Mindfulness is a practice that uses meditation to heighten attentiveness, focus and awareness, which can help resolve mental distress. With these proven benefits in mind, the Department of Defense (DoD) has begun to incorporate mindfulness techniques into military resilience training. Mindfulness training requires practice and dedication which can make the barrier of entry high. We hypothesize that the utilization of Extended Reality (ER) technologies to facilitate mindfulness training for active duty service members will help lower barriers of entry, enabling practice in-theatre. Consumers outside of the DoD currently use ER headsets to facilitate meditation and relaxation. We posit that ER headset audio and visual capabilities could be customized for active duty military to facilitate mindfulness at the edge. By utilizing mindfulness techniques, our aim is to assist warfighters in recovery from traumatic experiences, lowering the risk of PTSD. Students will conduct research, including an initial literature review to further support our hypothesis. Students will then begin building various ER prototypes that the user can select, incorporating visualizations, audio and/or haptics to facilitate mindfulness training.

This project seeks to determine the feasibility of using optic sensors to detect small reflections of light in fiber optic channels. These refractions exist as the result of randomness in the fiber from the manufacturing process and are unique to each segment of fiber. It is common to use randomness from semiconductors' manufacturing processes to establish unique fingerprints via PUFs, but this project seeks to use fiber optic channels as a less expensive material to achieve a similar security benefit for communications applications.

The Center for Space Hardware Assembly, Fabrication and Testing (C-SHAFT) at Georgia Tech (GT) is focused on research, prototyping and qualification of high-reliability micro-devices designed and built by the GT research community for deployment in outer space. The facility is the first “Class-S” cleanroom fabrication facility on campus, and will promote hands-on undergraduate and graduate education in interdisciplinary ‘space technology’ demonstration efforts. Georgia Tech has one of the largest and most diverse academic space research communities in the USA, with ~60 active faculty from AE, ECE, ME, CHBE, CHEM, PHYS, EAS, INTA and GTRI working in collaboration through the GT Center for Space Technology & Research (C-STAR). These researchers are developing a variety of space missions to study astrophysics, planetary science, robotics, materials science, and earth science. These explorations are most often achieved through fabrication of specialized sensors, space systems, and other micro-devices for integration within ‘CubeSat’ platforms. C-SHAFT fills a campus-wide capability void to facilitate and enable GT researchers to develop and deploy innovative experiments, architectures and designs in outer space. The research, prototype and qualification aspects of C-SHAFT are unique. C-SHAFT will utilize growing resources to develop prototyping, fabrication, assembly, test, and qualification capabilities to address the extreme requirements of the space flight environment (e.g., temperature, radiation, g-loads, pressure, magnetic fields, etc.). This Center provides a critically lacking campus resource ready to be utilized by an active and inclusive community of several dozen interdisciplinary space researchers at GT.

Communications systems and networks are at increased risk of being jammed, degraded, or denied. Furthermore, communications systems and networks are difficult to operate in congested and contested environment without requiring human intervention when interference is degrading or denying their operations. Our division is actively developing concealed, covert communication waveforms and protocols on software-defined radio (SDR) platforms to counter interference and jamming. By working with us, students will get hands-on experience on development digital signal processing algorithms on an SDR.

In general, spacecraft systems and subsystems are susceptible to a harsh space environment that includes energetic particle irradiation, exposure to atomic oxygen (AO) and solar photons, high vacuum, thermal cycling, and the micrometeoroidu2019s impact. Energy deposition from the environment leads to chemical changes in the material that manifests themselves in changes in myriad physical properties such as optical behavior, mechanical strength, electrical conductivity, and chemical reactivity. In turn, the functionality of the materials, as well as the spacecraft systems comprised of them, is degraded thus shortening the spacecraftu2019s lifetime. Therefore, a thorough understanding of the evolution of material properties throughout a planned mission lifetime is of primary importance when designing long-term space missions. In this GRIP Summer 2023 project, a group of researchers led by GTRI scientists will study the effect of the true and simulated space environments on the material properties of several heritage and novel spacecraft materials, including solar array coverglasses and spacecraft polymers.

System designers for modern-day embedded Cyber-Physical Systems (eCPSs) are routinely asked to perform the difficult task of optimizing tradeoffs between physical characteristics (Size, Weight, and Power), performance characteristics (speed, responsiveness), and most recently, security (runtime stability, behavioral security properties). While many single-core processor designs for eCPSs offer tight timing control and lower power, most have minimal guarantees of correctness. This leads to many well-known cybersecurity vulnerabilities such as crashes, reboots, general denial-of-service, as well as unique vulnerabilities associated with eCPSs(e.g. control loop instability). One way to architect a system design to combat these types of flaws is to conceptually mitigate these issues by separating high and low criticality tasks within the design. Often time however, this separation is at the system or software level, with no native support from the hardware/processor. In this project, students will leverage an open-source RISC-V processor core and leverage it to design and implement a resource-optimized dual-core embedded processor architecture that isolates high and low security tasks, and makes formal guarantees about the communication between both sides. This architecture will ensure bounded control execution on a hardware-isolated high-side coprocessor even if the low security side has reliability or security issues. Students will be responsible for designing an implementation of a dual core processor, with emphasis placed on the communication boundary between the high and low side cores. Students will verify this design by implementing it in SystemVerilog, testing in simulation and on an FPGA, and will work to formally verify the hardware design files. We are seeking one student to lead design of the processor, and a second student to lead the formal verification effort.

Digital microfluidics (DMF) is a technology that promises the ability to perform rapid transfer of small fluid volumes amenable to creation of computer-controlled automated chemical reactions. The PurpleDrop system is an open-source DMF hardware/software package developed at the University of Washington that provides a testbed for DMF experiments. We are particularly interested in using the PurpleDrop to perform storage, reconstitution, amplification, and ligation protocols for DNA, either in support of DNA data storage or biomedical applications. PurpleDrop performance depends on the solvent used, and, so far, water is the only solvent that has been tested extensively. For this project, we plan to set up a PurpleDrop system at GTRI and test its capabilities for acetone and other solvents needed for DNA protocols. In particular, we are interested in how efficiently we can move and recover different solvents, perform transfers necessary to support reactions, and how residual from transfers interacts with subsequent steps of relevant protocols.

Audio fingerprinting is used to automatically identify songs a person may be listening to in a noisy environment from a snippet in commercially available applications like Shazam. The system we propose is intended to recognize if a moving vehicle is wheel based or tracked based.nnInfrasound signals are ultra low frequency sounds that travel much farther acoustic signals. When a vehicle travels across the ground it transmits infrasound signals through the ground farther than acoustic signals travel through the air. In this project students will develop a neural network designed to ingest infrasound signals collected at the start of the project. This neural network will be trained to differentiate between tracked vehicles and wheeled vehicles. nnStudents will learn the fundamentals of audio collection, time series signal processing, audio information retrieval and machine learning techniques. Students will produce a system that can ingest audio data and positively identify if a vehicle is wheeled or tracked based on it's rolling sound.

The flow state, also known as 'flow' or 'being in the zone,' is a mental state characterized by complete immersion in the task at hand and a loss of self-consciousness. However, attaining this elusive state is not easy. Neuroscience has recently begun to understand the scientific origins of flow. Two main theories, the transient hypofrontality hypothesis (THH) and the Synchronization Theory of Flow (STF). Although fundamentally different, both theories share the role of the emotional tract in managing automatization of implicit processes and intrinsic reward, which can be manipulated through engineered systems. Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that involves applying a weak electrical current to the scalp via electrodes. It has been found to increase the likelihood of flow states in individuals engaged in a diverse example of tasks. However, direct electrical stimulation of the brain is not a natural phenomenon and may carry risks. Neural entrainment is the phenomenon in which external stimuli, such as light, sound, or vibration, can influence the activity of the nervous system; blue light can affect the body's internal clock or circadian rhythm. More recently, it has been found that flickering blue light at ~5.5 Hz can improve episodic memory. The mechanisms of this increase have similarities to brain activation during flow states, and a reasonable hypothesis can be made about the induction of flow. In this project, students will use Microsoft Hololens or similar augmented reality devices to construct a neural entrainment environment. They will then design an experiment to test improvements in athletic performance and focus. If these tasks are completed within the 10 weeks of the internship, students will begin dialogue with contacts at the Maneuver Battle Lab to explore testing on soldiers, with the ultimate goal of reducing combat losses in close combat through the use of flow entrainment.

We will use Fullerene-mediated cytoablation (FMCA) technology to facilitate ablative treatment at a microscopic level using our proposed Fullerene-Antibody Conjugate Energetic Nanoparticles (FACE-NP) therapeutic agent. Fullerenes are 60-carbon spherical molecules with strong non-linear optical properties. In particular, their optical absorption increases dramatically with light intensity. They undergo decomposition with rapid energy release when exposed to laser energy. First, we will modify fullerene surface to even increase their absorption to laser energy so that they will undergo decomposition even when exposed to low-level laser energy (1W/cm2). Also, we will increase the dispersion of fullerene into water by surface modification. Then we will conjugate the fullerenes to antibodies which are against BC-specific antigens. These FACE-NP will specifically target to BC and bind to antigens on cancer cells . Later these FACE-NPs bound with cancer cell will be laser scanned using an optical fiber. Bound FACE-NP should react with low level laser energy, resulting in targeted cancer cell removal.

Many organisms have developed complex strategies to achieve reliable, reversible adhesion to indiscriminate surfaces, particularly those with varying stiffness, roughness, and biofouling. Some of the most compelling structures used in these mechanisms are both hierarchical and exploit a fluid phase to enhance suction-based attachment. For example, the functional morphologies of the attachment surfaces of tree frogs, clingfish and remora all utilize tissue mechanics, topology and features such as micro- and nanofibular arrays to enhance attachment shear strength. Complex rheological fluids such as mucus also play a significant role. Recent work on elastomeric pillars has shown dramatic improvements in shear attachment when compared to smooth counterparts. In this project, students will investigate, design, fabricate and demonstrate hierarchical, biologically inspired multiphase attachment systems as an engineered solution for underwater or wet attachment. Fabrication routes such as laser micromachining, fugitive templating and soft lithography will be explored as means to achieve these structures. Biologically inspired prototypes will be evaluated via micro and nanostructural characterization and rheological properties as well as tested for attachment performance for a range of substrate conditions.

GTRI Quantum Systems Division needs a student to contribute to ongoing experiments in ion-trap quantum computing. The student would contribute to efforts to build new experimental apparatus. One example is a low-vibration, cryogenic vacuum chamber which ensures that ions are not lost from the trap and remain well aligned to incoming laser beams. Another example is a system of rack-mounted lasers with accompanying acousto-optic switches for controlling laser-beam frequencies and intensities. We also plan to redesign the sources of atomic calcium which provide the material to load the ion trap so that they can operate at lower power. There is also the possibility to participate more directly in the operation of an experiment and in the analysis of experimental data.

The United States is highly dependent on satellites for economic and strategic reasons. It is therefore critical to ensure that US-owned (as well as international or commercial) satellites are maintaining health and not in the path of increasingly dense debris fields. One of the primary means for monitoring satellites is through the use of telescope imagery. However, because the objects of interest are both small and far away, the resultant imagery is unresolved. This means that a major source of information for monitoring satellites takes the form of one-dimensional brightness time series. Expanding the amount of information that can be gleaned from unresolved satellite imagery is therefore critical to maximizing information on satellite status. nOne promising technique that has been under-explored for this purpose is polarization imaging. Polarization can reveal hidden information about a satelliteu2019s surface roughness and material composition in the form of encoded specular glints. These traits have led to the deployment of polarization in the field of computer vision for tasks such as segmentation of conducting from non-conducting materials and pose estimation of non-convex objects. Despite the promising nature of this technique in other fields, the development of algorithms for its utilization in spacecraft assessment is under-explored. This internship will seek to develop a basis for understanding how polarization can be deployed for spacecraft assessment. nThe students working on this project will go through multiple learning phases to create a basis for using polarization for satellite assessment: (1) deriving an understanding of the physics of polarization, (2) developing a material database of polarization properties, (3) using the database to simulate high-fidelity computer-generated imagery via the DIRSIG model, and (4) using the generated imagery to prove the capability of polarization for material unmixing.

The aim of Pufferfish is to create a model and simulation environment that synthetically produces LiDAR imaging of the ocean surface and water column. Characteristics of the GTRI LiDAR system are combined with knowledge of the ocean environment, ocean physics and phenomenology, and optical beam physics/radiative transfer in the water column to calculate simulated collections of point clouds. The project also covers topics which include fluid mechanics/physics, oceanography, and radiative transfer modeling. The prospective student would leverage recent developments in global ocean modeling capabilities, develop LiDAR beam modeling software, and/or estimate properties of the ocean. The student should have experience with programming, preferably in MATLAB, Python, and/or C/C++. Experience with finite element modeling or computational fluid dynamics is also desired.

Compared to more traditional industrial and manufacturing settings, robotics aimed at automating agricultural tasks face a unique set of challenges that stem from handling biomaterials. In the context of harvesting berries (our project for summer 2023), we are dealing with a deformable and non-uniform object that should ideally be picked at an optimal ripeness level. Building on our prior work, we will be utilizing soft robotics and a ripeness sensing technique based on near-infrared (NIR) spectroscopy to meet real world berry harvesting requirements. GRIP students will be tasked with the following: (1) iteratively designing, fabricating and testing a new kind of compact soft robotic gripper with integrated ripeness sensing specifically for blackberry harvesting, (2) integrating the gripper both mechanically and operationally onto an existing GTRI mobile robot platform, and (3) performing in situ field testing and benchmarking during the summer blackberry harvesting season. Our team will be working closely with horticultural researchers at the University of Georgia and with a local blackberry farm.

Students will design and create technology to address the code authorship attribution problem. Society has seen a recent increase in malicious applications of technology, such as the use of deepfakes to create misleading high-quality images or key logging malware to steal an identity. While authors of these malicious programs do not sign their work, a stylistic fingerprint is left in the code base. These markings may be used to identify the authors from a set of potential suspects (i.e. attribution).nnLeveraging Natural Language Processing (e.g. large language models such as GPT-3) and Topological Data Analysis (TDA), students will determine unique shapes and patterns in the code base/binaries, allowing for solutions to the attribution problem. NLP allows for the vectorization of the code while TDA uses advanced mathematical tools (not available in standard deep learning architecture) to identify unique structures in the shape of the data. This technology has the potential to significantly impact the current cyberspace environment by improving our ability to attribute the authors of malicious technology.

The most efficient and valuable radiation detection scintillator materials are halide-based single crystals, which are difficult to grow and easy to crack and degrade. Transparent halide ceramics with lower cost and higher stability have been long sought by DHS, DoD and DoE for nuclear threat detection applications. Halide (Cl, Br, or I based) scintillators are difficult to fabricate as transparent ceramics due to their lower thermochemical, environmental and processing stability in comparison to oxides. Recent success in the synthesis of stable halide/perovskite nanocrystal materials shines light on the development of transparent halide ceramics. This project will develop fabrication processes for transparent halide ceramics through colloidal synthesis of uniform and stable halide nanocrystals (less than 20nm) followed by hot pressing/vacuum sintering. The nano-sized precursor powders will significantly reduce the sintering temperature, increase ceramic density and transparency. Non-hygroscopic and cubic-structure Cs2HfCl6 ceramic system will be initially investigated while Li-containing halides such as Cs2LiYCl6 will be further developed for multi-mode gamma and neutron detection.

This project will examine various implementations of a compliance controller for a Universal Robots robotic arm. Compliance control is designed to allow the robot to achieve a Cartesian pose while simultaneously achieving a specified force profile. While these objectives seem to be contradictory, many simple tasks require such control. One simple example is inserting a peg into a hole. Following a precise Cartesian trajectory is likely to lead to the peg binding and becoming stuck. Allowing spring forces to subtly change the trajectory will ensure proper insertion. Under this project, students will investigate several open source packages that implement this form of control by porting them to our robotic hardware. Metrics will be developed to evaluate the quality of each package, and test methods will be implemented which allow the exploration of the metrics. The overall goal of the project is to find the best solution available for this form of controller on a six degree-of-freedom robotic arm. A stretch goal for the project is to extend the open source packages to work with your two-arm system. This would require compliant control for a 12 degree-of-freedom system.

Long-range optical imaging and communication systems must contend with a rapidly changing link when all or part of their paths intersect a volume of planetary atmosphere. Gases and aerosols attenuate the beam’s propagation while small changes in the index of refraction caused by temperature, water vapor, and pressure variations distort the beam’s wavefront. Each of these effects reduces the quality of the optical link. For imaging systems, these changes result in decreased contrast as well as a degradation in resolution. For laser communication systems, these changes increase the bit error rate and decrease system throughput. Wave optics, a subset of computational optics, models light as a propagating electromagnetic wave via Fourier analysis and linear systems theory. When coupled with precise knowledge of atmospheric characteristics, wave optics can be used as a tool to predict optical system statistical performance. In this program, students will use the principles governing free space optics to develop features of a wave optics simulation environment for electromagnetic signals propagating through time and space and demonstrate their efficacy in a simulated, short-range, ground-based imaging system.

Background: Many cross country and off-trail hikers know how difficult it can be to navigate through unknown woods and forests. Small arms teams also must often cross unknown terrain as part of their mission. This results in situations where teams come across an unknown obstacle, such as a deep ravine, and must back-track 10 miles back to the start of their 30 mile journey. Most soldiers describe this experience as an 'extreme emotional event.' In an effort to reduce these surprises, we hope to find a method to implement local-level route-planning on a Microsoft Hololens 2. nTask: Students will develop a dynamic local-level route-planning algorithm on the Microsoft Hololens 2 to help soldiers navigate in unfamiliar terrain. The algorithm will need to account for terrain height and rate 'cross-ability' of different terrain types. Students might need to apply some remote sensing data to classify the terrain, and allow soldiers to input different weights such as 'least difficult' or 'fastest available'. Students will gain lots of Unity development experience.

"The project proposed in this document extends the state of the art in systems model analysis with a focus on behavior models in the open standard Systems Modeling Language (SysML), a systems engineering extension of the Unified Modeling Language (UML) for software specification. The effort will automate translation of these models to an existing open logical solver language. The translation was informally specified in earlier work applying the Ontological Behavior Modeling (OBM) method to SysML behavior models, to unify the various behavior modeling techniques in SysML. Ontological approaches define formal rules for classifying individuals according to some modeling concept, behaviors in this case, which OBM applies to SysML behavior models. Semantics expressed only as free text in existing standards documents is brought into reusable libraries expressed in SysML, enabling logical methods of SysML behavior analysis. The proposed effort will extend beyond the simpler activity examples in earlier work, exploring more complex and intricate behaviors, enabled by an automated translator. This will support development of recommended model libraries that provide logical semantics for SysML behavior models, making them easier to learn and analyze, and contributing to the next generation of logically-grounded SysML standards.