Characterizing and Modeling Transient Behavior in Power Electronic Circuits with Wide Bandgap Semiconductors and in Maximum Power Point Tracking for Photovoltaic Systems

This dissertation examines the transient characteristics in next generation power electronic circuits at both the device-level and the systems-level. At the device-level, the effect of the parasitic capacitances on the switching performance of emerging wide bandgap semiconductors (WBG) is evaluated. Equivalent device models based on gallium nitride (GaN) and silicon carbide (SiC) are implemented in SaberRD and MATLAB, and transient switching characteristics are analyzed in great detail. The effects of the parasitic capacitances on detrimental circuit behavior such as “overshoot,” “ringing,” and “false turn-on” are investigated. The modeled results are supplemented and validated with experimental characterization of the devices in various power conversion circuits. The models can be used to aid in the design of next generation WBG devices so that the undesirable transient effects displayed by contemporary versions of these devices can be mitigated. At the systems-level, the transient overshoot demonstrated by conventional maximum power point tracking algorithms for photovoltaic power conversion systems is investigated. An adaptive controller is implemented so that the operating point can converge to the optimal power point rapidly with minimal overshoot. This new controller overcomes the parasitic components inherent to the power converter which limit its ability to deliver maximum power rapidly. It will be shown that with the new controller, the maximum power point is attainable in 4 milliseconds. The work accomplished in this dissertation lays a foundation for power electronic engineers to integrate semiconductor device theory with control theory to optimize the performance of next generation power conversion systems

Pathogen Detection with Loop Mediated Isothermal Amplification

Recently, a novel methodology termed Loop-mediated isothermal amplification (LAMP) was reported as the preferred technique for rapid diagnostic testing of pathogens. The advantages of LAMP over older techniques are its economic viability, rapidity and its obviation of complex instruments. Reported here is an interdisciplinary approach between medical practices and engineering to implement an affordable diagnostic device which employs LAMP for detection of pathogens. LAMP involves the optical excitation and detection of a pathogen sample mixed with a fluorescent dye as it is heated and amplified over an hour. The device reported here consists of readily available components which heat, optically excite and detect a LAMP sample. Finally, the integration will graphically display the amplification of the LAMP sample as a function of time on a palm top computer by exploiting the ubiquitous 802.11 wireless standard. The diagnostic box implemented here and its supporting components accurately discriminated between positive (infected) and negative (not infected) LAMP samples of various pathogens in approximately one hour. These results were verified using the standard method of pathogen diagnosis termed electrophoresis. Due to the low cost and portability of the device reported here, it poses as a potential solution to the need for quality point of care diagnostic tools in developing countries

AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming

The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture. In this context, building and updating a common map of the field is an essential but challenging task. The maps built using robots of different types show differences in size, resolution and scale, the associated geolocation data may be inaccurate and biased, while the repetitiveness of both visual appearance and geometric structures found within agricultural contexts render classical map merging techniques ineffective. In this paper we propose AgriColMap, a novel map registration pipeline that leverages a grid-based multimodal environment representation which includes a vegetation index map and a Digital Surface Model. We cast the data association problem between maps built from UAVs and UGVs as a multimodal, large displacement dense optical flow estimation. The dominant, coherent flows, selected using a voting scheme, are used as point-to-point correspondences to infer a preliminary non-rigid alignment between the maps. A final refinement is then performed, by exploiting only meaningful parts of the registered maps. We evaluate our system using real world data for 3 fields with different crop species. The results show that our method outperforms several state of the art map registration and matching techniques by a large margin, and has a higher tolerance to large initial misalignments. We release an implementation of the proposed approach along with the acquired datasets with this paper.Comment: Published in IEEE Robotics and Automation Letters, 201

Improved Thermoelectric Cooling Based on the Thomson Effect

Traditional thermoelectric cooling relies on the Peltier effect which produces a temperature drop limited by the figure of merit, zT. This cooling limit is not required from classical thermodynamics but can be traced to problems of thermoelectric compatibility. Alternatively, if a thermoelectric cooler can be designed to achieve full thermoelectric compatibility, lower temperature can be achieved even if the zT is low. In such a device the Thomson effect plays an important role. We present the theoretical concept of a “Thomson cooler,” for cryogenic cooling which is designed to maintain thermoelectric compatibility and we derive the requirements for the Seebeck coefficient

Isolated/non-isolated quad-inverter configuration for multilevel symmetrical/asymmetrical dual six-phase star-winding converter

This article presents the developments of a novel isolated/non-isolated quad inverter configuration for multilevel dual six-phase (twelve-phase) star-winding converter. The modular circuit consists of four standard voltage source inverters (VSIs). Each VSI is incorporated with one bi-directional switch (MOSFET/IGBT) per phase and links with the neutral line through two capacitors which allows symmetrical and asymmetrical operations. A modified single carrier five-level modulation (MSCFM) algorithm is developed and modulates each 2-level VSI as a 5-level output multilevel inverter. The entire AC converter is numerically modeled using Matlab/PLECS simulation software and the predicted behavior of the system is analyzed and presented. Good agreement is obtained between these results and the theoretical analysis. Suitable applications for the converter include (low-voltage/high-current) medium power systems, electrical vehicles, AC tractions, and ‘More-Electric Aircraft’ propulsion systems

WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming

We present a novel weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.Comment: 25 pages, 14 figures, MDPI Remote Sensin