Treasures @ UT Dallas
Not a member yet
7697 research outputs found
Sort by
Advancements in Hardware/firmware and Applications for CCi-MOBILE: a Cochlear Implant Research Platform
No full textHearing impairment is a pervasive problem which occurs due to the detrimental damage caused to
the inner ear. Assistive Hearing Devices such as Cochlear Implants (CIs) and Hearing Aids (HAs)
are designed to restore hearing, personalize rehabilitation, and enrich the listening experience.
Although signal processing and machine learning research has greatly improved audio processing,
the rigid design requirements of commercial CI sound processors make it difficult to explore novel
algorithms for research investigations and conduct longitudinal studies. This thesis presents the
design, development, clinical evaluation, and applications of CCi-MOBILE, a computationally
powerful signal processing testing platform built specifically for researchers in the CI/HA field
along with implementing multiple additional features to the platform. This custom-made, portable
research platform allows researchers to design and perform complex speech processing algorithm
assessment offline and in real-time through user-friendly, software-mediated open-source tools
with implants manufactured by Cochlear Corporation. The design includes a lightweight custom
circuit board comprising of an on-board FPGA to be used in conjunction with a computing
platform such as a PC/tablet/laptop/smartphone based on the requirement of CI/HA signal
processing algorithms. The processing pipeline for CI and HA stimulation is discussed followed
by results from an acute study with implant users’ speech intelligibility in quiet and noisy
conditions. The platform supports testing of algorithms for unilateral, bilateral, and bimodal
hearing impairment. A major obstruction to accurate source localization for bimodal and bilateral
CI users is the distortion of interaural time and level difference cues (ITD and ILD), and limited
ITD sensitivity. Various CI research interfaces developed by either academic or industry sponsored
research teams support proposed signal processing and psychoacoustic investigations but have
limited ability to efficiently validate bimodal and/or bilateral algorithms. To overcome such
challenges; verification, and validation of the synchronized bilateral (electric-electric) and bimodal
(electric-acoustic) outputs is performed, in an authenticated and efficient way, to support
localization algorithmic and experimental investigations. It has been hypothesized that variable
stimulation rate for exciting the electrode array can aid for better speech perception and increased
spectral information. Hence, a new multi-rate implant strategy including time-varying stimulation
rates has been proposed in this work. Lastly, expanding the capabilities of the platform to ensure
long-term sustainability, a real-time data streaming link between the platform and a cloud-based
data repository is established to enable remote-test facilities along with an algorithm
implementation and testing in naturalistic environments. We discuss implementation feasibility,
and hypothesized performance of these approaches individually, and collectively, on the
perceptual benefit for researchers working towards the welfare of the hearing-impaired
community
Multifunctional Carbon Nanotube Yarns for Artificial Muscles and Energy Harvesters
No full textThe superb mechanical, physical, and chemical properties of carbon nanotube (CNT) yarns have
promoted their application as function components that program actuation, sensing, and power
management for soft robotics and smart systems. Success in making artificial muscles that are
faster, more powerful, and that can provide larger strokes would expand their applications.
Efficient conversion of ambient mechanical energy into electrical energy is needed for diverse
applications, including self-powered wireless sensors, structural and human health monitoring
systems, and the extraction of energy from ocean waves.
Herein, the development of CNT yarn artificial muscles and mechanical energy harvesters are first
discussed, and the obtained understanding of underlying mechanisms provide guidance for
optimizing muscle and harvester performances. Next, unipolar stroke CNT yarn muscles are
described, in which muscle stroke changes between extreme potentials are additive and muscle
stroke remarkably increases with increasing potential scan rate. The normal decrease in stroke with
increasing scan rate, because of decreased capacitance, is overwhelmed by a dramatic increase in
effective ion size caused by electroosmotic pumping of solvent. These coiled carbon nanotube yarn
muscles contain a yarn guest that shifts the yarn’s potential of zero charge (pzc) by over a volt,
either positively or negatively. Such pzc shift agents include ion-exchange membrane polymers,
oxidized graphene platelets, and surfactants. Record muscle strokes, contractile work-per-cycle,
contractile power densities, and energy conversion efficiencies are obtained for unipolar muscles.
Then, powerful CNT yarn mechanical energy harvesters (we call twistrons) are described, which
are electrochemical artificial muscles run in reverse. Stretching a coiled CNT yarn can provide
large, reversible changes in electrochemical capacitance, which enables conversion of mechanical
energy to electrical energy. The performance of these twistron harvesters can be increased by
diverse fabrication methods: optimizing the structure of the precursor CNT forest, using stretchinduced alignment, thermal annealing under tension, and incorporating reduced graphene oxide
nanoplates. The peak output power at 1 Hz and at 30 Hz for a sinusoidal stretch were 0.73 and
3.19 kW/kg, which are 15- and 13-fold higher than for previous twistron harvesters at these
respective frequencies. This performance at 30 Hz was over 12-fold that of other prior-art
mechanical energy harvesters for frequencies between 1 Hz and 600 Hz. Last, the opportunities
and challenges for future practical applications of CNT yarns are highlighted
Automatic Test Generation for Program Repair
No full textSoftware bugs negatively impact product quality and user experience, necessitating efficient
debugging. Manual debugging is time-consuming and requires programmers’ expertise, making
automated program repair technologies essential as they offer potential solutions by automatically
identifying and fixing program bugs but face various limitations, such as low repair capability and
patch overfitting. This dissertation addresses these challenges in test-suite based automated
program repair by proposing and evaluating novel methods to improve test suite adequacy, patch
search space, algorithm search efficiency, and repair capability. The key contributions are: (1) an
novel patch generation module for search-based program repair that integrates repair templates
and a multi-population differential elite group strategy, improving candidate search space and
patch search capability, respectively; (2) the ARJANMT framework, which combines search-
based and neural-machine-translation-based repair techniques, resulting in superior repair
capability and patch correctness; (3) a local search strategy for automatic test case generation
utilizing adaptive simulated annealing and symbolic path constraints, generating high coverage
test cases within a limited time budget; (4) the PCA-DynaMOSA, an improved many-objective
optimization algorithm for test case generation based on dimensionality reduction, capable of
decomposing numerous objectives under different coverage criterion to address DynaMOSA's
limitations and enhance test case generation performance; (5) an investigation into the feasibility
and effectiveness of employing automatically generated test cases to mitigate patch overfitting
issues and boost the repair capability of automated program repair frameworks. These
contributions of this dissertation lead to advancements in the field of automated program repair
and test case generation, providing valuable insights and potential solutions to the program repair
and patch overfitting problem
Double Hybridization and Bordercanx Literary Works: How the United States Turned Mexican Americans Into the Forgotten People
No full text“Double colonization,” as a concept, was first introduced in the 1980s, to demonstrate how
women of formerly colonized and once indigenous societies were colonized twice—through both
patriarchal ideology and imperial ideology. Not long after this term and concept emanated, Homi
Bhabha reestablished the term “hybridity” as a theoretical advancement in The Location of
Culture (Bhabha 285). Through the existence of the colonizer and colonized, “hybridization” is
the doubling of these cultures as they intricately relate place and identity with time and societal
conditions. As Terry Goldie explains, the coming together of these cultures is neither essential by
nature nor an inevitable willingness; rather, it is the “putative superiority of the European and the
supposed inferiority of the native” (Goldie 61). Furthermore, the duality that these cultures
represent is not by any means Manichean in nature.
The duplication of “hybridity” cannot be singular as it pertains to one country over the other, and
it cannot be binary, because as James Clifford asserts, the two cultures here are in motion and
constant flux (Ashcroft, Griffiths, and Tiffin 164). Therefore, each location and culture is unique,
and according to Diana Brydon, the result of this merging produces altogether unique variables
(Ashcroft, Griffiths, and Tiffin 187). Establishing this cultural history in historiography is not a
linear or sequential process. Anthropologist Lee Berger likens human evolution to a “braided
stream,” similar to Clifford’s assertion that “there is no natural shape to configuration” (Berger
2019) (Ashcroft, Griffiths, and Tiffin x). Some indigenous populations have remained in one
location, while the environment around them changed by—“urbanization, habitation,
reindigenization, sinking roots, moving on, invading, and holding on” (Clifford 182-83).
Through a thorough examination of double hybridization on the effects of the Mexican American
population situated on the U.S.-Mexico Border, what becomes evident is that the segment of the
population that remained through the second disruption when northern Mexico became part of
the United States was intentionally targeted by the U.S. government and omitted from the
national and cultural identity.
Double hybridization reveals both agency and colonial oppression over time and has not been
given critical attention as the defining difference between United States Latinx literary works,
specifically between “Bordercanx” literary works and Latin American and American literary
works. Bordercanx literary works have specifically emerged from this in-between cultural space
and its people situated between Mexico and the United States. U.S.-Mexico Border people living
on the U.S. side of the Borderline have used the term “Bordercanx” as a counter concept to the
term “fronterizo/a” that those living on the Mexican side have used. Although the term is not
normally applied to Border writing, Bordercanx is used here to describe a population and their
artistic works specifically derived from this geocultural, geopolitical, and geographical location.
The referents of the term express the centrality and cultural displacement of the double
hybridized people who remained in the same location. The scholarly portion of the dissertation
consists of four chapters, which explore how the overall process of double hybridization turned
Mexican Americans into “the forgotten people,” as seen in Bordercanx literature.
Chapter 1 defines the U.S.-Mexico Border through a perimeter and regional approach to specify
the space and properly assess the anthropological and ethnographical understanding of that space
and provide a deeper understanding of it within a world context. Chapter 2 examines perimeter,
space, and location related to the theory of double hybridization that can only be seen in the
American Southwest Border region. Chapter 3 takes a contemporary look at the Latinx and
Hispanic people as an integral constituent of the population in the United States that is
intrinsically woven into the tapestry of the national identity by retracing the
geopolitical history and cultural history of each group, showing the commonalities and
differences between them. Finally, Chapter 4 demonstrates how double hybridization is the
defining element that separates U.S. Latinx—specifically Bordercanx literary works—from Latin
American and American literature, with a distinct focus on “body theory” as it pertains to
enslavement and labor, detainment and repatriation, location and positioning, and
Americanization through language suppression and racialization that transpired over two distinct
periods of disruption, resulting in the formation of “los olvidados,” “the forgotten people,” along
the U.S.-Mexico Border. The combination of all four chapters reveals a history and
circumstances that have shaped a distinct population with the Latinx community and American
identity over time. The fifth and final chapter applies the concept of double hybridization to a
compilation and layering process of creative work resulting in a children’s book through a selfdirected form of ethnomethodology called autoethnography, further situating this
multigenerational work within the framework of Bordercanx literature
Tissue Characterization Using H-scan Ultrasound Imaging
No full textBreast cancer is the second leading cause of mortality among women and affects more women
than any other type of cancer. Around 43,600 women in the U.S. died in 2021 from breast cancer.
Clinical studies have demonstrated that an early neoadjuvant response is a better predictor of the
patient’s recurrence-free survival than pathological complete response. Therefore, mammography,
ultrasound (US), and magnetic resonance imaging (MRI) have been widely used to determine
tumor response by tracking changes in tumor size using guidelines provided by the Response
Evaluation Criteria in Solid Tumors (RECIST). However, measurable changes in tumor size may
not be detectable until after multiple cycles of chemotherapy. In the interim, high cost and
unnecessary patient toxicity may be incurred for therapy regimens. Further, intratumor
heterogeneity poses a fundamental treatment challenge because different tumor subregions might
have different drug sensitivities. This implies that some therapeutic strategies might not be
effective against the whole tumor. Therefore, the use of noninvasive US for quantitative tissue
characterization has become an exciting research prospect. Herein the challenge is to find hidden
patterns in the US data to reveal more information about tissue function and pathology that cannot
be seen in the conventional US images. Circumventing some of the limitations associated with
traditional tissue characterization approaches, a new modality has been proposed for the US
classification of acoustic scatterers, such as cancer cells. Termed H-scan US imaging, this
technique relies on matching a model that describes US image formation to the mathematics of a
class of Gaussian-weighted Hermite polynomials. In short, it reveals the local frequency
dependence of different sized scatterers in soft tissue. In this dissertation work we demonstrate:
(1) application of a novel frequency-dependent attenuation correction technique improves H-scan
US imaging sensitivity to subtle changes at tissue depth. (2) propose 3-D H-scan imaging
technique to capture data from the entire tumor burden, visualization of any heterogenous tissue
patterns, and fundamentally improve any tissue characterization strategy and treatment response
determination and (3) propose volumetric H-scan US imaging to visualize breast cancer changes
during response to drug treatment including apoptotic activity, which is a hallmark feature of
effective anticancer therapy.
Our overarching hypothesis is that volumetric H-scan US imaging can detect early response to
chemotherapy in breast cancer tumors and provide vital prognostic data on treatment response and
tumor progression. Consequently, this would provide a new and safe approach to exploring the
tumor response to chemotherapy as early as possible and maximize effective therapy for an
individual patient, reduce morbidity, and constrain escalating health care costs associated with
overtreatment
Improved Spatial-temporal Neural Networks and the Application
No full textMultivariate time series (MTS) forecasting is the study of analyzing the change of multiple
variables over time, whose major objective is to predict the future based on existing historical data records. Such tasks are almost involved in the natural and social sciences in various
fields and have very broad development prospects, such as sociology, engineering, economics,
physics, etc. Therefore, it is also one of the hottest topics with significant research value
and strong practicability in recent years. In practice, one variable is not only determined
by its historical value but also influenced by other variables with which it has relationships.
Overall, there are three main types of dependencies between different samples: temporal
dependency, spatial dependency, and temporal-spatial dependency. Related research mainly
focuses on how to make the model more accurately capture those dependencies, especially
implicit ones. In recent years, due to the improvement of computing power and benefiting
from the large amount of data generated by internet-related technologies, the performance of
the data-driven model has made a spurt of progress and become one of the most eye-catching
fields worldwide. Thus, introducing deep learning methods that are the most popular data-
driven models into the MTS forecasting task has become the mainstream research direction
instantly.
In the thesis, we first review the previous work and summarize the main challenges faced
in current research. Then we design two novel models according to known limitations to
address those challenges.
The first one is named Fully Spatial-Temporal Graph Recurrent Convolutional Neural Networks (FGRCNN), which learns the adjacent matrix using a novel adaptive learning algorithm and extracts dependencies based on a sandwich-like framework. Besides, we also
propose two novel structures: Gated Convolutional ResNet (GCR) and Gated sequential
convolutional (GSC) block to improve the model’s performance.
The second model is Attention-Based Spatial-Temporal Fusion Neural Networks (ASTFNN),
which contains an improved spatial-temporal attention mechanism to capture all kinds of
dependencies simultaneously. And we also propose a dynamic graph generation algorithm
to make the model generate unique graph structures for each individual input.
After that, we train the model with several real-world datasets. Experimental results demonstrate that our method achieves state-of-the-art (SOTA) performance compared with other
competitive baseline approaches. Ablation experiments are also conducted to prove the positive contribution of each proposed sub-algorithm. Finally, the conclusions are given, and
future research topics are also summarized
On Combinatorial Design-based Test Generation
No full textCombinatorial testing (CT) is a testing technique that focuses on testing the interactions between the various factors of a given system. CT shows great potential for detecting faults, especially those that can only be detected by the specific value combinations of multiple factors (multi-factor faults). However, CT has not been widely adopted as a standard testing technique in the industry, which raises three main research questions that need to be answered: 1) how does CT perform in the real world; 2) can CT be applied to IoT systems – one of the most complicated types of systems need to be tested in the modern era; and 3) how should CT input models1 be constructed to ensure the superior fault detection effectiveness, especially when the testing budget is limited. This dissertation presents an empirical study of CT’s real-world effectiveness and proposes two approaches2 – CT-IoT and CT-Star – to answer the questions above. In the conducted empirical study, the performance of CT in terms of fault detection effectiveness was evaluated on eleven functionalities of five industrial systems with real faults using real-world settings. We compared the faults detected by CT with those detected by the techniques used by the in-house testing teams to evaluate whether CT can outperform industrial favored techniques. The results suggest that, despite some challenges, CT is an effective technique to detect faults, especially multi-factor faults, of software systems in industrial settings. Regarding applying CT to test IoT systems, a comprehensive literature review is conducted to evaluate the state-of-the-art approaches. It is discovered that applying CT to test IoT systems is challenging because CT cannot model IoT systems for testing. Moreover, the inability to handle complex constraints of IoT systems also hinders CT’s application to IoT systems. In response, a combinatorial testing path selection framework for IoT systems called CT-IoT is proposed. CTIoT systematically identifies and recommends testing paths in IoT systems for effective testing. Four coverage criteria that can help testers evaluate the testing thoroughness for IoT systems are also proposed. CT-IoT is evaluated on two real-world IoT systems in terms of coverage achievements. The results show the superiority of CT-IoT over a random approach. Last but not least, CT can be ineffective in detecting faults. If specific fault-triggering values are not included in the input model, the faults that can only be detected by the combinations of those values will remain undetected. If a systematic approach is not used to construct effective input models, the tester may struggle with mediocre testing results against real-world systems. A CT input model construction approach, called CT-Star, is proposed to create superior input models that can detect faults systematically. CT-Star includes various testing techniques to help practitioners create superior input models. It can also automatically tune the input model to reduce the number of test cases generated, helping practitioners meet limited testing budgets
Organic Photovoltaics for Outdoor and Indoor Applications
No full textOrganic Photovoltaics (OPVs) have been actively researched during the past three decades due to
their low cost, flexibility, solution processability, and robustness. With the development of novel
non-fullerene acceptor (NFA) materials, the power conversion efficiency (PCE) exceeds 18%
under the one-sun condition for single-junction devices. However, developing absorber material
alone is not enough to advance OPV performance because the opposite type of charge carriers is
likely to recombine at the electrodes when the active layer contacts intimately with the anode and
cathode. To mitigate the charge carrier recombination, it is critical to developing appropriate
interfacial materials to selectively pass the desired charges, i.e., positive ones, while blocking the
undesired negative ones. In Chapter 2 of this dissertation, solution-processed Mg doped CuCrO2
nanoparticles have been demonstrated as efficient hole transport layers. Mg doping effects from
structural, chemical, morphological, optical, and electronic properties of CuCrO2 are investigated.
Under the indoor condition, several high-performance indoor OPVs (IOPVs) have been reported
with the highest PCE surpassing 30%. Many groups have studied the light intensity dependence
of IOPVs. However, the IOPV performance as a function of color temperature has not been
carefully examined before. In Chapter 4 of this dissertation, the PCE as a function of correlated
color temperature (CCT) in several organic donor-acceptor systems is studied. Due to the different
absorption spectra of organic materials, two groups of behaviors, CCT-independent and CCTdependent, are seen, which provides a guidance on selecting suitable IOPVs for different
applications. In Chapter 5 of this dissertation, we focus on understanding the photocurrent
generation in OPV devices using two popular commercial NFAs. We show charge transport
determines the significant photocurrent difference. Understanding the mechanism behind higher
photocurrent is pivotal for developing NFA-based OPVs
Entrepreneurial and Strategic Decision-making in the Face of Adversity
No full textAdversity and crises can unexpectedly strike both entrepreneurial and established firms.
Responses to that adversity can mean the difference between survival and failure of the firms.
This dissertation explores decision-making in the face of adversity in three contexts involving
early-stage entrepreneurs, venture capitalist firms (VCs), and established firms. Chapter 1 uses a
conjoint analysis to study how the way mentors provide feedback to entrepreneurs regarding the
need to make a fundamental change to the venture and pivot affects whether the entrepreneurs
will be more likely to follow that advice and pivot. Chapter 2 introduces the concept of threatdefiant learning, through which firms can learn to resist the temptation to become rigid in the
face of crises, and instead respond defiantly. Threat-defiant learning is tested in the context of
VCs’ decisions to continue to invest in entrepreneurial ventures that face the crisis of a lawsuit.
Chapter 3 develops the concept of immoral entrenchment to explain why firms resist ethical
behavior in the form of recalling products associated with consumer harm, even when the moral
intensity of the situation is great. Overall, these papers contribute to the literature on crisis and
adversity broadly, and to research on cognition, entrepreneurship, mentorship, ethics, crisis, and
crisis management specifically
McLemore Award Recepients 2023
No full textPhoto of 2023 winners of the Ethel Ward-McLemore Award for Library Excellence (Left to Right): Megan Del Mar, Marcos Ortega, Marna Morland