Coding Strategies and Implementations of Compressive Sensing

This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others.

This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system.

Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity.

Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or information from a noisy environment. Using engineering efforts to accomplish the same task usually requires multiple detectors, advanced computational algorithms, or artificial intelligence systems. Compressive acoustic sensing incorporates acoustic metamaterials in compressive sensing theory to emulate the abilities of sound localization and selective attention. This research investigates and optimizes the sensing capacity and the spatial sensitivity of the acoustic sensor. The well-modeled acoustic sensor allows localizing multiple speakers in both stationary and dynamic auditory scene; and distinguishing mixed conversations from independent sources with high audio recognition rate.

The Political Economy of Climate Adaptation and Environmental Health: The Case of Ethiopia

The environment affects our health, livelihoods, and the social and political institutions within which we interact. Indeed, nearly a quarter of the global disease burden is attributed to environmental factors, and many of these factors are exacerbated by global climate change. Thus, the central research question of this dissertation is: How do people cope with and adapt to uncertainty, complexity, and change of environmental and health conditions? Specifically, I ask how institutional factors, risk aversion, and behaviors affect environmental health outcomes. I further assess the role of social capital in climate adaptation, and specifically compare individual and collective adaptation. I then analyze how policy develops accounting for both adaptation to the effects of climate and mitigation of climate-changing emissions. In order to empirically test the relationships between these variables at multiple levels, I combine multiple methods, including semi-structured interviews, surveys, and field experiments, along with health and water quality data. This dissertation uses the case of Ethiopia, Africa’s second-most populous nation, which has a large rural population and is considered very vulnerable to climate change. My fieldwork included interviews and institutional data collection at the national level, and a three-year study (2012-2014) of approximately 400 households in 20 villages in the Ethiopian Rift Valley. I evaluate the theoretical relationships between households, communities, and government in the process of adaptation to environmental stresses. Through my analyses, I demonstrate that water source choice varies by individual risk aversion and institutional context, which ultimately has implications for environmental health outcomes. I show that qualitative measures of trust predict cooperation in adaptation, consistent with social capital theory, but that measures of trust are negatively related with private adaptation by the individual. Finally, I describe how Ethiopia had some unique characteristics, significantly reinforced by international actors, that led to the development of an extensive climate policy, and yet with some challenges remaining for implementation. These results suggest a potential for adaptation through the interactions among individuals, communities, and government in the search for transformative processes when confronting environmental threats and climate change.

Adapting Arthur: The transformations and adaptations of Wirnt von Graffenberg's Wigalois

The stories of King Arthur and his noble knights have fascinated audiences for many centuries and continue to being retold and fashioned to attract modern audiences. Amongst these stories is the tale of Wigalois, the son of the reputable Gawain. This dissertation traces the story of Wigalois across different languages, cultures, and media in order to show how this is a shared German-Yiddish narrative. Furthermore, this dissertations challenges traditional understanding of adaptation within a diachronic and teleological framework by uncovering dialogical and dynamic processes inherent in this narrative tradition. Using the theoretical framework of a combined Adaptation Studies and Medieval Literature Studies’ notions of unstable texts my argumentation focuses on eight specific examples: Wirnt von Grafenberg’s Wigalois (1st half 13th ct.), Italian murals from the fourteenth century, Wigoleis von dem Rade (1483/93), Viduvilt (Yiddish, 16th ct.), Johann Christoph Wagenseil’s Belehrung der Jüdisch-Teutschen Red- und Schreibart (Yiddish and German, 1715), Gabein (Yiddish, 1789), the illustrations by Ludwig Richter (before 1851), and Die phantastischen Abenteuer der Glücksritters Wigalois (Comic, German, 2011).

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.