find optimal parameter values that reduce the likelihood of large overshoots and lengthy adjustment times. Ineffectively tuning PID parameters can have detrimental effects on autonomously …

Developing Resilient Defense Strategies Against Pheromone-Based Attacks In Foraging Robot Swarms, Ryan A. Luna

Theses and Dissertations

This thesis delves into the security of stochastic pheromone-based foraging algorithms within swarm robotic systems, a subset of foraging algorithms distinguished by their reliance on probabilistic decision-making mechanisms inspired by the natural world. Such algorithms face vulnerabilities in stigmergic communication that threaten to disrupt swarm operations. This research investigates these vulnerabilities, presenting two distinct contributions.

The first contribution examines the implementation of quarantine strategies as a defensive measure to isolate and mitigate the impact of fake resource attacks. By simulating these attacks, this study quantitatively assesses their detrimental effects on swarm efficiency and explores the efficacy …

Understanding Timing Error Characteristics From Overclocked Systolic Multiply-Accumulate Arrays In Fpgas, Andrew S. Chamberlin

All Graduate Theses and Dissertations, Fall 2023 to Present

Artificial Intelligence (AI) is one of the biggest fields of research for computer hardware right now. Hardware accelerators are chips (such as graphics cards) that are purpose built to be the best at a specific type of operation. AI hardware accelerators are a growing field of research. Part of hardware in general is a digital clock that controls the pace at which computations occur. If this clock runs too quickly, the hardware won't have enough time to finish its computation. We call that a timing error. This paper focuses on studying the characteristics of timing errors in a small custom …

Automatic Speech Recognition For Air Traffic Control Using Convolutional Lstm, Sakshi Nakashe

Electronic Theses, Projects, and Dissertations

The need for automatic speech recognition in air traffic control is critical as it enhances the interaction between the computer and human. Speech recognition helps to automatically transcribe the communication between the pilots and the air traffic controllers, which reduces the time taken for administrative tasks. This project aims to provide improvement to the Automatic Speech Recognition (ASR) system for air traffic control by investigating the impact of convolution LSTM model on ASR as suggested by previous studies. The research questions are: (Q1) Comparing the performance of ConvLSTM with other conventional models, how does ConvLSTM perform with respect to recognizing …

Deep Reinforcement Learning Of Variable Impedance Control For Object-Picking Tasks, Akshit Lunia

All Theses

The increasing deployment of robots in industries with varying tasks has accelerated the development of various control frameworks, enabling robots to replace humans in repetitive, exhaustive, and hazardous jobs. One critical aspect is the robots' interaction with their environment, particularly in unknown object-picking tasks, which involve intricate object weight estimations and calculations when lifting objects. In this study, a unique control framework is proposed to modulate the force exerted by a manipulator for lifting an unknown object, eliminating the need for feedback from a force/torque sensor. The framework utilizes a variable impedance controller to generate the required force, and an …

Hand Movement Analysis For Surgical Suturing Skill Assessment, Amir Mehdi Shayan

All Theses

To enhance patient safety, surgical education is increasingly incorporating simulation for formative skills assessment and training. However, many standardized assessment tools rely on human raters for performance assessment, which is resource-intensive and subjective. Simulators that provide automated and objective metrics from sensor data can address this limitation. This thesis presents an instrumented bench suturing simulator, patterned after the Clock Face (CF) radial suturing model from the Fundamentals of Vascular Surgery (FVS), for automated and objective assessment of open suturing skills by particularly focusing on biomechanical analysis of hand movements. For this research, 97 participants (35 attending surgeons and fellows, 32 …

Recommender System Design And Multi-Channel Pricing: Personalization Strategies For Online Platforms, Hao Zhang

Dissertations and Theses Collection (Open Access)

The advancement of mobile technology and rising consumer demands have contributed to the unprecedented growth of online platforms. In online platforms, recommender systems connect with multistakeholders who have different interests. Designing recommender systems to balance the benefit of multistakeholders is important for these platforms. In addition, price is an important factor influencing consumers’ purchase decisions. An increasing number of online platforms introduce multiple sales channels. Optimizing multiple-channel prices is vital for these platforms. Thus, this thesis designs multistakeholder recommender systems and multi-channel pricing strategies for online platforms through the following two works.

The first work focuses on designing multistakeholder recommender …

Simulating And Training Autonomous Rover Navigation In Unity Engine Using Local Sensor Data, Christopher Pace

Senior Honors Theses

Autonomous navigation is essential to remotely operating mobile vehicles on Mars, as communication takes up to 20 minutes to travel between the Earth and Mars. Several autonomous navigation methods have been implemented in Mars rovers and other mobile robots, such as odometry or simultaneous localization and mapping (SLAM) until the past few years when deep reinforcement learning (DRL) emerged as a viable alternative. In this thesis, a simulation model for end-to-end DRL Mars rover autonomous navigation training was created using Unity Engine, using local inputs such as GNSS, LiDAR, and gyro. This model was then trained in navigation in a …

Geometric Multi-Resolution Analysis Across Signals, Images, And Networks, Felicia Schenkelberg

Dartmouth College Master’s Theses

This research delves into the transformative potential of Geometric Multi-Resolution Analysis (GMRA) as a robust tool for dimensionality reduction and data analysis in the context of high-dimensional graphs. Statistical techniques for classification have historically been tailored for scenarios wherein the number of observations significantly exceeds the number of features, a paradigm characteristic of low-dimensional datasets. However, recent advancements in technologies have ushered in a transformative era in data collection practices across diverse domains, resulting in the acquisition of extensive feature measurements. As a result of this shift, datasets have transitioned into a high-dimensional realm wherein the number of features significantly …

Design And Application Of Smart Systems To Address Analytical Problems, Lucas B. Ayres

All Dissertations

This dissertation is a multidisciplinary effort that integrates low-cost analytical instrumentation, redox chemistry, and artificial intelligence to overcome existing limitations in the fields of wearable sensing technology, Deep Eutectic Solvents (DES), and antioxidant chemistry. The overall goal behind each implemented strategy is to enhance the accuracy, efficiency, and accessibility of analytical processes and technologies. A general overview of the thesis, along with the research outcomes is included in Chapter One. The theoretical framework of this dissertation is presented in Chapter Two. Chapter Three describes the development of a wearable platform (sensor and instrumentation) to rapidly detect (~20 minutes) S. aureus …

Cloud Computing Integration Into Mixed-Reality: Physical To Abstraction, Yassine Chahid, Patrick Slattery

Publications and Research

This research evaluates the progression of cloud computing and mixed-reality technologies, and to identify how these technologies influence advancements in the latter. Both cloud computing and mixed reality have significantly impacted the IT field and the services available to the public and various institutions. Cloud computing provides a valuable way to process information or allocate computational resources on otherwise limited hardware. Augmented or virtual reality hardware would greatly benefit from this by offloading resource-intensive tasks to other machines. The research methodology involves analyzing essential components of both innovations, divided into multiple categories. These components range from physical, hardware-based elements to …

Deep Learning Using Vision And Lidar For Global Robot Localization, Brett E. Gowling

Master's Theses

As the field of mobile robotics rapidly expands, precise understanding of a robot’s position and orientation becomes critical for autonomous navigation and efficient task performance. In this thesis, we present a snapshot-based global localization machine learning model for a mobile robot, the e-puck, in a simulated environment. Our model uses multimodal data to predict both position and orientation using the robot’s on-board cameras and LiDAR sensor. In an effort to minimize localization error, we explore different sensor configurations by varying the number of cameras and LiDAR layers used. Additionally, we investigate the performance benefits of different multimodal fusion strategies while …

A Smart Hybrid Enhanced Recommendation And Personalization Algorithm Using Machine Learning, Aswin Kumar Nalluri

Electronic Theses, Projects, and Dissertations

In today’s age of streaming services, the effectiveness and precision of recommendation systems are crucial in improving user satisfaction. This project introduces the Smart Hybrid Enhanced Recommendation and Personalization Algorithm (SHERPA) a cutting-edge machine learning approach aimed at transforming how movie suggestions are made. By combining Term Frequency Inverse Document Frequency (TF-IDF) for content based filtering and Alternating Squares (ALS) with Weighted Regularization for filtering SHERPA offers a sophisticated method for delivering tailored recommendations.

The algorithm underwent evaluation using a dataset that included over 50 million ratings from 480,000 Netflix users encompassing 17,000 movie titles. The performance of SHERPA was …

Cultural Awareness Application, Bharat Gupta

Electronic Theses, Projects, and Dissertations

In an increasingly interconnected global landscape, cultural awareness and competency have become indispensable skills for individuals and organizations alike. This paper introduces a pioneering cultural awareness application, grounded in the Cultural Orientation Model—a comprehensive framework devised by Dr. Walker [8]to guide individuals in understanding, appreciating, and effectively engaging with diverse cultures. The application encompasses ten primary dimensions, each representing fundamental aspects of social life shared by members of any socio-cultural environment. Through a combination of cultural education, interactive learning, guidance on cultural etiquette, and integration of cultural events, the application aims to foster empathy, tolerance, and effective cross-cultural communication skills. …

Automated Brain Tumor Classifier With Deep Learning, Venkata Sai Krishna Chaitanya Kandula

Electronic Theses, Projects, and Dissertations

Brain Tumors are abnormal growth of cells within the brain that can be categorized as benign (non-cancerous) or malignant (cancerous). Accurate and timely classification of brain tumors is crucial for effective treatment planning and patient care. Medical imaging techniques like Magnetic Resonance Imaging (MRI) provide detailed visualizations of brain structures, aiding in diagnosis and tumor classification^[8].

In this project, we propose a brain tumor classifier applying deep learning methodologies to automatically classify brain tumor images without any manual intervention. The classifier uses deep learning architectures to extract and classify brain MRI images. Specifically, a Convolutional Neural Network (CNN) …

Effectiveness Of Cnn-Lstm Models Used For Apple Stock Forecasting, Ethan White

Electronic Theses, Projects, and Dissertations

This culminating experience project investigates the effectiveness of convolutional neural networks mixed with long short-term memory (CNN-LSTM) models, and an ensemble method, extreme gradient boosting (XGBoost), in predicting closing stock prices. This quantitative analysis utilizes recent AAPL stock data from the NASDAQ index. The chosen research questions (RQs) are: RQ1. What are the optimal hyperparameters for CNN-LSTM models in stock price forecasting? RQ2. What is the best architecture for CNN-LSTM models in this context? RQ3. How can ensemble techniques like XGBoost effectively enhance the predictions of CNN-LSTM models for stock price forecasting?

The research questions were answered through a thorough …

Improved Test Fixture For Collecting Microcontact Performance And Reliability Data, Turja Nandy, Ronald A. Coutu Jr., Rafee Mahbub

Electrical and Computer Engineering Faculty Research and Publications

Microelectromechanical systems (MEMS) ohmic contact switches are considered to be a promising candidate for wireless communication applications. The longevity of MEMS switches is directly related to the reliability and performance of microcontacts. In this work, an improved microcontact test fixture with high actuation rates (KHz) and highly precise position control (nm) and force (nN) control was developed. Here, we collected microcontact performance data from initial contact tests (ICT) and microcontact reliability data from cold switched tests (CST). To perform these tests with our test fixture, we fabricated MEMS microcontact test structures with relatively high Young’s modulus electroplated Nickel (Ni)-based, fixed–fixed …

Detection Of Deficiencies And Data Analysis Of Bridge Members With Deep Convolutional Neural Networks, Bennett Jackson

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

Concrete cracks and structural steel corrosion are two of the most common defects in bridges. Quantifying and classifying these defects provide bridge inspectors and engineers with valuable data for assessing deterioration levels. However, the bridge inspection process is typically a subjective, time intensive, and tedious task, as defects can be overlooked or in locations not easily accessible. Previous studies have investigated deep learning-based inspection methods, implementing popular models such as Mask R-CNN and U-Net. The architectures of these models offer certain advantages depending on the required task. This thesis aims to evaluate and compare Mask R-CNN and U-Net regarding their …

Sliding Markov Decision Processes For Dynamic Task Planning On Uncrewed Aerial Vehicles, Trent Wiens

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

Mission and flight planning problems for uncrewed aircraft systems (UASs) are typically large and complex in space and computational requirements. With enough time and computing resources, some of these problems may be solvable offline and then executed during flight. In dynamic or uncertain environments, however, the mission may require online adaptation and replanning. In this work, we will discuss methods of creating MDPs for online applications, and a method of using a sliding resolution and receding horizon approach to build and solve Markov Decision Processes (MDPs) in practical planing applications for UASs. In this strategy, called a Sliding Markov Decision …

4-Channel Spatially Multiplexed Communication System In Single-Core Optical Fibers, Ce Su

Theses and Dissertations

This dissertation delves into exploring and advancing spatial domain / space division multiplexing (SDM) technologies within single-core optical fibers, a frontier in optical fiber communications poised to meet the burgeoning global demand for data transmission. At the heart of this research is the pursuit to significantly enhance the capacity and efficiency of optical fiber communication systems without necessitating additional fiber infrastructure. This work unveils a new paradigm in optical fiber communications characterized by a pioneering 4-channel SDM system through a meticulous process encompassing theoretical modeling, computational simulations, design innovations, and rigorous experimental validations. Theoretical contributions include the development of refined …

Towards Side-Channel Infrastructure For Software Implementations Of Pqc Algorithms, Tristen Teague

Graduate Theses and Dissertations

Post-Quantum Cryptography (PQC) is a new class of asymmetric cryptography algorithms that are supposed to be secure against both classical computers and quantum computers through Shor’s algorithm. Since PQC algorithms are currently being standardized, they will replace older standardized asymmetric algorithms (such as RSA) and will be deployed within the digital infrastructure. Before implementations of the PQC algorithms are placed into the infrastructure, they must undergo evaluation of both performance and security. One such security issue that needs large investigation before deployment are side-channels. Side-channel attacks (SCA) are a method of gathering information from the implementation, such as power-consumption and …

Ai-Powered Information Retrieval In Meeting Records And Transcripts Enhancing Efficiency And User Experience, Srushti Nitin Ghadge

Theses and Dissertations

This study compares the traditional search methods, which is to search from video recordings of the meetings by moving the slider back and forth or by keyword search in transcripts versus integrated AI video plus transcript search. Based on the previous test results, we introduced some human-centric design features to the AI and built a new enhanced AI search tool for information retrieval. For search technique efficiency testing, the method had two set of experiments. The first results of the experiment showed that AI-based search algorithms were more accurate and faster than conventional search approaches. Participants were also happier with …

Investigating Factors Influencing Blockchain Adoption In Saudi Healthcare Data Management, Noura Mohammad Alkhalifah

Theses and Dissertations

Blockchain technology can potentially address security and privacy issues concerning the collection, storage, and sharing of healthcare data. However, its adoption within the healthcare sector is nascent in Saudi Arabia. This underutilization prompted our investigation into the determinants influencing blockchain adoption, intending to fully empower the Saudi healthcare sector to leverage blockchain capabilities. To achieve this, an extensive literature review was conducted to identify the pivotal factors encompassing technology, organization, and environment (TOE) that affect the successful implementation of blockchain technologies in managing healthcare data within the Saudi context. Utilizing the TOE framework, this study formulated three hypotheses concerning the …

Hybrid Method Neighbor Node Discovery In Wireless Sensor Networks: A Framework, Sagar Mekala, Shahu Chatrapati Kaila, Jyothi Rani Matang

Makara Journal of Technology

Wireless devices are now being adapted for diverse purposes, such as healthcare, agriculture, transportation, and tactical operations, which present challenges in network formation owing to high device mobility. Current methods rely on discovery techniques for forming wireless sensor networks (WSNs); however, the existing research has been criticized for its high time complexity and redundant neighbor discovery process. In this study, we provide a hybrid strategy to effectively handle the difficulties of locating neighboring nodes in WSNs. Our method combines several strategies to produce precise and effective neighbor detection. Herein, shared memory–based discovery, a beacon technique, and range and distance overlap …

Building Software At Scale: Understanding Productivity As A Product Of Software Engineering Intrinsic Factors, Gauthier Ingende Wa Boway

Master's Theses

During our education at KSU, we have learned about various factors that affect productivity such as schedule, budget, and risks, but those are often controlled outside of what we could learn as software engineering principles, patterns, or practices. On top of that, other off-work factors such as health conditions, emotional distress, or political climate, just to name a few, could drastically affect the productivity of a software engineering team. We see a demarcation between those factors that affect productivity in software engineering but are not inherent to the discipline itself, which we call resistance factors, and the factors that are …

Secured Blockchain And Fractional Discrete Cosine Transform-Based Framework For Medical Images, Abhay Kumar Yadav, Virendra P. Vishwakarma

Makara Journal of Technology

Images can store large amounts of data and are useful for transmitting large amounts of information across different geographical locations using different cloud services. This data sharing increases the chances of cyber-attacks on digital images. Blockchain has properties that enable it to work as a solution to this problem, providing enhanced security and unchangeable storage. However, image size poses a challenge in image storage, as it increases the related storage cost. Compressing images using fractional discrete cosine transform (fctDCT) reduces the amount of data required to express an image securely. This paper presents a novel framework for securely storing and …

The Next Threat Landscape: Securing America’S Cyber-Physical Systems, Grayson Thomas

Honors College Theses

The goal of this research is to explore, identify, and enumerate the security threats that exist to industry current industrial controls systems (ICS) and supervisory control and data acquisition systems (SCADA). A scale lab similar to industry standard will be built and developed for research purposes. The Purdue Model for ICS and the Cyber Kill Chain will be referenced as frameworks for attack sequences, and the MITRE ATT&CK framework will be referenced for attack types. Attempts will be made to compromise the various pieces of our built SCADA system via configuration errors, software vulnerabilities, and deployment mistakes common with industrial …