This project combines the Boston Dynamics Spot robotic platform with virtual reality to develop a more intuitive remote inspection experience. Two means of visualizing camera data from Spot in real-time in a VR interface were developed and tested. The first approach uses point cloud data from the robot’s stereo cameras. Because of a bottleneck in data transmission latency, the second approach focuses on improved latency at the tradeoff of a slightly less natural two-dimensional display of data. This approach surrounds the human user in six planes that show the live JPEG-compressed image feed from each of the robot’s cameras: gripper, front right, front left, right, left, and back. The location of the user is tracked and sent back as movement commands to Spot; as the user moves in their space, the robot moves analogously in its space. The user can send additional commands, such as sitting and standing, via handheld controller buttons. Considerations for privacy in a remote inspection were also implemented, including features that protect sensitive information from being transmitted and ensure the robot can not approach certain areas. Possible applications for this project lie in its ability to facilitate more natural feeling remote inspections, including those of dangerous areas, spaces where direct access is difficult, and private or secure locations.

In the Radio Frequency Integrated Circuit design industry today, the design process is both complex and labor-intensive, demanding deep domain expertise and significant time investment. A designer first starts with target performance specifications. After establishing a general architecture, the designer then chooses topologies for each gain stage, iteratively adjusting parameters until the active portion of a circuit is produced. Then, the designer must match the impedances of each stage, utilizing predefined parameter sweeps and heuristic techniques to adjust and optimize their passive component designs. This process is extremely tedious, taking anywhere from a few weeks to several months depending on the complexity of a design. To address this issue and expedite the design process, this thesis tackles the development of both passive and active components by utilizing machine learning methods. Specifically, we utilize inverse design methods for passive structures and reinforcement learning for active components to synthesize power amplifier circuits from end-to-end algorithmically. Moreover, we consolidate these tools into graphical user interfaces to provide a ready-to-use product for RFIC design engineers anywhere.

The manufacturing of affordable pitch calling systems is imperative for providing equal opportunity for all college baseball teams to compete with the wide ranges in team budgets. In this project, I aimed to produce a reliable alternative to the industry leader for under 10% of the cost. I did so using microcontrollers with transceiver capabilities with an attached LCD screen. By transmitting via BLE, there is much less power consumption, which means a longer battery life. This independent project is meant to offer college baseball teams a chance to save valuable funds that would otherwise be effectively wasted. Ideally, these teams would invest the saved funds on more productive ways to improve their team, thus improving the overall level of play in college baseball.

Continuous device scaling and power scaling coupled with lithographic limitations have led to unacceptable levels of performance variation and an exponential increase in static power consumption. Effective measures to mitigate variation induced leakage and performance overhead are therefore greatly in demand. Previous compensation circuit solutions implore significant area-overhead solutions. In contrast, this thesis presents a low area overhead (512T) mix-signal compensation circuit, which can detect variation in performance and leakage and correct the variation to acceptable values.

The rapid emergence of advanced therapeutics has highlighted the need for innovative drug delivery systems (DDSs) capable of overcoming biological barriers and ensuring precise spatial and temporal control. This work focuses on the design and simulation of a dual-mode microfluidic DDS that combines electrochemical and magnetic actuation mechanisms to enable reliable and minimally invasive therapeutic delivery. Computational simulations demonstrate the system’s ability to maintain laminar flow under varying conditions, including different Reynolds numbers, fluid viscosities, and multi-input configurations, ensuring robust performance across diverse scenarios. Guidelines for fabrication have been established, detailing the integration of electrochemical drug release via gold membrane dissolution and a magnetic fallback system using iron-doped PDMS membranes for power-independent operation. The front-end fluid control system has been physically implemented on the macro-scale and addresses critical challenges in localized drug delivery by combining spatiotemporal precision, adaptability, and robustness.

Unmanned Surface Vehicles (USV) and Unmanned Underwater Vehicles (UUV) are essential for a wide range of applications, including deep-sea exploration, environmental monitoring, and sea-bed surveying for valuable minerals such as rare earth elements. Understanding fish locomotion is crucial for designing energy-efficient marine robotic vehicles that can travel either on the surface of or under the water without any human operators on board. This work introduces a low-cost miniature fish-like robot, BlueGuppy, that can swim up to approximately three body lengths per second and can turn with its sole actuator. First, we detail its hardware design, emphasizing the rationale behind specific design choices. Second, we perform both straight-line and turning experiments demonstrating how swimming speed scales with flapping frequency and how temporally asymmetric flapping enables controlled turning. Finally, we present ongoing efforts to develop a Proportional-Integral-Derivative (PID) controller framework for the robotic fish using Bluetooth Low Energy (BLE) and WiFi as a means of communication. This BlueGuppy project highlights the potential of free-swimming robotic models in advancing our understanding of fluid-swarm interactions within biological collectives and inspiring the development of next-generation marine robotic vehicles.

Beam steering is a technique that controls the direction of a beam of radiation as it travels from transmitter to receiver, allowing for focused delivery of energy to certain target locations. In the case of wireless communication, this allows a transmitter to compensate for the path loss signal attenuation it encounters when emitting over the air. Terahertz (THz) and sub-THz radiation—electromagnetic emittance ranging from 10 GHz to 10 THz—is considered to be the next frontier of wireless communication given its ability to provide much higher data speeds and support a larger user capacity compared to current networks. Additionally, sub-THz has gained significant traction in imaging and biomedical applications given its small wavelengths—which provide high sensing resolution—and high sensitivity to relevant biomarkers. Furthermore, the development of reliable means of controlling THz radiation is necessary for the development of these THz communication, imaging, and biomedical diagnostic systems. In this paper, we present a metasurface fabricated with a 2D array of C-shaped single split-ring resonators (SRRs) that when linearly polarized radiation at sub-THz (60 GHz) frequency is incident on the C-shaped single split-ring resonator metasurface, the radiation will be steered at some predetermined angle according to the metasurface’s C-shaped single split-ring resonator architecture. We show that this beam steering can be turned off via thin films of a depletion mode organic semiconductor such as Poly(ethylene dioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) and remains intact with thin films of accumulation mode organic semiconductors such as pG(2T-3T). Going forward we aim to apply these insights to make a reconfigurable metasurface whose beam steering properties can be turned on and off, likely by turning organic electrochemical transistors placed over the resonator gap openings and fabricated with PEDOT:PSS off and on respectively.

Due to their small size, micro aerial vehicles (MAVs – under 100 g) are ideal for agile exploration of a range of environments, particularly indoor ones. This size, however, greatly limits their payload capacity and affects their sensing and computational resources. Recent work demonstrates remarkable progress miniaturizing sensors, compute, and navigation algorithms to fit onboard MAVs, but the tasks that they can accomplish remain fundamentally constrained by their small size. Scaling laws mean that state of the art robotics autonomy requires ever increasing computation which simply cannot be achieved onboard MAVs. Typical MAV offboard computation uses a low latency line-of-sight radio link, but the prospects for non-line-of-sight cloud-based links via Wifi or 5G are becoming increasingly attractive as wireless technology improves. In this thesis I present CloudMAV, a Wifi-enabled navigation stack for MAVs. CloudMAV is designed from the ground up to support a reliable control link and a low latency video stream for offboard monocular navigation. I demonstrate that a Wifi network provides sufficient bandwidth to support a video stream and control with moderate latency and throughput. Through the use of Wifi, CloudMAV is able to navigate wherever it has connection and can access virtually unlimited computational resources in the cloud.

Deep learning has experienced widespread adoption across various disciplines and applications because of its versatile problem-solving capabilities. Such versatility arises from the diverse set of deep learning architectures that have proposed and optimized for different settings. The transformer is one such deep learning architecture: especially effective at learning the long-term relationships that characterize natural language, it has achieved state-of-the-art performance on language-related tasks. Its aptitude, however, is scale-dependent, and the scale required to achieve such striking performance leads to three significant inefficiencies in transformer-based language models: large memory footprints, high inference latencies, and high energy consumption – all of which render the deployment of transformers prohibitively expensive in general and entirely infeasible in resource-constrained environments. The recent introduction of efficient and performant differentiable logic networks (DLNs) as an alternative to standard neural networks may help alleviate these limitations when other techniques like pruning, quantization, parameter-efficient finetuning, knowledge distillation, and architecture modification fall short. The present work explores this possibility, replacing the feedforward neural networks of a pretrained transformer model with highly efficient DLNs to produce DLN-transformers (DLN-Ts). The DLN-Ts we synthesize here demonstrate similar performance to the baseline transformer model on the GLUE benchmark, with inferred improvements in memory use, inference latency, and energy consumption. The DLN-T, therefore, may be a viable precursor to a compact, fast, and low-energy language model.

Language acquisition and reasoning, two skills fundamental to human intelligence, both require internalizing and applying a collection of rules and structures derived from a limited set of examples. Crucially, this requires not only the ability to identify important structures in those examples, but also the ability to systematically combine learned structures in novel combinations, or systematicity. Interestingly, despite recent successes in the sphere of language generation, statistical learners like today's foundation models still fail to consistently reason systematically, suggesting a possible incompatibility between the current learning paradigm or model architectures and the acquisition of systematicity. We aim to develop a better understanding of the extent to which statistical learners are capable of generalizing to novel combinations, which we call compositional generalization, in systematic tasks. To that end, we identify some ways in which model architecture or optimization choices can encourage the inductive biases needed for models to generalize compositionally, as well as challenges in more realistic task settings.

This project presents the integrated design and optimization of a personal human wing glider system augmented by electric ducted fan (EDF) propulsion. Using OpenVSP, the full 3D geometry, including a realistic human mesh, fixed-wing structure, and EDF units, is modeled to assess aerodynamic performance. The system is aerodynamically optimized for glide and cruise conditions with a span-efficient wing design that effectively integrates wing and human geometry to maximize lift-to-drag ratio while minimizing induced drag. Flight operation constraints, including stall speed, weight-to-power ratio, and structural limits, are incorporated into a wing loading optimization algorithm to extract feasible design conditions.

Propulsion is achieved through a distributed EDF system, with six ducted fans symmetrically positioned along the wingspan. Each EDF is paired with a motor selected through an efficiency-based matching process that considers thrust, RPM, and power requirements across still hover, vertical climb, and cruise conditions. XROTOR simulations inform propeller blade twist, hub sizing, and exit area ratios to optimize thrust production and minimize power draw. The system’s thrust, power, torque, and throttle demands are cross-validated with motor performance curves to ensure high efficiency under all mission segments. The final system achieves efficient low-speed flight with well-defined power and thrust requirements, supporting stable transitions across glide, cruise, hover, and climb, and demonstrating viability for compact, wearable, EDF-assisted personal flight.

Most robotics systems currently in use rely on either an internal battery that is intermittently charged, or outside cables to provide power to local systems. As robots become smaller and approach micrometer or nanometer scales, sustaining battery and circuit technology to fit these size constraints become increasingly challenging. Magnetic origami robots give two major benefits. By integrating permanent magnetics within origami structures, magnetic fields can be used to extend or contract these robots and achieve both control and power transmission wirelessly. My research involves a twofold approach of both mechanical design and robotics testing. The first section of my Senior Thesis centers around constructing a 3-dimensional Helmholtz coil project that can manipulate magnetic objects such as foldable origami robots without direct contact. It will improve upon the existing device in use by providing a larger workspace volume inside the coil while generating an equally strong magnetic field of 60 mili-Teslas. The second section of my Senior Thesis revolves around using the current coil system to explore the resonance behavior of origami robots based on the Kresling origami cell. This is achieved by applying a continuous rotating magnetic field. At specific frequencies, we can cause these Kresling structures to collapse or expand through oscillation of a permanent magnet attached to the robot In this way, the robot can be actuated while consuming less power compared to a conventional static magnetic field approach. Because the resonance behavior is dependent on the material properties of the Kresling Robots, this research can pave the way for future research on isolated control of these structures across a variety of applications.

When working with biological cells, two key principles should guide the process: acceleration and accuracy. Estimating cell viability is a task that greatly benefits from methods that embody these characteristics. Traditionally, viable and dead cells have been classified manually using techniques such as cell staining with chemical reagents or fluorescence. However, these processes are limited by their reliance on manual intervention and the inherent fragility of cells. deep Through the methods introduced in this report, a third key principle is introduced: automation. Combining imaging techniques with deep learning algorithms presents a promising alternative, enabling the automation of single-cell viability classification. By leveraging deep learning and dimensionality reduction, systems can be developed to streamline and improve cell classification. To validate these systems, it is assumed that stained and unstained cells can be analyzed in a similar manner. This research aims to establish that no meaningful difference exists between stained and unstained cell images, laying a solid ground truth for the development of automated classification systems. These advancements hold significant potential for applications in cancer research, drug development, and stem cell studies.

To meet and improve system specifications in developing technologies, the Integrated Circuits (IC) industry began exploring 3-D IC design because it addresses the limitations of traditional 2-D chip design, offering enhanced performance, reduced power consumption, and increased integration density. However, the design and optimization of electromagnetic (EM) structures are critical processes for functionality, efficiency, labor, and costs. Traditional design methods are extensive, challenging, and limiting for particular circuit topologies optimal performance. Hence, a robust and nontraditional method is necessary to explore this complex design space efficiently and design an optimal EM structure. Recent research, conducted for planar EM structures from the Integrated Microsystems Research Lab (IMRL) at Princeton University, presents a reverse-model for circuit design, improving design time and creating a new design space to potentially reach a global optimal EM structure. A deep learning-based methodology provides a design procedure led by desired performance metrics, such as scattering parameters, rather than interfacing various electrical elements to execute a task, to then optimize through parametric sweeps. While research following this method developed concrete results, the next step is to utilize this framework to multilayer structures, adding depth and complexity to the traditional design methodologies. The development of 3-D requires techniques that handle tedious and laborious design tasks to be effective and reliable. This thesis presents the first steps towards a feasible 3-D EM synthesizer through MATLAB.

The myocardium is composed of sheets of brick-shaped cardiomyocytes embedded within an anisotropically aligned extracellular matrix of proteins. This intricate structural organization underlies the electrical and mechanical functionality of the heart. In vitro cardiac microphysiological systems (MPS) aim to replicate these structure-function relationships; however, existing platforms often struggle to acquire high-resolution electrophysiological data from three-dimensional tissues due to geometric mismatch, poor mechanical integration, or insufficient spatiotemporal coverage. In this thesis, we present electrically instrumented engineered cardiac ventricles that integrate ultra-flexible mesh electronics with fiber-aligned scaffolds fabricated via Focused Rotary Jet Spinning. This platform enables stimulation and recording of cardiac electrophysiology across physiologically relevant spatial and temporal domains while preserving the native tissue architecture. By providing high-fidelity, multimodal access to volumetric tissue dynamics, these electrically instrumented cardiac MPS advance the frontier of heart-on-a-chip technology and hold promise for applications in drug screening, disease modeling, and personalized cardiac medicine.

Layered transition metal dichalcogenides are an emerging class of atomically-thin, layered materials for use in solar cells, transistors, LEDs, and other optoelectronic devices. These materials are flexible and durable, with bandgaps that shift from indirect as a bulk film to direct as a monolayer, allowing for finely tunable devices. Two of these materials, WSe2 and WS2, have bandgaps around 2 eV, making them more versatile than other 2D materials such as graphene, which lacks a bandgap. This project focused on mono- and multilayer WSe2 and WS2 films, and determined their conductivity, established the position of the Fermi level, and introduced [RuCpMes]2, an air stable dimer, as an n-type dopant to determine shifts in Fermi level and changes in conductivity with doping. The introduction of the Ru dimer increased the conductivity of monolayer WSe2 films by 3 orders of magnitude and increased the conductivity of monolayer WS2 films by 4 orders of magnitude, indicating successful doping. The Fermi level of undoped monolayer WSe2 was found to be close to the conduction band minimum and the Fermi level of monolayer WS2 was found to be slightly below the conduction band minimum. After doping with [RuCpMes]2, the Fermi level of monolayer WSe2 did not shift significantly due to its proximity to the valence band minimum, but the Fermi level of monolayer WS2 shifted upward. Due to charges on the surface, the work function of both monolayer films decreased with doping, with a greater change in monolayer WS2.

This project builds upon prior work into the use of image tactile sensors to improve gripper performance through the adjustment of marker identifying algorithms and the initial construction of a physical gripping system. I focused on improving marker tracking algorithms, refining the physical design of sensor modules, and enabling real-time data processing. The previous image tactile sensing system has been enhanced through the development of new sensor designs, integration of a multi-camera system, and incorporation of a MySQL database for real-time data storage and analysis. Marker tracking accuracy and processing speed were significantly improved by introducing techniques like k-means clustering, watershed segmentation, and frame-skipping, alongside the use of diffused lighting and optimized illumination parameters. Three novel sensor modules were fabricated, including a black rubber module with manually placed markers, a clear silicone pad for small object interactions, and a gel-tip-inspired design for dynamic sensing. These modules demonstrated improved adaptability, scalability, and accuracy for tactile sensing. MySQL integration facilitated long-term data aggregation and enabled retrospective analysis of sensor performance, paving the way for machine learning applications in predictive force estimation. The database allows for the categorization of force measurements by sensor type, object size, and timestamp, enabling continuous system refinement. Real-time feedback was achieved by addressing prior computational challenges, reducing noise, and eliminating reliance on external lighting conditions. Testing with multiple gripper models validated the system's capability for precise tactile sensing in robotic manipulation tasks, allowing for accurate handling of a larger array of object sizes. The updated system supports continuous force tracking and object recognition during gripper operation. Future work will focus on miniaturizing the sensors, further optimizing image analysis algorithms, and leveraging accumulated data for enhanced system performance. This semester's advancements mark a significant step toward developing low-cost, efficient tactile sensing systems for applications in robotics, prosthetics, and human-robot interaction. Documenting these aspects of the implementation would make improving robotic gripping and navigation more accessible for a wider group of users. Because of the limitation of time, the physical calibration of the grippers and experiments regarding improvement to object manipulation performance are future considerations.

The fast-evolving nature of malware calls for the development of detection tools that work on attacks that were previously unseen. MalConv, a static classifier built on a convolutional neural network, is a significant step in this direction, but is unable to provide mathematical guarantees of its accuracy on its own. In this project, techniques that defend image classifiers from localized adversarial example attacks and calculate certified accuracy are applied to malware classifiers. In particular, De-Randomized Smoothed MalConv, an existing application of an image-based technique with a small receptive field, is extended for better performance on small files in models I call DRSM2 and PCM. DRSM2 improves DRSM to better utilize its base classifiers for small inputs; PCM applies PatchCleanser, an image-based technique with a large receptive field, to malware detection. Both models outperform the original DRSM, with DRSM2 achieving higher standard and certified accuracies but PCM providing certified accuracies for big perturbation sizes that DRSM2 cannot handle.

As computing evolves and the use of cloud environments increases, there is growing concern over security and data confidentiality. Virtual machine-based trusted execution environments (VM-based TEEs) have emerged as a promising solution due to their flexibility and scalability. This thesis presents a formal verification framework for reasoning about the correctness of the Intel TDX firmware—a central component of a prominent VM-based TEE. Using CBMC, a suite of abstraction strategies, and modular verification reasoning, the framework enables tractable analysis of this complex, real-world system. The methodology is specifically applied to the TD creation sequence, demonstrating that the verification process successfully validates correct flows and reliably detects errors when deviations occur. Beyond TDX, this work contributes to the broader goal of developing a scalable and rigorous methodology for evaluating secure processor architectures and ensuring their reliability in real-world deployments.

Drone-based item delivery has thus far in its short but innovative history revolved around a multi-rotor, miniature aircraft, performing a full landing to deliver packages. This system carries a range of technical and structural problems, including relatively poor flight dynamics, high battery power consumption, and range limitations. This thesis explores the deployment of electronically guided Smart Parachutes for drone-based package delivery, allowing the drone to perform a ‘fly-by’ during which it ejects the target’s package instead of the delivery requiring a full landing. Successfully designing and building a Smart Parachute delivery system repairs a wide range of social ills by adding yet more efficiency to the drone delivery network. Modern society suffers severe automotive congestion, pollution, theft, and latency between customer order and customer product receipt. A scaled drone delivery system mitigates the vast majority of these ills. As such, the project is worth pursuing.

The thesis specifically will explore a number of electromechanical solutions, highly reliant on granular, software-driven, location-sensing (via camera feed), as well as optimizing GPS coordination. These measurements will then feed into a rapid system state update algorithm to plan an optimal path for the Smart Parachute. Lastly, the system state algorithm will control a servo motor to steer the rigging of the parachute and update the feedback loop.