Engineering Commons^™

Industry 4.0 In The Retail Sector: Sustainability Of Food Retail With A Focus On Food Insecurity In Dayton, Oh, Katrina Alexis Coleman

Honors Theses

In recent years, large scale agricultural and food processing industries have experienced a great worldwide digital transformation. The advent of Industry 4.0, which has become popular in Europe, has helped many industries optimize their operations. Relatively new is the idea that food processing industries and other stakeholders in the food distribution supply chain cannot only optimize their processes but also, track and provide timely customer service. This has technical and managerial challenges that might limit the potential benefits of industry 4.0 in the efficient distribution of fresh food produce. For example, food retailers have to meet the increasing customer desire …

Effect Of Airfoil-Preserved Undulations On Wing Performance And Wingtip Vortex, Faith A. Loughnane

Honors Theses

The effect of undulation placement (leading edge, trailing edge, leading and trailing edge) on the wing performance and the wingtip vortex was investigated. Experiments were performed at the University of Dayton Low Speed Wind Tunnel (UD-LSWT) on undulated wings where the NACA 0012 airfoil cross-section is preserved along the wingspan. Sensitivity studies were done on the undulation wavelength along the span (λ/c 0.31, 0.21 and 0.15) and undulation placement (leading edge, trailing edge, and both leading and trailing edge). The leading edge undulations delayed stall until higher angles of attack, however, the maximum aerodynamic efficiency was reduced. The trailing edge …

Upper Extremity Motion Assessments In Virtual Reality Environments, Lanna Nicole Klausing

Honors Theses

Traditional upper extremity rehabilitation techniques often utilize tedious and repetitive reaching motions. Fully immersive virtual reality (VR), involving a VR headset, is a technology with the potential to have non-gaming uses and applications, specifically as an upper extremity rehabilitation tool. This study was designed with the long-term goal of evaluating immersive VR as an upper extremity rehabilitation tool. The purpose of this research is to quantify different movement deficits that may arise due to MS or Parkinson’s, and to understand how the motions of patients with MS or Parkinson’s may differ from healthy controls. This thesis documents the first step …

Multi-Modal Data Analysis And Fusion For Classification In 2d/3d Sensing, Jonathan P. Schierl

Honors Theses

This research would develop a method of more accurately detecting objects using machine learning. There is plenty of current research and algorithms to tackle this problem. Our approach would use a dataset gathered with 2-Dimensional Infrared Imagery as well as 3-Dimensional LiDAR Data. We would develop a deep learning network with the ability to “learn” using both of these datasets. This proposed fusion network will perform better than either of the individual networks.

Designing And Implementing A Peer-To-Peer Led Behavioral And Energy Reduction Program For Low-Income Neighborhoods, Jenn Hoody

Honors Theses

Reduction in energy consumption from fossil fuels is a necessary step toward combating climate change as more and more studies are revealing the catastrophic outcomes if the current trends do not change. Residential programs generally managed by energy utilities promoting energy cost savings and reduced consumption are being enacted to decrease the greenhouse emissions. However, thus far, little to no measures have been taken to extend the reach of such programs to low-income communities. Reducing household energy consumption would be particularly beneficial for these communities as it would lower utility bills for low-income households who spend a substantially greater portion …

Conditional Generative Adversarial Network Demosaicing Strategy For Division Of Focal Plane Polarimeters, Garrett Sargent, Bradley M. Ratliff, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Division of focal plane (DoFP), or integrated microgrid polarimeters, typically consist of a 2 × 2 mosaic of linear polarization filters overlaid upon a focal plane array sensor and obtain temporally synchronized polarized intensity measurements across a scene, similar in concept to a Bayer color filter array camera. However, the resulting estimated polarimetric images suffer a loss in resolution and can be plagued by aliasing due to the spatially-modulated microgrid measurement strategy. Demosaicing strategies have been proposed that attempt to minimize these effects, but result in some level of residual artifacts. In this work we propose a conditional generative adversarial …

Transfer-To-Transfer Learning Approach For Computer Aided Detection Of Covid-19 In Chest Radiographs, Barath Narayanan Narayanan, Russell C. Hardie, Vignesh Krishnaraja, Christina Karam, Venkata Salini Priyamvada Davuluru

Electrical and Computer Engineering Faculty Publications

The coronavirus disease 2019 (COVID-19) global pandemic has severely impacted lives across the globe. Respiratory disorders in COVID-19 patients are caused by lung opacities similar to viral pneumonia. A Computer-Aided Detection (CAD) system for the detection of COVID-19 using chest radiographs would provide a second opinion for radiologists. For this research, we utilize publicly available datasets that have been marked by radiologists into two-classes (COVID-19 and non-COVID-19). We address the class imbalance problem associated with the training dataset by proposing a novel transfer-to-transfer learning approach, where we break a highly imbalanced training dataset into a group of balanced mini-sets and …

Polarization-Selective Modulation Of Supercavity Resonances Originating From Bound States In The Continuum, Chan Kyaw, Riad Yahiaoui, Joshua A. Burrow, Viet Tran, Kyron Keelen, Wesley Sims, Eddie C. Red, Willie S. Rockward, Mikkel A. Thomas, Andrew M. Sarangan, Imad Agha, Thomas A. Searles

Electro-Optics and Photonics Faculty Publications

Bound states in the continuum (BICs) are widely studied for their ability to confine light, produce sharp resonances for sensing applications and serve as avenues for lasing action with topological characteristics. Primarily, the formation of BICs in periodic photonic band gap structures are driven by symmetry incompatibility; structural manipulation or variation of incidence angle from incoming light. In this work, we report two modalities for driving the formation of BICs in terahertz metasurfaces. At normal incidence, we experimentally confirm polarization driven symmetry-protected BICs by the variation of the linear polarization state of light. In addition, we demonstrate through strong coupling …

Atmospheric Turbulence Study With Deep Machine Learning Of Intensity Scintillation Patterns, Artem V. Vorontsov, Mikhail A. Vorontsov, Grigorii A. Fillimonov, Ernst Polnau

Electro-Optics and Photonics Faculty Publications

A new paradigm for machine learning-inspired atmospheric turbulence sensing is developed and applied to predict the atmospheric turbulence refractive index structure parameter using deep neural network (DNN)-based processing of short-exposure laser beam intensity scintillation patterns obtained with both: experimental measurement trials conducted over a 7 km propagation path, and imitation of these trials using wave-optics numerical simulations. The developed DNN model was optimized and evaluated in a set of machine learning experiments. The results obtained demonstrate both good accuracy and high temporal resolution in sensing. The machine learning approach was also employed to challenge the validity of several eminent atmospheric …

Self-Learning Algorithm To Predict Indoor Temperature And Cooling Demand From Smart Wifi Thermostat In A Residential Building, Kefan Huang, Kevin Hallinan, Robert Lou, Abdulrahman Alanezi, Salahaldin Alshatshati, Qiancheng Sun

Mechanical and Aerospace Engineering Faculty Publications

Smart WiFi thermostats have moved well beyond the function they were originally designed for; namely, controlling heating and cooling comfort in buildings. They are now also learning from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart WiFi thermostat data in residences to develop dynamic predictive models for room temperature and cooling/heating demand. These models can then be used to estimate the energy savings from new thermostat temperature schedules and estimate peak load reduction achievable from maintaining a residence in a minimum thermal comfort condition. …

Artificial Neural Network Discovery Of A Switchable Metasurface Reflector, J. R. Thompson, J. A. Burrow, P. J. Shah, J. Slagle, E. S. Harper, A. Van Rynbach, I. Agha, M. S. Mills

Electro-Optics and Photonics Faculty Publications

Optical materials engineered to dynamically and selectively manipulate electromag- netic waves are essential to the future of modern optical systems. In this paper, we simulate various metasurface configurations consisting of periodic 1D bars or 2D pillars made of the ternary phase change material Ge2Sb2Te5 (GST). Dynamic switching behavior in reflectance is exploited due to a drastic refractive index change between the crystalline and amorphous states of GST. Selectivity in the reflection and transmission spectra is manipulated by tailoring the geometrical parameters of the metasurface. Due to the immense number of possible metasurface configurations, we train deep neural networks capable of …

A Machine Learning Framework For Drop-In Volume Swell Characteristics Of Sustainable Aviation Fuel, Shane Kosir, Joshua Heyne, John Graham

Mechanical and Aerospace Engineering Faculty Publications

A machine learning framework has been developed to predict volume swell for 10 non-metallic materials submerged in neat compounds. The non-metallic materials included nitrile rubber, extracted nitrile rubber, fluorosilicone, low temp fluorocarbon, lightweight polysulfide, polythioether, epoxy (0.2 mm), epoxy (0.04 mm), nylon, and Kapton. Volume swell, a material compatibility concern, serves as a significant impediment for the minimization of the greenhouse gas emissions of aviation. Sustainable aviation fuels, the only near and mid-term solution to mitigating greenhouse gas emissions, are limited to low blend limits with conventional fuel due to material compatibility issues (i.e. O-ring swell). A neural network was …

Investigation Of Various Techniques For Controlled Void Formation In Fiberglass/Epoxy Composites, Donald A. Klosterman, Charles Browning, Issa Hakim, Kyle Lach

Chemical and Materials Engineering Faculty Publications

The effect of porosity in composite materials has been studied for years due to its deleterious effects on mechanical properties, especially matrix dominated properties. Currently there is an increasing use of composites in infrastructure worldwide, for example bridge components, residential and building structures, marine structures such as piers and docks, and large industrial chemical tanks. Most of these applications use fiberglass composites. Unfortunately, most of the published literature has focused on carbon fiber composites, in which fiber diameter and gas-fiber interactions are different than fiberglass composites. Therefore, the present study was undertaken to revisit the effect of porosity but specifically …

Work In Progress: Students' Perception Of Collaborative Online International Learning, Philip Appiah-Kubi, Jennifer Nichwitz

Engineering Management and Systems Faculty Publications

Teaching and learning in the digital age harness the opportunities created by internet technologies to distribute and learn various information all over the world. This is commonly referred to as connectivism, and it values the impact of stakeholder information appraisal skills, virtual and personal communication skills [1], and the sense of cultural awareness. Collaborative Online International Learning (COIL), a type of connectivism, typically involves instructors and students from at least two geographically and culturally distinct areas who collaborate virtually on a common project for four or more weeks.

The objective is to create a culturally diverse virtual environment where students …

Machine Learning Modeling Of Horizontal Photovoltaics Using Weather And Location Data, Christil Pasion, Torrey Wagner, Clay Koschnick, Steven Schuldt, Jada Williams, Kevin Hallinan

Mechanical and Aerospace Engineering Faculty Publications

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data-an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined …

Hybrid Machine Learning Architecture For Automated Detection And Grading Of Retinal Images For Diabetic Retinopathy, Barath Narayanan, Barath Narayanan, Russell C. Hardie, Manawaduge Supun De Silva, Nathaniel K. Kueterman

Electrical and Computer Engineering Faculty Publications

Purpose: Diabetic retinopathy is the leading cause of blindness, affecting over 93 million people. An automated clinical retinal screening process would be highly beneficial and provide a valuable second opinion for doctors worldwide. A computer-aided system to detect and grade the retinal images would enhance the workflow of endocrinologists. Approach: For this research, we make use of a publicly available dataset comprised of 3662 images. We present a hybrid machine learning architecture to detect and grade the level of diabetic retinopathy (DR) severity. We also present and compare simple transfer learning-based approaches using established networks such as AlexNet, VGG16, ResNet, …

Ensemble Malware Classification System Using Deep Neural Networks, Barath Narayanan Narayanan, Venkata Salini Priyamvada Davuluru

Electrical and Computer Engineering Faculty Publications

With the advancement of technology, there is a growing need of classifying malware programs that could potentially harm any computer system and/or smaller devices. In this research, an ensemble classification system comprising convolutional and recurrent neural networks is proposed to distinguish malware programs. Microsoft's Malware Classification Challenge (BIG 2015) dataset with nine distinct classes is utilized for this study. This dataset contains an assembly file and a compiled file for each malware program. Compiled files are visualized as images and are classified using Convolutional Neural Networks (CNNs). Assembly files consist of machine language opcodes that are distinguished among classes using …

Finding Patterns In Subsurface Using Bayesian Machine Learning Approach, Wang Hui

Civil and Environmental Engineering and Engineering Mechanics Faculty Publications

Stochastic simulation approaches and uncertainty quantification are usually adopted for gaining insight into variability in soil stratigraphy configurations. Previous investigations at geotechnical site characterization and interpretation can be broadly categorized into geostatistics- and process-based methods. On the other hand, modern site exploration techniques provide high-quality, dense datasets in physical spaces with high resolution, either directly from sensors (for example, cone penetration testing data) or indirectly from geophysical inversion (such as seismic inversion, electromagnetic induction inversion, and ground penetrating radar). In this work, anisotropy and heterogeneity are considered as possible patterns that inherently exist in the observations, and these are inferred …

Impact Of Team Formation Approach On Teamwork Effectiveness And Performance In An Upper-Level Undergraduate Chemical Engineering Laboratory Course, Erick S. Vasquez, Matthew J. Dewitt, Zachary J. West, Michael J. Elsass

Chemical and Materials Engineering Faculty Publications

This study focuses on the impact of team formation approach on teamwork effectiveness and performance spanning three years of instruction of the chemical engineering unit operations laboratory, which is an upper-level undergraduate laboratory course. Team formation approaches changed each year, and assessment tools, including peer-assessment, academic performance, and course evaluations, were employed to evaluate team performance. Approaches included three cases: instructor-selected teams based on GPA with the objective of a similar cumulative average GPA for each team, student self-selected teams, and a combination of self-selected teams with instructor-selected teams for a final experiment. For the third case, new teams were …

Organophosphorus-Hydrazides As Potential Reactive Flame Retardants For Epoxy, Alexander B. Morgan, Vladimir Benin, Donald A. Klosterman, Abdulhamid Bin Sulayman, Mustafa Mukhtar, Mary L. Galaska

Chemical and Materials Engineering Faculty Publications

For structural composites used in vehicles and aircraft, flame retardant chemistries which enhance char formation and reduce heat release are preferred. Phosphorus-based and phosphorus–nitrogen flame retardants for epoxies have been well studied to date, but phosphorus hydrazides have not been studied for their flame-retardant potential in epoxy. These hydrazides offer some novel structures and they can potentially offer a combination of vapor and condensed phase flame retardant action. A series of eight compounds were systematically investigated in this study as reactive flame retardants in a bisphenol F epoxy/aliphatic amine resin system at a level of 2.5 wt% phosphorus. Results suggest …

A Review Of A Collaborative Online International Learning, Philip Appiah-Kubi, Ebenezer Annan

Engineering Management and Systems Faculty Publications

Globalization has exacerbated the need for engineers who are capable of working in a cross-cultural environment. Multinational companies continuously seek for engineers who are interculturally competent and capable of conducting business successfully in a cross-cultural environment. However, the skills required to be successful in a multicultural environment are difficult to be taught in the traditional classroom. One of the most effective approaches to acquiring intercultural competency skills is through experiential learning. It is, therefore, not surprising that most colleges all over the world are devoting resources towards the internationalization of their classrooms and the campus community. This ensures that students …

Two-Stage Deep Learning Architecture For Pneumonia Detection And Its Diagnosis In Chest Radiographs, Barath Narayanan, Venkata Salini Priyamvada Davuluru, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

Approximately two million pediatric deaths occur every year due to Pneumonia. Detection and diagnosis of Pneumonia plays an important role in reducing these deaths. Chest radiography is one of the most commonly used modalities to detect pneumonia. In this paper, we propose a novel two-stage deep learning architecture to detect pneumonia and classify its type in chest radiographs. This architecture contains one network to classify images as either normal or pneumonic, and another deep learning network to classify the type as either bacterial or viral. In this paper, we study and compare the performance of various stage one networks such …

Corrections To ‘‘Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping’’, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

In the above article [1], Figure 2 was incorrect. Unfortunately, we mixed the color label of "CONV $\to $ BN $\to $ ReLu" and "Unpooling" in the CNN structure section of Figure 2. The color label of "CONV $\to $ BN $\to $ ReLu" should be orange while the color label of "Unpooling" should be green. Also, the word "Decoder" is misspelled. That same figure with the same error is also used for the graphic abstract. The corrected figure is given here. None of the sections in the figure is modified. The only change is in the color label of …

Mitosisnet: End-To-End Mitotic Cell Detection By Multi-Task Learning, Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Tj Bowen, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Mitotic cell detection is one of the challenging problems in the field of computational pathology. Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis. The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist. Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice. In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named"MitosisNet". MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region …

Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

Rising global temperatures over the past decades is directly affecting glacier dynamics. To understand glacier fluctuations and document regional glacier-state trends, glacier-boundary detection is necessary. Debris-covered glacier (DCG) mapping, however, is notoriously difficult using conventional geospatial technology methods. Therefore, in this research for automated DCG mapping, we evaluate the utility of a convolutional neural network (CNN), which is a deep learning feed-forward neural network. The CNN inputs include Landsat satellite images, an Advanced Land Observation Satellite (ALOS) digital elevation model (DEM) and DEM-derived land-surface parameters. Our CNN based deep-learning approach named GlacierNet was designed by appropriately choosing the type, number …

Ev Charging Behavior Analysis Using Hybrid Intelligence For 5g Smart Grid, Yi Shen, Wei Fang, Feng Ye, Michel Kadoch

Electrical and Computer Engineering Faculty Publications

With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select …

Ensemble Lung Segmentation System Using Deep Neural Networks, Redha A. Ali, Russell C. Hardie, Hussin K. Ragb

Electrical and Computer Engineering Faculty Publications

Lung segmentation is a significant step in developing computer-aided diagnosis (CAD) using Chest Radiographs (CRs). CRs are used for diagnosis of the 2019 novel coronavirus disease (COVID-19), lung cancer, tuberculosis, and pneumonia. Hence, developing a Computer-Aided Detection (CAD) system would provide a second opinion to help radiologists in the reading process, increase objectivity, and reduce the workload. In this paper, we present the implementation of our ensemble deep learning model for lung segmentation. This model is based on the original DeepLabV3+, which is the extended model of DeepLabV3. Our model utilizes various architectures as a backbone of DeepLabV3+, such as …

Patch-Based Gaussian Mixture Model For Scene Motion Detection In The Presence Of Atmospheric Optical Turbulence, Richard L. Van Hook, Russell C. Hardie

Electrical and Computer Engineering Faculty Publications

In long-range imaging regimes, atmospheric turbulence degrades image quality. In addition to blurring, the turbulence causes geometric distortion effects that introduce apparent motion in acquired video. This is problematic for image processing tasks, including image enhancement and restoration (e.g., superresolution) and aided target recognition (e.g., vehicle trackers). To mitigate these warping effects from turbulence, it is necessary to distinguish between actual in-scene motion and apparent motion caused by atmospheric turbulence. Previously, the current authors generated a synthetic video by injecting moving objects into a static scene and then applying a well-validated anisoplanatic atmospheric optical turbulence simulator. With known per-pixel truth …