Engineering Commons^™

Prediction Of Molecular Mutations In Diffuse Low-Grade Gliomas Using Mr Imaging Features, Zeina A. Shboul, James Chen, Khan M. Iftekharrudin

Electrical & Computer Engineering Faculty Publications

Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of low-grade gliomas using imaging features based on the updated classification. We introduce molecular (MGMT methylation, IDH mutation, 1p/19q co-deletion, ATRX mutation, and TERT mutations) prediction methods of low-grade gliomas with imaging. Imaging features are extracted from magnetic resonance imaging data and include texture features, fractal and multi-resolution fractal texture features, and volumetric features. Training models include …

Generation Of Large-Volume High-Pressure Plasma By Spatiotemporal Control Of Space Charge, Shirshak K. Dhali

Electrical & Computer Engineering Faculty Publications

Any attempt to scale pressure and volume of nonthermal plasma usually leads to instabilities due to the formation of localized space charge. The control of the plasma is limited by the discharge geometry, type of excitation, and gas composition. This article explores the possibility of controlling the space charge in a discharge with a spatially and temporally varying electric field. It is shown that a phase-staggered sinusoidal excitation to a set of conformal azimuthal electrodes in a cylindrical geometry leads to a traveling electric field. Simulations show that in space charge dominated transport, the charged species are dispersed both in …

Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

This guest editorial summarizes the Special Section on Machine Learning in Optics.

Semi-Supervised Adversarial Domain Adaptation For Seagrass Detection Using Multispectral Images In Coastal Areas, Kazi Aminul Islam, Victoria Hill, Blake Schaeffer, Richard Zimmerman, Jiang Li

Electrical & Computer Engineering Faculty Publications

Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from …

Bioenergetic Functions In Subpopulations Of Heart Mitochondria Are Preserved In A Non-Obese Type 2 Diabetes Rat Model (Goto-Kakizaki), Nicola Lai, C. M. Kummitha, F. Loy, R. Isola, C. L. Hoppel

Electrical & Computer Engineering Faculty Publications

A distinct bioenergetic impairment of heart mitochondrial subpopulations in diabetic cardiomyopathy is associated with obesity; however, many type 2 diabetic (T2DM) patients with high-risk for cardiovascular disease are not obese. In the absence of obesity, it is unclear whether bioenergetic function in the subpopulations of mitochondria is affected in heart with T2DM. To address this issue, a rat model of non-obese T2DM was used to study heart mitochondrial energy metabolism, measuring bioenergetics and enzyme activities of the electron transport chain (ETC). Oxidative phosphorylation in the presence of substrates for ETC and ETC activities in both populations of heart mitochondria in …

Thermoelectric Porous Mof Based Hybrid Materials, Engelbert Redel, Helmut Baumgart

Electrical & Computer Engineering Faculty Publications

Porous hybrid materials and MOF (Metal-Organic-Framework) films represent modern designer materials that exhibit many requirements of a near ideal and tunable future thermoelectric (TE) material. In contrast to traditional semiconducting bulk TE materials, porous hybrid MOF templates can be used to overcome some of the constraints of physics in bulk TE materials. These porous hybrid systems are amenable for simulation and modeling to design novel optimized electron-crystal phonon-glass materials with potentially very high ZT (figure of merit) numbers. Porous MOF and hybrid materials possess an ultra-low thermal conductivity, which can be further modulated by phonon engineering within their complex porous …

A Prototype Virginia Ground Station Network, Zach Leffke, Jonathon Black, Kevin Shinpaugh, Ian Harnett, Bryce Clegg, Nick Angle, Chris Goyne, Connor Segal, William 'Trace' Lacour, Mike Mcpherson, Dimitrie Popescu, Samuel Jensen, Jason Harris, Mary Sandy, Mike Miller

Electrical & Computer Engineering Faculty Publications

This paper provides a detailed technical description of a prototype ground station network, the Virginia Ground Station Network (VGSN), developed for the Virginia Cubesat Constellation (VCC) mission. Virginia Tech (VT), University of Virginia (UVA), and Old Dominion University (ODU) have each constructed ground stations to communicate with their respective VCC spacecraft. Initially, each university was responsible for commanding its own spacecraft via its own ground station. As the mission progressed, it was decided to network the ground stations and operations centers together to provide backup communications capability for the overall mission. The NASA Wallops Flight Facility (WFF) UHF smallsat ground …

Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos

Electrical & Computer Engineering Faculty Publications

Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer …

Flood Detection Using Multi-Modal And Multi-Temporal Images: A Comparative Study, Kazi Aminul Islam, Mohammad Shahab Uddin, Chiman Kwan, Jiang Li

Electrical & Computer Engineering Faculty Publications

Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that …

Context Aware Deep Learning For Brain Tumor Segmentation, Subtype Classification, And Survival Prediction Using Radiology Images, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction …

Real-Time Optimization Of Anti-Reflective Coatings For Cigs Solar Cells, Grace Rajan, Shankar Karki, Robert W. Collins, Nikolas J. Podraza, Sylvain Marsillac

Electrical & Computer Engineering Faculty Publications

A new method combining in-situ real-time spectroscopic ellipsometry and optical modeling to optimize the thickness of an anti-reflective (AR) coating for Cu(In,Ga)Se₂ (CIGS) solar cells is described and applied directly to fabricate devices. The model is based on transfer matrix theory with input from the accurate measurement of complex dielectric function spectra and thickness of each layer in the solar cell by spectroscopic ellipsometry. The AR coating thickness is optimized in real time to optically enhance device performance with varying thickness and properties of the constituent layers. Among the parameters studied, we notably demonstrate how changes in thickness of …

Effect Of Layer Thickness On Structural, Morphological And Superconducting Properties Of Nb3Sn Films Fabricated By Multilayer Sequential Sputtering, M. N. Sayeed, U. Pudasaini, C. E. Reece, G. V. Eremeev, H. E. Elsayed-Ali

Electrical & Computer Engineering Faculty Publications

Superconducting Nb₃Sn films can be synthesized by controlling the atomic concentration of Sn. Multilayer sequential sputtering of Nb and Sn thin films followed by high temperature annealing is considered as a method to fabricate Nb₃Sn films, where the Sn composition of the deposited films can be controlled by the thickness of alternating Nb and Sn layers. We report on the structural, morphological and superconducting properties of Nb₃Sn films fabricated by multilayer sequential sputtering of Nb and Sn films on sapphire substrates followed by annealing at 950 °C for 3 h. We have investigated the …

Priority Based Routing And Link Scheduling For Cognitive Radio Networks, Peng Jiang, Mitchell Zhou, Song Wen

Electrical & Computer Engineering Faculty Publications

To address the challenges caused by the time-varying rate requirement for multimedia communication sessions, we propose a Priority Based Routing and link Scheduling (PBRS) scheme for multi-hop cognitive radio networks. The objective is to minimize disruption to communication sessions due to channel switching as well as to minimize network resource consumption for multimedia applications based on a prioritized routing and resource allocation scheme. PBRS includes a priority based optimization formulation and an efficient algorithm to solve the problem. The main idea is to allocate the available resource to different types of services with their Quality of Experience (QoE) expectation as …

Low Temperature Plasma Jets: Characterization And Biomedical Applications, Mounir Laroussi

Electrical & Computer Engineering Faculty Publications

No abstract provided.

Efficacy Of Radiomics And Genomics In Predicting Tp53 Mutations In Diffuse Lower Grade Glioma, Zeina A. Shboul, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

An updated classification of diffuse lower-grade gliomas is established in the 2016 World Health Organization Classification of Tumors of the Central Nervous System based on their molecular mutations such as TP53 mutation. This study investigates machine learning methods for TP53 mutation status prediction and classification using radiomics and genomics features, respectively. Radiomics features represent patients' age and imaging features that are extracted from conventional MRI. Genomics feature is represented by patients’ gene expression using RNA sequencing. This study uses a total of 105 LGG patients, where the patient dataset is divided into a training set (80 patients) and testing set …

Deep Learning With Context Encoding For Semantic Brain Tumor Segmentation And Patient Survival Prediction, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

One of the most challenging problems encountered in deep learning-based brain tumor segmentation models is the misclassification of tumor tissue classes due to the inherent imbalance in the class representation. Consequently, strong regularization methods are typically considered when training large-scale deep learning models for brain tumor segmentation to overcome undue bias towards representative tissue types. However, these regularization methods tend to be computationally exhaustive, and may not guarantee the learning of features representing all tumor tissue types that exist in the input MRI examples. Recent work in context encoding with deep CNN models have shown promise for semantic segmentation of …

Generative Adversarial Networks For Visible To Infrared Video Conversion, Mohammad Shahab Uddin, Jiang Li, Chiman Kwan (Ed.)

Electrical & Computer Engineering Faculty Publications

Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) …

Efficient Incorporation Of Fatigue Damage Constraints In Wind Turbine Blade Optimization, Bryce Ingersoll, Andrew Ning

Faculty Publications

Wind turbine design is a challenging multidisciplinary optimization problem, where the aerodynamic shapes, structural member sizing, and material composition must all be determined and optimized. Some previous blade design methods incorporate static loading with an added safety factor to account for dynamic effects. Others incorporate dynamic loading, but in general limit the evaluation to a few design cases. By not fully incorporating the dynamic loading of the wind turbine, the final turbine blade design is either too conservative by overemphasizing the dynamic effects or infeasible by failing to adequately account for these effects. We propose an iterative method that estimates …

Proactive Content Caching In Future Generation Communication Networks: Energy And Security Considerations, Muhammad Ishtiaque Aziz Zahed

Theses: Doctorates and Masters

The proliferation of hand-held devices and Internet of Things (IoT) applications has heightened demand for popular content download. A high volume of content streaming/downloading services during peak hours can cause network congestion. Proactive content caching has emerged as a prospective solution to tackle this congestion problem. In proactive content caching, data storage units are used to store popular content in helper nodes at the network edge. This contributes to a reduction of peak traffic load and network congestion.

However, data storage units require additional energy, which offers a challenge to researchers that intend to reduce energy consumption up to 90% …

Pharmaceutical Manufacturing In The 21st Century: Identification And Analysis Of Key Elements, Robert N. Mills

Honors Program: Senior Projects (Public)

The availability of quality pharmaceutical products impacts the lives of the global population on a daily basis. The importance of product availability and quality within pharmaceutical manufacturing requires that quality systems put into place are capable of accurately and robustly capturing the quality compliance, or lack thereof, of the various stages of production. This review intends to analyze current expert-recommended best practices within key elements of a pharmaceutical manufacturing quality control system and provide recommendations throughout. The quality elements discussed include Quality by Design method validation, necessary equipment determination, and equipment qualification, followed by documentation control and data flow.

Control Of Energy Storage Utilisation For A Building Integrated Microgrid Using Multi-Objective Metaheuristic Optimisation Methods, Quang An Phan

Theses

The aim of this thesis was to develop and analyse optimisation strategies for commercial buildings with integrated microgrids, in order to find optimal trade-offs between maximising profitability and facilitating renewable energy from the national power grid. The continued proliferation of microgrids, as well as the increase in electricity produced by renewable energy sources on the Irish national grid has necessitated the requirement for these strategies. Models to simulate the performance of a photovoltaic system, wind turbine and battery bank were developed and validated. The most suitable optimisation algorithm to generate an optimal charge/discharge rate schedule for a battery bank was …

Viability Analysis Of Tralee’S District Heating System, Ed O’Connor

Theses

At present a large proportion of domestic and commercial heating systems in Ireland are fossil fuel based. Decarbonisation of Irelands heating system would mean a shift away from traditional fossil fuels, and a step towards more renewable and sustainable thermal energy systems. This thesis outlines a viability analysis of one of the options available to assist in the achievement of Ireland’s thermal energy goals and carbon emissions targets. The option considered in this proposal is the development of a wood chip fuelled District Heating System (DHS) to cater for the thermal needs of the town of Tralee. Several alternatives DHS …

ระบบระบุตำแหน่งภายในอาคารสำหรับสมาร์ตโฟนด้วยการใช้เทคนิคลายนิ้วมือของสัญญาณวายฟายเชิงกำหนด, ธีรภัทร์ วงศ์สุธีรา

Chulalongkorn University Theses and Dissertations (Chula ETD)

การระบุตำแหน่งภายในอาคารด้วยการใช้เทคนิคลายนิ้วมือของสัญญาณวายฟายมักจะทำงานอย่างไม่มีประสิทธิภาพเมื่อนำไปใช้งานในพื้นที่ขนาดใหญ่ นอกจากนี้ การระบุตำแหน่งดังกล่าวมักพบกับปัญหาตำแหน่งที่ไม่ตรงกัน ซึ่งทำให้เกิดความไม่แม่นยำในการระบุตำแหน่ง ยิ่งกว่านั้นการเปลี่ยนแปลงของสภาพแวดล้อมก็สามารถลดประสิทธิภาพโดยรวมของระบบได้เช่นกัน เพื่อที่จะแก้ปัญหาเหล่านี้ งานวิจัยชิ้นนี้ได้นำเสนอ ระบบระบุตำแหน่งภายในอาคารสำหรับสมาร์ตโฟนด้วยการใช้เทคนิคลายนิ้วมือของสัญญาณวายฟายเชิงกำหนด ระบบนี้ประกอบด้วยกันทั้งหมด 3 ส่วน ส่วนที่หนึ่งคืออัลกอริทึมสำหรับจัดหมวดหมู่พื้นที่ โดยส่วนนี้จะจำแนกคำร้องขอจากผู้ใช้งาน ว่าถูกส่งมาจากภายนอกอาคารหรือภายในอาคารใด เพื่อกรองคำร้องขอที่ถูกส่งมาจากพื้นที่ภายนอกอาคารหรือพื้นที่ที่ระบบไม่ได้ครอบคลุมออกไป ส่วนที่สองคืออัลกอริทึมระบุตำแหน่งภายในอาคาร ส่วนนี้จะใช้ข้อมูลจากส่วนแรก ในการลดขอบเขตการค้นหาลายนิ้วมือของสัญญาณลง ทำให้สามารถลดระยะเวลาในการประมวลผลเพื่อหาตำแหน่งที่แน่นอนได้อย่างมหาศาล นอกจากนี้อัลกอริทึมในส่วนนี้จะคำนึงถึงปัญหาตำแหน่งที่ไม่ตรงกัน เพื่อลดผลกระทบจากปัญหาดังกล่าว ส่วนที่สามคืออัลกอริทึมตรวจจับบีเอสเอสไอดีที่หายไปจากคำร้องขอจากผู้ใช้งานและปรับปรุงฐานข้อมูล เพื่อให้ระบบสามารถปรับตัวกับสภาวะแวดล้อมที่เปลี่ยนไปได้ จากผลการทดลอง ระบบที่นำเสนอสามารถจัดหมวดหมู่พื้นที่ ระบุตำแหน่งภายในอาคารและตรวจจับบีเอสเอสไอดีที่หายไปได้อย่างแม่นยำ โดยอัลกอริทึมสำหรับจัดหมวดหมู่พื้นที่สามารถลดระยะเวลาในการประมวลผลอย่างเห็นได้ชัดเมื่อเปรียบเทียบกับงานวิจัยที่ผ่านมา นอกจากนี้อัลกอริทึมตรวจจับบีเอสเอสไอดีที่หายไปสามารถเพิ่มประสิทธิภาพให้กับทั้งระบบที่นำเสนอและงานวิจัยที่ผ่านมาได้อย่างมาก

Development Of Radiation Transport Techniques For Modelling A High-Resolution Multi-Energy Photon Emission Tomography System, Seth Kilby

Doctoral Dissertations

”Nondestructive characterization techniques such as gamma tomography represent powerful tools for the analysis and quantification of physical defects and radionuclide concentrations within nuclear fuel forms. Gamma emission tomography, in particular, has the ability to utilize the inherent radiation within spent nuclear fuel to provide users with information about the migration and concentration of fission and activation products within the fuel form. Idaho National Laboratory is interested in using this technology to analyze new nuclear fuel forms for potential use in next generation nuclear reactors. In this work, two aspect of the system are analyzed. The first is a semi-analytic radiation …

Development Of Tools For Water Management In The Hatra Watershed (Northwestern Iraq) Using Satellite Technologies, Majid S. Mohamod

Doctoral Dissertations

“All around the world the demand for water is increasing, especially in arid and semi-arid regions, including Iraq which subject to continuous desertification that is worsening, more importantly the Jezira region in northwestern Iraq. Thus, it’s crucial to have a better strategy for water management. One of these strategies is to promote groundwater recharge for restoring the aquifer depletion. The successful groundwater recharge is limited by some potential data such as the annual water budge and precipitation measurements. The atomospheric and hydrological observations are limited by sparse population which tends to be less in arid and semi-arid regions. Therefore, an …

Integrating Resilience Into Military Infrastructure Mission Assurance Assessments And Decision Making, John Richards

Doctoral Dissertations

“This research created the Mission Assurance Resilience Matrix, a decision framework that integrates existing infrastructure assessment methods with emerging resilience research to model resilience under uncertainty as part of a detailed infrastructure management system. This framework enables military decision makers to easily visualize deficiencies in infrastructure resilience and assess where to most efficiently allocate resources. This research further extends results by including modules on training and education as a component of the scope of work.

There are three significant contributions of this research. The first identifies the gaps of how and where modeling under uncertainty, infrastructure systems management, and resilient …

Portable Leak Test System, Tyler Pulli, Joseph Arnell, Luke Osterhout, Dustyn Roberts

Mechanical and Aerospace Engineering

Idaho National Laboratories requires an airtight seal on transportation casks to ship radioactive material. To ensure that the cask is air-tight, a system needs to be designed to measure whether air is leaking out of the seal and determines if the cask passes or fails.

Hobbit Caves Access Bridge: Logan, Ut, Bailey Mabey, Katrina Mccoll, Hayley Sharkey, Spencer Tanner, Allison Valencia

Civil and Environmental Engineering

The National Forest Service proposed the reconstruction of a pedestrian bridge leading to the Logan Canyon Hobbit Caves. At the proposed location the previous bridge foundation failed leading to unsafe conditions.

Currently visitors must cross the creek on foot to access the Hobbit Caves. This disrupts the local habitat. This also creates a hazard for the public due to high variance in flow rate and water level of the creek. A new location was selected and the design criteria was created based upon this new location.

Test Dummy Positioning For Autoliv Ogden, Ut, Dianne Althouse, John Garrett, Ty Johnston, Ridge Jones

Mechanical and Aerospace Engineering

• The Team shall deliver a proof of concept prototype to Autoliv for its use in positioning test dummies for Out-of-Position (OOP) airbag testing

• This is to improve set-up timing and accuracy without operator to operator variation.

• The device should work for the placing of three-year-old, six- year-old, and 5th percentile female test dummies.

• There are two OOP: head on IP (instrumentation panel) and chest on IP.

Recycling Warehouse, Hunter Carter, John Jacobsen, Brady Layton, Ariel Silva, Wyatt Wray

Civil and Environmental Engineering

Valley Recycling is a recycling company located in Cache Valley. One of the main things they recycle is scrap metal that is sorted out into ferrous and nonferrous metals. The current focus is on the building that the nonferrous metals are sorted, cleaned, and processed in. The current building where this work is done does not meet the needs for the required work.