Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.

Passively Track WiFi Users with an Enhanced Particle Filter using Power-based Ranging

Passive positioning systems produce user location information for third-party providers of positioning services. Since the tracked wireless devices do not participate in the positioning process, passive positioning can only rely on simple, measurable radio signal parameters, such as timing or power information. In this work, we provide a passive tracking system for WiFi signals with an enhanced particle filter using fine-grained power-based ranging. Our proposed particle filter provides an improved likelihood function on observation parameters and is equipped with a modified coordinated turn model to address the challenges in a passive positioning system. The anchor nodes for WiFi signal sniffing and target positioning use software defined radio techniques to extract channel state information to mitigate multipath effects. By combining the enhanced particle filter and a set of enhanced ranging methods, our system can track mobile targets with an accuracy of 1.5m for 50% and 2.3m for 90% in a complex indoor environment. Our proposed particle filter significantly outperforms the typical bootstrap particle filter, extended Kalman filter and trilateration algorithms.

From irradiance to output power fluctuations: the pv plant as a low pass filter

The power generated by large grid-connected photovoltaic (PV) plants depends greatly on the solar irradiance. This paper studies the effects of the solar irradiance variability analyzing experimental 1-s data collected throughout a year at six PV plants, totaling 18 MWp. Each PV plant was modeled as a first order filter function based on an analysis in the frequency domain of the irradiance data and the output power signals. An empiric expression which relates the filter parameters and the PV plant size has been proposed. This simple model has been successfully validated precisely determining the daily maximum output power fluctuation from incident irradiance measurements.

Methodology for adapting the parameters of a fuzzy system using the extended Kalman filter

When we try to analyze and to control a system whose model was obtained only based on input/output data, accuracy is essential in the model. On the other hand, to make the procedure practical, the modeling stage must be computationally efficient. In this regard, this paper presents the application of extended Kalman filter for the parametric adaptation of a fuzzy model

Two-layer particle filter for multiple target detection and tracking

This paper deals with the detection and tracking of an unknown number of targets using a Bayesian hierarchical model with target labels. To approximate the posterior probability density function, we develop a two-layer particle filter. One deals with track initiation, and the other with track maintenance. In addition, the parallel partition method is proposed to sample the states of the surviving targets.

Analysis and optimized design of a distributed multi-stage EMC filter for an interleaved three-phase PWM-rectifier system for aircraft applications

Different possible input filter configurations for a modular three-phase PWM rectifier system consisting of three interleaved converter cells are studied. The system is designed for an aircraft application where MIL-STD-461E conducted EMI standards have to be met and system weight is a critical design issue. The importance of a LISN model on the simulated noise levels and the effect of interleaving and power unbalance between the different converter modules is discussed. The effect of the number of filter stages and the degree of distribution of the filter stages among the individual converter modules on the weight and losses of the input filter is studied and optimal filter structures are proposed.

The effect of adsorption, filter material and point of dilution on antibiotic elimination by haemofiltration - An in vitro study of levofloxacin

We studied an in vitro model of continuous venous-venous haemofiltration (CVVH), into which levofloxacin 100 mg was infused, to determine levofloxacin adsorption and to determine the effect of filter material and point of dilution (pre- or post-filter) on sieving coefficient. Mean (standard deviation; S.D.) adsorption was 18.7 (5.3) mg for the polyamide filter and 40.2 (2.0) mg for the polyacrylonitrile (PAN) filter (P < 0.001). Post-dilution resulted in a minor, but statistically significant, decrease in sieving coefficient (pre-dilution 0.96 (S.D. 0.10), post-dilution 0.88 (S.D. 0.11) with the PAN filter. These data indicate that the variability in published values for levofloxacin sieving coefficient are not due to variation in point of dilution or membrane type (PAN or polyamide). Significant adsorption of levofloxacin onto PAN filters occurs. (C) 2004 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.

Characterizing the physical and chemical properties of a vermicompost filter bed

Vermicompost filtration is a new on-site waste treatment system. Consequently, little is known about the filter medium properties. The aim of this preliminary study was to quantify physical and compositional properties of vermicompost filter beds that had been used to treat domestic solid organic waste and wastewater. This paper presents the trials performed on pilot-scale reactors filled with vermicompost from a full-scale vermicompost filtration system. Household solid organic waste and raw wastewater at the rate of 130 L/m(2)/d was applied to the reactor bed surface over a four-month period. It was found that fresh casts laid on the bed surface had a BOD of 1290 mg/g VS while casts buried to a depth of 10 cm had a BOD of 605 mg/g VS. Below this depth there was little further biodegradation of earthworm casts despite cast ages of up to five years. Solid material in the reactor accounted for only 7-10% of the reactor volume. The total voidage comprised of large free-draining pores, which accounted for 15-20% of the reactor volume and 60-70% micropores, able to hold up water against gravity. It was shown that water could flow through the medium micropores and macropores following a wastewater application. The wastewater flow characteristics were modeled by a two-region model based on the Richards Equation, an equation used to describe porous spatially heterogeneous materials.

Bayesian regression filter and the issue of priors

We propose a Bayesian framework for regression problems, which covers areas which are usually dealt with by function approximation. An online learning algorithm is derived which solves regression problems with a Kalman filter. Its solution always improves with increasing model complexity, without the risk of over-fitting. In the infinite dimension limit it approaches the true Bayesian posterior. The issues of prior selection and over-fitting are also discussed, showing that some of the commonly held beliefs are misleading. The practical implementation is summarised. Simulations using 13 popular publicly available data sets are used to demonstrate the method and highlight important issues concerning the choice of priors.

Development of an electrically tuneable Bragg grating filter in polymer optical fibre operating at 1.55 µm

We present a thorough study on the development of a polymer optical fibre-based tuneable filter utilizing an intra-core Bragg grating that is electrically tuneable, operating at 1.55 µm. The Bragg grating is made tuneable using a thin-film resistive heater deposited on the surface of the fibre. The polymer fibre was coated via the photochemical deposition of a Pd/Cu metallic layer with the procedure induced by VUV radiation at room temperature. The resulting device, when wavelength tuned via Joule heating, underwent a wavelength shift of 2 nm for a moderate input power of 160 mW, a wavelength to input power coefficient of -13.4 pm mW-1 and time constant of 1.7 s-1. A basic theoretical study verified that for this fibre type one can treat the device as a one-dimensional system. The model was extended to include the effect of input electrical power changes on the refractive index of the fibre and subsequently to changes in the Bragg wavelength of the grating, showing excellent agreement with the experimental measurements.

Chromatic dispersion effect in a microwave photonic filter using superstructured fiber Bragg grating and dispersive fiber

In this paper a microwave photonic filter using superstructured fiber Bragg grating and dispersive fiber is investigated. A theoretical model to describe the transfer function of the filter taking into consideration the spectral width of light source is established. Experiments are carried out to verify the theoretical analysis. Both theoretical and experimental results indicate that due to chromatic dispersion the source spectral width introduces an additional power penalty to the microwave photonic response of the filter.

Development of an electrically tuneable Bragg grating filter in polymer optical fibre operating at 1.55 νm

We present a thorough study on the development of a polymer optical fibre-based tuneable filter utilizing an intra-core Bragg grating that is electrically tuneable, operating at 1.55 νm. The Bragg grating is made tuneable using a thin-film resistive heater deposited on the surface of the fibre. The polymer fibre was coated via the photochemical deposition of a Pd/Cu metallic layer with the procedure induced by VUV radiation at room temperature. The resulting device, when wavelength tuned via Joule heating, underwent a wavelength shift of 2 nm for a moderate input power of 160 mW, a wavelength to input power coefficient of -13.4 pm mW-1 and time constant of 1.7 s-1. A basic theoretical study verified that for this fibre type one can treat the device as a one-dimensional system. The model was extended to include the effect of input electrical power changes on the refractive index of the fibre and subsequently to changes in the Bragg wavelength of the grating, showing excellent agreement with the experimental measurements. © 2007 IOP Publishing Ltd.

Chromatic dispersion effect in a microwave photonic filter using superstructured fiber Bragg grating and dispersive fiber

In this paper a microwave photonic filter using superstructured fiber Bragg grating and dispersive fiber is investigated. A theoretical model to describe the transfer function of the filter taking into consideration the spectral width of light source is established. Experiments are carried out to verify the theoretical analysis. Both theoretical and experimental results indicate that due to chromatic dispersion the source spectral width introduces an additional power penalty to the microwave photonic response of the filter. © 2005 Optical Society of America.

Use of active power filter to reduce the effects of harmonics in solid oxide fuel cells

The Solid Oxide Fuel Cell (SOFC) is a class of fuel cells that is capable of generating very high levels of power at high temperatures. SOFCs are used for stationary power generation and as Combined Heat and Power (CHP) systems. In spite of all the beneficial features of the SOFC, the propagation of ripple currents, due to nonlinear loads, is a challenging problem, as it interferes with the physical operation of the fuel cell. The purpose of this thesis is to identify the cause of ripples and attempt to eliminate or reduce the ripple propagation through the use of Active Power Filters (APF). To this end, a systematic approach to modeling the fuel cell to account for its nonlinear behavior in the presence of current ripples is presented. A model of a small fuel cell power system which consists of a fuel cell, a DC-DC converter, a single-phase inverter and a nonlinear load is developed in MATLAB/Simulink environment. The extent of ripple propagation, due to variations in load magnitude and frequency, are identified using frequency spectrum analysis. In order to reduce the effects of ripple propagation, an APF is modeled to remove ripples from the DC fuel cell current. The emphasis of this thesis is based on the idea that small fuel cell systems cannot implement large passive filters to cancel the effects of ripple propagation and hence, the compact APF topology effectively protects the fuel cell from propagating ripples and improves its electrical performance.