1,248 research outputs found

    Intelligent packet discarding policies for real-time traffic over wireless networks.

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    Yuen Ching Wan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 77-83).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Nature of Real-Time Traffic --- p.1Chapter 1.2 --- Delay Variability in Wireless Networks --- p.2Chapter 1.2.1 --- Propagation Medium --- p.3Chapter 1.2.2 --- Impacts of Network Designs --- p.5Chapter 1.3 --- The Keys - Packet Lifetime & Channel State --- p.8Chapter 1.4 --- Contributions of the Thesis --- p.8Chapter 1.5 --- Organization of the Thesis --- p.9Chapter 2 --- Background Study --- p.11Chapter 2.1 --- Packet Scheduling --- p.12Chapter 2.2 --- Call Admission Control (CAC) --- p.12Chapter 2.3 --- Active Queue Management (AQM) --- p.13Chapter 2.3.1 --- AQM for Wired Network --- p.14Chapter 2.3.2 --- AQM for Wireless Network --- p.17Chapter 3 --- Intelligent Packet Discarding Policies --- p.21Chapter 3.1 --- Random Packet Discard --- p.22Chapter 3.1.1 --- Variable Buffer Limit (VABL) --- p.22Chapter 3.2 --- Packet Discard on Expiration Likelihood (PEL) --- p.23Chapter 3.2.1 --- Working Principle --- p.24Chapter 3.2.2 --- Channel State Aware Packet Discard on Expiration Likelihood (CAPEL) --- p.26Chapter 3.3 --- System Modeling --- p.29Chapter 3.3.1 --- Wireless Channel as an Markov-Modulated Poisson Process (MMPP) --- p.30Chapter 3.3.2 --- System Analysis --- p.30Chapter 3.3.3 --- System Time Distribution --- p.33Chapter 3.3.4 --- Approximation of System Time Distribution by Gamma Distribution --- p.36Chapter 3.4 --- Goodput Analysis of Intelligent Packet Discarding Policies --- p.38Chapter 3.4.1 --- Variable Buffer Limit (VABL) --- p.38Chapter 3.4.2 --- CAPEL at the End-of-Line --- p.39Chapter 3.4.3 --- CAPEL at the Head-of-Line --- p.43Chapter 4 --- Performance Evaluation --- p.44Chapter 4.1 --- Simulation --- p.44Chapter 4.1.1 --- General Settings --- p.45Chapter 4.1.2 --- Choices of Parameters --- p.46Chapter 4.1.3 --- Variable Buffer Limit (VABL) --- p.49Chapter 4.1.4 --- CAPEL at the End-of-Line --- p.53Chapter 4.1.5 --- CAPEL at the Head-of-Line --- p.60Chapter 4.2 --- General Discussion --- p.64Chapter 4.2.1 --- CAPEL vs RED --- p.64Chapter 4.2.2 --- Gamma Approximation for System Time Distribution . --- p.69Chapter 5 --- Conclusion --- p.70Chapter A --- Equation Derivation --- p.73Chapter A.l --- Steady State Probabilities --- p.73Bibliography --- p.7

    Expressive movement generation with machine learning

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    Movement is an essential aspect of our lives. Not only do we move to interact with our physical environment, but we also express ourselves and communicate with others through our movements. In an increasingly computerized world where various technologies and devices surround us, our movements are essential parts of our interaction with and consumption of computational devices and artifacts. In this context, incorporating an understanding of our movements within the design of the technologies surrounding us can significantly improve our daily experiences. This need has given rise to the field of movement computing – developing computational models of movement that can perceive, manipulate, and generate movements. In this thesis, we contribute to the field of movement computing by building machine-learning-based solutions for automatic movement generation. In particular, we focus on using machine learning techniques and motion capture data to create controllable, generative movement models. We also contribute to the field by creating datasets, tools, and libraries that we have developed during our research. We start our research by reviewing the works on building automatic movement generation systems using machine learning techniques and motion capture data. Our review covers background topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. Building on our literature review, we present WalkNet, an interactive agent walking movement controller based on neural networks. The expressivity of virtual, animated agents plays an essential role in their believability. Therefore, WalkNet integrates controlling the expressive qualities of movement with the goal-oriented behaviour of an animated virtual agent. It allows us to control the generation based on the valence and arousal levels of affect, the movement’s walking direction, and the mover’s movement signature in real-time. Following WalkNet, we look at controlling movement generation using more complex stimuli such as music represented by audio signals (i.e., non-symbolic music). Music-driven dance generation involves a highly non-linear mapping between temporally dense stimuli (i.e., the audio signal) and movements, which renders a more challenging modelling movement problem. To this end, we present GrooveNet, a real-time machine learning model for music-driven dance generation

    Training deep neural density estimators to identify mechanistic models of neural dynamics

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    Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

    Model-Based Bayesian Inference, Learning, and Decision-Making with Applications in Communication Systems

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    This dissertation discusses the mathematical modeling of dynamical systems under uncertainty, Bayesian inference and learning of the unknown quantities, such as the system’s state and its parameters, and computing optimal decisions within these models. Probabilistic dynamical models achieve substantial performance gains for decision-making. Their ability to predict the system state depending on the decisions enables efficient learning with small amounts of data, and therefore make guided optimal decisions possible. Multiple probabilistic models for dynamical state-space systems under discrete-time and continuous-time assumptions are presented. They provide the basis to compute Bayesian beliefs and optimal decisions under uncertainty. Numerical algorithms are developed, by starting with the exact system description and making principled approximations to arrive at tractable algorithms for both inference and learning, as well as decision-making. The developed methods are showcased on communication systems and other commonplace applications. The specific contributions to modeling, inference and decision-making are outlined in the following. The first contribution is an inference method for non-stationary point process data, which is common, for example, in queues within communication systems. A hierarchical Bayesian non-parametric model with a gamma-distributional assumption on the holding times of the process serves as a basis. For inference, a computationally tractable method based on a Markov chain Monte Carlo sampler is derived and subsequently validated under the modeling assumption using synthetic data and in a real-data scenario. The second contribution is a fast algorithm for adapting bitrates in video streaming. This is achieved by a new algorithm for adaptive bitrate video streaming that uses a sparse Bayesian linear model for a quality-of-experience score. The algorithm uses a tractable inference scheme to extract relevant features from network data and builds on a contextual bandit strategy for decision making. The algorithm is validated numerically and an implementation and evaluation in a named data networking scenario is given. The third contribution is a novel method that exploits correlations in decision-making problems. Underlying model parameters can be inferred very data-efficiently, by building a Bayesian model for correlated count data from Markov decision processes. To overcome intractabilities arising in exact Bayesian inference, a tractable variational inference algorithm is presented exploiting an augmentation scheme. The method is extensively evaluated in various decision-making scenarios, such as, reinforcement learning in a queueing system. The final contribution is concerned with simultaneous state inference and decision-making in continuous-time partially observed environments. A new model for discrete state and action space systems is presented and the corresponding equations for exact Bayesian inference are discussed. The optimality conditions for decision-making are derived. Two tractable numerical schemes are presented, which exploit function approximators to learn the solution in the belief space. Applicability of the method is shown on several examples, including a scheduling algorithm under partial observability
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