Interrogating the spaces of personal photography : women, identity, and the cultural formation of photographic practice

Personal photographs permeate our lives from the moment we are born as they define who we are within our familial group and local communities. Archived in family albums or framed on living room walls, they continue on after our death as mnemonic artifacts referencing our gendered, raced, and ethnic identities. This dissertation examines salient instances of what women “do” with personal photographs, not only as authors and subjects but also as collectors, archivists, and family and cultural historians. This project seeks to contribute to more productive, complex discourse about how women form relationships and engage with the conventions and practices of personal photography. In the first part of this dissertation I revisit developments in the history of personal photography, including the advertising campaigns of the Kodak and Agfa Girls and the development of albums such as the Stammbuch and its predecessor, the carte-de-visite, that demonstrate how personal photography has functioned as a gendered activity that references family unity, sentimentalism for the past, and self-representation within normative familial and dominant cultural groups, thus suggesting its importance as a cultural practice of identity formation. The second and primary section of the dissertation expands on the critical analyses of Gillian Rose, Patricia Holland, and Nancy Martha West, who propose that personal photography, marketed to and taken on by women, double-exposes their gendered identities. Drawing on work by critics such as Deborah Willis, bell hooks, and Abigail Solomon-Godeau, I examine how the reconfiguration, recontextualization, and relocation of personal photographs in the respective work of Christine Saari, Fern Logan, and Katie Knight interrogates and complicates gendered, raced, and ethnic identities and cultural attitudes about them. In the final section of the dissertation I briefly examine select examples of how emerging digital spaces on the Internet function as a site for personal photography, one that both reinscribes traditional cultural formations while offering new opportunities for women for the display and audiencing of identities outside the family.

Fixed block configuration GDDs with block size 6 and (3, r)-regular graphs

Chapter 1 is used to introduce the basic tools and mechanics used within this thesis. Most of the definitions used in the thesis will be defined, and we provide a basic survey of topics in graph theory and design theory pertinent to the topics studied in this thesis. In Chapter 2, we are concerned with the study of fixed block configuration group divisible designs, GDD(n; m; k; λ1; λ2). We study those GDDs in which each block has configuration (s; t), that is, GDDs in which each block has exactly s points from one of the two groups and t points from the other. Chapter 2 begins with an overview of previous results and constructions for small group size and block sizes 3, 4 and 5. Chapter 2 is largely devoted to presenting constructions and results about GDDs with two groups and block size 6. We show the necessary conditions are sufficient for the existence of GDD(n, 2, 6; λ1, λ2) with fixed block configuration (3; 3). For configuration (1; 5), we give minimal or nearminimal index constructions for all group sizes n ≥ 5 except n = 10, 15, 160, or 190. For configuration (2, 4), we provide constructions for several families ofGDD(n, 2, 6; λ1, λ2)s. Chapter 3 addresses characterizing (3, r)-regular graphs. We begin with providing previous results on the well studied class of (2, r)-regular graphs and some results on the structure of large (t; r)-regular graphs. In Chapter 3, we completely characterize all (3, 1)-regular and (3, 2)-regular graphs, as well has sharpen existing bounds on the order of large (3, r)- regular graphs of a certain form for r ≥ 3. Finally, the appendix gives computational data resulting from Sage and C programs used to generate (3, 3)-regular graphs on less than 10 vertices.

Control design and genetic algorithm optimization for electrostatic MEMS

This dissertation discusses structural-electrostatic modeling techniques, genetic algorithm based optimization and control design for electrostatic micro devices. First, an alternative modeling technique, the interpolated force model, for electrostatic micro devices is discussed. The method provides improved computational efficiency relative to a benchmark model, as well as improved accuracy for irregular electrode configurations relative to a common approximate model, the parallel plate approximation model. For the configuration most similar to two parallel plates, expected to be the best case scenario for the approximate model, both the parallel plate approximation model and the interpolated force model maintained less than 2.2% error in static deflection compared to the benchmark model. For the configuration expected to be the worst case scenario for the parallel plate approximation model, the interpolated force model maintained less than 2.9% error in static deflection while the parallel plate approximation model is incapable of handling the configuration. Second, genetic algorithm based optimization is shown to improve the design of an electrostatic micro sensor. The design space is enlarged from published design spaces to include the configuration of both sensing and actuation electrodes, material distribution, actuation voltage and other geometric dimensions. For a small population, the design was improved by approximately a factor of 6 over 15 generations to a fitness value of 3.2 fF. For a larger population seeded with the best configurations of the previous optimization, the design was improved by another 7% in 5 generations to a fitness value of 3.0 fF. Third, a learning control algorithm is presented that reduces the closing time of a radiofrequency microelectromechanical systems switch by minimizing bounce while maintaining robustness to fabrication variability. Electrostatic actuation of the plate causes pull-in with high impact velocities, which are difficult to control due to parameter variations from part to part. A single degree-of-freedom model was utilized to design a learning control algorithm that shapes the actuation voltage based on the open/closed state of the switch. Experiments on 3 test switches show that after 5-10 iterations, the learning algorithm lands the switch with an impact velocity not exceeding 0.2 m/s, eliminating bounce.