A Sensor Fusion Algorithm for Filtering Pyrometer Measurement Noise in the Czochralski Crystallization Process

The Czochralski (CZ) crystallization process is used to produce monocrystalline silicon for solar cell wafers and electronics. Tight temperature control of the molten silicon is most important for achieving high crystal quality. SINTEF Materials and Chemistry operates a CZ process. During one CZ batch, two pyrometers were used for temperature measurement. The silicon pyrometer measures the temperature of the molten silicon. This pyrometer is assumed to be accurate, but has much high-frequency measurement noise. The graphite pyrometer measures the temperature of a graphite material. This pyrometer has little measurement noise. There is quite a good correlation between the two pyrometer measurements. This paper presents a sensor fusion algorithm that merges the two pyrometer signals for producing a temperature estimate with little measurement noise, while having significantly less phase lag than traditional lowpass- filtering of the silicon pyrometer. The algorithm consists of two sub-algorithms: (i) A dynamic model is used to estimate the silicon temperature based on the graphite pyrometer, and (ii) a lowpass filter and a highpass filter designed as complementary filters. The complementary filters are used to lowpass-filter the silicon pyrometer, highpass-filter the dynamic model output, and merge these filtered signals. Hence, the lowpass filter attenuates noise from the silicon pyrometer, while the graphite pyrometer and the dynamic model estimate those frequency components of the silicon temperature that are lost when lowpass-filtering the silicon pyrometer. The algorithm works well within a limited temperature range. To handle a larger temperature range, more research must be done to understand the process' nonlinear dynamics, and build this into the dynamic model

Spectral Line Removal in the LIGO Data Analysis System (LDAS)

High power in narrow frequency bands, spectral lines, are a feature of an interferometric gravitational wave detector's output. Some lines are coherent between interferometers, in particular, the 2 km and 4 km LIGO Hanford instruments. This is of concern to data analysis techniques, such as the stochastic background search, that use correlations between instruments to detect gravitational radiation. Several techniques of `line removal' have been proposed. Where a line is attributable to a measurable environmental disturbance, a simple linear model may be fitted to predict, and subsequently subtract away, that line. This technique has been implemented (as the command oelslr) in the LIGO Data Analysis System (LDAS). We demonstrate its application to LIGO S1 data.Comment: 11 pages, 5 figures, to be published in CQG GWDAW02 proceeding

The ACIGA Data Analysis programme

The Data Analysis programme of the Australian Consortium for Interferometric Gravitational Astronomy (ACIGA) was set up in 1998 by the first author to complement the then existing ACIGA programmes working on suspension systems, lasers and optics, and detector configurations. The ACIGA Data Analysis programme continues to contribute significantly in the field; we present an overview of our activities.Comment: 10 pages, 0 figures, accepted, Classical and Quantum Gravity, (Proceedings of the 5th Edoardo Amaldi Conference on Gravitational Waves, Tirrenia, Pisa, Italy, 6-11 July 2003

Cell-Type-Specific Cytokinin Distribution within the Arabidopsis Primary Root Apex

Cytokinins (CKs) play a crucial role in many physiological and developmental processes at the levels of individual plant components (cells, tissues, and organs) and by coordinating activities across these parts. High-resolution measurements of intracellular CKs in different plant tissues can therefore provide insights into their metabolism and mode of action. Here, we applied fluorescence-activated cell sorting of green fluorescent protein (GFP)-marked cell types, combined with solid-phase microextraction and an ultra-high-sensitivity mass spectrometry (MS) method for analysis of CK biosynthesis and homeostasis at cellular resolution. This method was validated by series of control experiments, establishing that protoplast isolation and cell sorting procedures did not greatly alter endogenous CK levels. The MS-based method facilitated the quantification of all the well known CK isoprenoid metabolites in four different transgenic Arabidopsis thaliana lines expressing GFP in specific cell populations within the primary root apex. Our results revealed the presence of a CK gradient within the Arabidopsis root tip, with a concentration maximum in the lateral root cap, columella, columella initials, and quiescent center cells. This distribution, when compared with previously published auxin gradients, implies that the well known antagonistic interactions between the two hormone groups are cell type specific

Alleviation of Zn toxicity by low water availability

Heavy metal contamination and drought are expected to increase in large areas worldwide. However, their combined effect on plant performance has been scantly analyzed. This study examines the effect of Zn supply at different water availabilities on morpho-physiological traits of Quercus suber L. in order to analyze the combined effects of both stresses. Seedlings were treated with four levels of zinc from 3 to 150 µM and exposed to low watering (LW) or high watering (HW) frequency in hydroponic culture, using a growth chamber. Under both watering regimes, Zn concentration in leaves and roots increased with Zn increment in nutrient solution. Nevertheless, at the highest Zn doses, Zn tissue concentrations were almost twice in HW than in LW seedlings. Functional traits as leaf photosynthetic rate and root hydraulic conductivity, and morphological traits as root length and root biomass decreased significantly in response to Zn supply. Auxin levels increased with Zn concentrations, suggesting the involvement of this phytohormone in the seedling response to this element. LW seedlings exposed to 150 µM Zn showed higher root length and root biomass than HW seedlings exposed to the same Zn dose. Our results suggest that low water availability could mitigate Zn toxicity by limiting internal accumulation. Morphological traits involved in the response to both stresses probably contributed to this response.This research was funded by the Spanish Ministry of Science and Innovation (Project GRACCIE, Programa Consolider-Ingenio 2010 (CSD 2007-00067) and SURVIVE (CGL-2011-30531-CO2-02)) and Generalitat Valenciana (FEEDBACKS-PROMETEO/2009/006). E. I. Hernández thanks the University of Alicante for her FPU research fellowship. CEAM is supported by Generalitat Valenciana

Aeroelastic Model Structure Computation for Envelope Expansion

Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modeling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion that may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of non-linear aeroelastic systems. The LASSO minimises the residual sum of squares with the addition of an l(Sub 1) penalty term on the parameter vector of the traditional l(sub 2) minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudo-linear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) Active Aeroelastic Wing project using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data

Global parameter identification of stochastic reaction networks from single trajectories

We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems Biology

Aspects of radiative K^+_e3 decays

We re-investigate the radiative charged kaon decay K+- --> pi0 e+- nu_e gamma in chiral perturbation theory, merging the chiral expansion with Low's theorem. We thoroughly analyze the precision of the predicted branching ratio relative to the non-radiative decay channel. Structure dependent terms and their impact on differential decay distributions are investigated in detail, and the possibility to see effects of the chiral anomaly in this decay channel is emphasized.Comment: 15 pages, 6 figure

Aperture synthesis for gravitational-wave data analysis: Deterministic Sources

Gravitational wave detectors now under construction are sensitive to the phase of the incident gravitational waves. Correspondingly, the signals from the different detectors can be combined, in the analysis, to simulate a single detector of greater amplitude and directional sensitivity: in short, aperture synthesis. Here we consider the problem of aperture synthesis in the special case of a search for a source whose waveform is known in detail: \textit{e.g.,} compact binary inspiral. We derive the likelihood function for joint output of several detectors as a function of the parameters that describe the signal and find the optimal matched filter for the detection of the known signal. Our results allow for the presence of noise that is correlated between the several detectors. While their derivation is specialized to the case of Gaussian noise we show that the results obtained are, in fact, appropriate in a well-defined, information-theoretic sense even when the noise is non-Gaussian in character. The analysis described here stands in distinction to ``coincidence analyses'', wherein the data from each of several detectors is studied in isolation to produce a list of candidate events, which are then compared to search for coincidences that might indicate common origin in a gravitational wave signal. We compare these two analyses --- optimal filtering and coincidence --- in a series of numerical examples, showing that the optimal filtering analysis always yields a greater detection efficiency for given false alarm rate, even when the detector noise is strongly non-Gaussian.Comment: 39 pages, 4 figures, submitted to Phys. Rev.