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.

Stochastic models of IEEE 802.11e wireless net works with multimedia applications

We provide a survey of some of our recent results ([9], [13], [4], [6], [7]) on the analytical performance modeling of IEEE 802.11 wireless local area networks (WLANs). We first present extensions of the decoupling approach of Bianchi ([1]) to the saturation analysis of IEEE 802.11e networks with multiple traffic classes. We have found that even when analysing WLANs with unsaturated nodes the following state dependent service model works well: when a certain set of nodes is nonempty, their channel attempt behaviour is obtained from the corresponding fixed point analysis of the saturated system. We will present our experiences in using this approximation to model multimedia traffic over an IEEE 802.11e network using the enhanced DCF channel access (EDCA) mechanism. We have found that we can model TCP controlled file transfers, VoIP packet telephony, and streaming video in the IEEE802.11e setting by this simple approximation.

Construction and use of linear regression models for processor performance analysis

Processor architects have a challenging task of evaluating a large design space consisting of several interacting parameters and optimizations. In order to assist architects in making crucial design decisions, we build linear regression models that relate Processor performance to micro-architecture parameters, using simulation based experiments. We obtain good approximate models using an iterative process in which Akaike's information criteria is used to extract a good linear model from a small set of simulations, and limited further simulation is guided by the model using D-optimal experimental designs. The iterative process is repeated until desired error bounds are achieved. We used this procedure to establish the relationship of the CPI performance response to 26 key micro-architectural parameters using a detailed cycle-by-cycle superscalar processor simulator The resulting models provide a significance ordering on all micro-architectural parameters and their interactions, and explain the performance variations of micro-architectural techniques.

Thermodynamic and structural studies of cavity formation in proteins suggest that loss of packing interactions rather than the hydrophobic effect dominates the observed energetics

The hydrophobic effect is widely believed to be an important determinant of protein stability. However, it is difficult to obtain unambiguous experimental estimates of the contribution of the hydrophobic driving force to the overall free energy of folding. Thermodynamic and structural studies of large to small substitutions in proteins are the most direct method of measuring this contribution. We have substituted the buried residue Phe8 in RNase S with alanine, methionine, and norleucine, Binding thermodynamics and structures were characterized by titration calorimetry and crystallography, respectively. The crystal structures of the RNase S F8A, F8M, and F8Nle mutants indicate that the protein tolerates the changes without any main chain adjustments, The correlation of structural and thermodynamic parameters associated with large to small substitutions was analyzed for nine mutants of RNase S as well as 32 additional cavity-containing mutants of T4 lysozyme, human lysozyme, and barnase. Such substitutions were typically found to result in negligible changes in Delta C-p and positive values of both Delta Delta H degrees and aas of folding. Enthalpic effects were dominant, and the sign of Delta Delta S is the opposite of that expected from the hydrophobic effect. Values of Delta Delta G degrees and Delta Delta H degrees correlated better with changes in packing parameters such as residue depth or occluded surface than with the change in accessible surface area upon folding. These results suggest that the loss of packing interactions rather than the hydrophobic effect is a dominant contributor to the observed energetics for large to small substitutions. Hence, estimates of the magnitude of the hydrophobic driving force derived from earlier mutational studies are likely to be significantly in excess of the actual value.

On the momentum balance in linear-combination models for the transition zone

The momentum balance of the linear-combination integral model for the transition zone is investigated for constant pressure flows. The imbalance is found to be small enough to be negligible for all practical purposes. [S0889-504X(00)00703-0].

Refinement of the thermodynamic properties of ruthenium dioxide and osmium dioxide

The standard Gibbs energies of formation of RuO2 and OsO2 at high temperature have been determined with high precision, using a novel apparatus that incorporates a buffer electrode between the reference and working electrodes, The buffer electrode absorbs the electrochemical flux of oxygen through the solid electrolyte from the electrode with higher oxygen chemical potential to the electrode with lower oxygen potential, The buffer electrode prevents polarization of the measuring electrode and ensures accurate data, The standard Gibbs energies of formation (Delta(f)G degrees) of RuO2, in the temperature range of 900-1500 K, and OsO2, in the range of 900-1200 K, can be represented by the equations Delta(f)G degrees(RuO2)(J/mol) = -324 720 + 354.21T - 23.490T In T Delta(f)G degrees(OsO2)(J/mol) = -304 740 + 318.80T - 18.444T In T where the temperature T is given in Kelvin and the deviation of the measurement is +/- 80 J/mol, The high-temperature heat ;capacities of RuO2 and OsO2 are measured using differential scanning calorimetry. The information for both the low- and high-temperature heat rapacity of RuO2 is coupled with the Delta(f)G degrees data obtained in this study to evaluate the standard enthalpy of formation of RuO2 at 298.15 K (Delta(f)H degrees(298.15K)). The low-temperature heat capacity of OsO2 has not been measured: therefore, the standard enthalpy and entropy of formation of OsO2 at 298.15 K (Delta(f)H degrees(298.15K) and S degrees(298.15K), respectively) are derived simultaneously through an optimization procedure from the high-temperature heat capacity and the Gibbs energy of formation. Both Delta fH degrees(298.15K) and S degrees(298.15K) are treated as variables in the optimization routine, For RuO2, the standard enthalpy of formation at 298.15 K is Delta fH degrees(298.15K) (RuO2) -313.52 +/- 0.08 kJ/mol, and that for OsO2 is Delta(f)H degrees(298.15K) (OSO2) = -295.96 +/- 0.08 kJ/mol. The standard entropy of OsO2 at 298.15 K that has been obtained from the optimization is given as S degrees(298.15K) (OsO2) = 49.8 +/- 0.2 J (mol K)(-1).

Prediction of inplane damping from deterministic and stochastic models

This paper reviews computational reliability, computer algebra, stochastic stability and rotating frame turbulence (RFT) in the context of predicting the blade inplane mode stability, a mode which is at best weakly damped. Computational reliability can be built into routine Floquet analysis involving trim analysis and eigenanalysis, and a highly portable special purpose processor restricted to rotorcraft dynamics analysis is found to be more economical than a multipurpose processor. While the RFT effects are dominant in turbulence modeling, the finding that turbulence stabilizes the inplane mode is based on the assumption that turbulence is white noise.

Studies directed towards the synthesis of schisanartane and related complex nortriterpenoids: construction of models of the peripheral ABC and FGH segments of rubrifloradilactone C

A conceptually unifying and flexible approach to the ABC and FGH segments of the nortriterpenoid rubrifloradilactone C, each embodying a furo[3,2-b]furanone moiety, from the appropriate Morita-Baylis-Hillman adducts is delineated. (C) 2010 Elsevier Ltd. All rights reserved.

Phase relations and thermodynamic properties of compounds in the pseudobinary system BaO-Y2O3

The coexisting phases in the pseudobinary system BaO-Y2O3 have been identified by equilibrating samples containing different amounts of component oxides at 1173, 1273 and 1373 K. Only two ternary oxides, BaY2O4 and Ba3Y4O9, have been found to be stable in the temperature range of investigation. Solid state galvanic cells: Pt, O2+BaO+BaF2double vertical barBaF2+2mol%Al2O3double vertical barBaF2+BaY2O4+Y2O3+O2, Pt and Pt, O2+BaO+BaF2double vertical barBaF2+2mol% Al2O3double vertical barBaF2+BaY2O4+Ba3Y4O9+O2, Pt have been employed for determining the Gibbs' energies of formation of BaY2O4 and Ba3Y4O9 from the component oxides in the range 850 to 1250 K. A composite solid electrolyte incorporating Al2O3-dispersed BaF2 was used in the cells. To prevent interaction between the Al2O3 powder and electrode materials, the solid electrolyte was coated with pure BaF2. The Gibbs' energies of formation of BaY2O4 and Ba3Y4O9 from component oxides are given by: Δf0 (BaY2O4, s)=−128,310+5.211T (±580) J mol−1, (850less-than-or-equals, slantTless-than-or-equals, slant1250 K) and ΔGfo(Ba3Y4O9, s)= −317,490 −24.704T (±1100) J mol−1, (850less-than-or-equals, slantTless-than-or-equals, slant1250 K).

Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

The problem of time variant reliability analysis of existing structures subjected to stationary random dynamic excitations is considered. The study assumes that samples of dynamic response of the structure, under the action of external excitations, have been measured at a set of sparse points on the structure. The utilization of these measurements m in updating reliability models, postulated prior to making any measurements, is considered. This is achieved by using dynamic state estimation methods which combine results from Markov process theory and Bayes' theorem. The uncertainties present in measurements as well as in the postulated model for the structural behaviour are accounted for. The samples of external excitations are taken to emanate from known stochastic models and allowance is made for ability (or lack of it) to measure the applied excitations. The future reliability of the structure is modeled using expected structural response conditioned on all the measurements made. This expected response is shown to have a time varying mean and a random component that can be treated as being weakly stationary. For linear systems, an approximate analytical solution for the problem of reliability model updating is obtained by combining theories of discrete Kalman filter and level crossing statistics. For the case of nonlinear systems, the problem is tackled by combining particle filtering strategies with data based extreme value analysis. In all these studies, the governing stochastic differential equations are discretized using the strong forms of Ito-Taylor's discretization schemes. The possibility of using conditional simulation strategies, when applied external actions are measured, is also considered. The proposed procedures are exemplifiedmby considering the reliability analysis of a few low-dimensional dynamical systems based on synthetically generated measurement data. The performance of the procedures developed is also assessed based on a limited amount of pertinent Monte Carlo simulations. (C) 2010 Elsevier Ltd. All rights reserved.

A general thermodynamic framework for understanding the behaviour of absorption chillers

In this article, a general definition of the process average temperature has been developed, and the impact of the various dissipative mechanisms on 1/COP of the chiller evaluated. The present component-by-component black box analysis removes the assumptions regarding the generator outlet temperature(s) and the component effective thermal conductances. Mass transfer resistance is also incorporated into the absorber analysis to arrive at a more realistic upper limit to the cooling capacity. Finally, the theoretical foundation for the absorption chiller T-s diagram is derived. This diagrammatic approach only requires the inlet and outlet conditions of the chiller components and can be employed as a practical tool for system analysis and comparison. (C) 2000 Elsevier Science Ltd and IIR. All rights reserved.

Comparison of ANN models for predicting water quality in distribution systems

Deterministic models have been widely used to predict water quality in distribution systems, but their calibration requires extensive and accurate data sets for numerous parameters. In this study, alternative data-driven modeling approaches based on artificial neural networks (ANNs) were used to predict temporal variations of two important characteristics of water quality chlorine residual and biomass concentrations. The authors considered three types of ANN algorithms. Of these, the Levenberg-Marquardt algorithm provided the best results in predicting residual chlorine and biomass with error-free and ``noisy'' data. The ANN models developed here can generate water quality scenarios of piped systems in real time to help utilities determine weak points of low chlorine residual and high biomass concentration and select optimum remedial strategies.

Numerically Exact Study of Polarizabilities and Hyperpolarizabilities of Correlated Conjugated Organic Models

Polarizabilities and Hyperpolarizabilities of conjugated organic chains are calculated using correlated model Hamiltonians. While correlations reduce the Polarizabilities and extend the range of linear response, the Hyperpolarizabilities essentially are unaffected by the same. This explains the apparently large Hyperpolarizabilities of conjugated electronic systems.

Ordering near the percolation threshold in models of two-dimensional interacting bosons with quenched dilution

Randomly diluted quantum boson and spin models in two dimensions combine the physics of classical percolation with the well-known dimensionality dependence of ordering in quantum lattice models. This combination is rather subtle for models that order in two dimensions but have no true order in one dimension, as the percolation cluster near threshold is a fractal of dimension between 1 and 2: two experimentally relevant examples are the O(2) quantum rotor and the Heisenberg antiferromagnet. We study two analytic descriptions of the O(2) quantum rotor near the percolation threshold. First a spin-wave expansion is shown to predict long-ranged order, but there are statistically rare points on the cluster that violate the standard assumptions of spin-wave theory. A real-space renormalization group (RSRG) approach is then used to understand how these rare points modify ordering of the O(2) rotor. A new class of fixed points of the RSRG equations for disordered one-dimensional bosons is identified and shown to support the existence of long-range order on the percolation backbone in two dimensions. These results are relevant to experiments on bosons in optical lattices and superconducting arrays, and also (qualitatively) for the diluted Heisenberg antiferromagnet La-2(Zn,Mg)(x)Cu1-xO4.