Southern Polytechnic College of Engineering and Engineering Technology

Our civil and environmental engineering projects, led by esteemed faculty, address critical environmental challenges and advance sustainable engineering practices. From analyzing metal content in recycled waste materials to developing biodegradable soil moisture sensors and creating durable 3D printed concrete, these projects aim to innovate and enhance our understanding of environmental impacts and sustainable solutions. Explore our projects and see how our scholars contribute to engineering advancements and environmental stewardship.

Return to the Main Project Listings Page Questions: Email Us

Civil and Environmental Engineering (Amy Gruss and Roneisha Worthy)

Feasibility Study of Cut-Flower Floating Wetlands on Nutrient Removal

First-Year Scholars: Roshni Chimmili & Aaron Zheng

  • In this research project students will design and implement a small-scale floating treatment wetland using cut-flowers in a mesocosm tanks for aquatic nitrogen and phosphorus removal. Floating treatment wetlands mimic the nutrient removal capabilities of natural wetlands on small floating rafts. Nutrient removal is vital as excess concentrations can lead to fish kills and degraded surface water bodies. While most research focuses on aquatic grasses, this research focuses on hydroponically grown cut-flower plants as an alternative. Not only do these cut-flowers have the potential for phytoremediation of impaired surface waters such as stormwater ponds and lakes, they also can provide a revenue source as well as adding a beautification element where installed.

    Outcomes of this study include the design and selection of plants for the floating treatment wetland, installation of aeration system, phosphorus and nitrogen dosing and concentration analysis, and biomass growth measurements.

  • Students will learn how to calculate basic water flow calculations, calculate concentration and dosing of chemicals at various volumes, measure nitrogen and phosphorus concentrations in a laboratory setting, grow cut-flowers in a hydroponic setup, and analyze and graph data.
  • During the initial phase, students will research the types of plants to be used for a floating treatment wetland and conduct a literature review. Subsequently, students will install a large mesocosm tank and grow the selected plants on a floating raft within the tank. Last, samples from the tank will be collected and measured for various water parameters within a laboratory setting and total growth of the biomass will be measured. 
  • Face-to-Face
  • Dr. Amy Gruss, agruss@kennesaw.edu

    Dr. Roneisha Worthy, rworthy@kennesaw.edu

Civil and Environmental Engineering (Parth Bhavsar)

Smart Transportation Solution for GA Parents

First-Year Scholars: Lorna Flowers, Joshua Greig, Cole McGough, Sai Palepu, Joshua Raj, & Erik Vaquero

  • Post Covid019, traffic congestion patterns have changed, specifically near elementary and middle school locations. More parents are choosing to use a personal vehicle as a primary mode of transportation to pick-up and drop-off their kids. In addition, parents of elementary and middle school kids who are part of the school choice program in Georgia must use the car to pick-up and drop-off their children. This process creates traffic congestion at intersections near schools during pick-up and drop-off times.

    The goal of this project is to develop a safe and efficient transportation solution for GA parents of elementary and middle school children.

    To achieve this goal, we will focus on two completing two objectives; (1) optimize traffic congestion at intersections near elementary and middle schools and (2) reduce total travel time for GA parents by developing a cloud-based application that integrates a smart-phone app with static vehicle detection sensors.

    Each objective will be accomplished by (1) understanding current state-of-the-art (i.e. what are current solutions being offered for the same problem); (2) identifying various possible solutions (i.e. brainstorming possible solutions and listing pros and cons of each solution); (3) developing the best possible solution (i.e. collecting data, developing traffic simulation models, or developing cloud-based applications).

    • Understand the research process.
    • Learn how to conduct a literature review.
    • Learn how to identify possible data collection sources online.
    • Learn how to collect data in the field with data collection devices.
    • Learn basics of traffic flow and traffic signal operations.
    • Learn how to develop and calibrate traffic simulation models.
    • Learn how to develop and test cloud-based applications.
    • Learn how to develop surveys for data collection.
    • Meet in-person or virtually.
    • Learn relevant skills from the PhD student or an instructor.
    • Perform specific tasks given at the end of the meeting.
    • Update research team about the completed tasks. 
  • Hybrid
  • Dr. Parth Bhavsar, pbhavsar@kennesaw.edu

Civil and Environmental Engineering (Da Hu and Adam Kaplan)

Exploring Beneath the Surface: Mapping the Underground with AI and Radar

First-Year Scholars: Alex Abounassar, Kimberly Barahona, Oscar Barahona, Kevin Gonzalez, & John Haly

  • Have you ever wondered what lies beneath the ground we walk on? Beneath our cities, parks, and schools lies a hidden world of pipes, cables, and natural structures, all critical to our daily lives yet invisible to the naked eye. This project, “Exploring Beneath the Surface,” offers a unique opportunity to uncover these hidden secrets using the latest technology in artificial intelligence (AI) and radar systems. Our journey begins with ground-penetrating radar (GPR), a remarkable tool that sends radar waves into the ground and captures images of what lies beneath without digging. It’s like having X-ray vision that can see underground! However, interpreting these radar images requires special expertise—this is where AI comes into play.

    In this project, you will help develop and train AI models, which are complex algorithms capable of learning patterns and making decisions. These models will learn to interpret radar images, helping us map underground structures quickly and accurately. By joining this team, you will gain hands-on experience with cutting-edge technology and contribute to making construction and urban planning safer and more efficient.

    You don’t need a background in technology or engineering to participate. We’re looking for curious, motivated students who are eager to learn and contribute to a team. Throughout the project, you will:

    • Learn how AI can be applied to solve real-world problems.
    • Gain experience with GPR technology and its applications.
    • Develop skills in data analysis and model training.
    • Work in a team setting, enhancing your collaboration and communication skills.

    This project is perfect for anyone interested in technology, engineering, environmental science, or urban studies. By the end of your first year, you’ll not only have a solid foundation in these innovative technologies but also real-world experience that can shape your future academic and career choices.

    Join us in peeling back the layers of the earth to discover the unseen, and let’s pave the way for future innovations together!

  • 1. Operate Ground-Penetrating Radar (GPR) to understand subsurface imaging.
    2. Train AI models for data interpretation.
    3. Analyze large datasets to extract insights.
    4. Solve Real-World problems using project-based learning.
    5. Understand interdisciplinary applications in technology and engineering.
    6. Develop teamwork skills in a collaborative environment.
    7. Experience advanced technologies in AI and radar systems.
    8. Learn about Real-World Applications of subsurface mapping.
  • 1. Conduct literature reviews to learn about the latest in subsurface mapping and AI.
    2. Operate GPR equipment to gather underground data.
    3. Implement AI techniques for data analysis.
    4. Refine AI models for improved accuracy.
    5. Document findings in a research log.
    6. Prepare presentations to share updates and results.
  • Hybrid
  • Dr. Da Hu, dhu3@kennesaw.edu

    Dr. Adam Kaplan, akaplan8@kennesaw.edu

Civil and Environmental Engineering (Adrijana Savic)

Investigation of the Flexural Strength Capacity of Concrete Made with Steel Fiber, Glass Powder and Fly Ash as Partial Replacement of Cement

First-Year Scholars: Nya Jones & Servando Pastor Izara

  • Steel Fiber Reinforced Concrete could be used to improve durability and toughness in concrete. Using steel fiber in production of concrete could eliminate the need for steel reinforcing. Cracking and fracturing under dynamic load could be prevented with enhancing the durability of concrete. Glass powder could improve the mechanical properties of concrete by the means of pozzolanic activity. Fly ash in concrete reduces cracking, permeability and bleeding creating high-durability concrete that is resistant to sulphates and alkali-aggregate reactions. The goal of this project is to produce high quality concrete mixture which could be resistant to dynamic loads, weathering with elimination the need for extra steel reinforcement with speeding the process of manufacturing precast elements. Steel fiber reinforced concrete has potential to reduce CO2, which is important for producing environmentally friendly concrete. This type of concrete could be widely used in infrastructure.

    The selected first year student will be guided by Dr. Adrijana Savic in this research effort. This research can make use of students with interest in civil engineering.

  • At the end of the project, students should be able to:

    1. Understand the materials needed to produce precast concrete.
    2. Develop critical thinking skills for problems encountered in the lab 
    3. Work effectively as part of a team
    4. Present their research to an audience (e.g., poster, oral presentation, performance, display)

  • Typical activities for this research include:

    1. Working in the lab to mix and cast steel fiber concrete beams. 
    2. Test the concrete cylinders under failure
    3. Collect data and compare the results: precast reinforcing concrete and steel fiber concrete.

  • Face-to-Face
  • Dr. Adrijana Savic, asavic@kennesaw.edu

 

Civil and Environmental Engineering (Metin Oguzmert)

Utilizing a Mid-Size Shake Table for Structural Dynamics and Earthquake Engineering Research and Education

First-Year Scholars: Gagandeep Narang & John Xu

  • This project will utilize the Quanser Shake Table II, a versatile mid-sized motion simulator, to advance research and education in structural dynamics and earthquake engineering. The equipment is capable of simulating various dynamic motions, including sine waves, chirp signals, and scaled versions of historical earthquake events like Northridge, Kobe, and El-Centro, to study the effects on structural models. It also provides the capability to incorporate additional earthquake profiles, which can be customized and scaled using data from the PEER Ground Motion Database, thereby broadening the range of potential experimental scenarios.

    The project will begin with a literature review on how similar mid-size shake tables have been used in research and teaching, providing a foundation for innovative approaches. Alternative prototype structures will be developed and tested, focusing on inelastic behavior to reflect realistic structural responses during earthquakes. These prototypes will then be integrated into classroom demonstrations, offering students hands-on experience.

    Although the equipment has been used for minor tests, this project aims to fully explore its potential, providing students with practical experience in operating advanced lab equipment and in data analysis using MATLAB and Simulink.

  • 1. Students will gain practical experience in setting up, designing, and operating lab equipment crucial to structural dynamics and earthquake engineering research.
    2. Students will learn how structures respond to real earthquake motions by observing and analyzing prototype models during shake table tests.
    3. Students will learn to collect and analyze digital data using MATLAB and Simulink, essential tools in engineering analysis.
    4. Involvement in the project will develop students' critical thinking and research skills, enabling them to synthesize information from existing studies and apply it to their work.
  • 1. Attend weekly progress meetings.
    2. Conduct a literature review on the use of mid-size shake tables.
    3. Assist in the setup and calibration of lab equipment.
    4. Develop and test prototype structures with a focus on inelastic behavior.
    5. Collect and analyze data using MATLAB/Simulink.
    6. Prepare reports and presentations for classroom demonstrations and potential conferences
  • Hybrid
  • Dr. Metin Oguzmert, moguzmer@kennesaw.edu

Civil and Environmental Engineering (Tien Yee and Da Hu):

Quantifying Flow in Streams Using Low Cost LSPIV System

First-Year Scholars: Lyra May

  • Streamflow is an important quantity for hydraulics and water resources studies. Researchers and practitioners rely heavily on data from gaging stations to estimate streamflow for their work. Due to the sparse distribution of stream gages, there remain many ungaged streams and basins. Setting up and maintaining a vast network of stream gages is expensive and hence impractical.

    This project will utilize an image processing technique, also known as the Large Scale Particle Image Velocimetry (LSPIV) for flow estimation. The LSPIV technique is capable of capturing surface velocity vectors and hence provide necessary information needed to estimate flowrates in strategic location along a stream. Motivated First-Year scholars will work in a team with graduate student(s) to accomplish for various tasks such as programming, laboratory work and field work to test the LSPIV system in the lab and in the field for flow estimation during various conditions. 

  • 1. Understand and apply knowledge in electronics and programming 
    2. student will be able to anticipate problems associated to fieldwork
    3. Be able to collect data in the lab and from the field
    4. Be able to articulate and present their research clearly

  • Participants to this project will be taught to think critically on what why the project is necessary and what the expected outcome is, through literature review.

  • Face-to-Face
  • Dr. Tien Yee, tyee@kennesaw.edu

    Dr. Da Hu, dhu3@kennesaw.edu

     

Civil and Environmental Engineering (Sunanda Dissanayake)

Highway Work Zone Crash Data Analysis to Improve Transportation Safety

First-Year Scholars: James Coker & Asiah Lightfoot

  • Have you or anyone you know ever been involved in a motor vehicle crash? Have you seen crashes when you travel, either as a driver or a passenger? Highway safety is a big concern in Georgia, and the number of crashes (also known as accidents, wrecks, etc.) and associated fatalities have increased considerably in recent years. The economic cost associated with these crashes is in the range of billions of dollars every year. During the last five years, from 2018 to 2022, there were 7,974 fatal crashes in Georgia, and as a result, 8,636 Georgians have lost their lives (GDOT). As we construct new roads and maintain the transportation network in good condition, highway work zones cannot be avoided. At such locations, the balance between the traffic flow and work zone activities needs to be maintained properly to ensure safety. However, unfortunately, during the last five years, 296 Georgians have lost their lives in highway work zones. In 2023 alone, 13,689 Georgians have been involved in crashes in work zones, resulting in 4,891 injuries.

    Work zones pose significant safety risks to both workers and drivers due to factors such as lane closures, reduced visibility, equipment presence, and such. Despite efforts by the Georgia Department of Transportation (GDOT), work zone crashes remain a persistent issue. This study analyzes crash data to identify trends, patterns, and contributing factors to work zone crashes in Georgia. The research will further investigate the relationship between work zone design and crash occurrence, with the goal of developing recommendations to enhance work zone safety.

  • Upon the completion of the project, the student will:

    • Understand the basic concepts in conducting research in the broad area of civil engineering
    • Understand how a literature review is conducted
    • Learn about basic data analysis practices
    • Understand how research findings could be derived by analyzing data
    • Be familiar with how research could be used to provide guidance on how work zone safety could be improved.
    • Educate themselves and others about the importance of good highway safety practices and the economic benefits associated with reducing crashes
  • The following are the major tasks the student will engage in depending on the stage of the project.

    • Conducting a literature review about work zone safety and understanding the general issues and concerns.
    • Gather data related to work zone crashes in Georgia (or part of the state) for the last five years.
    • Learn basic statistical methods that could be applied to analyze crash data.
    • Apply the most suitable methodology to analyze work Zone crash data and identify the critical factors affecting the safety in work zones.
    • Write down the activities completed, and record the time spent working on the project
    • Write an abstract and participate in the symposium.
  • Face-to-Face
  • Dr. Sunanda Dissanayake, sdissan1@kennesaw.edu

Civil and Environmental Engineering (Mahyar Amirgholy)

National Interactive Emission Modeling and Analysis Tool (NIEMAT): Machine Learning for Predicting Vehicle and Power Plant Emissions

First-Year Scholars: Jason Bradshaw Jr., Damian Martinez Gonzalez, & Sahil Patel

  • While electric vehicles (EVs) are often considered emission-free, their increasing adoption can lead to higher air pollutant emissions from vehicles running on fossil fuels due to traffic congestion caused by the additional EV trips, as well as from power plants due to the increased energy demand from the grid. This project aims to leverage machine learning to develop a large-scale tool for estimating emissions from vehicles and power plants in response to rising EV adoption in major cities, including Atlanta, Los Angeles, New York, and Seattle. This integrated tool enables estimating the energy consumption of EVs from the power grid, as well as the emissions of CO2, NOx, and PM2.5 from traditional vehicles, and the increase in CO2, CH4, and N2O emissions from power plants due to the energy demand from EVs, with the projected increase in EV adoption over the coming years in the United States. 

    • Enhance your expertise in machine learning and integrated modeling.
    • Learn to use the EPA's MOtor Vehicle Emission Simulator (MOVES).
    • Learn about the Cambium database developed by the National Renewable Energy Laboratory (NREL).
    • Collaborate with graduate students on this research project.
    • Biweekly meetings
    • Complete the assigned tasks 
  • Hybrid
  • Dr. Mahyar Amirgholy, mamirgho@kennesaw.edu

Civil and Environmental Engineering (Adam Kaplan)

Assessing Cyclist Safety on and Around Marietta Campus

First-Year Scholars: Mat Yun

  • The main goal of this project is to evaluate the safety of bicycle routes on and around our university campus.

    We aim to identify potential hazards and areas where improvements can be made to ensure that cycling is a safe and appealing mode of transportation for everyone in our university community.

    • Data Collection: Gather information on current cycling routes, traffic patterns, and previous incidents involving cyclists on campus.
    • Observation: Conduct field observations to see how cyclists, pedestrians, and vehicles interact.
    • Surveys: Distribute surveys to gather feedback from cyclists about their experiences and perceptions of safety.
    • Analysis: Use the data collected to identify high-risk areas and common factors contributing to unsafe conditions.
  • In the cyclist safety assessment project, a student's typical weekly activities include reviewing relevant literature, collecting data through field observations and surveys, and analyzing this data to identify safety issues. Weekly meetings with a mentor ensure consistent guidance and alignment with project goals. Tasks also involve drafting sections of reports and preparing presentations to communicate findings to stakeholders. These activities provide a comprehensive research experience, encompassing data collection, analysis, and practical communication, all under the structured mentorship of a faculty member in civil engineering.
  • Face-to-Face
  • Dr. Adam Kaplan, akaplan8@kennesaw.edu

Electrical and Computer Engineering (Sumit Chakravarty)

Maintaining Connectivity in High-Impact Disaster Situations with 5G -Software Defined Radios

First-Year Scholars: Drew Haase, Mit Patel, & Danny Tram

  • A Software Defined Radio (SDR) is a radio transceiver that is primarily defined in software. It allows radio engineers and researchers to easily control the hardware and implement and configure the physical layer in software. SDRs have been used in many scientific platforms to implement, test, and study different wireless technologies and protocols. In this project, we explore the 5G waveform generation function of the MATLAB 5G Toolbox to generate different uplink-downlink transmission frames. 

    The 5GNR (5G New Radio) 'knobs' include the bandwidth (BW), the physical resource block (PRB) allocation within the BW, the time slot occupation, the numerology, and the number of transmission layers, and the transmit (Tx) power. The generated waveform is transmitted over the air (OTA) using the GNU Radio software framework in conjunction with Universal Software Radio Peripheral (USRP) or other commercial SDR hardware. 

    In the proposed work, two SDRs are used to implement the test bed's transmitter and receiver sides. Each SDR is connected by USB 3.0 to a GNU Radio Companion computer. The transmitter and the receiver GNU Radio flowgraphs have a built-in spectrum analyzer (SA) block to visualize the fast Fourier transform of the signal for spectral analysis. We use the 5G Toolbox because of its flexibility to customize the uplink and downlink 5G NR transmission that complies with the 3GPP specifications. In addition, we use another MATLAB function to convert the in-phase and quadrature (IQ) samples to binary points for use with GNU Radio. GNU Radio is a free and open-source software development toolkit that provides the mechanisms and sample signal processing blocks to implement radios in software. It is compatible with low-cost SDR hardware to enable RF transmission. 

    Furthermore, it has a graphical user interface—GNU Radio Companion—to assemble radios by connecting blocks. Moreover, we use a Software Defined Radio (SDR) and P21XXCSR-EVB Energy Harvesting module (existing) to conduct experiments on RF energy harvesting from the SDR transmissions. Specifically, we are interested in obtaining insight into joint communication and RF energy harvesting, aiming to transmit as much information as possible under the constraints of providing sufficient RF energy for the receiving device to operate. Experiments that will enable our insights into this problem include comparing communication waveform designs, comparing power emission via USRP, and the effect of environmental conditions like separation distance, presence of nearby in-band transmissions, and other factors.  

  • There are many high-impact situations where there is loss of telecommunication, for example natural disasters such as hurricanes and places where conventional telecommunication infrastructure is damaged such as in Ukraine, currently. In these cases, software definable, rapid deployable wireless communication systems are urgently needed. Our students will conduct high-impact research to solve crucial telecommunication problems, develop soft and entrepreneurial, and networking skills. We target external grants and industry support. We also prepare the students to use the analytical approach of an engineer, be able to integrate software, hardware and communication systems, and network infrastructures, and develop proof-of-concept projects related to future telecommunications. 

    The students will develop essential articulation skills as we plan to present the work in seminars and other undergraduate symposiums.

  • Specifically, using the proposed test framework described above together with the related tutorials, the participant will be tasked with: 

    • Understand the basics of modern communications using the provided tutorials and simulations
    • Develop basic programming and testing of communications using the Matlab framework
    • Perform test simulations using the 5G toolbox in Matlab to learn and utilize the various aspects of the toolbox.
    • Familiarize with and utilize GNU Radio tools
    • Generate a variety of 5G NR frames using the testbed (data generation in Matlab followed by transmission via GNU Radio).
    • Manipulate the test parameters, including system bandwidth, waveform power, system gain, modulation, and subcarrier specifications. Analyze the results
    • Compare the testbed performance to simulation results.
    • Measure the energy harvested via signal transmission.
    • Innovate and Implement strategies to improve energy harvesting while maintaining transmission rates
  • Face-to-Face
  • Dr. Sumit Chakravarty, schakra2@kennesaw.edu

Electrical and Computer Engineering (Beibei Jiang)

Safer, Faster, and Last Longer: Developing the Next Generation All-Solid-State Lithium-Metal Batteries and Lithium-Sulfur Batteries

First-Year Scholars: Lily Liu, Josue Macias, Russell Park, Will Pixley, & Prajesh Santhanakrishnan

  • 1. Background
    The research on next-generation energy storage devices has garnered significant attention recently, driven by the increasing demand for high-performance energy storage systems in various applications, including electric vehicles, biocompatible medical devices, consumer electronics, etc. Among the forefront candidates, lithium-ion batteries have gained substantial interest due to their promising capabilities. However, the current battery technology still utilizes flammable organic liquid electrolyte, which makes them susceptible to fire incidents when short circuit happens under extreme conditions (such as collisions, high temperatures, etc). Furthermore, the inadequacy of current battery technology in meeting the escalating energy demands has necessitated the exploration of alternative anode materials. By replacing conventional carbon-based anodes with Li-metal anodes, it becomes possible to optimize energy density and address the growing energy requirements effectively. 

    2. Method and Objective 
    All the materials will be developed in our lab located in the Engineering Technology Center (Q building). The comprehensive characterization will be done using equipment located in our lab and other necessary shared facilities at Kennesaw State University.  Please check our website for more information. https://facultyweb.kennesaw.edu/bjiang1/index.php

    The project will employ a series of experimental techniques to synthesize and thoroughly characterize the novel solid electrolyte developed in our lab. Specifically, we will develop a solid electrolyte based on a novel fabrication method proposed by our team. We will fabricate all-solid-state lithium-metal batteries and lithium-sulfur batteries based on the newly developed solid electrolyte. We will investigate the interfaces between the solid electrolyte and the anode and cathode materials. In addition, modeling techniques will be employed to complement the experimental observations and facilitate a comprehensive analysis of the underlying mechanisms governing charge transport behaviors in the all-solid-state lithium-metal battery system.

    The project encompasses two primary objectives. Firstly, it aims to develop an innovative and high-performance solid electrolyte through the application of cost-effective roll-to-roll printing and polymerization techniques. Secondly, it aims to achieve a comprehensive understanding of the charge transport behavior across the interfaces. Through these endeavors, the aim is to minimize charge transfer resistance inside the solid electrolyte and across these interfaces, leading to a substantial enhancement in overall battery performance.

  • Students are expected to learn and develop the followings skills-based outcomes and techniques: 

    • Develop scientific research method and strict research attitude
    • Define the terminology associated with research and theory in their field
    • Describe past research studies in their field of study
    • Articulate how their research study makes a contribution to their academic field
    • Be able to find research resources through reading research papers
    • Be able to perform the basic material synthesis procedures
    • Be able to perform the basic material characterization techniques
    • Be able to perform the basic battery characterization techniques 
    • Familiar with testing and data collection, especially for Li-ion batteries
    • Familiar with curve fitting using linear and nonlinear regression 
    • Basic understanding of physical models and mechanisms related to Li-ion battery failure
    • Analyze, synthesize, organize, and interpret data from their research study
    • Work effectively as part of a team
    • Write a research paper 
    • Present their research/creative activity to audience (e.g., poster, oral presentation, performance, display)
    • Articulate what it means to be a scholar in their academic field
    • Articulate the ways in which their research participation helps prepare them for graduate school or career
    • Describe appropriate professional conduct (e.g., at conferences, when interacting with professionals in the field)

    Patents and publications are expected to be the outcome of this project. There is a chance to present this work in professional conferences.

  • Undergraduate students will team up with graduate students for each research project. I expect students to work 4-8 hrs per week. I expect to meet with students at least 1 time each week for mentoring and discussion.  

    Here is the type of activities that students will engage in each week:

    1. Receive training of any new experimental techniques 
    2. Perform the basic material synthesis experiment for developing the solid electrolyte 
    3. Characterizing the solid electrolyte developed in our lab 
    4. Analyzing the characterization results 
    5. Data analysis and/or failure analysis 
    6. Monitor the battery testing and collect battery testing data 
    7. Managing project progress and adjusting the design of experiments 

  • Hybrid
  • Dr. Beibei Jiang, bjiang1@kennesaw.edu

Electrical and Computer Engineering (Paul Lee, Garrett Hester, and Daphney Carter)

Novel Optical Sensors for Assessing the Effect of Aging on Muscle Health

First-Year Scholars: Aster Cheung, Jessica Mambo, & Samuel Samson

  • The primary goal of this project is to apply a novel light-based sensor (like how an Apple Watch monitors heart rate using green light) for noninvasive measurements of muscle health and function in older adults. Age-related reductions in muscle strength and function, known as “sarcopenia,” significantly impact mobility, quality of life, and mortality in older people. Noninvasive assessment of muscle function during exercise would be immensely valuable for the early diagnosis of sarcopenia and for monitoring the effectiveness of therapeutic interventions. Unfortunately, current technologies are insufficient for assessing oxygen delivery and utilization at the microvascular level during exercise, and existing laboratory methods are limited.

    Our light-based sensor directly measures muscle blood flow and oxygenation. During daily activities, the energy required for muscle function primarily comes from oxygen metabolism. Adequate oxygen delivery to muscles through normal blood flow is critical to meeting the metabolic demands during daily activities and exercise. This light-based technique allows for continuous, noninvasive monitoring of oxygen delivery and consumption in muscles during exercise. For  this project, we will place our sensors on one of the thigh muscles while human subjects perform knee extension exercise. We will recruit  young and  older adults to determine if there are differences in oxygen delivery and consumption between the two age groups.

    Throughout this project, the undergraduate student will engage in state-of-the-art biomedical research by applying novel medical devices to human subjects. The student will have the opportunity to receive mentoring from an interdisciplinary advisory group from the College of Engineering and Wellstar College of Health and Human Services. The student will gain a diverse range of skills, including basic engineering, biophotonics, muscle physiology, data acquisition and analysis in human subjects, and scientific communication, including symposium abstract writing and presentation skills.

    • Gain essential knowledge on biomedical optics 
    • Understand a principle of light-based sensors (Like Apple Watch) 
    • Experience in interdisciplinary research covering engineering and health
    • Experience in research directly involving human subjects 
    • Relevant training for ethics, safety and compliance
    • Collecting data on human subjects
    • Perform optical spectroscopic sensing and data analysis 
    • Enhance professional communication skills
  • Literature review in biomedical optics and applications in noninvasive muscle function monitoring 
    • Learn how to operate optical devices 
    • Assist with human data collection 
    • Data analysis on the acquired optical data
    • Present research progress in a weekly lab group meeting
    • Prepare presentations for conferences.  
  • Hybrid
  • Dr. Paul Lee, slee274@kennesaw.edu

    Dr. Garrett Hester, ghester4@kennesaw.edu

    Dr. Daphney Carter, dcart165@kennesaw.edu

Electrical and Computer Engineering (Hoseon Lee and Paul Lee)

Design and Test Microfluidic Devices for Biomedical Applications

First-Year Scholars: Conner Bhavsar, Jaden Dudley, Scott Eyre, Hieu Huynh, Alexis Ladao Blundell, & Manas Singh

  • Learn to design, fabricate, and test microfluidic devices in the lab for biomedical applications. One of the applications is early-stage viral infection testing with higher sensitivity and accuracy with lower false negatives compared to current rapid test kits. Using 3D printing, optical fiber core, and silicone material, we will be creating microfluidic channels that can detect magnetically tagged antigens and antibodies. Another biomedical application is phantoms to mimic the microvascular blood vessels in the head. These phantoms will be used to study the blood flow in the microvascular blood vessels with different sizes and different blood flow rates.

    This is a collaborative research with Emory and Georgia Tech research groups with opportunities to visit both campuses. 

    • 3D printing
    • Solidworks CAD software
    •  Prototype fabrication skills in the lab
    • Testing using optics and oscilloscopes
    • COMSOL Multiphysics simulation software
    • PowerPoint presentation skills
    • How to conduct literature review
    • Learn to write manuscripts for research publications
    • Attend research meetings
    • Present research updates
    • Conduct research in its various forms depending on the student's responsibilities (simulation, prototyping, literature review, testing, etc.)
  • Face-to-Face
  • Dr. Hoseon Lee, hoseon.lee@kennesaw.edu

    Dr. Paul Lee, slee274@kennesaw.edu

Electrical and Computer Engineering (Sandip Das)

Development of Memristor-based Artificial Synapses for Brain-like Neuromorphic Computer Chips of the Future

First-Year Scholars: Paxton Joseph, Benjamin Meza, Shlok Sohoni, & Trevor White

  • Neuromorphic electronics and neuromorphic computer chips can enable superior artificial intelligence. Developing such computer chips involve designing and fabricating artificial synapses – a key component for the transmission of electrical signals mimicking signal transmission within the human brain. In this project we focus on the development of Memristor-based artificial synapses. 

    A 'memristor' is a type of fundamental electronic device that has the feature of a memory and a resistor in one element. Such unique properties make memristors useful for building electronic circuits that mimic the synaptic connections in biological neural networks, enabling the development of artificial neurons and neural networks for futuristic neuromorphic computers that can process information like the human brain. Such neuromorphic computers find applications in Artificial Intelligence, Machine Learning, and brain-inspired computing systems. In addition, memristors can be used in many emerging technologies, such as non-volatile memories (ReRAM), Analog Signal Processing units, In-Memory Computing, Data Encryption, Hardware Security, Reconfigurable Logic Circuits, Smart Sensors, and Cognitive Computing systems that can adapt and learn from the environment, making them useful in intelligent robotics and autonomous systems.

    This FYSP project focuses on the research and development of memristor devices that are the basic building blocks for artificial synapses and neuromorphic computing chips. The specific aim of this project is to – design and fabricate a memristor system and test its performance/ characteristics in the laboratory. By the end of the project, we aim to demonstrate a working device/system that is capable of generating a pinched current-voltage hysteresis signature exhibited by memristors. The overarching goal is to develop low-cost memristor technology to accelerate the research, development, and education in the emerging field of neuromorphic computer chips. The first-year research scholars will learn how electronic devices and circuits can be designed and fabricated that can mimic the synapses of a human brain. They will gain invaluable knowledge and experience on memristor devices and learn how to fabricate an artificial synaptic device. Participating student researchers will receive hands-on training in electrical measurements and testing of devices and circuits, design and simulate circuits, learn to build complex circuits on a breadboard and/or PCB, and develop many important and useful engineering research skills through this project. We are looking forward to forming a multidisciplinary team with students from Electrical Engineering, Computer Engineering, Mechatronics Engineering, Computer Science, Information Technology, and Physics majors to work on this project.

  • 1. Learn how to perform literature research and formulate creative solutions.
    2. Build memristor device/circuits.
    3. Learn electronic test and measurement techniques.
    4. Learn how to operate various lab instruments.
    5. Design Experiments and implement test routines by embedded programming.
    6. Apply PCB design and PCB fabrication techniques.
    7. Implement data plot and data analysis techniques.
    8. Develop teamwork, presentation, and communication skills.
    • Study research articles to learn how the human brain functions and how electronic artificial synaptic devices can be used to mimic some of the brain’s functions.
    • Learn how memristors can be used as an artificial synaptic device and to create an artificial neuron or neural network.
    • Design and simulate circuits to realize an electronic memristor-type device.
    • Fabricate a memristor device/circuit and test its performance.
    • Perform experiments, acquire data using lab instruments, and analyze experimental data. 
    • Attend weekly research group meetings and update the PI.
    • Document the research results, make presentations and write reports.
  • Hybrid
  • Dr. Sandip Das, sdas2@kennesaw.edu

Electrical and Computer Engineering (Coskun Tekes)

Wearable Smart Hand Glove Development for Hand Gesture Prediction in Human-Robot Interaction

First-Year Scholars: Lukyan Hnidets, Aiden Jackson, Rodrigo Ledesma-Ledesma, & Abhijeet Surakanti

  • The project focuses on the development of a wearable smart glove designed to enhance human-robot interaction (HRI) through accurate hand gesture prediction. This glove will integrate various sensor technologies, including motion, flex and pressure sensors, to capture detailed hand movements and gestures. The data collected will be processed using machine learning algorithms to predict and interpret the user's intended actions, enabling a more intuitive smooth interaction with robotic systems. The smart glove is designed to connect what a person wants to do with how the robot responds, making human-robot interaction smoother and more efficient.

    A key aspect of the project is the design of the glove to be both comfortable and functional, allowing for continuous wear during extended periods of use without compromising accuracy or performance. The sensors will be strategically placed to cover the full range of hand movements, enabling accurate data capture. The project will also explore the integration of Electromyography sensors to train machine learning models for mapping muscle electrical activities to corresponding hand/finger intent of motion.

    In addition to the technical development, the project will involve extensive testing and validation to ensure the glove's reliability across different users and environments. This will include both controlled laboratory experiments and real-world scenarios to assess the glove's effectiveness in various HRI applications, such as industrial automation, assistive robotics, and virtual reality. Ultimately, the wearable smart glove aims to support research on robotic exoskeleton systems for stroke rehabilitation or upper limb amputation. 

    • Gain hands-on experience in integrating and configuring various sensors, such as motion, flex, pressure, and electromyography sensors.
    • Learn and advance 3D printing skills in designing and prototyping wearable technology that is both comfortable and functional
    • Learn how to conduct comprehensive testing and validation of the smart glove in both controlled and real-world environments
    • Explore the application of the smart glove in advanced fields like robotic exoskeletons for stroke rehabilitation or upper limb amputation
    • Develop the ability to apply/run machine learning algorithms to process sensor data, enabling the prediction and interpretation of user intent
    • Literature review for published material in the same field
    • Design and improve hand glove models 
    • Integration of the sensors
    • Experimental setup preparation
    • Test and validate performance
    • Attend weekly research meetings
    • Prepare write-ups and reports
  • Hybrid
  • Dr. Coskun Tekes, ctekes@kennesaw.edu 

Electrical and Computer Engineering (Yan Fang)

Neural-Symbolic AI for Robotic Learning and Planning

First-Year Scholars: Ty Alexander, Harris Aloys Henry Arockiaraj, Jnana Sai Harshini Kukunuri, & Jamil Smith

  • Neuro-symbolic AI (NSAI) represents an emerging AI paradigm that integrates neural, symbolic, and probabilistic approaches to enhance explainability, robustness, and enable learning from much less data in AI. Neural methods have proven highly effective in extracting complex features from data for tasks such as natural language processing and object detection. On the other hand, symbolic methods enhance explainability and reduce the dependence on extensive training data by incorporating established models of the physical world, and probabilistic methods enable cognitive systems to handle uncertainty more effectively, resulting in improved robustness under unstructured conditions. The synergistic fusion of neural, symbolic, and probabilistic methods positions NSAI as a promising paradigm capable of ushering in the third wave of AI. NSAI holds significant potential for enhancing real-time responses, energy efficiency, explainability, and trustworthiness of collaborative human-AI applications. 

    In this project, we will address the challenges of robotic learning and planning, which are complicated by continuous state spaces, continuous action spaces, and long task horizons. We will explore a bi-level planning scheme where symbolic AI planning in an outer loop guides continuous planning with neural models in an inner loop. Eventually, we expect to merge the model with the vector symbolic architecture and make it run smoothly on edge devices like microcontrollers or single-board computers for energy efficiency.

    In the proposed project, our first-year scholar will collaborate with other research lab members and learn basic concepts in neural symbolic AI. More importantly, the scholar will enjoy the opportunity to play with various robots, such as life-size robot dogs and small humanoids, for demonstrations. 

    •  Gain essential knowledge in neural-symbolic AI.
    • Explore the algorithm design under the hardware constraints and tradeoffs
    • Collaborate with other undergraduate and graduate students in our teams
    • Gain research skills systematically and understand the research process.
    • Acquire abilities in academic writing, presentation, and communication.
    • Improve skills in solving practical engineering problems.
    • Weekly meeting and report the progress.
    • Discuss the project with the advisor and collaborators.
    • Study algorithms and explore the implementation on hardware.
    • Evaluate the system performance.
    • Accomplish a final report and complete a research poster presentation. 
  • Hybrid
  • Dr. Yan Fang, yfang9@kennesaw.edu

Electrical Engineering (Cyril Okhio, Theodore Grosch, Tim Martin, and Austin Asgill)

Research on Brain Augmented Technology (BAT) and STEM-Peer Augmented Success & Support (STEM-PASS)

First-Year Scholars: Dylan Andersen, Liam Begley, Ololade Caulker, Janice Ryoo, & Dandre Williamson

  • The objective of this project is to use EMOTIV EEG Wireless Brain Sensors to design experiments that will enable researchers in the Virtually Integrated Project-Brain Augmented Technology (VIP-BAT) laboratory to continue to achieve brain augmentation for the reinforcement of learning engineering complex concepts and content.

    Designing environments that can calculate how much reward will adequately motivate an operand is of great interest in engineering, and in this research exercise, 3D Immersive Environments, EEG sensing, monitoring, and Data processing Tools, will be utilized. The following Tools are all within the KSU-Vertically Integrated Program-Brain Augmented Technology Research Laboratory - (1) 14-Channel EMOTIV EPOC X Wireless Mobile EEG System; (2) 32 Channel EMOTIV Wireless EEG Brainwear® (3) Real & Virtual Visualization & Simulation Environment and Tools; and (4) Immersion 3D Content Development Tools (zSpace). 

    The established community engagements and the Research Development assistance from Texas Instruments TI for the Robotic System Learning Kit (TI-RSLK) and Driver Interactive Virtual Simulation company for the Driver Simulation Software, will play pivotal roles as we further develop pathways for the STEM-PASS Program. This effort is multi-disciplinary and comprises of the Engineering College & the Psychology department Students & Faculty, and the Marietta Schools' Teachers and students (especially females & underserved students). This effort will also provide a foundation for responses to RFPs from Agencies such as the National Science Foundation NSF, Naval Research Office NRO and the Army Research Office ARO, in the foreseeable future.

  • At the end of each Program period, participants would have demonstrated the value of the following:

    1. Mentoring relationships and the role that gender plays in STEM mentoring, particularly cross-gender mentoring relationships and whether they encourage positive socialization to the field in the same manner as same-gender mentoring relationships.
    2. The role of gender in different types of mentoring models and in the terms of mentoring relationships (i.e., formal, or informal). For instance, studies could examine whether males and females in STEM fields receive the same benefits through formal and informal mentoring programs or whether mentoring relationships that utilize the citizen model facilitate the retention of females within STEM disciplines.
    3. The elements of successful mentoring relationships formed by females in STEM disciplines to provide a more holistic picture of what factors need to be included in the design of such mentoring programs for maximum benefits.

    The Program activities would help students:

    1. Learn and understand Engineering, and Neuroscience concepts.
    2. Learn Experimental Design, Assembly and Testing.
    3. Learn Micro-Controller interfaces with Sensors, Actuators & Motors.
    4. Learn about 3D Immersive Environments, EEG sensing, and Data processing.
    1. Group discussions about being good Team Player
    2. Review Literature on EEG Applications and Robotics
    3. CODE Development exercises.
    4. Data Collection and Analysis.
    5. Regular Presentation of work and state of the art before an audience.
    6. Write Reports on study.
    7. Regular Group Meetings to build rapport/relationships.
    8. Spring Symposium of Scholars presentations.
  • Face-to-Face
  • Dr. Cyril Okhio, cokhio@kennesaw.edu

    Dr. Theodore Grosch, tgrosch@kennesaw.edu

    Dr. Tim Martin, tmarti61@kennesaw.edu

    Dr. Austin Asgill, aasgill@kennesaw.edu

Engineering Technology (Aaron Adams, David Stollberg, and Cameron Coates)

Testing and Simulation of Effects of Raster Angle and Infill Density of Specimens Created Using Additive Manufacturing

First-Year Scholars: Abel Huerta, Eric Miller, & Luke Whitcomb

  • Fused deposition modeling (FDM) is one of the additive manufacturing processes gaining ground in today's industry. FDM or material extrusion (MEX) printing can create parts with “infill” which refers to the amount of material inside the part, and “raster angle", which refers to the amount of material inside the part. FDM creates parts in which the properties depend on the direction which the parts are made.

    Because of the layer-by-layer process of manufacturing parts, different properties can occur depending on the direction of manufacture, often called anisotropy. Despite its advantages, the inability to predict failure and the variability of the mechanical properties of parts manufactured through this process is still difficult. This research aims to study the influence of these parameters on additively manufactured specimens subjected to tensile and impact loads and better understand the anisotropic nature. To advance the understanding of this anisotropic nature, tensile and impact specimens will be manufactured using material extrusion and tested according to ASTM standards.

    In addition to physical testing, computer-aided models will be refined as needed to reflect the “real world” behavior better to understand the anisotropic nature. The computer-aided models
    and physical tests will be compared to verify the accuracy of simulations in predicting failure. The results of the research will aid engineers in the analysis of parts prior to manufacture.

  • Students will develop research and testing techniques through real-world and novel testing. These skills will be invaluable to young researchers who want to contribute and impact through modern additive manufacturing.

    Furthermore, the skills acquired through this program will be helpful for someone wanting to become a researcher or attend graduate school.

  • Students will perform scholarly research and perform physical testing.

    Students will also be responsible for meaningful contributions, as well as weekly meetings and deadlines.

  • Face-to-Face
  • Dr. Aaron Adams, aadam224@kennesaw.edu

    Dr. David Stollberg, dstollbe@kennesaw.edu

    Dr. Cameron Coates, ccoates4@kennesaw.edu

Engineering Technology (David Stollberg and Aaron Adams)

Anisotropy of 3D Printed Materials

First-Year Scholars: Preston Coutinho & Keily Zendejas Benavides

  • The subject research in Additive manufacturing (AM) (aka 3D printing) involves research into the material properties of 3D printed polylactide (PLA) tensile test specimens and the assessment of the properties depending on the orientation of the part during manufacturing.  An isotropic material is one which has the same properties throughout, regardless of direction or orientation.  An anisotropic material will have different properties depending on the orientation; a 3D printed part, may have a texture or grain developed by the direction of the extruded polymer which may have different mechanical properties (strength, hardness, toughness, etc.) when subjected to loads in different directions.  Test specimens are 3D printed and then tensile tested, Charpy impact test and hardness tested (macro and micro tests).  Specimens are 3D printed horizontally, vertically and at a 45 degree angles.

    • Students will learn to design parts in CAD (using SolidWorks), 3D print and print specimens. 
    • Students will learn to tensile test specimens, to perform Charpy impact tests and to operate a hardness tester and microhardness tester.
    • Student will also learn to take and present data in oral and written form.
       
    • Design parts in SolidWorks.
    • Operate 3D printers.  
    • Organize and plan experiments. 
    • Conduct testing. 
    • Analyze results and plan for new experiments.
    • Assist in preparation of technical papers and presentations on the research.
  • Face-to-Face
  • Dr. David Stollberg, dstollbe@kennesaw.edu

    Dr. Aaron Adams, aadam224@kennesaw.edu

Industrial and Systems Engineering (Robert Keyser and Lin Li)

The Use of Emerging Technologies in the Blood Donation Process

First-Year Scholars: Kimberly Kaleta & Fardus Kazi

  • Abstract:
    Blood transfusion is a crucial service of health care systems and contributes to saving and improving millions of lives every year [1], given the increasing complexity of medical and surgical interventions, and improves both life expectancy and quality of life of blood recipients. Since the manufacture of blood in the laboratory is currently impossible, continual blood donor recruitment is critical due to declining blood donations from donors, which threatens national supplies in many countries, including the United States [2] [3]. Various types of emerging technologies, such as mHealth apps [4], virtual reality [5], and blockchain technology [6] to name a few, are being employed in the blood donation industry to attract new donors, process blood products, or deliver blood products to healthcare partners. Artificial intelligence (AI) is an emerging technology that is gaining widespread attention in virtually every industry today, including the blood donation industry [7]. In this research, we explore both current and potential uses of emerging technologies in the procurement, processing, and distribution of blood products to meet the nations’ daily blood supply needs.

    References:
    1. Fordham J, Dhingra N. Towards 100% voluntary blood donation. A global framework for action. World Health Organization, Geneva, 2010.
    2. Simon T. Where have all the blood donors gone? A personal reflection on the crisis in America’s volunteer blood program. Transfusion. 2003: 43(2):273-279. https://doi.org/10.1046/j.1537-2995.2003.00325.x.
    3. Sullivan M, Cotton, R, Read E, Wallace, E. Blood collection and transfusion in the United States in 2001. Transfusion. 2007;47(3):385-394. DOI: https://doi.org/10.1046/j.1537-2995.2003.00325.x. PMID: 12559025.
    4. Li L, Valero M, Keyser R, Ukuku A, Zheng D. Mobile applications for encouraging blood donation: A systematic review and case study. Digital Health. 2023;9:1-15. https://doi.org/10.1177/20552076231203603. PMID: 37822963. PMCID: PMC10563464.
    5. Abbott Laboratories. Taking blood donation to a new dimension of reality. 2023. https://www.abbott.com/corpnewsroom/diagnostics-testing/taking-blood-donation-to-a-new-dimension-of-reality.html.
    6. Varghese A, Thilak K, Thomas S. Technological advancements, digital transformation, and future trends in blood transfusion services. Int J Adv Med. 2024;11(2):147-152. https://doi.org/10.18203/2349-3933.ijam20240368
    7. Raturi M, Dhiman Y, Khatiwada B, Gaur D, Adhikari B, Rawat P. The role of artificial intelligence in optimizing the donation process and predicting blood thresholds. Transfus Clin Biol. 2023;30(4):458-459. https://doi.org/10.1016/j.tracli.2023.08.004. PMID: 37597607.

    1. Students will learn how to effectively conduct a Literature Review.
    2. Students will improve their teamwork, time management, writing, and oral presentation skills.
    3. Students will build responsibility, commitment, and accountability in meeting deadlines with high-quality work.
    1. We will spend several weeks conducting a Literature Review and categorizing manuscripts by common themes.
    2. We will spend several weeks on a journal manuscript prep.
    3. Students will prepare a poster or oral presentation for the 2025 IISE Healthcare Systems Process Improvement Conference in Feb 2025.
    4. Students will prepare a poster or oral presentation for the SP25 KSU Symposium of Student Scholars in April 2025.
  • Online
  • Dr. Robert Keyser, rkeyser@kennesaw.edu

    Dr. Lin Li, lli19@kennesaw.edu

Industrial and Systems Engineering (Luisa Valentina Nino de Valladares)

 A Patient-Centered Decision Support System to Improve Diabetes Management

First-Year Scholars: Joselin Reyes

  • Diabetes is predicted to have a worldwide prevalence of 5.4% by 2025, which translates into 300
    million individuals. The potential impact on society is enormous and an additional urgency has evolved because of increments in obesity, insulin resistance, and type 2 diabetes patients. Long-term complications associated with diabetes can be prevented by intensive glycemic management which requires keeping track of blood sugar levels to see what makes them go up or down.

    Improved health outcomes have been observed in patients who have been educated and skilled to self manage their diabetes. However, both patients and clinicians’ beliefs, attitudes, health literacy,
    communication, and knowledge about diabetes are identified barriers that affect diabetes self management. This project will explore a unique, dynamic data-driven framework for the management and treatment of diabetes by giving patients and providers the opportunity to measure, gather, and customize the display of glucose level data and use the information to support better decision-making to improve medical outcomes.

    This project will advance the knowledge of diabetes, helping in reducing misperceptions regarding the potential seriousness of the disease which can further generate changes in attitudes, beliefs, behaviors, and understanding about the importance of diabetes self-management and care. The student working on the project will perform a literature review by searching different journal databases to gather information about the past and current methods used by patients and clinicians to self-manage diabetes.

    The ultimate objective of this project is to develop a dynamic data-driven framework supported by a non-invasive monitoring glucose device to increase understanding of diabetes care, reduce barriers, increase confidence and motivation needed to improve one’s diabetes self-care management and move towards a collaborative diabetes management and treatment approach.

  • Students will be guided through a methodical and organized process to learn the skills required for
    conducting a literature review. This process encompasses various stages such as effective search
    strategies, critical reading, categorizing relevant information, and summarizing key findings. 
    By the end of the learning experience, students will have compiled their findings into a structured
    spreadsheet, which will serve as a repository for the information they have gathered and
    systematically processed during the literature review journey.

    Moreover, students will take their learning a step further by crafting the preliminary draft of a literature review Journal paper. This practical exercise will help them apply their acquired knowledge in a real-world context, thus reinforcing their understanding of the literature review process and its significance in scholarly research.

    • Searching journal articles, conference articles, and books related to the topic.
    • Reading the articles/books.
    • Identifying if the articles meet the criteria to be part of the study.
    • Summarizing (incorporating the articles into the spreadsheet). 
    • Writing the literature review paper.
  • Hybrid
  • Dr. Luisa Valentina Nino de Valladares, lvallad1@kennesaw.edu

Industrial and Systems Engineering (Lin Li and Robert Keyser)

Latinx Participation in Blood Donation Survey

First-Year Scholars: Emma De Leon Navarro

  • Blood transfusion is a crucial service of health care systems and contributes to saving and improving millions of lives every year [1], given the increasing complexity of medical and surgical interventions, and improves both life expectancy and quality of life of blood recipients. Since the manufacture of blood in the laboratory is currently impossible, continual blood donor recruitment is critical due to declining blood donations from donors, which threatens national supplies in many countries, including the United States [2] [3]. In this research, we will administer a blood donation survey specifically targeting the Latinx community, an underrepresented population in the blood donation process. We intend to administer the survey via the Georgia Dept. of Public Health and the KSU student organization, HOLA. The FYSP student will obtain CITI certification in Human Subjects research, be added to our IRB application, assist with data analysis, manuscript prep, posters, and presentations at the KSU Symposium of Student Scholars (FA24 or SP25) and the Healthcare Systems Process Improvement (HSPI) conference in Atlanta in Feb 2025.

    References:
    1. Fordham J, Dhingra N. Towards 100% voluntary blood donation. A global framework for action. World Health Organization, Geneva, 2010.
    2. Simon T. Where have all the blood donors gone? A personal reflection on the crisis in America’s volunteer blood program. Transfusion. 2003: 43(2):273-279. https://doi.org/10.1046/j.1537-2995.2003.00325.x.
    3. Sullivan M, Cotton, R, Read E, Wallace, E. Blood collection and transfusion in the United States in 2001. Transfusion. 2007;47(3):385-394. DOI: https://doi.org/10.1046/j.1537-2995.2003.00325.x. PMID: 12559025.

  • 1. Describe past research studies in their field of study 
    2. Evaluate research studies they see in the media or encounter in literature 
    3. Write papers and research reports 
    4. Develop problem-solving skills 
    5. Present their research activity to an audience. 
  • 1. Find papers related to the problem 
    2. Summarize papers 
    3. Find gaps in the research 
    4. Discuss research directions with faculty mentors.
    5. Meet weekly with faculty mentors.
    6. Write research papers 
  • Hybrid
  • Dr. Lin Li, lli19@kennesaw.edu

    Dr. Robert Keyser, rkeyser@kennesaw.edu

Industrial and Systems Engineering (Awatef Ergai, Jeanne Law, Brayden Milam, Robert Keyser, Valentina Nino, Lin Li, and Lauren Matheny)

Generative AI in Engineering Education: Stakeholder Perceptions and Utilization

First-Year Scholars: Sai Siddarth Dara, Violet Huggins, Mika Ann Maas, & Aziriah Martin

  • The rapid advancement of artificial intelligence (AI) technologies, particularly generative AI, has significantly impacted various sectors, including engineering education and practice. Generative AI encompasses algorithms, such as Generative Adversarial Networks (GANs) and transformer models (e.g., GPT-4), that can create new content, including text, images, and designs, based on learned patterns from large datasets (Goodfellow et al., 2014; Vaswani et al., 2017). The integration of generative AI in the engineering industry is transforming how engineering tasks are performed, from design automation to predictive maintenance (Davenport & Ronanki, 2018). Recent surveys have highlighted that employers expect engineering graduates to possess AI-related skills and knowledge (Brynjolfsson & McAfee, 2017). This study aims to bridge the gap between academic training and industry expectations by surveying faculty, students, and employers. This study investigates the perceptions and utilization of generative AI among engineering faculty, students, and employers, providing a comprehensive understanding of its current and potential impact on engineering education and the industry.
  • In this project, students will develop critical skills that are important for both research and professional growth. They will learn how to conduct detailed literature reviews, describe ethical research practices and apply them to a research study, complete Citi training, and become IRB certified. Students will gain practical experience in collecting data and analyzing results. They will also enhance their ability to communicate findings clearly through reports and presentations. Additionally, the project will help students build teamwork and project management skills as they collaborate to meet deadlines and complete tasks.
  • The duties from week to week will differ, but they will engage in the following activities:

    • Get Citi certified (IRB)
    • Conduct Literature Review
    • Analyze survey data
    • Attend research meetings
    • Contribute to discussions
    • Prepare presentations for the study
  • Hybrid
  • Dr. Awatef Ergai, aergai@kennesaw.edu

    Dr. Jeanne Law, jlaw29@kennesaw.edu

    Dr. Brayden Milam, bmilam3@kennesaw.edu

    Dr. Robert Keyser, rkeyser@kennesaw.edu

    Dr. Valentina Nino, lvallad1@kennesaw.edu

    Dr. Lin Li, lli19@kennesaw.edu

    Dr. Lauren Matheny, lmathen1@kennesaw.edu

Mechanical Engineering (Jungkyu Park)

Thermal Transport Properties of Deformed Nanomaterials

First-Year Scholars: Joshua Ellison

  • In this research project, students will engage in advanced nanoscale computational modeling to investigate the thermal transport properties of novel low-dimensional nanomaterials, specifically graphene and hexagonal boron nitride (h-BN). This research, part of a newly funded NSF project, aims to advance our understanding of these cutting-edge materials. Students will delve into the mechanical behavior of nanomaterials, exploring their response under various conditions at the nanoscale, and gain hands-on experience with molecular dynamics simulations, learning how to set up, run, and interpret results from complex computational models.

    As part of the research process, students will document their findings and methodologies thoroughly, improving their technical writing skills and preparing detailed technical reports. Participating in this project will allow students to stay abreast of the latest developments in the field of nanomaterials, work with state-of-the-art computational tools and techniques, and gain insights directly applicable to both academic and industry settings. The skills and knowledge acquired through this research are highly valuable and transferable, providing significant assets whether students choose to pursue further academic research or enter the industry. Additionally, students will work closely with faculty members and other researchers, fostering a collaborative environment that encourages the exchange of ideas and peer learning, thus enhancing their research experience and building a network of professional relationships that can benefit their future careers.

    In summary, this research project offers a comprehensive learning experience that combines theoretical knowledge with practical application, equipping students with a deeper understanding of nanomaterials, advanced computational skills, and enhanced technical writing abilities crucial for their professional development in the field of engineering and materials science.

  • While working on this project, students will develop a range of valuable skills and techniques essential for academic research and professional development in engineering and materials science. They will gain hands-on experience with computational modeling, particularly molecular dynamics simulations, to study the thermal transport properties of nanomaterials like graphene and hexagonal boron nitride. Students will deepen their understanding of nanomaterial mechanics, use advanced simulation software, and improve their technical writing by documenting findings in detailed reports. They will enhance their data analysis and problem-solving abilities, collaborate closely with faculty and peers, and learn effective time management. Critical thinking will be fostered as they analyze results and question assumptions, while presentation skills will be honed through opportunities to present their research. This comprehensive skill set will prepare students for successful careers in scientific research and beyond.
  • Each week, students will engage in various activities designed to build their skills and advance the research project. They will set up and run molecular dynamics simulations, analyze the resulting data, and read recent scientific papers to stay informed about the latest developments in the field. Weekly group meetings with faculty advisors and peers will provide a forum for discussing progress, sharing challenges, and receiving feedback, while technical writing tasks will help students document their research process and results in detailed reports. Periodic workshops will teach specific skills such as advanced data analysis techniques and effective use of simulation software. Students may also conduct complementary experimental activities to validate their computational models, participate in problem-solving sessions to address specific research challenges, and collaborate with peers on joint tasks. Towards the end of each week, students will prepare and deliver presentations summarizing their progress and plans for the upcoming week, ensuring continuous feedback and enhancing their communication skills. These structured activities provide a balanced and comprehensive research experience, equipping students with the necessary skills and knowledge to succeed in their academic and professional endeavors.
  • Face-to-Face
  • Dr. Jungkyu Park, jpark186@kennesaw.edu

Mechanical Engineering (Ayse Tekes and Coskun Tekes)

Design, Development, and Control Of An Upper Body Compliant Exoskeleton

First-Year Scholars: Layla Burnette, Isaiah Iwayemi, Nylah Mack, Carlos Mendoza-Lomeli, Andrew Parella, & Sabrina Scarpinato

  • Exoskeleton based robotic devices have been increasingly popular in recent years for upper extremity disorders. Several studies have reported promising results on the effectiveness of these robotic assistive systems for the rehabilitation procedures of stroke survivors. These systems mainly focus on assisting the patients with the guidance of physical therapist during long term rehabilitation protocol. Therefore, almost all designs are unportable systems having large and cumbersome that are installed and mounted inside physical therapy clinics. Although these devices play an important role on regaining the effected body functions for the patients, there is a growing demand for a wearable active exoskeleton device which can provide power amplification and motion support to assist patient’s daily living activities.

    The goal of this research is to design and implement a 3D printed user-friendly wearable exoskeleton device which can be donned on upper limb and assist the daily activities of the patients with neuromuscular disorders providing support on their complete arm. The key idea in this project is to use 3D printing to manufacture the main exoskeleton parts. This provides the advantage of designing a lightweight exoskeleton body having minimum number of pieces and assembly requirements. Achieving a light exoskeleton is specifically important to increase children patient’s access and utilization of such systems which is a huge drawback of currently available commercial devices. On the other hand, the proposed design and manufacturing method also provides high flexibility to implement custom exoskeletons easily to improve ergonomics of the user. Student will be working closely with a team of undergraduate students and a PhD student.

  • I strongly encourage my students who are interested in pursuing their graduate degree to gain research experience as undergraduates. This kind of experience can also impact the future career of undergraduates. Hands-on learning enables senior students to apply the skills learned in the classroom to their studies. They will have the opportunity to take the knowledge gained and apply it to find out new solutions to the existing well-defined problems. It is often really cumbersome to have a deep understanding of the theoretical concepts when it's presented in a two-dimensional manner via lecture and textbook without the benefit of direct experience. Learning to conduct literature reviews; discovering the process of methods selection; controlling for variables; conducting experiments by selecting the most suitable equipment; statistically working through the results; and preparing for presentation are key skills that will be fine-tuned through the students' research studies. Specifically in this project, student will be working closely with a team of undergraduate students and a PhD student.

    1. Conduct literature review
    2. Get training on 3D printing
    3. Design complex mechanisms in SolidWorks, student will be given instructions on how to strengthen their skills on drawing cad models if necessary
    4. Develop designs by 3D printing
    5. Assemble parts
    6. Actuate DC motors using Arduino, Phyton, and Raspberry Pi
    7. Test the performance of the prototype
    8. Acquire, analyze and interpret data from setup
    9. Work effectively as part of a team
    10. Write a research paper
    11. Present their research paper at related conference in 2025
  • Face-to-Face
  • Dr. Ayse Tekes, atekes@kennesaw.edu

    Dr. Coskun Tekes, ctekes@kennesaw.edu

Mechanical Engineering (Lei Shi)

Creating a Digital Twin of the Human Heart Using Machine Learning

First-Year Scholars: Yusuf Al Kadi, Emmanuel Crawford, Skylar Herb, Luke Jackson, Anshu Rao, Aissatou Sarr, & Josiah Ware

  • This project aims to develop a cutting-edge digital twin of the human heart through advanced machine learning techniques. A digital twin is a virtual model of a physical entity—in this case, the human heart—that can simulate its behavior in real-time using patient-specific data. Our research integrates diverse data sources, including ECG (electrocardiogram), pressure measurements, and 4D CT imaging, to create highly accurate and personalized models of heart function.

    The project will engage first-year students in a comprehensive research experience, where they will contribute to various aspects of this innovative study. Students will work on tasks such as data preprocessing, machine learning model development, and simulation validation. By participating, students will gain hands-on experience with cutting-edge technologies and learn how to apply machine learning to solve complex biomedical problems.

    Throughout the year, students will be involved in:

    1. Data Analysis: Handling and analyzing real patient data, including ECG signals, pressure readings, and 4D CT images. Students will learn how to preprocess and prepare these datasets for modeling.

    2. Model Development: Building and training machine learning models to simulate heart mechanics. Students will work with advanced algorithms and learn how to optimize models for accuracy.

    3. Simulation and Validation: Running simulations to compare model predictions with actual patient data. Students will assess the performance of the digital twin and make necessary adjustments.

    4. Presentation and Reporting: Preparing and presenting their findings at the Symposium of Student Scholars in April. Students will also be responsible for documenting their progress and contributing to mid-year and end-of-semester evaluations.

    Mentorship will include regular meetings to guide students through their research tasks and ensure they meet project milestones. Students will also participate in mandatory orientation sessions and complete required progress reports. This project provides a unique opportunity for first-year scholars to delve into interdisciplinary research, bridging machine learning and biomedical sciences, while developing essential research skills and contributing to the advancement of personalized medicine.

  • In working on the project "Creating a Digital Twin of the Human Heart Using Machine Learning," students will gain valuable skills and techniques essential for both research and professional development. Key skills include:

    1. Data Analysis and Preprocessing: Students will learn to handle and preprocess complex datasets, including ECG signals, pressure measurements, and 4D CT images. They will acquire skills in data cleaning, normalization, and transformation to prepare data for analysis.

    2. Machine Learning Techniques: Students will gain experience in applying machine learning algorithms to develop predictive models. They will learn about various techniques, including supervised learning, neural networks, and deep learning. Practical experience with model training, validation, and optimization will be key components.

    3. Programming and Software Tools: Proficiency in programming languages such as Python will be developed, along with familiarity with libraries and frameworks like TensorFlow, PyTorch, and scikit-learn. Students will also gain experience with data visualization tools and software for model implementation.

    4. Simulation and Modeling: Students will learn to create and validate simulations of heart mechanics using digital twin technology. They will understand how to integrate real patient data into these models and assess their performance against actual outcomes.

    5. Critical Thinking and Problem-Solving: The project will enhance students' problem-solving skills by challenging them to address complex research questions and optimize their models based on performance metrics and data analysis.

    6. Communication and Presentation: Students will develop skills in communicating complex research findings effectively. They will prepare and present their results at the Symposium of Student Scholars, learning how to articulate their research processes and outcomes to diverse audiences.

  • Students will engage in a variety of activities each week as part of the project "Creating a Digital Twin of the Human Heart Using Machine Learning." These activities will include:

    1. Data Segmentation and Processing: Weekly, students will work on segmenting 3D models from CT data. This involves using specialized software to extract relevant structures and features from the imaging data. They will also preprocess and prepare these models for further analysis.

    2. Machine Learning Techniques: Students will dedicate time each week to learning and applying machine learning techniques. This includes studying various algorithms, such as neural networks and deep learning models, and understanding their applications in simulating heart mechanics. They will implement these techniques using programming languages and tools like Python and PyTorch.

    3. Algorithm Training and Simulation: Students will apply machine learning algorithms to train models with the prepared data. This involves running simulations, adjusting model parameters, and refining their approaches based on performance metrics. They will work on developing and validating accurate simulations of heart function.

    4. Bi-Weekly Meetings: Every two weeks, students will meet with me to discuss their progress, present their results, and receive feedback. These meetings will provide an opportunity to address challenges, refine techniques, and ensure alignment with research objectives.

    5. Presentation Preparation: At the end of each semester, students will prepare a presentation to showcase their work and outcomes. This includes summarizing their research activities, data analysis, and results. They will practice presenting their findings clearly and effectively to both peers and faculty. 

  • Hybrid
  • Dr. Lei Shi, lshi@kennesaw.edu

Mechanical Engineering (Ashish Aphale)

Metal Oxide based Electrode Development for Electrochemical Energy Storage Devices

First-Year Scholars: Davis Pham & Elliott Yancey

  • Combining solar and wind power with reliable energy storage technology like batteries and capacitors is vital to meet our growing clean energy needs and reach net-zero emission goals. To make these energy storage systems more efficient, we focus on improving the materials used in the electrodes—the parts of batteries and capacitors where the energy reactions occur. Our project aims to explore and enhance metal-oxide materials to boost electrode performance. By studying these materials at the nanoscale, we can better understand the key processes that drive their electrochemical behavior.

    Our research will develop innovative methods to create high aspect ratio nanostructures. These nanostructures will increase the surface area and stability of the electrodes, leading to better performance and longer-lasting energy storage systems. This advancement is a step forward in building a sustainable and clean energy future.

  • 1. Understand the importance and basic principles of clean energy technology.
    2. Conduct hands-on experimental research, generating results and performing data analysis for making meaningful conclusions.
    3. Learn the cutting-edge characterization techniques used in materials science.
    4. Engage in writing research articles and conference proceedings. 
  • The student will conduct weekly experimental duties. The student will participate in weekly meetings with the research group to discuss the progress and plan for future experimental work. Conduct hand-on experiments in lab and analyze the generated data.
  • Face-to-Face
  • Dr. Ashish Aphale, aaphale@kennesaw.edu

Mechanical Engineering (Gaurav Sharma)

Numerical Analysis of Wing Vortex Interaction in Tandem Wing Configuration

First-Year Scholars: Hannah Ahmed, Brennan Anderson, Matthew Ballard, James Banks, Frank Kellett, Abdullah Quadri, Lea Scott, & Choloe White

  • The project focuses on investigating the aerodynamic behavior of tandem wing configurations, a topic of significant importance in the design of advanced aircraft and unmanned aerial vehicles (UAVs). The primary objective is to understand the complex interactions between the vortices generated by the leading wing and their impact on the aerodynamic performance of the trailing wing. This research is driven by the need to optimize tandem wing arrangements, which can potentially offer higher lift-to-drag ratios, improved stability, and enhanced maneuverability in various flight regimes.

    The study will employ advanced computational fluid dynamics (CFD) techniques to simulate the flow fields around the tandem wing configuration. The numerical analysis will be conducted using high-fidelity CFD software, with turbulence modeling approaches such as Reynolds-Averaged Navier-Stokes (RANS), Large Eddy Simulation (LES), or Detached Eddy Simulation (DES) to capture the intricate details of vortex formation, shedding, and interaction. The simulation results will provide detailed insights into the wake dynamics, vortex trajectories, and their influence on the pressure distribution, lift, drag, and overall aerodynamic efficiency of the tandem wing arrangement.

    A key aspect of the project is the parametric study, where different wing configurations, spacing, and angles of attack will be analyzed to determine their effect on vortex interaction and aerodynamic performance. The study will also explore the effects of varying Reynolds numbers to understand the performance characteristics across different flight conditions. The results from the numerical simulations will be validated against available experimental data or empirical correlations to ensure accuracy and reliability.

    The outcomes of this research are expected to contribute to the design guidelines for tandem-wing aircraft, enabling the development of more efficient and high-performance aerial vehicles. The findings will have implications for both military and civilian applications, where tandem wing configurations are being considered for their potential advantages in specific mission profiles.

    Overall, this project aims to advance the understanding of vortex interactions in tandem wing arrangements, providing valuable insights that can lead to innovative design strategies in the aerospace industry. The research will culminate in the development of comprehensive design recommendations and potential optimization strategies for future tandem-wing aircraft designs.

  • Students participating in this project will develop a robust set of skills and techniques that are essential for careers in aerospace engineering and related fields.

    Firstly, students will gain hands-on experience with advanced computational fluid dynamics (CFD) software, learning how to set up, run, and analyze simulations of complex aerodynamic systems. They will become proficient in using CFD tools such as ANSYS Fluent, which are industry standards, and will learn how to apply different turbulence modeling approaches like Reynolds-Average Navier-Stokes Equations (RANS), Large Eddy Simulation (LES), or Detached Eddy Simulation (DES) to capture the intricate details of vortex dynamics.

    In addition to technical software skills, students will develop strong analytical and problem-solving abilities. They will learn to interpret the complex data generated from simulations, extracting meaningful insights into the behavior of vortices in tandem wing configurations. This process will involve understanding and applying fundamental aerodynamic principles, as well as critically evaluating the accuracy and reliability of their results.

    Moreover, students will enhance their research and experimental design skills by conducting a parametric study to explore the effects of different wing configurations, spacing, and angles of attack. They will learn how to design experiments that test specific hypotheses, control variables effectively, and draw conclusions based on empirical data.

    Collaboration and communication skills will also be a significant focus. Students will work in teams, learning how to collaborate effectively, share responsibilities, and integrate their contributions into a cohesive research effort. They will also develop the ability to communicate their findings clearly and concisely, whether through written reports, presentations, or discussions, which is critical for any professional or academic career.

    Overall, this project provides students with a comprehensive skill set that prepares them for future challenges in both academic and industry settings.

  • Throughout the two-semester duration of this project, students will engage in a series of structured and progressive activities designed to build their skills, knowledge, and research capabilities. The proposed tentative plan for two semesters is as follows:

    Semester 1:

    Weeks 1-3: Orientation and Background Research

    • Students will start by reviewing fundamental concepts in aerodynamics and computational fluid dynamics (CFD).
    • They will engage in literature reviews to understand current research on tandem wing configurations and vortex interactions.

    Weeks 4-6: CFD Software Training

    • Hands-on training sessions with CFD software (e.g., ANSYS Fluent) will be conducted.
    • Students will complete basic tutorials to become familiar with setting up simulations, mesh generation, and running simple flow analyses.

    Weeks 7-10: Simulation Setup and Preliminary Runs

    • Students will begin setting up initial simulations of a basic tandem wing configuration.
    • They will define the geometry, select appropriate turbulence models, and perform preliminary simulation runs.

    Weeks 11-15: Data Analysis and Mid-Semester Review

    • Students will analyze the initial simulation data, focusing on vortex formation and flow characteristics.
    • A mid-semester review will be conducted, where students present their findings and receive feedback to guide the next phase of the project.

    Semester 2:

    Weeks 1-4: Advanced Simulation Runs

    • Students will refine their simulation models based on feedback and extend their analysis to more complex wing configurations.
    • They will conduct a parametric study to explore the effects of varying spacing, angles of attack, and Reynolds numbers.

    Weeks 5-8: Validation and Comparison

    • Students will validate their simulation results against existing experimental data or empirical correlations.
    • They will perform comparative analyses to evaluate the accuracy and reliability of their models.

    Weeks 9-12: Documentation and Presentation Preparation

    • Students will compile their findings into a comprehensive report.
    • They will develop presentations for internal reviews, conferences, or potential publications.

    Weeks 13-15: Final Review and Reflection

    • A final project review will be held, where students present their complete research findings.
    • They will reflect on the research process, discussing lessons learned and potential future research directions.
  • Hybrid
  • Dr. Gaurav Sharma, gsharma3@kennesaw.edu

Mechanical Engineering (Mehdi Sarmast)

The Siemens - Software and Hardware Setup Package

First-Year Scholars: Omar Gervacio-Pedraza

  • The project involves learning to use Siemens software and setting up a test rig, which could be a plate or a model of a wing. The student will work with equipment such as Data Acquisition systems and a shaker. This hands-on experience will teach them how to collect data from a rig, like a wing, by applying vibrations. Welcome to the world of Modal Analysis.
  • While working on this project, students will learn and develop the following skills and techniques:

    1) Modal analysis
    2) Siemens software
    3) Vibration testing
    4) Rig setup
    5) Data collection
    6) Result validation
    7) Data analysis
    8) How to write a report

  • Each week, students will engage in a variety of activities, including:

    a) Watching YouTube tutorials or attending webinars to learn Siemens software.
    b) Gaining an understanding of how the equipment operates.
    c) Setting up the vibration testing rig.
    d) Collecting data from the completed tests.
    e) Analyzing the data.

  • Face-to-Face
  • Dr. Mehdi Sarmast, msarmast@kennesaw.edu

Mechanical Engineering (Sathish Kumar Gurupatham)

AI-Driven Fruit Quality Inspection: Utilizing Detectron2 for Automated Bruise Detection and Analysis

First-Year Scholars: Bao Nguyen & Moneesh Rajaram

  • This research aims to advance artificial intelligence (AI) for detecting bruises and assessing the quality of fruits using cameras at manufacturing sites. By improving AI techniques, we can enhance fruit quality and extend shelf life. The study employs convolutional neural networks (CNNs), semantic segmentation, and object detection.

    The research will begin with annotating images of bruised fruits using bounding boxes to create a comprehensive dataset. We then apply preprocessing techniques and data augmentation to optimize the dataset for training AI models. Common challenges include variations in lighting across different cameras and different fruit orientations. AI models will be developed to accurately identify bruised areas on fruit surfaces. We will use various segmentation techniques for locating bruised regions and semantic segmentation for higher accuracy. Object detection will further assess and classify defects by measuring the size and severity of bruises. These AI models will be integrated into fruit sorting automation systems. High-speed fruit production often relies on automated sorting to handle large volumes, as manual sorting is insufficient and can cause additional damage. AI techniques will address practical issues such as varying illumination and inconsistent fruit orientations during sorting, reducing fruit loss and improving efficiency. The models' performance will be evaluated using industry-standard metrics to ensure accuracy, reliability, and effectiveness in real-world settings. 

    Overall, this research seeks to bridge the gap between advanced AI techniques and practical applications in agriculture. By developing a lightweight, AI-driven fruit quality assessment system, the study aims to improve fruit sorting and quality control practices, particularly by efficiently identifying and grouping bruised fruits for lower-priced sales.

  • The students' learning outcomes from the project on leveraging AI for fruit quality control could include:

    1. Application of AI Techniques: Students will be able to apply advanced AI techniques such as convolutional neural networks, semantic segmentation, and object detection to solve real-world problems in agriculture.

    2. Data Collection and Preprocessing: Students will learn to collect and preprocess high-resolution images of fruits, ensuring consistency in lighting and orientation, which are crucial for training effective AI models.

    3. Model Development and Evaluation: Students will develop AI models capable of identifying and measuring bruises on fruits. They will also evaluate these models using industry-relevant metrics to ensure accuracy and reliability in practical settings.

    4. Problem-Solving Skills: Through the project, students will enhance their problem-solving skills by addressing challenges such as varying lighting conditions and fruit orientations in the application of AI.

    5. Integration of AI in Industry: Students will understand how to integrate AI models into automated systems, improving efficiency and accuracy in fruit sorting processes, which is crucial for high-speed operations in the industry.

    6. Research and Development: Students will contribute to bridging the gap between theoretical AI techniques and practical agricultural applications, gaining insights into research and development processes.

  • Students will be working on these phases:

    Introduction and Overview

    - Orientation session introducing the project objectives, importance of fruit quality control, and the role of AI.

    Data Collection

    - Collect high-resolution images of various fruits with different levels of bruising.

    Data Annotation

    - Annotate the collected images to mark bruised areas and categorize defects.

    Preprocessing Techniques

    - Learn and apply preprocessing techniques to optimize images for model training (e.g., adjusting lighting and orientation).

    Model Selection and Training

    - Introduction to various AI models (CNNs, semantic segmentation) and begin training selected models on the dataset.

    Model Evaluation

    - Evaluate the trained models using industry-relevant metrics (accuracy, reliability).

    Integration into Automated Systems

    - Explore how the developed models can be integrated into automated fruit sorting systems.

    Addressing Real-World Challenges

    - Discuss and devise strategies to tackle challenges such as varying lighting conditions and fruit orientations.

    Continuous Improvement and Feedback Loop

    - Analyze how ongoing use of AI can improve model accuracy and performance over time.

    Final Presentation and Reporting

    - Prepare a presentation summarizing findings, model performance, and potential impacts on the fruit industry.

  • Face-to-Face
  • Dr. Sathish Kumar Gurupatham, sgurupat@kennesaw.edu

Mechanical Engineering (Dal Hyung Kim)

Biomimetic Gait Modeling from Small Walking Creatures in Nature Using Motion Compensator

First-Year Scholars: Robert Henderson, Michaella Hopfner, Jonathan Jackson, & Josephine Phillips

  • Biomimetics, the emulation of nature's models, systems, and elements, plays a crucial role in advancing the field of robotics, particularly in the development of multi-legged robots. These robots, inspired by the complex and efficient locomotion mechanisms found in nature, hold significant promise for a variety of applications, from search and rescue missions to exploration of difficult terrains. The biomimetic approach is advantageous because it allows for the development of robots that are not only more efficient but also more adaptable to diverse environments. Nature has perfected the art of movement over millions of years, and by studying the gait patterns of small, multi-legged creatures, researchers can design robots that mimic these natural movements, leading to more stable, agile, and energy-efficient robotic systems. Hexapod robots, which are often modeled after insects, are a prime example of biomimetics, as they can navigate uneven terrain with remarkable stability and precision.

    The main objective of this project is to investigate various biomimetic gait locomotion observed in small creatures, such as insects, using a specially developed locomotion compensator. This will involve the modification of an existing locomotion compensator to create a large-scale model capable of accurately capturing and analyzing the gait patterns of various insects found around our campus. In addition, we will develop a Central Pattern Generator (CPG) model for each gait pattern observed. The CPG models will be crucial in understanding the underlying principles of these natural movements, allowing for a comparative analysis of the similarities and differences in gait patterns among various multi-legged creatures. This research is expected to contribute valuable insights into the biomechanics of small animals, which can then be applied to the design of more efficient and effective multi-legged robots.

    The expected outcome of this project is the design and development of a biomimetic multi-legged robot that emulates the gait patterns found in small creatures, using compliant mechanisms. This robot will serve as a tangible demonstration of the effectiveness of biomimetic principles in robotics. Additionally, the project will study the efficiency of these biomimetic gait patterns in comparison to artificial gait patterns currently used in robotic systems. By exploring the various locomotion techniques of small animals, the project aims to identify the most efficient and adaptable gait patterns, ultimately contributing to the advancement of robotics technology. This project is expected to be conducted by undergraduate students in various backgrounds fields such as mechanical engineering and biology.

  • The project will provide students with a comprehensive set of skills and techniques that are essential for both their academic development and future professional careers. Through hands-on research, collaboration, and mentorship, students will gain valuable experience in the following areas:

    • Proficiency in data collection and analysis, focusing on gait patterns of small insects.
    • Experience in modifying and operating a locomotion compensator, including mechanical and electronic components.
    • Development of computational modeling skills through the creation and refinement of Central Pattern Generator (CPG) models.
    • Familiarity with software tools for simulation, analysis, and data visualization.
    • Application of scientific methods for hypothesis testing and data-driven decision-making.
    • Development of creative problem-solving techniques for modifying existing tools and approaches.
    • Experience in troubleshooting and iterative design to overcome research challenges.
    • Enhancement of teamwork and collaboration skills through engagement with peers and mentors.
    • Improvement in communication abilities, including presenting research findings in written reports, oral presentations, and possibly academic publications.

    These outcomes will prepare students not only for continued success in their academic careers but also for future roles in research and industry, making them well-rounded and skilled contributors to their fields.

  • Throughout the project, students will engage in a variety of activities designed to build their skills progressively and contribute to the overall research objectives. The weekly duties will include a combination of hands-on experiments, data analysis, team meetings, and documentation, ensuring a well-rounded research experience. The following are some examples of activities the students are expected to conduct.

    * Research Activities (Data Collection and Analysis)

    - Locomotion Compensator Redesign and Operation: Students will redesign and operate the modified locomotion compensator to capture the gait patterns. They will learn how to calibrate the equipment and ensure accurate data collection.
       - Insect Observation and Recording: Students will collect gait pattern data from small insects found around the campus. This will involve setting up and using a motion compensator to record the movements of these insects.
       - Video Analysis and Modeling: After collecting data, students will analyze the recorded videos to extract precise gait patterns. They will then begin developing and refining Central Pattern Generator (CPG) models based on this data.

    * Weekly Meetings and Documentation:

    - Weekly Progress Meetings: Students will participate in weekly team meetings with their peers and mentors to discuss progress, troubleshoot challenges, and plan for the upcoming week.
       - Research Log Maintenance: Students will maintain a detailed research log, documenting their weekly activities, observations, and any modifications made to the experimental setup.
       - Report Writing: As the project progresses, students will draft sections of the final research report, summarizing their findings and reflecting on their experiences.

    These duties will provide students with a comprehensive understanding of the research process, from data collection to analysis and reporting, ensuring they are fully engaged in each aspect of the project.

  • Face-to-Face
  • Dr. Dal Hyung Kim, dkim97@kennesaw.edu

Robotics and Mechatronics Engineering (Razvan Voicu and Muhammad Tanveer)

Artificial General Intelligence - Control of Real-time Ecosystems (AGI CORE)

First-Year Scholars: Sean Haque, J.R. Johnson, Ming Lin, & Luke Wharton

  • Welcome to the future of Artificial General Intelligence (AGI)! Our project, AGI CORE, focuses on the exciting and groundbreaking field of AGI, particularly its application in real-time control of ecosystems involving robotics and various environments. Imagine a world where machines can understand, learn, and adapt just like humans, creating seamless interactions between intelligent machines like robots, their surroundings, and humans.

    AGI is in its inception phases, making this an incredibly exciting time to get involved. Our project aims to develop intelligent systems capable of real-time decision-making and autonomous control. These systems will be able to manage complex environments, such as smart homes, autonomous vehicles, robotic assistants, and biomedical applications. Picture a smart healthcare system where AGI can analyze patient data, monitor vital signs with embedded sensors, and assist in surgeries with robotic precision.

    Joining AGI CORE in the VOICU (Vision of Immortal Cell Understanding) lab will put you at the forefront of this emerging discipline. We have access to cutting-edge robotic systems, sensors, and embedded units, providing you with the tools to explore and innovate. We seek enthusiastic students eager to grow and learn during and beyond the first-year program. This project offers a unique opportunity to work on cutting-edge technology, develop technical and soft skills, and contribute to significant advancements in AGI. You will hone your programming abilities, work with advanced AI algorithms, and gain experience with real-time systems. Additionally, you will enhance your problem-solving, teamwork, and communication skills, essential for professional growth.

    Whether you're passionate about robotics, artificial intelligence, biomedical applications, or innovative problem-solving, AGI CORE provides a platform to explore and expand your horizons. We invite students from all disciplines to join us, as there is room for growth for those in the sciences, including biology, chemistry, physics, humanities, arts, and beyond. Imagine an AGI system developing new music or art. AGI, with its human-like intelligence, seeks to understand and interact with the world in a way that integrates knowledge from diverse fields, making your unique perspective invaluable.

    You'll collaborate with a diverse team of students and faculty, gaining hands-on experience and developing skills that will set you apart in the rapidly evolving tech landscape. Join us in shaping the future of intelligent systems and making a meaningful impact on the world. Together, we'll push the boundaries of what AGI can achieve and pave the way for a smarter, more connected future.

  • Participating in the AGI CORE project will equip students with a diverse set of skills and techniques, preparing them for future careers in AI, robotics, and other related fields. Here are some of the key outcomes:

    Technical Skills: Students will gain proficiency in programming languages such as Python and C++, which are essential for developing AGI systems. They will learn to implement machine learning algorithms, work with neural networks, manage large datasets, and understand the fundamentals of artificial intelligence. Additionally, they will explore and understand multimodal AGI, integrating various forms of data such as visual, auditory, and textual inputs to create more robust intelligent systems.

    Hands-on Experience: Through practical projects, students will design, build, and test intelligent systems. They will work with advanced robotics platforms, sensors, and embedded units, gaining experience in hardware integration and real-time system control.

    Research Skills: Students will develop strong research abilities, including literature review, hypothesis formulation, experimental design, and data analysis. They will document their findings and prepare research papers for publication, contributing to the academic community.

    Soft Skills: The project emphasizes developing soft skills such as problem-solving, teamwork, and communication. Students will collaborate with peers and mentors, enhancing their ability to work effectively in diverse teams and present their ideas clearly.

    Interdisciplinary Knowledge: Students will learn to approach problems from multiple perspectives by integrating knowledge from various fields. This holistic understanding is crucial for developing innovative AGI solutions that interact seamlessly with human environments.

    Professional Growth: The experience gained through AGI CORE will make students attractive candidates for internships, jobs, and further academic pursuits. They will build a portfolio of work demonstrating their capabilities and accomplishments.

    By the end of the project, students will have a comprehensive skill set and practical experience, positioning them for success in the rapidly evolving tech landscape.

  • Students involved in the AGI CORE project will engage in various weekly activities, ensuring a well-rounded and immersive learning experience. Here's a breakdown of potential weekly duties:

    Programming and Development: Students will dedicate time to coding and developing algorithms for AGI systems, including developing AI Assistants. This includes writing and testing code, debugging, and optimizing performance to ensure efficient operation of intelligent systems.

    Research and Literature Review: Students will review scholarly articles and research papers to stay updated with the latest advancements in AGI, helping them to understand current trends, identify gaps in the field, and generate new ideas for their projects.

    Experimentation: Students will conduct experiments to test their AGI models. This involves setting up experiments, collecting data, and analyzing results to evaluate the effectiveness of their solutions. They will iterate on their designs based on the findings.

    Team Meetings: Regular meetings will be held to discuss progress, share insights, and plan upcoming tasks. These meetings allow students to collaborate, seek feedback, and refine their approaches.

    Documentation: Students will document their work, including coding procedures, experimental methods, and findings, which will help them keep track of their progress and prepare them for writing research papers and reports.

    Mentorship Sessions: Students will have one-on-one sessions with faculty mentors to receive guidance, ask questions, and discuss challenges. 

    Presentations: Periodically, students will present their work to the team or at academic forums. This helps them develop their presentation skills and gain confidence in sharing their research with others.

    Interdisciplinary Collaboration: Students will engage in activities integrating knowledge from various fields, encouraging them to think creatively and approach problems from multiple perspectives.

    By participating in these activities, students will gain a comprehensive understanding of AGI, develop practical skills, and prepare for future academic, professional, and start-up endeavors.

  • Hybrid
  • Dr. Razvan Voicu, rvoicu@kennesaw.edu

    Dr. Muhammad Tanveer, mtanveer@kennesaw.edu

Robotics and Mechatronics Engineering (Muhammad Tanveer and Razvan Voicu)

Precision Farming with Husky UGV: Enhancing Agricultural Efficiency through Robotics

First-Year Scholars: Montreal Fletcher, Hunter Richards, Sheraz Saudagar, & Whitney Youmans

  • As the agricultural sector embraces advanced technologies, precision farming stands out as a key strategy for enhancing efficiency, productivity, and sustainability. This project aims to fuse robotics with precision agriculture by employing the Husky Unmanned Ground Vehicle (UGV) to gather and analyze farm data. The Husky UGV, known for its robustness and versatility, will serve as the main robotic platform, enabling students to optimize farming practices and support sustainable agricultural management.

    Objectives

    Data Collection: Utilize the Husky UGV to collect diverse farm data, including soil moisture, temperature, humidity, and crop health indicators.
    Precision Agriculture: Leverage collected data to facilitate precision agriculture techniques like site-specific crop management and variable rate application of inputs.
    Robotic Integration: Develop and implement advanced sensor integration and data processing techniques to enhance the Husky UGV's capabilities.
    Sustainability: Support sustainable farming practices by minimizing resource wastage and improving crop yields.
    Student Engagement: Offer hands-on experience in robotics, data analytics, and agricultural sciences.

    Project Components

    Robotic Platform: The Husky UGV, equipped with a range of sensors and cameras, will navigate the farm to collect real-time data.
    Sensor Integration: Various sensors will be integrated, including:

    • Soil Moisture Sensors for irrigation planning.
    • Thermal Cameras to detect plant stress or disease.
    • Multispectral Cameras for crop health assessment.
    • LIDAR for farm topography mapping.

    Data Processing and Analysis: Implement advanced algorithms for data interpretation, using machine learning and statistical analysis to provide actionable insights.
    Precision Farming Techniques: Insights from data analysis will guide variable rate technology (VRT), predictive modeling, and crop monitoring.
    Sustainability Practices: Focus on reducing water usage, minimizing chemical inputs, and enhancing soil health.

    Expected Outcomes

    • Improved Farm Productivity through precision agriculture techniques.
    • Resource Optimization by precise application of water, fertilizers, and pesticides.
    • Enhanced Student Learning in robotics and data analytics.
    • Sustainable Farming Practices that promote responsible resource use and long-term sustainability.
  • Working on this project will equip students with a diverse set of skills and techniques that are essential for a career in robotics, data science, and precision agriculture. Students will gain hands-on experience in the following areas:

    C++ Programming: Students will develop their proficiency in C++, a crucial language for robotics and embedded systems. They will learn to write efficient and optimized code for the Husky UGV’s operations, sensor integrations, and data processing tasks. This will include writing and debugging code to control the UGV and interact with various sensors and hardware components.

    ROS (Robot Operating System): The project will provide extensive experience with ROS, a flexible framework for writing robot software. Students will learn to use ROS for developing software that enables the Husky UGV to navigate, collect data, and communicate with other systems. They will become familiar with ROS tools and libraries for sensor data acquisition, data processing, and control algorithms.

    Python Programming: In addition to C++, students will enhance their skills in Python, a versatile language commonly used in data science and machine learning. They will use Python to develop algorithms for data analysis, machine learning models, and visualization tools. Python’s simplicity and powerful libraries make it ideal for rapid prototyping and data-driven decision-making.

    Mathematics and Data Analysis: The project involves significant mathematical and statistical analysis. Students will apply mathematical concepts to process sensor data, model environmental conditions, and interpret results. They will use statistical methods and machine learning algorithms to analyze patterns in the data, derive insights, and make predictions about farm conditions.

    Interdisciplinary Problem-Solving: Through the integration of robotics, data science, and agriculture, students will develop a holistic approach to problem-solving. They will learn to collaborate across disciplines, apply theoretical knowledge to practical problems, and innovate solutions for sustainable farming practices.

    Overall, this project will provide a comprehensive learning experience that combines technical skills with creative problem-solving, preparing students for future challenges in robotics and agriculture.

  • Each week, students will participate in a variety of hands-on and collaborative activities designed to develop their skills and understanding of robotics and precision agriculture:

    Programming and Software Development: Students will spend time coding in C++ and Python, writing and testing scripts for controlling the Husky UGV and processing data from various sensors. This will include debugging and refining their code to ensure reliable operation and accurate data collection.

    ROS (Robot Operating System) Practice: Each week, students will work with ROS to build, test, and improve the software that enables the Husky UGV to perform its tasks. This involves setting up ROS nodes, configuring launch files, and managing data flow between different components.

    Sensor Integration: Students will regularly check and calibrate sensors on the Husky UGV, such as soil moisture sensors, thermal cameras, and multispectral cameras. They will ensure that sensors are properly integrated and functioning correctly to collect accurate data from the farm.

    Data Analysis and Interpretation: Using Python and statistical tools, students will analyze the data collected by the Husky UGV. This will involve organizing data, applying basic statistical methods, and creating visualizations to understand patterns and insights.

    Team Meetings and Collaboration: Weekly team meetings will allow students to discuss their progress, share findings, and plan upcoming tasks. They will collaborate to solve problems, exchange ideas, and develop strategies for improving the UGV’s performance and data accuracy.

    Documentation and Reporting: Students will maintain documentation of their weekly activities, code changes, and data analysis results. This will include writing brief reports summarizing their findings and any challenges they encountered.

    Project Management: Students will also learn to manage their time and resources effectively, setting goals for the week and tracking their progress. They will prioritize tasks and coordinate with team members to ensure the project stays on schedule.

  • Face-to-Face
  • Dr. Muhammad Tanveer, mtanveer@kennesaw.edu

    Dr. Razvan Voicu, rvoicu@kennesaw.edu

Robotics and Mechatronics Engineering (Amir Ali Amiri Moghadam and Turaj Ashuri)

Development of a Walking Robot as a Personal Assistant

First-Year Scholars: Navaneeth Jayakanth, Maharshi Naik, & Dylan Ross

  • This project focuses on the design, modeling, and control of a walking robot intended to serve as a personal assistant within indoor environments. The primary objective is to develop an affordable robotic system by minimizing the degrees of freedom and the number of actuators required for basic locomotion. The initial phase aims to achieve stable straight-line walking, with future phases expanding the robot’s capabilities to include turning and stair climbing. The development process involves CAD design, 3D printing, and the integration of servomotors and microcontrollers for precise control. This project lays the groundwork for a cost-effective, functional robotic assistant tailored for indoor applications.
    • CAD design
    • 3D printing
    • Microcontrollers
    • Weekly reports
    • 3D printing
    • Programing
  • Face-to-Face
  • Dr. Amir Ali Amiri Moghadam, aamirimo@kennesaw.edu

    Dr. Turaj Ashuri, tashuri@kennesaw.edu

Robotics and Mechatronics Engineering (Turaj Ashuri and Amir Ali Amiri Moghadam)

Campus Navigator: A Mobile App for Seamless University Navigation

First-Year Scholars: Amarion Chanona, Hafsa Mohammed, Ali Rahimzadehfard, Mathias Rossi, & Rachab Wilson

  • The objective of this project is to develop a mobile application that allows students, staff, and visitors to easily navigate Marietta campus. The app will provide real-time directions to various campus locations, including academic buildings, administrative offices, libraries, dining areas, parking lots, and more. 

    Key features will include:

    Interactive Campus Map: A detailed, interactive map with searchable building names, departments, and facilities.

    Indoor Navigation: Turn-by-turn navigation for navigating large buildings, using Wi-Fi or Bluetooth beacons for positioning.

    Parking Locator: Guidance and directions to the nearest parking lot.

    Customizable Routes: Ability to generate routes based on preferences like shortest distance, accessibility needs, or quickest path.
        
    Event and Class Integration: Integration with university systems to show class locations, event venues, and upcoming activities.

    The development will be done by a team of students, each focusing on different technical and management aspects, ensuring a well-rounded and comprehensive final product. The project will be completed over two semesters, with a goal of launching a beta version for testing by the end of the second semester.

  • Technical Skills Development: Students will gain experience with mobile development frameworks, APIs for map integration, and database management for storing building and routing data.

    Team Collaboration and Project Management: Students will learn to work collaboratively using agile methodologies, with experience in sprint planning, task delegation, and progress tracking.

    Problem Solving and Innovation: The team will face challenges like integrating indoor navigation technologies, providing accessible routes, and ensuring cross-platform compatibility, fostering problem-solving and innovative thinking.

    User Experience Design: Students will design an intuitive app, considering accessibility, ease of use, and aesthetic appeal.

    Testing and Quality Assurance: Comprehensive testing (unit, integration, and user acceptance) will ensure that the app is reliable, accurate, and user-friendly.

  • Research and Literature Review: Students will read academic papers, analyze existing apps, and gather information on relevant technologies.

    Project Planning and Meetings: Regular team meetings for sprint planning, progress tracking, and collaborative discussions.

    Design and Prototyping: Developing designs, creating wireframes, and refining prototypes based on feedback.

    Data Collection and Mapping: Gathering campus data, mapping buildings, and setting up databases for location and route information.

    Coding and Development: Writing code to implement features, followed by regular testing and debugging.

    Documentation and Reporting: Maintaining technical documentation, drafting progress reports, and preparing presentations.

    User Testing, Final Checks, and Deployment: Conducting beta tests, gathering feedback, making improvements, performing final QA checks, and preparing the app for launch with user training materials.

  • Hybrid
  • Dr. Turaj Ashuri, tashuri@kennesaw.edu

    Dr. Amir Ali Amiri Moghadam, aamirimo@kennesaw.edu