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Metaheuristic Optimization for Automatic Arrangement of Power Electronics Components in a Shipboard Electrical Distribution System
No full textThis thesis proposes a novel methodology for the automatic placement of Power Electronics Building Blocks (PEBBs) in modular, integrated power corridor designs. These building blocks, which are created and tested offsite for a variety of applications, are currently placed manually during the design process, a method that is time-consuming and suboptimal. To address this challenge, we reduce the placement problem to a 2D bin-packing problem, leveraging a hybrid approach combining Genetic Algorithms and Simulated Annealing. This approach enables the generation of optimized placements that find the extremes of arbitrary heuristics, including minimizing routing distance and power density, effectively improving both design efficiency and system performance. The proposed methodology offers a significant step toward automating and optimizing the layout of power electronic components in complex systems.M.Eng
Boosting hydrogel conductivity via water-dispersible conducting polymers for injectable bioelectronics
No full textBioelectronic devices hold transformative potential for healthcare diagnostics and therapeutics. Yet, traditional electronic implants often require invasive surgeries and are mechanically incompatible with biological tissues. Injectable hydrogel bioelectronics offer a minimally invasive alternative that interfaces with soft tissue seamlessly. A major challenge is the low conductivity of bioelectronic systems, stemming from poor dispersibility of conductive additives in hydrogel mixtures. We address this issue by engineering doping conditions with hydrophilic biomacromolecules, enhancing the dispersibility of conductive polymers in aqueous systems. This approach achieves a 5-fold increase in dispersibility and a 20-fold boost in conductivity compared to conventional methods. The resulting conductive polymers are molecularly and in vivo degradable, making them suitable for transient bioelectronics applications. These additives are compatible with various hydrogel systems, such as alginate, forming ionically cross-linkable conductive inks for 3D-printed wearable electronics toward high-performance physiological monitoring. Furthermore, integrating conductive fillers with gelatin-based bioadhesive hydrogels substantially enhances conductivity for injectable sealants, achieving 250% greater sensitivity in pH sensing for chronic wound monitoring. Our findings indicate that hydrophilic dopants effectively tailor conducting polymers for hydrogel fillers, enhancing their biodegradability and expanding applications in transient implantable biomonitoring
What's in a Query: Polarity-Aware Distribution-Based Fair Ranking
No full textWWW ’25, April 28-May 2, 2025, Sydney, NSW, AustraliaMachine learning-driven rankings, where individuals (or items) are ranked in response to a query, mediate search exposure or attention in a variety of safety-critical settings. Thus, it is important to ensure that such rankings are fair. Under the goal of equal opportunity, attention allocated to an individual on a ranking interface should be proportional to their relevance across search queries. In this work, we examine amortized fair ranking -- where relevance and attention are cumulated over a sequence of user queries to make fair ranking more feasible in practice. Unlike prior methods that operate on expected amortized attention for each individual, we define new divergence-based measures for attention distribution-based fairness in ranking (DistFaiR), characterizing unfairness as the divergence between the distribution of attention and relevance corresponding to an individual over time. This allows us to propose new definitions of unfairness, which are more reliable at test time. Second, we prove that group fairness is upper-bounded by individual fairness under this definition for a useful class of divergence measures, and experimentally show that maximizing individual fairness through an integer linear programming-based optimization is often beneficial to group fairness. Lastly, we find that prior research in amortized fair ranking ignores critical information about queries, potentially leading to a fairwashing risk in practice by making rankings appear more fair than they actually are
Development of an Apparatus and Testing Strategy for Characterizing Rolling Resistance of Omnidirectional Wheels
No full textOmnidirectional wheels (omni wheels) are a type of wheel technology similar to caster wheels but capable of simultaneous longitudinal and lateral motion, making them suitable for holonomic motion applications. In recent years, their popularity has grown substantially in areas such as educational robotics, autonomous vehicles, and industrial automation. Despite their similarity to caster wheels in both function and application, omni wheels are a much less mature technology and few agreed-upon standards exist for their design and testing. This thesis covers the design of a test procedure and its requisite test apparatus to characterize the rolling resistance of omni wheels across various test conditions, and focuses specifically on the mechanical and electrical design of an apparatus which can measure the rolling resistance coefficient of omni wheels while modulating their load weight, travel angle, and travel speed.M.Eng
Wireless, Battery-Free, High-Sensitivity 5G RF Energy Harvesters for Next Generation IoT Sensor Tags
No full textThe Internet of Things (IoT) is revolutionizing various industries, enabling a new wave of smart applications such as automated asset tracking in warehouses, substation monitoring in smart grids, and precision agriculture. However, as IoT devices proliferate, powering these devices in a sustainable and maintenance-free manner has become a critical challenge. Traditional IoT systems rely on batteries, which present issues of limited lifespan, environmental impact, and maintenance costs, especially in large-scale deployments. As a result, the development of battery-free IoT devices powered by ambient energy harvesting has gained significant attention. Among various energy-harvesting technologies, radio frequency (RF) energy harvesting has emerged as a promising solution for powering IoT devices. By harvesting energy from ambient RF signals in licensed frequency bands, RF energy-harvesting systems eliminate the need for batteries and allow for continuous, maintenance-free operation. This is especially crucial in environments where battery replacement is impractical or impossible, such as in large industrial warehouses, remote infrastructure, and hazardous environments. However, achieving high sensitivity and reliable operation in RF energy-harvesting systems poses several challenges. High-sensitivity rectifiers are required to capture and convert weak RF signals into usable energy, but integrating these rectifiers with ultra-low power baseband data processing circuits remains a significant hurdle. Moreover, antenna-rectifier matching calibration must be compatible with the duty-cycled operation of these tags, where brief communication periods are followed by long charging intervals. Additionally, the antenna system must be robust to detuning when placed on various objects, ensuring that the system can operate effectively in diverse environments. This thesis presents two integrated circuits to work towards these goals. The first chip is designed with the goal of minimizing the charging time as much as possible, which is critical in scenarios such as inventory management in warehouses, and tamper detection. The goal was to achieve < 1-minute charging time while maintaining sensitivity competitive with the state-of-the-art. Unlike previous harvesters that either focused solely on sensitivity without integrating baseband processing and communication, or included those features but considered continuous communication at low sensitivity, the IC developed in this work achieves a sensitivity of −31 dBm and is capable of backscattering data approximately 18 seconds after a cold start. It also provides a detailed description of the difficulty of achieving higher sensitivities at higher 5G frequencies. The second chip in this thesis builds upon the first one and integrates an analog front-end to convert sensor data for environmental monitoring. We implemented an antenna-rectifier calibration method that is maintained as long as there if RF power, even though the tag goes into long charging periods. Even though the charging time, or the data readout interval, for these tags is more relaxed compared to the inventory management applications, we have also developed a design methodology to minimize the energy required to generate a data packet for backscattering, through which we were able to keep the charging time at 4 minutes while having additional functionalities and backscattering at a higher data rate compared to the first chip. Finally, a simple shielding method was implemented to enable the tags to be placed on any objects without resonance frequency detuning. All of these were achieved while still obtaining a sensitivity of −30 dBm, competitive with the state of the art. In addition, the third project investigates the use of heterogeneously integrated “beyondCMOS” devices to enhance overall rectifier performance. These emerging devices, fabricated by Palacios Group at MIT, show promise in overcoming sensitivity limitations commonly found in rectifiers, thereby extending the range and coverage of energy-harvesting IoT systems. We conduct a detailed characterization of these devices, highlighting their unique physical behaviors not present in standard CMOS technology, and provide system-level design guidelines for building improved rectifiers. Preliminary simulation results show that rectifiers using negative-capacitance field-effect transistors (NCFETs) can harvest up to four times as much power than their CMOS-based counterparts, while maintaining the same sensitivity. This thesis outlines the design, implementation, and evaluation of all three systems. The two aforementioned ICs are tested both in simulation and in real-world scenarios such as a typical office environment. Meanwhile, the novel device technologies are explored through simulation, demonstrating their significant potential for next-generation rectifier design.Ph.D
Advancing Tendon-Driven Robotic Systems: From Climbing Robots to String Actuators
No full textTendon-driven mechanisms provide a range of benefits for robotic systems, particularly by allowing actuators to be mounted at the base of a manipulator and reducing its inertia. This thesis explores two projects that exploit and advance tendon-driven mechanisms: a wheeled-grasping hybrid climbing robot with modular tendon-driven grasping arms and a hybrid twisted-winching string actuator. Called CLIMR (Cabled Limb Interlocking Modular Robot), the novel climbing robot adapts to columns of varying diameters by adding or removing modular arm links. CLIMR also features capabilities like self-locking (the ability of the robot to stay on the column without power), autonomous grasping, and rotation around the column axis. Mathematical models describe conditions for self-locking, vertical wheeled climbing, and complete grasping of a column. Simulations and experimental results validate the proposed models. The insights from CLIMR are then extended into general design strategies for future developments of similar hybrid climbing robots, focusing on methods to inform design decisions and assess metrics such as adaptability. Ultimately, this work provides a comprehensive framework for designing hybrid climbing robots, highlighting the potential of autonomous solutions for environments where climbing tall structures is critical. Stemming from this climbing robot work is a novel actuator system combining a twisted string actuator (TSA) with a winch mechanism. Relative to traditional hydraulic and pneumatic systems, TSAs are compact but face limitations in stroke length and velocity. This TSA-winch system overcomes these constraints without risking overtwisting by providing both high displacement winching and high force twisting modes. The design features a rotating turret that houses a winch and a worm gear transmission driven by a through-hole drive shaft. Models are developed for the combined displacement and velocity control of this system. Experiments validate the open loop model as well as the closed loop model, which uses a conductive string feedback controller with a gain scheduling and control effort allocation scheme. For specific cases that require large displacement winching followed by high force twisting over several repeatable cycles, an alternate design sacrifices complete string state control and replaces a motor with passive automatic clutches to achieve a seamless transition between modes triggered by the string load. The models of the clutch torque thresholds for this version of the actuator are verified by experiments. Overall, this research contributes to the development of more versatile and efficient actuation systems for tendon-driven robotic applications.Ph.D
Is There Super-Normal Profit in Real Estate Development?*
No full textThis paper explores the question of whether real estate development (RED) projects systematically present positive net present value (NPV) and therefore, provide super-normal profit. Such projects are the products of a business operation that governs the exercise of the real call option on development that is represented by developable land. We present a framework for considering super-normal profit in the RED industry, and then in light of that framework we examine RED projects produced by publicly-traded equity real estate investment trusts (REITs). We find strong evidence of positive correlation between REITs’ Tobin-Q ratios, indicative of positive NPV, and the ratio of development assets to total assets in the firm, controlling for other factors. The nature of the firm’s Tobin’s-Q metric is such that the implied added firm value is net of land cost and net of overhead and search costs associated with the RED business operation. While our findings do not prove a direction of causality between REITs’ RED activity and positive NPV, the robust positive correlation controlling for other factors raises interesting implications which are discussed in the paper
Probabilistic Inference for Inference Time Scaling of Language Models
No full textLarge language models (LLMs) have achieved significant performance gains via scaling up model sizes and/or data. However, recent evidence suggests diminishing returns from such approaches, motivating a pivot to scaling test-time compute. Existing deterministic inference-time scaling methods, usually with reward models, cast the task as a search problem, but suffer from a key limitation: early pruning. Due to inherently imperfect reward models, promising trajectories may be discarded prematurely, leading to suboptimal performance. We propose a novel inference-time scaling approach by adapting particle-based Monte Carlo methods. Our method maintains a diverse set of candidates and robustly balances exploration and exploitation. Our empirical evaluation demonstrates that our particle filtering methods have a 4–16x better scaling rate over deterministic search counterparts on both various challenging mathematical and more general reasoning tasks. Using our approach, we show that Qwen2.5-Math-1.5B-Instruct surpasses GPT-4o accuracy in only 4 rollouts, while Qwen2.5-Math-7B-Instruct scales to o1 level accuracy in only 32 rollouts. Our work not only presents an effective method to inference-time scaling, but also connects rich literature in probabilistic inference with inference-time scaling of LLMs to develop more robust algorithms in future work. Code, videos, and further information available at probabilistic-inference-scaling.github.io/S.M
Performance and Analysis of a Deployable DiffractiveOptical Element for Small Satellite Missions
No full textAs space missions push toward smaller, lighter, and more deployable instrumentation, diffractive optical elements (DOEs) offer a compelling alternative to traditional optics. Their ability to focus light through engineered phase profiles rather than curved surfaces allows for large-aperture, flat optics that are far lighter and easier to package for launch. However, this benefit comes with trade-offs: DOEs are sensitive to wavelength mismatch, manufacturing errors, and environmental deformations—especially thermal gradients and membrane tensioning in space. This thesis develops a comprehensive framework for understanding and simulating the performance of DOEs under realistic operating conditions. Beginning from first principles, the work contrasts geometric and wave-optical models for Fresnel zone plates and multilevel diffractive lenses, leading to quantitative predictions of diffraction efficiency and PSF quality under non-idealities. A key contribution is the analytical and numerical analysis of how uniform thickness errors, wavelength mismatches, and thermal expansions degrade optical performance, both in efficiency and wavefront fidelity. To evaluate these effects in detail, a flexible simulation tool was developed in MATLAB, enabling both Fourier and integral-based propagation through arbitrarily deformed DOEs. These models are applied to a conceptual space-based LIDAR system—SPECIES—that uses a deployable DOE optic to demonstrate the feasibility and limitations of this approach. The results show that DOEs can tolerate some global deformations - for example, a 1 mm deformation results in a 38% performance loss in an F3 LiDAR system with a 1 mm detector diameter. However, they remain highly sensitive to fine-scale shape errors, posing significant challenges for high-precision applications like fiber coupling or imaging. The findings provide new insight into the tolerances, benefits, and trade-offs of DOEbased systems in space, and lay the groundwork for future missions seeking to leverage lightweight diffractive optics for remote sensing and optical communication.S.M
On the potential of microtubules for scalable quantum computation
No full textWe examine the quantum coherence properties of tubulin heterodimers arranged into the protofilaments of cytoskeletal microtubules. In the physical model proposed by the authors, the microtubule interiors are treated as high-Q quantum electrodynamics (QED) cavities that can support decoherence-resistant entangled states under physiological conditions, with decoherence times of the order of O ( 10 - 6 ) s. We identify strong electric dipole interactions between tubulin dimers and ordered water dipole quanta within the microtuble interior as the mechanism responsible for the extended coherence times. Classical nonlinear (pseudospin) σ -models describing solitonic excitations are reinterpreted as emergent quantum-coherent—or possibly pointer—states, arising from incomplete collapse of dipole-aligned quantum states. These solitons mediate dissipation-free energy transfer along microtubule filaments. We discuss logic-gate-like behaviour facilitated by microtubule-associated proteins, and outline how such structures may enable scalable, ambient-temperature quantum computation, with the fundamental unit of information storage realized as a quDit encoded in the tubulin dipole state. We further describe a process akin to “decision-making” that emerges following an external stimulus, whereby optimal, energy-loss-free signal and information transport pathways are selected across the microtubular network. Finally, we propose experimental approaches—including Rabi-splitting spectroscopy and entangled surface plasmon probes—to validate the use of biomatter as a substrate for scalable quantum computation