Current Estimation Techniques Research Articles

Integration of GLAS and Landsat TM data for aboveground biomass estimation

Current regional aboveground biomass estimation techniques, such as those that require extensive fieldwork or airborne light detection and ranging (lidar) data for validation, are time and cost intensive. The use of freely available satellite-based data for carbon stock estimation mitigates both the cost and the spatial limitations of field-based techniques. Spaceborne lidar data have been demonstrated as useful for aboveground biomass (AGBM) estimation over a wide range of biomass values and forest types. However, the application of these data is limited because of their spatially discrete nature. Spaceborne multispectral sensors have been used extensively to estimate AGBM, but these methods have been demonstrated as inappropriate for forest structure characterization in high-biomass mature forests. This study uses an integration of ICESat Geospatial Laser Altimeter System (GLAS) lidar and Landsat data to develop methods to estimate AGBM in an area of south-central British Columbia, Canada. We compare estimates with a reliable AGBM map of the area derived from high-resolution airborne lidar data to assess the accuracy of satellite-based AGBM estimates. Further, we use the airborne lidar dataset in combination with forest inventory data to explore the relationship between model error and canopy height, AGBM, stand age, canopy rugosity, mean diameter at breast height (DBH), canopy cover, terrain slope, and dominant species type. GLAS AGBM models were shown to reliably estimate AGBM (R2 = 0.77) over a range of biomass conditions. A partial least squares AGBM model using Landsat input data to estimate AGBM (derived from GLAS) had an R2 of 0.60 and was found to underestimate AGBM by an average of 26 Mg/ha per pixel when applied to areas outside of the GLAS transect. This study demonstrates that Landsat and GLAS data integration are most useful for forests with less than 120 Mg/ha of AGBM, less than 60 years of age, and less than 60% canopy cover. These techniques have high associated error when applied to areas with greater than 200 Mg/ha of AGBM.

High mobility orthogonal frequency division multiple access channel estimation using basis expansion model

Owing to the loss of subcarrier orthogonalities in high-speed applications, the use of conventional frequency-domain-based channel estimation in high mobility orthogonal frequency division multiple access (OFDMA) systems such as mobile WiMax may give rise to an unacceptable high channel estimation error floor. To alleviate this problem, the authors develop some basis expansion model (BEM)-based estimation schemes for the OFDMA uplink. Specifically, the authors express the time-varying channel as a superposition of a small number of complex exponential basis functions spanning the entire Doppler range, and then formulate least square (LS) and linear minimum mean square error (LMMSE) algorithms to estimate the basis coefficients for two different types of pilot patterns. The authors also derive the respective Cramer-Rao lower bounds for these estimators. It has been shown that the time domain BEM using a pilot scheme where pilots are placed over time axis will give better performance under a high Doppler scenario. Lastly, using simulation results, the proposed algorithms have been found to have better estimation accuracy over current frequency domain estimation techniques. This is in addition to the advantage that the proposed algorithms have in general a lower computational complexity.

Estimating the Policy Preferences of United States Courts of Appeals Judges

Despite recent advances in public law scholarship and political methodology, scholars still lack sophisticated data on the United States Courts of Appeals and the policy preferences of the judges who serve on them. To remedy this deficiency, we employ item response theory models to estimate circuit court judges' ideal points. In this paper, we present some initial results from our project. Specifically, we gathered data to estimate ideal points for judges serving on the First Circuit Court (1925-2008) and the D.C. Circuit Court (1977-1985). Our data provide more accurate results than current estimation techniques and, at the same time, highlight critical limitations with those methods currently used. Additionally, our approach allows scholars to compare circuit judges' ideal points with those from other circuits, the Supreme Court, Congress, and the president.

Assessment of blood volume flow in slightly curved arteries from a single velocity profile

Non-invasive estimation of arterial blood volume flow (BVF) has become a central issue in assessment of cardiovascular risk. Poiseuille and Womersley approaches are commonly used to assess the BVF from centerline velocity, but both methods neglect the influence of curvature. Based on the assumption that the velocity in curved tubes as function of the circumferential position for a given radial position can be approximated by a cosine, the BVF can also be estimated by averaging velocities at opposite radial positions, referred to as the cosine θ model (CTM). This study investigates the accuracy of BVF estimation in slightly curved arteries for BVF waveforms obtained in the brachial artery of 6 volunteers. Computational fluid dynamics simulations were used to compute the influence of curvature on velocity profiles. The BVF was then estimated from the simulation results with the CTM and methods based on Poiseuille, Womersley and using the center stream velocity and the velocity waveform at the position where the maximum velocity is observed, and compared to the prescribed BVF. The simulations show that the influence of curvature is strongest when the flow decelerates. For Poiseuille and Womersley, the time average BVF was underestimated by maximally 10.4% and 7.8% for a radius of curvature of 50 and 100 mm, respectively. The estimation error is lower for the CTM and equals 4.2% and 1.2% for a radius of curvature of 50 and 100 mm, respectively. From this study, we can conclude that the velocity waveform at the position of the maximum rather than the center stream velocity waveform combined with the Womersley method should be chosen. The CTM improves current estimation techniques if in-vivo velocity distributions are available.

Methods for estimating neural firing rates, and their application to brain-machine interfaces.

Neural spike trains present analytical challenges due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of a spike train's underlying firing rate. Numerous methods for estimating neural firing rates have been developed in recent years, but to date no systematic comparison has been made between them. In this study, we review both classic and current firing rate estimation techniques. We compare the advantages and drawbacks of these methods. Then, in an effort to understand their relevance to the field of neural prostheses, we also apply these estimators to experimentally gathered neural data from a prosthetic arm-reaching paradigm. Using these estimates of firing rate, we apply standard prosthetic decoding algorithms to compare the performance of the different firing rate estimators, and, perhaps surprisingly, we find minimal differences. This study serves as a review of available spike train smoothers and a first quantitative comparison of their performance for brain-machine interfaces.

Etude des systèmes d'horodatage

Power and energy dissipation are currently critical design constraints in Digital and Signal Processing (DSP) applications for embedded software ; the increasing weight of the software implies that software consumption mut also be controlled. Moreover, the software dissipation is highly dependant of the processor architecture where the code has to be executed. To guide efficiently the required optimisation all over the design flow, reliable estimations are necessary. We present here an ovrview of the main current estimation techniques, the models they are relying on, their characteristics and applications domains, their respective advantages and drawbacks and eventually the available tools.

Toward a Model for Local Drug Delivery in Abdominal Aortic Aneurysms

The formation of an abdominal aortic aneurysm (AAA) may eventually result in rupture, an event associated with a 50% mortality rate. This work represents a first step toward improving current stress estimation techniques and local transport simulations in AAA. Toward this aim, a computational parametric study was performed on an axisymmetric cylindrical FEM of a 5 cm AAA with a 1.5 cm thick intraluminal thrombus (ILT). Both the AAA wall and ILT were modeled as porohyperelastic PHE materials using estimated values of AAA wall and ILT permeability. While no values for AAA wall permeability could be found in the literature, the value of ILT permeability was taken from a previous investigation by Adolph et al.(7) Peak stresses, fluid velocities, and local pore pressure values within the ILT and wall were recorded and analyzed as a function of the cardiac cycle. While peak wall stress values for the PHE models did not largely differ from corresponding solid finite element simulations (186.2 N/cm(2) vs. 186.5 N/cm(2)), the stress in the abluminal region of the ILT increased by 17.4% (7.7 N/cm(2) vs. 6.5 N/cm(2)). Pore pressure values were relatively constant through the ILT while there were significant pore pressure gradients present in the AAA wall. The magnitude of fluid velocities varied in magnitude and direction throughout the cardiac cycle with large fluctuations occurring on the luminal surface. The combination of the patient-specific PHE AAA FEMs with mass transport simulations will result in spatially and time-varying concentration distributions within AAA, which may benefit future pharmaceutical treatments of AAA.

A priori wirelength and interconnect estimation based on circuit characteristics

Interconnect prediction is very important for early feasibility studies in modern design flows. Most of the current interconnect estimation techniques estimate either the average or the total wirelength and some qualitative measure of routing demand for circuits. A priori techniques estimate these parameters without actually performing circuit placement. We propose a new a priori interconnect and wirelength estimation methodology for island style field programmable gate arrays (FPGAs). For a given design, we estimate bounding box semiperimeter wirelengths of all nets for an optimized placement and the minimum number of tracks per channel required for successful routing on an FPGA device. We analyze the structural characteristics of circuits and limitations posed by the FPGA architecture to derive a consistent model for wirelength and routing demand estimation. We identify reconvergences present in a circuit as an important global circuit characteristic in wirelength prediction. Our overall results show that we have an average error of 11.6% w.r.t. semiperimeter wirelength measured from the optimized layout using VPR. Also, the number of routing tracks per channel is predicted with an average error of 13.2% of the detailed routing results from VPR.

Improved representation of diurnal variability of rainfall retrieved from the Tropical Rainfall Measurement Mission Microwave Imager adjusted Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) system

Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) is a satellite infrared‐based algorithm that produces global estimates of rainfall at resolutions of 0.25° × 0.25° and a half‐hour. In this study the model parameters of PERSIANN are routinely adjusted using coincident rainfall derived from the Tropical Rainfall Measurement Mission Microwave Imager (TMI). The impact of such an adjustment on capturing the diurnal variability of rainfall is examined for the Boreal summer of 2002. General evaluations of the PERSIANN rainfall estimates with/without TMI adjustment were conducted using U.S. daily gauge rainfall and nationwide radar network (weather surveillance radar) 1988 Doppler data. The diurnal variability of PERSIANN rainfall estimates with TMI adjustment is improved over those without TMI adjustment. In particular, the amounts of afternoon and morning maximums in rainfall diurnal cycles improved by 14.9% and 26%, respectively, and the original 2–3 hours of time lag in the phase of diurnal cycles improved by 1–2 hours. In addition, the rainfall estimate with TMI adjustment has higher correlation (0.75 versus 0.63) and reduced bias (+8% versus −11%) at monthly 0.25° × 0.25° resolution than that without TMI adjustment and consistently shows higher correlation (0.62 versus 0.51) and lower bias (+22% versus −30%) at daily 0.25° × 0.25° scale. This study provides evidence that the TMI, which measures instantaneous rain rates from the TRMM platform flying on a non‐Sun‐synchronous orbit, enables PERSIANN to capture more realistic diurnal variations of rainfall. This study also reveals the limitation of current satellite rainfall estimation techniques in retrieving the rainfall diurnal features and suggests that further investigation of precipitation generation in different periods of cloud life cycles might help resolve this limitation.

An adaptation of ridge regression for improved estimation of kinetic model parameters from PET studies

The quantitative analysis of dynamic positron emission tomography (PET) data to obtain kinetic constants in compartmental models involves the use of nonlinear weighted least squares regression. Current estimation techniques often have poor mean square error estimation properties. Ridge regression is a technique that has been found to have potential for improving mean square error when adapted to the nonlinear PET estimation problem. The effectiveness of ridge regression in this context, however, relies heavily on the correct selection of an unknown biasing parameter and the precise specification of a penalty function. In this study, an approach is explored for improving the effectiveness of ridge regression by incorporation of more rigorous Bayesian formulations for specification of the ridge penalty function. Using a variance component model, a prior covariance for the ridge penalty term is developed. An adaptive approach to the selection of the biasing parameter is also evaluated. The adaptive selection of the biasing parameter was not shown to improve estimation over more standard ridge estimation techniques. Ridge regression with the Bayesian formulation for the penalty, however, reduces current ridge regression parameter loss by up to 16% when the penalty closely reflects the true kinetic parameter covariance structure and performs comparably to the current method when the penalty does not.

Modeling of Induction Machines Including Stator and Rotor Slot Effects

Three-phase as well as single-phase induction machines form the backbone of a substantial portion of the adjustable-speed drives' applications. Among the current estimation techniques available for speed-sensorless induction motor drives, speed measurement based on rotor or principal slot harmonics (RSH or PSH) detection in machine line current happens to be a prominent one. In practice, two predominant RSH can be seen. The present paper attempts to investigate the effect of slot permeance on the production of RSH. Simulation studies on a small motor but with a popular stator, rotor slot, and pole combination suggest that their effect is less significant compared to rotor space harmonics effect. Also, out of the two aforementioned RSH, the cause of the less significant slot harmonic is shown to be the negative-sequence-current-induced reverse rotating field. Voltage unbalances and constructional imperfections can cause negative-sequence current even in a healthy machine. Thus, although the less significant RSH is absent in the simulated line current of an idealized machine running from a balanced supply, it can be detected in an actual machine.

Measuring Credit Spread Risk

It is widely known that the possibility of default makes the expected return distribution for financial products that are subject to credit risk highly skewed and fat-tailed. Recent development of an unbiased tail index estimator enables modeling of the additional risk presented by changes in swap spreads. Tests on data from the U.S., the U.K., Germany, and Japan indicate that we tend to grossly underestimate the risk of large spread widenings and tightenings. Estimation of swap spread risk is dramatically improved when the severity of the additional downside risk is measured and incorporated into current estimation techniques. These results are crucial in improving credit risk management and pricing out-of-the-money credit derivatives.

Selection criteria of induction machines for speed-sensorless drive applications

Induction motors, both three and single phase, are used extensively for adjustable-speed drives' applications. These machines are structurally very robust and are a primary source of motive power and speed control where DC machines cannot be used. For closed-loop control of these machines, sensorless speed estimation is usually preferred. Among the current estimation techniques available for speed-sensorless induction motor drives, speed measurement based on rotor-slot-related harmonic detection in machine line current happens to be a prominent one. While these harmonics can be strong in certain kinds of machines, some other machines may exhibit very weak rotor slot harmonics that can be obscured by noise. Skewing, slot shapes and types, structural unbalances, etc., also have a prominent effect on the detectability of these harmonics. This paper attempts to investigate this problem based on the interaction of pole pairs, number of rotor bars, and stator winding. Although the analysis and experimental results have been mainly provided for three-phase squirrel-cage induction motors, single-phase and slip-ring induction motors have also been addressed. Further, it has been shown that eccentricity-related fault detection could also be easily accommodated with this kind of speed detection technique at no or negligible extra cost when certain motors are selected.

A comparison of various terminal-gate relationships for interconnect prediction in VLSI circuits

Over the years, different interpretations of Rent's rule and different ways of estimating the Rent parameters have emerged. In general, these parameters are extracted from the average terminal-gate relationship for a set of circuit modules. We show that this relationship (the Rent characteristic) strongly depends on the definition of the circuit modules. These can be generated in many different ways, either from the topology of the circuit graph or, in a geometric way, by cutting regions from a circuit layout. The resulting Rent parameters can be quite far apart. This paper discusses the fundamental differences between the topological and the two geometric interpretations of the Rent characteristic that are expected to be most appropriate for current wirelength estimation techniques. Our discussion is based on experimental data, as well as on a theoretical model that can be used to estimate certain geometric Rent characteristics from the topological Rent parameters. Using this model, we derive a theoretical lower limit to the value of the average geometric Rent exponent. We also study the impact of the placement approach and placement quality on the geometric Rent characteristics.

Modeling layout tools to derive forward estimates of area and delay at the RTL level

Forward estimates of area and delay facilitate effective decision-making when searching the solution space of digital designs. Current estimation techniques focus on modeling the layout result and fail to deliver timely or accurate estimates. This paper presents a novel approach to deriving these area and delay estimates at the RTL level by modeling the layout tool, rather than the layout result. This approach uses machine learning techniques to capture the relationships between general design features (i.e., topology, connectivity, common input, and common output) and layout concepts (i.e., relative placement). Experiments illustrate the formulation of the training set for machine learning in this domain, and also show how we can derive different tool models. Finally, they show how we can use the resultant model to derive forward estimates of area and delay in real-world designs.

Estimation of short-term postmortem interval utilizing core body temperature: a new algorithm

The use of temperature-based short-term postmortem interval (PMI) estimation methods can be useful to homicide investigators at the scene of a questionable death; however, a number of current PMI estimation techniques have high error rates such that they are of limited utility to law enforcement investigators and forensic professionals. These methods fail to control confounding errors present in individual data. An averages-based method of short term postmortem interval estimation was compared to eight other methods, and found to predict postmortem interval considerably more accurately, and for much longer periods of time.

Lumped modelling of open-circuit turbogenerator operational parameters

Solid-rotor turbine-generators have commonly been derived from the fitting of curves to terminal characteristics obtained by measurement or from finite element simulations. The paper presents a new method to represent the open-circuit operational inductances of a large turbogenerator of 150 MVA. The method differs from current estimation techniques because the lumped model and its parameters are determined simultaneously. It is based on a finite-element electromagnetic linear analysis of the solid-rotor machine cross section in the frequency domain. Network theory is used to characterise the quadrature-axis model where the problems of model structure and parameter determination are treated. A q-axis equivalent circuit with one damper winding and frequency dependent parameters is found. In addition, an analysis of the machine d-axis is presented. It is based in an electromagnetic study of the "black box" two-port network, where the open-circuit operational inductances are represented by frequency dependent parameters.

Optimal estimation of power spectral density by means of a time-varying autoregressive approach

A new time-varying autoregressive modeling has been proposed as a tool for time–frequency analysis of nonstationary time series. The method allows a good estimation of both frequency and amplitude of the spectrum and offers a new point of view for the evaluation of the parametric approach when applied to spectral analysis. The good performance is related to the adaptive choice of time-varying coefficients of the autoregressive model. The problems in parametric spectral analysis related to the unsatisfactory amplitude estimate have been successfully overcome by means of a compensation procedure inserted in the designed technique. The evaluation of the proposed approach has been performed on both synthetic and real signals which have been chosen as significant examples of typical nonstationary data. The performance of the method has been compared to classical techniques such as short-time Fourier analysis and autoregressive Burg algorithm. The results show the usefulness of the proposed approach in overcoming many of the difficulties encountered in current spectral estimation techniques by means of parametric approaches.

Estimating software project effort using analogies

Accurate project effort prediction is an important goal for the software engineering community. To date most work has focused upon building algorithmic models of effort, for example COCOMO. These can be calibrated to local environments. We describe an alternative approach to estimation based upon the use of analogies. The underlying principle is to characterize projects in terms of features (for example, the number of interfaces, the development method or the size of the functional requirements document). Completed projects are stored and then the problem becomes one of finding the most similar projects to the one for which a prediction is required. Similarity is defined as Euclidean distance in n-dimensional space where n is the number of project features. Each dimension is standardized so all dimensions have equal weight. The known effort values of the nearest neighbors to the new project are then used as the basis for the prediction. The process is automated using a PC-based tool known as ANGEL. The method is validated on nine different industrial datasets (a total of 275 projects) and in all cases analogy outperforms algorithmic models based upon stepwise regression. From this work we argue that estimation by analogy is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques.

Predicting salmon biomass remotely using a digital stereo-imaging technique

Effective fish farm management requires accurate information on fish biomass in order to control feeding regimes, stocking densities and ultimately the optimum time to harvest the stock. Current methods for biomass estimation are deemed inaccurate and may also be stressful to the fish. Therefore, the ability to remotely predict fish weight and biomass was tested using a non-invasive, digital stereo-camera system. The camera system was arranged in a vertical set-up, which grabbed images of fish viewed from the side. Specific combinations of fin-fin, body depth and length dimensions, visible from side views of fish, were measured from stereo-images and these estimates were incorporated into a series of multifactor regression equations that were used to predict weight. Biomass of Atlantic salmon ( Salmo salar L.) was estimated to within 0.4% of the real value and individual weight was determined with an error of −0.1 ± 9.0%. Thus, the results from this study suggest that biomass can be predicted with a high degree of accuracy using a stereo-camera system, and the method has the advantage of greatly reduced stress levels in fish, compared with current biomass estimation techniques.