Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Turaga, Pavan
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
Antibiotic resistance is a very important issue that threatens mankind. As bacteria
are becoming resistant to multiple antibiotics, many common antibiotics will soon
become ineective. The ineciency of current methods for diagnostics is an important
cause of antibiotic resistance, since due to their relative slowness, treatment plans
are often based on physician's experience rather than on test results, having a high
chance of being inaccurate or not optimal. This leads to a need of faster, pointof-
care (POC) methods, which can provide results in a few hours. Motivated by
recent advances on computer vision methods, three projects have been developed
for bacteria identication and antibiotic susceptibility tests (AST), with the goal of
speeding up the diagnostics process. The rst two projects focus on obtaining features
from optical microscopy such as bacteria shape and motion patterns to distinguish
active and inactive cells. The results show their potential as novel methods for AST,
being able to obtain results within a window of 30 min to 3 hours, a much faster
time frame than the gold standard approach based on cell culture, which takes at
least half a day to be completed. The last project focus on the identication task,
combining large volume light scattering microscopy (LVM) and deep learning to
distinguish bacteria from urine particles. The developed setup is suitable for pointof-
care applications, as a large volume can be viewed at a time, avoiding the need
for cell culturing or enrichment. This is a signicant gain compared to cell culturing
methods. The accuracy performance of the deep learning system is higher than chance
and outperforms a traditional machine learning system by up to 20%.
are becoming resistant to multiple antibiotics, many common antibiotics will soon
become ineective. The ineciency of current methods for diagnostics is an important
cause of antibiotic resistance, since due to their relative slowness, treatment plans
are often based on physician's experience rather than on test results, having a high
chance of being inaccurate or not optimal. This leads to a need of faster, pointof-
care (POC) methods, which can provide results in a few hours. Motivated by
recent advances on computer vision methods, three projects have been developed
for bacteria identication and antibiotic susceptibility tests (AST), with the goal of
speeding up the diagnostics process. The rst two projects focus on obtaining features
from optical microscopy such as bacteria shape and motion patterns to distinguish
active and inactive cells. The results show their potential as novel methods for AST,
being able to obtain results within a window of 30 min to 3 hours, a much faster
time frame than the gold standard approach based on cell culture, which takes at
least half a day to be completed. The last project focus on the identication task,
combining large volume light scattering microscopy (LVM) and deep learning to
distinguish bacteria from urine particles. The developed setup is suitable for pointof-
care applications, as a large volume can be viewed at a time, avoiding the need
for cell culturing or enrichment. This is a signicant gain compared to cell culturing
methods. The accuracy performance of the deep learning system is higher than chance
and outperforms a traditional machine learning system by up to 20%.
ContributorsIriya, Rafael (Author) / Turaga, Pavan (Thesis advisor) / Wang, Shaopeng (Committee member) / Grys, Thomas (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2020