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- All Subjects: Signal Processing
- Creators: Electrical Engineering Program
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.
This Honors Thesis is a continuation of Prof. Lauren Hayes’s and Dr. Xin Luo’s research initiative, Haptic Electronic Audio Research into Musical Experience (HEAR-ME), which investigates how to enhance the musical listening experience for CI users using a wearable haptic system. The goals of this Honors Thesis are to adapt Prof. Hayes’s system code from the Max visual programming language into the C++ object-oriented programming language and to study the results of the developed C++ codes. This adaptation allows the system to operate in real-time and independently of a computer.
Towards these goals, two signal processing algorithms were developed and programmed in C++. The first algorithm is a thresholding method, which outputs a pulse of a predefined width when the input signal falls below some threshold in amplitude. The second algorithm is a root-mean-square (RMS) method, which outputs a pulse-width modulation signal with a fixed period and with a duty cycle dependent on the RMS of the input signal. The thresholding method was found to work best with speech, and the RMS method was found to work best with music. Future work entails the design of adaptive signal processing algorithms to allow the system to work more effectively on speech in a noisy environment and to emphasize a variety of elements in music.
The idea for this thesis emerged from my senior design capstone project, A Wearable Threat Awareness System. A TFmini-S LiDAR sensor is used as one component of this system; the functionality of and signal processing behind this type of sensor are elucidated in this document. Conceptual implementations of the optical and digital stages of the signal processing is described in some detail. Following an introduction in which some general background knowledge about LiDAR is set forth, the body of the thesis is organized into two main sections. The first section focuses on optical processing to demodulate the received signal backscattered from the target object. This section describes the key steps in demodulation and illustrates them with computer simulation. A series of graphs capture the mathematical form of the signal as it progresses through the optical processing stages, ultimately yielding the baseband envelope which is converted to digital form for estimation of the leading edge of the pulse waveform using a digital algorithm. The next section is on range estimation. It describes the digital algorithm designed to estimate the arrival time of the leading edge of the optical pulse signal. This enables the pulse’s time of flight to be estimated, thus determining the distance between the LiDAR and the target. Performance of this algorithm is assessed with four different levels of noise. A calculation of the error in the leading-edge detection in terms of distance is also included to provide more insight into the algorithm’s accuracy.