Computer Engineering Commons^™

Protecting Return Address Integrity For Risc-V Via Pointer Authentication, Yuhe Zhao

Masters Theses

Embedded systems based on lightweight microprocessors are becoming more prevalent in various applications. However, the security of them remains a significant challenge due to the limited resources and exposure to external threats. Especially, some of these devices store sensitive data and control critical devices, making them high-value targets for attackers. Software security is particularly important because attackers can easily access these devices on the internet and obtain control of them by injecting malware.

Return address (RA) hijacking is a common software attack technique used to compromise control flow integrity (CFI) by manipulating memory, such as return-to-libc attacks. Several methods have …

Blockchain Design For A Secure Pharmaceutical Supply Chain, Zhe Xu

Masters Theses

In the realm of pharmaceuticals, particularly during the challenging times of the COVID-19 pandemic, the supply chain for drugs has faced significant strains. The increased demand for vaccines and therapeutics has revealed critical weaknesses in the current drug supply chain management systems. If not addressed, these challenges could lead to severe societal impacts, including the rise of counterfeit medications and diminishing trust in government authorities.

The study identified that more than the current strategies, such as the Drug Supply Chain Security Act (DSCSA) in the U.S., which focuses on unique authentication and traceability codes for prescription drugs, is needed to …

Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim

Masters Theses

Due to significant investment, research, and development efforts over the past decade, deep neural networks (DNNs) have achieved notable advancements in classification and regression domains. As a result, DNNs are considered valuable intellectual property for artificial intelligence providers. Prior work has demonstrated highly effective model extraction attacks which steal a DNN, dismantling the provider’s business model and paving the way for unethical or malicious activities, such as misuse of personal data, safety risks in critical systems, or spreading misinformation. This thesis explores the feasibility of model extraction attacks on mobile devices using aggregated runtime profiles as a side-channel to leak …

Developing A Flexible System For A Friendly Robot To Ease Dementia (Fred) Using Cloud Technologies And Software Design Patterns, Robert James Bray

Masters Theses

In this work, we designed two prototypes for a friendly robot to ease dementia (FRED). This affordable social robot is designed to provide company to older adults with cognitive decline, create reminders for important events and tasks, like taking medication, and providing cognitive stimulus through games. This project combines several cloud technologies including speech-to-text, cloud data storage, and chat generation in order to provide high level interactions with a social robot. Software design patterns were employed in the creation of the software to produce flexible code base that can sustain platform changes easily, including the framework used for the graphical …

Analyzing The System Features, Usability, And Performance Of A Containerized Application On Cloud Computing Systems, Anoop Abraham

Masters Theses

This study analyzed the system features, usability, and performance of three serverless cloud computing platforms: Google Cloud’s Cloud Run, Amazon Web Service’s App Runner, and Microsoft Azure’s Container Apps. The analysis was conducted on a containerized mobile application designed to track real-time bus locations for San Antonio public buses on specific routes and provide estimated arrival times for selected bus stops. The study evaluated various system-related features, including service configuration, pricing, and memory & CPU capacity, along with performance metrics such as container latency, Distance Matrix API response time, and CPU utilization for each service. Easy-to-use usability was also evaluated …

Design And Fabrication Of A Trapped Ion Quantum Computing Testbed, Christopher A. Caron

Masters Theses

Here we present the design, assembly and successful ion trapping of a room-temperature ion trap system with a custom designed and fabricated surface electrode ion trap, which allows for rapid prototyping of novel trap designs such that new chips can be installed and reach UHV in under 2 days. The system has demonstrated success at trapping and maintaining both single ions and cold crystals of ions. We achieve this by fabricating our own custom surface Paul traps in the UMass Amherst cleanroom facilities, which are then argon ion milled, diced, mounted and wire bonded to an interposer which is placed …

Sel4 On Risc-V - Developing High Assurance Platforms With Modular Open-Source Architectures, Michael A. Doran Jr

Masters Theses

Virtualization is now becoming an industry standard for modern embedded systems. Modern embedded systems can now support multiple applications on a single hardware platform while meeting power and cost requirements. Virtualization on an embedded system is achieved through the design of the hardware-software interface. Instruction set architecture, ISA, defines the hardware-software interface for an embedded system. At the hardware level the ISA, provides extensions to support virtualization.

In addition to an ISA that supports hypervisor extensions it is equally important to provide a hypervisor completely capable of exploiting the benefits of virtualization for securing modern embedded systems. Currently there does …

Interactive Architecture - Intervention Of Virtual Business On Commercial Space, Yihao George Xu

Masters Theses

Traditional mall-based restaurants, such as P.F. Chang’s in the Providence Place Mall, have primarily focused on site-based dining and bar services. However, the food provided by this chain restaurant often lacks depth, and customers seldom learn the story behind the dishes. This thesis explores the integration of mixed reality technology within the physical environment of P.F. Chang’s, an American Chinese restaurant chain with over 300 locations, aiming to transform it into an authentic Shanghai food culture experience. This experience combines virtual and physical stimuli to evoke various periods of Chinese history, providing a unique encounter for customers.

The proposed transformation …

Unveiling Pain: Wearables For Objective Pain Measurement, Hanqing Tang

Masters Theses

">">Pain perception is a subjective experience that differs significantly among individuals, often leading to inconsistencies in assessment and management. A critical issue within this context is the gender bias in pain evaluation, which contributes to unequal treatment and perpetuates gender inequality within the healthcare system. This thesis presents an in-depth investigation of the problem and proposes the development of a wearable device for objective pain assessment. Physiological parameters — Electrocardiography (ECG) can be collected from cardiac sound signals auscultated by fabrics via nanometre-scale vibrations. Machine learning methods can accurately classify heart rate and acute pain intensity of participants. …

Optimizing The Placement Of Multiple Uav--Lidar Units Under Road Priority And Resolution Requirements, Zachary Michael Osterwisch

Masters Theses

"Real-time road traffic information is crucial for intelligent transportation systems (ITS) applications, like traffic navigation or emergency response management, but acquiring such data is tremendously challenging in practice because of the high costs and inefficient placement of sensors. Some modern ITS applications contribute to this problem by equipping vehicles with multiple light detection and ranging (LiDAR) sensors, which are expensive and gather data inefficiently; one solution that avoids vehicle-mounted LiDAR acquisition has been to install elevated LiDAR instruments along roadways, but this approach remains unrefined. The eventual development of sixth-generation (6G) wireless communication will enable new, creative solutions to solve …

Incorporating Novel Sensors For Reading Human Health State And Motion Intent Into Real-Time Computing Systems, Adam Sawyer

Masters Theses

"Integrating sensors that read states of the human body into everyday life is an increasing desire, especially with the rise of deep learning which requires vast stores of data to make predictions. This work explores integrating these sensors into the human experience through two methods and recording the results. The first of these methods integrates a MXene based field-effect transistor sensor for the 2019-nCov spike protein with a mobile app. This allows the user to read how saturated their breath is with Covid-19. The second method integrates 3D-printed pressure sensors, and a motion capture system, into a glove to read …

A Hybrid Framework For Critical Infrastructures Interdependency Modeling, Simulation, And Analysis, David Corder Hinton

Masters Theses

"Flow system models, also known as flow network models, encompass vastly complex, ever-expanding problem sets which comprise the foundation for maintenance, operation, and improvement of critical infrastructures around the world. The stable operation of these vast critical infrastructures is fundamental to the continued advancement of modern society. These infrastructures are tightly interdependent and vulnerable to interruption by both natural circumstance and malicious targeting. This necessitates representation of such critical infrastructures and their multi-domain interdependencies in defense focused constructive and virtual simulation environments as a matter of national interest and security. By breadth exploration of the problem space, this work body …

Benchmarking Of Embedded Object Detection In Optical And Radar Scenes, Vijaysrinivas Rajagopal

Masters Theses

A portable, real-time vital sign estimation protoype is developed using neural network- based localization, multi-object tracking, and embedded processing optimizations. The system estimates heart and respiration rates of multiple subjects using directional of arrival techniques on RADAR data. This system is useful in many civilian and military applications including search and rescue.

The primary contribution from this work is the implementation and benchmarking of neural networks for real time detection and localization on various systems including the testing of eight neural networks on a discrete GPU and Jetson Xavier devices. Mean average precision (mAP) and inference speed benchmarks were performed. …

Personalizing Student Graduation Paths Using Expressed Student Interests, Nicolas Charles Dobbins

Masters Theses

"This work proposes an intelligent recommender approach to facilitate personalized education and help students in planning their path to graduation. The original research contribution of this work is to develop a recommender approach that pervasively personalizes and optimizes a student’s path to graduation by accounting for the student’s career interests and academic background. The approach is a multi-objective optimization problem, subject to institutional constraints, with the goal of optimizing the graduation path with respect to one or more criteria, such as time-to-graduation, credit hours taken, and alignment with student’s career interests. The efficacy of the approach is illustrated and verified …

Analysis Of Multiple Adversarial Attacks On Convolutional Neural Networks, Burcum Eken

Masters Theses

The thesis studies different kind of adversarial attacks on Convolutional Neural Network by using electric load data set in order to fool deep neural network. With the improvement of Deep Learning methods, their securities and vulnerabilities have become an important research subject. An adversary who gains access to the model and data sets may add some perturbations to the datasets, which may cause significant damage to the system. By using adversarial attacks, it shows how much these attacks affect the system and shows the attacks' success in this research.

Development Of A Ppg Sensor Array As A Wearable Device For Monitoring Cardiovascular Metrics, Jose Ignacio Rodriguez-Labra

Masters Theses

Wearable devices with integrated sensors for tracking human vitals are widely used for a variety of applications, including exercise, wellness, and health monitoring. Photoplethysmography (PPG) sensors use pulse oximetry to measure pulse rate, cardiac cycle, oxygen saturation, and blood flow by passing a light beam of variable wavelength through the skin and measuring its reflection. A multi-channel PPG wearable system was developed to include multiple nodes of pulse oximeters, each capable of using different wavelengths of light. The system uses sensor fusion along with a machine learning model to perform feature extraction of relevant cardiovascular metrics across multiple pulse oximeters …

Evaluation Of Robust Deep Learning Pipelines Targeting Low Swap Edge Deployment, David Carter Cornett

Masters Theses

The deep learning technique of convolutional neural networks (CNNs) has greatly advanced the state-of-the-art for computer vision tasks such as image classification and object detection. These solutions rely on large systems leveraging wattage-hungry GPUs to provide the computational power to achieve such performance. However, the size, weight and power (SWaP) requirements of these conventional GPU-based deep learning systems are not suitable when a solution requires deployment to so called "Edge" environments such as autonomous vehicles, unmanned aerial vehicles (UAVs) and smart security cameras.

The objective of this work is to benchmark FPGA-based alternatives to conventional GPU systems that have the …

Action : Adaptive Cache Block Migration In Distributed Cache Architectures, Chandra Sekhar Mummidi

Masters Theses

Increasing number of cores in chip multiprocessors (CMP) result in increasing traffic to last-level cache (LLC). Without commensurate increase in LLC bandwidth, such traffic cannot be sustained resulting in loss of performance. Further, as the number of cores increases, it is necessary to scale up the LLC size; otherwise, the LLC miss rate will rise, resulting in a loss of performance. Unfortunately, for a unified LLC with uniform cache access time, access latency increases with cache size, resulting in performance loss. Previously, researchers have proposed partitioning the cache into multiple smaller caches interconnected by a communication network which increases aggregate …

Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi

Masters Theses

Quantitative structure-activity relationship (QSAR) models based on machine learning algorithms are powerful tools to expedite drug discovery processes and therapeutics development. Given the cost in acquiring large-sized training datasets, it is useful to examine if QSAR analysis can reasonably predict drug activity with only a small-sized dataset (size < 100) and benchmark these small-dataset QSAR models in application-specific studies. To this end, here we present a systematic benchmarking study on small-dataset QSAR models built for prediction of effective Wnt signaling inhibitors, which are essential to therapeutics development in prevalent human diseases (e.g., cancer). Specifically, we examined a total of 72 two-dimensional (2D) QSAR models based on 4 best-performing algorithms, 6 commonly used molecular fingerprints, and 3 typical fingerprint lengths. We trained these models using a training dataset (56 compounds), benchmarked their performance on 4 figures-of-merit (FOMs), and examined their prediction accuracy using an external validation dataset (14 compounds). Our data show that the model performance is maximized when: 1) molecular fingerprints are selected to provide sufficient, unique, and not overly detailed representations of the chemical structures of drug compounds; 2) algorithms are selected to reduce the number of false predictions due to class imbalance in the dataset; and 3) models are selected to reach balanced performance on all 4 FOMs. These results may provide general guidelines in developing high-performance small-dataset QSAR models for drug activity prediction.

Internet Infrastructures For Large Scale Emulation With Efficient Hw/Sw Co-Design, Aiden K. Gula

Masters Theses

Connected systems are becoming more ingrained in our daily lives with the advent of cloud computing, the Internet of Things (IoT), and artificial intelligence. As technology progresses, we expect the number of networked systems to rise along with their complexity. As these systems become abstruse, it becomes paramount to understand their interactions and nuances. In particular, Mobile Ad hoc Networks (MANET) and swarm communication systems exhibit added complexity due to a multitude of environmental and physical conditions. Testing these types of systems is challenging and incurs high engineering and deployment costs. In this work, we propose a scalable MANET emulation …

A Cloud Infrastructure For Large Scale Health Monitoring In Older Adult Care Facilities, Uchechukwu Gabriel David

Masters Theses

Technology development in the sub-field of older adult care has always been on the back-burner compared to other healthcare areas. But with increasing life expectancy, this is poised to change. With the increasing older adult population, the current older adult care facilities and personnel are struggling to keep up with demand. Research conducted in the Netherlands [1] found 33,000 older adults were awaiting admission into a home for the elderly showing that demand far exceeds availability. This huge demand for older adult care has resulted in a decrease in the quality of care being provided. A recent study involving older …

Design And Simulation Of A Supervisory Control System For Hybrid Manufacturing, Michael Buckley

Masters Theses

The research teams of Dr. Bill Hamel, Dr. Bradley Jared and Dr. Tony Schmitz were tasked by the Office of Naval Research to create a hybrid manufacturing process for a reduced scale model of a naval ship propeller. The base structure of the propeller is created using Wire Arc Additive Manufacturing (WAAM), which is then scanned to compare created geometry to desired geometry. The propeller is then machined down to match the desired geometry. This process is iterated upon until the final product meets design tolerances. Due to the complex nature and numerous industrial machines used in the process, it …

Hardware Acceleration In Image Stitching: Gpu Vs Fpga, Joshua David Edgcombe

Masters Theses

Image stitching is a process where two or more images with an overlapping field of view are combined. This process is commonly used to increase the field of view or image quality of a system. While this process is not particularly difficult for modern personal computers, hardware acceleration is often required to achieve real-time performance in low-power image stitching solutions. In this thesis, two separate hardware accelerated image stitching solutions are developed and compared. One solution is accelerated using a Xilinx Zynq UltraScale+ ZU3EG FPGA and the other solution is accelerated using an Nvidia RTX 2070 Super GPU. The image …

Lecture Video Transformation Through An Intelligent Analysis And Post-Processing System, Xi Wang

Masters Theses

Lecture videos are good sources for people to learn new things. Students commonly use online videos to explore various domains. However, some recorded videos are posted on online platforms without being post-processed due to technology and resource limitations. In this work, we focus on the research of developing an intelligent system to automatically extract essential information, including the main instructor and screen, in a lecture video in several scenarios by using modern deep learning techniques. This thesis aims to combine the extracted essential information to render the videos and generate a new layout with a smaller file size than the …

Analysis Of Hardware Accelerated Deep Learning And The Effects Of Degradation On Performance, Samuel C. Leach

Masters Theses

As convolutional neural networks become more prevalent in research and real world applications, the need for them to be faster and more robust will be a constant battle. This thesis investigates the effect of degradation being introduced to an image prior to object recognition with a convolutional neural network. As well as experimenting with methods to reduce the degradation and improve performance. Gaussian smoothing and additive Gaussian noise are both analyzed degradation models within this thesis and are reduced with Gaussian and Butterworth masks using unsharp masking and smoothing, respectively. The results show that each degradation is disruptive to the …

A Secure Architecture For Defense Against Return Address Corruption, Grayson J. Bruner

Masters Theses

The advent of the Internet of Things has brought about a staggering level of inter-connectivity between common devices used every day. Unfortunately, security is not a high priority for developers designing these IoT devices. Often times the trade-off of security comes at too high of a cost in other areas, such as performance or power consumption. This is especially prevalent in resource-constrained devices, which make up a large number of IoT devices. However, a lack of security could lead to a cascade of security breaches rippling through connected devices. One of the most common attacks used by hackers is return …

Ticknet: A Lightweight Deep Classifier For Tick Recognition, Li Wang

Masters Theses

The world is increasingly controlled by machine learning and deep learning. Deep neural networks are becoming powerful, encroaching on many tasks in computer vision system areas previously seen as the unique domain of humans, such as image classification, object detection, semantic segmentation, and instance segmentation. The success of a deep learning model at a specific application is determined by a sequence of choices, like what kind of deep neural network will be used, what data to be fed into the deep model, and what manners will be adopted to train a deep model.

The goal of this work is to …

Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii

Masters Theses

“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …

Topological Biclustering Artmap, Raghu Yelugam

Masters Theses

”Detection of gene mutations is central for assessing genetic factors affecting disease predisposition, genetic causes of a particular disease, and gene-targeted treatment. DNA microarray methods are widely used to detect mutations by contrasting the expression levels of thousands of genes together under varying experimental conditions. The experimental conditions could be diseased cell states compared with the normal cell states. Biclustering, a robust exploratory data analysis tool, can be applied to microarray data to detect subsets of genes that co-express highly only for a subset of experimental conditions. Such detection is crucial for gaining insights into gene regulatory networks, differential gene …

Network Virtualization And Emulation Using Docker, Openvswitch And Mininet-Based Link Emulation, Narendra Prabhu

Masters Theses

With the advent of virtualization and artificial intelligence, research on networked systems has progressed substantially. As the technology progresses, we expect a boom in not only the systems research but also in the network of systems domain. It is paramount that we understand and develop methodologies to connect and communicate among the plethora of devices and systems that exist today. One such area is mobile ad-hoc and space communication, which further complicates the task of networking due to myriad of environmental and physical conditions. Developing and testing such systems is an important step considering the large investment required to build …