Variance estimation with Chao's sampling scheme

We show that the Hájek (Ann. Math Statist. (1964) 1491) variance estimator can be used to estimate the variance of the Horvitz–Thompson estimator when the Chao sampling scheme (Chao, Biometrika 69 (1982) 653) is implemented. This estimator is simple and can be implemented with any statistical packages. We consider a numerical and an analytic method to show that this estimator can be used. A series of simulations supports our findings.

A simple variance formula for population size estimators by conditioning

This note considers the variance estimation for population size estimators based on capture–recapture experiments. Whereas a diversity of estimators of the population size has been suggested, the question of estimating the associated variances is less frequently addressed. This note points out that the technique of conditioning can be applied here successfully which also allows us to identify sources of variation: the variance due to estimation of the model parameters and the binomial variance due to sampling n units from a population of size N. It is applied to estimators typically used in capture–recapture experiments in continuous time including the estimators of Zelterman and Chao and improves upon previously used variance estimators. In addition, knowledge of the variances associated with the estimators by Zelterman and Chao allows the suggestion of a new estimator as the weighted sum of the two. The decomposition of the variance into the two sources allows also a new understanding of how resampling techniques like the Bootstrap could be used appropriately. Finally, the sample size question for capture–recapture experiments is addressed. Since the variance of population size estimators increases with the sample size, it is suggested to use relative measures such as the observed-to-hidden ratio or the completeness of identification proportion for approaching the question of sample size choice.

Stabilization of a helical water chain in a metal-organic host of a trinuclear Schiff base complex

Three heterometallic trinuclear Schiff base complexes, [{GuL(1)(H2O)}(2)Ni(CN)(4)]center dot 4H(2)O (1), [{CuL2(H2O)}(2)Ni(CN)(4)] (2), and [{CuL3(H2O)}(2)Ni(CN)(4)] (3) (HL1 = 7-amino-4-methyl-5-azahept-3-en-2-one, HL2 = 7-methylamino-4-methyl-5-azahept-3-en-2-one, and HL3 = 7-dimethylamino-4-methyl-5-azahept-3-en-2-one), were synthesized. All three complexes were characterized by elemental analysis, IR and UV spectroscopies, and thermal analysis. Two of them (1 and 3) were also characterized by single crystal X-ray crystallography. Complex 1 forms a hydrogen-bonded one-dimensional metal-organic framework that stabilizes a helical water chain into its cavity, but when any of the amine hydrogen atoms of the Schiff base are replaced by methyl groups, as in L 2 and L 3, the water chain, vanishes, showing explicitly the importance of the host-guest H-bonding interactions for the stabilization of a water cluster.

Stabilization of two smallest possible diastereomeric beta-hairpins in a water soluble tetrapeptide containing non-coded alpha-amino isobutyric acid (Aib) and m-amino benzoic acid

Single crystal X-ray diffraction study reveals that the water soluble tetrapeptide H2N-Ile-Aib-Leu-m-ABA-CO2H, containing non-coded Aib (alpha-amino isobutyric acid) and m-ABA (meta-amino benzoic acid), crystallizes with two smallest possible diastereomeric beta-hairpin molecules in the asymmetric unit. Although in both of the molecules the chiralities at Ile(1) and Leu(3) are S, a conformational reversal in the back bone chain is observed to produce the beta-hairpins with beta-turn conformations of type II and II'. Interestingly Aib which is known to adopt helical conformation, adopts unusual semi-extended conformation with phi: -49.5(5)degrees, psi: 135.2(5)degrees in type II and phi: 50.6(6)degrees. psi: -137.0(4)degrees in type II' for occupying the i + 1 position of the beta-turns. The two hairpin molecules are further interlocked through intermolecular hydrogen bonds and electrostatic interactions between CO2- and -+NH3 groups to form dimeric supramolecular beta-hairpin aggregate in the crystal state. The CD measurement and 2D NMR study of the peptide in aqueous medium support the existence of beta-hairpin structure in water. (C) 2009 Elsevier B.V. All rights reserved.

Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems

In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

Vertical velocity variance and skewness in clear and cloud-topped boundary layers as revealed by Doppler lidar

In this paper, observations by a ground-based vertically pointing Doppler lidar and sonic anemometer are used to investigate the diurnal evolution of boundary-layer turbulence in cloudless, cumulus and stratocumulus conditions. When turbulence is driven primarily by surface heating, such as in cloudless and cumulus-topped boundary layers, both the vertical velocity variance and skewness follow similar profiles, on average, to previous observational studies of turbulence in convective conditions, with a peak skewness of around 0.8 in the upper third of the mixed layer. When the turbulence is driven primarily by cloud-top radiative cooling, such as in the presence of nocturnal stratocumulus, it is found that the skewness is inverted in both sign and height: its minimum value of around −0.9 occurs in the lower third of the mixed layer. The profile of variance is consistent with a cloud-top cooling rate of around 30Wm−2. This is also consistent with the evolution of the thermodynamic profile and the rate of growth of the mixed layer into the stable nocturnal boundary layer from above. In conditions where surface heating occurs simultaneously with cloud-top cooling, the skewness is found to be useful for diagnosing the source of the turbulence, suggesting that long-term Doppler lidar observations would be valuable for evaluating boundary-layer parametrization schemes. Copyright c 2009 Royal Meteorological Society

Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

Auto-weighted minimum-variance self-tuning controller

A self-tuning controller which automatically assigns weightings to control and set-point following is introduced. This discrete-time single-input single-output controller is based on a generalized minimum-variance control strategy. The automatic on-line selection of weightings is very convenient, especially when the system parameters are unknown or slowly varying with respect to time, which is generally considered to be the type of systems for which self-tuning control is useful. This feature also enables the controller to overcome difficulties with non-minimum phase systems.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Robust blind deconvolution algorithm: variance approximation and series decoupling

An alternative blind deconvolution algorithm for white-noise driven minimum phase systems is presented and verified by computer simulation. This algorithm uses a cost function based on a novel idea: variance approximation and series decoupling (VASD), and suggests that not all autocorrelation function values are necessary to implement blind deconvolution.

Hydrogen-bond assisted stabilization of the less favored conformation of a tridentate Schiff base ligand in dinuclear nickel(II) complex: an experimental and theoretical study

The Schiff base ligand, HL (2-[1-(3-methylamino-propylimino)-ethyl]-phenol), the 1:1 condensation product of 2-hydroxy acetophenone and N-methyl-1,3-diaminopropane, has been synthesized and characterized by X-ray crystallography as the perchlorate salt [H2L]ClO4 (1). The structure consists of discrete [H2L](+) cations and perchlorate anions. Two dinuclear Ni-II complexes, [Ni2L2(NO2)(2)] (2), [Ni2L2(NO3)(2)] (3) have been synthesized using this ligand and characterized by single crystal X-ray analyses. Complexes 2 and 3 are centrosymmetric dimers in which the Ni-II ions are in distorted fac- and mer-octahedral environments, respectively, bridged by two mu(2)-phenolate ions of deprotonated ligand, L. The plane of the phenyl rings and the Ni2O2 basal plane are nearly coplanar in 2 but almost perpendicular in 3. We have studied and explained this different behavior using high level DFT calculations (RI-BP86/def2-TZVP level of theory). The conformation observed in 3, which is energetically less favorable, is stabilized via intermolecular non-covalent interactions. Under the excitation of ultraviolet light, characteristic fluorescence of compound 1 was observed; by comparison fluorescence intensity decreases in case of compound 3 and completely quenched in compound 2.

A linear CDMA detector based on minimum variance distortionless response (MVDR)

A bit-level processing (BLP) based linear CDMA detector is derived following the principle of minimum variance distortionless response (MVDR). The combining taps for the MVDR detector are determined from (1) the covariance matrix of the matched filter output, and (2) the corresponding row (or column) of the user correlation matrix. Due to the interference suppression capability of MVDR and the fact that no inversion of the user correlation matrix is involved, the influence of the synchronisation errors is greatly reduced. The detector performance is demonstrated via computer simulations (both synchronisation errors and intercell interference are considered).