Digital Commons Network^™

Security And Privacy Analysis Of Wearable Health Device, Abm Kamrul Islam Riad

Symposium of Student Scholars

Wearable technology allows for consumers to record their healthcare data for either personal or clinical use via portable devices. As advancements in this technology continue to rise, the use of these devices has become more widespread. In this paper, we examine the significant security and privacy features of three health tracker devices: Fitbit, Jawbone and Google Glass. We also analyze the devices' strength and how the devices communicate via its Bluetooth pairing process with mobile devices. We explore possible malicious attacks through Bluetooth networking. The outcomes of this analysis illustrate how these devices allow third parties to access sensitive information, …

Securing Global Positioning Systems In A Blockchain Using Vehicle-To-Everything Communications, Malik Purefoy

Symposium of Student Scholars

Intelligent transportation systems (ITS) are being deployed globally to support vehicular safety innovation using wireless communication. The ITS paradigm of vehicle-to-everything communication (V2X) enables vehicles equipped with an on-board unit (OBU) to communicate with a roadside unit (RSU) infrastructure to provide safe intersection movements between similarly equipped vehicles, pedestrians, and animal life. However, the current paradigm relies heavily on the availability of a Global Positioning System (GPS) for ensuring road user safety. In a doomsday scenario where GPS becomes unavailable, collision avoidance services provided through V2X may be rendered unavailable. The proposed solution is to develop a secure and reliable …

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 …

Complimenting A Chlamydomonas Reinhardtii Mutation Using Cell Penetrating Peptides, Matthew Mckenzie

Symposium of Student Scholars

Complimenting a Chlamydomonas reinhardtii Mutation Using Cell Penetrating Peptides

Matthew McKenzie and Jennifer L. Cooper

Chlamydomonas reinhardtii is a photosynthetic model organism most notable for its easily manipulatable genetics. C. reinhardtii uses flagella to swim and optimize its growth conditions in the light. We plan to use cell penetrating peptides (CPPs) to compliment C. reinhardtii that is affected with the IFT46 mutation. Cell penetrating peptides are short peptides that can move across a cell membrane. The novel CPP that we are using is called TaT-CaM. It consists of the trans-activator of transcription (TaT) and the calmodulin domain (CaM) that binds …

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.

R-Lstm: Time Series Forecasting For Covid-19 Confirmed Cases With Lstm-Based Framework, Mohammad Masum

Symposium of Student Scholars

The coronavirus disease 2019 (COVID-19) caused a pandemic outbreak affecting 213 nations worldwide. Global policymakers are imposing many measures to slow and reduce the rapid growth of infections. On the other hand, the healthcare system is encountering significant challenges for a massive number of COVID-19 confirmed or suspected individuals seeking treatment. Therefore, estimating the number of confirmed cases is necessary to provide valuable insights into the growth of the outbreak and facilitate the policy-making process. In this study, we apply ARIMA models as well as LSTM-based recurrent neural networks to forecast the daily cumulative confirmed cases. The LSTM architecture generates …

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 …

Car Ratings Take A Back Seat To Vehicle Type: Outcomes Of Suv Versus Passenger Car Crashes, Dietrich Jehle, Albert Arslan, Chirag Doshi, Clay O'Brien

HCA Healthcare Journal of Medicine

Background

Car safety ratings are routinely utilized in making automobile purchase decisions. These 1- to 5-star ratings are based on crash test data comparing vehicles of similar type, size and weight.

Objectives

We hypothesized that car safety ratings are less important than vehicle factors such as vehicle type and weight in predicting outcomes of head-on crashes.

Methods

A retrospective study was conducted on severe head-on motor vehicle crashes entered into the FARS (Fatality Analysis Reporting System) database between 1995 and 2010. This database includes all US motor vehicle crashes that resulted in a death within 30 days of the accident. …

Ephemeris Vol. Ii

Ephemeris

No abstract provided.

Exploring Potentials Of Leftover Spaces Using Urban Metamorphosis, Huda Maatouk, Marwan Halabi, Hiba Mohsen, Maged Youssef

BAU Journal - Society, Culture and Human Behavior

In a dynamic world, the challenge behind reaching utopia is a result of the continuity of change and the metamorphosis. Metamorphosis, from the Greek meta signifying the change and morphe referring to the form, is a change and adaptation of the form or nature of something into a different one through time. However, when it comes to urban metamorphosis, it is a tension between the traditional living and the raise of technology through history, and this adaptation is affecting cities, buildings, identity, and environment. Urban metamorphosis of undeveloped non-organized cities had created urban and social fragmentation; unorganized cities are broken …

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 …

On Non-Linear Network Embedding Methods, Huong Yen Le

Dissertations

As a linear method, spectral clustering is the only network embedding algorithm that offers both a provably fast computation and an advanced theoretical understanding. The accuracy of spectral clustering depends on the Cheeger ratio defined as the ratio between the graph conductance and the 2nd smallest eigenvalue of its normalizedLaplacian. In several graph families whose Cheeger ratio reaches its upper bound of Theta(n), the approximation power of spectral clustering is proven to perform poorly. Moreover, recent non-linear network embedding methods have surpassed spectral clustering by state-of-the-art performance with little to no theoretical understanding to back them.

The dissertation includes work …

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) …