Error Probability Estimation Research Articles

Identifying insects with incomplete DNA barcode libraries, African fruit flies (Diptera: Tephritidae) as a test case.

We propose a general working strategy to deal with incomplete reference libraries in the DNA barcoding identification of species. Considering that (1) queries with a large genetic distance with their best DNA barcode match are more likely to be misidentified and (2) imposing a distance threshold profitably reduces identification errors, we modelled relationships between identification performances and distance thresholds in four DNA barcode libraries of Diptera (n = 4270), Lepidoptera (n = 7577), Hymenoptera (n = 2067) and Tephritidae (n = 602 DNA barcodes). In all cases, more restrictive distance thresholds produced a gradual increase in the proportion of true negatives, a gradual decrease of false positives and more abrupt variations in the proportions of true positives and false negatives. More restrictive distance thresholds improved precision, yet negatively affected accuracy due to the higher proportions of queries discarded (viz. having a distance query-best match above the threshold). Using a simple linear regression we calculated an ad hoc distance threshold for the tephritid library producing an estimated relative identification error <0.05. According to the expectations, when we used this threshold for the identification of 188 independently collected tephritids, less than 5% of queries with a distance query-best match below the threshold were misidentified. Ad hoc thresholds can be calculated for each particular reference library of DNA barcodes and should be used as cut-off mark defining whether we can proceed identifying the query with a known estimated error probability (e.g. 5%) or whether we should discard the query and consider alternative/complementary identification methods.

Calculating the required number of bits in the function of confidence level and error probability estimation

This paper proposes the calculation of the required number of bits transmitted in the system, in order to achieve the desired level of confidence. Proposed solution is based on the selected value for the bit error probability, and statistical confidence levels. In addition, required error probability for telecommunications protocols and data transfer protocols is discussed, overview of the BER (Bit Error Ratio) testing equipment performance is provided, and compromise of testing time in relation to the statistical level of confidence depending on BER is examined.

Nonlinear parametric identification of stochastic discrete plants based on generalized probabilistic criteria

Studying alternative (with respect to conventional) methods of stochastic parameter identification of nonlinear discrete plants is shown to be of current interest. For the first time, generalized probabilistic criteria for discrete time are proposed to solve the problem of parametric identification of nonlinear stochastic plants. A parametric identification algorithm is proposed based on the criterion of the minimum probability of estimation error. A toy example is considered to illustrate the efficiency of the proposed approach.

Choosing appropriate covariance matrices in a nonparametric analysis of factorials in block designs

The standard nonparametric, rank-based approach to the analysis of dependent data from factorial designs is based on an estimated unstructured (UN) variance–covariance matrix, but the large number of variance–covariance terms in many designs can seriously affect test performance. In a simulation study for a factorial arranged in blocks, we compared estimates of type-I error probability and power based on the UN structure with the estimates obtained with a more parsimonious heterogeneous-compound-symmetry structure (CSH). Although tests based on the UN structure were anti-conservative with small number of factor levels, especially with four or six blocks, they became conservative at higher number of factor levels. Tests based on the CSH structure were anti-conservative, and results did not depend on the number of factor levels. When both tests were anti-conservative, tests based on the CSH structure were less so. Although use of the CSH structure is concluded to be more suitable than use of the UN structure for the small number of blocks typical in agricultural experiments, results suggest that further improvement of test statistics is needed for such situations.

Dimensionality reduction by minimizing nearest-neighbor classification error

There is a great interest in dimensionality reduction techniques for tackling the problem of high-dimensional pattern classification. This paper addresses the topic of supervised learning of a linear dimension reduction mapping suitable for classification problems. The proposed optimization procedure is based on minimizing an estimation of the nearest neighbor classifier error probability, and it learns a linear projection and a small set of prototypes that support the class boundaries. The learned classifier has the property of being very computationally efficient, making the classification much faster than state-of-the-art classifiers, such as SVMs, while having competitive recognition accuracy. The approach has been assessed through a series of experiments, showing a uniformly good behavior, and competitive compared with some recently proposed supervised dimensionality reduction techniques.

Method of estimating human error probabilities in construction for structural reliability analysis based on analytic hierarchy process and failure likelihood index method

Human error (HE) is the most important factor influencing on structural safety because its effect often exceeds the random deviation. Large numbers of facts have shown that structural failures may be caused by the gross error due to HE. So it is essential to analyze HE in construction. The crucial work of human error analysis (HEA) is the estimation of human error probability (HEP) in construction. The method for estimating HEP, analytic hierarchy process and failure likelihood index method (AHP-FLIM), is introduced in this paper. The method also uses the process of expert judgment within the failure likelihood index method (FLIM). A numerical example shows the effectiveness of the methods proposed.

Human Error Probability Estimation for Process Risk Assessment with Emphasis on Control Room Operations

Human Error Probability Estimation for Process Risk Assessment with Emphasis on Control Room Operations

Error probabilities of noncoherent and coherent FSK in the presence of frequency and phase offsets for two-hop relay networks

We analyze the error performance of two-hop relay networks adopting frequency shift keying (FSK) over frequency flat Rayleigh fading channels. It is assumed that relay networks consist of a source, a relay, and a destination without a direct path signal from the source to the destination and the relay adopts the amplify-and-forward protocol with a fixed gain. Firstly, considering imperfect frequency and phase synchronization, we obtain the exact error probability expressions for noncoherent and coherent binary FSK (BFSK). Secondly, assuming perfect frequency and phase synchronization, we derive a closed-form error probability approximation for coherent M-ary FSK (MFSK). The proposed methods can also be used for the error performance analysis of classical one-hop FSK systems with perfect/imperfect frequency and phase synchronization. The obtained error probability expressions will help the design of two-hop relay networks adopting FSK in determining the system parameters such as the transmission power at the source, the amplifying coefficient at the relay, and the maximum affordable frequency and phase offsets to satisfy the required error performance.

Optimal estimation of the position of non-Gaussian impulse noise in images

An algorithm for optimal estimation (in the sense of the minimum average risk criterion) of the spatial position of non-Gaussian impulse noise in digital images is proposed based on the signal difference between the center of the slide aperture and its other elements. An expression for the boundary values of the admissible signal interval is obtained that provides the potentially achievable zero probability of noise estimation error. Results of numerical studies of the efficiency of the synthesized algorithm show its advantages over the well-known nonoptimal algorithm.

Bit error probability approximation for short-time Fourier transform based nonstationary interference excision in DS-SS systems

The problem addressed in this paper is bit error probability (BEP) approximation in direct sequence spread-spectrum (DS-SS) systems that implement the short-time Fourier transform (STFT) as a means of nonstationary jammer excision. The jammer, previously concentrated in the time–frequency ( t– f) plane, it is suppressed by removing its t– f signature via a binary mask. The applied binary mask gives rise to odd higher-order central moments of the decision variable d, i.e., introduces a deviation in the probability density function of d, f d ( x ) , from a Gaussian law. Two analytical approximations to f d ( x ) are proposed, Gaussian approximation (GA) and Hermite–Gaussian approximation (HGA). The HGA takes advantage of the Hermite polynomials and central moments of d thus reducing the approximation error introduced by the GA. Analytical expressions for the mean, variance and third central moment of d, when the STFT of the received signal is modified by an arbitrary two-dimensional function, are derived and numerically confirmed. The HGA outperforms the GA for various types of monocomponent and multicomponent jammers, which was verified by simulations.

Cross-layer enhanced time scheduling for multi-band OFDM UWB networks

Multi-band Orthogonal Frequency Division multiplexing based Ultra Wide-band (MB-OFDM UWB) technology is one of the strong alternatives for high data rate wireless personal area networks (WPANs) with low power consumption. The capacity of such systems is degraded by multi-path fading, shadowing, multi-user interference and noise. To improve system capacity under these adverse effects, in this paper, we devise cross-layer time scheduling methods, Proportional Time Scheduling with Modiano Algorithm (PTS-MA) and Proportional Time Scheduling with Channel State Information (PTS-CSI), in which scheduling and link adaptation are performed using instantaneous bit error probability (IBEP) estimates obtained through Modiano's algorithm and our novel estimation technique, respectively. We evaluate the performance of the PTS schemes by using numerical experiments. Simulation results suggest PTS-CSI scheduler as the most promising candidate for practical MB-OFDM UWB WPANs with high capacity and fair throughput distribution.

On the performance of a maximum likelihood method with delayed correlation for the coarse carrier frequency offset estimation of OFDM signals with multiple preamble symbols

Estimation of carrier frequency offset (CFO) is an important issue in the design of a wireless receiver that employs orthogonal frequency division multiplexing (OFDM) techniques. In this paper, using the ten short training symbols specified in the signal format of the IEEE 802.11a WLAN, we investigate the performance of a coarse CFO estimation scheme for OFDM signals with multiple preamble symbols. This scheme, which we call DC-ML, employs the maximum likelihood (ML) method with delayed correlation (DC). For AWGN channels under moderate signal to noise ratio (SNR) conditions, we develop an analysis to evaluate the variance of estimation error (VEER). The analysis is corroborated in light of simulations, and compared with the formulated Cramer-Rao lower bound (CRLB). VEER of the DC-ML in a multipath environment is studied via simulations. Numerical results show that a certain parameter combination can result in minimum VEER. Simulation results justify that the probability of estimation error (PEER) approximates Gaussian distribution in both AWGN and multipath scenarios. We also present a Two-Branch DC-ML (TBDC-ML) scheme, which comprises two correlation branches of DC-ML, and an associative ambiguity resolution algorithm. Numerical examples reveal that TBDC-ML outperforms DC-ML in both VEER and PEER. Assuming that the estimation error resulting from the two branches is jointly Gaussian, we derive the joint probability density function (pdf) and validate it via simulations.

A criterion of minimum of estimation error probability in a problem of nonlinear parametric identification

Solution of stochastic nonlinear identification problem is proposed. An algorithm of discrete observer parameter identification, based on utilization of criterion of minimal estimation error probability, is synthesized. To illustrate proposed approach efficiency we consider a model example.

Knowledge-aided covariance matrix estimation: a MAXDET approach

The authors consider the problem of knowledge-aided covariance matrix estimation and its application to adaptive radar detection. The authors assume that an a priori (knowledge-based) estimate of the disturbance covariance M, derived from a physical scattering model of the terrain and/or of the environment, is available. Hence, starting from a set of secondary data, the authors evaluate the maximum likelihood (ML) estimate of M assuming that it lies in a suitable neighbourhood of the a priori estimate and formulate this ML estimation in terms of a convex optimisation problem which falls within the class of MAXDET problems. Both the cases of unstructured and structured disturbance covariance are considered. At the analysis stage, the authors assess the performance of the new knowledge-aided covariance estimators both in terms of estimation error and detection probability achievable by a class of adaptive detectors. The results highlight that, if the a priori knowledge is reliable, satisfactory levels of performance can be achieved with considerably less training data than those exploited by conventional algorithms.

Probabilistic Error Modeling for Nano-Domain Logic Circuits

In nano-domain logic circuits, errors generated are transient in nature and will arise due to the uncertainty or the unreliability of the computing element itself. This type of errors - which we refer to as dynamic errors - are to be distinguished from traditional faults and radiation related errors. Due to these highly likely dynamic errors, it is more appropriate to model nano-domain computing as probabilistic rather than deterministic. We propose a probabilistic error model based on Bayesian networks to estimate this expected output error probability, given dynamic error probabilities in each device since this estimate is crucial for nano-domain circuit designers to be able to compare and rank designs based on the expected output error. We estimate the overall output error probability by comparing the outputs of a dynamic error-encoded model with an ideal logic model. We prove that this probabilistic framework is a compact and minimal representation of the overall effect of dynamic errors in a circuit. We use both exact and approximate Bayesian inference schemes for propagation of probabilities. The exact inference shows better time performance than the state-of-the art by exploiting conditional independencies exhibited in the underlying probabilistic framework. However, exact inference is worst case NP-hard and can handle only small circuits. Hence, we use two approximate inference schemes for medium size benchmarks. We demonstrate the efficiency and accuracy of these approximate inference schemes by comparing estimated results with logic simulation results. We have performed our experiments on <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LGSynth'93</i> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ISCAS'85</i> benchmark circuits. We explore our probabilistic model to calculate: 1) error sensitivity of individual gates in a circuit; 2) compute overall exact error probabilities for small circuits; 3) compute approximate error probabilities for medium sized benchmarks using two stochastic sampling schemes; 4) compare and vet design with respect to dynamic errors; 5) characterize the input space for desired output characteristics by utilizing the unique backtracking capability of Bayesian networks (inverse problem); and 6) to apply selective redundancy to highly sensitive nodes for error tolerant designs.

Inclusion of human errors assessment in failure frequency analysis—A case study for the transportation of ammonia by rail in Malaysia

AbstractRisk is defined as the probability of a specific adverse event occurring within a specific period, while Quantitative risk assessment (QRA) is the development of a quantitative estimate of risk based on engineering evaluation and mathematical techniques by combining estimate of incident consequences and frequencies. In view of the increase in the use of railways as the mode of transportation for hazardous materials throughout the world, the associated risk analysis should be taken into concern. In this study, the failure frequency of the transportation of ammonia from the Petronas fertilizers Kedah (PFK) plant in Gurun (Northern part of Peninsular Malaysia), to the Chemical Company Malaysia (CCM) fertilizer's facilities in Port Klang (South‐western part of Peninsular Malaysia) was evaluated by incorporating the human error assessment. The study highlighted the importance of human error contributions in the failure frequency analysis and its impact on the selected failure scenarios. Besides, it also shows that the application of the human error assessment and reduction technique (HEART), which is a useful human reliability analysis tool, should be used in parallel with the fuzzy arithmetic approach to reduce the uncertainties involved in the estimation of human error probabilities, and hence, to reduce the likelihood of incorrect risks estimates being assessed. The results suggested that the commonly applied approach in quantitative risk assessments, which only consider equipment failures in the failure frequency estimations, are clearly an underestimate of the potential causes of failures leading to hazardous material releases, and hence, the calculated risks based do not reflect the actual risks. © 2008 American Institute of Chemical Engineers Process Saf Prog, 2009

Mutual‐information‐based adaptive coding and modulation in bit‐interleaved OFDM systems using punctured LDPC codes

AbstractAdaptive coding, modulation and power allocation are well‐known techniques for enhancing the performance of systems transmitting over quasi‐static fading channels. Especially their application in orthogonal frequency division multiplexing (OFDM)‐based systems promises gains in spectral efficiency. However, an open issue is the joint optimisation of code‐rate, modulation formats and power allocation if codewords are mapped onto sub‐symbols with heterogeneous channel quality and modulation formats. In this paper, we present an adaptive coding, modulation and, optionally, power allocation scheme that maximises the throughput under a codeword error rate constraint for bit‐interleaved coded OFDM systems using rate‐compatible punctured block‐circulant low‐density parity‐check codes (RCP‐BLDPC). The average mutual‐information per code bit is used as single parameter for the codeword error probability estimation. The algorithms are evaluated within the context of the OFDMA/TDMA system designed within the EU FP6 Integrated Project WINNER. Copyright © 2008 John Wiley &amp; Sons, Ltd.

Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry

A mass spectrum produced via tandem mass spectrometry can be tentatively matched to a peptide sequence via database search. Here, we address the problem of assigning a posterior error probability (PEP) to a given peptide-spectrum match (PSM). This problem is considerably more dif.cult than the related problem of estimating the error rate associated with a large collection of PSMs. Existing methods for estimating PEPs rely on a parametric or semiparametric model of the underlying score distribution. We demonstrate how to apply non-parametric logistic regression to this problem. The method makes no explicit assumptions about the form of the underlying score distribution; instead, the method relies upon decoy PSMs, produced by searching the spectra against a decoy sequence database, to provide a model of the null score distribution. We show that our non-parametric logistic regression method produces accurate PEP estimates for six different commonly used PSM score functions. In particular, the estimates produced by our method are comparable in accuracy to those of PeptideProphet, which uses a parametric or semiparametric model designed speci.cally to work with SEQUEST. The advantage of the non-parametric approach is applicability and robustness to new score functions and new types of data. C++ code implementing the method as well as supplementary information is available at http://noble.gs. washington.edu/proj/qvality

Constrained Decentralized Estimation Over Noisy Channels for Sensor Networks

Decentralized estimation of a noise-corrupted source parameter by a bandwidth-constrained sensor network feeding, through a noisy channel, a fusion center is considered. The sensors, due to bandwidth constraints, provide binary representatives of a noise-corrupted source parameter. Recently, proposed decentralized, distributed estimation, and power scheduling methods do no consider errors occurring during the transmission of binary observations from the sensors to fusion center. In this paper, we extend the decentralized estimation model to the case where imperfect transmission channels are considered. The proposed estimator, which operates on additive channel noise corrupted versions of quantized noisy sensor observations, is approached from maximum likelihood (ML) perspective. The resulting ML estimate is a root, in the region of interest (ROI), of a derivative polynomial function. We analyze the natural logarithm of the polynomial within the ROI showing that the function is log-concave, thereby indicating that numerical methods, such as Newton's algorithm, can be utilized to obtain the optimal solution. Due to complexity and implementation issues associated with the numerical methods, we derive and analyze simpler suboptimal solutions, i.e., the two-stage and mean estimators. The two-stage estimator first estimates the binary observations from noisy fusion center observations utilizing a threshold operation, followed by an estimate of the source parameter. The optimal threshold is the maximum a posteriori (MAP) detector for binary detection and minimizes the probability of binary observation estimation error. Optimal threshold expressions for commonly utilized light-(Gaussian) and heavy-tailed (Cauchy) channel noise models are derived. The mean estimator simply averages the noisy fusion center observations. The output variances of means of the proposed suboptimal estimators are derived. In addition, a computational complexity analysis is presented comparing the proposed ML optimal and suboptimal two-stage and mean estimators. Numerical examples evaluating and comparing the performance of proposed ML, two-stage and mean estimators are also presented.

Robust CDMA Multiuser Detectors: Probability-Constrained Versus the Worst-Case-Based Design

In this letter, a robust code-division multiple-access (CDMA) multiuser detection technique which is based on the probability-constrained optimization approach is developed, and its relationship to the popular worst-case-based robust CDMA multiuser detection technique is established. The important advantage of the proposed probability-constrained optimization-based approach with respect to the worst-case-based design is that in the former approach, the parameter of the uncertainty region of the worst-case-based design is quantified in terms of the outage probability and second-order statistics of the user signature estimation error. A simulation example demonstrates the advantages of such statistically motivated choice of the parameter of the uncertainty region.