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- Creators: Bakkaloglu, Bertan
- Creators: Turaga, Pavan
- Member of: ASU Electronic Theses and Dissertations
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Description
A massive volume of data is generated at an unprecedented rate in the information age. The growth of data significantly exceeds the computing and storage capacities of the existing digital infrastructure. In the past decade, many methods are invented for data compression, compressive sensing and reconstruction, and compressed learning (learning directly upon compressed data) to overcome the data-explosion challenge. While prior works are predominantly model-based, focus on small models, and not suitable for task-oriented sensing or hardware acceleration, the number of available models for compression-related tasks has escalated by orders of magnitude in the past decade. Motivated by this significant growth and the success of big data, this dissertation proposes to revolutionize both the compressive sensing reconstruction (CSR) and compressed learning (CL) methods from the data-driven perspective. In this dissertation, a series of topics on data-driven CSR are discussed. Individual data-driven models are proposed for the CSR of bio-signals, images, and videos with improved compression ratio and recovery fidelity trade-off. Specifically, a scalable Laplacian pyramid reconstructive adversarial network (LAPRAN) is proposed for single-image CSR. LAPRAN progressively reconstructs images following the concept of the Laplacian pyramid through the concatenation of multiple reconstructive adversarial networks (RANs). For the CSR of videos, CSVideoNet is proposed to improve the spatial-temporal resolution of reconstructed videos. Apart from CSR, data-driven CL is discussed in the dissertation. A CL framework is proposed to extract features directly from compressed data for image classification, objection detection, and semantic/instance segmentation. Besides, the spectral bias of neural networks is analyzed from the frequency perspective, leading to a learning-based frequency selection method for identifying the trivial frequency components which can be removed without accuracy loss. Compared with the conventional spatial downsampling approaches, the proposed frequency-domain learning method can achieve higher accuracy with reduced input data size. The methodologies proposed in this dissertation are not restricted to the above-mentioned applications. The dissertation also discusses other potential applications and directions for future research.
ContributorsXu, Kai (Author) / Ren, Fengbo (Thesis advisor) / Li, Baoxin (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Open Design is a crowd-driven global ecosystem which tries to challenge and alter contemporary modes of capitalistic hardware production. It strives to build on the collective skills, expertise and efforts of people regardless of their educational, social or political backgrounds to develop and disseminate physical products, machines and systems. In contrast to capitalistic hardware production, Open Design practitioners publicly share design files, blueprints and knowhow through various channels including internet platforms and in-person workshops. These designs are typically replicated, modified, improved and reshared by individuals and groups who are broadly referred to as ‘makers’.
This dissertation aims to expand the current scope of Open Design within human-computer interaction (HCI) research through a long-term exploration of Open Design’s socio-technical processes. I examine Open Design from three perspectives: the functional—materials, tools, and platforms that enable crowd-driven open hardware production, the critical—materially-oriented engagements within open design as a site for sociotechnical discourse, and the speculative—crowd-driven critical envisioning of future hardware.
More specifically, this dissertation first explores the growing global scene of Open Design through a long-term ethnographic study of the open science hardware (OScH) movement, a genre of Open Design. This long-term study of OScH provides a focal point for HCI to deeply understand Open Design's growing global landscape. Second, it examines the application of Critical Making within Open Design through an OScH workshop with designers, engineers, artists and makers from local communities. This work foregrounds the role of HCI researchers as facilitators of collaborative critical engagements within Open Design. Third, this dissertation introduces the concept of crowd-driven Design Fiction through the development of a publicly accessible online Design Fiction platform named Dream Drones. Through a six month long development and a study with drone related practitioners, it offers several pragmatic insights into the challenges and opportunities for crowd-driven Design Fiction. Through these explorations, I highlight the broader implications and novel research pathways for HCI to shape and be shaped by the global Open Design movement.
This dissertation aims to expand the current scope of Open Design within human-computer interaction (HCI) research through a long-term exploration of Open Design’s socio-technical processes. I examine Open Design from three perspectives: the functional—materials, tools, and platforms that enable crowd-driven open hardware production, the critical—materially-oriented engagements within open design as a site for sociotechnical discourse, and the speculative—crowd-driven critical envisioning of future hardware.
More specifically, this dissertation first explores the growing global scene of Open Design through a long-term ethnographic study of the open science hardware (OScH) movement, a genre of Open Design. This long-term study of OScH provides a focal point for HCI to deeply understand Open Design's growing global landscape. Second, it examines the application of Critical Making within Open Design through an OScH workshop with designers, engineers, artists and makers from local communities. This work foregrounds the role of HCI researchers as facilitators of collaborative critical engagements within Open Design. Third, this dissertation introduces the concept of crowd-driven Design Fiction through the development of a publicly accessible online Design Fiction platform named Dream Drones. Through a six month long development and a study with drone related practitioners, it offers several pragmatic insights into the challenges and opportunities for crowd-driven Design Fiction. Through these explorations, I highlight the broader implications and novel research pathways for HCI to shape and be shaped by the global Open Design movement.
ContributorsFernando, Kattak Kuttige Rex Piyum (Author) / Kuznetsov, Anastasia (Thesis advisor) / Turaga, Pavan (Committee member) / Middel, Ariane (Committee member) / Takamura, John (Committee member) / Arizona State University (Publisher)
Created2020
Description
Cameras have become commonplace with wide-ranging applications of phone photography, computer vision, and medical imaging. With a growing need to reduce size and costs while maintaining image quality, the need to look past traditional style of cameras is becoming more apparent. Several non-traditional cameras have shown to be promising options for size-constraint applications, and while they may offer several advantages, they also usually are limited by image quality degradation due to optical or a need to reconstruct a captured image. In this thesis, we take a look at three of these non-traditional cameras: a pinhole camera, a diffusion-mask lensless camera, and an under-display camera (UDC).
For each of these cases, I present a feasible image restoration pipeline to correct for their particular limitations. For the pinhole camera, I present an early pipeline to allow for practical pinhole photography by reducing noise levels caused by low-light imaging, enhancing exposure levels, and sharpening the blur caused by the pinhole. For lensless cameras, we explore a neural network architecture that performs joint image reconstruction and point spread function (PSF) estimation to robustly recover images captured with multiple PSFs from different cameras. Using adversarial learning, this approach achieves improved reconstruction results that do not require explicit knowledge of the PSF at test-time and shows an added improvement in the reconstruction model’s ability to generalize to variations in the camera’s PSF. This allows lensless cameras to be utilized in a wider range of applications that require multiple cameras without the need to explicitly train a separate model for each new camera. For UDCs, we utilize a multi-stage approach to correct for low light transmission, blur, and haze. This pipeline uses a PyNET deep neural network architecture to perform a majority of the restoration, while additionally using a traditional optimization approach which is then fused in a learned manner in the second stage to improve high-frequency features. I show results from this novel fusion approach that is on-par with the state of the art.
For each of these cases, I present a feasible image restoration pipeline to correct for their particular limitations. For the pinhole camera, I present an early pipeline to allow for practical pinhole photography by reducing noise levels caused by low-light imaging, enhancing exposure levels, and sharpening the blur caused by the pinhole. For lensless cameras, we explore a neural network architecture that performs joint image reconstruction and point spread function (PSF) estimation to robustly recover images captured with multiple PSFs from different cameras. Using adversarial learning, this approach achieves improved reconstruction results that do not require explicit knowledge of the PSF at test-time and shows an added improvement in the reconstruction model’s ability to generalize to variations in the camera’s PSF. This allows lensless cameras to be utilized in a wider range of applications that require multiple cameras without the need to explicitly train a separate model for each new camera. For UDCs, we utilize a multi-stage approach to correct for low light transmission, blur, and haze. This pipeline uses a PyNET deep neural network architecture to perform a majority of the restoration, while additionally using a traditional optimization approach which is then fused in a learned manner in the second stage to improve high-frequency features. I show results from this novel fusion approach that is on-par with the state of the art.
ContributorsRego, Joshua D (Author) / Jayasuriya, Suren (Thesis advisor) / Blain Christen, Jennifer (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2020
Description
Infants born before 37 weeks of pregnancy are considered to be preterm. Typically, preterm infants have to be strictly monitored since they are highly susceptible to health problems like hypoxemia (low blood oxygen level), apnea, respiratory issues, cardiac problems, neurological problems as well as an increased chance of long-term health issues such as cerebral palsy, asthma and sudden infant death syndrome. One of the leading health complications in preterm infants is bradycardia - which is defined as the slower than expected heart rate, generally beating lower than 60 beats per minute. Bradycardia is often accompanied by low oxygen levels and can cause additional long term health problems in the premature infant.The implementation of a non-parametric method to predict the onset of brady- cardia is presented. This method assumes no prior knowledge of the data and uses kernel density estimation to predict the future onset of bradycardia events. The data is preprocessed, and then analyzed to detect the peaks in the ECG signals, following which different kernels are implemented to estimate the shared underlying distribu- tion of the data. The performance of the algorithm is evaluated using various metrics and the computational challenges and methods to overcome them are also discussed.
It is observed that the performance of the algorithm with regards to the kernels used are consistent with the theoretical performance of the kernel as presented in a previous work. The theoretical approach has also been automated in this work and the various implementation challenges have been addressed.
It is observed that the performance of the algorithm with regards to the kernels used are consistent with the theoretical performance of the kernel as presented in a previous work. The theoretical approach has also been automated in this work and the various implementation challenges have been addressed.
ContributorsMitra, Sinjini (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Moraffah, Bahman (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2020
Description
The ability to identify unoccupied resources in the radio spectrum is a key capability for opportunistic users in a cognitive radio environment. This paper draws upon and extends geometrically based ideas in statistical signal processing to develop estimators for the rank and the occupied subspace in a multi-user environment from multiple temporal samples of the signal received at a single antenna. These estimators enable identification of resources, such as the orthogonal complement of the occupied subspace, that may be exploitable by an opportunistic user. This concept is supported by simulations showing the estimation of the number of users in a simple CDMA system using a maximum a posteriori (MAP) estimate for the rank. It was found that with suitable parameters, such as high SNR, sufficient number of time epochs and codes of appropriate length, the number of users could be correctly estimated using the MAP estimator even when the noise variance is unknown. Additionally, the process of identifying the maximum likelihood estimate of the orthogonal projector onto the unoccupied subspace is discussed.
ContributorsBeaudet, Kaitlyn (Author) / Cochran, Douglas (Thesis advisor) / Turaga, Pavan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2014
Description
Motion estimation is a core task in computer vision and many applications utilize optical flow methods as fundamental tools to analyze motion in images and videos. Optical flow is the apparent motion of objects in image sequences that results from relative motion between the objects and the imaging perspective. Today, optical flow fields are utilized to solve problems in various areas such as object detection and tracking, interpolation, visual odometry, etc. In this dissertation, three problems from different areas of computer vision and the solutions that make use of modified optical flow methods are explained.
The contributions of this dissertation are approaches and frameworks that introduce i) a new optical flow-based interpolation method to achieve minimally divergent velocimetry data, ii) a framework that improves the accuracy of change detection algorithms in synthetic aperture radar (SAR) images, and iii) a set of new methods to integrate Proton Magnetic Resonance Spectroscopy (1HMRSI) data into threedimensional (3D) neuronavigation systems for tumor biopsies.
In the first application an optical flow-based approach for the interpolation of minimally divergent velocimetry data is proposed. The velocimetry data of incompressible fluids contain signals that describe the flow velocity. The approach uses the additional flow velocity information to guide the interpolation process towards reduced divergence in the interpolated data.
In the second application a framework that mainly consists of optical flow methods and other image processing and computer vision techniques to improve object extraction from synthetic aperture radar images is proposed. The proposed framework is used for distinguishing between actual motion and detected motion due to misregistration in SAR image sets and it can lead to more accurate and meaningful change detection and improve object extraction from a SAR datasets.
In the third application a set of new methods that aim to improve upon the current state-of-the-art in neuronavigation through the use of detailed three-dimensional (3D) 1H-MRSI data are proposed. The result is a progressive form of online MRSI-guided neuronavigation that is demonstrated through phantom validation and clinical application.
The contributions of this dissertation are approaches and frameworks that introduce i) a new optical flow-based interpolation method to achieve minimally divergent velocimetry data, ii) a framework that improves the accuracy of change detection algorithms in synthetic aperture radar (SAR) images, and iii) a set of new methods to integrate Proton Magnetic Resonance Spectroscopy (1HMRSI) data into threedimensional (3D) neuronavigation systems for tumor biopsies.
In the first application an optical flow-based approach for the interpolation of minimally divergent velocimetry data is proposed. The velocimetry data of incompressible fluids contain signals that describe the flow velocity. The approach uses the additional flow velocity information to guide the interpolation process towards reduced divergence in the interpolated data.
In the second application a framework that mainly consists of optical flow methods and other image processing and computer vision techniques to improve object extraction from synthetic aperture radar images is proposed. The proposed framework is used for distinguishing between actual motion and detected motion due to misregistration in SAR image sets and it can lead to more accurate and meaningful change detection and improve object extraction from a SAR datasets.
In the third application a set of new methods that aim to improve upon the current state-of-the-art in neuronavigation through the use of detailed three-dimensional (3D) 1H-MRSI data are proposed. The result is a progressive form of online MRSI-guided neuronavigation that is demonstrated through phantom validation and clinical application.
ContributorsKanberoglu, Berkay (Author) / Frakes, David (Thesis advisor) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2018
Description
There is an increasing demand for fully integrated point-of-load (POL) isolated DC-DC converters that can provide an isolation barrier between the primary and the secondary side, while delivering a low ripple, low noise regulated voltage at their isolated sides to a high dynamic range, sensitive mixed signal devices, such as sensors, current-shunt-monitors and ADCs. For these applications, smaller system size and integration level is important because the whole system may need to fit to limited space. Traditional methods for providing isolated power are discrete solutions using bulky transformers. Miniaturization of isolated POL regulators is becoming highly desirable for low power applications.
A fully integrated, low noise isolated point-of-load DC-DC converter for supply regulation of high dynamic range analog and mixed signal sensor signal-chains is presented. The isolated DC-DC converter utilizes an integrated planar air-core micro-transformer as a coupled resonator and isolation barrier and enables direct connection of low-voltage mixed signal circuits to higher supply rails. The air core transformer is driven at its primary resonant frequency of 100 MHz to achieve maximum power transfer. A mixed-signal perturb-and-observe based frequency search algorithm is developed to improve maximum power transfer efficiency by 60% across the isolation barrier compared to fixed driving frequency method. The isolated converter’s output ripple is reduced by utilizing spread spectrum clocking in the driver. An isolated PMOS LDO in the secondary side is used to suppress switching noise and ripple by 21dB. Conducted and radiated EMI distribution on the IC is measured by a set of integrated ring oscillator based noise sensors with -68dBm noise sensitivity. The proposed isolated converter achieves highest level of integration with respect to earlier reported integrated isolated converters, while providing 50V on-chip junction isolation without the need for extra silicon post-processing steps.
A fully integrated, low noise isolated point-of-load DC-DC converter for supply regulation of high dynamic range analog and mixed signal sensor signal-chains is presented. The isolated DC-DC converter utilizes an integrated planar air-core micro-transformer as a coupled resonator and isolation barrier and enables direct connection of low-voltage mixed signal circuits to higher supply rails. The air core transformer is driven at its primary resonant frequency of 100 MHz to achieve maximum power transfer. A mixed-signal perturb-and-observe based frequency search algorithm is developed to improve maximum power transfer efficiency by 60% across the isolation barrier compared to fixed driving frequency method. The isolated converter’s output ripple is reduced by utilizing spread spectrum clocking in the driver. An isolated PMOS LDO in the secondary side is used to suppress switching noise and ripple by 21dB. Conducted and radiated EMI distribution on the IC is measured by a set of integrated ring oscillator based noise sensors with -68dBm noise sensitivity. The proposed isolated converter achieves highest level of integration with respect to earlier reported integrated isolated converters, while providing 50V on-chip junction isolation without the need for extra silicon post-processing steps.
ContributorsLiu, Chengxi (Author) / Bakkaloglu, Bertan (Thesis advisor) / Kiaei, Sayfe (Committee member) / Song, Hongjiang (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this dissertation a new wideband circular HIS is proposed. The circular periodicity made it possible to illuminate the surface with a cylindrical TEMz wave and; a novel technique is utilized to make it wideband. Two models are developed to analyze the
reflection characteristics of the proposed HIS.
The circularly symmetric high impedance surface is used as a ground plane for the design of a low-profile loop and spiral radiating elements. It is shown that a HIS with circular periodicity provides a wider operational bandwidth for curvilinear radiating elements such, such as loops and spirals, compared to canonical rectangular HISs.
It is also observed that, with the aid of a circular HIS ground plane the gain of a loop and a spiral increases compared to when a perfect magnetic conductor (PMC) or rectangular HIS is used as a ground plane. The circular HIS was fabricated and the loop and spiral elements were placed individually in close proximity to it.
Also, due to the growing demand for low-radar signature (RCS) antennas for advanced airborne vehicles, curved and flexible HIS ground planes, which meet both the aerodynamic and low RCS requirements, have recently become popular candidates within the antenna and microwave technology. This encouraged us, to propose a spherical HIS where a 2-D curvature is introduced to the previously designed flat HIS.
The major problem associated with spherical HIS is the impact of the curvature on its reflection properties. After characterization of the flat circular HIS, which is addressed in the first part of this dissertation, a spherical curvature is introduced to the flat circular HIS and its impact on the reflection properties was examined when it was illuminated with the same cylindrical TEMz wave. The same technique, as for the flat HIS ground plane, is utilized to make the spherical HIS wideband. A loop and spiral element were placed in the vicinity of the curved HIS and their performanceswere investigated. The HISs were also fabricated and measurements were conducted to verify the simulations. An excellent agreement was observed.
reflection characteristics of the proposed HIS.
The circularly symmetric high impedance surface is used as a ground plane for the design of a low-profile loop and spiral radiating elements. It is shown that a HIS with circular periodicity provides a wider operational bandwidth for curvilinear radiating elements such, such as loops and spirals, compared to canonical rectangular HISs.
It is also observed that, with the aid of a circular HIS ground plane the gain of a loop and a spiral increases compared to when a perfect magnetic conductor (PMC) or rectangular HIS is used as a ground plane. The circular HIS was fabricated and the loop and spiral elements were placed individually in close proximity to it.
Also, due to the growing demand for low-radar signature (RCS) antennas for advanced airborne vehicles, curved and flexible HIS ground planes, which meet both the aerodynamic and low RCS requirements, have recently become popular candidates within the antenna and microwave technology. This encouraged us, to propose a spherical HIS where a 2-D curvature is introduced to the previously designed flat HIS.
The major problem associated with spherical HIS is the impact of the curvature on its reflection properties. After characterization of the flat circular HIS, which is addressed in the first part of this dissertation, a spherical curvature is introduced to the flat circular HIS and its impact on the reflection properties was examined when it was illuminated with the same cylindrical TEMz wave. The same technique, as for the flat HIS ground plane, is utilized to make the spherical HIS wideband. A loop and spiral element were placed in the vicinity of the curved HIS and their performanceswere investigated. The HISs were also fabricated and measurements were conducted to verify the simulations. An excellent agreement was observed.
ContributorsAmiri, Mikal Askarian (Author) / Balanis, Constantine A (Thesis advisor) / Aberle, James T (Committee member) / Bakkaloglu, Bertan (Committee member) / Trichopoulos, Georgios C (Committee member) / Arizona State University (Publisher)
Created2018
Description
A Microbial fuel cell (MFC) is a bio-inspired carbon-neutral, renewable electrochemical converter to extract electricity from catabolic reaction of micro-organisms. It is a promising technology capable of directly converting the abundant biomass on the planet into electricity and potentially alleviate the emerging global warming and energy crisis. The current and power density of MFCs are low compared with conventional energy conversion techniques. Since its debut in 2002, many studies have been performed by adopting a variety of new configurations and structures to improve the power density. The reported maximum areal and volumetric power densities range from 19 mW/m2 to 1.57 W/m2 and from 6.3 W/m3 to 392 W/m3, respectively, which are still low compared with conventional energy conversion techniques. In this dissertation, the impact of scaling effect on the performance of MFCs are investigated, and it is found that by scaling down the characteristic length of MFCs, the surface area to volume ratio increases and the current and power density improves. As a result, a miniaturized MFC fabricated by Micro-Electro-Mechanical System(MEMS) technology with gold anode is presented in this dissertation, which demonstrate a high power density of 3300 W/m3. The performance of the MEMS MFC is further improved by adopting anodes with higher surface area to volume ratio, such as carbon nanotube (CNT) and graphene based anodes, and the maximum power density is further improved to a record high power density of 11220 W/m3. A novel supercapacitor by regulating the respiration of the bacteria is also presented, and a high power density of 531.2 A/m2 (1,060,000 A/m3) and 197.5 W/m2 (395,000 W/m3), respectively, are marked, which are one to two orders of magnitude higher than any previously reported microbial electrochemical techniques.
ContributorsRen, Hao (Author) / Chae, Junseok (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Phillips, Stephen (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2016
Description
When dancers are granted agency over music, as in interactive dance systems, the actors are most often concerned with the problem of creating a staged performance for an audience. However, as is reflected by the above quote, the practice of Argentine tango social dance is most concerned with participants internal experience and their relationship to the broader tango community. In this dissertation I explore creative approaches to enrich the sense of connection, that is, the experience of oneness with a partner and complete immersion in music and dance for Argentine tango dancers by providing agency over musical activities through the use of interactive technology. Specifically, I create an interactive dance system that allows tango dancers to affect and create music via their movements in the context of social dance. The motivations for this work are multifold: 1) to intensify embodied experience of the interplay between dance and music, individual and partner, couple and community, 2) to create shared experience of the conventions of tango dance, and 3) to innovate Argentine tango social dance practice for the purposes of education and increasing musicality in dancers.
ContributorsBrown, Courtney Douglass (Author) / Paine, Garth (Thesis advisor) / Feisst, Sabine (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2017