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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Description
In UAVs and parking lots, it is typical to first collect an enormous number of pixels using conventional imagers. This is followed by employment of expensive methods to compress by throwing away redundant data. Subsequently, the compressed data is transmitted to a ground station. The past decade has seen the

In UAVs and parking lots, it is typical to first collect an enormous number of pixels using conventional imagers. This is followed by employment of expensive methods to compress by throwing away redundant data. Subsequently, the compressed data is transmitted to a ground station. The past decade has seen the emergence of novel imagers called spatial-multiplexing cameras, which offer compression at the sensing level itself by providing an arbitrary linear measurements of the scene instead of pixel-based sampling. In this dissertation, I discuss various approaches for effective information extraction from spatial-multiplexing measurements and present the trade-offs between reliability of the performance and computational/storage load of the system. In the first part, I present a reconstruction-free approach to high-level inference in computer vision, wherein I consider the specific case of activity analysis, and show that using correlation filters, one can perform effective action recognition and localization directly from a class of spatial-multiplexing cameras, called compressive cameras, even at very low measurement rates of 1\%. In the second part, I outline a deep learning based non-iterative and real-time algorithm to reconstruct images from compressively sensed (CS) measurements, which can outperform the traditional iterative CS reconstruction algorithms in terms of reconstruction quality and time complexity, especially at low measurement rates. To overcome the limitations of compressive cameras, which are operated with random measurements and not particularly tuned to any task, in the third part of the dissertation, I propose a method to design spatial-multiplexing measurements, which are tuned to facilitate the easy extraction of features that are useful in computer vision tasks like object tracking. The work presented in the dissertation provides sufficient evidence to high-level inference in computer vision at extremely low measurement rates, and hence allows us to think about the possibility of revamping the current day computer systems.
ContributorsKulkarni, Kuldeep Sharad (Author) / Turaga, Pavan (Thesis advisor) / Li, Baoxin (Committee member) / Chakrabarti, Chaitali (Committee member) / Sankaranarayanan, Aswin (Committee member) / LiKamWa, Robert (Committee member) / Arizona State University (Publisher)
Created2017
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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

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.
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