Digital Commons Network^™

Doppler Radar-Based Non-Contact Health Monitoring For Obstructive Sleep Apnea Diagnosis: A Comprehensive Review, Vinh Phuc Tran, Adel Ali Al-Jumaily, Syed Mohammed Shamsul Islam

Research outputs 2014 to 2021

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring …

Efficient Machine Learning: Models And Accelerations, Zhe Li

Dissertations - ALL

One of the key enablers of the recent unprecedented success of machine learning is the adoption of very large models. Modern machine learning models typically consist of multiple cascaded layers such as deep neural networks, and at least millions to hundreds of millions of parameters (i.e., weights) for the entire model. The larger-scale model tend to enable the extraction of more complex high-level features, and therefore, lead to a significant improvement of the overall accuracy. On the other side, the layered deep structure and large model sizes also demand to increase computational capability and memory requirements. In order to achieve …

Expanding The Lisa Horizon From The Ground, Kaze Wong, Ely D. Kovetz, Curt Cutler, Emanuele Berti

Faculty and Student Publications (Open Access)

No abstract provided.

Increasing Revenue By Applying Machine Learning To Congestion Management In Sdn, Nabarun Jana

Theses

With the advent of 5G, IoT and 4k videos, online gaming, movie streaming and other data intensive applications, the demand for data is sky rocketing. Due to this surge in data, the load on the network increases. This heightened network load causes degradation in network performance. Which can lead to the customer Service Provider (CSP)s loosing revenue if the Service Level Agreement (SLA) are not met.

This report describes how machine learning techniques such as tit for tat can be applied to telecom networks. Machine learning applied to telecom networks help detect congestion and maintain SLAs while increasing yield (revenue). …

Learning About Large Scale Image Search: Lessons From Global Scale Hotel Recognition To Fight Sex Trafficking, Abby Stylianou

McKelvey School of Engineering Graduate Student Theses & Dissertations

Hotel recognition is a sub-domain of scene recognition that involves determining what hotel is seen in a photograph taken in a hotel. The hotel recognition task is a challenging computer vision task due to the properties of hotel rooms, including low visual similarity between rooms in the same hotel and high visual similarity between rooms in different hotels, particularly those from the same chain. Building accurate approaches for hotel recognition is important to investigations of human trafficking. Images of human trafficking victims are often shared by traffickers among criminal networks and posted in online advertisements. These images are often taken …

Decoding Complexity In Metabolic Networks Using Integrated Mechanistic And Machine Learning Approaches, Tolutola Timothy Oyetunde

McKelvey School of Engineering Graduate Student Theses & Dissertations

How can we get living cells to do what we want? What do they actually ‘want’? What ‘rules’ do they observe? How can we better understand and manipulate them? Answers to fundamental research questions like these are critical to overcoming bottlenecks in metabolic engineering and optimizing heterologous pathways for synthetic biology applications. Unfortunately, biological systems are too complex to be completely described by physicochemical modeling alone.

In this research, I developed and applied integrated mechanistic and data-driven frameworks to help uncover the mysteries of cellular regulation and control. These tools provide a computational framework for seeking answers to pertinent biological …

Functional Principal Component Analysis Of Vibrational Signal Data: A Functional Data Analytics Approach For Fault Detection And Diagnosis Of Internal Combustion Engines, Justin Blake Mcmahan

Theses and Dissertations

Fault detection and diagnosis is a critical component of operations management systems. The goal of FDD is to identify the occurrence and causes of abnormal events. While many approaches are available, data-driven approaches for FDD have proven to be robust and reliable. Exploiting these advantages, the present study applied functional principal component analysis (FPCA) to carry out feature extraction for fault detection in internal combustion engines. Furthermore, a feature subset that explained 95% of the variance of the original vibrational sensor signal was used in a multilayer perceptron to carry out prediction for fault diagnosis. Of the engine states studied …

Enabling Auditing And Intrusion Detection Of Proprietary Controller Area Networks, Brent C. Stone

Theses and Dissertations

The goal of this dissertation is to provide automated methods for security researchers to overcome ‘security through obscurity’ used by manufacturers of proprietary Industrial Control Systems (ICS). `White hat' security analysts waste significant time reverse engineering these systems' opaque network configurations instead of performing meaningful security auditing tasks. Automating the process of documenting proprietary protocol configurations is intended to improve independent security auditing of ICS networks. The major contributions of this dissertation are a novel approach for unsupervised lexical analysis of binary network data flows and analysis of the time series data extracted as a result. We demonstrate the utility …

Data Driven Approach To Thermal Comfort Model Design, Mostafa Rafaiejokandan

Durham School of Architectural Engineering and Construction: Dissertations, Theses, and Student Research

Apart from the dominant environmental factors such as relative humidity, radiant, and ambient temperatures, studies have confirmed that thermal comfort significantly depends on internal personal parameters such as metabolic rate, age, and health status. This study reviews the sensitivity of the Predicted Mean Vote (PMV) thermal comfort model relative to its environmental and personal parameters of a group of people in a space. PMV model equations adapted in ASHRAE Standard 55–Thermal Environmental Conditions for Human Occupancy, are used in this investigation to conduct a parametric study by generating and analyzing multi-dimensional comfort zone plots. It has been found that personal …

Multi-Modal Deep Learning To Understand Vision And Language, Shagan Sah

Theses

Developing intelligent agents that can perceive and understand the rich visual world around us has been a long-standing goal in the field of artificial intelligence. In the last few years, significant progress has been made towards this goal and deep learning has been attributed to recent incredible advances in general visual and language understanding. Convolutional neural networks have been used to learn image representations while recurrent neural networks have demonstrated the ability to generate text from visual stimuli. In this thesis, we develop methods and techniques using hybrid convolutional and recurrent neural network architectures that connect visual data and natural …

Learning-Based Analysis On The Exploitability Of Security Vulnerabilities, Adam Bliss

Computer Science and Computer Engineering Undergraduate Honors Theses

The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing …

Dwrelu : Double Weighted Rectifier Linear Unit An Activation Function With Trainable Scaling Parameter, Bhaskar Chandra Trivedi

Computer Science and Engineering Theses - Archive

Deep Neural Network have become very popular for computer vision application in recent years. At the same time, it remains important to understand the different implementation choices that need to be made when designing a neural network and to thoroughly investigate existing and novel alternatives for those choices. One of those choices is the activation function. The ReLU activation function is a widely used activation function. It discards all the values below zero and keeps the ones greater than zero. Variations such as Leaky ReLU and Parametric ReLU do not discard values, so that gradiants are nonzero for the entire …

Health Monitoring Of Atlas Data Center Clusters And Failure Analysis, Meenakshi Balasubramanian

Computer Science and Engineering Theses - Archive

Monitoring the health of data center clusters is an integral part of any industrial facility. ATLAS is one of the High Energy Physics experiments at the Large Hadron Collider (LHC) at CERN. ATLAS DDM (Distributed Data Management) is a system that manages data transfer, staging, deletions and experimental data on the LHC grid. Currently, the DDM system relies on Rucio software, with Cloud based object storage and No-SQL solutions. It is a cumbersome process in the current system, to fetch and analyze the transfer, staging and deletion metrics of a specific site for any regional center. In this thesis, a …

Large-Scale Deep Learning With Application In Medical Imaging And Bio-Informatics, Zheng Xu

Computer Science and Engineering Dissertations - Archive

With the recent advancement of the deep learning technology in the artificial intelligence area, nowadays people's lives have been drastically changed. However, the success of deep learning technology mostly relies on large-scale high-quality data-sets. The complexity of deeper model and larger scale datasets have brought us significant challenges. Inspired by this trend, in this dissertation, we focus on developing efficient and effective large-scale deep learning techniques in solving real-world problems, like cell detection in hyper-resolution medical image or drug screening from millions of compound candidates. With respect to the hyper-resolution medical imaging cell detection problem, the challenges are mainly the …

A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab

Electronic Theses and Dissertations

The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …

Generating Exploration Mission-3 Trajectories To A 9:2 Nrho Using Machine Learning, Esteban Guzman

Master's Theses

The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA’s Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can be an abstract task and relies on …

Efficacy Of Deep Learning In Support Of Smart Services, Basheer Mohammed Basheer Qolomany

Dissertations

The massive amount of streaming data generated and captured by smart service appliances, sensors and devices needs to be analyzed by algorithms, transformed into information, and minted to extract knowledge to facilitate timely actions and better decision making. This can lead to new products and services that can dramatically transform our lives. Machine learning and data analytics will undoubtedly play a critical role in enabling the delivery of smart services. Within the machine-learning domain, Deep Learning (DL) is emerging as a superior new approach that is much more effective than any rule or formula used by traditional machine learning. Furthermore, …

Machine Learning Methods To Map Stabilizer Effectiveness Based On Common Soil Properties, Amit Gajurel

Boise State University Theses and Dissertations

Unconfined compressive strength (UCS) has been widely used as one of the primary criteria for the selection of optimum type and amount of chemical stabilizer for subgrade/base stabilization. Guidelines established by various state and federal agencies aid in selecting these optimum values by recommending an initial type and amount based on a wide range of soil index properties. A significant number of laboratory trials have to be done to establish the optimum type and amount of stabilizer for a given target strength. This process takes a copious amount of time, money, and the workforce. In addition to that, the finite …

Amplifying The Prediction Of Team Performance Through Swarm Intelligence And Machine Learning, Erick Michael Harris

Master's Theses

Modern companies are increasingly relying on groups of individuals to reach organizational goals and objectives, however many organizations struggle to cultivate optimal teams that can maximize performance. Fortunately, existing research has established that group personality composition (GPC), across five dimensions of personality, is a promising indicator of team effectiveness. Additionally, recent advances in technology have enabled groups of humans to form real-time, closed-loop systems that are modeled after natural swarms, like flocks of birds and colonies of bees. These Artificial Swarm Intelligences (ASI) have been shown to amplify performance in a wide range of tasks, from forecasting financial markets to …

Improving Ultra-Wideband Localization By Detecting Radio Misclassification, Cory A. Mayer

Master's Theses

The Global Positioning System (GPS) and other satellite-based positioning systems are often a key component in applications requiring localization. However, accurate positioning in areas with poor GPS coverage, such as inside buildings and in dense cities, is in increasing demand for many modern applications. Fortunately, recent developments in ultra-wideband (UWB) radio technology have enabled precise positioning in places where it was not previously possible by utilizing multipath-resistant wide band pulses.

Although ultra-wideband signals are less prone to multipath interference, it is still a bottleneck as increasingly ambitious projects continue to demand higher precision. Some UWB radios include on-board detection of …

Making A Good Thing Better: Enhancing Password/Pin-Based User Authentication With Smartwatch, Bing Chang, Yingjiu Li, Qiongxiao Wang, Wen-Tao Zhu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Wearing smartwatches becomes increasingly popular in people’s lives. This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer’s password has already been revealed. This idea is motivated by our observation that a sensor-rich smartwatch is capable of tracking the wrist motions of its bearer typing a password or PIN, which can be used as an authentication factor. The major challenge in this research is that a sophisticated attacker may imitate a user’s typing behavior as shown in previous research on keystroke dynamics based user authentication. We address this …

Machine Learning The Harness Track: A Temporal Investigation Of Race History On Prediction, Robert P. Schumaker

Journal of International Technology and Information Management

Machine learning techniques have shown their usefulness in accurately predicting greyhound races. Many of the studies within this domain focus on two things; win-only wagers and using a very particular combination of race history. Our study investigates altering these properties and studying the results. In particular we found a race history combination that optimizes our S&C Racing system’s predictions on seven different wager types. From this, S&C Racing posted an impressive 50.44% accuracy in selecting winning wagers with a payout of $609.34 and a betting return of $10.06 per dollar wagered.

Cleaver: Classification Of Everyday Activities Via Ensemble Recognizers, Samantha Hsu

Master's Theses

Physical activity can have immediate and long-term benefits on health and reduce the risk for chronic diseases. Valid measures of physical activity are needed in order to improve our understanding of the exact relationship between physical activity and health. Activity monitors have become a standard for measuring physical activity; accelerometers in particular are widely used in research and consumer products because they are objective, inexpensive, and practical. Previous studies have experimented with different monitor placements and classification methods. However, the majority of these methods were developed using data collected in controlled, laboratory-based settings, which is not reliably representative of real …

Computational Support For Predicting Requirement Change Volatility In Complex System Design, Phyo Htet Hein

Theses and Dissertations

Requirements play critical role in the design process as objective statements of stakeholders’ expectations. Design process is iterative, and requirements are also constantly changed and updated to reflect stakeholders’ expectations, design changes, regulations, resource limitations, etc. Managing requirement change is one of the most important requirement management tasks. Since requirements are driving factors in product development from initial concept development stage to final production, mismanaged requirement changes can adversely affect project health leading to monetary and time losses. The ability to assess a requirement change and predict its propagation early in the design process will enable engineers to make informed …

Landmine Detection Using Semi-Supervised Learning., Graham Reid

Electronic Theses and Dissertations

Landmine detection is imperative for the preservation of both military and civilian lives. While landmines are easy to place, they are relatively difficult to remove. The classic method of detecting landmines was by using metal-detectors. However, many present-day landmines are composed of little to no metal, necessitating the use of additional technologies. One of the most successful and widely employed technologies is Ground Penetrating Radar (GPR). In order to maximize efficiency of GPR-based landmine detection and minimize wasted effort caused by false alarms, intelligent detection methods such as machine learning are used. Many sophisticated algorithms are developed and employed to …

Blood-Based Biomarkers For Predicting The Risk For Five-Year Incident Coronary Heart Disease In The Framingham Heart Study Via Machine Learning, Meeshanthini V. Dogan, Steven R.H. Beach, Ronald L. Simons, Amaury Lendasse, Brandan Penaluna, Robert A. Philibert

Engineering Management and Systems Engineering Faculty Research & Creative Works

An Improved Approach for Predicting the Risk for Incident Coronary Heart Disease (CHD) Could Lead to Substantial Improvements in Cardiovascular Health. Previously, We Have Shown that Genetic and Epigenetic Loci Could Predict CHD Status More Sensitively Than Conventional Risk Factors. Herein, We Examine Whether Similar Machine Learning Approaches Could Be Used to Develop a Similar Panel for Predicting Incident CHD. Training and Test Sets Consisted of 1180 and 524 Individuals, respectively. Data Mining Techniques Were Employed to Mine for Predictive Biosignatures in the Training Set. an Ensemble of Random Forest Models Consisting of Four Genetic and Four Epigenetic Loci Was …

Towards Scalable Characterization Of Noisy, Intermediate-Scale Quantum Information Processors, Travis Luke Scholten

Physics & Astronomy ETDs

In recent years, quantum information processors (QIPs) have grown from one or two qubits to tens of qubits. As a result, characterizing QIPs – measuring how well they work, and how they fail – has become much more challenging. The obstacles to characterizing today’s QIPs will grow even more difficult as QIPs grow from tens of qubits to hundreds, and enter what has been called the “noisy, intermediate-scale quantum” (NISQ) era. This thesis develops methods based on advanced statistics and machine learning algorithms to address the difficulties of “quantum character- ization, validation, and verification” (QCVV) of NISQ processors. In the …

Embedded Cyclegan For Shape-Agnostic Image-To-Image Translation, Ram Longman

Theses

Image-to-Image translation is the task of translating images between domains while maintaining the identity of the images. The task can be used for entertainment purposes and applications, data augmentation, semantic image segmentation, and more. Generative Adversarial Networks (GANs), and in particular Conditional GANs have recently shown incredible success in image-to-image translation and semantic manipulation. However, such methods require paired data, meaning that an image must have ground-truth translations across domains. Cycle-consistent GANs solve this problem by using unpaired data. Such methods work well for translations that involve color and texture changes but fail when shape changes are required. This research …

Artificial Intelligence, Data Analytics And Sports Biomechanics: A New Era Or A False Dawn?, Jacqueline Alderson, Bill Johnson

ISBS Proceedings Archive

The adoption of wearable technologies combined with emerging and evolving data science techniques has the potential to do for sports biomechanists what the industrial revolution did for manufacturing. Yet the full capability of these technologies and methods will likely only be realised if sport scientists are embedded in their application and driving the questions being asked.

Head Impact Monitoring: What New Methodologies Could Do For Concussion Biomechanics, Enora Le Flao, Patria Hume, Doug King

ISBS Proceedings Archive

Concussion has become a world-wide concern for sports participants. In-vivo head impacts monitoring has long been proposed as a way of identifying and even helping to prevent concussions. Several head impact devices were developed to measure head kinematics on the field, allowing the study of a wide range of sports. However, after fifteen years of data collection and despite a better understanding of injury mechanisms, concussion biomechanics still presents numerous challenges. This study aimed to summarize current knowledge of head impact monitoring via narrative and systematic reviews. The discussion was focused on how technology might have limited previous research, and …