Computer Engineering Commons^™

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 …

Fair Fault-Tolerant Approach For Access Point Failures In Networked Control System Greenhouses, Mohammed Ali Yaslam Ba Humaish

Theses and Dissertations

Greenhouse Networked Control Systems (NCS) are popular applications in modern agriculture due to their ability to monitor and control various environmental factors that can affect crop growth and quality. However, designing and operating a greenhouse in the context of NCS could be challenging due to the need for highly available and cost-efficient systems. This thesis presents a design methodology for greenhouse NCS that addresses these challenges, offering a framework to optimize crop productivity, minimize costs, and improve system availability and reliability. It contributes several innovations to the field of greenhouse NCS design. For example, it recommends using the 2.4GHz frequency …

Few-Shot Learning For Ner Using Maml, Nourchene Bargaoui

Theses and Dissertations

This thesis investigates the application of Few-Shot Learning (FSL) using Model-Agnostic Meta-Learning (MAML) to enhance Named Entity Recognition (NER) within the domain of Natural Language Processing (NLP), specifically focusing on chemical datasets. The primary challenge addressed is the impracticality of relying on extensive annotated datasets, especially in specialized fields like chemistry. The research primarily explores the concept of Few-Shot Learning, aiming to train models on minimal data while maintaining performance across diverse tasks. It delves into the N-way K-shot methodology, where "N" represents the number of classes and "K" signifies the number of examples per class. This approach is further …

Efficient Connectivity Management And Path Planning For Iot And Uav Networks, Amirahmad Chapnevis

Theses and Dissertations

This dissertation explores how to better manage resources in mobile networks, especially for enhancing the performance of Unmanned Aerial Vehicles (UAV)-supported IoT networks. We explored ways to set up a flexible communication architecture that can handle large IoT deployments by making good use of mobile core network resources like bearers and data paths. We developed strategies that meet the needs of IoT networks and enhance network performance. We also developed and tested a system that combines traffic from several mobile devices that use the same user identity and network resources within the core mobile network. We used everyday smartphones, SIM …

Qasm-To-Hls: A Framework For Accelerating Quantum Circuit Emulation On High-Performance Reconfigurable Computers, Anshul Maurya

Theses and Dissertations

High-performance reconfigurable computers (HPRCs) make use of Field-Programmable Gate Arrays (FPGAs) for efficient emulation of quantum algorithms. Generally, algorithm-specific architectures are implemented on the FPGAs and there is very little flexibility. Moreover, mapping a quantum algorithm onto its equivalent FPGA emulation architecture is challenging. In this work, we present an automation framework for converting quantum circuits to their equivalent FPGA emulation architectures. The framework processes quantum circuits represented in Quantum Assembly Language (QASM) and derives high-level descriptions of the hardware emulation architectures for High-Level Synthesis (HLS) on HPRCs. The framework generates the code for a heterogeneous architecture consisting of a …

Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad

Theses and Dissertations

Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …

A New Algorithm For Encounter Generation: Encounters From Actual Trajectories (Enact), James Anthony Ritchie Iii

Theses and Dissertations

There is ongoing research at the Federal Aviation Administration (FAA) and other private industries to examine a concept for delegated separation in multiple classes of airspace to allow unmanned aircraft systems (UAS) to remain well clear of other aircraft. Detect and Avoid (DAA) capabilities are one potential technology being examined to maintain separation. To evaluate these DAA capabilities, input traffic scenarios are needed, but current approaches are limited by the breadth of the traffic recordings available. This thesis derives a new mathematical algorithm that uses great circle navigation equations in an Earth spherical model and an accurate aircraft performance model …

Physical Layer Security With Unmanned Aerial Vehicles For Advanced Wireless Networks, Aly Sabri Abdalla

Theses and Dissertations

Unmanned aerial vehicles (UAVs) are emerging as enablers for supporting many applications and services, such as precision agriculture, search and rescue, temporary network deployment, coverage extension, and security. UAVs are being considered for integration into emerging wireless networks as aerial users, aerial relays (ARs), or aerial base stations (ABSs). This dissertation proposes employing UAVs to contribute to physical layer techniques that enhance the security performance of advanced wireless networks and services in terms of availability, resilience, and confidentiality. The focus is on securing terrestrial cellular communications against eavesdropping with a cellular-connected UAV that is dispatched as an AR or ABS. …

Robust And Uncertainty-Aware Software Vulnerability Detection Using Bayesian Recurrent Neural Networks, Orune Aminul

Theses and Dissertations

Software systems are prone to code defects or vulnerabilities, resulting in several cyberattacks such as hacking, identity breach and information leakage leading to system failure. Vulnerabilities in software systems have severe societal implications, including threats to public safety, financial damage, and even risks to national security. Identifying and mitigating software vulnerabilities is critical to protect organizations and societies from potential threats. Machine learning algorithms have been employed to detect and classify potential vulnerabilities in software source code automatically. However, these algorithms are not robust to noise or malicious attacks and cannot quantify uncertainty in the model’s output. Quantifying uncertainty in …

Leveraging Programmable Switches To Enhance The Performance Of Networks: Active And Passive Deployments, Elie Kfoury

Theses and Dissertations

The performance of networks today is drastically affected by: 1) switches equipped with large buffers, referred to as “bloated buffers”: due to the lack of programmability and traffic visibility in legacy switches, operators nowadays configure large buffers statically without considering the characteristics or dynamics of flows. Such buffers increase the delays on packets, causing the Quality of Service (QoS) of networked applications (e.g., voice over IP, web browsing) to degrade; 2) switches forwarding packets on a best-effort basis: traffic crossing a switch is heterogeneous in many ways. Mixing such traffic in a single queue without any QoS measures can drastically …

Digital Health Design For Improving Treatment Decisions, Akanksha Singh

Theses and Dissertations

In the age of artificial intelligence and large datasets, information retrieval by querying large databases is an impossible task for the common user due to the information overload. Recommender Systems (RS) for commercial applications like YouTube, Amazon and Netflix were designed to support users by finding items of interest based on their user profiles and various filtering techniques. Health Recommender Systems (HRS) is a category of RSs that provides immense opportunities for application across several healthcare domains and contexts including treatment decision support. Unlike RS applications that focus on analyzing consumer choices, a key differentiator for HRS applications is the …

User Profiling Through Zero-Permission Sensors And Machine Learning, Ahmed Elhussiny

Theses and Dissertations

With the rise of mobile and pervasive computing, users are often ingesting content on the go. Services are constantly competing for attention in a very crowded field. It is only logical that users would allot their attention to the services that are most likely to adapt to their needs and interests. This matter becomes trivial when users create accounts and explicitly inform the services of their demographics and interests. Unfortunately, due to privacy and security concerns, and due to the fast nature of computing today, users see the registration process as an unnecessary hurdle to bypass, effectively refusing to provide …

Mixed-Criticality Scheduling Using Reinforcement Learning, Omar Elseadawy

Theses and Dissertations

Mixed-criticality (MC) scheduling is necessary for many safety-critical real-time embedded systems, as a failure of high-criticality jobs could lead to fatal accidents. With the emergence of software technologies in software-defined vehicles in the automotive and avionics industries, studying Mixed-Critically (MC) systems is essential to their safety standards, similar to ISO26262. The real-time operation of MC systems makes it an inherently online problem, such that the scheduler is only aware of the jobs that are currently released at any point in time and has no knowledge of future jobs. Due to the overhead cost of preemption, this study focuses on enforcing …

Towards Optimal Operation And Control Of Emerging Electric Distribution Networks, Jimiao Zhang

Theses and Dissertations

The growing integration of power-electronics converters enabled components causes low inertia in the evolving electric distribution networks, which also suffer from uncertainties due to renewable energy sources, electric demands, and anomalies caused by physical or cyber attacks, etc. These issues are addressed in this dissertation. First, a virtual synchronous generator (VSG) solution is provided for solar photovoltaics (PVs) to address the issues of low inertia and system uncertainties. Furthermore, for a campus AC microgrid, coordinated control of the PV-VSG and a combined heat and power (CHP) unit is proposed and validated. Second, for islanded AC microgrids composed of SGs and …

A Graph-Based Approach For Adaptive Serious Games, Nidhi G. Patel

Theses and Dissertations

Traditional education systems are based on the one-size-fits-all approach, which lacks personalization, engagement, and flexibility necessary to meet the diverse needs and learning styles of students. This encouraged researchers to focus on exploring automated, personalized instructional systems to enhance students’ learning experiences. Motivated by this remark, this thesis proposes a personalized instructional system using a graph method to enhance a player’s learning process by preventing frustration and avoiding a monotonous experience. Our system uses a directional graph, called an action graph, for representing solutions to in-game problems based on possible player actions. Through our proposed algorithm, a serious game integrated …

Secure And Efficient Federated Learning, Xingyu Li

Theses and Dissertations

In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to …

Cross-Platform Development Of Wake-Up-Word, Christopher Ryan Woodle

Theses and Dissertations

The goal of this project will be to explore cross-platform implementation of Wake-Up- Word (WUW). To enable the development of future speech-based artificial intelligence applications, it is important to have robust and accessible implementations of WUW. Adoption of Unix based operating systems continues to expand for server, backend, and embedded applications, therefore a WUW implementation in Unix will become essential. As web technologies continue to grow, WUW will also need to be implemented in web, using technologies such as JavaScript and Web Assembly (WASM). This project encompasses porting the previous implementation of WUW from Microsoft Windows to Unix, building a …

A Semantic Web Approach To Fault Tolerant Autonomous Manufacturing, Fadi El Kalach

Theses and Dissertations

The next phase of manufacturing is centered on making the switch from traditional automated to autonomous systems. Future Factories are required to be agile, allowing for more customized production, and resistant to disturbances. Such production lines would have the capability to reallocate resources as needed and eliminate downtime while keeping up with market demands. These systems must be capable of complex decision making based on different parameters such as machine status, sensory data, and inspection results. Current manufacturing lines lack this complex capability and instead focus on low level decision making on the machine level without utilizing the generated data …

Real-Time Facial Expression Recognition Using Edge Ai Accelerators, Mark Heath Smith

Theses and Dissertations

Facial expression recognition is a popular and challenging area of research in machine learning applications. Facial expressions are critical to human communication and allow us to convey complex thoughts and emotions beyond spoken language. The complexity of facial expressions creates a difficult problem for computer vision systems, especially edge computing systems. Current Deep Learning (DL) methods rely on large-scale Convolutional Neural Networks (CNN) which require millions of floating point operations (FLOPS) to accomplish similar image classification tasks. However, on edge and IoT devices, large-scale convolutional models can cause problems due to memory and power limitations. The intent of this work …

Initiating Change In Care: Socially Assistive Robots, Sooraj Sushama

Theses and Dissertations

Socially assistive robots (SAR) are autonomous machines equipped with sensors and software that allow them to interact socially with humans. SAR robots are commonly used in healthcare settings to provide patients with non-clinical support, such as conversation and emotional companionship. SARs can also deliver reminders, monitor vital signs, and provide educational information about health conditions or medications. Researchers have studied SAR applications in detail. Additionally, there has been prior research on SAR where users' sociodemographic factors and technology acceptance were studied. But even though the backbone of SAR is an advanced technology, no known research has been done on users' …

Wifi Sensing At The Edge Towards Scalable On-Device Wireless Sensing Systems, Steven M. Hernandez

Theses and Dissertations

WiFi sensing offers a powerful method for tracking physical activities using the radio-frequency signals already found throughout our homes and offices. This novel sensing modality offers continuous and non-intrusive activity tracking since sensing can be performed (i) without requiring wearable sensors, (ii) outside the line-of-sight, and even (iii) through the wall. Furthermore, WiFi has become a ubiquitous technology in our computers, our smartphones, and even in low-cost Internet of Things devices. In this work, we consider how the ubiquity of these low-cost WiFi devices offer an unparalleled opportunity for improving the scalability of wireless sensing systems. Thus far, WiFi sensing …

Machine Learning Models To Automate Radiotherapy Structure Name Standardization, Priyankar Bose

Theses and Dissertations

Structure name standardization is a critical problem in Radiotherapy planning systems to correctly identify the various Organs-at-Risk, Planning Target Volumes and `Other' organs for monitoring present and future medications. Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and `Other' organs is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. We compare both traditional methods and deep neural network-based approaches on the multimodal vision-language prostate cancer …

Portable Robotic Navigation Aid For The Visually Impaired, Lingqiu Jin

Theses and Dissertations

This dissertation aims to address the limitations of existing visual-inertial (VI) SLAM methods - lack of needed robustness and accuracy - for assistive navigation in a large indoor space. Several improvements are made to existing SLAM technology, and the improved methods are used to enable two robotic assistive devices, a robot cane, and a robotic object manipulation aid, for the visually impaired for assistive wayfinding and object detection/grasping. First, depth measurements are incorporated into the optimization process for device pose estimation to improve the success rate of VI SLAM's initialization and reduce scale drift. The improved method, called depth-enhanced visual-inertial …

Virtual Plc Platform For Security And Forensics Of Industrial Control Systems, Syed Ali Qasim

Theses and Dissertations

Industrial Control Systems (ICS) are vital in managing critical infrastructures, including nuclear power plants and electric grids. With the advent of the Industrial Internet of Things (IIoT), these systems have been integrated into broader networks, enhancing efficiency but also becoming targets for cyberattacks. Central to ICS are Programmable Logic Controllers (PLCs), which bridge the physical and cyber worlds and are often exploited by attackers. There's a critical need for tools to analyze cyberattacks on PLCs, uncover vulnerabilities, and improve ICS security. Existing tools are hindered by the proprietary nature of PLC software, limiting scalability and efficiency.

To overcome these challenges, …

Improving The Flexibility And Robustness Of Machine Tending Mobile Robots, Richard Ethan Hollingsworth

Theses and Dissertations

While traditional manufacturing production cells consist of a fixed base robot repetitively performing tasks, the Industry 5.0 flexible manufacturing cell (FMC) aims to bring Autonomous Industrial Mobile Manipulators (AIMMs) to the factory floor. Composed of a wheeled base and a robot arm, these collaborative robots (cobots) operate alongside people while autonomously performing tasks at different workstations. AIMMs have been tested in real production systems, but the development of the control algorithms necessary for automating a robot that is a combination of two cobots remains an open challenge before the large scale adoption of this technology occurs in industry. Currently popular …

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 …