FAST LINEAR ESTIMATION METHODS FOR VECTOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. Three linear methods for estimating parameter values of vector auto-regressive moving-average (VARMA) models which are in general at least an order of magnitude faster than maximum likelihood estimation are developed in this paper. Simulation results for different model structures with varying numbers of component series and observations suggest that the accuracy of these procedures is in most cases comparable with maximum likelihood estimation. Procedures for estimating parameter standard error are also discussed and used for identification of nonzero elements in the VARMA polynomial structures. These methods can also be used to establish the order of the VARMA structure. We note, however, that the primary purpose of these estimates is to generate initial estimates for the nonzero parameters in order to reduce subsequent computational time of more efficient estimation procedures such as exact maximum likelihood.     

Comparison of entropy and complexity measures for the assessment of depth of sedation

Entropy and complexity of the electroencephalogram (EEG) have recently been proposed as measures of depth of anesthesia and sedation. Using surrogate data of predefined spectrum and probability distribution we show that the various algorithms used for the calculation of entropy and complexity actually measure different properties of the signal. The tested methods, Shannon entropy (ShEn), spectral entropy, approximate entropy (ApEn), Lempel-Ziv complexity (LZC), and Higuchi fractal dimension (HFD) are then applied to the EEG signal recorded during sedation in the intensive care unit (ICU). It is shown that the applied measures behave in a different manner when compared to clinical depth of sedation score--the Ramsay score. ShEn tends to increase while the other tested measures decrease with deepening sedation. ApEn, LZC, and HFD are highly sensitive to the presence of high-frequency components in the EEG signal.     

The importance of accurate visibility parameterization during atmospheric correction: impact on boreal forest classification

Observation of the Earth's surface from spaceborne platforms is complicated by the various layers of the Earth's atmosphere that reflect, scatter, and attenuate electromagnetic radiation passing through them, thus influencing (upward or downward) the signal strength recorded at the sensor relative to the true quantity of radiance reflected from the observed surfaces. The magnitude and spatial distribution of atmospheric effects is non-stationary and will vary due to numerous factors. While the effect of these factors cannot be eliminated completely, the understanding of radiative transfer physics, atmospheric states, and electromagnetic wave propagation permits much of these effects to be appropriately modelled and minimized. Such corrections for atmospheric effects permit the extraction of more accurate physical properties of surface materials and states from imagery than if atmospheric effects were ignored. Modelling of atmospheric effects with radiative transfer models, however, requires appropriate parameterization. We explore the sensitivity of the important visibility parameter of the popular Atmospheric and Topographic Correction (ATCOR) model for atmospheric correction over boreal forest land cover. Further, we provide a methodology for estimating reasonable values for the visibility parameter in the event that this information is not readily available. Our sensitivity analyses, performed on Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery from northern Québec and Ontario, rely on both incremental adjustments to the visibility parameter to assess the degree of atmospheric effect removal and the cascading effect on land-cover classification. We build confidence around our measures using a spatial bootstrapping analysis within each of the two images we analyse. Our analysis demonstrates that exceeding a magnitude of error of approximately 2 km in estimating a visibility parameter values can decrease classification accuracy by nearly 10%. Our assessments of the spatial structure of the mitigated atmospheric component within our scenes, testing for complete spatial randomness, clustering of like values, or evenness in value distributions are inconclusive, but hint towards more clustered results with greater magnitudes of parameterization error.     

Study of the factors determining the mobility of ions in the polypyrrole films doped with aromatic sulfonate anions

The electrochemical redox behavior of the polypyrrole (PPy) films doped with benzenesulfonate (BS), p-toluenesulfonate (pTS) and naphtalene-1,5-disulfonate (NDS) was investigated by cyclic voltammetry, the chemical composition of the films was determined by XDS, the surface morphology was characterized by AFM, and the interactions between pyrrole oligomers and the anions were modeled with quantum chemical methods. It is the first detailed comparative study of the properties of these interesting systems with two complementing interactions (electrostatic and aromatic stacking). It was shown than these relatively similar aromatic sulfonate anions have rather different behavior in PPy films. The results showed that the redoxactivity of polypyrrole films doped with sulfonate ions increases in the sequence pTS < BS < NDS. The comparison of voltammograms measured in the presence of different anions with PPy films synthesized in the same solution show that the PPy/pTS films have the most densely packed and the PPy/NDS films the least densely packed structure of the three. The mobility of ions in these films is mainly determined by the packing of the films, which in turn partly depends on the interactions between the dopant-anion and polymer chains.     

Transport of carbon-11-methionine is enhanced by insulin

The anabolic effects of insulin are not restricted to carbohydrate and lipid metabolism but also include protein metabolism. However, the effects of insulin on protein metabolism have been difficult to demonstrate in vivo. Amino acid transport is partly regulated by insulin according to the experimental data. PET provides a way to measure fractional uptake rates of amino acids. The purpose of this study was to measure the effect of insulin on amino acid transport from the plasma to the human parotid glands. METHODS: We compared the uptake of L-[methyl-11C]methionine ([11C]methionine) into the parotid glands and cerebellum in seven healthy volunteers during the fasting state and euglycemic insulin clamp technique (1 mU/kg per minute). RESULTS: The fractional uptake rate of [11C]methionine was increased by 31% for the right parotid gland (p = 0.003) and by 29% for the left parotid gland (p = 0.009) during insulin clamp, while the increase was 19% for the cerebellum (p = 0.01). The concentration of amino acids typical for the hormone-sensitive transport system A was 11% lower during insulin infusion than in the fasting state. CONCLUSION: The uptake of methionine into brain tissue does not seem to be under major control by insulin, while the transport of methionine in the parotid glands is stimulated by insulin. PET provides a sophisticated method to study the transport system of amino acids in vivo.     

High accuracy CMOS position-sensitive photodetector (PSD)

A high-accuracy position-sensitive photodetector (PSD) for integrated sensor systems is presented. A 2-axis PSD composed of a dense array of vertical phototransistors and two arrays of polysilicon resistors was implemented using standard CMOS technology. Test results show better resolution and linearity as compared to a high-quality conventional PSD     

Bi-level optimization for a dynamic multiobjective problem

In this article, a bi-level optimization problem covering upper (design) and lower (operation) levels is defined and a solution procedure for bi-level optimization problems is presented. This is devised as a dynamic multiobjective optimization problem, i.e. the values of the control and state variables change over a predefined time horizon and several competing criteria are optimized simultaneously. Moreover, the interaction between the upper and lower levels is analysed. The benefits of bi-level dynamic multiobjective optimization are illustrated in detail by examining an industrial case in which the design of a paper mill (upper level) and the mill operation (lower level) are optimized at the same time. However, the problem definition and the solution procedure are not limited to any specific application but can be exploited in many different industrial areas.