Engineering Commons^™

Towards Qos-Based Embedded Machine Learning, Tom Springer, Erik Linstead, Peiyi Zhao, Chelsea Parlett-Pelleriti

Engineering Faculty Articles and Research

Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications including computer vision, speech recognition, healthcare efficiency, industrial IoT, robotics and many more. However, there is a critical limitation in implementing ML algorithms efficiently on embedded platforms: the computational and memory expense of many machine learning models can make them unsuitable in resource-constrained environments. Therefore, to efficiently implement these memory-intensive and computationally expensive algorithms in an embedded computing environment, innovative resource management techniques are required at the …

Reality Analagous Synthetic Dataset Generation With Daylight Variance For Deep Learning Classification, Thomas Lee, Susan Mckeever, Jane Courtney

Conference papers

For the implementation of Autonomously navigating Unmanned Air Vehicles (UAV) in the real world, it must be shown that safe navigation is possible in all real world scenarios. In the case of UAVs powered by Deep Learning algorithms, this is a difficult task to achieve, as the weak point of any trained network is the reduction in predictive capacity when presented with unfamiliar input data. It is possible to train for more use cases, however more data is required for this, requiring time and manpower to acquire. In this work, a potential solution to the manpower issues of exponentially scaling …

Automated Identification Of Astronauts On Board The International Space Station: A Case Study In Space Archaeology, Rao Hamza Ali, Amir Kanan Kashefi, Alice C. Gorman, Justin St. P. Walsh, Erik J. Linstead

Art Faculty Articles and Research

We develop and apply a deep learning-based computer vision pipeline to automatically identify crew members in archival photographic imagery taken on-board the International Space Station. Our approach is able to quickly tag thousands of images from public and private photo repositories without human supervision with high degrees of accuracy, including photographs where crew faces are partially obscured. Using the results of our pipeline, we carry out a large-scale network analysis of the crew, using the imagery data to provide novel insights into the social interactions among crew during their missions.

Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors And Machine Learning Algorithms, Rahul Soangra, Yuxin Wen, Hualin Yang, Marybeth Grant-Beuttler

Physical Therapy Faculty Articles and Research

Idiopathic toe walking (ITW) is a gait abnormality in which children’s toes touch at initial contact and demonstrate limited or no heel contact throughout the gait cycle. Toe walking results in poor balance, increased risk of falling, and developmental delays among children. Identifying toe walking steps during walking can facilitate targeted intervention among children diagnosed with ITW. With recent advances in wearable sensing, communication technologies, and machine learning, new avenues of managing toe walking behavior among children are feasible. In this study, we investigate the capabilities of Machine Learning (ML) algorithms in identifying initial foot contact (heel strike versus toe …

Addressing The "Leaky Pipeline": A Review And Categorisation Of Actions To Recruit And Retain Women In Computing Education, Alina Berry, Susan Mckeever, Brenda Murphy, Sarah Jane Delany

Conference papers

Gender imbalance in computing education is a well-known issue around the world. For example, in the UK and Ireland, less than 20% of the student population in computer science, ICT and related disciplines are women. Similar figures are seen in the labour force in the field across the EU. The term "leaky pipeline"; is often used to describe the lack of retention of women before they progress to senior roles. Numerous initiatives have targeted the problem of the leaky pipeline in recent decades. This paper provides a comprehensive review of initiatives related to techniques used to boost recruitment and improve …

Generating Reality-Analogous Datasets For Autonomous Uav Navigation Using Digital Twin Areas, Thomas Lee, Susan Mckeever, Jane Courtney

Conference papers

In order for autonomously navigating Unmanned Air Vehicles(UAVs) to be implemented in day-to-day life, proof of safe operation will be necessary for all realistic navigation scenarios. For Deep Learning powered navigation protocols, this requirement is challenging to fulfil as the performance of a network is impacted by how much the test case deviates from data that the network was trained on. Though networks can generalise to manage multiple scenarios in the same task, they require additional data representing those cases which can be costly to gather. In this work, a solution to this data acquisition problem is suggested by way …

Project Metamorphosis: Designing A Dynamic Framework For Converting Musical Compositions Into Paintings, Rao Hamza Ali, Grace Fong, Erik Linstead

Engineering Faculty Articles and Research

The authors present an automated, rule-based system for converting piano compositions into paintings. Using a color-note association scale presented by Edward Maryon in 1919, which correlates 12-tone scale with 12 hues of the color circle, the authors present a simple approach for extracting colors associated with each note played in a piano composition. The authors also describe the color extraction and art generation process in detail, as well as the process for creating “moving art,” which imitates the progression of a musical piece in real time. They share and discuss artworks generated for four well-known piano compositions.

Blockchain Storage – Drive Configurations And Performance Analysis, Jesse Garner, Aditya A. Syal, Ronald C. Jones

Other Student Works

This project will analyze the results of trials implementing various storage methods on Geth nodes to synchronize and maintain a full-archive state of the Ethereum blockchain. The purpose of these trials is to gain deeper insight to the process of lowering cost and increasing efficiency of blockchain storage using available technologies, analyzing results of various storage drives under similar conditions. It provides performance analysis and describes performance of each trial in relation to the others.

Evaluation Of Selected Computer Software For Concussion Recovery And Diagnosis, J.P. Jensen

Honors Theses

Acquired traumatic brain injuries, such as concussions, impact many athletes participating in sports, particularly at the high school, collegiate, and professional levels. The risks posed by concussions – particularly when an athlete suffers repeated injuries – demands that protocols and tools be developed to maximize athlete health and safety. Computer technology can perform critical roles in the analysis and management of concussions. While specialized devices in the areas of imaging and impact sensing, are most associated with concussion management, researchers within the last two decades have increasingly explored the incorporation of various consumer technologies into the identification and treatment of …

Designing A Digital Electronics Lab, Ben Buckwalter

Honors Theses

Hardware electronics tools can be more expensive than a new learner is willing to invest resulting in a barrier to entry. Furthermore, it can be difficult for a new learner to know where to start when learning electronics. To resolve this issue, we first explore current mobile app solutions that provide free electronics tooling. Then, we propose a design for a new mobile app that contains basic function generator and oscilloscope functionality as well as the learning resources necessary in order to guide new learners in the basics of digital electronics.

Cognality Vr: Exploring A Mobile Vr App With Multiple Stakeholders To Reduce Meltdowns In Autistic Children, Louanne E. Boyd, Espen Garner, Ian Kim, Gianna Valencia

Engineering Faculty Articles and Research

Many autistic children can have difficulty communicating, understanding others, and interacting with new and unfamiliar environments. At times they may suffer from a meltdown. The major contributing factor to meltdowns is sensory overwhelm. Technological solutions have shown promise in improving the quality of life for autistic children-however little exists to manage meltdowns. In this work with stakeholders, we design and deploy a low cost, mobile VR application to provide relief during sensory discomfort. Through the analysis of surveys from 88 stakeholders from a variety of groups (i.e., autistic adults, children with autism, parents of autistic individuals, and medical practitioners), we …

Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen

Engineering Faculty Articles and Research

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …

Manipulating Image Luminance To Improve Eye Gaze And Verbal Behavior In Autistic Children, Louanne Boyd, Vincent Berardi, Deanna Hughes, Franceli L. Cibrian, Jazette Johnson, Viseth Sean, Eliza Delpizzo-Cheng, Brandon Mackin, Ayra Tusneem, Riya Mody, Sara Jones, Karen Lotich

Engineering Faculty Articles and Research

Autism has been characterized by a tendency to attend to the local visual details over surveying an image to understand the gist–a phenomenon called local interference. This sensory processing trait has been found to negatively impact social communication. Although much work has been conducted to understand these traits, little to no work has been conducted to intervene to provide support for local interference. Additionally, recent understanding of autism now introduces the core role of sensory processing and its impact on social communication. However, no interventions to the end of our knowledge have been explored to leverage this relationship. This work …

Seabem: An Artificial Intelligence Powered Web Application To Predict Cover Crop Biomass, Aime Christian Tuyishime, Andrea Basche

Honors Theses

SEABEM, the Stacked Ensemble Algorithms Biomass Estimator Model, is a web application with a stacked ensemble of Machine Learning (ML) algorithms running on the backend to predict cover crop biomass for locations in Sub-Saharan. The SEABEM model was developed using a previously developed database of crop growth and yield that included site characteristics such as latitude, longitude, soil texture (sand, silt, and clay percentages), temperature, and precipitation. The goal of SEABEM is to provide global farmers, mainly small-scale African farmers, the knowledge they need before practicing and benefiting from cover crops while avoiding the expensive and time-consuming operations that come …

A Deep Learning-Based Approach To Extraction Of Filler Morphology In Sem Images With The Application Of Automated Quality Inspection, Md. Fashiar Rahman, Tzu-Liang Bill Tseng, Jianguo Wu, Yuxin Wen, Yirong Lin

Engineering Faculty Articles and Research

Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and particles and their obscure presence in SEM images. Convolution Neural Networks (CNNs) have been shown to be very effective at object recognition in digital images. This paper proposes an automatic filler detection system in SEM images, utilizing a Mask Region-based CNN architecture. The proposed system …

Three Wave Mixing In Epsilon-Near-Zero Plasmonic Waveguides For Signal Regeneration, Nicholas Mirchandani, Mark C. Harrison

Engineering Faculty Articles and Research

Vast improvements in communications technology are possible if the conversion of digital information from optical to electric and back can be removed. Plasmonic devices offer one solution due to optical computing’s potential for increased bandwidth, which would enable increased throughput and enhanced security. Plasmonic devices have small footprints and interface with electronics easily, but these potential improvements are offset by the large device footprints of conventional signal regeneration schemes, since surface plasmon polaritons (SPPs) are incredibly lossy. As such, there is a need for novel regeneration schemes. The continuous, uniform, and unambiguous digital information encoding method is phase-shift-keying (PSK), so …

A Neural Network Based Proportional Hazard Model For Iot Signal Fusion And Failure Prediction, Yuxin Wen, Xingxin Guo, Junbo Son, Jianguo Wu

Engineering Faculty Articles and Research

Accurate prediction of remaining useful life (RUL) plays a critical role in optimizing condition-based maintenance decisions. In this paper, a novel joint prognostic modeling framework that simultaneously combines both time-to-event data and multi-sensor degradation signals is proposed. With the increasing use of IoT devices, unprecedented amounts of diverse signals associated with the underlying health condition of in-situ units have become easily accessible. To take full advantage of the modern IoT-enabled engineering systems, we propose a specialized framework for RUL prediction at the level of individual units. Specifically, a Bayesian linear regression model is developed for the multi-sensor degradation signals and …

Sok: Analysis Of Software Supply Chain Security By Establishing Secure Design Properties, Chinenye Okafor, Taylor R. Schorlemmer, Santiao Torres-Arias, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

This paper systematizes knowledge about secure software supply chain patterns. It identifies four stages of a software supply chain attack and proposes three security properties crucial for a secured supply chain: transparency, validity, and separation. The paper describes current security approaches and maps them to the proposed security properties, including research ideas and case studies of supply chains in practice. It discusses the strengths and weaknesses of current approaches relative to known attacks and details the various security frameworks put out to ensure the security of the software supply chain. Finally, the paper highlights potential gaps in actor and operation-centered …

Reflecting On Recurring Failures In Iot Development, Dharun Anandayuvaraj, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

As IoT systems are given more responsibility and autonomy, they offer greater benefits, but also carry greater risks. We believe this trend invigorates an old challenge of software engineering: how to develop high-risk software-intensive systems safely and securely under market pressures? As a first step, we conducted a systematic analysis of recent IoT failures to identify engineering challenges. We collected and analyzed 22 news reports and studied the sources, impacts, and repair strategies of failures in IoT systems. We observed failure trends both within and across application domains. We also observed that failure themes have persisted over time. To alleviate …

Exploiting Input Sanitization For Regex Denial Of Service, Efe Barlas, Xin Du, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Web services use server-side input sanitization to guard against harmful input. Some web services publish their sanitization logic to make their client interface more usable, e.g., allowing clients to debug invalid requests locally. However, this usability practice poses a security risk. Specifically, services may share the regexes they use to sanitize input strings — and regex-based denial of service (ReDoS) is an emerging threat. Although prominent service outages caused by ReDoS have spurred interest in this topic, we know little about the degree to which live web services are vulnerable to ReDoS.

In this paper, we conduct the first black-box …

Discrepancies Among Pre-Trained Deep Neural Networks: A New Threat To Model Zoo Reliability, Diego Montes, Pongpatapee Peerapatanapokin, Jeff Schultz, Chengjun Guo, Wenxin Jiang, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Training deep neural networks (DNNs) takes significant time and resources. A practice for expedited deployment is to use pre-trained deep neural networks (PTNNs), often from model zoos.collections of PTNNs; yet, the reliability of model zoos remains unexamined. In the absence of an industry standard for the implementation and performance of PTNNs, engineers cannot confidently incorporate them into production systems. As a first step, discovering potential discrepancies between PTNNs across model zoos would reveal a threat to model zoo reliability. Prior works indicated existing variances in deep learning systems in terms of accuracy. However, broader measures of reliability for PTNNs from …

An Empirical Study On The Impact Of Deep Parameters On Mobile App Energy Usage, Qiang Xu, James C. Davis, Y Charlie Hu, Abhilash Jindal

Department of Electrical and Computer Engineering Faculty Publications

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these "deep" parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. …

Reflections On Software Failure Analysis, Paschal C. Amusuo, Aishwarya Sharma, Siddharth R. Rao, Abbey Vincent, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Failure studies are important in revealing the root causes, behaviors, and life cycle of defects in software systems. These studies either focus on understanding the characteristics of defects in specific classes of systems or the characteristics of a specific type of defect in the systems it manifests in. Failure studies have influenced various software engineering research directions, especially in the area of software evolution, defect detection, and program repair.

In this paper, we reflect on the conduct of failure studies in software engineering. We reviewed a sample of 52 failure study papers. We identified several recurring problems in these studies, …

A Systematic Review Of How Cloud Infrastructure And Gdpr Have Affected Digital Investigations In A Multinational Business Context, Stuart Fraser, A.Omar Portillo-Dominguez

Other

With cloud infrastructure becoming an ever more popular platform for business network implementations, and with ever-tightening data protection regulation, the ability to carry out digital investigations has become more difficult. This has led to areas of research that have looked to restore the balance to digital investigations in this environment. These areas include the use of blockchain, data tracing, and digital forensics as a service. With so many methods to consider, this article looks at how each method aims to return the balance and make it possible to carry out an investigation that complies with new privacy regulations (e.g., the …