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- All Subjects: Image Processing
- Creators: Electrical Engineering Program
The Fourier representation of a signal or image is equivalent to its native representation in the sense that the signal or image can be reconstructed exactly from its Fourier transform. The Fourier transform is generally complex-valued, and each value of the Fourier spectrum thus possesses both magnitude and phase. Degradation of signals and images when Fourier phase information is lost or corrupted has been studied extensively in the signal processing research literature, as has reconstruction of signals and images using only Fourier magnitude information. This thesis focuses on the case of images, where it examines the visual effect of quantifiable levels of Fourier phase loss and, in particular, studies the merits of introducing varying degrees of phase information in a classical iterative algorithm for reconstructing an image from its Fourier magnitude.
This creative project is an extension of the work being done as part of Senior Design in<br/>developing the See-Through Car Pillar, a system designed to render the forward car pillars in a car<br/>invisible to the driver so they can have an unobstructed view utilizing displays, sensors, and a<br/>computer. The first half of the paper provides the motivation, design and progress of the project, <br/>while the latter half provides a literature survey on current automobile trends, the viability of the<br/>See-Through Car Pillar as a product in the market through case studies, and alternative designs and <br/>technologies that also might address the problem statement.
recent years, the ability of science to do so has been scrutinized. Attempts to reproduce
findings in diverse fields demonstrate that many results are unreliable and do not
generalize across contexts. In response to these concerns, many proposals for reform have
emerged. Although promising, such reforms have not addressed all aspects of scientific
practice. In the social sciences, two such aspects are the diversity of study participants
and incentive structures. Most efforts to improve scientific practice focus on replicability,
but sidestep issues of generalizability. And while researchers have speculated about the
effects of incentive structures, there is little systematic study of these hypotheses. This
dissertation takes one step towards filling these gaps. Chapter 1 presents a cross-cultural
study of social discounting – the purportedly fundamental human tendency to sacrifice
more for socially-close individuals – conducted among three diverse populations (U.S.,
rural Indonesia, rural Bangladesh). This study finds no independent effect of social
distance on generosity among Indonesian and Bangladeshi participants, providing
evidence against the hypothesis that social discounting is universal. It also illustrates the
importance of studying diverse human populations for developing generalizable theories
of human nature. Chapter 2 presents a laboratory experiment with undergraduates to test
the effect of incentive structures on research accuracy, in an instantiation of the scientific
process where the key decision is how much data to collect before submitting one’s
findings. The results demonstrate that rewarding novel findings causes respondents to
make guesses with less information, thereby reducing their accuracy. Chapter 3 presents
an evolutionary agent-based model that tests the effect of competition for novel findings
on the sample size of studies that researchers conduct. This model demonstrates that
competition for novelty causes the cultural evolution of research with smaller sample
sizes and lower statistical power. However, increasing the startup costs to conducting
single studies can reduce the negative effects of competition, as can rewarding
publication of secondary findings. These combined chapters provide evidence that
aspects of current scientific practice may be detrimental to the reliability and
generalizability of research and point to potential solutions.