VictorJuiz/thesis huggingface dataset:
Space Vector Modulation of Multi-level and Multi-module Converters for High Power Applications

This thesis presents and investigates Space Vector Modulation (SVM) switching strategies for (i) a multi-level Diode-Clamped Converter (DCC) and (ii) a multi-module Voltage-Sourced Converter (VSC) system in which each module is a conventional two-level VSC. Although the SVM strategies are general and applicable for n-level DCC and n-module VSC systems, this text only concentrates on five-level DCC and four-module VSC systems. For a five-level DCC, a computationally efficient SVM algorithm is proposed. The algorithm, that is based on a classifier Neural Network (NN), reduces the computational time for the SVM realization. Therefore, adequate saving of processor execution time, in each sampling period of SVM, is provided to carry out other functions, e.g. the calculations required for DC-capacitor voltage balancing task. The thesis also proposes a DC-capacitor voltage balancing strategy to counteract the voltage drift phenomenon of (i) a passive-front-end five-level DCC, and (ii) a back-to-back connected five-level DCC system. The proposed balancing strategy, that is based on augmenting the proposed SVM algorithm, takes advantage of the redundant switching states to minimize a quadratic cost function associated with voltage deviations of the DC-capacitors. The salient features of the proposed balancing strategy are (i) online calculation of SVM to select the best switching states, (ii) minimization of switching frequency, (iii) minimization of the THD content of the AC-side voltage, and (iv) no requirement for additional power circuitry. For a four-module VSC system a sequential sampling SVM strategy is proposed. The proposed strategy (i) provides harmonic cancellation/minimization at the net AC-side voltage of the multi-module VSC system, and (ii) offers a low switching frequency for each VSC module. Technical feasibility of the proposed SVM strategies for a five-level DCC and a four-module VSC system, as a STATCOM and a back-to-back HVDC system, are investigated and presented. The studies are conducted in the time-domain, in the PSCAD/EMTDC software environment.

Keywords: Power Electronics
High Power Converters
Advisor: Iravani, Mohammad Reza
A Cognitive Radio Tracking System for Indoor Environments

Advances in wireless communication have enabled mobility of personal computing services equipped with sensing and computing capabilities. This has motivated the development of location-based services (LBS) that are implemented on top of existing communication infrastructures to cater to changing user contexts. To enable and support the delivery of LBS, accurate, reliable, and realtime user location information is needed. This thesis introduces a cognitive dynamic system for tracking the position of mobile users using received signal strength (RSS) in Wireless Local Area Networks (WLAN). The main challenge in WLAN positioning is the unpredictable nature of the RSS-position relationship. Existing system rely on a set of training samples collected at a set of anchor points with known positions in the environment to characterize this relationship. The first contribution of this thesis is the use of nonparametric kernel density estimation for minimum mean square error positioning using the RSS training data. This formulation enables the rigorous study of state-space filtering in the context of WLAN positioning. The outcome is the Nonparametric Information (NI) filter, a novel recursive position estimator that incorporates both RSS measurements and a dynamic model of pedestrian motion during estimation. In contrast to traditional Kalman filtering approaches, the NI filter does not require the explicit knowledge of RSS-position relationship and is therefore well-suited for the WLAN positioning problem. The use of the dynamic motion model by the NI filter leads to the design of a cognitive dynamic tracking system. This design harnesses the benefits of feedback and position predictions from the filter to guide the selection of anchor points and radio sensors used during estimation. Experimental results using real measurement from an office environment demonstrate the effectiveness of proactive determination of sensing and estimation parameters in mitigating difficulties that arise due to the unpredictable nature of the indoor radio environment. In particular, the results indicate that the proposed cognitive design achieves an improvement of 3.19m (56\%) in positioning error relative to memoryless positioning alone.

Keywords: Positioning
Localization
Location determination
Geolocation
WLAN Positioning
Fingerprinting
Nonparametric Information Filter
Cognitive Dynamic System
Location-based services
Received signal strength
Advisor: Plataniotis, Konstantinos N.
Venetsanopoulos, Anastasios N.
The Design of Table-centric Interactive Spaces

The Design of Table-Centric Interactive Spaces, by Daniel J. Wigdor A thesis submitted in partial conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Computer Science, University of Toronto © Copyright by Daniel J. Wigdor, 2008 Direct-touch tabletops offer compelling uses as direct, multi-touch, multi-user displays for face to face collaborative work. As task complexity and group size increase, the addition to the tabletop of multiple, vertical displays allows for more information content, while reducing the need to multiplex the tabletop display area. We dub such systems table-centric interactive spaces. Although compelling, these spaces offer unique challenges. In particular, the displays in such spaces are seen by the users at angles not typically found in combination in other environments. First, the viewing imagery shown on a horizontal display by seated participants means that that imagery is distorted, receding away from the users’ eyes. Second, the sharing of information by users sitting around a horizontal display necessitates that on-screen content be oriented at non-optimal angles for some subset of those users. Third, positioning vertical displays around the table means that some subset of the seated users will be looking at those displays at odd angles. In this thesis, we investigate the challenges associated with these viewing angles. We begin with a examination of related work, including tabletop technology and interaction techniques. Next, we report the results of controlled experiments measuring performance of reading, graphical perception, and ancillary display control under the angles we identified. Next, we present a set of design issues encountered in our work with table-centric spaces. We then review a series of interaction techniques built to address those issues. These techniques are evaluated through the construction and validation of an application scenario. Through these examinations, we hope to provide designers with insights as to how to enable users to take full advantage of ancillary displays, while maintaining the advantages and affordances of collocated table-centric work.

Keywords: Tabletop
Multi-Touch
HCI
Human Computer Interaction
Surface Computing
Magnitude Perception
Control orientation
world in miniature
Advisor: Balakrishnan, Ravin
Using Ambient Radio Environment to Support Practical Pervasive Computing

Mobile applications can benefit from increased awareness of the device's context. Unfortunately, existing solutions for inferring context require special purpose sensors or beacons on the mobile devices or in the physical environment. This requirement significantly limits the deployment of these solutions. In this thesis, I argue that mobile devices can infer a substantial amount of their context by leveraging their existing wireless interfaces to monitor ambient radio sources, such as GSM cell towers or WiFi access points. I focus on two important problems in context-aware computing: localization of mobile devices and detecting proximity between mobile devices for authentication purposes. Specifically, I present an accurate localization system based on fingerprinting of GSM signals. I show that the key to more accurate GSM localization is the use of wide signal strength fingerprints that include readings from a large number of base stations. Next, I present a method that addresses the key drawback of fingerprint-based localization systems - the need to collect extensive measurements to train the system in every target environment. Finally, I show how radio environment sensing can be used to secure the communication of devices that come within close proximity. Removing the need for additional hardware on the mobile devices and in the physical environment renders the approach that I present amenable for widespread deployment.

Keywords: mobile computing
localization
context-awareness
secure pairing
Advisor: Lara, Eyal de
LaMarca, Anthony
A Graph Approach to Measuring Text Distance

Text comparison is a key step in many natural language processing (NLP) applications in which texts can be classified on the basis of their semantic distance (how similar or different the texts are). For example, comparing the local context of an ambiguous word with that of a known word can help identify the sense of the ambiguous word. Typically, a distributional measure is used to capture the implicit semantic distance between two pieces of text. In this thesis, we introduce an alternative method of measuring the semantic distance between texts as a combination of distributional information and relational/ontological knowledge. In this work, we propose a novel distance measure within a network-flow formalism that combines these two distinct components in a way that they are not treated as separate and orthogonal pieces of information. First, we represent each text as a collection of frequency-weighted concepts within a relational thesaurus. Then, we make use of a network-flow method which provides an efficient way of measuring the semantic distance between two texts by taking advantage of the inherently graphical structure in an ontology. We evaluate our method in a variety of NLP tasks. In our task-based evaluation, we find that our method performs well on two of three tasks. We introduce a novel measure which is intended to capture how well our network-flow method perform on a dataset (represented as a collection of frequency-weighted concepts). In our analysis, we find that an integrated approach, rather than a purely distributional or graphical analysis, is more effective in explaining the performance inconsistency. Finally, we address a complexity issue that arises from the overhead required to incorporate more sophisticated concept-to-concept distances into the network-flow framework. We propose a graph transformation method which generates a pared-down network that requires less time to process. The new method achieves a significant speed improvement, and does not seriously hamper performance as a result of the transformation, as indicated in our analysis.

Keywords: Graph Approaches in NLP
Semantic Distance
Distributional Methods
Ontological Methods
Advisor: Stevenson, Suzanne
Merging and Consistency Checking of Distributed Models

Large software projects are characterized by distributed environments consisting of teams at different organizations and geographical locations. These teams typically build multiple overlapping models, representing different perspectives, different versions across time, different variants in a product family, different development concerns, etc. Keeping track of the relationships between these models, constructing a global view, and managing consistency are major challenges. Model Management is concerned with describing the relationships between distributed models, i.e., models built in a distributed development environment, and providing systematic operators to manipulate these models and their relationships. Such operators include, among others, Match, for finding relationships between disparate models, Merge, for combining models with respect to known or hypothesized relationships between them, Slice, for producing projections of models and relationships based on given criteria, and Check-Consistency, for verifying models and relationships against the consistency properties of interest. In this thesis, we provide automated solutions for two key model management operators, Merge and Check-Consistency. The most novel aspects of our work on model merging are (1) the ability to combine arbitrarily large collections of interrelated models and (2) support for toleration of incompleteness and inconsistency. Our consistency checking technique employs model merging to reduce the problem of checking inter-model consistency to checking intra-model consistency of a merged model. This enables a flexible way of verifying global consistency properties that is not possible with other existing approaches. We develop a prototype tool, TReMer+, implementing our merge and consistency checking approaches. We use TReMer+ to demonstrate that our contributions facilitate understanding and refinement of the relationships between distributed models.

Keywords: Model Management
Model Merging
Consistency Checking
Advisor: Easterbrook, Steve
Learning Probabilistic Models for Visual Motion

A fundamental goal of computer vision is the ability to analyze motion. This can range from the simple task of locating or tracking a single rigid object as it moves across an image plane, to recovering the full pose parameters of a collection of nonrigid objects interacting in a scene. The current state of computer vision research, as with the preponderance of challenges that comprise "artificial intelligence", is that the abilities of humans can only be matched in very narrow domains by carefully and specifically engineered systems. The key to broadening the applicability of these successful systems is to imbue them with the flexibility to handle new inputs, and to adapt automatically without the manual intervention of human engineers. In this research we attempt to address this challenge by proposing solutions to motion analysis tasks that are based on machine learning. We begin by addressing the challenge of tracking a rigid object in video, presenting two complementary approaches. First we explore the problem of learning a particular choice of appearance model---principal components analysis (PCA)---from a very limited set of training data. However, PCA is far from the only appearance model available. This raises the question: given a new tracking task, how should one select the most-appropriate models of appearance and dynamics? Our second approach proposes a data-driven solution to this problem, allowing the choice of models, along with their parameters, to be learned from a labelled video sequence. Next we consider motion analysis at a higher-level of organization. Given a set of trajectories obtained by tracking various feature points, how can we discover the underlying non-rigid structure of the object or objects? We propose a solution that models the observed sequence in terms of probabilistic "stick figures", under the assumption that the relative joint angles between sticks can change over time, but their lengths and connectivities are fixed. We demonstrate the ability to recover the invariant structure and the pose of articulated objects from a number of challenging datasets.

Keywords: computer vision
machine learning
Advisor: Zemel, Richard S.
DescribeX: A Framework for Exploring and Querying XML Web Collections

The nature of semistructured data in web collections is evolving. Even when XML web documents are valid with regard to a schema, the actual structure of such documents exhibits significant variations across collections for several reasons: an XML schema may be very lax (e.g., to accommodate the flexibility needed to represent collections of documents in RSS feeds), a schema may be large and different subsets used for different documents (e.g., this is common in industry standards like UBL), or open content models may allow arbitrary schemas to be mixed (e.g., RSS extensions like those used for podcasting). A schema alone may not provide sufficient information for many data management tasks that require knowledge of the actual structure of the collection. Web applications (such as processing RSS feeds or web service messages) rely on XPath-based data manipulation tools. Web developers need to use XPath queries effectively on increasingly larger web collections containing hundreds of thousands of XML documents. Even when tasks only need to deal with a single document at a time, developers benefit from understanding the behaviour of XPath expressions across multiple documents (e.g., what will a query return when run over the thousands of hourly feeds collected during the last few months?). Dealing with the (highly variable) structure of such web collections poses additional challenges. This thesis introduces DescribeX, a powerful framework that is capable of describing arbitrarily complex XML summaries of web collections, providing support for more efficient evaluation of XPath workloads. DescribeX permits the declarative description of document structure using all axes and language constructs in XPath, and generalizes many of the XML indexing and summarization approaches in the literature. DescribeX supports the construction of heterogenous summaries where different document elements sharing a common structure can be declaratively defined and refined by means of path regular expressions on axes, or axis path regular expression (AxPREs). DescribeX can significantly help in the understanding of both the structure of complex, heterogeneous XML collections and the behaviour of XPath queries evaluated on them. Experimental results demonstrate the scalability of DescribeX summary refinements and stabilizations (the key enablers for tailoring summaries) with multi-gigabyte web collections. A comparative study suggests that using a DescribeX summary created from a given workload can produce query evaluation times orders of magnitude better than using existing summaries. DescribeX’s light-weight approach of combining summaries with a file-at-a-time XPath processor can be a very competitive alternative, in terms of performance, to conventional fully-fledged XML query engines that provide DB-like functionality such as security, transaction processing, and native storage.

Keywords: XML
Summaries
Framework
Semistructured
Web
XPath
Advisor: Miller, Renee J.
Mechanisms of Channel Arrest and Spike Arrest Underlying Metabolic Depression and the Remarkable Anoxia-tolerance of the Freshwater Western Painted Turtle (Chrysemys picta bellii)

Anoxia is an environmental stress that few air-breathing vertebrates can tolerate for more than a few minutes before extensive neurodegeneration occurs. Some facultative anaerobes, including the freshwater western painted turtle Chrysemys picta bellii, are able to coordinately reduce ATP demand to match reduced ATP availability during anoxia, and thus tolerate prolonged insults without apparent detriment. To reduce metabolic rate, turtle neurons undergo channel arrest and spike arrest to decrease membrane ion permeability and neuronal electrical excitability, respectively. However, although these adaptations have been documented in turtle brain, the mechanisms underlying channel and spike arrest are poorly understood. The aim of my research was to elucidate the cellular mechanisms that underlie channel and spike arrest and the neuroprotection they confer on the anoxic turtle brain. Using electrophysiological and fluorescent imaging techniques, I demonstrate for the first time that: 1) the α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) undergoes anoxia-mediated channel arrest; 2) delta opioid receptors (DORs), and 3) mild mitochondrial uncoupling via mitochondrial ATP-sensitive K+ channels result in an increase in cytosolic calcium concentration and subsequent channel arrest of the N-methyl-D-aspartate receptor, preventing excitotoxic calcium entry, and 4) reducing nitric oxide (NO) production; 5) the cellular concentration of reactive oxygen species (ROS) decreases with anoxia and ROS bursts do not occur during reoxygenation; and 6) spike arrest occurs in the anoxic turtle cortex, and that this is regulated by increased neuronal conductance to chloride and potassium ions due to activation of γ–amino-butyric acid receptors (GABAA and GABAB respectively), which create an inhibitory electrical shunt to dampen neuronal excitation during anoxia. These mechanisms are individually critical since blockade of DORs or GABA receptors induce excitotoxic cell death in anoxic turtle neurons. Together, spike and channel arrest significantly reduce neuronal excitability and individually provide key contributions to the turtle’s long-term neuronal survival during anoxia. Since the turtle is the most anoxia-tolerant air-breathing vertebrate identified, these results suggest that multiple mechanisms of metabolic suppression acting in concert are essential to maximizing anoxia-tolerance.

Keywords: anoxia
reptile
neuroscience
ischemia
facultative anaerobe
NMDA receptor
AMPA receptor
reactive oxygen species
ischemic preconditioning
mKATP channel
GABA
delta opioid receptor
Advisor: Buck, Leslie
Label-free Detection of Oligonucleotide Microarrays by the Scanning Kelvin Nanoprobe

The Kelvin measurement is a sensitive and label-free method based on work function measurements. Work function, the minimum energy required to extract an electron from a metallic material, can be shifted by ionic charges and dipoles present on the surface. The scanning Kelvin nanoprobe (SKN), a probe-based microscopic imaging device, was used in the detection of work function changes induced by surface-immobilized oligonucleotide / DNA microarrays. The scanning Kelvin nanoprobe was able to study DNA microarrays smaller than 100 µm in size, produced with solution concentrations lower than 10 µmol/L. The limit of detection was estimated to be 15 ng DNA. Better than ± 10% relative variation was achieved for replicate spots. It was observed that higher surface densities of immobilized DNA molecules produced greater work function changes than lower surface densities. Surface saturation with increasing solution concentrations was observed as well. Also, longer strands of DNA produced greater work function changes than shorter strands. Statistical analysis of the results confirmed that non-complementary DNA strands could be differentiated from complementary strands by the Kelvin measurement. Single base mismatches on the complementary DNA strands were also detected by the Kelvin measurement. Different substrate materials were tested in the search for reliable and inexpensive sample slides with satisfactory DNA immobilization efficiency. Materials such as silicon wafers, gold-coated glass slides, gold-coated stainless steel slides, and gold compact discs (CD) were tested. A surface property comparison of gold-coated glass slides and compact discs was made by atomic force microscopy (AFM), and revealed very different microscopic features. The effect of cleaning on gold-coated glass slides was examined by time-of-flight secondary ion mass spectrometry (TOF-SIMS). Technical improvements were made to the SKN equipment progressively. Several revisions to the tip holder design have been employed for better electromagnetic shielding, enhanced robustness and easier tip change. An older signal generator was replaced with a professional PC audio card to provide more stable signal and more convenient on-screen fine tuning, also at a reduced cost. The Labview-based controlling program has also been improved through multiple iterations.

Keywords: Kelvin probe
DNA
oligonucleotide
microarray
Advisor: Thompson, Michael