Estimation of signals in colored non gaussian noise based on Gaussian Mixture models

Non-Gaussianity of signals/noise often results in significant performance degradation for systems, which are designed using the Gaussian assumption. So non-Gaussian signals/noise require a different modelling and processing approach. In this paper, we discuss a new Bayesian estimation technique for non-Gaussian signals corrupted by colored non Gaussian noise. The method is based on using zero mean finite Gaussian Mixture Models (GMMs) for signal and noise. The estimation is done using an adaptive non-causal nonlinear filtering technique. The method involves deriving an estimator in terms of the GMM parameters, which are in turn estimated using the EM algorithm. The proposed filter is of finite length and offers computational feasibility. The simulations show that the proposed method gives a significant improvement compared to the linear filter for a wide variety of noise conditions, including impulsive noise. We also claim that the estimation of signal using the correlation with past and future samples leads to reduced mean squared error as compared to signal estimation based on past samples only.

Asymptotic behaviour of point processes with Markov-dependent intervals

This splitting techniques for MARKOV chains developed by NUMMELIN (1978a) and ATHREYA and NEY (1978b) are used to derive an imbedded renewal process in WOLD's point process with MARKOV-correlated intervals. This leads to a simple proof of renewal theorems for such processes. In particular, a key renewal theorem is proved, from which analogues to both BLACKWELL's and BREIMAN's forms of the renewal theorem can be deduced.

Asymptotic behaviour and boundedness of linear systems with time varying coefficients

Motivated by developments in spacecraft dynamics, the asymptotic behaviour and boundedness of solution of a special class of time varying systems in which each term appears as the sum of a constant and a time varying part, are analysed in this paper. It is not possible to apply standard textbook results to such systems, which are originally in second order. Some of the existing results are reformulated. Four theorems which explore the relations between the asymptotic behaviour/boundedness of the constant coefficient system, obtained by equating the time varying terms to zero, to the corresponding behaviour of the time varying system, are developed. The results show the behaviour of the two systems to be intimately related, provided the solutions of the constant coefficient system approach zero are bounded for large values of time, and the time varying terms are suitably restrained. Two problems are tackled using these theorems.

Anomalous Behavior of Linear Hydrocarbon Chains in Water-DMSO Binary Mixture at Low DMSO Concentration

We find that at a mole fraction 0.05 of DMSO (x(DMSO) = 0.05) in aqueous solution, a linear hydrocarbon chain of intermediate length (n = 30-40) adopts the most stable collapsed conformation. In pure water, the same chain exhibits an intermittent oscillation between the collapsed and the extended coiled conformations. Even when the mole fraction of DMSO in the bulk is 0.05, the concentration of the same in the first hydration layer around the hydrocarbon of chain length 30 (n = 30) is as large as 17%. Formation of such hydrophobic environment around the hydrocarbon chain may be viewed as the reason for the collapsed conformation gaining additional stability. We find a second anomalous behavior to emerge near x(DMSO) = 0.15, due to a chain-like aggregation of the methyl groups of DMSO in water that lowers the relative concentration of the DMSO molecules in the hydration layer. We further find that as the concentration of DMSO is gradually increased, it progressively attains the extended coiled structure as the stable conformation. Although Flory-Huggins theory (for binary mixture solvent) fails to predict the anomaly at x(DMSO) = 0.05, it seems to capture the essence of the anomaly at 0.15.

Fluctuation quench and hydrophobic collapse of polymers and biopolymers in water-DMSO binary mixture at low DMSO concentration

Binary mixtures have strong influence on activities of polymers and biopolymers even at low cosolvent concentration. Among the several aqueous binary mixtures studied, water-DMSO especially stands out for its unusual behavior at certain specific concentrations of DMSO. In the present work, we study the effect of water-DMSO binary mixture on polymers and biopolymers by taking a simple linear hydrocarbon chain of intermediate length (n = 30) and the protein lysozyme, respectively. We find that at a mole fraction of 0.05 of DMSO (x(DMSO) = 0.05) in aqueous solution, the hydrocarbon chain adopts the collapsed conformation as the most stable and rigid state. In this case of 0.05 mole fraction of DMSO in bulk, the DMSO concentration in the first hydration layer around the polymer is found to be as large as 17%. Formation of such hydrophobic environment around the polymer is the reason for the collapsed state gaining so much stability. Interestingly, similar quench of conformational fluctuation is also observed for the protein investigated. It is observed that in the case of alkane polymer chains, long wavelength fluctuation gets easily quenched, the polymer being purely hydrophobic. However, in case of the protein, quench of fluctuation is prominent only at the hydrophobic surface, and quench of long wavelength fluctuation becomes insignificant for the full protein. As protein contains both hydrophobic and hydrophilic moieties, the extent of quench of conformational fluctuation with respect to that in pure water is almost half for the biopolymer complex (16.83%) than the same for pure hydrophobic polymer chain (32.43%).

Validation-Based Sparse Gaussian Process Classifier Design

Gaussian processes (GPs) are promising Bayesian methods for classification and regression problems. Design of a GP classifier and making predictions using it is, however, computationally demanding, especially when the training set size is large. Sparse GP classifiers are known to overcome this limitation. In this letter, we propose and study a validation-based method for sparse GP classifier design. The proposed method uses a negative log predictive (NLP) loss measure, which is easy to compute for GP models. We use this measure for both basis vector selection and hyperparameter adaptation. The experimental results on several real-world benchmark data sets show better orcomparable generalization performance over existing methods.

Semi-Supervised Classification Using Sparse Gaussian Process Regression

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.

Common underlying steering curves for motorcycles in steady turns

We study the steady turn behaviours of some light motorcycle models on circular paths, using the commercial software package ADAMS-Motorcycle. Steering torque and steering angle are obtained for several path radii and a range of steady forward speeds. For path radii much greater than motorcycle wheelbase, and for all motorcycle parameters including tyre parameters held fixed, dimensional analysis can predict the asymptotic behaviour of steering torque and angle. In particular, steering torque is a function purely of lateral acceleration plus another such function divided by path radius. Of these, the first function is numerically determined, while the second is approximated by an analytically determined constant. Similarly, the steering angle is a function purely of lateral acceleration, plus another such function divided by path radius. Of these, the first is determined numerically while the second is determined analytically. Both predictions are verified through ADAMS simulations for various tyre and geometric parameters. In summary, steady circular motions of a given motorcycle with given tyre parameters can be approximately characterised by just one curve for steering torque and one for steering angle.

A Multi-layer Perceptron based Non-linear Mixture Model to estimate class abundance from mixed pixels

Sub-pixel classification is essential for the successful description of many land cover (LC) features with spatial resolution less than the size of the image pixels. A commonly used approach for sub-pixel classification is linear mixture models (LMM). Even though, LMM have shown acceptable results, pragmatically, linear mixtures do not exist. A non-linear mixture model, therefore, may better describe the resultant mixture spectra for endmember (pure pixel) distribution. In this paper, we propose a new methodology for inferring LC fractions by a process called automatic linear-nonlinear mixture model (AL-NLMM). AL-NLMM is a three step process where the endmembers are first derived from an automated algorithm. These endmembers are used by the LMM in the second step that provides abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual proportions are fed to multi-layer perceptron (MLP) architecture as input to train the neurons which further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. AL-NLMM is validated on computer simulated hyperspectral data of 200 bands. Validation of the output showed overall RMSE of 0.0089±0.0022 with LMM and 0.0030±0.0001 with the MLP based AL-NLMM, when compared to actual class proportions indicating that individual class abundances obtained from AL-NLMM are very close to the real observations.

A mixture model approach for formant tracking and the robustness of student's-t distribution

We address the problem of robust formant tracking in continuous speech in the presence of additive noise. We propose a new approach based on mixture modeling of the formant contours. Our approach consists of two main steps: (i) Computation of a pyknogram based on multiband amplitude-modulation/frequency-modulation (AM/FM) decomposition of the input speech; and (ii) Statistical modeling of the pyknogram using mixture models. We experiment with both Gaussian mixture model (GMM) and Student's-t mixture model (tMM) and show that the latter is robust with respect to handling outliers in the pyknogram data, parameter selection, accuracy, and smoothness of the estimated formant contours. Experimental results on simulated data as well as noisy speech data show that the proposed tMM-based approach is also robust to additive noise. We present performance comparisons with a recently developed adaptive filterbank technique proposed in the literature and the classical Burg's spectral estimator technique, which show that the proposed technique is more robust to noise.

Speeded up Gaussian mixture model algorithm for background subtraction

Adaptive Gaussian Mixture Models (GMM) have been one of the most popular and successful approaches to perform foreground segmentation on multimodal background scenes. However, the good accuracy of the GMM algorithm comes at a high computational cost. An improved GMM technique was proposed by Zivkovic to reduce computational cost by minimizing the number of modes adaptively. In this paper, we propose a modification to his adaptive GMM algorithm that further reduces execution time by replacing expensive floating point computations with low cost integer operations. To maintain accuracy, we derive a heuristic that computes periodic floating point updates for the GMM weight parameter using the value of an integer counter. Experiments show speedups in the range of 1.33 - 1.44 on standard video datasets where a large fraction of pixels are multimodal.

Interdependent cache analyses for better precision and safety

One of the challenges for accurately estimating Worst Case Execu-tion Time(WCET) of executables is to accurately predict their cache behaviour. Various techniques have been developed to predict the cache contents at different program points to estimate the execution time of memory-accessing instructions. One of the most widely used techniques is Abstract Interpretation based Must Analysis, which de-termines the cache blocks guaranteed to be present in the cache, and hence provides safe estimation of cache hits and misses. However,Must Analysis is highly imprecise, and platforms using Must Analysis have been known to produce blown-up WCET estimates. In our work, we propose to use May Analysis to assist the Must Analysis cache up-date and make it more precise. We prove the safety of our approach as well as provide examples where our Improved Must Analysis provides better precision. Further, we also detect a serious flaw in the original Persistence Analysis, and use Must and May Analysis to assist the Persistence Analysis cache update, to make it safe and more precise than the known solutions to the problem.

STUDENT'S-t MIXTURE MODEL BASED MULTI-INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

We address the problem of multi-instrument recognition in polyphonic music signals. Individual instruments are modeled within a stochastic framework using Student's-t Mixture Models (tMMs). We impose a mixture of these instrument models on the polyphonic signal model. No a priori knowledge is assumed about the number of instruments in the polyphony. The mixture weights are estimated in a latent variable framework from the polyphonic data using an Expectation Maximization (EM) algorithm, derived for the proposed approach. The weights are shown to indicate instrument activity. The output of the algorithm is an Instrument Activity Graph (IAG), using which, it is possible to find out the instruments that are active at a given time. An average F-ratio of 0 : 7 5 is obtained for polyphonies containing 2-5 instruments, on a experimental test set of 8 instruments: clarinet, flute, guitar, harp, mandolin, piano, trombone and violin.

Some recent results on the stability of linear time varying systems

Some theorems derived recently by the authors on the stability of multidimensional linear time varying systems are reported in this paper. To begin with, criteria based on Liapunov�s direct method are stated. These are followed by conditions on the asymptotic behaviour and boundedness of solutions. Finally,L 2 andL ? stabilities of these systems are discussed. In conclusion, mention is made of some of the problems in aerospace engineering to which these theorems have been applied.