Manipulator motion planning has conventionally been solved using sampling and optimization-based algorithms that are agnostic to embodiment and environment configurations. However, these algorithms plan on a fixed environment representation approximated using shape primitives, and hence struggle to find solutions for cluttered and dynamic environments. Furthermore, these algorithms fail to produce solutions for complex unstructured environments under real-time bounds. Neural Motion Planners (NMPs) are an appealing alternative to algorithmic approaches as they can leverage parallel computing for planning while incorporating arbitrary environmental constraints directly from raw sensor observations. Contemporary NMPs successfully transfer to different environment variations, however, fail to generalize across embodiments. This thesis proposes "AnyNMP'', a generalist motion planning policy for zero-shot transfer across different robotic manipulators and environments. The policy is conditioned on semantically segmented 3D pointcloud representation of the workspace thus enabling implicit sim2real transfer. In the proposed approach, templates are formulated for manipulator kinematics and ground truth motion plans are collected for over 3 million procedurally sampled robots in randomized environments. The planning pipeline consists of a state validation model for differentiable collision detection and a sampling based planner for motion generation. AnyNMP has been validated on 5 different commercially available manipulators and showcases successful cross-embodiment planning, achieving an 80% average success rate on baseline benchmarks.

Since the invention of the automobile, engineers have been designing and making newer and newer improvements to them in order to provide customers with safer, faster, more reliable, and more comfortable vehicles. With each new generation, new technology can be seen being introduced into mainstream products, one of which that is currently being pushed is that of autonomy. Established brand manufacturers and small research teams have been dedicated for years to find a way to make the automobile autonomous with none of them being able to confidently answer that they have found a solution. Among the engineering community there are two schools of thought when solving this issue: camera and LiDAR; some believe that only cameras and computer vision are required while other believe that LiDAR is the solution. The most optimal case is to use both cameras and LiDAR’s together in order to increase reliability and ensure data confidence. Designers are reluctant to use LiDAR systems due to their massive weight, cost, and complexity; with too many moving components, these systems are very bulky and have multiple costly, moving parts that eventually need replacement due to their constant motion. The solution to this problem is to develop a solid-state LiDAR system which would solve all those issues previously stated and this research takes it one level further and looks into a potential prototype for a solid-state camera and Lidar package. Currently no manufacturer offers a system that contains a solid-state LiDAR system and a solid-state camera with computing capabilities, all manufacturers provided either just the camera, just the Lidar, or just the computation ability. This design will also use of the shelf COTS parts in order to increase reproducibility for open-source development and to reduce total manufacturing cost. While keeping costs low, this design is also able to keep its specs and performance on par with that of a well-used commercial product, the Velodyne VL50.

The integration of Distributed Energy Resources (DER), including wind energy and photovoltaic (PV) panels, into power systems, increases the potential for events that could lead to outages and cascading failures. This risk is heightened by the limited dynamic information in energy grid datasets, primarily due to sparse Phasor Measurement Units (PMUs) placement. This data quality issue underscores the need for effective methodologies to manage these challenges. One significant challenge is the data gaps in low-resolution (LR) data from RTU and smart meters, hindering robust machine learning (ML) applications. To address this, a systematic approach involves preparing data effectively and designing efficient event detection methods, utilizing both intrinsic physics and extrinsic correlations from power systems. The process begins by interpolating LR data using high-resolution (HR) data, aiming to create virtual PMUs for improved grid management. Current interpolation methods often overlook extrinsic spatial-temporal correlations and intrinsic governing equations like Ordinary Differential Equations (ODEs) or Differential Algebraic Equations (DAEs). Physics-Informed Neural Networks (PINNs) are used for this purpose, though they face challenges with limited LR samples. The solution involves exploring the embedding space governed by ODEs/DAEs, generating extrinsic correlations for initial LR data imputation, and enforcing intrinsic physical constraints for refinement. After data preparation, event data dimensions such as spatial, temporal, and measurement categories are recovered in a tensor. To prevent overfitting, common in traditional ML methods, tensor decomposition is used. This technique merges intrinsic and physical information across dimensions, yielding informative and compact feature vectors for efficient feature extraction and learning in event detection. Lastly, in grids with insufficient data, knowledge transfer from grids with similar event patterns is a viable solution. This involves optimizing projected and transferred vectors from tensor decomposition to maximize common knowledge utilization across grids. This strategy identifies common features, enhancing the robustness and efficiency of ML event detection models, even in scenarios with limited event data.

Antenna arrays are widely used in wireless communication, radar, remote sensing, and other fields. Compared to traditional linear antenna arrays, novel nonlinear antenna arrays have fascinating advantages in terms of structural simplicity, lower cost, wider bandwidth, faster scanning speed, and lower side-lobe levels. This dissertation explores a novel design of a phased array antenna with an augmented scanning range, aiming to establish a clear connection between mathematical principles and practical circuitry. To achieve this goal, the Van der Pol (VDP) model is applied to a single-transistor oscillator to obtain the isolated limit cycle. The coupled oscillators are then integrated into a 1 times 7 coupled phased array, using the Keysight PathWave Advanced Design System (ADS) for tuning and optimization. The VDP model is used for analytic study of bifurcation, quasi-sinusoidal oscillation, quasi-periodic chaos, and oscillator death, while ADS schematics guide engineering implementation and physical fabrication. The coupled oscillators drive cavity-backed antennas, forming a one-dimensional scanning antenna array of 1 times 7. The approaches for increasing the scanning range performance are discussed.

The objective of this dissertation is to study the optical and radiative properties of inhomogeneous metallic structures. In the ongoing search for new materials with tunable optical characteristics, porous metals and nanowires provides an extensive design space to engineer its optical response based on the morphology-dependent phenomena.This dissertation firstly discusses the use of aluminum nanopillar array on a quartz substrate as spectrally selective optical filter with narrowband transmission for thermophotovoltaic systems. The narrow-band transmission enhancement is attributed to the magnetic polariton resonance between neighboring aluminum nanopillars. Tuning of the resonance wavelengths for selective filters was achieved by changing the nanopillar geometry. It concludes by showing improved efficiency of Gallium-Antimonide thermophotovoltaic system by coupling the designed filter with the cell. Next, isotropic nanoporous gold films are investigated for applications in energy conversion and three-dimensional laser printing. The fabricated nanoporous gold samples are characterized by scanning electron microscopy, and the spectral hemispherical reflectance is measured with an integrating sphere. The effective isotropic optical constants of nanoporous gold with varying pore volume fraction are modeled using the Bruggeman effective medium theory. Nanoporous gold are metastable and to understand its temperature dependent optical properties, a lab-scale fiber-based optical spectrometer setup is developed to characterize the in-situ specular reflectance of nanoporous gold thin films at temperatures ranging from 25 to 500 oC. The in-situ and the ex-situ measurements suggest that the ii specular, diffuse, and hemispherical reflectance varies as a function of temperature due to the morphology (ligament diameter) change observed. The dissertation continues with modeling and measurements of the radiative properties of porous powders. The study shows the enhanced absorption by mixing porous copper to copper powder. This is important from the viewpoint of scalability to get end products such as sheets and tubes with the requirement of high absorptance that can be produced through three-dimensional printing. Finally, the dissertation concludes with recommendations on the methods to fabricate the suggested optical filters to improve thermophotovoltaic system efficiencies. The results presented in this dissertation will facilitate not only the manufacturing of materials but also the promising applications in solar thermal energy and optical systems.

The strong demand for the advancing of Moore’s law on device size scaling down has accelerated the miniaturization of passive devices. Among these important electronic components, inductors are facing challenges because the inductance value, which is strongly dependent on the coil number for the air core inductor case, will be sacrificed when the size is shrinking. Adding magnetic core is one of the solutions due to its enhancement of inductance density but it will also add complexity to the fabrication process, and the core loss induced by the eddy current at high frequency is another drawback. In this report, the output of this research will be presented, which has three parts. In the first part, the CoZrTaB thin films are sputtered on different substrates and characterized comprehensively. The laminated CoZrTaB thin films have been also investigated, showing low coercivity and anisotropy field on both Si and polyimide substrates. Also, the different process conditions that could affect the magnetic properties are investigated. In the second part, Ansys Maxwell software is used to optimize the lamination profile and the magnetic core inductor structure. The measured M-H loop is imported to improve the simulation accuracy. In the third part, a novel method to fabricate the magnetic core inductors on flexible substrates is proposed. The sandwich magnetic core inductor is fabricated and assembled with flipchip bonder. The measurement result shows that this single-turn magnetic core inductor can achieve up to 24% inductance enhancement and quality factor of 7.42. The super low DC resistance (< 60 mΩ) proves that it is a good candidate to act as the passive component in the power delivery module and the use of polyimide-based substrate extends its compatibility to more packaging form factors.

Although the increasing penetration of electric vehicles (EVs) has reduced the emissionof the greenhouse gas caused by vehicles, it would lead to serious congestion on-road and in charging stations. Strategic coordination of EV charging would benefit the transportation system. However, it is difficult to model a congestion game, which includes choosing charging routes and stations. Furthermore, conventional algorithms cannot balance System Optimization and User Equilibrium, which can cause a huge waste to the whole society. To solve these problems, this paper shows (1) a congestion game setup to optimize and reveal the relationship between EV users, (2) using ε – Nash Equilibrium to reduce the inefficient impact from the self-minded the behavior of the EV users, and (3) finding the relatively optimal solution to approach Pareto-Optimal solution. The proposed method can reduce more total EVs charging time and most EV users’ charging time than existing methods. Numerical simulations demonstrate the advantages of the new method compared to the current methods.

The performance of voltage source inverter (VSI) in terms of output waveform quality, conversion efficiency and common mode noise depends greatly on the pulse width modulation (PWM) method. In this work, a low-loss space vector PWM i.e., 240°-clamped space vector PWM (240CPWM) is proposed to improve the performance of VSIs in electric/hybrid electric vehicles (EV/HEVs) and grid connected photovoltaic (PV) systems. The salient features of 240CPWM include 240° clamping of each phase pole to positive or negative DC bus in a fundamental cycle ensuring that switching losses are reduced by a factor of seven as compared to conventional space vector PWM (CSVPWM) at unity power factor. Zero states are completely eliminated and only two nearest active states are used ensuring that there is no penalty in terms of total harmonic distortion (THD) in line current. The THD of the line current is analyzed using the notion of stator flux ripple and compared with conventional and discontinuous PWM method. Discontinuous PWM methods achieve switching loss reduction at the expense of higher THD while 240CPWM achieves a much greater loss reduction without impacting the THD. The analysis and performance of 240CPWM are validated on a 10 kW two-stage experimental prototype. Common mode voltage (CMV) and leakage current characteristics of 240CPWM are analyzed in detail. It is shown analytically that 240CPWM reduces the CMV and leakage current as compared to other PWM methods while simultaneously reducing the switching loss and THD. Experimental results from a 10-kW hardware prototype conform to the analytical discussions and validate the superior performance of 240CPWM. 240CPWM requires a six-pulse dynamic DC link voltage that introduces low frequency harmonics in DC input current and/or AC line currents that can affect maximum power point tracking, battery life or THD in line current. Four topologies have been proposed to minimize the low frequency harmonics in input and line currents in grid-connected PV system with 240CPWM. In order to achieve further benefits in terms of THD and device stress reduction, 240CPWM is extended to three-level inverters. The performance metrics such as THD and switching loss for 240CPWM are analyzed in three-level inverter.

Voltage Source Inverter (VSI) is an integral component that converts DC voltage to AC voltage suitable for driving the electric motor in Electric Vehicles/Hybrid Electric Vehicles (EVs/HEVs) and integration with electric grid in grid-connected photovoltaic (PV) converter. Performance of VSI is significantly impacted by the type of Pulse Width Modulation (PWM) method used.In this work, a new PWM method called 240° Clamped Space Vector PWM (240CPWM) is studied extensively. 240CPWM method has the major advantages of clamping a phase to the positive or negative rail for 240° in a fundamental period, clamping of two phases simultaneously at any given instant, and use of only active states, completely eliminating the zero states. These characteristics lead to a significant reduction in switching losses of the inverter and lower DC link capacitor current stress as compared to Conventional Space Vector PWM. A unique six pulse dynamically varying DC link voltage is required for 240CPWM instead of constant DC link voltage to maintain sinusoidal output voltage. Voltage mode control of DC-DC stage with Smith predictor is developed for shaping the dynamic DC link voltage that meets the requirements for fast control. Experimental results from a 10 kW hardware prototype with 10 kHz switching frequency validate the superior performance of 240CPWM in EV/HEV traction inverters focusing on loss reduction and DC link capacitor currents. Full load efficiency with the proposed 240CPWM for the DC-AC stage even with conventional Silicon devices exceeds 99%. Performance of 240CPWM is evaluated in three phase grid-connected PV converter. It is verified experimentally that 240CPWM performs well under adverse grid conditions like sag/swell and unbalance in grid voltages, and under a wide range of power factor. Undesired low frequency harmonics in inverter currents are minimized using the Harmonic Compensator that results in Total Harmonic Distortion (THD) of 3.5% with 240CPWM in compliance with grid interconnection standards. A new, combined performance index is proposed to compare the performance of different PWM schemes in terms of switching loss, THD, DC link current stress, Common Mode Voltage and leakage current. 240CPWM achieves the best value for this index among the PWM methods studied.