An Audio-Band Discrete-Time ΔΣ ADC Using Ring-amp and KT/C Noise Cancellation in 45nm CMOS

(Georgia Institute of Technology, 2023-12-18) Yao, Huang

In this thesis, we designed an audio-band DT ΔΣ ADC using ring amplifiers and kT/C noise cancellation. The ADC is designed and simulated in NCSU 45nm CMOS with a 1.2V supply. It achieves a peak SNDR and DR of 100dB and 105dB with a 2mW power consumption, yielding a Schreier Figure of Merit (FoM) of 171.dB and a Walden FoM of 482fJ/step. We show that a ring amplifier is a good candidate for low-voltage DT circuits such as an SC integrator, and the kT/C noise cancellation technique can provide a better noise performance in a ΔΣ ADC. In the future, we would optimize the amplifier to improve the noise performance further and to have lower power consumption
Evaluation of Convolutional Neural Networks for Modeling Blast Propagation in Multi-room Bunkers

(Georgia Institute of Technology, 2023-12-15) Luo, Felix

The rapid evaluation of blasts in enclosed geometrically complex spaces has long eluded the design of safer blast-resistant structures. Traditional methods of determining blast responses in enclosed geometrically complex spaces oftentimes rely on the use of traditional computational fluid dynamics (CFD) solvers to compute the entire flow field of the structure. This method has an enormous computational burden, especially considering that blasts are highly transient in nature and require the transient pressure fluctuations to be determined to formulate an accurate blast response prediction. However, more efficient methods of blast evaluation are desired such that parametric sweeps or optimization processes can be performed at low cost to provide a tool for iterative design. To rectify this gap in capabilities, a convolutional neural network based (CNN) model was developed to provide rapid blast predictions for 2D structures to establish this capability to aid in the design of more blast resistant structures. This approach leverages the inherent spatial awareness of CNNs to provide predictions for peak pressures since blasts in enclosed spaces are highly dependent on the spatial relationships between blast locations and wall location. This approach provides a nearly 5,000 times speed up against CFD simulations used within this study with good convergence of errors, correlation coefficients, predicted and truth values and distributions in all situational evaluations. These computational advantages, in part, comes from using the CNN based model to directly predict peak pressures whereas traditional CFD solvers require iterations to propagate fluid flows over time. However, some limitations do exist with respect to higher errors, such as model training costs, and the capability to predict 3D structures. Nonetheless, the results provide a characterization of the capabilities CNN based models in predicting peak pressures from blasts in enclosed spaces. From these evaluations and studies, a model which can provide significant computational savings while maintaining a similar accuracy can be obtained, which enables the rapid iterative design of more blast resistant structures.
A Sliding-Window Matrix Pencil Method for Aeroelastic Design Optimization with Limit-Cycle Oscillation Constraints

(Georgia Institute of Technology, 2023-12-15) Golla, Tarun

This paper presents a new approach for constraining limit-cycle oscillations in aeroelastic design optimization. The approach builds on a gradient-oriented limit-cycle oscillation constraint that bounds the recovery rate to equilibrium, bypassing the need for bifurcation diagrams. Previous work demonstrated the constraint using recovery rates approximated via a conservative approach. This work introduces a new approach to accurately evaluate recovery rates from transient simulations. The approach uses the matrix pencil method within a time window that slides along the time history for the quantity of interest, allowing this damping identification method to resolve amplitude-variant nonlinear effects. The new sliding-window matrix pencil method is verified with reference recovery rates from envelope finite differencing of the dynamic responses induced with a large initial perturbation of a typical aeroelastic section. Sensitivity analyses identify optimal parameters to obtain accurate recovery rates while minimizing computational costs. The new developments are then demonstrated by optimizing the typical section subject to the proposed limit-cycle oscillation constraint along with flutter and side constraints. The results are compared with previous work that solved the same optimization problem by evaluating the limit-cycle oscillation constraint using approximate recovery rates. The limit-cycle oscillation constraint based on the new sliding-window matrix pencil method allows the optimizer to achieve a less conservative design solution while satisfying the constraints. This methodology was additionally extended through the optimization of a more complex 3-variable optimization. The implementation was further ported into a modular framework within which results were verified, allowing for future extensions to this methodology. This work is anticipated to pave the way for larger-scale aeroelastic design optimizations subject to limit-cycle oscillation constraints.
Enhancement of Ankle Fusion through FK506 Induced Osteogenesis

(Georgia Institute of Technology, 2023-12-14) Huffman, Nicholas

Ankle Arthrodesis is a common surgical procedure that typically involves the fusion of the tibia and talus of the patient. During surgery, the surgeon uses screws and plates to compress the bones together and cease plantar and dorsiflexion motion [1]. However, one of the main complications with the surgery is the non-union of bones. This can be due to loosening of the screws or failure to grow new bone in the joint space. Our team hypothesized that introducing an additional orthobiologic into the system would assist in bone formation and reducing non-union rates. In this study, we evaluated the effectiveness of osteogenic drugs to improve bone fusion within ankle arthrodesis. One such molecule we evaluated is FK506 (Tacrolimus), an FDA approved drug for treating organ transplant rejection. We implemented a cell culture model to test out the osteogenic potential of FK506. Bovine Marrow Derived Cells (MDCs) were cultured for 1-2 weeks and evaluated with Alizarin Red S Staining, Results were also tested with hMSCs. ALP Activity, and Gene Expression. We found that FK506 significantly affects Alizarin Red S staining within our MDCs. Additionally, we identified that rhPDGF-bb could be a potential adjuvant to FK506 treatment. Though future work will be needed to confirm the effects of rhPDGF-bb within an in vivo model. It was also noticed that there was significant variation associated with the MDC results between donors. We will look to answer those questions with flow cytometry in future experiments. Following those results, we tested our model within a rabbit ankle model to evaluate effectiveness.
Explicit Group Sparse Projection for Machine Learning

(Georgia Institute of Technology, 2023-12-14) Ohib, Riyasat

The concept of sparse solutions in classical machine learning is noted for its efficiency and has parallels in the natural world, such as in the mammalian visual cortex. This biological basis provides an inspiration for the importance of sparsity in computational models. Sparsity is increasingly relevant in machine learning, especially in non-negative matrix factorization (NMF), where it aids in interpretability and efficiency. NMF involves breaking down a non-negative matrix into simpler components, with sparsity ensuring these components distinctly represent data features, simplifying interpretation. In deep learning, sparse model parameters lead to more efficient computation, quicker training and inference, and in some cases, more robust models. As models grow in size, the role of inducing sparsity becomes even more crucial. In this thesis, we design a new sparse projection method for a set of vectors that guarantees a desired average sparsity level measured leveraging the popular Hoyer measure. Existing approaches either project each vector individually or require the use of a regularization parameter which implicitly maps to the average $\ell_0$-measure of sparsity. Instead, in our approach we set the \revise{Hoyer} sparsity level for the whole set explicitly and simultaneously project a group of vectors with the \revise{Hoyer} sparsity level of each vector tuned automatically. Hence, we call this the Group Sparse Projection (GSP). We show that the computational complexity of our projection operator is linear in the size of the problem. GSP can be used in particular to sparsify the columns of a matrix, which we use to compute sparse low-rank matrix approximations (namely, sparse NMF). We showcase the efficacy of our approach in both supervised and unsupervised learning tasks on image datasets including MNIST and CIFAR10. In non-negative matrix factorization, our approach yields competitive reconstruction errors against state-of-the-art algorithms. In neural network pruning, the sparse models produced by our method have competitive accuracy at corresponding sparsity values compared to existing methods.
Design of a Long-Range and Retrodirective Tunneling Tag

(Georgia Institute of Technology, 2023-12-13) Saetia, Christopher Austin

The rise of the Internet of Things has increasingly integrated radio-frequency identification (RFID) technology in many practical applications that deal with localization, communication, and more. Specifically, RFID tags are designed to be low-powered and inexpensively deployable. Passive tags in particular harvest energy from ambient transmitted signals in their environment to operate their circuitry and backscatter a response on the same transmitted signal. They do not depend on an internal battery or power source; hence, their communication range with a reader is usually limited compared to a tag or sensor node that has its own power source and dedicated transmitter. To meet the vision of deploying backscatter, passive tags on a large scale, new tag architecture for improved read-range needs to be investigated. This thesis aims to design a 915 MHz retrodirective, tunneling tag by loading a rat-race coupler with tunneling reflection amplifiers. The thesis’ goal is to explore whether this proposed design has improved backscatter capabilities to help improve its read-range and investigate the effects of retrodirectivity and reflection amplification on the tag’s overall response.
Hardware Security at the Edge: A Discussion of Hardware Security Challenges and Tradeoffs Under Resource Constraints with Selected Example Primitives

(Georgia Institute of Technology, 2023-12-13) Ellis, Zachary

The explosion of smart devices into all realms of day-to-day life has been of great benefit to humanity allowing the measurement and control of many parts of the everyday in an automated fashion. Consumers can now choose to monitor parts of their home or collect data on their body to stay more informed on their health and spending. This unprecedented era of personalized data also poses a great security challenge for individuals and corporations to keep this potentially sensitive data secure. Many of these new devices operate in mobile or otherwise low power settings and are at a constraint for resources maximizing accuracy and communication speed. This work aims to address the impact of added security measures to these devices on a hardware level, quantifying the area, power, and speed impacts of added security with two example hardware primitives that may be used in edge devices now and in the future.
Anti-Jam Measures For Protected Tactical Waveform

(Georgia Institute of Technology, 2023-12-13) Moturu, Amar

This thesis explores various possible improvements to the Protected Tactical Waveform standard. These improvements mainly center on the log-on process in which users initially attempt to access the network and how certain signal processing techniques can be employed in order to combat jamming effects. Improvements are explored in the domain of the initial synchronization process as well as in the areas of adaptive antenna arrays, direction of arrival estimation, and beamforming techniques. The performance of these improvements are characterized both in DVB-S2 and PTW within a MATLAB simulation framework.
Residual Deterrence In Action: Exploring The Impact Of License Plate Readers In Warner Robins' Smart City Digital Twin Model

(Georgia Institute of Technology, 2023-12-13) Sonar, Nidhi Sandip

With an emphasis on crime prevention, this thesis investigates the residual effect of License Plate Readers (LPRs) within a Smart City Digital Twin (SCDT) model. It highlights the integration of Artificial Intelligence (AI) and LPRs for improved surveillance while tracing the historical background of crime. The study presents the Warner Robins SCDT model, which shows encouraging outcomes in the use of dynamic LPR deployment to curb criminal acts. The residual effect of LPRs, which shows continued deterrence even after relocation, is analyzed using statistical tests on the crime records of Warner Robins (WR) and the location of cameras during the intervention period of eighteen weeks. The results demonstrate the presence of a residual effect for the period under consideration, and the possibility of yielding unique results or insights through advanced testing and research based on different parameters.
Surface passivation for enhanced stability and performance in perovskite solar cells

(Georgia Institute of Technology, 2023-12-13) Sharma, Sakshi

Lead halide perovskite solar cells (PSC) have emerged as promising next generation photovoltaics. Their unique ABX3 stoichiometry- where ‘A’ is a monovalent cation, ‘B’ is a divalent metal cation and ‘X’ is a halogen- provides tremendous potential for composition and bandgap engineering to obtain desired optoelectronic properties, enabling high power conversion efficiencies exceeding 25%. Despite their growing appeal, commercialization of PSC technology faces challenges due to device instabilities in ambient conditions. Particularly, device interfaces between the active perovskite layer and adjacent charge transport layers are vulnerable to defects which can accelerate perovskite degradation under environmental stressors such as heat, moisture, or oxygen, limiting their long-term viability. Interfaces also significantly impact charge transport, collection and recombination mechanisms in devices and thus require optimization. To address these challenges, research has concentrated on interface modification to passivate surface defects, protect the bulk of perovskite from external environment, and tune the charge transfer properties at the surface. Conjugated organic ammonium salts have been used at interfaces to introduce hydrophobicity on the perovskite film and promote charge delocalization brought on by conjugation. However, most surface treatment strategies relying on organic molecules introduce an electrically insulating spacer layer under thermal stress. Heat induced diffusion of molecules can reconstruct the interface into lower dimensional phases, which impedes charge extraction and affects photo-conversion efficiency (PCE) of devices. This brings a tradeoff between the benefits of passivation and charge extraction. For proper interface design, it is essential to study the thermal behavior of these passivation layers and establish their relationship with the optoelectronic properties of solar cells. This work explores the thermal behavior of passivation agents, specifically employing long-chain thiophene-functionalized π-conjugated molecules (2TI and 4TmI, with two and four thiophene rings, respectively) on interfacial structural stability and charge extraction. Tailoring the steric hindrance of the bulky cations used to treat perovskite surfaces presents an opportunity to control cation mobility, and consequently any phase changes resulting at elevated temperatures. Structural studies reveal that the length of the cation backbone regulates the rate of interfacial perovskite structure reconstruction on prolonged heating. Consequently, faster phase conversion is observed in 2TI compared to larger 4TmI, with the formation of a n=1 A’PbI4 two- dimensional phase which consists of inorganic PbI6 octahedra monolayers separated by an organic spacer layer, A’ being either 2T or 4Tm. The oligothiophene tail in these molecules further contributes to spacer layer conductivity, prompting distinct charge extraction and recombination behaviors in 2TI versus 4TmI passivated devices, confirmed by synchrotron-based X-ray measurements. Results show that despite the observed phase changes, 2TI treated devices can tune the surface potential to promote efficient hole extraction to the overlying hole transport layer and reduce carrier recombination. This interfacial steric engineering translates to high performing passivated solar cells, with 2TI/CsFAPbI3 devices exhibiting efficiency exceeding 20%, an open-circuit voltage of 1.07 V and minimal changes under continuous thermal exposure. By identifying the nature and impact of heat induced dynamical structural changes at passivated perovskite interfaces, this work highlights the key to surface functionalization so that solar cell performances can be maintained at high operating temperatures.