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Title: Beam-Wave Interaction for a Terahertz Solid-state Amplifier
Creator: Hodek, Matthew Steven
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The push of conventional electronic amplifier technologies into the deep submillimeter wavelength and THz frequency ranges of the electromagnetic (EM) spectrum has been limited by constraints on their fundamental physics of operation and fabrication limitations. At the same time, optical amplifier technologies can only access this spectral region using inefficient frequency down-conversion. This struggle for practical power amplifiers in the THz band will likely require a new type of...
Show moreThe push of conventional electronic amplifier technologies into the deep submillimeter wavelength and THz frequency ranges of the electromagnetic (EM) spectrum has been limited by constraints on their fundamental physics of operation and fabrication limitations. At the same time, optical amplifier technologies can only access this spectral region using inefficient frequency down-conversion. This struggle for practical power amplifiers in the THz band will likely require a new type of amplifier and has led to a desire for a solid-state beam-wave style amplifier using semiconductor fabrication techniques. While there has been considerable progress in creating transistors in the THz region, the small size required to achieve the needed transit times and gate capacitances generally precludes them from producing power above 1 mW. Vacuum electronic devices (VEDs), such as traveling wave amplifiers (TWAs), have also shown great progress into this band. A TWA is an example of a beam-wave style device where gain is achieved by transferring energy from an electron beam to an EM wave at electrically large length scales. However, as traditional TWAs are scaled to higher frequencies, the shrinking wavelength makes fabrication of the corresponding interaction circuit structures and miniscule beam tunnels increasingly difficult through micro-machining or other subtractive metal shaping. Thus, combining the strengths of both these systems into a single device has some merit. Solid-state TWAs have been attempted over many years without success largely due to slow electron drift velocities resulting in beam equivalents that are unsuitable for synchronization with EM slow-wave structures. One possible path towards a beam-wave style THz solid-state amplifier is to couple to a plasma wave characterized by phase propagation much faster than the electron velocity limited by scattering in a material, but this requires a substantial redevelopment of the fundamental beam-wave interaction analysis. Presented here is a novel analysis built upon the prior work on solid-state and VED TWAs with a primary difference in the nature of the charge carrier behavior. In this work the electron beam, which was previously described as bulk carriers in a semiconductor, is now formed with an un-gated 2D electron gas (2DEG). A freely propagating plasma wave is present in the dense 2DEG and takes the place of the typical space charge wave present in VED devices. Example calculations are compared to a generic VED TWA behavior and the basic performance of a realizable device is analyzed through the use of a gallium nitride heterostructure material system and achievable fabrication strategies. It is shown that the concept of a TWA using a 2DEG plasma wave is not practical at best, and fundamentally flawed at worst. However, the understanding gained lays some of the groundwork for other possible beam-wave interaction style amplifiers using a fast 2DEG plasma wave.
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Title: COLLABORATIVE DISTRIBUTED DEEP LEARNING SYSTEMS ON THE EDGES
Creator: Zeng, Xiao
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Deep learning has revolutionized a wide range of fields. In spite of its success, most deep learning systems are proposed in the cloud, where data are processed in a centralized manner with abundant compute and network resources. This raises a problem when deep learning is deployed on the edge where distributed compute resources are limited. In this dissertation, we propose three distributed systems to enable collaborative deep learning on the edge. These three systems target different...
Show moreDeep learning has revolutionized a wide range of fields. In spite of its success, most deep learning systems are proposed in the cloud, where data are processed in a centralized manner with abundant compute and network resources. This raises a problem when deep learning is deployed on the edge where distributed compute resources are limited. In this dissertation, we propose three distributed systems to enable collaborative deep learning on the edge. These three systems target different scenarios and tasks. The first system dubbed Distream is a distributed live video analytics system based on the smart camera-edge cluster architecture. Distream fully utilizes the compute resources at both ends to achieve optimized system performance. The second system dubbed Mercury is a system that addresses the key bottleneck of collaborative learning. Mercury enhances the training efficiency of on-device collaborative learning without compromising the accuracies of the trained models. The third system dubbed FedAce is a distributed training system that improves training efficiency under federated learning setting where private on-device data are not allowed to be shared among local devices. Within each participating client, FedAce achieves such improvement by prioritizing important data. In the server where model aggregation is performed, FedAce exploits the client importance and prioritizes important clients to reduce stragglers and reduce the total number of rounds. In addition, FedAce conducts federated model compression to reduce the per-round communication cost and obtains a compact model after training completes. Extensive experiments show that the proposed three systems are able to achieve significant improvements over status-quo systems.
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Title: Control of MSU jumping robot
Creator: Alsaedi, Emad
Date: 2014
Collection: Electronic Theses & Dissertations
Description: The idea of Miniature jumping robots is taking exactly from the animal living around us. A gecko lizard is a perfect example for a jumping balancing Robot, in an experiment, it was seen how gecko lizard can balance landing using the moment of the tail. Another example is a falling cat midair self-righting. These miniature jumping robot are becoming widely used in real life. One cannot expect how useful to use such robot at times where human presence is at danger. Like in the case of wars or...
Show moreThe idea of Miniature jumping robots is taking exactly from the animal living around us. A gecko lizard is a perfect example for a jumping balancing Robot, in an experiment, it was seen how gecko lizard can balance landing using the moment of the tail. Another example is a falling cat midair self-righting. These miniature jumping robot are becoming widely used in real life. One cannot expect how useful to use such robot at times where human presence is at danger. Like in the case of wars or natural disaster (as in earth quakes or nuclear like disaster as Fukushima leaks in Japan). The MSU jumper robot is unique in terms of weight and size; however, there is some control problem in the case of landing procedure, as well as self-righting and maneuvering in midair. Designing a controller for MSU jumping robot is challenging, the controller has to response in half a second as the jumping period is close to 2 second, that short period made it almost impossible for the robot to resist uncertainties or unmolded dynamics, as well as changes in the mass of moment of inertia of the body due to change of body shape. We managed to add mini wings to the robot to prolong jumping period and the stabilize landing procedure, as well as to enable the robot to estimate the mass of moment of inertia for the body , and all of that for the controller at the tail to force the body to land on the desired edge. MSU jumper robot has swept greatly throughout robotics media and industry due to the tininess and light weight properties. A light weight that doesn't exceed 28 g and a maximum size of 6.5 cm is what made the robot special in its types of all jumping robots.
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Title: DEVELOPMENT AND APPLICATIONS OF SENSOR INTEGRATED HYBRID RFID SYSTEM
Creator: Mondal, Saikat
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Today, Radio Frequency Identification (RFID) has become a multibillion-dollar industry. RFID is primarily used for numerous object tagging and tracking applications such as road toll collection service, facility access control, asset tracking in a supply chain to name a few. Batteryless tag architecture and modern fabrication technology has enabled the miniaturization and cost reduction of ultra-high frequency (UHF) passive RFIDs. There is growing interest in the use of RFIDs for supply chain...
Show moreToday, Radio Frequency Identification (RFID) has become a multibillion-dollar industry. RFID is primarily used for numerous object tagging and tracking applications such as road toll collection service, facility access control, asset tracking in a supply chain to name a few. Batteryless tag architecture and modern fabrication technology has enabled the miniaturization and cost reduction of ultra-high frequency (UHF) passive RFIDs. There is growing interest in the use of RFIDs for supply chain sensors (e.g., agricultural products and pharmaceuticals), underground object tagging (e.g., plastic pipes), low-power wearable devices, etc. However, the RFID designs in their current form cannot meet the stringent requirements of these new applications. Four key challenges that hinders the direct adoption of existing RFIDs for these applications are: (a) clutter effect, (b) integration of sensors, (c) response time, and (d) prolonged RF transmission from RFID reader for continuous sensor monitoring. In this work, the fundamental limitations of conventional RFID system are described in detail first, followed by proposed solutions leading to new RFID designs. First, a dual frequency harmonic RFID system is proposed and demonstrated to mitigate the clutter effect. Second, a low power digital interface sensing platform is demonstrated for electrochemical pH sensor, targeted towards biochemical applications as an example. Third, detailed analysis is performed at the component level to understand the efficiency and response time dependence of the energy harvester within the RFID designs. Based on the analysis, a model was proposed to estimate the response time of a conventional RFID tag. Fourth, a dual mode RFID is proposed and demonstrated to reduce the transmit time of RFID reader, hence reducing the effective RF transmission. Furthermore, the compatibility of integrating these solutions together in a single platform are presented. This is important as more than one challenge can be present in a single application. Finally, a hybrid RFID configuration is proposed and demonstrated that is capable of simultaneously mitigating all these four challenges.
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Title: DOWNLINK RESOURCE BLOCKS POSITIONING AND SCHEDULING IN LTE SYSTEMS EMPLOYING ADAPTIVE FRAMEWORKS
Creator: Abusaid, Osama M.
Date: 2018
Collection: Electronic Theses & Dissertations
Description: The present expansions in size and complexity of LTE networks is hindering their performance and their reliability. This hindrance is manifested in deteriorating performance in the User Equipment’s throughput and latency as a consequence to deteriorating the E-node B downlink throughput. This is leading to the need of smart E Node Base with various capabilities adapting to the changing communication environment. The proposed work aims at developing Self Organization (SO) techniques and...
Show moreThe present expansions in size and complexity of LTE networks is hindering their performance and their reliability. This hindrance is manifested in deteriorating performance in the User Equipment’s throughput and latency as a consequence to deteriorating the E-node B downlink throughput. This is leading to the need of smart E Node Base with various capabilities adapting to the changing communication environment. The proposed work aims at developing Self Organization (SO) techniques and frameworks for LTE networks at the Resource Blocks (RB) scheduling management level. After reviewing the existing literature on Self Organization techniques and scheduling strategies that have been recently implemented in other wireless networks, we identify several contrasting needs that can jointly be addressed. The deployment of the introduced algorithms in the communication network is expected to lead to improved and upgraded overall network performance. The main feature of the LTE networks family is the feed-back that the cell receives from the users. The feedback includes the down link channel assessment based on the User Equipment (UE) measure Channel Quality Indicator (CQI) in the last Transmission Time Interval (TTI). This feed-back should be the main decision factor in allocating Resource Blocks (RBs) among users. The challenge is how could one maps the users’ data onto the RBs based on the CQI. The Thesis advances two approaches towards that end:- the allocation among the current users for the next TTI should be mapped, consistent with historical feed-back CQI received from users over prior transmission durations. This approach also aims at offering a solution to the bottle-neck capacity issue in the scheduling of LTE networks. To that end, we present an implementation of a modified Self Organizing Map (SOM) algorithm for mapping incoming data into RBs. Such an implementation can handle the collective cell enabling our cell to become smarter. The criteria in measuring the E-node Base performance include throughput, fairness and the trade-off between these attributes.- Another promising and complementary approach is to tailor Recurrent Neural Networks (RNNs) to implement optimal dynamic mappings of the Resource Blocks (RBs) in response to the history sequence of the Channel Quality Indicator CQI feedback. RNNs can successfully build its own internal state over the entire training CQI sequence and consequently make the prediction more viable. With this dynamic mapping technique, the prediction will be more accurate to changing time-varying channel environments. Overall, the collective cell management would become more intelligent and would be adaptable to changing environments. Consequently, a significant performance improvement can be achieved at lower cost. Moreover, a general tunability of the scheduling system becomes possible which would incorporate a trade-off between system complexity and QoS.
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Title: DYNAMIC ANTENNA ARRAY FOR ACTIVE INCOHERENT MILLIMETER-WAVE SENSING
Creator: Chen, Daniel S.
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The need for fast and reliable sensing at millimeter-wave frequencies has been increasing dramaticallyin recent years for a wide range of applications. Imaging has been of particular interest since the wavelengths at millimeter-wave frequencies provide good resolution and are capable to propagate through obscurants such as smoke, clouds, and clothing with negligible attenuation. While various implementations for millimeter-wave imaging have been developed, the new technique of active...
Show moreThe need for fast and reliable sensing at millimeter-wave frequencies has been increasing dramaticallyin recent years for a wide range of applications. Imaging has been of particular interest since the wavelengths at millimeter-wave frequencies provide good resolution and are capable to propagate through obscurants such as smoke, clouds, and clothing with negligible attenuation. While various implementations for millimeter-wave imaging have been developed, the new technique of active incoherent millimeter-wave (AIM) imaging is of particular interest because it solves fundamental limitations inherent in other approaches. Furthermore, AIM enables imaging with significantly fewer elements than phased arrays and costs less than passive imagers. This is enabled by actively transmitting noise signals, allowing the system to capture scene information in the spatial frequency domain. In this work, I explore the use of array dynamics to further reduce the hardware requirements of AIM imaging by introducing a new degree of freedom in the array design. By dynamically changing the locations of receiving antennas in a sparse array through motions, the spatial frequency domain can be efficiently sampled using as few as two antennas. In this thesis, I demonstrate the use of array dynamics to generate imagery and show a new concept for imageless object identification based on sampling unique spatial frequency features associated with physical shapes of objects. This non-imaging approach further reduces the required number of antennas. The designed rotational dynamic antenna array operates at 38 GHz and leverages noise transmitting sources as required by the AIM technique. Two receivers are designed with adjustable distance in-between, enabling a sparse linear array to be synthesized. Simulation and experimental measurements using the AIM based rotational dynamic antenna array are discussed for imaging and imageless classification.
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Title: Data-Driven Modeling and Control of Nonlinear and Complex Systems with Application on Automotive Systems
Creator: Chen, Kaian
Date: 2022
Collection: Electronic Theses & Dissertations
Description: As data is becoming more readily accessible from various systems, data-driven modeling and control approaches have gained increased popularity among both researchers and practitioners. Compared to its first-principle counterparts, data-driven approaches require minimal domain knowledge and calibration efforts, which is especially appealing to nonlinear and complex systems. In this thesis, two efficient data-driven frameworks, indirect and direct, for the control of nonlinear and complex...
Show moreAs data is becoming more readily accessible from various systems, data-driven modeling and control approaches have gained increased popularity among both researchers and practitioners. Compared to its first-principle counterparts, data-driven approaches require minimal domain knowledge and calibration efforts, which is especially appealing to nonlinear and complex systems. In this thesis, two efficient data-driven frameworks, indirect and direct, for the control of nonlinear and complex systems are presented.The indirect method first involves an efficient online system identification approach with a composite local model structure. We introduce the concept of evolving Spatial Temporal Filters (eSTF) that dynamically transforms an incoming input-output data stream into a nonlinear combination of local models. Each local model is assigned with an ellipsoid-shaped cluster that is used to define its validity zone, and a distance metric that combines the Mahalanobis distance to the clusters and the scaled local model prediction error is exploited to compute the local model composition weights. The cluster and model parameters are efficiently updated online using input-output data stream, enabling adaptive system identification with efficient computations. With the identified eSTF model structure, we then develop an efficient quasi-linear parameter varying (qLPV) based stochastic model predictive controller (SMPC) for a class of nonlinear systems subject to chance constraints and additive disturbance. The qLPV form is established with the scheduling variable and a set of linear time-invariant models obtained from the eSTF system identification approach. To handle chance constraints, probabilistic reachable sets -- the probabilistic analogy of robust reachable sets -- are exploited to tighten the constraints to robustly guarantee constraint satisfaction despite model uncertainties and additive disturbances.A shifted scheduling variable strategy is designed such that the resultant MPC optimization can be efficiently solved by solving a series of quadratic programming problems. The indirect data-driven modeling and control pipeline is successfully applied to automotive powertrain systems with great system identification and control performance demonstrated. On the other hand, we further develop a direct data-driven control paradigm that leverages behavioural system theory, and directly generates control commands from input/output data without the need of a parametric model. We exploit singular value decomposition (SVD)-based order reduction to significantly reduce the online computation complexity without degrading the control performance. This control paradigm is successfully applied to battery fast charging, which has complex dynamics and is difficult to model. Furthermore, the direct data-driven approach heavily relies on the intensive collection and sharing of data, which raises serious privacy concerns, especially for systems with multiple agents. Therefore, we develop a privacy-preserving data-enabled predictive control scheme where we exploit affine-masking to protect the privacy of shared input/output data. It is then applied to the control of connected and automated vehicles (CAVs) in a mixed traffic environment with promising results demonstrated through comprehensive simulations.
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Title: Design and Analysis of Sculpted Rotor Interior Permanent Magnet Machines
Creator: Hayslett, Steven Lee
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Design of interior permanent magnet electrical machines is complex. Interior permanent magnet machines offer a good balance of cost, efficiency, and torque/power density. Maximum torque and power production of an interior permanent magnet machine is achieved through balancing design choices related to the permanent magnet and salient features. The embedded magnet within the salient structure of the rotor lamination results in an increase in harmonic content. In addition, interaction of the...
Show moreDesign of interior permanent magnet electrical machines is complex. Interior permanent magnet machines offer a good balance of cost, efficiency, and torque/power density. Maximum torque and power production of an interior permanent magnet machine is achieved through balancing design choices related to the permanent magnet and salient features. The embedded magnet within the salient structure of the rotor lamination results in an increase in harmonic content. In addition, interaction of the armature, control angle, and rotor reluctance structure creates additional harmonic content. These harmonics result in increased torque ripple, radial forces, losses, and other unwanted phenomena. Further improvements in torque and power density, and techniques to minimize harmonics, are necessary. Typical interior permanent magnet machine design results at the maximum torque per amp condition are at neither the maximum magnet nor maximum salient torque, but at the best combination of the two. The use of rotor surface features to align the magnet and the reluctance axis allows for improvement of torque and power density. Reduction of flux and torque harmonics is also possible through careful design of rotor sculpt features that are included at or near the surface of the rotor. Finite element models provide high fidelity and accurate results to machine performance but do not give insight into the relationship between design parameters and performance. Winding factor models describe the machine with a set of Fourier series equations, providing access to the harmonic information of both parameters and performance. Direct knowledge of this information provides better insight, a clear understanding of interactions, and the ability to develop a more efficient design process. A new analytical winding function model of the single-V IPM machine is introduced, which considers the sculpted rotor and how this model can be used in the design approach of machines.Rotor feature trends are established and utilized to increase design intuition and reduce dependency upon the lengthy design of experiment optimization processes. The shape and placement of the rotor features, derived from the optimization process, show the improvement in torque average and torque ripple of the IPM machine.
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Title: Design and control of a multilevel inverter for electric vehicles
Creator: Matteson, Arthur W.
Date: 2008
Collection: Electronic Theses & Dissertations

Title: Design of an analog computer
Creator: Bush, Nyle Eugene
Date: 1948
Collection: Electronic Theses & Dissertations

Title: Design study of a miniaturized multi-layered metamaterial-inspired dynamically tunable antenna
Creator: Myers, Joshua C.
Date: 2014
Collection: Electronic Theses & Dissertations
Description: A multi-layered metamaterial inspired minaturized antenna with a pixel grid loading structure is introduced in this work. The antenna consists of two layers separated by a thin dielectric substrate. The first layer contains a folded monopole antenna surrounded by a metal pixel based loading structure, while the second layer is envisioned to consist of a photo conductive pixel grid utilized to tune the antenna. The state of each pixel is controlled by a binary genetic algorithm, which is...
Show moreA multi-layered metamaterial inspired minaturized antenna with a pixel grid loading structure is introduced in this work. The antenna consists of two layers separated by a thin dielectric substrate. The first layer contains a folded monopole antenna surrounded by a metal pixel based loading structure, while the second layer is envisioned to consist of a photo conductive pixel grid utilized to tune the antenna. The state of each pixel is controlled by a binary genetic algorithm, which is implemented with a Matlab-HFSS interface. As a proof of concept, the pixel grid on the second layer is initially made of a metal conductor. HFSS simulations show that the second layer has a wide tuning ability with the appropriate state formed through optimization. A wide range of other conductivities are also shown toprovide pixel combinations that meet the required antenna characteristics. The radiation efficiency of the antenna with the second layer is also examined and optimized, and the theoretical tuning range is investigated. The fabrication of multiple antenna configurations with the pixels made of a metal conductor are explored. Thin PET films are first investigated to be used as simple loading elements that can be placed directly on the antenna. However, the airgap and misalignment between the layers caused by this method is shown to be too large to overcome. A novel multi layer fabrication technique is then investigated which uses a SU-8 photoresist as the dielectric layer. This layer can be spun directly onto the antenna, essentially eliminating any airgap problems. The alignment with this process is also much better than the previews method. Multiple antenna configurations corresponding to a wide frequency range are constructed using this fabrication method. The measured reflection coefficients and radiation patterns are shown to be in good agreement with HFSS simulations, successfully demonstrating the ability to dramatically tune the antenna with a second pixel grid
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Title: Development of electrospun nanofiber biosensor and nuclear magnetic resonance based biosensor for rapid pathogen detection
Creator: Luo, Yilun
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Water and food contaminated with pathogens cause millions of hospitalizations and thousands of deaths, and costly recalls in food and retail industries annually. Among them, the Shiga Toxin-producing Escherichia coli (STEC) causes foodborne outbreaks every year, which leads to more than 265,000 illnesses, 3,600 hospitalizations, and 30 deaths in the United States alone. Currently approved detection methods, such as culturing and colony counting, or polymerase chain reaction (PCR), provide...
Show moreWater and food contaminated with pathogens cause millions of hospitalizations and thousands of deaths, and costly recalls in food and retail industries annually. Among them, the Shiga Toxin-producing Escherichia coli (STEC) causes foodborne outbreaks every year, which leads to more than 265,000 illnesses, 3,600 hospitalizations, and 30 deaths in the United States alone. Currently approved detection methods, such as culturing and colony counting, or polymerase chain reaction (PCR), provide accurate diagnosis. However, these methods require long detection time (ranging from 6 to 24 hours), high testing cost, large-sized equipment, and/or skilled personnel, limiting their application in controlling outbreaks, reducing recall loss, or on-field diagnosis in developing countries. In this dissertation research, two biosensors were developed based on electrospun nanofiber and nuclear magnetic resonance (NMR) for rapid detection of STEC with high sensitivity. The electrospun biosensor was designed as lateral-flow immuno-sensor based on magnetic nanoparticles (MNPs) and electrospun nanofibers. The MNPs were coated with conductive nano-shells and functionalized with antibody to extract target pathogen by immunomagnetic separation. Biocompatible nanofibrous membrane was synthesized by electrospinning technique, which was optimized for nano-porous structure and excellent capillary properties. The electrospun membrane was functionalized with antibody to capture the MNP-pathogen conjugates by lateral-flow separation. As a result, the membrane’s conductivity was proportional to pathogen concentration, which could be measured by a portable impedance analyzer. Owing to the novel nanostructure, the surface area and mass transfer rate were significantly increased. This improved the biochemical binding effect and sensor signal to noise ratio. The biosensor’s sensitivity limit was 61 colony forming units per milliliter (CFU/mL) and 104 cell culture infective dose per milliliter (CCID/mL) for bacterial and viral samples, respectively, with detection time of 8 min. The electrospun biosensor has advantages of low cost and high sensitivity, which can be used for on-field biodefense and food safety applications. In the second work, a portable nuclear magnetic resonance (pNMR) biosensor was developed based on antibody functionalized MNPs as proximity biomarkers of the pathogen, which induced micro-magnetic variation to accelerate NMR resonance signal decay. The pNMR was designed using a hand-held magnet of 0.47 Tesla, a high-power radio frequency (RF) transmitter, and an ultra-low noise receiver capable of detecting 0.1 μV NMR signal. The pNMR biosensor assay and sensing mechanism was used in detecting E. coli O157:H7, and sensitivity limit was 76 CFU/mL in water samples and 92 CFU/mL in milk samples with detection time of 1 min. The pNMR biosensor is innovative for bacterial detection in food matrices and can be extended to other microbial or viral organisms by changing the antibody specificity. Besides, the pNMR biosensor can be used for on-field healthcare diagnostic and biodefense applications owing to its advantages of portability and speed of detection.
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Title: Dynamic LED-based Optical Localization of a Mobile Robot
Creator: Greenberg, Jason N.
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Autonomous mobile robots operating in areas with poor GPS and wireless coverage (e.g., underwater) must rely on alternative localization and communication techniques to navigate the field, share their data, and accomplish other missions. This dissertation is focused on the design of an LED-based optical localization system that achieves Simultaneous Localization and Communication (SLAC), where the bearing angles, needed for establishing optical line-of-sight (LOS) for LED-based communication...
Show moreAutonomous mobile robots operating in areas with poor GPS and wireless coverage (e.g., underwater) must rely on alternative localization and communication techniques to navigate the field, share their data, and accomplish other missions. This dissertation is focused on the design of an LED-based optical localization system that achieves Simultaneous Localization and Communication (SLAC), where the bearing angles, needed for establishing optical line-of-sight (LOS) for LED-based communication between beacon (base) nodes and a mobile robot, are used to triangulate and thereby localize the robot. A two-dimensional (2D) setup is considered in this work.First, the measurement process and procedural steps necessary for implementing the localization scheme are developed. Critical to the success of this scheme is the maintenance of the LOS, which is difficult due to the robot's mobile nature. A Kalman filtering-based approach is proposed to predict the mobile robot's position, allowing the system to reduce the overhead of establishing and maintaining the LOS, therefore significantly improving the quality of the localization and communication. The effectiveness of this approach is evaluated with extensive simulation and experiments, including a comparison to an alternative approach not using Kalman filtering-based location prediction.The initial design of the localization system involves two base nodes, which could result in a singularity problem in position measurement when the mobile robot is close to forming a collinear relationship with the base nodes. To address this issue, a setup involving more than two base nodes is considered, where one could dynamically change the base node pair for localization. An important design consideration for this approach is how to best exploit the redundancy in base nodes to provide robust localization. A sensitivity metric is introduced to characterize the level of uncertainty in the position estimate relative to the bearing angle measurement error, to dynamically select a desired pair of beacon nodes. The proposed solution is evaluated with simulation and experimentation, in a setting of three beacons nodes and one mobile node, and its efficacy is demonstrated via comparison with multiple alternative approaches. The aforementioned work assumes that the bearing angles with respect to all base nodes are captured simultaneously (or when the robot is at a single location). Consequently, because scanning for the light intensity to determine the bearing angle takes time, a stop-and-go motion has to be used to ensure that the robot is at a single location during the angle measurement process, which significantly slows the robot's movement. To counter this issue, a scheme is proposed to dynamically localize a robot undergoing continuous movement, by exploiting the velocity prediction from Kalman filtering to properly correlate two consecutive measurements of bearing angles relative to the base nodes. Simulation and experiments show that, with this approach, the robot can be successfully localized when it is continuously moving.
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Title: EFFECT OF GATE-OXIDE DEGRADATION ON ELECTRICAL PARAMETERS OF SILICON AND SILICON CARBIDE POWER MOSFETS
Creator: KARKI, UJJWAL
Date: 2019
Collection: Electronic Theses & Dissertations
Description: The power MOSFET (Metal Oxide Semiconductor Field Effect Transistor) is recognized as a crucial component of many power-electronic systems. The physical structure of both Silicon and Silicon Carbide power MOSFETs require an oxide layer as a dielectric material between their gate terminal and the semiconductor surface. The gate-oxide material, which is predominantly silicon dioxide, slowly degrades under the presence of an electric field. Over time, the degradation process significantly alters...
Show moreThe power MOSFET (Metal Oxide Semiconductor Field Effect Transistor) is recognized as a crucial component of many power-electronic systems. The physical structure of both Silicon and Silicon Carbide power MOSFETs require an oxide layer as a dielectric material between their gate terminal and the semiconductor surface. The gate-oxide material, which is predominantly silicon dioxide, slowly degrades under the presence of an electric field. Over time, the degradation process significantly alters the electrical parameters of power MOSFETs, causing a negative impact on performance, reliability, and efficiency of power converters they are used in. In order to monitor this, the electrical parameters are utilized as precursors (or failure indicators) of gate-oxide degradation.Despite extensive investigation of gate-oxide degradation in Silicon (Si) power MOSFETs, the research literature has not attributed a consistent variation pattern to its gate-oxide degradation precursors. This dissertation investigates the variation pattern of existing precursors: a) threshold voltage, b) gate-plateau voltage, and c) on-resistance. While confirming the previously reported dip-and-rebound variation pattern of the threshold voltage and the gate-plateau voltage, a similar dip-and-rebound variation pattern is also identified in the on-resistance of Si power MOSFETs. Furthermore, a new online precursor of gate-oxide degradation— the gate-plateau time, is proposed and demonstrated to exhibit a similar dip-and-rebound variation pattern. The gate-plateau time is also shown to be the most sensitive online precursor for observing the rebound phenomenon. In addition, the analytical expressions are derived to correlate the effect of gate-oxide degradation with simultaneous dip-and-rebound variation pattern in all four precursors. The dip-and-rebound variation pattern is experimentally confirmed by inducing accelerated gate-oxide degradation in two different commercial Si power MOSFETs. While multiple electrical parameters have been identified as precursors for monitoring the gate-oxide degradation in Si MOSFETs, very few precursors have been proposed for Silicon Carbide (SiC) power MOSFETs. This dissertation proposes that in addition to the threshold voltage, the other online precursors identified for Si power MOSFETs: the gate-plateau voltage and the gate-plateau time, are also effective for monitoring the effect of gate-oxide degradation process in SiC power MOSFETs. Though the gate-oxide material is the same in both Si and SiC power MOSFETs, the effect of gate-oxide degradation on the variation pattern of electrical parameters is different. In contrast to the dip-and-rebound variation pattern of precursors in Si MOSFETs, the research literature has attributed a consistent linear-with-log-stress-time variation pattern to the threshold-voltage shift in SiC power MOSFETs. It is shown that both the gate-plateau voltage and the gate-plateau time increase in a linear-with-log-stress-time manner similar to the threshold voltage. The analytical expressions are derived to correlate the effect of gate-oxide degradation with simultaneous linear-with-log-stress-time variation pattern in all three online precursors. The increasing trend of precursors is experimentally confirmed by inducing accelerated gate-oxide degradation in both planar and trench-gate commercial SiC power MOSFETs under high voltage, high temperature, and hard-switching conditions.
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Title: ENABLING REAL-TIME COMMUNICATION FOR HUMAN AUGMENTATION SYSTEMS VIA UNOBTRUSIVE HIGH BANDWIDTH MACHINE TO HUMAN ELECTROTACTILE PERIPHERAL NERVE STIMULATION
Creator: Parsnejad, Sina
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The advent of sensor technologies and the resulting abundance of information together with modern advanced processing capabilities makes improving human lives via human augmentation technologies ever more appealing. To establish a new effective form of human-machine-communication (M2HC) for augmentation, this dissertation explores non-invasive peripheral nerve stimulation via electrotactile waveforms. This dissertation conducts extensive convergence research between the fields of psychology,...
Show moreThe advent of sensor technologies and the resulting abundance of information together with modern advanced processing capabilities makes improving human lives via human augmentation technologies ever more appealing. To establish a new effective form of human-machine-communication (M2HC) for augmentation, this dissertation explores non-invasive peripheral nerve stimulation via electrotactile waveforms. This dissertation conducts extensive convergence research between the fields of psychology, electrical engineering, neuroscience and human augmentation and established innovations to create distinct sensations that can be utilized as iconic electrotactile M2HC. Existing electrotactile stimulation models deliver a limited range of distinct sensations, making iconic communication challenging. To address this issue, we created a software/hardware infrastructure, including novel electrotactile electrode arrays and improved stimulation circuitry, that allows for rapid prototyping and testing various electrotactile innovations. We created a model for electrotactile waveform generation (MEWS) wherein a train of high-frequency electrotactile pulses is shaped into electrotactile waveforms through a multi-layer on-off-keying modulation forgoing the need for constant frequency recalibration and making painful sensations less likely to happen. Using MEWS, we conducted multiple human trials on 15 volunteering participants stimulating a total of ~6000 electrotactile sensations which led us to create 13 distinct electrotactile waveform with an accuracy of 85.4%. To increase the number of messages that can be delivered by electrotactile stimulation, a model for creating varying electrotactile waveforms (MOVES) was created based on linguistic concept of phonemes and taking a semi-heuristic approach to creating electrotactile waveforms. Using MOVES we conducted multiple human trials on 21 volunteering participants stimulating a total of ~5000 electrotactile sensations. Our human trials proved that MOVES was able to create 24 distinct sensations with an accuracy of 89% that can be used to convey messages through iconic communication and has the potential to expand further beyond the 24 messages. The number of messages delivered by MOVES pentuples the best recorded number of distinct electrotactile sensations in literature.
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Title: EXTENDED REALITY (XR) & GAMIFICATION IN THE CONTEXT OF THE INTERNET OF THINGS (IOT) AND ARTIFICIAL INTELLIGENCE (AI)
Creator: Pappas, Georgios
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The present research develops a holistic framework for and way of thinking about Deep Technologies related to Gamification, eXtended Reality (XR), the Internet of Things (IoT), and Artificial Intelligence (AI). Starting with the concept of gamification and the immersive technology of XR, we create interconnections with the IoT and AI implementations. While each constituent technology has its own unique impact, our approach uniquely addresses the combinational potential of these technologies...
Show moreThe present research develops a holistic framework for and way of thinking about Deep Technologies related to Gamification, eXtended Reality (XR), the Internet of Things (IoT), and Artificial Intelligence (AI). Starting with the concept of gamification and the immersive technology of XR, we create interconnections with the IoT and AI implementations. While each constituent technology has its own unique impact, our approach uniquely addresses the combinational potential of these technologies that may have greater impact than any technology on its own. To approach the research problem more efficiently, the methodology followed includes its initial division into smaller parts. For each part of the research problem, novel applications were designed and developed including gamified tools, serious games and AR/VR implementations. We apply the proposed framework in two different domains: autonomous vehicles (AVs), and distance learning.Specifically, in chapter 2, an innovative hybrid tool for distance learning is showcased where, among others, the fusion with IoT provides a novel pseudomultiplayer mode. This mode may transform advanced asynchronous gamified tools to synchronous by enabling or disabling virtual events and phenomena enhancing the student experience. Next, in Chapter 3, along with gamification, the combination of XR with IoT data streams is presented but this time in an automotive context. We showcase how this fusion of technologies provides low-latency monitoring of vehicle characteristics, and how this can be visualized in augmented and virtual reality using low-cost hardware and services. This part of our proposed framework provides the methodology of creating any type of Digital Twin with near real-time data visualization.Following that, in chapter 4 we establish the second part of the suggested holistic framework where Virtual Environments (VEs), in general, can work as synthetic data generators and thus, be a great source of artificial suitable for training AI models. This part of the research includes two novel implementations the Gamified Digital Simulator (GDS) and the Virtual LiDAR Simulator.Having established the holistic framework, in Chapter 5, we now “zoom in” to gamification exploring deeper aspects of virtual environments and discuss how serious games can be combined with other facets of virtual layers (cyber ranges,virtual learning environments) to provide enhanced training and advanced learning experiences. Lastly, in chapter 6, “zooming out” from gamification an additional enhancement layer is presented. We showcase the importance of human-centered design of via an implementation that tries to simulate the AV-pedestrian interactions in a virtual and safe environment.
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Title: Electro-mechanical analogies
Creator: Eggert, William C.
Date: 1917
Collection: Electronic Theses & Dissertations

Title: Empowering Internet of Things with the Emerging Wireless Infrastructures and Technologies
Creator: Yang, Deliang
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Wireless technologies have been evolved rapidly, whose infrastructures are built and delivered speedily. The emerging wireless technologies offer new solutions for data communication, monitoring, sensing, and edge computing, etc. The fast growth of wireless networks generates not only opportunities for new applications, but also issues in high energy consumption, unexpected latency, and potential privacy breach. In this dissertation, we propose two novel cyber-physical systems to demonstrate...
Show moreWireless technologies have been evolved rapidly, whose infrastructures are built and delivered speedily. The emerging wireless technologies offer new solutions for data communication, monitoring, sensing, and edge computing, etc. The fast growth of wireless networks generates not only opportunities for new applications, but also issues in high energy consumption, unexpected latency, and potential privacy breach. In this dissertation, we propose two novel cyber-physical systems to demonstrate the possibility of empowering new IoT services and applications by leveraging the emerging wireless charging infrastructures and benchmarking the energy performance of end nodes in low-power wireless networks, respectively. First, we present QID, the first system that identifies a Qi-compliant device during wireless charging in real-time using wireless charging fingerprints. QID employs a 2-dimensional motion unit to emulate a variety of multi-coil designs of Qi, which allows for fine-grained device fingerprinting. With the novel mobile coil design and a set of novel fingerprints from oscillator and controller patterns, QID achieves high device recognition accuracy by using ensembled Machine Learning algorithms. With the prevalence of public wireless charging stations, our results also have important implications for mobile user privacy. Second, we develop a novel benchmarking ecosystem, called \textit{NB-Scope}, to study the energy performance of the Narrowband Internet of Things (NB-IoT) network. NB-Scope adopts a hierarchical design, resolving the heterogeneity in network operators, node module vendors, and location profiles, to allow for the fusion of fine-grained diagnostic traces and current measurement. We then conduct a large-scale field measurement study consisting of 30 nodes deployed at over 1,200 locations in 3 regions for three months. Our in-depth analysis of the collected 49 GB traces showed that NB-IoT nodes yield significantly imbalanced energy consumption in the wild, up to a ratio of 75:1, which may lead to short battery lifetime and frequent network partition. By extensive data analysis, we identify several key factors, including diverse network coverage levels, long-tail power profile, and excessive control message repetitions, that lead to high variance in the energy performance. Finally, we explore the optimization of NB-IoT base station settings on a software-defined eNodeB testbed and suggest several important design aspects that can be considered by future NB-IoT specifications and chipsets. Our study on the NB-IoT network provides important insights into the energy consumption of low-power wide-area networks and empowers the IoT applications.
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Title: Enabling Soft Robotic Systems : New Solutions to stiffness Tuning, Sensing, and Actuation Control
Creator: Al-Rubaiai, Mohammed
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Soft robots have appealing advantages of being highly flexible and adaptable to complex environments. This dissertation is focused on advancing key enabling elements for soft robots, including providing new solutions to stiffness-tuning, integrated sensing, and modeling and control of soft actuation materials.First, a compact and cost-effective mechanism for stiffness-tuning is proposed based on a 3D-printed conductive polylactic acid (CPLA) material. The conductive nature of the CPLA allows...
Show moreSoft robots have appealing advantages of being highly flexible and adaptable to complex environments. This dissertation is focused on advancing key enabling elements for soft robots, including providing new solutions to stiffness-tuning, integrated sensing, and modeling and control of soft actuation materials.First, a compact and cost-effective mechanism for stiffness-tuning is proposed based on a 3D-printed conductive polylactic acid (CPLA) material. The conductive nature of the CPLA allows convenient control of temperature and stiffness via Joule heating in a reversible manner. A gripper composed of two soft actuators as fingers is fabricated to demonstrate localized gripping posture, passive shape holding, and the ability to carry load in a desired locked configuration.Second, two types of integrated sensors are proposed. The first type is 3D-printed strain sensors that can be co-fabricated with soft robot bodies. Three commercially available conductive filaments are explored, among which the conductive thermoplastic polyurethane (ETPU) filament shows the highest sensitivity (gauge factor of 20) and working strain range of 0$\%$–12.5$\%$. The ETPU strain sensor exhibits an interesting behavior where the conductivity increases with the strain. In addition, the resistance change of the ETPU sensor in a doubly-clamped configuration in response to a wind stimulus is characterized, and the sensor shows sensitivity to wind velocity beyond 3.5 m/s. We then present a soft pressure-mapping sensing system that is lightweight and low-cost, and can be integrated with inflatable or textile structures with minimal impact on the original substrate characteristics. The sensing system involves two layers of piezoresistive foil and three layers of conductive copper sheets, stacked on top of each other in an orderly manner, to detect the magnitude and the location of applied load, respectively. Extensive experiments on a sensor prototype with dimensions of 35$\times$500 mm mounted on an inflatable tube are conducted to demonstrate the capability of the proposed scheme in simultaneous measurement of deformation location and magnitude. In particular, it is shown that the specific design approach minimizes the coupling of location and magnitude measurements, resulting in minimal complexity for data processing. Finally, we investigate the modeling and control of soft actuation materials, specifically accommodating their nonlinear dynamics. Polyvinyl chloride (PVC) gel actuators are considered in this work. A nonlinear, control-oriented Hammerstein model, with a polynomial nonlinearity preceding a transfer function, is proposed to capture the amplitude and bias-dependent frequency response of PVC gel actuators. A trajectory-tracking controller is developed, where an inverse is used to cancel the effect of the nonlinearity and a disturbance estimator/compensator is adopted to mitigate the influence of model uncertainties and disturbances. The efficacy of the proposed modeling and control approach is demonstrated experimentally in comparison with alternative methods, where the PVC actuator is commanded to track references of varying frequencies and waveforms.
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Title: FLEXIBLE MICROELECTRONICS FOR PREVENTING AND MANAGING VISION LOSS
Creator: Mazaheri Kouhani, Mohammad Hossein
Date: 2020
Collection: Electronic Theses & Dissertations
Description: In this thesis, we investigate and develop three flexible microelectronic technologies for managing glaucoma, slowing down neural degeneration and improving the delivery of bionic vision using visual prosthetic technologies. The first device focuses on monitoring intraocular pressure as a risk factor that is commonly known to induce blindness in glaucoma patients. The second device is designed and built to further facilitate the investigation of the hypothesis whether stimulating visual...
Show moreIn this thesis, we investigate and develop three flexible microelectronic technologies for managing glaucoma, slowing down neural degeneration and improving the delivery of bionic vision using visual prosthetic technologies. The first device focuses on monitoring intraocular pressure as a risk factor that is commonly known to induce blindness in glaucoma patients. The second device is designed and built to further facilitate the investigation of the hypothesis whether stimulating visual cortex with light slows down the degeneration of optic nerve pathways in the visual circuitry in the brains of animals and eventually be able to optimize the parameters and maximize the positive effects shown in earlier studies. The third project reports a successful attempt in improving a currently existing visual prosthetic implantable device made by a company called Second Sight Medical Products Inc. For the first project, we create a contact lens that incorporates a pressure sensor and sends out continuous data on the pressure of the eyes to external devices through a pair of goggles that communicates with the lens on the surface of the patient’s eye. The second part of the thesis focuses on the development of a device that helps with investigating the effect of optogenetic stimulation on slowing down the degenerative processes in neural pathways that lead to loss of retinal cells and eventually blindness. Thirdly, we develop a new coating technology for the currently existing microelectrodes that some versions of them are currently commercially available for the delivery of bionic vision directly through a flexible microelectrode array implanted on the visual cortex of humans. These three technologies described, developed and advanced in this thesis allow a multi-factorial approach to preventing vision loss and managing blindness caused by multitude of reasons including but not limited to glaucoma, macular degeneration, and retinitis pigmentosa. While the wearable contact lens can help monitoring the pressure in the eye round-the-clock, the cortical prosthetic devices using either light or electricity stimulate the visual cortex that can either slow down vision loss or reintroduce a new sensory domain called bionic vision for blind patients. We demonstrate proof of concept for the wearable pressure sensing contact lens by demonstrating the responsivity in ex vivo experiments on enucleated animal eyes and the eyes intact in post-mortem dog and rabbit heads. Next we show proof of concept for a wireless and miniaturized optogenetic stimulator device designed for experiments on mice. These micro-controlled light delivery systems are compact and low-cost allowing future experiments in vivo that can further demonstrate the efficacy of light delivery in battling and slowing down or perhaps stop vison loss caused by various degenerative and progressive neural diseases. Last but not the least, we advance a commercially existing visual prosthetic system and develop and incorporate new coating material for its electrical stimulation electrodes so that it can better deliver bionic vision to those patients who have already lost their vision.
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