Engineering Commons^™

Assessing Magnetic Iron Oxide Nanoparticles Properties Under Different Thermal Treatments, Erick S. Vasquez, Evan M. Prehn, Keisha B. Walters

Chemical and Materials Engineering Faculty Publications

Magnetic nanoparticle structures have been examined as potential carrier vehicles and substrates in a wide range of applications where they undergo mechanical, chemical and/or thermal manipulation to allow for their modification, conjugation and transport. For safe and effective use, it is imperative to not only measure the initial physicochemical and structural properties of nanomaterials, but also identify and quantify any property changes related to a loss of chemical and/or physical integrity during processing and usage conditions. In this study an assessment of iron oxide magnetic nanoparticle thermal stability using modulated differential scanning calorimetry (mDSC) and a controlled-heating system is conducted …

A Transdisciplinary Collaboration And Innovation Education Model And Experience, Brian Laduca, Michelle Hayford, Adrienne Ausdenmoore, Jerome Yorke, Kevin Hallinan, Rebecca Blust, Anne R. Crecelius, Philip Appiah-Kubi, Jennifer Katz-Buonconintro, Jana Bennett, Jackie Marshall Arnold, Connie L. Bowman, Castel Sweet

Engineering Management and Systems Faculty Publications

As the interconnectedness of the world grows, the need to prepare college students capable of addressing complexity likewise grows. In this context, the University of Dayton has developed and tested a transdisciplinary model for education. This model links multiple classes from different disciplines via a common theme and within a common space. It also employs an educational model premised on the following trajectory: disciplinary content development / transdisciplinary observation (empathy); transdisciplinary disruption leading to “A-Ha” observations which transform the disciplinary directions; and lastly transdisciplinary informed design and research. Central to this model is a 3,500 square foot common space used …

Participatory Sensing-Based Geospatial Localization Of Distant Objects For Disaster Preparedness In Urban Built Environments, Hongjo Kim, Youngjib Ham

Civil and Environmental Engineering and Engineering Mechanics Faculty Publications

Although the benefit of participatory sensing for collecting local data over large areas has long been recognized, it has not been widely used for various applications such as disaster preparation due to a lack of geospatial localization capability with respect to a distant object. In such applications, objects of interest need to be ro- bustly localized and documented for supporting data-driven decision-making in site inspection and resource mobilization. However, participatory sensing is inappropriate to localize a distant object due to the absence of ranging sensors in citizens' mobile devices; thus, the localization accuracy varies to a large extent. To address …

Sustainable Aviation Fuels Approval Streamlining: Auxiliary Power Unit Lean Blowout Testing, Erin E. Peiffer, Joshua S. Heyne, Meredith Colket

Mechanical and Aerospace Engineering Faculty Publications

An underpinning hindrance in the market penetration of sustainable aviation fuel is the approval process for alternative jet fuels. One solution to this is to develop low-cost screening tools that can be implemented earlier in the approval process. Auxiliary power unit combustors historically show the most sensitivity to physical and volatile fuel properties, making it a useful tool in assessing potential alternative jet fuel effects at test conditions representative of operability stability limits. It is hypothesized that these observations can be explained via timescale analysis considering fuel droplet breakup and evaporation, combustor mixing, and chemical reactivity timescales on the progression …

Optothermal Microbubble Assisted Manufacturing Of Nanogap-Rich Structures For Active Chemical Sensing, Farzia Karim, Erick S. Vasquez, Yvonne Sun, Chenglong Zhao

Chemical and Materials Engineering Faculty Publications

Guiding analytes to the sensing area is an indispensable step in a sensing system. Most of the sensing systems apply a passive sensing method, which waits for the analytes to diffuse towards the sensor. However, passive sensing methods limit the detection of analytes to a picomolar range on micro/nanosensors for a practical time scale. Therefore, active sensing methods need to be used to improve the detection limit in which the analytes are forced to concentrate on the sensors. In this article, we have demonstrated the manufacturing of nanogap-rich structures for active chemical sensing. Nanogap-rich structures are manufactured from metallic nanoparticles …

Simulation Of The Impact Of Connected And Automated Vehicles At A Signalized Intersection, Hamad Bader Almobayedh, Deogratias Eustace, Philip Appiah-Kubi

Civil and Environmental Engineering and Engineering Mechanics Faculty Publications

Intersections are locations with higher likelihood of crash occurences and sources of traffic congestion as they act as bottlenecks compared with other parts of the roadway networks. Consequently, connected and automated vehicles (CAVs) can help to improve the efficiency of the roadways by reducing traffic congestion and traffic delays. Since CAVs are expected to take control from drivers (human control) in making many important decisions, thus they are expected to minimize driver (human) errors in driving tasks. Therefore, CAVs potential benefits of eliminating driver error include an increase in safety (crash reduction), smooth vehicle flow to reduce emissions, and reduce …

Optimization And Structural Stability Of Gold Nanoparticle–Antibody Bioconjugates, Robert T. Busch, Farzia Karim, John Weis, Yvonne Sun, Chenglong Zhao

Chemical and Materials Engineering Faculty Publications

Gold nanoparticles (AuNPs) bound with biomolecules have emerged as suitable biosensors exploiting unique surface chemistries and optical properties. Many efforts have focused on antibody bioconjugation to AuNPs resulting in a sensitive bioconjugate to detect specific types of bacteria. Unfortunately, bacteria thrive under various harsh environments, and an understanding of bioconjugate stability is needed. Here, we show a method for optimizing Listeria monocytogenes polyclonal antibodies bioconjugation mechanisms to AuNPs via covalent binding at different pH values, from 2 to 11, and 2-(N-morpholino)ethanesulfonic acid (MES), 3-(N-morpholino)propanesulfonic acid, NaOH, HCl conditions. By fitting Lorentz curves to the amide I and II regions, we …

Computational And Experimental Approach To Understanding The Structural Interplay Of Self-Assembled End-Terminated Alkanethiolates On Gold Surfaces, Juganta K. Roy, Erick S. Vasquez, Henry P. Pinto, Swati Kumari, Keisha B. Walters, Jerzy Leszcynski

Chemical and Materials Engineering Faculty Publications

Applications of self-assembled monolayers (SAMs) on surfaces are prevalent in modern technologies and drives the need for a better understanding of the surface domain architecture of SAMs. To explore structural interaction at the interface between gold surfaces and a hydroxyl-terminated alkanethiol, 11-hydroxy-1-undecanethiol, (C11TH) we have employed a combined computational and experimental approach. Density functional theory (DFT) calculations were carried out on the thiol–gold interface using both the Perdew–Burke–Ernzerhof (PBE) and van der Waals (optB86b) density functionals. Our ab initio molecular dynamics (AIMD) simulations revealed that the interface consists of four different distinguished phases, each with different C11TH orientations. Experiments involved …

Development Of A Methodology For Characterizing Reaction Kinetics, Rheology, And In-Situ Compaction Of Polyimide Prepregs During Cure, James Raymond Magato, Donald A. Klosterman

Chemical and Materials Engineering Faculty Publications

PMR-type polyimide prepregs are challenging to fabricate into high quality composites due to volatiles that are generated and must be removed in situ during processing. The current work was conducted to develop accurate, reliable, and practical characterization techniques of the prepreg rheology, volatile generation, and subsequent volatile removal from the prepreg during composite fabrication. Thermal analysis was used to characterize volatile generation, reaction rates, and rheology. A novel approach was used to measure the thickness of the prepreg in situ during vacuum bag/oven processing using a high-temperature LVDT. Experimental results are presented for the commercially available RM-1100 polyimide/carbon prepreg system, …

Fusion Of Interpolated Frames Superresolution In The Presence Of Atmospheric Optical Turbulence, Russell C. Hardie, Michael A. Rucci, Barry K. Karch, Alexander J. Dapore, Douglas R. Droege, Joseph C. French

Electrical and Computer Engineering Faculty Publications

An extension of the fusion of interpolated frames superresolution (FIF SR) method to perform SR in the presence of atmospheric optical turbulence is presented. The goal of such processing is to improve the performance of imaging systems impacted by turbulence. We provide an optical transfer function analysis that illustrates regimes where significant degradation from both aliasing and turbulence may be present in imaging systems. This analysis demonstrates the potential need for simultaneous SR and turbulence mitigation (TM). While the FIF SR method was not originally proposed to address this joint restoration problem, we believe it is well suited for this …

A Computationally Efficient U-Net Architecture For Lung Segmentation In Chest Radiographs, Barath Narayanan, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Lung segmentation plays a crucial role in computer-aided diagnosis using Chest Radiographs (CRs). We implement a U-Net architecture for lung segmentation in CRs across multiple publicly available datasets. We utilize a private dataset with 160 CRs provided by the Riverain Medical Group for training purposes. A publicly available dataset provided by the Japanese Radiological Scientific Technology (JRST) is used for testing. The active shape model-based results would serve as the ground truth for both these datasets. In addition, we also study the performance of our algorithm on a publicly available Shenzhen dataset which contains 566 CRs with manually segmented lungs …

The Effect Of Fabric Architecture On The Processing And Properties Of Composites Made By Vacuum Assisted Resin Transfer Molding, Francois Ntakobatagize, Oscar Ntakontagize, Donald A. Klosterman

Chemical and Materials Engineering Faculty Publications

The goal of this research project was to evaluate and compare the effect of fabric architecture on the processing and properties of composites made by Vacuum Assisted Resin Transfer Molding (VARTM). The fabric architectures investigated included plain weave, satin weave, and warp-knit unidirectional. The fiber types included E-glass and standard modulus carbon fiber. Flat panels were fabricated with a lab scale VARTM process using an epoxy resin system. Fabric plies were cut to 45 cm x 30 cm (18 in. x 12 in.), and the number of plies used depended on the fiber areal weight of each fabric to produce …

Generation Of Silver Nanoparticle Pharmacokinetic Profiles In A Lung Model, Rachel Galaska

Honors Theses

Nanomaterial technologies are becoming increasingly prevalent in consumer and industrial applications, including drug delivery, energy harvesting, environmental applications, and medicine due to their unique physiochemical properties. As nanomaterial use increases, so too does human exposure. This has made it progressively more important to understand the toxicological effects of nanomaterials and their interactions with the human body. Silver nanoparticles (AgNPs) are one of the most commonly used nanomaterials due to their antibacterial properties. As inhalation is one of the most common exposure routes, understanding the toxicity of these AgNPs on lung tissue was studied. Using A549 cells for a lung tissue …

Energy Analysis And Orbit Simulation Of Actuating Cubesat Solar Arrays, Justin T. Ehren

Honors Theses

CubeSats are used in space research to explore new technologies and detect data to gain a better understanding of various areas of research and subjects affecting human life. CubeSats rely on a solar array to generate energy from the sun and perform their various functions in space. This research studies the energy capturing potential of various solar array configurations and positioning devices for CubeSats. The location and orientation of a CubeSat is simulated with MATLAB for both geo-synchronous and sunsynchronous orbits. Two degree-of-freedom (DoF) positioning devices are sufficient to continuously adjust the photovoltaic array to face towards the sun. Lower …

Towards A Pre-Processing Algorithm For Automated Arrhythmia Detection, Sarah Miller

Honors Theses

There are a variety of different wearable fitness/cardiac monitoring devices that are currently used in many people’s day to day life. The primary cardiac function of these devices is to monitor heart rate, however we believe that they could be utilized to detect different forms of arrhythmia. In order to categorize and identify different forms of arrhythmia, we are utilizing published EKG data sets from existing databases as a basis for machine learning. The challenge that comes from the existing data sets is that the format they present the data in does not lend itself to machine learning, which requires …

Effect Of Activated Carbon On The Performance Of A Solar Thermal Adsorptive Refrigerator (Star) Using Activated Carbon-Ethanol, Joshua Romo

Honors Theses

The Solar Thermal Adsorptive Refrigerator (STAR) project at the University of Dayton seeks to bridge sustainability and the need for reliable refrigeration in developing communities. Cost-effective construction, operation, and maintenance as well as the use of a sustainable adsorption pair, activated carbon-ethanol, give STAR great potential in the realm of humanitarian engineering. This project explores the effect of using two activated carbon brands, 8x16 and CocoPlus, on the cyclic performance of the STAR system. Although both brands have similar specifications given by the manufacturer, one (8x16) inhibited successful performance while the other (CocoPlus) enabled it. This project highlights both the …

A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language …

Investigation Of The Effect Of Vehicle Color On Safety, Deogratias Eustace, Fayez Khalaf Alanazi, Peter W. Hovey

Civil and Environmental Engineering and Engineering Mechanics Faculty Publications

No abstract provided.

Iact Undergraduate Certificate In Applied Creativity (Year 2 - 2019), Brian Laduca

IACT Certificate Program

At the Institute of Applied Creativity for Transformation (IACT) at ArtStreet, we seek to empower a creatively confident 21st-century student with the ability to discover, invent and innovate ambiguous ideas through a disruptive design process that will impact today’s ever-changing global world regardless of degree focus.

IACT is home to the nation’s first undergraduate certificate in Applied Creativity for Transformation. Open to undergraduate students of any major, the certificate is a first step in achieving the University of Dayton’s vision of innovation, applied creativity, entrepreneurship and community engagement for the common good.

Performance Analysis Of Machine Learning And Deep Learning Architectures For Malaria Detection On Cell Images, Barath Narayanan, Redha Ali, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Plasmodium malaria is a parasitic protozoan that causes malaria in humans. Computer aided detection of Plasmodium is a research area attracting great interest. In this paper, we study the performance of various machine learning and deep learning approaches for the detection of Plasmodium on cell images from digital microscopy. We make use of a publicly available dataset composed of 27,558 cell images with equal instances of parasitized (contains Plasmodium) and uninfected (no Plasmodium) cells. We randomly split the dataset into groups of 80% and 20% for training and testing purposes, respectively. We apply color constancy and spatially resample all images …

Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Given that there are numerous amounts of unlabeled data available for usage in training neural networks, it is desirable to implement a neural network architecture and training paradigm to maximize the ability of the latent space representation. Through multiple perspectives of the latent space using adversarial learning and autoencoding, data requirements can be reduced, which improves learning ability across domains. The entire goal of the proposed work is not to train exhaustively, but to train with multiperspectivity. We propose a new neural network architecture called Active Recall Network (ARN) for learning with less labels by optimizing the latent space. This …

Recurrent Residual U-Net For Medical Image Segmentation, Md Zahangir Alom, Christopher Yakopcic, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep …

Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai

Electrical and Computer Engineering Faculty Publications

To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic …

A Survey Of Techniques For Mobile Service Encrypted Traffic Classification Using Deep Learning, Pan Wang, Xuejiao Chen, Feng Ye, Zhixin Sun

Electrical and Computer Engineering Faculty Publications

The rapid adoption of mobile devices has dramatically changed the access to various net- working services and led to the explosion of mobile service traffic. Mobile service traffic classification has been a crucial task that attracts strong interest in mobile network management and security as well as machine learning communities for past decades. However, with more and more adoptions of encryption over mobile services, it brings a lot of challenges about mobile traffic classification. Although classical machine learning approaches can solve many issues that port and payload-based methods cannot solve, it still has some limitations, such as time-consuming, costly handcrafted …