An approach is presented for simultaneously estimating target states and signal-to-noise ratio (SNR) in the framework of the probabilistic multiple hypothesis tracking (PMHT). The approach, named PMHT-S, utilises the ...
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An approach is presented for simultaneously estimating target states and signal-to-noise ratio (SNR) in the framework of the probabilistic multiple hypothesis tracking (PMHT). The approach, named PMHT-S, utilises the expectation-maximisation (EM) algorithm to obtain the maximum a posteriori estimates of target states and SNR's of multiple targets in the presence of false measurements. The missing data of the EM algorithm consists of measurement-to-target assignments as well as a set of fictitious geometric and signal strength measurements each associated with a target under the hypothesis that the target has been undetected. This formulation creates new algorithmic approaches for solving PMHT problems such that information on missed targets may be exploited. It is shown that the auxiliary function of the PMHT-S is additively separable as the sum of a function of target states and a function of target SNR's. The pair, as a result, can be independently maximised in each EM iteration to update target states and SNR's. The computational advantage of the separation is substantial even for a small number of targets. Explicit expressions of the auxiliary function of the PMHT-S are given. Monte Carlo simulations were performed to assess estimation performance of the PMHT-S for target tracking examples.
The problem of iterative data detection and channel estimation for space-time block coded continuous phase modulation (CPM) signals over quasi-static flat fading channels is studied in this study. Firstly, the combina...
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The problem of iterative data detection and channel estimation for space-time block coded continuous phase modulation (CPM) signals over quasi-static flat fading channels is studied in this study. Firstly, the combination of space-time block code with CPM is not as straightforward as with linear modulation schemes, because of the requirement of phase continuity and the associated inherent memory of CPM. Therefore a novel block construction method is proposed to insert a tail sequence into each block for ensuring the phase continuity. Secondly, at the receiver the expectation-maximisation algorithm is applied to channel estimation and the estimated channel information is used to evaluate a posteriori probability for data detection. The channel estimation and data detection work iteratively. Simulation results show that the spectral efficiency of transmission is improved by the proposed block construction method and that the proposed iterative receiver outperforms its non-iterative counterpart significantly.
The expectationmaximisation (EM) algorithm has proven to be effective for a range of identification problems. Unfortunately, the way in which the EM algorithm has previously been applied has proven unsuitable for the...
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ISBN:
(纸本)9781424477456
The expectationmaximisation (EM) algorithm has proven to be effective for a range of identification problems. Unfortunately, the way in which the EM algorithm has previously been applied has proven unsuitable for the commonly employed innovations form model structure. This paper addresses this problem, and presents a previously unexamined method of EM algorithm employment. The results are profiled, which indicate that a hybrid EM/gradient-search technique may in some cases outperform either a pure EM or a pure gradient-based search approach.
Humans as well as humanoid robots can use a large number of degrees of freedom to solve very complex motor tasks. The high-dimensionality of these motor tasks adds difficulties to the control problem and machine learn...
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ISBN:
(纸本)9781479971749
Humans as well as humanoid robots can use a large number of degrees of freedom to solve very complex motor tasks. The high-dimensionality of these motor tasks adds difficulties to the control problem and machine learning algorithms. However, it is well known that the intrinsic dimensionality of many human movements is small in comparison to the number of employed DoFs, and hence, the movements can be represented by a small number of synergies encoding the couplings between DoFs. In this paper, we want to apply Dimensionality Reduction (DR) to a recent movement representation used in robotics, called Probabilistic Movement Primitives (ProMP). While ProMP have been shown to have many benefits, they suffer with the high-dimensionality of a robotic system as the number of parameters of a ProMP scales quadratically with the dimensionality. We use probablistic dimensionality reduction techniques based on expectation maximization to extract the unknown synergies from a given set of demonstrations. The ProMP representation is now estimated in the low-dimensional space of the synergies. We show that our dimensionality reduction is more efficient both for encoding a trajectory from data and for applying Reinforcement Learning with Relative Entropy Policy Search (REPS).
To address the rapid and non-destructive detection of meat spoilage microorganisms during aerobic storage at chill and abuse temperatures, Fourier transform infrared spectroscopy (FTIR) with the help of an intelligent...
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To address the rapid and non-destructive detection of meat spoilage microorganisms during aerobic storage at chill and abuse temperatures, Fourier transform infrared spectroscopy (FTIR) with the help of an intelligent-based identification system was attempted in this work. The objective of this study is to associate simultaneously spectral data with microbiological data (log counts), for Total Viable Counts, Pseudomonas spp., Brochothrix thermosphacta, Lactic Acid Bacteria and Enterobacteriaceae. The dual purpose of the proposed modelling scheme is not only to classify meat samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from FTIR spectra. An Extended Normalised Radial Basis Function neural network has been implemented, and the Bayesian Ying-Yang expectationmaximisationalgorithm has been utilised together with novel splitting operations to determine network's size and parameter set. The dimensionality reduction of spectral data has been addressed by the implementation of a fuzzy principal component algorithm. Results confirmed the superiority of the adopted methodology compared to other schemes such as multilayer perceptron and the partial least squares techniques and indicated that spectral information obtained by FTIR spectroscopy during beef spoilage, in combination with an efficient choice of a learning-based modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage. (C) 2014 Elsevier Ltd. All rights reserved.
This paper presents new methods for the recognition and categorization of object properties such as surface texture, weight, and compliance using a multi-modal artificial skin mounted on both arms of a humanoid. In ad...
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ISBN:
(纸本)9781479971749
This paper presents new methods for the recognition and categorization of object properties such as surface texture, weight, and compliance using a multi-modal artificial skin mounted on both arms of a humanoid. In addition, it introduces two novel feature descriptors, which are useful for providing high-level information to learning algorithms. The artificial skin has built-in 3-axis accelerometer, normal force, proximity, and temperature sensors. To explore different surface textures and weights, objects were left sliding between the NAO humanoid's arms. The caused vibration was detected by accelerometers. Surface texture and weight recognition models were learned from the extracted features of the vibration signals thanks to two learning algorithms, namely the support vector machine (SVM) and the expectation Maximization (EM). In order to recognize objects having different compliances, SVM and EM took into account total amount of forces applied by the arms to hold the object firmly. The experimental results show that the humanoid can distinguish between different objects having different surface textures and weights with a recognition rate of 100%. Furthermore, it can categorize objects with hard and soft surfaces and classify objects having similar compliance with 100% and 70% accuracy rates respectively.
This paper introduces a modification to the EM (expectation Maximization) algorithm potentially allowing reliable convergence to the ML (Maximum Likelihood) parameter estimate for a set of previously intractable probl...
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ISBN:
(纸本)9781479930012
This paper introduces a modification to the EM (expectation Maximization) algorithm potentially allowing reliable convergence to the ML (Maximum Likelihood) parameter estimate for a set of previously intractable problems. The modification is based on the MCEM (Monte Carlo EM) algorithm, which substitutes sample averages for the explicit calculation of expectation. A problem with previous algorithms is that the number of samples required for convergence and the generally convergence behavior was uncertain. Using information geometric principles, we arrive at a new formulation that ensures convergence with probability one. Further, we begin an investigation attempting to minimize the number of samples required to obtain an acceptable approximation of the ML estimate. This algorithm is well suited to solve numerous challenging statistical problems.
The challenge of localizing number of concurrent acoustic sources in reverberant enclosures is addressed in this paper. We formulate the localization task as a maximum likelihood (ML) parameter estimation problem, and...
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ISBN:
(纸本)9781479931095
The challenge of localizing number of concurrent acoustic sources in reverberant enclosures is addressed in this paper. We formulate the localization task as a maximum likelihood (ML) parameter estimation problem, and develop a distributed expectation-maximization (DEM) procedure, based on the Incremental EM (IEM) framework. The algorithm enables localization of the speakers without a center point. Unlike direction search, localization is a distributed task in nature, since the sensors must be spatially deployed. Taking advantage of the distributed constellation of the sensors we propose a distributed algorithm that enables multiple processing nodes and considers communication constraints between them. The proposed DEM has surprising advantages over conventional expectation-maximization (EM) schemes. Firstly, it is less sensitive to initial conditions. Secondly, it converges much faster than the conventional EM. The proposed algorithm is tested by an extensive simulation study.
A home healthcare monitoring system is a useful tool to check the cardiovascular condition in our daily life. Instead of diagnostic medical equipment, low-cost infrared cameras can measure vein images noninvasively an...
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ISBN:
(纸本)9781479969449
A home healthcare monitoring system is a useful tool to check the cardiovascular condition in our daily life. Instead of diagnostic medical equipment, low-cost infrared cameras can measure vein images noninvasively and simply. However, the recorded image may result in low contrast with external noise. An efficient image filtering method to assess the state of peripheral veins will enable the early detection of disease. Therefore, a real-time analyzer with a new filtering method was developed for evaluating vein images acquired from a near-infrared camera. The novel image filter was automatically designed by the genetic algorithm with the expectation maximization algorithm, and it was able to alter the low image quality. If the real-time analyzer developed for the assessment of venous states is incorporated into e-healthcare applications, it could be easily distributed through smartphones or tablets.
Detecting fish in submarine environment is a challenge due to the properties of the water such as light absorption and scattering. In this work, we present a method for preprocessing images in submarine environment. I...
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ISBN:
(纸本)9781479949182
Detecting fish in submarine environment is a challenge due to the properties of the water such as light absorption and scattering. In this work, we present a method for preprocessing images in submarine environment. In the first step, we model the underwater environment as overlapp of two processes. The first process is considered as a Poisson distribution, while the second one is considered as a Gaussian mixture. The resulting distribution is called Poisson-Gaussian mixture (PGM). To estimate the noise parameters, we propose an iterative algorithm based on the expectation maximization approach. This allows us to jointly estimate the scale of the Poisson parameter as well as the standard deviation and the mean of all Gaussian distributions. In order to facilitate the detection of objects, to correct the illumination problem of the scene and to restore the colors, we integrate a color correction algorithm. Finally, detection and localization of fish complete the pre-processing in the images. To obtain medium or small regions, the mean shift algorithm is used with a reduced threshold. In the segmentation process, the proposed detector scan the image region by region. This detector allows to estimate statistically the type of the region (object or non-object). The method is tested under different underwater conditions. Experimental results show that the proposed approach outperforms conventional methods.
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