VictorJuiz/thesis huggingface dataset:
High-speed Properties of 1.55-micron-wavelength Quantum Dot Semiconductor Amplifiers and Comparison with Higher-Dimensional Structures
Keywords: quantum dot
semiconductor optical amplifiers
all-optical switching
semiconductor nonlinear dynamics
semiconductor lasers
nonlinear optics
semiconductor physics
linewidth enhancement factor
two-photon absorption
quantum well
quantum dash
ultrafast lasers
optical signal processing

This thesis reports an experimental characterization of the ultrafast gain and refractive index dynamics of a novel InAs/InGaAsP/InP quantum-dot (QD) semiconductor optical amplifier (SOA) operating near 1.55-µm wavelengths, assessing its high-speed performance characteristics for the first time. The thesis also studies the influence of the degree of quantum confinement on the dynamics of SOAs by comparing the zero-dimensional (0-D) QD's dynamics to those in 1-D InAs/InAlGaAs/InP quantum-dash (QDash), and 2-D InGaAsP/InGaAsP/InP quantum-well (QW) SOAs, both of which also operate near 1.55-µm wavelengths, and are made with matching or similar materials and structures. The ultrafast (around 1 ps) and long-lived (up to 2 ns) amplitude and phase dynamics of the SOAs are characterized via advanced heterodyne pump-probe measurements with 150-femtosecond resolution. It is found that the QD SOA has an 80-picosecond amplitude, and 110-picosecond phase recovery lifetime in the gain regime, 4-6 times faster than the QDash and QW recovery lifetimes, as well as reduced ultrafast transients, giving it the best properties for high-speed (> 100 Gb/s) all-optical signal processing in the important telecommunications wavelength bands. An impulse response model is developed and used to analyze the dynamics, facilitating a comparison of the gain compression factors, time-resolved linewidth enhancement factors (alpha-factors), and instantaneous dynamic coefficients (two-photon absorption and nonlinear refractive-index coefficients) amongst the three structures. The quantum-dot device is found to have the lowest effective alpha-factor, 2-10, compared to 8-16 in the QW, as well as time-resolved alpha-factors lower than in the QW—promising for reduced-phase-transient operation at high bitrates. Significant differences in the alpha-factors of lasers with the same structure are found, due to the differences between gain changes that are induced optically or through the electrical bias. The relative contributions of stimulated transitions and free-carrier absorption to the total carrier heating dynamics in SOAs of varying dimensionality are also reported for the first time. Examining the QD electroluminescence and linear gain spectra in combination with the carrier dynamics also brings about conclusions on the nature of the quantum confinement, dot energy-level structure, and density of states—aspects of the material that have not been previously well understood.
Large-scale Peer-to-peer Streaming: Modeling, Measurements, and Optimizing Solutions
Keywords: Computer networking
Peer-to-Peer Streaming

Peer-to-peer streaming has emerged as a killer application in today's Internet, delivering a large variety of live multimedia content to millions of users at any given time with low server cost. Though successfully deployed, the efficiency and optimality of the current peer-to-peer streaming protocols are still less than satisfactory. In this thesis, we investigate optimizing solutions to enhance the performance of the state-of-the-art mesh-based peer-to-peer streaming systems, utilizing both theoretical performance modeling and extensive real-world measurements. First, we model peer-to-peer streaming applications in both the single-overlay and multi-overlay scenarios, based on the solid foundation of optimization and game theories. Using these models, we design efficient and fully decentralized solutions to achieve performance optimization in peer-to-peer streaming. Then, based on a large volume of live measurements from a commercial large-scale peer-to-peer streaming application, we extensively study the real-world performance of peer-to-peer streaming over a long period of time. Highlights of our measurement study include the topological characterization of large-scale streaming meshes, the statistical characterization of inter-peer bandwidth availability, and the investigation of server capacity utilization in peer-to-peer streaming. Utilizing in-depth insights from our measurements, we design practical algorithms that advance the performance of key protocols in peer-to-peer live streaming. We show that our optimizing solutions fulfill their design objectives in various realistic scenarios, using extensive simulations and experiments.
Cooperative Communication Schemes in Wireless Networks: A Cross Layer Approach
Keywords: Electrical & Computer Engineering
Wireless Networks
Cooperative Communication

In order to improve the Quality of Service in wireless networks it is crucial to design and optimize the communication algorithms based on the underlying Physical and Link Layers. In this thesis we show that if instead of the link abstraction used in traditional wireless networking we rely on the much broader definition of a link, used in the context of cooperative communication, we can improve the performance of relay transmission systems operating over the wireless medium. From a networking perspective there are a whole host of layering and cross-layer design issues that enable one to propose optimal cooperative algorithms for wireless communication. Most of the research in this area has been concentrated on the physical layer issues. In this thesis, we consider the interaction of the physical layer cooperative link with the higher layers, in particular the Medium Access Control Layer, and show that by appropriate protocol design we can improve the performance of wireless networks by using cooperation. Enabling cooperation among nodes in an optimal manner can lead to significant increase in the throughput for multi-hop wireless networks. We study and design cooperative protocols that lead to this throughput increase and quantify the appropriate level of cooperation among the users which leads to improving QoS.
Time and Frequency Evolution of the Precursors in Dispersive Media and their Applications
Keywords: Precursors,
Finite Difference Time Domain Method

Until now, few rigorous studies of the precursors in structures exhibiting superluminal group velocities have been performed. One dimensional photonic crystals(1DPC) and active Lorentzian media are among the ones which are able to exhibit superluminal propagation. In the first part of the thesis we have studied the evolution of the precursors in active Lorentzian media and 1DPC. The problem of the propagation of the precursors in active Lorentzian media is addressed, by employing the steepest descent method to provide a detailed description of the propagation of the pulse inside the dispersive medium in the time domain. The problem of the time and frequency evolution of the precursors in 1DPC is studied, using the finite-difference time-domain (FDTD) techniques in conjunction with joint time-frequency analysis (JTFA). Our study clearly shows that the precursor fields associated with superluminal pulse propagation travel at subluminal speeds. It is also shown that FDTD analysis and JTFA can be combined to study the dynamic evolution of the transient and steady state pulse propagation in dispersive media. The second part of the thesis concentrates on the applications of the precursors. An interesting property of the precursors is their lower than exponential attenuation rate inside a lossy dielectric, such as water. This property of the precursors has made them an interesting candidate for applications such as ground penetrating radar and underwater communication. It was recently pointed out that a pulse which is generated inside of water and assumes the shape of the Brillouin precursor would be optimally suited for long range propagation in water (described by the single-pole Debye model). Here, we have considered the optimal pulse propagation problem, accounting for the interaction of the pulse with the air/water interface at oblique incidence. In addition, we argue that pulse excitations which are rough approximation of the Brillouin precursor will eventually evolve into the Brillouin precursor itself shortly after they enter water. Therefore, the excitation of a long-propagating pulse is not sensitive to its shape. Finally, we studied the performance of the optimized pulse in terms of the energy of the scattered field from an object inside water. Based on the simulation results the optimized pulse scattered field has higher energy compared to pulses with the same energy and different temporal distribution. The FDTD technique is employed in all the simulations.
Space Vector Modulation of Multi-level and Multi-module Converters for High Power Applications
Keywords: Power Electronics
High Power Converters

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.
A Cognitive Radio Tracking System for Indoor Environments
Keywords: Positioning
Localization
Location determination
Geolocation
WLAN Positioning
Fingerprinting
Nonparametric Information Filter
Cognitive Dynamic System
Location-based services
Received signal strength

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.
The Design of Table-centric Interactive Spaces
Keywords: Tabletop
Multi-Touch
HCI
Human Computer Interaction
Surface Computing
Magnitude Perception
Control orientation
world in miniature

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.
Using Ambient Radio Environment to Support Practical Pervasive Computing
Keywords: mobile computing
localization
context-awareness
secure pairing

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.
A Graph Approach to Measuring Text Distance
Keywords: Graph Approaches in NLP
Semantic Distance
Distributional Methods
Ontological Methods

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.
Merging and Consistency Checking of Distributed Models
Keywords: Model Management
Model Merging
Consistency Checking

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.
Learning Probabilistic Models for Visual Motion
Keywords: computer vision
machine learning

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.