Matching Items (1,187)
Filtering by
- All Subjects: Electrical Engineering
- Status: Published
Description
A model of self-heating is incorporated into a Cellular Monte Carlo (CMC) particle-based device simulator through the solution of an energy balance equation (EBE) for phonons. The EBE self-consistently couples charge and heat transport in the simulation through a novel approach to computing the heat generation rate in the device under study. First, the moments of the Boltzmann Transport equation (BTE) are discussed, and subsequently the EBE of for phonons is derived. Subsequently, several tests are performed to verify the applicability and accuracy of a nonlinear iterative method for the solution of the EBE in the presence of convective boundary conditions, as compared to a finite element analysis solver as well as using the Kirchhoff transformation. The coupled electrothermal characterization of a GaN/AlGaN high electron mobility transistor (HEMT) is then performed, and the effects of non-ideal interfaces and boundary conditions are studied.
The proposed thermal model is then applied to a novel $\Pi$-gate architecture which has been suggested to reduce hot electron generation in the device, compared to the conventional T-gate. Additionally, small signal ac simulations are performed for the determination of cutoff frequencies using the thermal model as well.
Finally, further extensions of the CMC algorithm used in this work are discussed, including 1) higher-order moments of the phonon BTE, 2) coupling to phonon Monte Carlo simulations, and 3) application to other large-bandgap, and therefore high-power, materials such as diamond.
The proposed thermal model is then applied to a novel $\Pi$-gate architecture which has been suggested to reduce hot electron generation in the device, compared to the conventional T-gate. Additionally, small signal ac simulations are performed for the determination of cutoff frequencies using the thermal model as well.
Finally, further extensions of the CMC algorithm used in this work are discussed, including 1) higher-order moments of the phonon BTE, 2) coupling to phonon Monte Carlo simulations, and 3) application to other large-bandgap, and therefore high-power, materials such as diamond.
ContributorsMerrill, Ky (Author) / Saraniti, Marco (Thesis advisor) / Goodnick, Stephen (Committee member) / Smith, David (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2020
Description
An optimal energy scheduling procedure is essential in an isolated environment such as naval submarines. Conventional naval submarines include diesel-electric propulsion systems, which utilize diesel generators along with batteries and fuel cells. Submarines can charge the batteries by running diesel-electric generators only at the surface or at snorkeling depth. This is the most dangerous time for submarines to be detectable by acoustic and non-acoustic sensors of enemy assets. Optimizing the energy resources while reducing the need for snorkeling is the main factor to enhance underwater endurance. This thesis introduces an energy management system (EMS) as a supervisory tool for the officers onboard to plan energy schedules in order to complete various missions. The EMS for a 4,000-ton class conventional submarine is developed to minimize snorkeling and satisfy various conditions of practically designed missions by optimizing the energy resources, such as Lithium-ion batteries, Proton-exchange membrane fuel cells, and diesel-electric generators. Eventually, the optimized energy schedules with the minimum snorkeling hours are produced for five mission scenarios. More importantly, this EMS performs deterministic and stochastic operational scheduling processes to provide secured optimal schedules which contains outages in the power generation and storage systems.
ContributorsJeon, Byeongdoo (Author) / Hedman, Mojdeh Khorsand (Thesis advisor) / Holbert, Keith E. (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2020
Description
Programmable Metallization Cell (PMC) devices are, in essence, redox-based
solid-state resistive switching devices that rely on ion transport through a solid electrolyte (SE) layer from anode to cathode. Analysis and modeling of the effect of different fabrication and processing parameter/conditions on PMC devices are crucial for future electronics. Furthermore, this work is even more significant for devices utilizing back-end- of-line (BEOL) compatible materials such as Cu, W, their oxides and SiOx as these devices offer cost effectiveness thanks to their inherent foundry-ready nature. In this dissertation, effect of annealing conditions and cathode material on the performance of Cu-SiOx vertical devices is investigated which shows that W-based devices have much lower forming voltage and initial resistance values. Also, higher annealing temperatures first lead to an increase in forming voltage from 400 °C to 500 °C, then a drastic decrease at 550 °C due to Cu island formation at the Cu/SiOx interface. Next, the characterization and modeling of the bilayer Cu2O/Cu-WO3 obtained by annealing the deposited Cu/WO3 stacks in air at BEOL-compatible temperatures is presented that display unique characteristics for lateral PMC devices. First, thin film oxidation kinetics of Cu is studied which show a parabolic relationship with annealing time and an activation energy of 0.70 eV. Grown Cu2O shows a cauliflower-like morphology where feature size on the surface increase with annealing time and temperature. Then, diffusion kinetics of Cu in WO3 is examined where the activation energy of diffusion of Cu into WO3 is calculated to be 0.74 eV. Cu was found to form clusters in the WO3 host which was revealed by imaging. Moreover, using the oxidation and diffusion analyses, a Matlab model is established for modeling the bilayer for process and annealing-condition optimization. The model is built to produce the resulting Cu2O thickness and Cu concentration in Cu-WO3. Additionally, material characterization, preliminary electrical results along with modeling of lateral PMC devices utilizing the bilayer is also demonstrated. By tuning the process parameters such as deposited Cu thickness and annealing conditions, a low-resistive Cu2O layer was achieved which dramatically enhanced the electrodeposition growth rate for lateral PMC devices.
solid-state resistive switching devices that rely on ion transport through a solid electrolyte (SE) layer from anode to cathode. Analysis and modeling of the effect of different fabrication and processing parameter/conditions on PMC devices are crucial for future electronics. Furthermore, this work is even more significant for devices utilizing back-end- of-line (BEOL) compatible materials such as Cu, W, their oxides and SiOx as these devices offer cost effectiveness thanks to their inherent foundry-ready nature. In this dissertation, effect of annealing conditions and cathode material on the performance of Cu-SiOx vertical devices is investigated which shows that W-based devices have much lower forming voltage and initial resistance values. Also, higher annealing temperatures first lead to an increase in forming voltage from 400 °C to 500 °C, then a drastic decrease at 550 °C due to Cu island formation at the Cu/SiOx interface. Next, the characterization and modeling of the bilayer Cu2O/Cu-WO3 obtained by annealing the deposited Cu/WO3 stacks in air at BEOL-compatible temperatures is presented that display unique characteristics for lateral PMC devices. First, thin film oxidation kinetics of Cu is studied which show a parabolic relationship with annealing time and an activation energy of 0.70 eV. Grown Cu2O shows a cauliflower-like morphology where feature size on the surface increase with annealing time and temperature. Then, diffusion kinetics of Cu in WO3 is examined where the activation energy of diffusion of Cu into WO3 is calculated to be 0.74 eV. Cu was found to form clusters in the WO3 host which was revealed by imaging. Moreover, using the oxidation and diffusion analyses, a Matlab model is established for modeling the bilayer for process and annealing-condition optimization. The model is built to produce the resulting Cu2O thickness and Cu concentration in Cu-WO3. Additionally, material characterization, preliminary electrical results along with modeling of lateral PMC devices utilizing the bilayer is also demonstrated. By tuning the process parameters such as deposited Cu thickness and annealing conditions, a low-resistive Cu2O layer was achieved which dramatically enhanced the electrodeposition growth rate for lateral PMC devices.
ContributorsBalaban, Mehmet Bugra (Author) / Kozicki, Michael N (Thesis advisor) / Barnaby, Hugh J (Committee member) / Goryll, Michael (Committee member, Committee member) / Arizona State University (Publisher)
Created2020
Description
Impedance-modulated metasurfaces are compact artificially-engineered surfaces whose surface-impedance profile is modulated with a periodic function. These metasurfaces function as leaky-wave antennas (LWAs) that are capable of achieving high gains and narrow beamwidths with thin and light-weight structures. The surface-impedance modulation function for the desired radiation characteristics can be obtained using the holographic principle, whose application in antennas has been investigated extensively.
On account of their radiation and physical characteristics, modulated metasurfaces can be employed in automotive radar, 5G, and imaging applications. Automotive radar applications might require the antennas to be flush-mounted on the vehicular bodies that can be curved. Hence, it is necessary to analyze and design conformal metasurface antennas. The surface-impedance modulation function is derived for cylindrically-curved metasurfaces, where the impedance modulation is along the cylinder axis. These metasurface antennas are referred to as axially-modulated cylindrical metasurface LWAs (AMCLWAs). The effect of curvature is modeled, the radiation characteristics are predicted analytically, and they are validated by simulations and measurements.
Communication-based applications, like 5G and 6G, require the generation of multiple beams with polarization diversity, which can be achieved using a class of impedance-modulated metasurfaces referred to as polarization-diverse holographic metasurfaces (PDHMs). PDHMs can form, one at a time, a pencil beam in the desired direction with horizontal polarization, vertical polarization, left-hand circular polarization (LHCP), or right-hand circular polarization (RHCP). These metasurface antennas are analyzed, designed, measured, and improved to include the ability to frequency scan.
In automotive radar and other imaging applications, the performance of metasurface antennas can be impacted by the formation of standing waves due to multiple reflections between the antenna and the target. The monostatic RCS of the metasurface antenna is reduced by modulating its surface impedance with a square wave, to avert multiple reflections. These square-wave-modulated metasurfaces are referred to as checkerboard metasurface LWAs, whose radiation and scattering characteristics, for normal incidence parallel polarization, are analyzed and measured.
On account of their radiation and physical characteristics, modulated metasurfaces can be employed in automotive radar, 5G, and imaging applications. Automotive radar applications might require the antennas to be flush-mounted on the vehicular bodies that can be curved. Hence, it is necessary to analyze and design conformal metasurface antennas. The surface-impedance modulation function is derived for cylindrically-curved metasurfaces, where the impedance modulation is along the cylinder axis. These metasurface antennas are referred to as axially-modulated cylindrical metasurface LWAs (AMCLWAs). The effect of curvature is modeled, the radiation characteristics are predicted analytically, and they are validated by simulations and measurements.
Communication-based applications, like 5G and 6G, require the generation of multiple beams with polarization diversity, which can be achieved using a class of impedance-modulated metasurfaces referred to as polarization-diverse holographic metasurfaces (PDHMs). PDHMs can form, one at a time, a pencil beam in the desired direction with horizontal polarization, vertical polarization, left-hand circular polarization (LHCP), or right-hand circular polarization (RHCP). These metasurface antennas are analyzed, designed, measured, and improved to include the ability to frequency scan.
In automotive radar and other imaging applications, the performance of metasurface antennas can be impacted by the formation of standing waves due to multiple reflections between the antenna and the target. The monostatic RCS of the metasurface antenna is reduced by modulating its surface impedance with a square wave, to avert multiple reflections. These square-wave-modulated metasurfaces are referred to as checkerboard metasurface LWAs, whose radiation and scattering characteristics, for normal incidence parallel polarization, are analyzed and measured.
ContributorsRamalingam, Subramanian (Author) / Balanis, Constantine A. (Thesis advisor) / Aberle, James T. (Committee member) / Palais, Joseph C. (Committee member) / Trichopoulos, Georgios C. (Committee member) / Arizona State University (Publisher)
Created2020
Description
For the last 50 years, oscillator modeling in ranging systems has received considerable
attention. Many components in a navigation system, such as the master oscillator
driving the receiver system, as well the master oscillator in the transmitting system
contribute significantly to timing errors. Algorithms in the navigation processor must
be able to predict and compensate such errors to achieve a specified accuracy. While
much work has been done on the fundamentals of these problems, the thinking on said
problems has not progressed. On the hardware end, the designers of local oscillators
focus on synthesized frequency and loop noise bandwidth. This does nothing to
mitigate, or reduce frequency stability degradation in band. Similarly, there are not
systematic methods to accommodate phase and frequency anomalies such as clock
jumps. Phase locked loops are fundamentally control systems, and while control
theory has had significant advancement over the last 30 years, the design of timekeeping
sources has not advanced beyond classical control. On the software end,
single or two state oscillator models are typically embedded in a Kalman Filter to
alleviate time errors between the transmitter and receiver clock. Such models are
appropriate for short term time accuracy, but insufficient for long term time accuracy.
Additionally, flicker frequency noise may be present in oscillators, and it presents
mathematical modeling complications. This work proposes novel H∞ control methods
to address the shortcomings in the standard design of time-keeping phase locked loops.
Such methods allow the designer to address frequency stability degradation as well
as high phase/frequency dynamics. Additionally, finite-dimensional approximants of
flicker frequency noise that are more representative of the truth system than the
tradition Gauss Markov approach are derived. Last, to maintain timing accuracy in
a wide variety of operating environments, novel Banks of Adaptive Extended Kalman
Filters are used to address both stochastic and dynamic uncertainty.
attention. Many components in a navigation system, such as the master oscillator
driving the receiver system, as well the master oscillator in the transmitting system
contribute significantly to timing errors. Algorithms in the navigation processor must
be able to predict and compensate such errors to achieve a specified accuracy. While
much work has been done on the fundamentals of these problems, the thinking on said
problems has not progressed. On the hardware end, the designers of local oscillators
focus on synthesized frequency and loop noise bandwidth. This does nothing to
mitigate, or reduce frequency stability degradation in band. Similarly, there are not
systematic methods to accommodate phase and frequency anomalies such as clock
jumps. Phase locked loops are fundamentally control systems, and while control
theory has had significant advancement over the last 30 years, the design of timekeeping
sources has not advanced beyond classical control. On the software end,
single or two state oscillator models are typically embedded in a Kalman Filter to
alleviate time errors between the transmitter and receiver clock. Such models are
appropriate for short term time accuracy, but insufficient for long term time accuracy.
Additionally, flicker frequency noise may be present in oscillators, and it presents
mathematical modeling complications. This work proposes novel H∞ control methods
to address the shortcomings in the standard design of time-keeping phase locked loops.
Such methods allow the designer to address frequency stability degradation as well
as high phase/frequency dynamics. Additionally, finite-dimensional approximants of
flicker frequency noise that are more representative of the truth system than the
tradition Gauss Markov approach are derived. Last, to maintain timing accuracy in
a wide variety of operating environments, novel Banks of Adaptive Extended Kalman
Filters are used to address both stochastic and dynamic uncertainty.
ContributorsEchols, Justin A (Author) / Bliss, Daniel W (Thesis advisor) / Tsakalis, Konstantinos S (Committee member) / Berman, Spring (Committee member) / Mittelmann, Hans (Committee member) / Arizona State University (Publisher)
Created2020
Description
Modern digital applications have significantly increased the leakage of private and sensitive personal data. While worst-case measures of leakage such as Differential Privacy (DP) provide the strongest guarantees, when utility matters, average-case information-theoretic measures can be more relevant. However, most such information-theoretic measures do not have clear operational meanings. This dissertation addresses this challenge.
This work introduces a tunable leakage measure called maximal $\alpha$-leakage which quantifies the maximal gain of an adversary in inferring any function of a data set. The inferential capability of the adversary is modeled by a class of loss functions, namely, $\alpha$-loss. The choice of $\alpha$ determines specific adversarial actions ranging from refining a belief for $\alpha =1$ to guessing the best posterior for $\alpha = \infty$, and for the two specific values maximal $\alpha$-leakage simplifies to mutual information and maximal leakage, respectively. Maximal $\alpha$-leakage is proved to have a composition property and be robust to side information.
There is a fundamental disjoint between theoretical measures of information leakages and their applications in practice. This issue is addressed in the second part of this dissertation by proposing a data-driven framework for learning Censored and Fair Universal Representations (CFUR) of data. This framework is formulated as a constrained minimax optimization of the expected $\alpha$-loss where the constraint ensures a measure of the usefulness of the representation. The performance of the CFUR framework with $\alpha=1$ is evaluated on publicly accessible data sets; it is shown that multiple sensitive features can be effectively censored to achieve group fairness via demographic parity while ensuring accuracy for several \textit{a priori} unknown downstream tasks.
Finally, focusing on worst-case measures, novel information-theoretic tools are used to refine the existing relationship between two such measures, $(\epsilon,\delta)$-DP and R\'enyi-DP. Applying these tools to the moments accountant framework, one can track the privacy guarantee achieved by adding Gaussian noise to Stochastic Gradient Descent (SGD) algorithms. Relative to state-of-the-art, for the same privacy budget, this method allows about 100 more SGD rounds for training deep learning models.
This work introduces a tunable leakage measure called maximal $\alpha$-leakage which quantifies the maximal gain of an adversary in inferring any function of a data set. The inferential capability of the adversary is modeled by a class of loss functions, namely, $\alpha$-loss. The choice of $\alpha$ determines specific adversarial actions ranging from refining a belief for $\alpha =1$ to guessing the best posterior for $\alpha = \infty$, and for the two specific values maximal $\alpha$-leakage simplifies to mutual information and maximal leakage, respectively. Maximal $\alpha$-leakage is proved to have a composition property and be robust to side information.
There is a fundamental disjoint between theoretical measures of information leakages and their applications in practice. This issue is addressed in the second part of this dissertation by proposing a data-driven framework for learning Censored and Fair Universal Representations (CFUR) of data. This framework is formulated as a constrained minimax optimization of the expected $\alpha$-loss where the constraint ensures a measure of the usefulness of the representation. The performance of the CFUR framework with $\alpha=1$ is evaluated on publicly accessible data sets; it is shown that multiple sensitive features can be effectively censored to achieve group fairness via demographic parity while ensuring accuracy for several \textit{a priori} unknown downstream tasks.
Finally, focusing on worst-case measures, novel information-theoretic tools are used to refine the existing relationship between two such measures, $(\epsilon,\delta)$-DP and R\'enyi-DP. Applying these tools to the moments accountant framework, one can track the privacy guarantee achieved by adding Gaussian noise to Stochastic Gradient Descent (SGD) algorithms. Relative to state-of-the-art, for the same privacy budget, this method allows about 100 more SGD rounds for training deep learning models.
ContributorsLiao, Jiachun (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Committee member) / Zhang, Junshan (Committee member) / Dasarathy, Gautam (Committee member) / Arizona State University (Publisher)
Created2020
Description
In this work, the stacked values of battery energy storage systems (BESSs) of various power and energy capacities are evaluated as they provide multiple services such as peak shaving, frequency regulation, and reserve support in an ‘Arizona-based test system’ - a simplified, representative model of Salt River Project’s (SRP) system developed using the resource stack information shared by SRP. This has been achieved by developing a mixed-integer linear programming (MILP) based optimization model that captures the operation of BESS in the Arizona-based test system. The model formulation does not include any BESS cost as the objective is to estimate the net savings in total system operation cost after a BESS is deployed in the system. The optimization model has been formulated in such a way that the savings due to the provision of a single service, either peak shaving or frequency regulation or spinning reserve support, by the BESS, can be determined independently. The model also allows calculation of combined savings due to all the services rendered by the BESS.
The results of this research suggest that the savings obtained with a BESS providing multiple services are significantly higher than the same capacity BESS delivering a single service in isolation. It is also observed that the marginal contribution of BESS reduces with increasing BESS energy capacity, a result consistent with the law of diminishing returns. Further, small changes in the simulation environment, such as factoring in generator forced outage rates or projection of future solar penetration, can lead to changes as high as 10% in the calculated stacked value.
The results of this research suggest that the savings obtained with a BESS providing multiple services are significantly higher than the same capacity BESS delivering a single service in isolation. It is also observed that the marginal contribution of BESS reduces with increasing BESS energy capacity, a result consistent with the law of diminishing returns. Further, small changes in the simulation environment, such as factoring in generator forced outage rates or projection of future solar penetration, can lead to changes as high as 10% in the calculated stacked value.
ContributorsTripathy, Sujit Kumar (Author) / Tylavsky, Daniel J (Thesis advisor) / Pal, Anamitra (Committee member) / Wu, Meng M (Committee member) / Arizona State University (Publisher)
Created2020
Description
Due to the rapid penetration of solar power systems in residential areas, there has
been a dramatic increase in bidirectional power flow. Such a phenomenon of bidirectional
power flow creates a need to know where Photovoltaic (PV) systems are
located, what their quantity is, and how much they generate. However, significant
challenges exist for accurate solar panel detection, capacity quantification,
and generation estimation by employing existing methods, because of the limited
labeled ground truth and relatively poor performance for direct supervised learning.
To mitigate these issue, this thesis revolutionizes key learning concepts to (1)
largely increase the volume of training data set and expand the labelled data set by
creating highly realistic solar panel images, (2) boost detection and quantification
learning through physical knowledge and (3) greatly enhance the generation estimation
capability by utilizing effective features and neighboring generation patterns.
These techniques not only reshape the machine learning methods in the GIS
domain but also provides a highly accurate solution to gain a better understanding
of distribution networks with high PV penetration. The numerical
validation and performance evaluation establishes the high accuracy and scalability
of the proposed methodologies on the existing solar power systems in the
Southwest region of the United States of America. The distribution and transmission
networks both have primitive control methodologies, but now is the high time
to work out intelligent control schemes based on reinforcement learning and show
that they can not only perform well but also have the ability to adapt to the changing
environments. This thesis proposes a sequence task-based learning method to
create an agent that can learn to come up with the best action set that can overcome
the issues of transient over-voltage.
been a dramatic increase in bidirectional power flow. Such a phenomenon of bidirectional
power flow creates a need to know where Photovoltaic (PV) systems are
located, what their quantity is, and how much they generate. However, significant
challenges exist for accurate solar panel detection, capacity quantification,
and generation estimation by employing existing methods, because of the limited
labeled ground truth and relatively poor performance for direct supervised learning.
To mitigate these issue, this thesis revolutionizes key learning concepts to (1)
largely increase the volume of training data set and expand the labelled data set by
creating highly realistic solar panel images, (2) boost detection and quantification
learning through physical knowledge and (3) greatly enhance the generation estimation
capability by utilizing effective features and neighboring generation patterns.
These techniques not only reshape the machine learning methods in the GIS
domain but also provides a highly accurate solution to gain a better understanding
of distribution networks with high PV penetration. The numerical
validation and performance evaluation establishes the high accuracy and scalability
of the proposed methodologies on the existing solar power systems in the
Southwest region of the United States of America. The distribution and transmission
networks both have primitive control methodologies, but now is the high time
to work out intelligent control schemes based on reinforcement learning and show
that they can not only perform well but also have the ability to adapt to the changing
environments. This thesis proposes a sequence task-based learning method to
create an agent that can learn to come up with the best action set that can overcome
the issues of transient over-voltage.
ContributorsHashmy, Syed Muhammad Yousaf (Author) / Weng, Yang (Thesis advisor) / Sen, Arunabha (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
Transportation plays a significant role in every human's life. Numerous factors, such as cost of living, available amenities, work style, to name a few, play a vital role in determining the amount of travel time. Such factors, among others, led in part to an increased need for private transportation and, consequently, leading to an increase in the purchase of private cars. Also, road safety was impacted by numerous factors such as Driving Under Influence (DUI), driver’s distraction due to the increase in the use of mobile devices while driving. These factors led to an increasing need for an Advanced Driver Assistance System (ADAS) to help the driver stay aware of the environment and to improve road safety.
EcoCAR3 is one of the Advanced Vehicle Technology Competitions, sponsored by the United States Department of Energy (DoE) and managed by Argonne National Laboratory in partnership with the North American automotive industry. Students are challenged beyond the traditional classroom environment in these competitions, where they redesign a donated production vehicle to improve energy efficiency and to meet emission standards while maintaining the features that are attractive to the customer, including but not limited to performance, consumer acceptability, safety, and cost.
This thesis presents a driver assistance system interface that was implemented as part of EcoCAR3, including the adopted sensors, hardware and software components, system implementation, validation, and testing. The implemented driver assistance system uses a combination of range measurement sensors to determine the distance, relative location, & the relative velocity of obstacles and surrounding objects together with a computer vision algorithm for obstacle detection and classification. The sensor system and vision system were tested individually and then combined within the overall system. Also, a visual and audio feedback system was designed and implemented to provide timely feedback for the driver as an attempt to enhance situational awareness and improve safety.
Since the driver assistance system was designed and developed as part of a DoE sponsored competition, the system needed to satisfy competition requirements and rules. This work attempted to optimize the system in terms of performance, robustness, and cost while satisfying these constraints.
EcoCAR3 is one of the Advanced Vehicle Technology Competitions, sponsored by the United States Department of Energy (DoE) and managed by Argonne National Laboratory in partnership with the North American automotive industry. Students are challenged beyond the traditional classroom environment in these competitions, where they redesign a donated production vehicle to improve energy efficiency and to meet emission standards while maintaining the features that are attractive to the customer, including but not limited to performance, consumer acceptability, safety, and cost.
This thesis presents a driver assistance system interface that was implemented as part of EcoCAR3, including the adopted sensors, hardware and software components, system implementation, validation, and testing. The implemented driver assistance system uses a combination of range measurement sensors to determine the distance, relative location, & the relative velocity of obstacles and surrounding objects together with a computer vision algorithm for obstacle detection and classification. The sensor system and vision system were tested individually and then combined within the overall system. Also, a visual and audio feedback system was designed and implemented to provide timely feedback for the driver as an attempt to enhance situational awareness and improve safety.
Since the driver assistance system was designed and developed as part of a DoE sponsored competition, the system needed to satisfy competition requirements and rules. This work attempted to optimize the system in terms of performance, robustness, and cost while satisfying these constraints.
ContributorsBalaji, Venkatesh (Author) / Karam, Lina J (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Ultrasound B-mode imaging is an increasingly significant medical imaging modality for clinical applications. Compared to other imaging modalities like computed tomography (CT) or magnetic resonance imaging (MRI), ultrasound imaging has the advantage of being safe, inexpensive, and portable. While two dimensional (2-D) ultrasound imaging is very popular, three dimensional (3-D) ultrasound imaging provides distinct advantages over its 2-D counterpart by providing volumetric imaging, which leads to more accurate analysis of tumor and cysts. However, the amount of received data at the front-end of 3-D system is extremely large, making it impractical for power-constrained portable systems.
In this thesis, algorithm and hardware design techniques to support a hand-held 3-D ultrasound imaging system are proposed. Synthetic aperture sequential beamforming (SASB) is chosen since its computations can be split into two stages, where the output generated of Stage 1 is significantly smaller in size compared to the input. This characteristic enables Stage 1 to be done in the front end while Stage 2 can be sent out to be processed elsewhere.
The contributions of this thesis are as follows. First, 2-D SASB is extended to 3-D. Techniques to increase the volume rate of 3-D SASB through a new multi-line firing scheme and use of linear chirp as the excitation waveform, are presented. A new sparse array design that not only reduces the number of active transducers but also avoids the imaging degradation caused by grating lobes, is proposed. A combination of these techniques increases the volume rate of 3-D SASB by 4\texttimes{} without introducing extra computations at the front end.
Next, algorithmic techniques to further reduce the Stage 1 computations in the front end are presented. These include reducing the number of distinct apodization coefficients and operating with narrow-bit-width fixed-point data. A 3-D die stacked architecture is designed for the front end. This highly parallel architecture enables the signals received by 961 active transducers to be digitalized, routed by a network-on-chip, and processed in parallel. The processed data are accumulated through a bus-based structure. This architecture is synthesized using TSMC 28 nm technology node and the estimated power consumption of the front end is less than 2 W.
Finally, the Stage 2 computations are mapped onto a reconfigurable multi-core architecture, TRANSFORMER, which supports different types of on-chip memory banks and run-time reconfigurable connections between general processing elements and memory banks. The matched filtering step and the beamforming step in Stage 2 are mapped onto TRANSFORMER with different memory configurations. Gem5 simulations show that the private cache mode generates shorter execution time and higher computation efficiency compared to other cache modes. The overall execution time for Stage 2 is 14.73 ms. The average power consumption and the average Giga-operations-per-second/Watt in 14 nm technology node are 0.14 W and 103.84, respectively.
In this thesis, algorithm and hardware design techniques to support a hand-held 3-D ultrasound imaging system are proposed. Synthetic aperture sequential beamforming (SASB) is chosen since its computations can be split into two stages, where the output generated of Stage 1 is significantly smaller in size compared to the input. This characteristic enables Stage 1 to be done in the front end while Stage 2 can be sent out to be processed elsewhere.
The contributions of this thesis are as follows. First, 2-D SASB is extended to 3-D. Techniques to increase the volume rate of 3-D SASB through a new multi-line firing scheme and use of linear chirp as the excitation waveform, are presented. A new sparse array design that not only reduces the number of active transducers but also avoids the imaging degradation caused by grating lobes, is proposed. A combination of these techniques increases the volume rate of 3-D SASB by 4\texttimes{} without introducing extra computations at the front end.
Next, algorithmic techniques to further reduce the Stage 1 computations in the front end are presented. These include reducing the number of distinct apodization coefficients and operating with narrow-bit-width fixed-point data. A 3-D die stacked architecture is designed for the front end. This highly parallel architecture enables the signals received by 961 active transducers to be digitalized, routed by a network-on-chip, and processed in parallel. The processed data are accumulated through a bus-based structure. This architecture is synthesized using TSMC 28 nm technology node and the estimated power consumption of the front end is less than 2 W.
Finally, the Stage 2 computations are mapped onto a reconfigurable multi-core architecture, TRANSFORMER, which supports different types of on-chip memory banks and run-time reconfigurable connections between general processing elements and memory banks. The matched filtering step and the beamforming step in Stage 2 are mapped onto TRANSFORMER with different memory configurations. Gem5 simulations show that the private cache mode generates shorter execution time and higher computation efficiency compared to other cache modes. The overall execution time for Stage 2 is 14.73 ms. The average power consumption and the average Giga-operations-per-second/Watt in 14 nm technology node are 0.14 W and 103.84, respectively.
ContributorsZhou, Jian (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Wenisch, Thomas F. (Committee member) / Ogras, Umit Y. (Committee member) / Arizona State University (Publisher)
Created2019