Accurate information of power network parameters is essential for performing various power system monitoring and control tasks including state estimation, economic dispatch, and contingency analysis. In this paper, we present a novel approach of power network parameter correction wherein we exploit the sparse nature of parameter errors. Parameter error correction...
Crowdsourcing is a popular paradigm to address the high demands for labeled data in big data deluge. It aims to produce accurate labels by effectively integrating noisy, non-expert labeling from crowdsourced workers (annotators). The machine learning community has been studying effective crowdsourcing methods for many years, and many models and...
Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...
Structural health monitoring (SHM) systems perform automated non-destructive damage detection and characterization for a variety of large structures including civil structures such as bridges and aerospace structures such as aircrafts and space vehicles. The goals of SHM include preventing catastrophic structural failures, increasing reliability, reducing maintenance costs, and increasing the...
Ring amplifiers (ringamps) have shown excellent power efficiency in the latest state-of-the-art analog to digital converters (ADCs). This thesis describes circuit techniques to ensure robust operation of ringamps using standard analog techniques and proportional-integral-derivative(PID) controller analogy. Large-signal and small-signal analysis of a ringamp are performed using simple RC settling and...
This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...
We consider multiple Compressive Sensing (CS) problems wherein the supports of signal vectors of CS problems are restricted to satisfy a collection of joint logical constraints, which we refer to as coupling constraints. We consider a case where the coupling constraints are encoded in a graph and present a sequential...
This thesis concerns the development of a direct torque control strategy using a sliding mode control approach to optimize the power output of an oscillating water column (OWC) wave energy converter (WEC). The OWC WEC is a device that has a submerged vertical tail tube open at both ends, which...
Magnetic particle imaging (MPI) is a biomedical imaging technique which detects the presence of magnetic nanoparticles which have been introduced into the specimen prior to imaging. MPI has shown promise for real-time imaging with spatial resolution comparable to magnetic resonance imaging (MRI). MPI research has focused predominantly on the development...
Cold air pools are spatiotemporal phenomena that occur when cold air from higher elevations roll down the slope to accumulate in lower elevations. Behaviors like this lead to microclimate anomalies such as the city of Corvallis (Oregon) experiencing persistent cold weather even on a sunny day. We analyze multivariate temperature...