Congratulations to the funded 2021 Texas A&M Engineering Experiment Station (TEES) research collaborations.
$10,000 Award
CyberEye
Team members: Abhijit Nag, Texas A&M University – Central Texas; Anitha Chennamaneni, Texas A&M – Central Texas; Mohsen Aghashahi, Texas A&M San Antonio; and Erdogen Dogdu, Angelo State
This project proposes an artificial intelligence-led approach that is coupled with systems’ dynamical models to improve the capability to absorb, withstand and recover from adversarial events.
To achieve this, we use a hierarchical distributed cross-layer intelligence to improve situational awareness. This will look at relevant behaviors from the different interdependent systems to perform real-time detection and mitigation. The systems’ cyber-physical control layers then can synthesize the best course of action to mitigate the ongoing threats, leveraging adaptive capacity resources on the cyber and physical layers of the different interdependent systems.
Optimization of Marine Propulsion System
Team members: Jeff Sammons, Texas A&M – College Station; Irvin Osborne-Lee, Prairie View A&M; Lucina Kuusisto, Texas A&M – Commerce; Saadat Mirza, Texas A&M – College Station
Marine transportation is significantly different when compared to its land and air counterparts. Unlike them, this mode of transportation is characterized by higher torque and lower speed requirements.
This is usually achieved by employing multiple gears that help in operating the motor at a relatively lower speed, and the desired speed-torque can be obtained by shifting gears. This gear mechanism adds cost and weight, generates significant noise, poor efficiency at higher gear ratios, and requires regular maintenance.
$5,000 Award
Beavers Pipeline Engineering
Team members: Zhenhua Huang, University of North Texas; Poorya Hajyalikhani, Tarleton State University; Maurizio Manzo, University of North Texas; and Yuehua Wang, Texas A&M – Commerce
Millions of miles of utility pipelines are implemented all over the world to transfer essential fluids like fresh water and fossil fuel. In Texas hundreds of miles of underground utility pipelines are laid to feed cities with their needs.
Unfortunately, Texas soil conditions are complex because of the wide variety of soil types, soil characteristics, climate, vegetation, geology and landscape. More than 1,300 different kinds of soil are recognized in Texas. The most concern relates to the expansive soils that are unsuitable for construction and that cause pipeline problems and threaten the structural integrity of the pipeline system.
Secure Lambda
Team members: Mohamed Chouikha, Prairie View A&M University; Dilma Da Silva, Texas A&M University- College Station; and Damiano Torre , Texas A&M – Central Texas
In this proposal, four principal investigators from three institutions will collaborate to explore a new paradigm for secure serverless computing with the internet of things applications. Serverless computing is emerging recently as the future trend for cloud computing, which allows developers to build and run applications without having to manage servers.
Amazon’s AWS function, Microsoft Azure functions, Google’s multiple Cloud functions, and IBM Open-Whisk are all developed under this scenario. The internet of things, with the lightweight, short lifespan features, will dominate in serverless computing. But there are a lack of strategies to analyze and act on preventing attacks on this new platform.
IoC (Internet of Clothing)
Team members: Sunil Khatri, Texas A&M – College Station
There is strong demand for clothing-based sensing/transmission devices, resulting in a possibility of an “internet-of-clothing.” A key requirement of such clothing-based sensing platforms is the need to eliminate batteries, for safety reasons.
In this project, we will demonstrate a means to charge such clothing-based sensors wirelessly and communicate their sensed data to a central hub. In our proposal, an infrared (IR) laser, placed in a central hub, will power a flexible photoreceiver/sensor/transmitter patch that is attached to the clothing. The energy received by the patch from the hub will power the sensor, and the collected sensor data will be communicated back to the hub by way of IR diodes.
Sustainable Water Remediation Technologies Team
Team members: Saravanan Ramasamy, Angelo State University
Remediation of water off emerging contaminants, such as drug-resistant bacteria, dyes, fertilizers and forever chemicals (PFAS) is not possible using the existing train of water treatment technologies. These treatments include chlorination, ozonation, UV treatment and filtration.
Next-generation technologies, such as photocatalysis, could be used to remove these emerging contaminants. Accomplishing such a task on a large scale requires that the nano-photocatalyst used for water remediation be separated from water following the treatment, and reactivated for future use. Such a removal is also essential to ensure that the photocatalyst used for water remediation does not contaminate water by itself.
Cyber Hard Hat
Team members: LaTasha Taylor-Starr, Texas A&M University – College Station; and Mohammad Shafinul, Angelo State University
Now more than ever, with the substantial increase in people online given the 2020 pandemic, the vulnerability to cyber attacks has increased for many sectors including local governments, higher education, independent school districts and small businesses.
The effects of a major ransomware attack can be expensive and crippling. This program will offer college and university students, and their independent school district and local government partners, the opportunity to engage in real-world, hands-on instruction for assessing company network and human vulnerabilities to keep these sectors a step ahead of attackers. This will take the form of a 10-week internship in some cases, involving network assessments and the delivery of social engineering trainings according to best practices.
$2,500 Award
Learning Bridge to Industry Success
Team members: Lucas Loafman, Texas A&M University – Central Texas; Vinay Gonela, Texas A&M – Central Texas; Hye Jung Kang, Texas A&M – Texarkana; and Diana Elrod, Texas Woman’s University
The demand for skilled science, technology, engineering and mathematics (STEM) workers is great. It is predicted that demand will increase and exceed the quantity of workers. Workers aging out of the workforce, lack of advanced STEM skills for those currently in the workforce, and increased economic expansion are contributing factors to this deficit.
Team TMCS
Team members: Zheng O’Neill, Texas A&M University- College Station; Xiaofeng Nie, Texas A&M College Station; and Oscar Lopez, Texas A&M University – McAllen
There is a need for real-time information that will provide the general public with accurate ground evacuation routes to safe, operable shelter that meets their individual needs. Evacuations during natural disasters have a history of being difficult to enforce and manage, as well as being somewhat chaotic on the transportation network.
Decisions about evacuation routes are largely based on information from government and media sources. However, the mistrust on government agencies and over reliance on social media outlets have complicated decision-making processes, possibly increasing the danger to the population.
Fluorescent Fire-Retardants
Team member: Qingsheng Wang, Texas A&M University – College Station
Nanomaterials like ZnO/ZnS have fire-retardant properties that can be coated on fabrics or doped in plastics. Our aim is to develop an imaging technique (alternate to SEM and TEM) to study the distribution of these fire-retardant nanoparticles on fabrics or in plastics.
Highly fluorescent semiconductor nanocrystals (quantum dots) can be encapsulated in ZnO shell or surface modified to link onto ZnO micro-rods. Coating/doping of these particles would add fire-retardancy to the substrate. Fluorescence imaging can be used to study the binding efficiency, distribution, dispersion and stability of the nanoparticles. This imaging method can be extended to study other fire-retardant materials
POA-Group
Team member: Tracy Hammond, Texas A&M University – College Station
This project will provide the ability to assess and demonstrate the fairness, transparency, explainability, impartiality, and accountability of fairness of artificial intelligence (AI) systems in education. The outcome of this research will enhance the implementation and contribution to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society in education.
