Output Bias Research Articles

Addendum: “An ultrafast quantum random number generator with provably bounded output bias based on photon arrival time measurements” [Appl. Phys. Lett. 98, 171105 (2011)

First Page

Linking statistical bias description to multiobjective model calibration

In the absence of model deficiencies, simulation results at the correct parameter values lead to an unbiased description of observed data with remaining deviations due to observation errors only. However, this ideal cannot be reached in the practice of environmental modeling, because the required simplified representation of the complex reality by the model and errors in model input lead to errors that are reflected in biased model output. This leads to two related problems: First, ignoring bias of output in the statistical model description leads to bias in parameter estimates, model predictions and, in particular, in the quantification of their uncertainty. Second, as there is no objective choice of how much bias to accept in which output variable, it is not possible to design an “objective” model calibration procedure. The first of these problems has been addressed by introducing a statistical (Bayesian) description of bias, the second by suggesting the use of multiobjective calibration techniques that cannot easily be used for uncertainty analysis. We merge the ideas of these two approaches by using the prior of the statistical bias description to quantify the importance of multiple calibration objectives. This leads to probabilistic inference and prediction while still taking multiple calibration objectives into account. The ideas and technical details of the suggested approach are outlined and a didactical example as well as an application to environmental data are provided to demonstrate its practical feasibility and computational efficiency.

Analog Building Blocks for Organic Smart Sensor Systems in Organic Thin-Film Transistor Technology on Flexible Plastic Foil

In this work, we present the implementation and measurement results of all analog building blocks of an organic smart sensor system on foil. The presented building blocks are a 1D and a 2D 4 × 4 pixel flexible capacitive touch sensor with a sample rate of 1.5 kS/s, a DC-connected two-stage opamp with a 20 dB DC gain, a Dickson DC-DC up-converter with output bias voltages up to 60 V and down to - 40 V which are used as a bias voltage in the other building blocks, and a ΔΣ ADC with a 26.5 dB precision and a band width of 15.6 Hz. The sensors, the opamp, the DC-DC converter and the ADC respectively consume 6 μA, 15 μA, 1 μA and 100 μA from a 15 V power supply.

Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods

AbstractQuantifying the effects of future changes in the frequency of precipitation extremes is a key challenge in assessing the vulnerability of hydrological systems to climate change but is difficult as climate models do not always accurately simulate daily precipitation. This article compares the performance of four published techniques used to reduce the bias in a regional climate model precipitation output: (1) linear, (2) nonlinear, (3) γ‐based quantile mapping and (4) empirical quantile mapping. Overall performance and sensitivity to the choice of calibration period were tested by calculating the errors in the first four statistical moments of generated daily precipitation time series and using a cross‐validation technique. The study compared the 1961–2005 precipitation time series from the regional climate model HadRM3.0‐PPE‐UK (unperturbed version) with gridded daily precipitation time series derived from rain gauges for seven catchments spread throughout Great Britain. We found that while the first and second moments of the precipitation frequency distribution can be corrected robustly, correction of the third and fourth moments of the distribution is much more sensitive to the choice of bias correction procedure and to the selection of a particular calibration period. Overall, our results demonstrate that, if both precipitation data sets can be approximated by a γ‐distribution, the γ‐based quantile‐mapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation data sets cannot adequately be approximated using a γ‐distribution, the nonlinear method is more effective at reducing the bias, but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period. However, it should be borne in mind that bias correction introduces additional uncertainties, which are greater for higher order moments. Copyright © 2012 Royal Meteorological Society

Breaking Monte Carlo - Industry Standard Option Model Limits - Implications for Investors and Corporate Finance

Pushing models to extremes can expose output biases that stem from underlying assumptions. In the case of industry standard option valuation models, long term, high volatility securities provide a stress test vehicle. For instance, in evaluating a stock with 60% volatility, industry standard option valuation models imply that 69.2% of the stock’s value reflects the likelihood that the share price will rise 600% (septuple) within 10 years. For a portfolio of stocks with 60% volatility, industry standard models project roughly 10% of the holdings will septuple over the course of a decade. In dealing with high volatility securities over long time periods, do outcomes projected by industry standard option models match up with empirical observations? Or, are the predicted extraordinary returns for a significant percentage of high volatility stocks artifacts of a slant in the models’ foundations? Do both an assumption of a log normal random distribution of stock prices and the absence of a “reversion to the mean” link to fair value make industry standard option models systematically overweight improbable upside events? Is reported volatility biased upwards when measured under the presumption of a log normal distribution of stock prices, and does that bias in measured volatility also contribute to overweighting improbable upside stock price events? Considerable discussion has been given to the “black swan” issue (systematic underweighting of improbable events), but the issue here is just the reverse. What are the implications? If widely used option pricing models are systematically skewed to overestimate the likelihood of upside stock price appreciation over long time periods, can investors with superior models find arbitrage opportunities? Are long term executive and employee option packages overvalued with, consequently, excessive expense recognized at grant? Might corporate finance managers find compelling capital strategies that existing pricing models erroneously dismiss?

ELECTROACTIVE POLYMER TRANSDUCERS BIASED FOR OPTIMAL OUTPUT

Electroactive polymer transducers are disclosed. They are biased in a manner that provides for increased force and/or stroke output, thereby offering improved work potential and power output capacity. The biasing may offer additional or alternate functional advantage in terms of matching transducer performance with a given application such as a normally-closed valve. The improved biasing (including increased output biasing) may utilize negative spring rate biasing and/or a combination of negative or zero-rate biasing with positive rate biasing to achieve the desired ends.

Resonance asymmetry phenomenon in waveguide-type optical ring resonator gyro

The intensity output of a silica waveguide ring resonator (WRR) was found to show an asymmetric resonance characteristic. To further analyze and better design the resonator micro optic gyro (RMOG) with a waveguide-type ring resonator, in-depth research of the characteristics of the resonance asymmetry is fully developed for the first time, to the best of our knowledge. Four possible sources of the resonance asymmetry are analyzed and their respective contributions are compared. These four error sources are differential normal mode losses in the coupler, the backscattering induced noises, the polarization fluctuations and the optical Kerr effect. The differential normal mode loss is suspected to be the major contributor to the observed resonance asymmetry in the fabricated silica WRR. Asymmetric resonance curve produces a large output bias error in the RMOG based on the phase modulation technique, which is related to the modulation frequency differences between the CW and CCW lightwaves. Theoretical analysis shows that replacing the reflector WRR with a transmitter one is helpful to eliminate the effect of the different normal mode losses in the coupler on the resonance asymmetry.

A New Learning Algorithm for Single hidden Layer Feedforward Neural Networks

high dimensional pattern recognition problems, the learning speed of gradient based training algorithms (back-propagation) is generally very slow. Local minimum, improper learning rate and over-fitting are some of the other issues. Extreme learning machine was proposed as a non-iterative learning algorithm for single-hidden layer feed forward neural network (SLFN) to overcome these issues. The input weight and biases are chosen randomly in ELM which makes the classification system of non- deterministic behavior. In this paper, a new learning algorithm is presented in which the input weights and the hidden layer biases of SLFN are assigned from basis vectors generated by training space. The output weights and biases are decided through simple generalized inverse operation on output matrix of hidden layer. This makes very fast learning speed and better generalization performance in comparison to conventional learning algorithm as well as ELM.

Hemodynamic monitoring by transpulmonary thermodilution and pulse contour analysis in critically ill children

To summarize the physiologic principles underlying the hemodynamic monitoring using the PiCCO device (Pulsion, Munich, Germany) incorporating the transpulmonary thermodilution technique, the pulse contour cardiac output, and estimation of the arterial pressure variation method. Analysis and review of the current literature. A MEDLINE-based literature search using the key words transpulmonary thermodilution, pulse contour analysis, cardiac output, animal models, and child. The bias and precision of cardiac output measured by transpulmonary thermodilution are reliable. The reproducibility for repeated measurements is approximately 5% and the percentage error is approximately 15%. Transpulmonary thermodilution may adequately track changes in cardiac output in animals submitted to hypovolemic conditions and during volume loading. Conversely, data from experimental and clinical studies suggest that continuous monitoring of cardiac output using pulse contour analysis requires careful interpretation because periodic recalibration with transpulmonary thermodilution is necessary. Transpulmonary thermodilution-derived static indicator of cardiac preload (global end-diastolic volume, intrathoracic blood volume) may be more sensitive than conventional measurements of vascular filling pressure. However, the value of stroke volume variation or pulse pressure variation have not been evaluated in pediatric patients. Further studies are needed to determine whether theoretical assumptions underlying the measurement of extravascular lung water are valid in children. The PiCCO device may be a useful adjunct for hemodynamic monitoring in critically ill children. Further studies are needed to clarify the reliability and clinical value of pulse contour method and extravascular lung water measurement.

Estimator for photon counting energy selective x-ray imaging with multibin pulse height analysis.

This paper describes a noniterative estimator for the energy dependent information from photon counting detectors with multibin pulse height analysis (PHA). The estimator uses the two function decomposition of the attenuation coefficient [R. E. Alvarez and A. Macovski, Phys. Med. Biol. 21, 733-744 (1976)] and its output is the line integrals of the basis set coefficients. The output noise variance and bias is compared to other noniterative estimators and to the Cramèr-Rao lower bound (CRLB). The estimator first computes an initial estimate from a linearized maximum likelihood estimator. The errors in the initial estimates are determined at a set of points from measurements on a calibration phantom. The errors at these known points are interpolated to create two-dimensional look up tables of corrections to the initial estimates. During image acquisition, the linearized maximum likelihood estimate for each data point is used as an input to the correction look up tables, and the final output is the sum of the estimate and the correction. The performance of the estimator is compared to generalizations of the polynomial and rational polynomial estimators for multibin data. The estimators are compared by the mean square error (MSE) and its components, the bias, and the variance of the output. The variance is also compared to the CRLB. The performance is simulated with two to five bins PHA data. The CRLB at a fixed object thickness is also computed as a function of the number of bins. For two bin data, all the estimators' variances are equal to the CRLB. With three or more bins, only the proposed estimator achieves the CRLB while the others, which were not optimized for noise performance, have much larger output variance. The bias of the proposed estimator is equal to the polynomial estimator for calibration phantoms with 40 or more steps, that is, 1600 combinations of basis materials, but is larger than the rational polynomial bias. In all cases at the photon counts tested, the MSE is essentially equal to the variance, indicating that the bias errors are negligible compared to the variance. The estimator provides a noniterative method to compute the energy dependent information from multibin PHA data that achieves the CRLB over a wide range of operating conditions and has low output bias. The estimator can be calibrated based on the measurements of a calibration phantom; so, it does not require measurements of the x-ray energy spectrum or the detector response functions.

An ultrafast quantum random number generator with provably bounded output bias based on photon arrival time measurements

We report the implementation of a quantum random number generator based on photon arrival times. Due to fast and high resolution timing we are able to generate the highest bitrate of any current generator based on photon arrival times. Bias in the raw data due to the exponential distribution of the arrival times is removed by postprocessing which is directly integrated in the field programmable logic of the timing electronics.

An integrated 10 GHz low-noise phase-locked loop with improved PVT tolerance

An integrated phase-locked loop (PLL) with low phase noise is presented, which is robust with respect to variations of device parameters with process, supply voltage, and temperature (PVT). The low-noise CMOS voltage-controlled oscillator (VCO) employs two varactors for fine and coarse tuning. By using a CMOS charge pump with output biasing, the dc fine tuning voltage of the VCO and the loop dynamics of the PLL are well defined and fairly independent of PVT variations. Device noise in the charge pump and linearity of the phase detector are much improved by a two-transistor charge pump architecture for fine tuning. We measured a phase noise below ?131 dBc/Hz at 10 MHz offset and below ?94 dBc/Hz at 10 kHz offset over a tuning range of 1.2 GHz. An integrated phase error below 0.6° was measured, corresponding to an rms jitter below 160 fs. The chip was produced in a 0.25 μm low-cost SiGe BiCMOS technology, occupies a chip area of 2.25 mm2 and draws 60 mA from a 3 V supply.

TFT-LCD关机横纹缺陷分析

The horizontal block stripe,as an image quality defect,was analyzed by means of output signals and bias voltage stress(BVS) test.The results showed that the defect was correlated with discharging efficiency.Further,two approaches were proposed,one was to improve TFT characteristic,the other was to add reset function.The combined experiments indicated that discharging promotion can improve the defect.Especially the latter is most optimal to eliminate the phenomenon.

The learning of moment neuronal networks

We consider how to train the recently developed moment neuronal networks (MNN) with the IF model activated by the renewal process synaptic inputs, which encodes the input information into the mean and variance of the interspike intervals. Here we apply an error minimization technique to train the networks and mathematically derive a corresponding back-propagation learning rule based on the error regression, which seeks to minimize the error combination of the output means and variances of networks. As a result, we present a more biologically plausible so-called second order spiking perceptron (SOSP). We show through various examples that such system, even a single neuron, is able to perform various complex non-linear tasks like the XOR problem which is impossible to be solved by traditional single-layer perceptrons. Moreover such perceptron can be trained to implement a practical and classical dynamic learning task: simulating the trajectory of an arm movement. Among including the second order statistics in computations, such perceptron offers a significant advantage over their predecessors, in that it involves the learning accuracy of output variance by directly introducing the variance term in the error presentation. Thus we can train not only the output bias (mean error) but also the output noises (variances), and also can reach the trade-off between the output bias and output variance by the adjustment of the penalty factor in error function, due to a specific learning task. Indeed, this is the most important advantage over other kinds of spiking neural networks in existent.

Area Method for a Biased Relay Feedback System

The relay feedback system often shows an asymmetric response because of initial transient states, disturbances, and process nonlinearity. To restore a symmetric response of the relay feedback system, an iterative adjustment of input or output bias is required. Instead of trying to obtain a symmetric response, the asymmetric response can be analyzed to estimate the ultimate data or a parametric model of process. However, the asymmetric response causes additional errors in estimating the ultimate properties of the process. An area method is proposed to reduce these errors. Because integrals (areas) of the relay responses reduce the effects of the high-order harmonic terms significantly, the proposed method shows better accuracy in obtaining frequency response data and parametric models compared with previous approaches.

A single-mode single-polarization monolithically silica waveguide ring resonator used in microoptic gyro

A single-mode single-polarization waveguide (SMSPW) ring resonator for the microoptic gyro (MOG) was proposed, and it could be monolithically integrated onto a silica planar lightwave circuit. We have presented the design of a three-dimensional SMSPW ring resonator in which the TM mode cuts off and the TE mode transmits by taking into consideration the stress-induced birefringence effect of the Si-based silica waveguide using the effective-index method. The characteristics of the light propagation across the SMSPW resonator were studied by a three-dimensional finite difference beam propagation method. Numerical simulation results showed the SMSPW ring resonator has a high extinction ratio over 50 dB cm−1 for the TM mode, low propagation loss of 0.055 dB cm−1 for the TE mode, and a high finesse of 36. A rate detection limit of 0.8° h−1 can be achieved. Without increasing the complexity of the waveguide fabrication process, this structure can solve the MOGs output bias instabilities induced by polarization fluctuations and provide a new solution for MOGs.

An integrated 8-12 GHz fractional-N frequency synthesizer in SiGe BiCMOS for satellite communications

We present an integrated fractional-N low-noise frequency synthesizer for satellite applications. By using two integrated VCOs and combining digital and analog tuning techniques, a PLL lock range from 8 to 12 GHz is achieved. Due to a small VCO fine tuning gain and optimized charge pump output biasing, the phase noise is low and almost constant over the tuning range. All 16 sub-bands show a tuning range above 900 MHz each, allowing temperature compensation without sub-band switching. This makes the synthesizer robust against variations of the device parameters with process, supply voltage, temperature and aging. The measured phase noise is ?87 dBc/Hz and ?106 dBc/Hz at 10 kHz and 1 MHz offset, respectively. In integer-N mode, phase noise values down to ?98 dBc/Hz at 10 kHz and ?111 dBc/Hz at 1 MHz offset, respectively, were measured.

Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time‐dependent parameters

A recently developed technique for identifying continuous‐time, time‐dependent, stochastic model parameters is embedded in a general framework for identifying causes of bias and reducing bias in dynamic models. In contrast to the usual approach of considering bias in model output with an autoregressive error model or a stochastic process, we make the attempt to correct for bias within the model or even in model input. This increases the potential of learning about the causes of bias and of subsequently correcting deficits of the deterministic model structure. The time‐dependent parameters as formulated in our approach can also consistently be used for adding stochasticity to the model without losing precise fulfilment of conservation laws used for deriving the model equations. An additional advantage of the suggested procedure is that it makes it possible to derive more realistic uncertainty bounds of internal model variables than is the case when bias is only considered for measured model output. This is important for mechanistic models in which internal variables have a direct physical meaning. The concept is illustrated by an application to a simple eight‐parameter conceptual hydrological model. This application demonstrates the feasibility of the proposed approach and gives an impression of its potential for application to a large class of nonlinear, dynamic models.

Analysis and Construction of Correctors

A post-processing function is used to reduce or eliminate statistical weaknesses of physical random number generators. The output bias of a n-bit input m-bit output function is examined, when the input bits are biased. For this purpose, the definition of (n, m, t)-corrector is introduced; many characterizations and properties of a corrector are presented. It appears as a generalization of the notion of resilience for a vectorial function. Several constructions of correctors from old correctors are also proposed.

A SELF-OSCILLATING SWITCHING POWER AMPLIFIER

A self-oscillating switching power amplifier is proposed. We use feedback to reduce the output DC bias current and to produce self-oscillation. Further, filters are added in the feedback loop to reduce the quantization noise. The experimental results show that the proposed circuit has 0.25% total harmonic distortion (THD) and the efficiency of output power of 840 μW reaches 90.1% while the DC output bias current is 8 μA and the supply voltage is 1.5 V.