Performance comparison of the optimal and the zero-forcing beamforming algorithms under practical conditions

The optimal and the zero-forcing beamformers are two commonly used algorithms in the subspace-based blind beamforming technology. The optimal beamformer is regarded as the algorithm with the best output SINR. The zero-forcing algorithm emphasizes the co-channel interference cancellation. This paper compares the performance of these two algorithms under some practical conditions: the effect of the finite data length and the existence of the angle estimation error. The investigation reveals that the zero-forcing algorithm can be more robust in the practical environment than the optimal algorithm.

Quantitative precipitation estimation over ocean using Bayesian approach from microwave observations during the typhoon season

We have developed a new Bayesian approach to retrieve oceanic rain rate from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), with an emphasis on typhoon cases in the West Pacific. Retrieved rain rates are validated with measurements of rain gauges located on Japanese islands. To demonstrate improvement, retrievals are also compared with those from the TRMM/Precipitation Radar (PR), the Goddard Profiling Algorithm (GPROF), and a multi-channel linear regression statistical method (MLRS). We have found that qualitatively, all methods retrieved similar horizontal distributions in terms of locations of eyes and rain bands of typhoons. Quantitatively, our new Bayesian retrievals have the best linearity and the smallest root mean square (RMS) error against rain gauge data for 16 typhoon overpasses in 2004. The correlation coefficient and RMS of our retrievals are 0.95 and ~2 mm hr-1, respectively. In particular, at heavy rain rates, our Bayesian retrievals outperform those retrieved from GPROF and MLRS. Overall, the new Bayesian approach accurately retrieves surface rain rate for typhoon cases. Accurate rain rate estimates from this method can be assimilated in models to improve forecast and prevent potential damages in Taiwan during typhoon seasons.

Extended optimal estimation techniques for sea surface temperature from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI)

Sea surface temperature (SST) can be estimated from day and night observations of the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) by optimal estimation (OE). We show that exploiting the 8.7 μm channel, in addition to the “traditional” wavelengths of 10.8 and 12.0 μm, improves OE SST retrieval statistics in validation. However, the main benefit is an improvement in the sensitivity of the SST estimate to variability in true SST. In a fair, single-pixel comparison, the 3-channel OE gives better results than the SST estimation technique presently operational within the Ocean and Sea Ice Satellite Application Facility. This operational technique is to use SST retrieval coefficients, followed by a bias-correction step informed by radiative transfer simulation. However, the operational technique has an additional “atmospheric correction smoothing”, which improves its noise performance, and hitherto had no analogue within the OE framework. Here, we propose an analogue to atmospheric correction smoothing, based on the expectation that atmospheric total column water vapour has a longer spatial correlation length scale than SST features. The approach extends the observations input to the OE to include the averaged brightness temperatures (BTs) of nearby clear-sky pixels, in addition to the BTs of the pixel for which SST is being retrieved. The retrieved quantities are then the single-pixel SST and the clear-sky total column water vapour averaged over the vicinity of the pixel. This reduces the noise in the retrieved SST significantly. The robust standard deviation of the new OE SST compared to matched drifting buoys becomes 0.39 K for all data. The smoothed OE gives SST sensitivity of 98% on average. This means that diurnal temperature variability and ocean frontal gradients are more faithfully estimated, and that the influence of the prior SST used is minimal (2%). This benefit is not available using traditional atmospheric correction smoothing.

Sea surface temperature estimation from the Geostationary Operational Environmental Satellite 12 (GOES-12)

This paper describes the techniques used to obtain sea surface temperature (SST) retrievals from the Geostationary Operational Environmental Satellite 12 (GOES-12) at the National Oceanic and Atmospheric Administration’s Office of Satellite Data Processing and Distribution. Previous SST retrieval techniques relying on channels at 11 and 12 μm are not applicable because GOES-12 lacks the latter channel. Cloud detection is performed using a Bayesian method exploiting fast-forward modeling of prior clear-sky radiances using numerical weather predictions. The basic retrieval algorithm used at nighttime is based on a linear combination of brightness temperatures at 3.9 and 11 μm. In comparison with traditional split window SSTs (using 11- and 12-μm channels), simulations show that this combination has maximum scatter when observing drier colder scenes, with a comparable overall performance. For daytime retrieval, the same algorithm is applied after estimating and removing the contribution to brightness temperature in the 3.9-μm channel from solar irradiance. The correction is based on radiative transfer simulations and comprises a parameterization for atmospheric scattering and a calculation of ocean surface reflected radiance. Potential use of the 13-μm channel for SST is shown in a simulation study: in conjunction with the 3.9-μm channel, it can reduce the retrieval error by 30%. Some validation results are shown while a companion paper by Maturi et al. shows a detailed analysis of the validation results for the operational algorithms described in this present article.

Optimal estimation of sea surface temperature from split-window observations

Optimal estimation (OE) improves sea surface temperature (SST) estimated from satellite infrared imagery in the “split-window”, in comparison to SST retrieved using the usual multi-channel (MCSST) or non-linear (NLSST) estimators. This is demonstrated using three months of observations of the Advanced Very High Resolution Radiometer (AVHRR) on the first Meteorological Operational satellite (Metop-A), matched in time and space to drifter SSTs collected on the global telecommunications system. There are 32,175 matches. The prior for the OE is forecast atmospheric fields from the Météo-France global numerical weather prediction system (ARPEGE), the forward model is RTTOV8.7, and a reduced state vector comprising SST and total column water vapour (TCWV) is used. Operational NLSST coefficients give mean and standard deviation (SD) of the difference between satellite and drifter SSTs of 0.00 and 0.72 K. The “best possible” NLSST and MCSST coefficients, empirically regressed on the data themselves, give zero mean difference and SDs of 0.66 K and 0.73 K respectively. Significant contributions to the global SD arise from regional systematic errors (biases) of several tenths of kelvin in the NLSST. With no bias corrections to either prior fields or forward model, the SSTs retrieved by OE minus drifter SSTs have mean and SD of − 0.16 and 0.49 K respectively. The reduction in SD below the “best possible” regression results shows that OE deals with structural limitations of the NLSST and MCSST algorithms. Using simple empirical bias corrections to improve the OE, retrieved minus drifter SSTs are obtained with mean and SD of − 0.06 and 0.44 K respectively. Regional biases are greatly reduced, such that the absolute bias is less than 0.1 K in 61% of 10°-latitude by 30°-longitude cells. OE also allows a statistic of the agreement between modelled and measured brightness temperatures to be calculated. We show that this measure is more efficient than the current system of confidence levels at identifying reliable retrievals, and that the best 75% of satellite SSTs by this measure have negligible bias and retrieval error of order 0.25 K.

Calibration of structure in a distributed forecasting model for a semiarid flash flood: dynamic surface storage and channel roughness

Flash floods pose a significant danger for life and property. Unfortunately, in arid and semiarid environment the runoff generation shows a complex non-linear behavior with a strong spatial and temporal non-uniformity. As a result, the predictions made by physically-based simulations in semiarid areas are subject to great uncertainty, and a failure in the predictive behavior of existing models is common. Thus better descriptions of physical processes at the watershed scale need to be incorporated into the hydrological model structures. For example, terrain relief has been systematically considered static in flood modelling at the watershed scale. Here, we show that the integrated effect of small distributed relief variations originated through concurrent hydrological processes within a storm event was significant on the watershed scale hydrograph. We model these observations by introducing dynamic formulations of two relief-related parameters at diverse scales: maximum depression storage, and roughness coefficient in channels. In the final (a posteriori) model structure these parameters are allowed to be both time-constant or time-varying. The case under study is a convective storm in a semiarid Mediterranean watershed with ephemeral channels and high agricultural pressures (the Rambla del Albujón watershed; 556 km 2 ), which showed a complex multi-peak response. First, to obtain quasi-sensible simulations in the (a priori) model with time-constant relief-related parameters, a spatially distributed parameterization was strictly required. Second, a generalized likelihood uncertainty estimation (GLUE) inference applied to the improved model structure, and conditioned to observed nested hydrographs, showed that accounting for dynamic relief-related parameters led to improved simulations. The discussion is finally broadened by considering the use of the calibrated model both to analyze the sensitivity of the watershed to storm motion and to attempt the flood forecasting of a stratiform event with highly different behavior.

Essays on the credit channel of monetary policy: a case study for Brazil

The onset of the financial crisis in 2008 and the European sovereign crisis in 2010 renewed the interest of macroeconomists on the role played by credit in business cycle fluctuations. The purpose of the present work is to present empirical evidence on the monetary policy transmission mechanism in Brazil with a special eye on the role played by the credit channel, using different econometric techniques. It is comprised by three articles. The first one presents a review of the literature of financial frictions, with a focus on the overlaps between credit activity and the monetary policy. It highlights how the sharp disruptions in the financial markets spurred central banks in developed and emerging nations to deploy of a broad set of non conventional tools to overcome the damage on financial intermediation. A chapter is dedicated to the challenge face by the policymaking in emerging markets and Brazil in particular in the highly integrated global capital market. This second article investigates the implications of the credit channel of the monetary policy transmission mechanism in the case of Brazil, using a structural FAVAR (SFAVAR) approach. The term “structural” comes from the estimation strategy, which generates factors that have a clear economic interpretation. The results show that unexpected shocks in the proxies for the external finance premium and the credit volume produce large and persistent fluctuations in inflation and economic activity – accounting for more than 30% of the error forecast variance of the latter in a three-year horizon. Counterfactual simulations demonstrate that the credit channel amplified the economic contraction in Brazil during the acute phase of the global financial crisis in the last quarter of 2008, thus gave an important impulse to the recovery period that followed. In the third articles, I make use of Bayesian estimation of a classical neo-Keynesian DSGE model, incorporating the financial accelerator channel developed by Bernanke, Gertler and Gilchrist (1999). The results present evidences in line to those already seen in the previous article: disturbances on the external finance premium – represented here by credit spreads – trigger significant responses on the aggregate demand and inflation and monetary policy shocks are amplified by the financial accelerator mechanism. Keywords: Macroeconomics, Monetary Policy, Credit Channel, Financial Accelerator, FAVAR, DSGE, Bayesian Econometrics

Measurement of the top quark pair production cross section in the lepton plus jets channel in proton-antiproton collisions at root s=1.96 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Analysis of ion channel stochastic signals for biosensing

In biological world, life of cells is guaranteed by their ability to sense and to respond to a large variety of internal and external stimuli. In particular, excitable cells, like muscle or nerve cells, produce quick depolarizations in response to electrical, mechanical or chemical stimuli: this means that they can change their internal potential through a quick exchange of ions between cytoplasm and the external environment. This can be done thanks to the presence of ion channels, proteins that span the lipid bilayer and act like switches, allowing ionic current to flow opening and shutting in a stochastic way. For a particular class of ion channels, ligand-gated ion channels, the gating processes is strongly influenced by binding between receptive sites located on the channel surface and specific target molecules. These channels, inserted in biomimetic membranes and in presence of a proper electronic system for acquiring and elaborating the electrical signal, could give us the possibility of detecting and quantifying concentrations of specific molecules in complex mixtures from ionic currents across the membrane; in this thesis work, this possibility is investigated. In particular, it reports a description of experiments focused on the creation and the characterization of artificial lipid membranes, the reconstitution of ion channels and the analysis of their electrical and statistical properties. Moreover, after a chapter about the basis of the modelling of the kinetic behaviour of ligand gated ion channels, a possible approach for the estimation of the target molecule concentration, based on a statistical analysis of the ion channel open probability, is proposed. The fifth chapter contains a description of the kinetic characterisation of a ligand gated ion channel: the homomeric α2 isoform of the glycine receptor. It involved both experimental acquisitions and signal analysis. The last chapter represents the conclusions of this thesis, with some remark on the effective performance that may be achieved using ligand gated ion channels as sensing elements.

Energy-efficient Communication and Estimation in Wireless Sensor Networks

This thesis focuses on the energy efficiency in wireless networks under the transmission and information diffusion points of view. In particular, on one hand, the communication efficiency is investigated, attempting to reduce the consumption during transmissions, while on the other hand the energy efficiency of the procedures required to distribute the information among wireless nodes in complex networks is taken into account. For what concerns energy efficient communications, an innovative transmission scheme reusing source of opportunity signals is introduced. This kind of signals has never been previously studied in literature for communication purposes. The scope is to provide a way for transmitting information with energy consumption close to zero. On the theoretical side, starting from a general communication channel model subject to a limited input amplitude, the theme of low power transmission signals is tackled under the perspective of stating sufficient conditions for the capacity achieving input distribution to be discrete. Finally, the focus is shifted towards the design of energy efficient algorithms for the diffusion of information. In particular, the endeavours are aimed at solving an estimation problem distributed over a wireless sensor network. The proposed solutions are deeply analyzed both to ensure their energy efficiency and to guarantee their robustness against losses during the diffusion of information (against information diffusion truncation more in general).

Estimation of the degree of polarization through computational sensing

The degree of polarization of a refected field from active laser illumination can be used for object identifcation and classifcation. The goal of this study is to investigate methods for estimating the degree of polarization for refected fields with active laser illumination, which involves the measurement and processing of two orthogonal field components (complex amplitudes), two orthogonal intensity components, and the total field intensity. We propose to replace interferometric optical apparatuses with a computational approach for estimating the degree of polarization from two orthogonal intensity data and total intensity data. Cramer-Rao bounds for each of the three sensing modalities with various noise models are computed. Algebraic estimators and maximum-likelihood (ML) estimators are proposed. Active-set algorithm and expectation-maximization (EM) algorithm are used to compute ML estimates. The performances of the estimators are compared with each other and with their corresponding Cramer-Rao bounds. Estimators for four-channel polarimeter (intensity interferometer) sensing have a better performance than orthogonal intensities estimators and total intensity estimators. Processing the four intensities data from polarimeter, however, requires complicated optical devices, alignment, and four CCD detectors. It only requires one or two detectors and a computer to process orthogonal intensities data and total intensity data, and the bounds and estimator performances demonstrate that reasonable estimates may still be obtained from orthogonal intensities or total intensity data. Computational sensing is a promising way to estimate the degree of polarization.

Estimation of vertical groundwater fluxes into a streambed through continuous temperature profile monitoring and the relationship of groundwater fluxes to coaster brook trout spawning habitat

We hypothesized that the spatial distribution of groundwater inflows through river bottom sediments is a critical factor associated with the selection of coaster brook trout (a life history variant of Salvelinus fontinalis,) spawning sites. An 80-m reach of the Salmon Trout River, in the Huron Mountains of the upper peninsula of Michigan, was selected to test the hypothesis based on long-term documentation of coaster brook trout spawning at this site. Throughout this site, the river is relatively similar along its length with regard to stream channel and substrate features. A monitoring well system consisting of an array of 27 wells was installed to measure subsurface temperatures underneath the riverbed over a 13-month period. The monitoring well locations were separated into areas where spawning has and has not been observed. Over 200,000 total temperature measurements were collected from 5 depths within each of the 27 monitoring wells. Temperatures within the substrate at the spawning area were generally cooler and less variable than river temperatures. Substrate temperatures in the non-spawning area were generally warmer, more variable, and closely tracked temporal variations in river temperatures. Temperature data were inverted to obtain subsurface groundwater velocities using a numerical approximation of the heat transfer equation. Approximately 45,000 estimates of groundwater velocities were obtained. Estimated velocities in the spawning and non-spawning areas confirmed that groundwater velocities in the spawning area were primarily in the upward direction, and were generally greater in magnitude than velocities in the non-spawning area. In the non-spawning area there was a greater occurrence of velocities in the downward direction, and velocity estimates were generally lesser in magnitude than in the spawning area. Both the temperature and velocity results confirm the hypothesis that spawning sites correspond to areas of significant groundwater influx to the river bed.

High precision range estimation techniques for multi-path wireless environments

Range estimation is the core of many positioning systems such as radar, and Wireless Local Positioning Systems (WLPS). The estimation of range is achieved by estimating Time-of-Arrival (TOA). TOA represents the signal propagation delay between a transmitter and a receiver. Thus, error in TOA estimation causes degradation in range estimation performance. In wireless environments, noise, multipath, and limited bandwidth reduce TOA estimation performance. TOA estimation algorithms that are designed for wireless environments aim to improve the TOA estimation performance by mitigating the effect of closely spaced paths in practical (positive) signal-to-noise ratio (SNR) regions. Limited bandwidth avoids the discrimination of closely spaced paths. This reduces TOA estimation performance. TOA estimation methods are evaluated as a function of SNR, bandwidth, and the number of reflections in multipath wireless environments, as well as their complexity. In this research, a TOA estimation technique based on Blind signal Separation (BSS) is proposed. This frequency domain method estimates TOA in wireless multipath environments for a given signal bandwidth. The structure of the proposed technique is presented and its complexity and performance are theoretically evaluated. It is depicted that the proposed method is not sensitive to SNR, number of reflections, and bandwidth. In general, as bandwidth increases, TOA estimation performance improves. However, spectrum is the most valuable resource in wireless systems and usually a large portion of spectrum to support high performance TOA estimation is not available. In addition, the radio frequency (RF) components of wideband systems suffer from high cost and complexity. Thus, a novel, multiband positioning structure is proposed. The proposed technique uses the available (non-contiguous) bands to support high performance TOA estimation. This system incorporates the capabilities of cognitive radio (CR) systems to sense the available spectrum (also called white spaces) and to incorporate white spaces for high-performance localization. First, contiguous bands that are divided into several non-equal, narrow sub-bands that possess the same SNR are concatenated to attain an accuracy corresponding to the equivalent full band. Two radio architectures are proposed and investigated: the signal is transmitted over available spectrum either simultaneously (parallel concatenation) or sequentially (serial concatenation). Low complexity radio designs that handle the concatenation process sequentially and in parallel are introduced. Different TOA estimation algorithms that are applicable to multiband scenarios are studied and their performance is theoretically evaluated and compared to simulations. Next, the results are extended to non-contiguous, non-equal sub-bands with the same SNR. These are more realistic assumptions in practical systems. The performance and complexity of the proposed technique is investigated as well. This study’s results show that selecting bandwidth, center frequency, and SNR levels for each sub-band can adapt positioning accuracy.

Use of Calcium Channel Blockers is Associated with Mortality in Patients with Chronic Kidney Disease.

BACKGROUND/AIMS The use of antihypertensive medicines has been shown to reduce proteinuria, morbidity, and mortality in patients with chronic kidney disease (CKD). A specific recommendation for a class of antihypertensive drugs is not available in this population, despite the pharmacodynamic differences. We have therefore analysed the association between antihypertensive medicines and survival of patients with chronic kidney disease. METHODS Out of 2687 consecutive patients undergoing kidney biopsy a cohort of 606 subjects with retrievable medical therapy was included into the analysis. Kidney function was assessed by glomerular filtration rate (GFR) estimation at the time point of kidney biopsy. Main outcome variable was death. RESULTS Overall 114 (18.7%) patients died. In univariate regression analysis the use of alpha-blockers and calcium channel antagonists, progression of disease, diabetes mellitus (DM) type 1 and 2, arterial hypertension, coronary heart disease, peripheral vascular disease, male sex and age were associated with mortality (all p<0.05). In a multivariate Cox regression model the use of calcium channel blockers (HR 1.89), age (HR 1.04), DM type 1 (HR 8.43) and DM type 2 (HR 2.17) and chronic obstructive pulmonary disease (HR 1.66) were associated with mortality (all p < 0.05). CONCLUSION The use of calcium channel blockers but not of other antihypertensive medicines is associated with mortality in primarily GN patients with CKD.

A Logarithmic Image Prior for Blind Deconvolution

Blind Deconvolution consists in the estimation of a sharp image and a blur kernel from an observed blurry image. Because the blur model admits several solutions it is necessary to devise an image prior that favors the true blur kernel and sharp image. Many successful image priors enforce the sparsity of the sharp image gradients. Ideally the L0 “norm” is the best choice for promoting sparsity, but because it is computationally intractable, some methods have used a logarithmic approximation. In this work we also study a logarithmic image prior. We show empirically how well the prior suits the blind deconvolution problem. Our analysis confirms experimentally the hypothesis that a prior should not necessarily model natural image statistics to correctly estimate the blur kernel. Furthermore, we show that a simple Maximum a Posteriori formulation is enough to achieve state of the art results. To minimize such formulation we devise two iterative minimization algorithms that cope with the non-convexity of the logarithmic prior: one obtained via the primal-dual approach and one via majorization-minimization.