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Metastability-zone Based Quantization for High-speed Analog-to-digital Converters
No full textHigh-speed analog-to-digital converters (ADCs) are highly demanded in both wireless and
wireline communication systems. These systems normally require 6 to 8 bits resolution for
further data processing and preferrable even higher resolution in some areas like satellite
communication. However, due to the weak signal arrived at receivers, the system effective
least significant bit (LSB) becomes small. In ADCs with traditional quantization technique,
the LSB is always limited by the comparator offset. Meanwhile, high clock rate comparator
with small LSB can easily fall into metastable state during quantization, which is a dominant
source of bit-error-rate (BER).
In this dissertation, a high-speed ADC quantization method based on the metastability-zones
is presented. Compared with traditional comparator based quantization method, which
requires sufficient regeneration time and large enough LSB to generate valid logic outputs,
the proposed method can significantly reduce the regeneration time to achieve a higher
sampling rate and a finer quantization scale. To prove the proposed quantization method, a
MATLAB model is created for mathematically verification. Two 2-GS/s, 6-bit flash ADCs
with different calibration schemes are designed in 130nm CMOS for block level verification.
At the end, a 3.2-GS/s 6-bit flash ADC prototype is designed and fabricated in 65nm CMOS.
The measured results show that the system achieves 5mV least significant bit, which is much
smaller than the traditional flash ADCs using comparator based quantization and is in the
range of comparator with interpolation technique design. At the same time, the sampling
rate with proposed quantization method is higher. The figure-of-merit of 263 fJ/conv-step
is the best compared with the state of the art design with similar resolution and sampling
rate
Standard Cell Library Composition Optimization for Advanced Process Nodes
No full textThe quality of standard cell libraries, which serve as the building blocks for Application
Specific Integrated Circuits (ASICs), is heavily influenced by their range of logic cell functions
and drive strengths. However, the sheer vastness of contemporary libraries imposes high
costs and considerable challenges in generation, maintenance, and characterization, especially
during technology node transitions. In response to these issues, we present a methodology
that seeks the minimum subset of logic functions and drive strengths in any given ASIC
standard cell library that does not sacrifice power, performance or area (PPA) compared
to the use of the full library. Our methodology, which is developed around state-of-the-art
Electronic Design Automation (EDA) tools, has been applied to standard cell libraries for
four FinFET-based technologies (i.e., 16nm, 12nm, 7nm, and 5nm) and evaluated on a range
of benchmark circuits and industrial designs. Results show that the standard cell libraries can
be reduced to around 16 different combinational logic functions, a single drive for each flip-flop
type, and a fraction of the full set of drive strengths and yet not sacrifice any meaningful
power, performance, and area. In turn, this implies that library generation, maintenance and
characterization time could be reduced by more than an order of magnitude
Experiential Learning to Support Community Based Climate Change Adaptation in the Zambezi Region of Namibia
No full textThe consequences of climate change have a profound impact on communities around the world
over a long period of time. For the majority population in particular, it is essential to adapt to the
implications of climate change in order to ensure their own wellbeing and the wellbeing of future
generations. The actions of the current generation have a significant impact on the well-being of
future generations who are unable to respond to these decisions. In this context, behavioral games
can provide a platform for reflection and discussion on intergenerational equity and climate change
adaptation strategies. Using an Intergenerational Goods Game and a visioning workshop, we
investigate whether experiential learning supports community-based adaptation to climate change
in the Zambezi region of Namibia. Our results indicate a significant increase in self-efficacy after
participating in the game and workshop, as well as an increased demand for information about
climate change impacts. However, there is no notable impact on the relationship with future
generations and on awareness of the multigenerational dilemma. Overall, this study confirms the
effectiveness of the experiential learning approach and motivates further research on behavior
change in the context of community-based climate change adaptation
Towards High-frequency High-efficiency Hybrid DC-DC Converters for Large-conversion-ratio Applications
No full textGrowing demands for energy-efficient electronics and trend of miniaturization increase the needs
of the high-conversion-ratio non-isolated DC-DC converters. They use single step to directly
realize large voltage conversions, thereby greatly reducing the system distribution loss and
improving the power density through high-frequency operation. The high-conversion-ratio singlestep DC-DC converters gain popularity among various applications such as automotive systems,
data center & telecommunication applications, and consumer electronics.
Since the conventional DC-DC converters have technical limitations to operate in large-voltageconversion conditions, four different types of hybrid DC-DC converters, which support all stepup, step-down, and step-up / -down voltage conversions for different applications, are developed
in this research. These hybrid converters possess some of the following benefits: lower voltage
stress of power switches, reduced inductor current and its ripple, enlarged switch on-time under
high-conversion-ratio scenarios, doubled effective frequency, multiple DC outputs, flyingcapacitor self balancing, etc.
Among them, this research first develops a three-switch ZVS hybrid step-up / -down power
converter for automotive applications. It not only supports a wide input range of 9-V – 45-V and
delivers a maximum 48-W output power, but also achieves peak power efficiencies of 98% and
96% in step-down and -up modes at 2-MHz frequency. The volume of external components in the
converter is also reduced by at least five times compared with the prior art.
Two different hybrid converters are also proposed for large input-to-output step-down
applications. The first one is a dual-path hybrid Dickson (DPHD) converter with large outputcurrent capability. Through using a 7:1 prototype with 25-V silicon MOSFETs, high voltage
conversions from 36 – 65-V VIN to 1 – 2-V VOUT with the maximum output current up to 32 A
were validated. The DPHD converter achieves a high peak power efficiency of 92.7% under 48-
V-to-2-V conversion and a power density of 481 W/in3
that is at least 2 times higher than other
prior single-inductor hybrid converters. The second one is a class of Ladder-based capacitorassisted dual-inductor (CADI) hybrid converters suitable for high-frequency operations. The
proposed topology was validated using a 3-to-1 Ladder-based CADI converter with fully on-chip
power nMOS transistors. It supports voltage conversions from a wide input range of 36 – 55 V to
an output of 1 – 2 V at high frequencies of 2.5 MHz – 5 MHz. Specifically, under 48-V-to-1-V
direct conversion, the proposed converter is the first to achieve competitive high power efficiencies
at a high frequency from 2.5 MHz to 5 MHz.
Finally, a hybrid step-up converter with a scalable VOUT / VIN conversion ratio boosting (SCRB)
scheme is developed for LED backlighting applications. It can produce large output-to-input
voltage conversions while generating multiple DC output voltages. Through a four-stage prototype,
the converter produces four different outputs, achieves the highest VOUT / VIN conversion ratio and
switching frequency, and obtains high peak efficiencies under high-conversion-ratio and highfrequency conditions
Performance Analysis of Large-scale NGSO Satellites-based Radio Astronomy and Sky Radio Quiet Zones
No full textLarge-scale non-geostationary orbit (NGSO) satellite communication (SatCom) systems
(SCSs) are emerging to play an important role for future global wireless communication.
However, satellite communication systems with more than thousands of satellites raise a
serious concern of radio frequency interference (RFI) to the ground-based radio astronomy
system (RAS). This situation becomes more serious as the SatCom industry is rapidly
expanding the number of communicating satellites which increases RFI, while the RAS is
also advancing with enhanced radio astronomical observation (RAO) capability requiring
better protection against RFI.
To address this impending RFI issue, this dissertation focuses on two approaches, namely,
space-based RAS and sky radio quiet zone. First, the satellite-based RASs including low earth
orbit (LEO)-based or medium earth orbit (MEO)-based RASs are considered to lower the
impact from SCSs on the higher orbit onto the ground RASs and the lower orbit RASs. We find out the required SCS emission mask for each RAS so that both systems can avoid RFI.
Then, we investigate three typical radio astronomy metrics such as maximum baseline distance
(MBD), the number of simultaneously observing telescopes, and the signal to interference plus
noise power ratio (SINR) performance for NGSO satellite-based RAS. Additionally, we also
explore the advantages of NGSO satellite-based RAS from a communication side. Our analysis
shows that the large-scale NGSO satellite-based RAS can offer more spectrum access to both
SCS and RAS. Secondly, we analyze RFI from the large-scale NGSO SatCom system to a
ground-based RAS to identify dominant RFI contributors and then propose two types of sky
radio quiet zone (SRQZ), namely, telescope-centered (TC)-SRQZ and RAO direction-centered
(DC)-SRQZ. We investigate peak RFI and average RFI suppression characteristics of the two
SRQZ types when applied individually alone and jointly. We evaluate the RFI characteristics
with/without SRQZs for a few representative ground RAS receiver locations under a low
earth orbit SatCom system as well as a medium earth orbit SatCom system. We present
and discuss extensive RFI performance results and their dependency on the specifics of the
RFI scenarios. These results show that appropriately designed SRQZs provide significant RFI suppression. We also offer guidance on the choice of SRQZ type/deployment, related
parameter settings, and practical implementation aspects
Pandemic Fantasies: Discourses of Escapism During COVID-19
No full textPandemic Fantasies: Discourses of Escapism During COVID-19 observes media environments
that proliferated during the COVID-19 pandemic, how discourses of escapism were expressed
within these environments, and how those environments contributed and shifted conversations
about our relationship escapism and media use. By closely examining the intersections between
fiction and real life as described by audiences and fans, I examine the shifting discourse around
escapism as the pandemic pushed many people towards digital media interactions. Applying
theories of critical media studies and fandom studies, this dissertation challenges normative
critiques of popular media as meaningless while also questioning alternative narratives of
empowerment and misinformation. This dissertation asks how escapist media discourses shaped
the perception and reception of media engagements during the first year of the COVID-19
pandemic, what a counter-discourse regarding these media engagements suggests about those
who partake in those media environments, and what these counter-discourses and media
engagements suggest when we approach them as serious attempts at sensemaking and
worldbuilding.
Escapism is, to its detractors, a derivative practice wherein a person attempts to escape their
daily lives by seeking out something else, such as a work of fiction, an online community, or a
vacation. As a result, escapism is presented as a break from reality, time set apart and kept at bay
with skepticism of its constructed nature for fear of losing oneself to fantasy. Yet the pandemic
provoked a mass exodus of sorts to any escape route—from Animal Crossing to QAnon—that
could provide a haven for the weeks, months, and years of COVID protocols and social
distancing measures. In contrast to this dominant perception, I analyze narratives about what is
characterized as escapist media consumption—media practices that are frequently regarded as
“disconnected” from reality yet powerful enough to change people for good or ill. Taking
seriously these narratives of escapism’s ability to transform lives, I interrogate how escapism
helped shape the pandemic
Autonomous Estimation of Foot Bone Structure Based on Scanned Foot Surface Topography
No full textThe main goal of this master’s thesis was to develop an autonomous algorithm for the
construction of an approximate surface model for a foot that would also generate the corresponding underlying bone structure model through non-invasive means for the purpose of
enhancing finite element analysis (FEA) simulations for functionally personalized footwear.
The algorithm consists of the following components: (1) measurement of the foot surface
topography using LED light stereo imaging to obtain a scanned foot model; (2) machine
learning and post-processing sub-algorithms for the localization of a set of anatomical landmarks on the given scanned foot model; (3) an optimization procedure to determine the
most optimal transformation to approximate a given surface scan utilizing these anatomical
landmarks; and (4) the application of the optimal transformation on a reference bone model
to generate a patient-specific bone structure model. This algorithm was tested on synthetic
data that was generated from scanning a physical foot model. The results demonstrated the
robustness of the algorithm to construct approximate bone structures from a variety of white
light scanned foot surface geometries. This work provides a protocol to synthesize various
techniques into one comprehensive autonomous algorithm which is further enhanced by the
introduction of machine learning to determine the anatomical landmarks of any given foot
scan without the need for human intervention
Necessary Evils: the Role of Horror in Modern and Contemporary Literature
No full textIn my extensive studies of horror, I have found that the genre of horror has typically not been
taken seriously in its own right. This can be extended to the occurrence of horror in other types
of literature. Horror, if recognized at all, is viewed as a component of the story, and not
necessarily as significant or as relevant as other aspects of the book. This dissertation
approaches the problem of how literary horror can, genre or otherwise, be recognized as a part of
legitimate and influential academic study and why such study is important. To do so,
I examine multiple works of both genre horror and literary horror, using established literary
theories to analyze and understand these written works. I also examine multiple works not
classified as horror yet contain instances of significant horror to show that horror exists past the
genre. I utilize literary theories such as the uncanny, the monstrous, Kristeva’s theory of
abjection and the Jungian shadow to show the literary merits of these works.
To not read horror is to ignore aspects of life that act as a mirror reflecting society and individual
fears at any point in time. Such willful evasion can be detrimental. Dismissing horror as merely
entertainment avoids the social and cultural deceits it can expose. Horror is an interpretation of
what is both desired and feared in our lives. Its omnipresence makes it critical to be understood
as a vehicle used to acknowledge and understand our fears, and ultimately determine the best
way to handle them
The Synthesis and Characterization of Homometallic and Mixed Metal-organic Frameworks and Their Magnetic Properties
No full textMetal-Organic Frameworks (MOFs) are highly crystalline materials formed through the
coordination of organic linkers to metal centers. MOFs are known for having high surface areas
and well-defined pore structures. MOFs have been used for a wide variety of applications such as
gas separations, gas storage, catalysis, sensors, water purifiers, energy storage, and drug delivery.
Rare earth MOFs exhibit coordination and structural diversity as well as interesting magnetic and
luminescence properties. This work discusses the synthesis of rare earths and transition metal
MOFs for magnetic applications. The new discovery of fluorine extracted from organic molecules
using rare earth ions, and the synthesis of fluoro-bridged rare earth MOFs is described
Toward Highly Reliable, Smart and Open Optical Networks
No full textAs more software and applications requiring greater bandwidth are developed and become
more popular, the need for a reliable and high-speed communication network grows. Optical
transport network is uniquely positioned to deliver the required speed and capacity. The
development of software defined optical networking technology has dramatically improved
the operability, flexibility, and effectiveness of optical networks. Through modeling and
algorithm optimization of network resource management and monitoring of network signal
transmission quality, it improves the awareness of the network, reduces the network failure
rate, and improves the network operation efficiency. At the same time, the proposal of
open optical network breaks the barrier between different vendors, unifies the standard of
each interface, and greatly accelerates the development of the optical network. Open optical
network also accelerates the optimization and management of network resources, providing
prerequisites for the development of more applications, technologies, and algorithms.
In this dissertation, the analytical models for the network blocking probability under first-fit
algorithm are introduced. The effect of optical signal quality of transmission margin on the
performance of optical networks is explored and a method using neural network to model
the WSS filtering penalty is proposed. An open optical network architecture, OpenROADM,
is introduced, and the applications developed based on this open optical network platform
are described. Moreover, an emulator that can work with the OpenROADM simulator to
provide a realistic optical network environment is developed