Engineering Commons^™

Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso

Theses and Dissertations--Electrical and Computer Engineering

The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …

A Secure And Distributed Architecture For Vehicular Cloud And Protocols For Privacy-Preserving Message Dissemination In Vehicular Ad Hoc Networks, Hassan Mistareehi

Theses and Dissertations--Computer Science

Given the enormous interest in self-driving cars, Vehicular Ad hoc NETworks (VANETs) are likely to be widely deployed in the near future. Cloud computing is also gaining widespread deployment. Marriage between cloud computing and VANETs would help solve many of the needs of drivers, law enforcement agencies, traffic management, etc. The contributions of this dissertation are summarized as follows: A Secure and Distributed Architecture for Vehicular Cloud: Ensuring security and privacy is an important issue in the vehicular cloud; if information exchanged between entities is modified by a malicious vehicle, serious consequences such as traffic congestion and accidents can …

Peer-To-Peer Energy Trading In Smart Residential Environment With User Behavioral Modeling, Ashutosh Timilsina

Theses and Dissertations--Computer Science

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.

Trading energy among users in a decentralized fashion has been referred …

Determining Power System Fault Location Using Neural Network Approach, Edward O. Ojini

Theses and Dissertations--Electrical and Computer Engineering

Fault location remains an extremely pivotal feature of the electric power grid as it ensures efficient operation of the grid and prevents large downtimes during fault occurrences. This will ultimately enhance and increase the reliability of the system. Since the invention of the electric grid, many approaches to fault location have been studied and documented. These approaches are still effective and are implemented in present times, and as the power grid becomes even more broadened with new forms of energy generation, transmission, and distribution technologies, continued study on these methods is necessary. This thesis will focus on adopting the artificial …

Bone Quality And Fractures In Women With Osteoporosis Treated With Bisphosphonates For 1 To 14 Years, Hartmut H. Malluche, Jin Chen, Florence Lima, Lucas J. Liu, Marie-Claude Monier-Faugere, David A. Pienkowski

Internal Medicine Faculty Publications

Oral bisphosphonates are the primary medication for osteoporosis, but concerns exist regarding potential bone-quality changes or low-energy fractures. This cross-sectional study used artificial intelligence methods to analyze relationships among bisphosphonate treatment duration, a wide variety of bone-quality parameters, and low-energy fractures. Fourier transform infrared spectroscopy and histomorphometry quantified bone-quality parameters in 67 osteoporotic women treated with oral bisphosphonates for 1 to 14 years. Artificial intelligence methods established two models relating bisphosphonate treatment duration to bone-quality changes and to low-energy clinical fractures. The model relating bisphosphonate treatment duration to bone quality demonstrated optimal performance when treatment durations of 1 to 8 …

Innovative Computational Methods For Pharmaceutical Problem Solving A Review Part I: The Drug Development Process, Heather R. Campbell, Robert A. Lodder

Pharmaceutical Sciences Faculty Publications

Computational methods have provided pharmaceutical scientists and engineers a means to go beyond what's possible with experimental testing alone. Providing a means to study active pharmaceutical ingredients (API), excipients, and drug interactions at or near-atomic levels. This paper provides a review of this and other innovative computational methods used for solving pharmaceutical problems throughout the drug development process. Part one of two this paper will emphasize the role of computational methods and game theory in solving pharmaceutical challenges.

Innovative Computational Methods For Pharmaceutical Problem Solving A Review Part Ii: Serious Gaming, Heather R. Campbell, Robert A. Lodder

Pharmaceutical Sciences Faculty Publications

Serious gaming has begun to take a foothold in pharmaceutical problem-solving. Companies such as Akili's Interactive are seeing success in the form of positive clinical trial results and FDA approval of digital therapeutics. Academic researchers have begun exploring novel uses for serious gaming in the way of protein design and more with promising results. This paper provides a review of such topics in addition to topics of game repurposing- repurposing a game originally intended for entertainment into a serious game-such as Minecraft and America's Army. Reviewing these topics this paper shows the utility of serious gaming as a tool for …

Weakly Supervised Learning For Multi-Image Synthesis, Muhammad Usman Rafique

Theses and Dissertations--Electrical and Computer Engineering

Machine learning-based approaches have been achieving state-of-the-art results on many computer vision tasks. While deep learning and convolutional networks have been incredibly popular, these approaches come at the expense of huge amounts of labeled data required for training. Manually annotating large amounts of data, often millions of images in a single dataset, is costly and time consuming. To deal with the problem of data annotation, the research community has been exploring approaches that require less amount of labelled data.

The central problem that we consider in this research is image synthesis without any manual labeling. Image synthesis is a classic …

Artificial Intelligence And Soft Computing In Smart Structural Systems, Sajad Javadinasab Hormozabad

Theses and Dissertations--Civil Engineering

Next-generation smart cities are the key feature in the next chapter of human life. Cities that employ innovative and technology-driven solutions to improve the sustainability, resilience, prosperity, and amenity of the community are considered smart cities. Development of smart cities requires fundamental innovations in many technical and technological aspects including those contributing to smart structures. Smart technologies improve the structural performance against natural disasters like earthquakes, hurricanes, tornados, and promote the sustainability of structural systems. Next-generation smart structures encompass a variety of technologies including Structural Control (SC) and Structural Health Monitoring (SHM). SC covers methodologies and technologies that modify the …

A User Study Of A Wearable System To Enhance Bystanders’ Facial Privacy, Alfredo J. Perez, Sherali Zeadally, Scott Griffith, Luis Y. Matos Garcia, Jaouad A. Mouloud

Information Science Faculty Publications

The privacy of users and information are becoming increasingly important with the growth and pervasive use of mobile devices such as wearables, mobile phones, drones, and Internet of Things (IoT) devices. Today many of these mobile devices are equipped with cameras which enable users to take pictures and record videos anytime they need to do so. In many such cases, bystanders’ privacy is not a concern, and as a result, audio and video of bystanders are often captured without their consent. We present results from a user study in which 21 participants were asked to use a wearable system called …

Β-Amyloid And Tau Drive Early Alzheimer's Disease Decline While Glucose Hypometabolism Drives Late Decline, Tyler C. Hammond, Xin Xing, Chris Wang, David Ma, Kwangsik Nho, Paul K. Crane, Fanny Elahi, David A. Ziegler, Gongbo Liang, Qiang Cheng, Lucille M. Yanckello, Nathan Jacobs, Ai-Ling Lin

Sanders-Brown Center on Aging Faculty Publications

Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer’s disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer’s Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aβ …

Recent Shrinkage And Fragmentation Of Bluegrass Landscape In Kentucky, Bo Tao, Yanjun Yang, Jia Yang, S. Ray Smith, James F. Fox, Alex C. Ruane, Jinze Liu, Wei Ren

Plant and Soil Sciences Faculty Publications

The Bluegrass Region is an area in north-central Kentucky with unique natural and cultural significance, which possesses some of the most fertile soils in the world. Over recent decades, land use and land cover changes have threatened the protection of the unique natural, scenic, and historic resources in this region. In this study, we applied a fragmentation model and a set of landscape metrics together with the satellite-derived USDA Cropland Data Layer to examine the shrinkage and fragmentation of grassland in the Bluegrass Region, Kentucky during 2008–2018. Our results showed that recent land use change across the Bluegrass Region is …

Estimating Free-Flow Speed With Lidar And Overhead Imagery, Armin Hadzic

Theses and Dissertations--Computer Science

Understanding free-flow speed is fundamental to transportation engineering in order to improve traffic flow, control, and planning. The free-flow speed of a road segment is the average speed of automobiles unaffected by traffic congestion or delay. Collecting speed data across a state is both expensive and time consuming. Some approaches have been presented to estimate speed using geometric road features for certain types of roads in limited environments. However, estimating speed at state scale for varying landscapes, environments, and road qualities has been relegated to manual engineering and expensive sensor networks. This thesis proposes an automated approach for estimating free-flow …

Fault Identification On Electrical Transmission Lines Using Artificial Neural Networks, Christopher W. Asbery

Theses and Dissertations--Electrical and Computer Engineering

Transmission lines are designed to transport large amounts of electrical power from the point of generation to the point of consumption. Since transmission lines are built to span over long distances, they are frequently exposed to many different situations that can cause abnormal conditions known as electrical faults. Electrical faults, when isolated, can cripple the transmission system as power flows are directed around these faults therefore leading to other numerous potential issues such as thermal and voltage violations, customer interruptions, or cascading events. When faults occur, protection systems installed near the faulted transmission lines will isolate these faults from the …

A Comparative Analysis Of Reinforcement Learning Applied To Task-Space Reaching With A Robotic Manipulator With And Without Gravity Compensation, Jonathan Fugal

Theses and Dissertations--Electrical and Computer Engineering

Advances in computing power in recent years have facilitated developments in autonomous robotic systems. These robotic systems can be used in prosthetic limbs, wearhouse packaging and sorting, assembly line production, as well as many other applications. Designing these autonomous systems typically requires robotic system and world models (for classical control based strategies) or time consuming and computationally expensive training (for learning based strategies). Often these requirements are difficult to fulfill. There are ways to combine classical control and learning based strategies that can mitigate both requirements. One of these ways is to use a gravity compensated torque control with reinforcement …

Influence Spread In Two-Layer Interdependent Networks: Designed Single-Layer Or Random Two-Layer Initial Spreaders?, Hana Khamfroush, Nathaniel Hudson, Samuel Iloo, Mahshid R. Naeini

Computer Science Faculty Publications

Influence spread in multi-layer interdependent networks (M-IDN) has been studied in the last few years; however, prior works mostly focused on the spread that is initiated in a single layer of an M-IDN. In real world scenarios, influence spread can happen concurrently among many or all components making up the topology of an M-IDN. This paper investigates the effectiveness of different influence spread strategies in M-IDNs by providing a comprehensive analysis of the time evolution of influence propagation given different initial spreader strategies. For this study we consider a two-layer interdependent network and a general probabilistic threshold influence spread model …

A Generative Human-Robot Motion Retargeting Approach Using A Single Rgbd Sensor, Sen Wang, Xinxin Zuo, Runxiao Wang, Ruigang Yang

Computer Science Faculty Publications

The goal of human-robot motion retargeting is to let a robot follow the movements performed by a human subject. Typically in previous approaches, the human poses are precomputed from a human pose tracking system, after which the explicit joint mapping strategies are specified to apply the estimated poses to a target robot. However, there is not any generic mapping strategy that we can use to map the human joint to robots with different kinds of configurations. In this paper, we present a novel motion retargeting approach that combines the human pose estimation and the motion retargeting procedure in a unified …

Procure-To-Pay Software In The Digital Age: An Exploration And Analysis Of Efficiency Gains And Cybersecurity Risks In Modern Procurement Systems, Drew Lane

MPA/MPP/MPFM Capstone Projects

Procure-to-Pay (P2P) softwares are an integral part of the payment and procurement processing functions at large-scale governmental institutions. These softwares house all of the financial functions related to procurement, accounts payable, and often human resources, helping to facilitate and automate the process from initiation of a payment or purchase, to the actual disbursal of funds. Often, these softwares contain budgeting and financial reporting tools as part of the offering. As such an integral part of the financial process, these softwares obviously come at an immense cost from a set of reputable vendors. In the case of government, these vendors mainly …

A Compiler Target Model For Line Associative Registers, Paul S. Eberhart

Theses and Dissertations--Electrical and Computer Engineering

LARs (Line Associative Registers) are very wide tagged registers, used for both register-wide SWAR (SIMD Within a Register )operations and scalar operations on arbitrary fields. LARs include a large data field, type tags, source addresses, and a dirty bit, which allow them to not only replace both caches and registers in the conventional memory hierarchy, but improve on both their functions. This thesis details a LAR-based architecture, and describes the design of a compiler which can generate code for a LAR-based design. In particular, type conversion, alignment, and register allocation are discussed in detail.

Image-Based Roadway Assessment Using Convolutional Neural Networks, Weilian Song

Theses and Dissertations--Computer Science

Road crashes are one of the main causes of death in the United States. To reduce the number of accidents, roadway assessment programs take a proactive approach, collecting data and identifying high-risk roads before crashes occur. However, the cost of data acquisition and manual annotation has restricted the effect of these programs. In this thesis, we propose methods to automate the task of roadway safety assessment using deep learning. Specifically, we trained convolutional neural networks on publicly available roadway images to predict safety-related metrics: the star rating score and free-flow speed. Inference speeds for our methods are mere milliseconds, enabling …

Relation Prediction Over Biomedical Knowledge Bases For Drug Repositioning, Mehmet Bakal

Theses and Dissertations--Computer Science

Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying other essential relations (e.g., causation, prevention) between biomedical entities is also critical to understand biomedical processes. Hence, it is crucial to develop automated relation prediction systems that can yield plausible biomedical relations to expedite the discovery process. In this dissertation, we demonstrate three approaches to predict treatment relations between biomedical entities for the drug repositioning task …

Facepet: Enhancing Bystanders' Facial Privacy With Smart Wearables/Internet Of Things, Alfredo J. Perez, Sherali Zeadally, Luis Y. Matos Garcia, Jaouad A. Mouloud, Scott Griffith

Information Science Faculty Publications

Given the availability of cameras in mobile phones, drones and Internet-connected devices, facial privacy has become an area of major interest in the last few years, especially when photos are captured and can be used to identify bystanders’ faces who may have not given consent for these photos to be taken and be identified. Some solutions to protect facial privacy in photos currently exist. However, many of these solutions do not give a choice to bystanders because they rely on algorithms that de-identify photos or protocols to deactivate devices and systems not controlled by bystanders, thereby being dependent on the …

X-Search: An Open Access Interface For Cross-Cohort Exploration Of The National Sleep Research Resource, Licong Cui, Ningzhou Zeng, Matthew Kim, Remo Mueller, Emily Ruth Hankosky, Susan Redline, Guo-Qiang Zhang

Computer Science Faculty Publications

Background: The National Sleep Research Resource (NSRR) is a large-scale, openly shared, data repository of de-identified, highly curated clinical sleep data from multiple NIH-funded epidemiological studies. Although many data repositories allow users to browse their content, few support fine-grained, cross-cohort query and exploration at study-subject level. We introduce a cross-cohort query and exploration system, called X-search, to enable researchers to query patient cohort counts across a growing number of completed, NIH-funded studies in NSRR and explore the feasibility or likelihood of reusing the data for research studies.

Methods: X-search has been designed as a general framework with two loosely-coupled components: …

Query-Constraint-Based Mining Of Association Rules For Exploratory Analysis Of Clinical Datasets In The National Sleep Research Resource, Rashmie Abeysinghe, Licong Cui

Computer Science Faculty Publications

Background: Association Rule Mining (ARM) has been widely used by biomedical researchers to perform exploratory data analysis and uncover potential relationships among variables in biomedical datasets. However, when biomedical datasets are high-dimensional, performing ARM on such datasets will yield a large number of rules, many of which may be uninteresting. Especially for imbalanced datasets, performing ARM directly would result in uninteresting rules that are dominated by certain variables that capture general characteristics.

Methods: We introduce a query-constraint-based ARM (QARM) approach for exploratory analysis of multiple, diverse clinical datasets in the National Sleep Research Resource (NSRR). QARM enables rule mining on …

Compact Hardware Implementation Of A Sha-3 Core For Wireless Body Sensor Networks, Yi Yang, Debiao He, Neeraj Kumar, Sherali Zeadally

Information Science Faculty Publications

One of the most important Internet of Things applications is the wireless body sensor network (WBSN), which can provide universal health care, disease prevention, and control. Due to large deployments of small scale smart sensors in WBSNs, security, and privacy guarantees (e.g., security and safety-critical data, sensitive private information) are becoming a challenging issue because these sensor nodes communicate using an open channel, i.e., Internet. We implement data integrity (to resist against malicious tampering) using the secure hash algorithm 3 (SHA-3) when smart sensors in WBSNs communicate with each other using the Internet. Due to the limited resources (i.e., storage, …

Sensor Technologies For Intelligent Transportation Systems, Juan Guerrero-Ibáñez, Sherali Zeadally, Juan Contreras-Castillo

Information Science Faculty Publications

Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the …

Scheduling Based On Interruption Analysis And Pso For Strictly Periodic And Preemptive Partitions In Integrated Modular Avionics, Hui Lu, Qianlin Zhou, Zongming Fei, Rongrong Zhou

Computer Science Faculty Publications

Integrated modular avionics introduces the concept of partition and has been widely used in avionics industry. Partitions share the computing resources together. Partition scheduling plays a key role in guaranteeing correct execution of partitions. In this paper, a strictly periodic and preemptive partition scheduling strategy is investigated. First, we propose a partition scheduling model that allows a partition to be interrupted by other partitions, but minimizes the number of interruptions. The model not only retains the execution reliability of the simple partition sets that can be scheduled without interruptions, but also enhances the schedulability of the complex partition sets that …

A Fast And Robust Extrinsic Calibration For Rgb-D Camera Networks, Po-Chang Su, Ju Shen, Wanxin Xu, Sen-Ching S. Cheung, Ying Luo

Electrical and Computer Engineering Faculty Publications

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. Practical applications often use sparsely-placed cameras to maximize visibility, while using as few cameras as possible to minimize cost. In general, it is challenging to calibrate sparse camera networks due to the lack of shared scene features across different camera views. In this paper, we propose a novel algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Our work has a number of novel features. First, to cope …

Application Of Acoustic Emission And Machine Learning To Detect Codling Moth Infested Apples, Mengxing Li, Nader Ekramirad, Ahmed Rady, Akinbode A. Adedeji

Biosystems and Agricultural Engineering Faculty Publications

Incidence of codling moth (CM) (Cydia pomonella L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. 'GoldRush‘ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect …

Self-Image Multimedia Technologies For Feedforward Observational Learning, Nkiruka M. A. Uzuegbunam

Theses and Dissertations--Electrical and Computer Engineering

This dissertation investigates the development and use of self-images in augmented reality systems for learning and learning-based activities. This work focuses on self- modeling, a particular form of learning, actively employed in various settings for therapy or teaching. In particular, this work aims to develop novel multimedia systems to support the display and rendering of augmented self-images. It aims to use interactivity (via games) as a means of obtaining imagery for use in creating augmented self-images. Two multimedia systems are developed, discussed and analyzed. The proposed systems are validated in terms of their technical innovation and their clinical efficacy in …