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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
Description
Recent years have seen fin field effect transistors (finFETs) dominate modern complementary metal oxide semiconductor (CMOS) processes, [1][2], e.g., at the sub 20 nm technology nodes, as they alleviate short channel effects, provide lower leakage, and enable some continued VDD scaling. However, a realistic finFET based predictive process design kit (PDK) that supports investigation into both circuit and physical design, encompassing all aspects of digital design, for academic use has been unavailable. While the finFET based FreePDK15 was supplemented with a standard cell library, it lacked full physical verification (LVS) and parasitic extraction at the time [3][4]. Consequently, the only available sub 45 nm educational PDKs are the planar CMOS based Synopsys 32/28 nm and FreePDK45 (45 nm PDK) [5][6]. The cell libraries available for those processes are not realistic since they use large cell heights, in contrast to recent industry trends. Additionally, the SRAM rules and cells provided by these PDKs are not realistic. Because finFETs have a 3D structure, which affects transistor density, using planar libraries scaled to sub 22 nm dimensions for research is likely to give poor accuracy.
Commercial libraries and PDKs, especially for advanced nodes, are often difficult to obtain for academic use, and access to the actual physical layouts is even more restricted. Furthermore, the necessary non disclosure agreements (NDAs) are un manageable for large university classes and the plethora of design rules can distract from the key points. NDAs also make it difficult for the publication of physical design as these may disclose proprietary design rules and structures.
This work focuses on the development of realistic PDKs for academic use that overcome these limitations. These PDKs, developed for the N7 and N5 nodes, even before 7 nm and 5 nm processes were available in industry, are thus predictive. The predictions have been based on publications of the continually improving lithography, as well as estimates of what would be available at N7 and N5. For the most part, these assumptions have been accurate with regards to N7, except for the expectation that extreme ultraviolet (EUV) lithography would be widely available, which has turned out to be optimistic.
Commercial libraries and PDKs, especially for advanced nodes, are often difficult to obtain for academic use, and access to the actual physical layouts is even more restricted. Furthermore, the necessary non disclosure agreements (NDAs) are un manageable for large university classes and the plethora of design rules can distract from the key points. NDAs also make it difficult for the publication of physical design as these may disclose proprietary design rules and structures.
This work focuses on the development of realistic PDKs for academic use that overcome these limitations. These PDKs, developed for the N7 and N5 nodes, even before 7 nm and 5 nm processes were available in industry, are thus predictive. The predictions have been based on publications of the continually improving lithography, as well as estimates of what would be available at N7 and N5. For the most part, these assumptions have been accurate with regards to N7, except for the expectation that extreme ultraviolet (EUV) lithography would be widely available, which has turned out to be optimistic.
ContributorsVashishtha, Vinay (Author) / Clark, Lawrence T. (Thesis advisor) / Allee, David R. (Committee member) / Ogras, Umit Y. (Committee member) / Seo, Jae sun (Committee member) / Arizona State University (Publisher)
Created2019
Description
Limited spectral access motivates technologies that adapt to diminishing resources and increasingly cluttered environments. A joint positioning-communications system is designed and implemented on \acf{COTS} hardware. This system enables simultaneous positioning of, and communications between, nodes in a distributed network of base-stations and unmanned aerial systems (UASs). This technology offers extreme ranging precision ($<$ 5 cm) with minimal bandwidth (10 MHz), a secure communications link to protect against cyberattacks, a small form factor that enables integration into numerous platforms, and minimal resource consumption which supports high-density networks. The positioning and communications tasks are performed simultaneously with a single, co-use waveform, which efficiently utilizes limited resources and supports higher user densities. The positioning task uses a cooperative, point-to-point synchronization protocol to estimate the relative position and orientation of all users within the network. The communications task distributes positioning information between users and secures the positioning task against cyberattacks. This high-performance system is enabled by advanced time-of-arrival estimation techniques and a modern phase-accurate distributed coherence synchronization algorithm. This technology may be installed in ground-stations, ground vehicles, unmanned aerial systems, and airborne vehicles, enabling a highly-mobile, re-configurable network with numerous applications.
ContributorsHerschfelt, Andrew (Author) / Bliss, Daniel W (Thesis advisor) / Cochran, Douglas (Committee member) / Richmond, Christ (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2019
Description
Physical activity helps in reducing the risk of many chronic diseases, and plays a key role in maintaining good health of an individual. Just Walk is an intensively adaptive physical activity intervention, which has been designed based on system identification and control engineering principles. The goal of Just Walk is to design interventions that are responsive to an individual's changing needs, and thus encourage the individual to increase the number of steps walked.
Regularization is widely used in the field of machine learning. The goal of this thesis is to see how classical system identification principles in combination with machine learning methods like regularization help towards getting improved model estimates for complex systems. Estimating individual behavioral models using traditional prediction error methods can be done using an order selection. However, this method is can be computationally expensive due to the extensive search performed on a large set of order combination. If order selection is not done properly, it can cause bias (low order) and variance (high order) issues. In such cases regularization plays an important role in addressing the bias-variance trade-off.
One of the most important applications of identifying individual behavioral models is to understand what factors impact most the behavior of the person. Here "factors" can be considered as inputs (designed or environmental) to the participant over the course of the study, and the "behavior" is the step count of the participant under study. This is done by estimating models with different input combinations and then seeing which combinations of inputs (influence behavior most) give the best model estimate (best describe behavior of the person). As a part of this thesis, it is studied how regularized models can give a better estimation of personalized behavioral models, for the Just Walk study, which can further help in designing personalized interventions.
Regularization is widely used in the field of machine learning. The goal of this thesis is to see how classical system identification principles in combination with machine learning methods like regularization help towards getting improved model estimates for complex systems. Estimating individual behavioral models using traditional prediction error methods can be done using an order selection. However, this method is can be computationally expensive due to the extensive search performed on a large set of order combination. If order selection is not done properly, it can cause bias (low order) and variance (high order) issues. In such cases regularization plays an important role in addressing the bias-variance trade-off.
One of the most important applications of identifying individual behavioral models is to understand what factors impact most the behavior of the person. Here "factors" can be considered as inputs (designed or environmental) to the participant over the course of the study, and the "behavior" is the step count of the participant under study. This is done by estimating models with different input combinations and then seeing which combinations of inputs (influence behavior most) give the best model estimate (best describe behavior of the person). As a part of this thesis, it is studied how regularized models can give a better estimation of personalized behavioral models, for the Just Walk study, which can further help in designing personalized interventions.
ContributorsMandal, Tarunima (Author) / Rivera, Daniel E (Thesis advisor) / Si, Jennie (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2020
Description
Flexible conducting materials have been in the forefront of a rapidly transforming electronics industry, focusing on wearable devices for a variety of applications in recent times. Over the past few decades, bulky, rigid devices have been replaced with a surging demand for thin, flexible, light weight, ultra-portable yet high performance electronics. The interconnects available in the market today only satisfy a few of the desirable characteristics, making it necessary to compromise one feature over another. In this thesis, a method to prepare a thin, flexible, and stretchable inter-connect is presented with improved conductivity compared to previous achievements. It satisfies most mechanical and electrical conditions desired in the wearable electronics industry. The conducting composite, prepared with the widely available, low cost silicon-based organic polymer - polydimethylsiloxane (PDMS) and silver (Ag), is sandwiched between two cured PDMS layers. These protective layers improve the mechanical stability of the inter-connect. The structure can be stretched up to 120% of its original length which can further be enhanced to over 250% by cutting it into a serpentine shape without compromising its electrical stability. The inter-connect, around 500 µm thick, can be integrated into thin electronic packaging. The synthesis process of the composite material, along with its electrical and mechanical and properties are presented in detail. Testing methods and results for mechanical and electrical stability are also illustrated over extensive flexing and stretching cycles. The materials put into test, along with conductive silver (Ag) - polydimethylsiloxane (PDMS) composite in a sandwich structure, are copper foils, copper coated polyimide (PI) and aluminum (Al) coated polyethylene terephthalate (PET).
ContributorsNandy, Mayukh (Author) / Yu, Hongbin (Thesis advisor) / Chan, Candace (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2020
Description
Technological advances in low power wearable electronics and energy optimization techniques
make motion energy harvesting a viable energy source. However, it has not been
widely adopted due to bulky energy harvester designs that are uncomfortable to wear. This
work addresses this problem by analyzing the feasibility of powering low wearable power
devices using piezoelectric energy generated at the human knee. We start with a novel
mathematical model for estimating the power generated from human knee joint movements.
This thesis’s major contribution is to analyze the feasibility of human motion energy harvesting
and validating this analytical model using a commercially available piezoelectric
module. To this end, we implemented an experimental setup that replicates a human knee.
Then, we performed experiments at different excitation frequencies and amplitudes with
two commercially available Macro Fiber Composite (MFC) modules. These experimental
results are used to validate the analytical model and predict the energy harvested as a function
of the number of steps taken in a day. The model estimates that 13μWcan be generated
on an average while walking with a 4.8% modeling error. The obtained results show that
piezoelectricity is indeed a viable approach for powering low-power wearable devices.
make motion energy harvesting a viable energy source. However, it has not been
widely adopted due to bulky energy harvester designs that are uncomfortable to wear. This
work addresses this problem by analyzing the feasibility of powering low wearable power
devices using piezoelectric energy generated at the human knee. We start with a novel
mathematical model for estimating the power generated from human knee joint movements.
This thesis’s major contribution is to analyze the feasibility of human motion energy harvesting
and validating this analytical model using a commercially available piezoelectric
module. To this end, we implemented an experimental setup that replicates a human knee.
Then, we performed experiments at different excitation frequencies and amplitudes with
two commercially available Macro Fiber Composite (MFC) modules. These experimental
results are used to validate the analytical model and predict the energy harvested as a function
of the number of steps taken in a day. The model estimates that 13μWcan be generated
on an average while walking with a 4.8% modeling error. The obtained results show that
piezoelectricity is indeed a viable approach for powering low-power wearable devices.
ContributorsBandyopadhyay, Shiva (Author) / Ogras, Umit Y. (Thesis advisor) / Fan, Deliang (Committee member) / Trichopoulos, Georgios (Committee member) / Arizona State University (Publisher)
Created2020