Engineering Commons^™

Design And Test Of Novel Vehicle-To-Everything Packet For Sensor Arrays In Under Roadway Deployment, Marquis Brown

Symposium of Student Scholars

Title: Design and Test of Novel Vehicle-to-Everything Packet for Sensor Arrays in Under Roadway Deployment

Poster Presentation Undergraduate Student(s): Hakeem Wilson, Marquis Brown Research Mentor(s): Dr. Billy Kihei

Vehicle-to-Everything (V2X) communications is a burgeoning technology that will enable safer, greener, and more productive usage of roadways. In the V2X paradigm, all equipped vehicles and infrastructure can communicate with each other in real-time. By utilizing V2X communications as an Internet-of-Things gateway, a sensor array installed under roadways, allows real-time monitoring of road conditions which increases overall road safety and road maintenance cost. In our research, we have utilized the WAVE Short …

Detecting Primitive Jamming Attacks Using Machine Learning In Vehicle-To-Everything Networks​, Hakeem Wilson

Symposium of Student Scholars

Vehicle-to-Everything communications (V2X) is gaining additional ground as an upcoming ad hoc safety network. In V2X, basic safety messages are used for exchanging critical information between vehicles at a set broadcast rate. However, jamming attacks on the safety spectrum could deny V2X radios the ability to save lives on the roadway. This preliminary work analyzes two types of primitive jamming attacks performed on target V2X devices. Lab results reveal that V2X networks are easily susceptible to jamming attacks, due to all V2X standards lacking a requirement to detect/mitigate jamming. To avert this threat and promote safety of life on the …

Novel Vehicle-To-Everything Rollover Antenna, Aya Alazzawi

Symposium of Student Scholars

Rollover Vehicle-to-Everything Antenna

Poster Presentation Undergraduate Student: Aya Alazzawi

Research Mentor(s): Dr. Billy Kihei

Soft robotics is a new field of engineering ready to be explored, because of its use of unconventional materials it can withstand copious amounts of stress which makes it ideal for applications involving vehicular accidents. In this groundbreaking work, we attempt to integrate soft robotics technologies with Vehicle-to-Everything (V2X) communications. Specifically, we introduce the preliminary planning stages for creating a novel V2X rollover antenna that can reduce road deaths and provide more reliable accident data. An up-right roof-mounted antenna could be damaged during a roll-over accident …

Motion Detection On A Frequency Jumping Rfid Signal ​ Using Machine Learning, Trevor Stanca

Symposium of Student Scholars

Radio Frequency IDentification (RFID) is a well-known technology in wireless communication. It is hypothesized that the capabilities of RFID can be extended by reading an ID number and detecting movement around the reader during the read. Following regulatory standards, this study presents the foundation for a software defined RFID reader that may simultaneously detects and classifies the type of movement during the interrogation operation. A frequency hopping signal in unlicensed 5.8GHz can be analyzed using machine learning to extract a Doppler profile. We effectively collect information about an object through RFID by potentially detecting the speed of the object or …

The Influence Of Mechanical Properties Associated With Exercise Equipment Padding On Maximizing Performance And Minimizing Potential Soft-Tissue Injury When Performing High-Intensity Weight Lifting Exercises, Robert Hewston

Symposium of Student Scholars

The intention of this study is the measurement of mechanical properties such as resilience and compressibility associated with exercise equipment padding in order to determine the ability to minimize potential soft-tissue injuries while performing high-intensity weight lifting exercises. ASTM standards were used to test the resilience and compressibility of five different exercise equipment padding specimens. The specimens consisted of standard bench press foam, composite foam, polyethylene foam, a single yoga mat foam, and double yoga mat foam. The resilience and compressibility are tested to determine the amount of deformation each sample can experience and the level of energy returned back …

A Special Slab-Wall Connection In Post-Tensioned Concrete, Mohammad Jonaidi

Symposium of Student Scholars

Post tensioned concrete is a widely used method in the construction of the slab of high-rise buildings, podium (transferred) slab of wood-framed structures, bridges and so on. The effects of slab shortening due to concrete shrinkage and post-tensioning compression should be considered in the design of the structure. Various connections, between slab and wall, are used in the industry. However, the behavior of a special connection which consists of steel reinforcement dowels wrapped with compressible material has not been investigated. This research study investigates a case that requires a permanent slip detail in which the dowel between the shear wall …

Long Distance Li-Fi, Adeyemi Fagbemi

Symposium of Student Scholars

Wi-Fi has been in existence since 1998 providing an increase in the amount of data transmitted and generated by machines. With the growing market demand for network bandwidth, it will make it nearly impossible for typical radio frequency (RF) technologies like Wi-Fi to supply the resources needed for the data transmission. As a complementary solution, Li-Fi was introduced in 2011 as a method of data transfer through the use light of light sources. Li-Fi employs the use of visible light spectrum which has 1000 more times the bandwidth of typical RF spectrums. This study investigates the distance to which this …

Lifting Flow Over A Cylinder As A Lift System, Kyle Mello

Symposium of Student Scholars

The goal of this project is to conceptually design a lift system consisting of rotating cylinders that may improve certain flight characteristics when compared to conventional wings for a typical two passenger airplane. The functions of flaps, ailerons, lift to weight ratio and structural responses are considered. The cylindrical model is developed within CFD simulation software such that it generates the same lift produced by the wings of a Cessna 172 wings at cruising speed. Incompressible flow theoretical solutions are initially shown to compare favorably with the computational predictions for elementary cylindrical designs.

The rotating cylinder aircraft generates the same …

Pulmon-C: A Real-Time Monitoring Framework Of Pulmonary Function, Md Saiful Islam, Maria Valero, Shahriar Hossain

Symposium of Student Scholars

This project will develop PulMon-C, a real-time monitoring framework of pulmonary function to diagnose COVID-19 patients who are being self-quarantined at home. The tool will identify anomalies in breathe rate and predict pulmonary deterioration to raise alert for immediate actions. The uniqueness of the tool is using non-invasive sensors placed under-mattress that are able to communicate data about the respiratory signal. The customer segment of PulMon-C will be the diagnosed COVID-19 patients and healthcare providers. PulMon-C will assist with the remote monitoring of COVID-19 patients as an urgent need in the USA and will bring larger impact in delivering …

Oral Minimal Model For Gestational Diabetes, Sarah Peters

Symposium of Student Scholars

Gestational diabetes is one of the most common issues that a pregnant woman encounters that could result in harm to both the woman and child. Due to this issue, the woman’s glucose and insulin levels should be carefully monitored throughout her pregnancy to assess the need for prescribed diabetic medication to help regulate those levels. In this research, the objectives is to develop a MATLAB computer program of the Oral Minimal Model, which is a model that can be used to estimate a person’s insulin sensitivity from an oral glucose tolerance test (OGTT) where plasma glucose and insulin levels are …

Sterilizing Masks With Uvc Light, Christopher Focht

Symposium of Student Scholars

This experiment focused on the construction and testing of a UVC light box and the treatment of facemasks with UVC light. Exposure rates were calculated and measured to ensure efficacy of the light box. Masks were contaminated with bacteria and exposed for set time intervals, after which the bacteria were collected, cultured, and counted. Early results show that the masks in the box had no bacteria survive past ten seconds. This research is ongoing with different bacteria strains and mask types.

Source Localization Of Electroencephalogram (Eeg) Waves With Convolutional Neural Network, Terence Onyewuenyi

Symposium of Student Scholars

This paper investigates the use of deep learning as a means for quantification and source localization of prioritizing electroencephalogram (EEG) waves for the purpose of detecting different eye states of human subjects. The Convolutional Deep Learning tool is trained to recognize EEG reading corresponding to a set of different eye movements as generated by watching different action scenes. The results also predict whether the subjects' eyes are open or closed. Source localization is performed next on the EEG data to focus on the different EEG components which primarily contribute to the activity. This was done by using a convolutional neural …

Short-Term Crash Risk Prediction Considering Proactive, Reactive, And Driver Behavior Factors, Sina Darban Khales

Dissertations

Providing a safe and efficient transportation system is the primary goal of transportation engineering and planning. Highway crashes are among the most significant challenges to achieving this goal. They result in significant societal toll reflected in numerous fatalities, personal injuries, property damage, and traffic congestion. To that end, much attention has been given to predictive models of crash occurrence and severity. Most of these models are reactive: they use the data about crashes that have occurred in the past to identify the significant crash factors, crash hot-spots and crash-prone roadway locations, analyze and select the most effective countermeasures for reducing …

Colloidal Quantum Dot (Cqd) Based Mid-Wavelength Infrared Optoelectronics, Shihab Bin Hafiz

Dissertations

Colloidal quantum dot (CQD) photodetectors are a rapidly emerging technology with a potential to significantly impact today’s infrared sensing and imaging technologies. To date, CQD photodetector research is primarily focused on lead-chalcogenide semiconductor CQDs which have spectral response fundamentally limited by the bulk bandgap of the constituent material, confining their applications to near-infrared (NIR, 0.7-1.0 um) and short-wavelength infrared (SWIR, 1-2.5 um) spectral regions. The overall goal of this dissertation is to investigate a new generation of CQD materials and devices that advances the current CQD photodetector research toward the technologically important thermal infrared region of 3-5 ?m, known as …

Advances In Modeling Gas Adsorption In Porous Materials For The Characterization Applications, Max A. Maximov

Dissertations

The dissertation studies methods for mesoporous materials characterization using adsorption at various levels of scale and complexity. It starts with the topic introduction, necessary notations and definitions, recognized standards, and a literature review.

Synthesis of novel materials requires tailoring of the characterization methods and their thorough testing. The second chapter presents a nitrogen adsorption characterization study for silica colloidal crystals (synthetic opals). These materials have cage-like pores in the range of tens of nanometers. The adsorption model can be described within a macroscopic approach, based on the Derjaguin-Broekhoff-de Boer (DBdB) theory of capillary condensation. A kernel of theoretical isotherms is …

Gradient Free Sign Activation Zero One Loss Neural Networks For Adversarially Robust Classification, Yunzhe Xue

Dissertations

The zero-one loss function is less sensitive to outliers than convex surrogate losses such as hinge and cross-entropy. However, as a non-convex function, it has a large number of local minima, andits undifferentiable attribute makes it impossible to use backpropagation, a method widely used in training current state-of-the-art neural networks. When zero-one loss is applied to deep neural networks, the entire training process becomes challenging. On the other hand, a massive non-unique solution probably also brings different decision boundaries when optimizing zero-one loss, making it possible to fight against transferable adversarial examples, which is a common weakness in deep learning …

Multi-Stage Stochastic Optimization And Reinforcement Learning For Forestry Epidemic And Covid-19 Control Planning, Sabah Bushaj

Dissertations

This dissertation focuses on developing new modeling and solution approaches based on multi-stage stochastic programming and reinforcement learning for tackling biological invasions in forests and human populations. Emerald Ash Borer (EAB) is the nemesis of ash trees. This research introduces a multi-stage stochastic mixed-integer programming model to assist forest agencies in managing emerald ash borer insects throughout the U.S. and maximize the public benets of preserving healthy ash trees. This work is then extended to present the first risk-averse multi-stage stochastic mixed-integer program in the invasive species management literature to account for extreme events. Significant computational achievements are obtained using …

Towards Adversarial Robustness With 01 Lossmodels, And Novel Convolutional Neural Netsystems For Ultrasound Images, Meiyan Xie

Dissertations

This dissertation investigates adversarial robustness with 01 loss models and a novel convolutional neural net systems for vascular ultrasound images.

In the first part, the dissertation presents stochastic coordinate descent for 01 loss and its sensitivity to adversarial attacks. The study here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required.

In the second part, this dissertation proposes sign activation network with a novel gradient-free stochastic coordinate descent algorithm and its ensembling model. The study here finds that the ensembling model gives a high minimum distortion (as measured by …

Stochastic Programming And Agent-Based Simulation Approaches For Epidemics Control And Logistics Planning, Xuecheng Yin

Dissertations

This dissertation addresses the resource allocation challenges of fighting against infectious disease outbreaks. The goal of this dissertation is to formulate multi-stage stochastic programming and agent-based models to address the limitations of former literature in optimizing resource allocation for preventing and controlling epidemics and pandemics. In the first study, a multi-stage stochastic programming compartmental model is presented to integrate the uncertain disease progression and the logistics of resource allocation to control a highly contagious infectious disease. The proposed multi-stage stochastic program, which involves various disease growth scenarios, optimizes the distribution of treatment centers and resources while minimizing the total expected …

Modeling Dewetting, Demixing, And Thermal Effects In Nanoscale Metal Films, Ryan Howard Allaire

Dissertations

Thin film dynamics, particularly on the nanoscale, is a topic of extensive interest. The process by which thin liquids evolve is far from trivial and can lead to dewetting and drop formation. Understanding this process involves not only resolving the fluid mechanical aspects of the problem, but also requires the coupling of other physical processes, including liquid-solid interactions, thermal transport, and dependence of material parameters on temperature and material composition. The focus of this dissertation is on the mathematical modeling and simulation of nanoscale liquid metal films, which are deposited on thermally conductive substrates, liquefied by laser heating, and subsequently …

Modeling And Design Optimization For Membrane Filters, Yixuan Sun

Dissertations

Membrane filtration is widely used in many applications, ranging from industrial processes to everyday living activities. With growing interest from both industrial and academic sectors in understanding the various types of filtration processes in use, and in improving filter performance, the past few decades have seen significant research activity in this area. Experimental studies can be very valuable, but are expensive and time-consuming, therefore theoretical studies offer potential as a cost-effective and predictive way to improve on current filter designs. In this work, mathematical models, derived from first principles and simplified using asymptotic analysis, are proposed for: (1) pleated membrane …

Shale Softening Based On Pore Network And Laboratory Investigations, Di Zhang

Dissertations

This dissertation consists of two major parts: Firstly, experimental investigation of four major shale softening mechanisms and quantifications of structural parameters. Secondly, numerical simulations of nano-scale flow behaviors using the previous experiments determined parameters based on modified pore network modeling.

Hydraulic fracturing is widely applied to economical gas production from shale reservoirs. Still, the gradual swelling of the clay micro/nano-pores due to retained fluid from hydraulic fracturing causes a gradual reduction of gas production. Four different gas-bearing shale samples are investigated to quantify the expected shale swelling due to hydraulic fracturing. These shale samples are subject to heated deionized (DI) …

Data-Driven Learning For Robot Physical Intelligence, Leidi Zhao

Dissertations

The physical intelligence, which emphasizes physical capabilities such as dexterous manipulation and dynamic mobility, is essential for robots to physically coexist with humans. Much research on robot physical intelligence has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has limitations handling dynamic skills and compound actions. In this dissertation, a composite learning scheme which goes beyond LfD and integrates robot learning from human definition, demonstration, and evaluation is proposed. This method tackles …

Feedstock Powders For Reactive Structural Materials, Daniel Hastings

Dissertations

Metals as fuels have higher energy density per unit mass or volume compared to any hydrocarbon. At the same time, metals are common structural materials. Exploring metals as reactive structural materials may combine their high energy density with attractive mechanical properties. Preparing such materials, however, is challenging. Requirements that need to be met for applications include density, strength, and stability enabling the component to sustain the structure during its desired operation; added requirements are the amount and rate of the energy release upon impact or shock. Powder technology and additive manufacturing are approaches considered for design of reactive structural materials. …

Analysis Of Container Throughput: Demand Forecast And Seaport Competitiveness Assessment, Hussain Talat Sulaimani

Dissertations

Seaports play a crucial role in the container industry, where they act as important nodes in the transport chain to facilitate international trade. In a competitive market, port capacity plays a significant role in defining its competitive position to attract demand and avoid congestion. Failing to provide suitable capacity results in the loss of market share. Therefore, port decision-makers face the challenge of maintaining and developing suitable port facilities to provide efficient services to port users. One of the aspects that decision-makers consider in the planning and development process is analyzing container demand. The analysis of container demand can be …

Towards Understanding The Role Of Central Processing In Release From Masking, Nima Alamatsaz

Dissertations

People with normal hearing have the ability to listen to a desired target sound while filtering out unwanted sounds in the background. However, most patients with hearing impairment struggle in noisy environments, a perceptual deficit which current hearing aids and cochlear implants cannot resolve. Even though peripheral dysfunction of the ears undoubtedly contribute to this deficit, surmounting evidence has implicated central processing in the inability to detect sounds in background noise. Therefore, it is essential to better understand the underlying neural mechanisms by which target sounds are dissociated from competing maskers. This research focuses on two phenomena that help suppress …

Constructive Solution Methodologies To The Capacitated Newsvendor Problem And Surrogate Extension, Pinyuan Shan

Dissertations

The newsvendor problem is a single-period stochastic model used to determine the order quantity of perishable product that maximizes/minimizes the profit/cost of the vendor under uncertain demand. The goal is to fmd an initial order quantity that can offset the impact of backlog or shortage caused by mismatch between the procurement amount and uncertain demand. If there are multiple products and substitution between them is feasible, overstocking and understocking can be further reduced and hence, the vendor's overall profit is improved compared to the standard problem. When there are one or more resource constraints, such as budget, volume or weight, …

Towards Quality By Design (Qbd) Of Pharmaceutical Oral Films Loaded With Poorly Watersoluble Drugs, Eylul Cetindag

Dissertations

Oral films are an emerging drug delivery dosage form with numerous advantages such as ease of handling, the possibility to circumvent the first-pass metabolism, better patient compliance, capability for continuous manufacturing, and as a platform for personalized medicine. As films are different from other final dosages by their dimensions and their matrix form, further research is required to better understand the impact of excipient and drug on product performance to assure a consistent and good quality product. Therefore, this dissertation aims to contribute towards the processability and manufacturability of films by examining the impact of the materials and processes. First, …

Learning Of Radar System For Target Detection, Wei Jiang

Dissertations

In this dissertation, the problem of data-driven joint design of transmitted waveform and detector in a radar system is addressed. Two novel learning-based approaches to waveform and detector design are proposed based on end-to-end training of the radar system. The first approach consists of alternating supervised training of the detector for a fixed waveform and reinforcement learning of the transmitter for a fixed detector. In the second approach, the transmitter and detector are trained simultaneously. Various operational waveform constraints, such as peak-to-average-power ratio (PAR) and spectral compatibility, are incorporated into the design. Unlike traditional radar design methods that rely on …

Exploring Fused Deposition Modeling (Fdm) Three-Dimensional Printing Tablet Design Options For Pharmaceutical Dosage Forms, Guluzar Gorkem Buyukgoz

Dissertations

This dissertation examines the use of Fused Deposition Modeling (FDM) based three-dimensional (3D) printing approach for developing patient-specific dosage forms and addressing related technical challenges in such drug delivery systems. The first main objective is to explore pharmaceutical tablet design options using novel FDM 3D printing technology as the drug delivery platform such that drug form and tablet properties are tailored by considering patient age-specific formulations and dissolution control. Of the five different design options, two proposed options meet the main objective of providing similar drug release, whereas the popular option of fixed drug concentration but differing tablet size could …