The use of Gaussian Mixture Models (GMM), adapted through the expectation Minimization(EM) algorithm, is not rare in Audio Analysis for Surveillance Applications and Environmental sound recognition. Their use is found...
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ISBN:
(纸本)9781424441242
The use of Gaussian Mixture Models (GMM), adapted through the expectation Minimization(EM) algorithm, is not rare in Audio Analysis for Surveillance Applications and Environmental sound recognition. Their use is founded on the good qualities of GMM models when aimed at approximating Probability Density Functions (PDF) of random variables. But in some cases, where models are to be adapted from small sample sets instead of large but generic databases, a problem of balance between model complexity and sample size may play an important role. From this perspective, we show, through simple sound classification experiments, that constrained GMM, with fewer degrees of freedom, as compared to GMM with full covariance matrices, provide better classification performances. Moreover, pushing this argument even further, we also show that a Parzen model can do even better than usual GMM.
The paper presents a novel approach to identification of stochastic nonlinear dynamic systems using efficient approximation methods. The motivation behind this work is to develop a computationally efficient and robust...
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ISBN:
(纸本)9781424453634
The paper presents a novel approach to identification of stochastic nonlinear dynamic systems using efficient approximation methods. The motivation behind this work is to develop a computationally efficient and robust algorithm for estimation of wastewater treatment plant model parameters. The mathematical model of the plant is required for the application of advanced predictive control algorithms and condition monitoring. The presented algorithm employs the expectation-Maximization algorithm to compute the Maximum likelihood estimates of the unknown model parameters. The algorithm uses the Unscented Transformation (UT) to approximate the posterior distribution of the random variable that undergoes a nonlinear transformations. The advantage of this approach lies in efficient approximation methods that greatly reduce the computational load of the algorithm and is therefore suitable for on-line implementation.
This paper presents an innovative method for tourist market segmentation-based on dominant movement patterns of tourists;that is, the travel sequences or patterns used by tourists most frequently. There were three ste...
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This paper presents an innovative method for tourist market segmentation-based on dominant movement patterns of tourists;that is, the travel sequences or patterns used by tourists most frequently. There were three steps to achieve this goal. In the first step, general log-linear models were adopted to identify the dominant movement patterns, while the second step was to discover the characteristics of the groups of tourists who travelled with these patterns. The expectation-maximisation algorithm was then used to partition tourist segments in terms of socio-demographic and travel behavioural variables. The third step was to select target markets based upon the earlier analysis. These methods were applied to a sample of tourists, over the period of a week, on Phillip Island, Victoria, Australia. A significant outcome of this research is that it will assist tourism organisations to identify tourism market segments and develop better tour packages and more efficient marketing strategies aligned to the characteristics of the tourists. (C) 2009 Elsevier Ltd. All rights reserved.
We propose a method to classify human trajectories, modeled by a set of motion vector fields, each tailored to describe a specific motion regime. Trajectories are modeled as being composed of segments corresponding to...
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ISBN:
(纸本)9781424479948
We propose a method to classify human trajectories, modeled by a set of motion vector fields, each tailored to describe a specific motion regime. Trajectories are modeled as being composed of segments corresponding to different motion regimes, each generated by one of the underlying motion fields. Switching among the motion fields follows a probabilistic mechanism, described by a field of stochastic matrices. This yields a space-dependent motion model which can be estimated using an expectation-maximization (EM) algorithm. To address the model selection question (how many fields to use?), we adopt a discriminative criterion based on classification accuracy on a held out set. Experiments with real data (human trajectories in a shopping mall) illustrate the ability of the proposed approach to classify complex trajectories into high level classes (client versus non-client).
The impulse response of a typical wireless multipath channel can be modeled as a tapped delay line filter whose non-zero components are sparse relative to the channel delay spread. In this paper, a novel method of est...
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ISBN:
(纸本)9781424456383
The impulse response of a typical wireless multipath channel can be modeled as a tapped delay line filter whose non-zero components are sparse relative to the channel delay spread. In this paper, a novel method of estimating such sparse multipath fading channels for OFDM systems is explored. In particular, Sparse Bayesian Learning (SBL) techniques are applied to jointly estimate the sparse channel and its second order statistics, and a new Bayesian Cramer-Rao bound is derived for the SBL algorithm. Further, in the context of OFDM channel estimation, an enhancement to the SBL algorithm is proposed, which uses an expectation Maximization (EM) framework to jointly estimate the sparse channel, unknown data symbols and the second order statistics of the channel. The EM-SBL algorithm is able to recover the support as well as the channel taps more efficiently, and/or using fewer pilot symbols, than the SBL algorithm. To further improve the performance of the EM-SBL, a threshold-based pruning of the estimated second order statistics that are input to the algorithm is proposed, and its mean square error and symbol error rate performance is illustrated through Monte-Carlo simulations. Thus, the algorithms proposed in this paper are capable of obtaining efficient sparse channel estimates even in the presence of a small number of pilots.
This paper addresses the problem of transmitting data to multiple mobile stations using a decode-and-forward strategy. Precoding vectors are used in relays to cancel out multiple access interference at the mobile stat...
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ISBN:
(纸本)9781424480166
This paper addresses the problem of transmitting data to multiple mobile stations using a decode-and-forward strategy. Precoding vectors are used in relays to cancel out multiple access interference at the mobile stations. Statistical distribution of signal to noise ratio (SNR) is approximated by an expectation maximization algorithm. Based on this distribution, system performance is evaluated for low and high SNR. Simulation results confirm the analytic calculations and show that the maximum diversity advantage can be obtained, which is the product of the number of antennas at each relay by the number of relays minus the total number of system constraints.
Inspired from the mechanism of Fuzzy C-means (FCMs) which introduces a degree of fuzziness on the dissimilarity function based on distances, a fuzzy expectation Maximization (EM) algorithm for Gaussian Mixture Models ...
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ISBN:
(纸本)9781424469208
Inspired from the mechanism of Fuzzy C-means (FCMs) which introduces a degree of fuzziness on the dissimilarity function based on distances, a fuzzy expectation Maximization (EM) algorithm for Gaussian Mixture Models (GMMs) is proposed in this paper. In the fuzzy EM algorithm, the dissimilarity function is defined as the multiplicative inverse of probability density function. Different from FCMs, the defined dissimilarity function is based on the exponential function of the distance. The fuzzy EM algorithm is compared with normal EM algorithm in terms of fitting degree and convergence speed. The experimental results in modeling random data and various characters demonstrate the ability of the proposed algorithm in reducing the computational cost of GMMs.
In this paper, we propose an efficient carrier frequency offset (CFO) estimation technique based on the space alternating generalized expectation-maximization (SAGE) for uplink orthogonal frequency division multiple a...
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ISBN:
(纸本)9781424456383
In this paper, we propose an efficient carrier frequency offset (CFO) estimation technique based on the space alternating generalized expectation-maximization (SAGE) for uplink orthogonal frequency division multiple access (OFDMA) systems. In general, the SAGE method transforms a multidimensional search problem into a sequence of one-dimensional searches, which greatly simplifies the estimation procedure. However, the conventional algorithms based on the SAGE method require a large amount of computations to estimate the CFO due to exhaustive grid search. To reduce the computational burden, we exploit the leakage on the fast Fourier transform (FFT) output of the received signal after the multiple access interference is removed by the SAGE method. Then, this leakage-based approach reduces the complexity of the conventional SAGE algorithm regardless of an employed carrier assignment scheme by avoiding grid search. Simulation results show that our modified SAGE algorithm approaches the Cramer Rao bound at all signal to noise ratio (SNR) region with greatly reduced complexity compared to the conventional SAGE algorithms.
In this paper we consider greedy scheduling algorithms in wireless networks, i.e., the schedules are computed by adding links greedily based on some priority vector. Two special cases are considered: 1) Longest Queue ...
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ISBN:
(纸本)9781424456383
In this paper we consider greedy scheduling algorithms in wireless networks, i.e., the schedules are computed by adding links greedily based on some priority vector. Two special cases are considered: 1) Longest Queue First (LQF) scheduling, where the priorities are computed using queue lengths, and 2) Static Priority (SP) scheduling, where the priorities are preassigned. We first propose a closed-form lower bound stability region for LQF scheduling, and discuss the tightness result in some scenarios. We then propose an lower bound stability region for SP scheduling with multiple priority vectors, as well as a heuristic priority assignment algorithm, which is related to the well-known expectation-Maximization (EM) algorithm. The performance gain of the proposed heuristic algorithm is finally confirmed by simulations.
We present an approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables. The demonstrated skill is first encoded in a compact form through a modified version of Dyn...
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ISBN:
(纸本)9781424466757
We present an approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables. The demonstrated skill is first encoded in a compact form through a modified version of Dynamic Movement Primitives (DMP) which encapsulates correlation information. expectation-Maximization based Reinforcement Learning is then used to modulate the mixture of dynamical systems initialized from the user's demonstration. The approach is evaluated on a torque-controlled 7 DOFs Barrett WAM robotic arm. Two skill learning experiments are conducted: a reaching task where the robot needs to adapt the learned movement to avoid an obstacle, and a dynamic pancake-flipping task.
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