The central focus of this thesis is the design, fabrication and characterization of amorphous oxide semiconductor (AOS) thin-film transistor (TFT) current mirrors. The thin-film deposition and circuit fabrication methods used to realize zinc tin oxide (ZTO) TFT
current mirrors are addressed in order to elucidate the processing challenges for this...
The primary objective of this thesis is to develop a process for fabricating integrated circuits based on thin-film transistors (TFTs) using zinc tin oxide (ZTO) as the channel layer. ZTO, in contrast to indium- or gallium-based amorphous oxide semiconductors (AOS), is perceived to be a more commercially viable AOS choice...
The prevalence of Internet-of-Things (IoT) applications leads to an increasing focus on the design and optimization of sensor nodes. Battery lifetime and associated costs of battery replacement often limits the long term operation and viability of sensor nodes. RF wireless energy harvesting on the other hand can be appealing since...
The number of wind turbines and wind farms in the Pacific Northwest has increased dramatically in the past six years, which represents a significant amount of electrical generation capacity connected to the public electric grid. However, the variable nature of wind sometimes introduces excessive power, or conversely shortages, in power...
Low-distortion architecture is widely used in wideband discrete-time switched-capacitor delta-sigma ADC design. However, it suffers from the power-hungry active adder and critical timing for quantization and dynamic element matching (DEM). To solve this problem, this dissertation presents a delta-sigma modulator architecture with shifted loop delays. In this project, shifted loop...
There is a significant need in recent mobile communication and wireless broadband
systems for high-performance analog-to-digital converters (ADCs) that have wide
bandwidth (BW>5-MHz) and high data rate (>100-Mbps). A delta-sigma ADC is
recognized as a power-efficient ADC architecture when high resolution (>12-b) is
required. This is due to several advantages...
The thesis focuses on activity recognition from sensor data, which has spurred a great deal of interest due to its impact on health care and security. Previous work on activity recognition from multivariate time series data has mainly applied supervised learning techniques which require a high degree of annotation effort...
Efficient time-series analysis can impact multiple application domains such as motif discovery in gene analysis or music data, extracting spectro-temporal patterns in acoustic scene analysis, or annotating and classifying electrical bio-signals (such as ECG, EEG, and EMG) for medical applications.
Time-series analysis involves a variety of tasks.
To predict future...
In supervised learning, label information can be provided at different levels of granularity. For small datasets, it is possible to acquire a label for each data instance. However, in the big-data regime, this fine granularity approach is prohibitively costly. For example, in semi-supervised learning, only a limited number of samples...
Approximate string matching is commonly used to align genetic sequences (DNA
or RNA) to determine their shared characteristics. In contrast with the standard
dynamic programming methods which use local edit distance models, the Walking
Tree heuristic method was created to handle non-local changes, e.g., translocations,
inversions, and duplications, altogether and...