A conditionally linearized Monte Carlo filter in non-linear structural dynamics

State and parameter estimations of non-linear dynamical systems, based on incomplete and noisy measurements, are considered using Monte Carlo simulations. Given the measurements. the proposed method obtains the marginalized posterior distribution of an appropriately chosen (ideally small) subset of the state vector using a particle filter. Samples (particles) of the marginalized states are then used to construct a family of conditionally linearized system of equations and thus obtain the posterior distribution of the states using a bank of Kalman filters. Discrete process equations for the marginalized states are derived through truncated Ito-Taylor expansions. Increased analyticity and reduced dispersion of weights computed over a smaller sample space of marginalized states are the key features of the filter that help achieve smaller sample variance of the estimates. Numerical illustrations are provided for state/parameter estimations of a Duffing oscillator and a 3-DOF non-linear oscillator. Performance of the filter in parameter estimation is also assessed using measurements obtained through experiments on simple models in the laboratory. Despite an added computational cost, the results verify that the proposed filter generally produces estimates with lower sample variance over the standard sequential importance sampling (SIS) filter.

Analysis of a Filter Bag Reactor for gaseous pollution abatement

The conveying zone and the filter bag zone of a Filter Bag Reactor have been analysed as individual reactors. The gas and solid particles flow almost in plug flow through the pneumatic conveying section. In the filter bag the height of the packed column varies with time, a cell model has been used to calculate the concentration of outgoing stream. The total conversion obtained is the sum of conversions in each section of the reactor.

First Report of a Toxic Nodularia spumigena (Nostocales/Cyanobacteria) Bloom in Sub-Tropical Australia. II. Bioaccumulation of Nodularin in Isolated Populations of Mullet (Mugilidae)

Fish collected after a mass mortality at an artificial lake in south-east Queensland, Australia, were examined for the presence of nodularin as the lake had earlier been affected by a Nodularia bloom. Methanol extracts of muscle, liver, peritoneal and stomach contents were analysed by HPLC and tandem mass spectrometry; histological examination was conducted on livers from captured mullet. Livers of sea mullet (Mugil cephalus) involved in the fish kill contained high concentrations of nodularin (median 43.6 mg/kg, range 40.8-47.8 mg/kg dry weight; n = 3) and the toxin was also present in muscle tissue (median 44.0 mu g/kg, range 32.3-56.8 mu g/kg dry weight). Livers of fish occupying higher trophic levels accumulated much lower concentrations. Mullet captured from the lake 10 months later were also found to have high hepatic nodularin levels. DNA sequencing of mullet specimens revealed two species inhabiting the study lake: M. cephalus and an unidentified mugilid. The two mullet species appear to differ in their exposure and/or uptake of nodularin, with M. cephalus demonstrating higher tissue concentrations. The feeding ecology of mullet would appear to explain the unusual capacity of these fish to concentrate nodularin in their livers; these findings may have public health implications for mullet fisheries and aquaculture production where toxic cyanobacteria blooms affect source waters. This report incorporates a systematic review of the literature on nodularin measured in edible fish, shellfish and crustaceans.

Analysis And Design Of Two Layered Ultra Wide Band Filter

Design of an Ultra Wide Band (UWB) filter over 3.1 GHz to 10.6 GHz using broad side coupled and spur lines in microstrip medium suitable for UWB communications has been presented in this paper. Parameters of broad side coupled lines have been appropriately chosen to achieve ultra wide band response. Spur lines have been incorporated at the input and output feed lines of the filter to improve the stop band rejection characteristics of the filter. Filter has been analyzed based on circuit models and full wave simulations. Experimental results of the filter designed using the proposed structure has been verified against the results obtained from circuit models and full wave simulations. The results match satisfactorily. Stop band rejection of better than 20 dB was obtained over the frequencies of 13 GHz to 18.2 GHz. Overall size of the filter is 40 x 18 x 0.787 mm(3).

A Minimax Filter for Systems with Large Plant Uncertainties Using Measurements Corrupted by Colored Noise

A minimax filter is derived to estimate the state of a system, using observations corrupted by colored noise, when large uncertainties in the plant dynamics and process noise are presen.

Wavefront filter for double-exposure holographic interferometry

Measurements of small phase shifts by double-exposure holographic interferometry are facilitated, and ambiguities in the sign of the phase shift eliminated, by introducing a background pattern of interference fringes. A simple and reliable optical system for this purpose utilizing a rotating wedge is described, with which fringes of any desired orientation and spacing can conveniently be obtained. It is shown how this system can be used under certain conditions for measurements of small mechanical deformations.

Constrained linear minimax filter for systems with large plant uncertainties

This paper presents a method of designing a minimax filter in the presence of large plant uncertainties and constraints on the mean squared values of the estimates. The minimax filtering problem is reformulated in the framework of a deterministic optimal control problem and the method of solution employed, invokes the matrix Minimum Principle. The constrained linear filter and its relation to singular control problems has been illustrated. For the class of problems considered here it is shown that the filter can he constrained separately after carrying out the mini maximization. Numorieal examples are presented to illustrate the results.

Constrained linear filter and its relation to singular optimal control

This paper presents an analysis of an optimal linear filter in the presence of constraints on the moan squared values of the estimates from the viewpoint of singular optimal control. The singular arc has been shown to satisfy the generalized Legcndrc-Clebseh condition and Jacobson's condition. Both the cases of white measurement noise and coloured measurement noise are considered. The constrained estimate is shown to be a linear transformation of the unconstrained Kalman estimate.

A semi-analytical particle filter for identification of nonlinear oscillators

Particle filters find important applications in the problems of state and parameter estimations of dynamical systems of engineering interest. Since a typical filtering algorithm involves Monte Carlo simulations of the process equations, sample variance of the estimator is inversely proportional to the number of particles. The sample variance may be reduced if one uses a Rao-Blackwell marginalization of states and performs analytical computations as much as possible. In this work, we propose a semi-analytical particle filter, requiring no Rao-Blackwell marginalization, for state and parameter estimations of nonlinear dynamical systems with additively Gaussian process/observation noises. Through local linearizations of the nonlinear drift fields in the process/observation equations via explicit Ito-Taylor expansions, the given nonlinear system is transformed into an ensemble of locally linearized systems. Using the most recent observation, conditionally Gaussian posterior density functions of the linearized systems are analytically obtained through the Kalman filter. This information is further exploited within the particle filter algorithm for obtaining samples from the optimal posterior density of the states. The potential of the method in state/parameter estimations is demonstrated through numerical illustrations for a few nonlinear oscillators. The proposed filter is found to yield estimates with reduced sample variance and improved accuracy vis-a-vis results from a form of sequential importance sampling filter.

The role of phosphorus as a regulator of bloom-forming diazotrophic cyanobacteria in the Baltic Sea

Eutrophication favours harmful algal blooms worldwide. The blooms cause toxic outbreaks and deteriorated recreational and aesthetic values, causing both economic loss and illness or death of humans and animals. The Baltic Sea is the world s only large brackish water habitat with recurrent blooms of toxic cyanobacteria capable of biological fixation of atmospheric nitrogen gas. Phosphorus is assumed to be the main limiting factor, along with temperature and light, for the growth of these cyanobacteria. This thesis evaluated the role of phosphorus nutrition as a regulating factor for the occurrence of nitrogen-fixing cyanobacteria blooms in the Baltic Sea, utilising experimental laboratory and field studies and surveys on varying spatial scales. Cellular phosphorus sources were found to be able to support substantial growth of the two main bloom forming species Aphanizomenon sp. and Nodularia spumigena. However, N. spumigena growth seemed independent of phosphorus source, whereas, Aphanizomenon sp. grew best in a phosphate enriched environment. Apparent discrepancies with field observations and experiments are explained by the typical seasonal temperature dependent development of Aphanizomenon sp. and N. spumigena biomass allowing the two species to store ambient pre-bloom excess phosphorus in different ways. Field experiments revealed natural cyanobacteria bloom communities to be predominantly phosphorus deficient during blooms. Phosphate additions were found to increase the accumulation of phosphorus relatively most in the planktonic size fraction dominated by the nitrogen-fixing cyanobacteria. Aphanizomenon sp. responded to phosphate additions whereas the phosphorus nutritive status of N. spumigena seemed independent of phosphate addition. The seasonal development of phosphorus deficiency is different for the two species with N. spumigena showing indications of phosphorus deficiency during a longer time period in the open sea. Coastal upwelling introduces phosphorus to the surface layer during nutrient deficient conditions in summer. The species-specific ability of Aphanizomenon sp. and N. spumigena to utilise phosphate enrichment of the surface layer caused by coastal upwelling was clarified. Typical bloom time vertical distributions of biomass maxima were found to render N. spumigena more susceptible to advection by surface currents caused by coastal upwellings. Aphanizomenon sp. populations residing in the seasonal thermocline were observed to be able to utilise the phosphate enrichment and a bloom was produced with a two to three week time lag subsequent to the relaxation of upwelling. Consistent high concentrations of dissolved inorganic phosphorus, caused by persistent internal loading of phosphorus, was found to be the main source of phosphorus for large-scale pelagic blooms. External loads were estimated to contribute with only a fraction of available phosphorus for open sea blooms. Remineralization of organic forms of phosphorus along with vertical mixing to the permanent halocline during winter set the level of available phosphorus for the next growth season. Events such as upwelling are important in replenishing phosphate concentrations during the nutrient deplete growth season. Autecological characteristics of the two main bloom forming species favour Aphanizomenon sp. populations in utilising the abundant excess phosphate concentrations and phosphate pulses mediated through upwelling. Whilst, N. spumigena displays predominant phosphorus limited growth mode and relies on more scarce cellular phosphorus stores and presumably dissolved organic phosphorus compounds for growth. The Baltic Sea is hypothesised to be in an inhibited state of recovery due to the extensive historical external nutrient loading, extensive internal phosphorus loading and the substantial nitrogen load caused by cyanobacteria nitrogen fixation. This state of the sea is characterised as a vicious circle .

A Kalman filter based strategy for linear structural system identification based on multiple static and dynamic test data

The problem of identification of stiffness, mass and damping properties of linear structural systems, based on multiple sets of measurement data originating from static and dynamic tests is considered. A strategy, within the framework of Kalman filter based dynamic state estimation, is proposed to tackle this problem. The static tests consists of measurement of response of the structure to slowly moving loads, and to static loads whose magnitude are varied incrementally; the dynamic tests involve measurement of a few elements of the frequency response function (FRF) matrix. These measurements are taken to be contaminated by additive Gaussian noise. An artificial independent variable τ, that simultaneously parameterizes the point of application of the moving load, the magnitude of the incrementally varied static load and the driving frequency in the FRFs, is introduced. The state vector is taken to consist of system parameters to be identified. The fact that these parameters are independent of the variable τ is taken to constitute the set of ‘process’ equations. The measurement equations are derived based on the mechanics of the problem and, quantities, such as displacements and/or strains, are taken to be measured. A recursive algorithm that employs a linearization strategy based on Neumann’s expansion of structural static and dynamic stiffness matrices, and, which provides posterior estimates of the mean and covariance of the unknown system parameters, is developed. The satisfactory performance of the proposed approach is illustrated by considering the problem of the identification of the dynamic properties of an inhomogeneous beam and the axial rigidities of members of a truss structure.

Learning Feature Trajectories Using Gabor Filter Bank for Human Activity Segmentation and Recognition

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using dynamic time warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique.

Robust joint precoder/receive filter designs for multiuser MIMO downlink

In this paper, we consider robust joint linear precoder/receive filter designs for multiuser multi-input multi-output (MIMO) downlink that minimize the sum mean square error (SMSE) in the presence of imperfect channel state information at the transmitter (CSIT). The base station (BS) is equipped with multiple transmit antennas, and each user terminal is equipped with one or more receive antennas. We consider a stochastic error (SE) model and a norm-bounded error (NBE) model for the CSIT error. In the case of CSIT error following SE model, we compute the desired downlink precoder/receive filter matrices by solving the simpler uplink problem by exploiting the uplink-downlink duality for the MSE region. In the case of the CSIT error following the NBE model, we consider the worst-case SMSE as the objective function, and propose an iterative algorithm for the robust transceiver design. The robustness of the proposed algorithms to imperfections in CSIT is illustrated through simulations.

Citizens breaking out of filter bubbles: Urban screens as civic media

Social media platforms risk polarising public opinions by employing proprietary algorithms that produce filter bubbles and echo chambers. As a result, the ability of citizens and communities to engage in robust debate in the public sphere is diminished. In response, this paper highlights the capacity of urban interfaces, such as pervasive displays, to counteract this trend by exposing citizens to the socio-cultural diversity of the city. Engagement with different ideas, networks and communities is crucial to both innovation and the functioning of democracy. We discuss examples of urban interfaces designed to play a key role in fostering this engagement. Based on an analysis of works empirically-grounded in field observations and design research, we call for a theoretical framework that positions pervasive displays and other urban interfaces as civic media. We argue that when designed for more than wayfinding, advertisement or television broadcasts, urban screens as civic media can rectify some of the pitfalls of social media by allowing the polarised user to break out of their filter bubble and embrace the cultural diversity and richness of the city.

Single-resolution and multiresolution extended-Kalman-filter-based reconstruction approaches to optical refraction tomography

The problem of reconstruction of a refractive-index distribution (RID) in optical refraction tomography (ORT) with optical path-length difference (OPD) data is solved using two adaptive-estimation-based extended-Kalman-filter (EKF) approaches. First, a basic single-resolution EKF (SR-EKF) is applied to a state variable model describing the tomographic process, to estimate the RID of an optically transparent refracting object from noisy OPD data. The initialization of the biases and covariances corresponding to the state and measurement noise is discussed. The state and measurement noise biases and covariances are adaptively estimated. An EKF is then applied to the wavelet-transformed state variable model to yield a wavelet-based multiresolution EKF (MR-EKF) solution approach. To numerically validate the adaptive EKF approaches, we evaluate them with benchmark studies of standard stationary cases, where comparative results with commonly used efficient deterministic approaches can be obtained. Detailed reconstruction studies for the SR-EKF and two versions of the MR-EKF (with Haar and Daubechies-4 wavelets) compare well with those obtained from a typically used variant of the (deterministic) algebraic reconstruction technique, the average correction per projection method, thus establishing the capability of the EKF for ORT. To the best of our knowledge, the present work contains unique reconstruction studies encompassing the use of EKF for ORT in single-resolution and multiresolution formulations, and also in the use of adaptive estimation of the EKF's noise covariances. (C) 2010 Optical Society of America