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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- All Subjects: Diffusion processes
- All Subjects: vernacular
- Creators: Turaga, Pavan
Description
Modern systems that measure dynamical phenomena often have limitations as to how many sensors can operate at any given time step. This thesis considers a sensor scheduling problem in which the source of a diffusive phenomenon is to be localized using single point measurements of its concentration. With a linear diffusion model, and in the absence of noise, classical observability theory describes whether or not the system's initial state can be deduced from a given set of linear measurements. However, it does not describe to what degree the system is observable. Different metrics of observability have been proposed in literature to address this issue. Many of these methods are based on choosing optimal or sub-optimal sensor schedules from a predetermined collection of possibilities. This thesis proposes two greedy algorithms for a one-dimensional and two-dimensional discrete diffusion processes. The first algorithm considers a deterministic linear dynamical system and deterministic linear measurements. The second algorithm considers noise on the measurements and is compared to a Kalman filter scheduling method described in published work.
ContributorsNajam, Anbar (Author) / Cochran, Douglas (Thesis advisor) / Turaga, Pavan (Committee member) / Wang, Chao (Committee member) / Arizona State University (Publisher)
Created2016
Description
This thesis aims to explore the language of different bodies in the field of dance by analyzing
the habitual patterns of dancers from different backgrounds and vernaculars. Contextually,
the term habitual patterns is defined as the postures or poses that tend to re-appear,
often unintentionally, as the dancer performs improvisational dance. The focus lies in exposing
the movement vocabulary of a dancer to reveal his/her unique fingerprint.
The proposed approach for uncovering these movement patterns is to use a clustering
technique; mainly k-means. In addition to a static method of analysis, this paper uses
an online method of clustering using a streaming variant of k-means that integrates into
the flow of components that can be used in a real-time interactive dance performance. The
computational system is trained by the dancer to discover identifying patterns and therefore
it enables a feedback loop resulting in a rich exchange between dancer and machine. This
can help break a dancer’s tendency to create similar postures, explore larger kinespheric
space and invent movement beyond their current capabilities.
This paper describes a project that distinguishes itself in that it uses a custom database
that is curated for the purpose of highlighting the similarities and differences between various
movement forms. It puts particular emphasis on the process of choosing source movement
qualitatively, before the technological capture process begins.
the habitual patterns of dancers from different backgrounds and vernaculars. Contextually,
the term habitual patterns is defined as the postures or poses that tend to re-appear,
often unintentionally, as the dancer performs improvisational dance. The focus lies in exposing
the movement vocabulary of a dancer to reveal his/her unique fingerprint.
The proposed approach for uncovering these movement patterns is to use a clustering
technique; mainly k-means. In addition to a static method of analysis, this paper uses
an online method of clustering using a streaming variant of k-means that integrates into
the flow of components that can be used in a real-time interactive dance performance. The
computational system is trained by the dancer to discover identifying patterns and therefore
it enables a feedback loop resulting in a rich exchange between dancer and machine. This
can help break a dancer’s tendency to create similar postures, explore larger kinespheric
space and invent movement beyond their current capabilities.
This paper describes a project that distinguishes itself in that it uses a custom database
that is curated for the purpose of highlighting the similarities and differences between various
movement forms. It puts particular emphasis on the process of choosing source movement
qualitatively, before the technological capture process begins.
ContributorsIyengar, Varsha (Author) / Xin Wei, Sha (Thesis advisor) / Turaga, Pavan (Committee member) / Coleman, Grisha (Committee member) / Arizona State University (Publisher)
Created2016