Error Rate Estimation Research Articles

Maple species identification based on leaf hyperspectral imaging data

The present study aimed to identify the leaves of Acer campestre L., A. negundo L. and A. saccharinum L. The experiment was carried out under controlled laboratory conditions in 2021. A Cubert UHD-185 camera was used for hyperspectral imaging (HSI). The content of photosynthetic pigments was determined by the spectrophotometric method. HSI and determination of pigments were carried out during the growing season of maples with intervals between experiments of 7–10 days. The time interval in the vegetation of maples was determined, in which the identification of their species by the random forest (RF) method occurs most accurately. Based on the strength of the correlation with photosynthetic pigments, factor loadings of principal component analysis (PCA), results of the t-test and analysis of variance (ANOVA), a group of vegetation indices (VIs) was selected, the values of which are associated with the species characteristics of maples. It has been established that VIs CRI1, Carter4, Datt3, DPI, EVI, Gitelson2, GMI1, Green_NDVI, MTVI, PRI, REP_Li, TCARI, TVI are significant for the identification of A. campestre, A. negundo and A. saccharinum. The OOB estimate of error rate does not exceed 7% when classified by the RF method using selected VIs. When training on the data of Jul 02, the testing error in the time interval from Jul 09 to Aug 24 did not exceed 20%.

Line Balancing Based on Error Rate Estimation with Artificial Neural Networks in Assembly Line Operations

In this study, in the assembly line systems consisting of the operations in interaction with each other; To reduce the number of faulty products, to prevent poor quality and to reduce the production time, Error Ratio Estimation with Artificial Neural Networks and probabilistic Line Balancing method have been performed. The error rate estimation provides information on which jeans models should be applied in the improvement work to eliminate existing errors in place. In the study, using the Levenberg - Marquardt Learning Algorithm, machine learning was determined by the experimental design method. At the same time, it has been used as an artificial intelligence algorithm in the multi-directional decision making stages, estimation and line balancing parts. In Assembly Line Equilibration, it has been aimed to re-stabilize the unbalanced line with the influence of post-forecasting process recovery. The Probabilistic Line Balancing method has been used because the processing times are stochastic (variable) and statistical data and mathematical algorithms (digital algorithms can be created). When the results are examined, a successful forecasting process has been carried out for two different five-pocket jeans models which has been selected and it has been seen that the work components of the probabilistic line balancing method enable it to be precisely assigned to work stations. And it has given reliable results.

Hierarchical Bayesian non-response models for error rates in forensic black-box studies.

Forensic science plays a critical role in the United States criminal legal system. Historically, however, most feature-based fields of forensic science, including firearms examination and latent print analysis, have not been shown to be scientifically valid. Recently, black-box studies have been proposed as a means of assessing whether these feature-based disciplines are valid, at least in terms of accuracy, reproducibility and repeatability. In these studies, forensic examiners frequently either do not respond to every test item or select an answer equivalent to 'don't know'. Current black-box studies do not account for these high levels of missingness in statistical analyses. Unfortunately, the authors of black-box studies typically do not share the data necessary to meaningfully adjust estimates for the high proportion of missing responses. Borrowing from work in the context of small area estimation, we propose the use of hierarchical Bayesian models that do not require auxiliary data to adjust for non-response. Using these models, we offer the first formal exploration of the impact that missingness is playing in error rate estimations reported in black-box studies. We show that error rates currently reported as low as 0.4% could actually be at least 8.4% in models accounting for non-response where inconclusive decisions are counted as correct, and over 28% when inconclusives are counted as missing responses. These proposed models are not the answer to the missingness problem in black-box studies. But with the release of auxiliary information, they can be the foundation for new methodologies to adjust for missingness in error rate estimations. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Estimation of bit error rate in 2×2 and 4×4 multi-input multi-output-orthogonal frequency division multiplexing systems

<span lang="EN-US">Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with multiple input antennas and multiple output antennas in dynamic environments face the challenge of channel estimation. To overcome this challenge and to improve the performance and signal-to-noise ratio, in this paper we used the Kalman filter for the correct estimation of the signal in dynamic environments. To obtain the original signal at the receiver end bit error rate factor plays a major role. If the signal to noise ratio is high and the bit error rate is low then signal strength is high, the signal received at the receiver end is almost similar to the </span><em><span lang="EN-US">i<sup>th</sup></span></em><span lang="EN-US"> transmitted signal. The dynamic tracking characteristic of Kalman filter is used to establish a dynamic space-time codeword and a collection of orthogonal pilot sequences to prevent interference among transmissions in this paper. Using the simulation, the Kalman filter method can be compared to the other channel estimation method presented in this paper that can track time-varying channels rapidly.</span>

A New Polar Code Design Based on Reciprocal Channel Approximation

This paper revisits polar code design for a binary-input additive white Gaussian noise (BI-AWGN) channel when successive cancellation (SC) decoding is applied at the receiver. We focus on the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reciprocal channel approximation (RCA)</i> , which is often adopted in the design of low-density parity-check (LDPC) codes. Implementation of RCA requires the computation of the mutual information of BPSK signaling as well as a corresponding function known as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reciprocal channel mapping</i> , and thus we develop rigorous closed-form approximations of these that are easy to calculate numerically and also valid over a wide range of SNR. Through numerical evaluation we find that, compared to approaches based on the popular Gaussian approximation (GA) as well as the so-called improved GA (IGA), the proposed RCA approach offers better estimates of the bit error rate of polarized channels <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">with no additional computational cost</i> . As a result, polar codes designed by the proposed RCA can achieve further improvement in terms of block error rate (BLER) performance. The gain achieved by the new approach becomes significant as the codeword length increases.

Prototyping the Symmetry-Based Chaotic Communication System Using Microcontroller Unit

Chaos-based communications are a promising application of chaos theory and nonlinear dynamics. Their key features include concealed transmission, high security, and native broadband signals. Many studies have recently been published devoted to this technology. However, the practical implementations of chaos-based communications are rare due to multiple shortcomings: high hardware requirements, complex signal processing algorithms, and a lack of efficient modulation techniques for chaotic signals. In this study, we consider a simple hardware prototype of a coherent chaos-based communication system based on a novel type of modulation: adaptive symmetry of the finite-difference scheme used in a chaos generator. We explicitly demonstrate the possibility of covertly transmitting data using a chaotic transmitter and receiver implemented in a general-purpose microcontroller unit. A comparison between traditional parameter and symmetry modulation is given through a return map analysis and bit error rate estimation. The communication secrecy is analyzed using quantified return map analysis. The obtained results confirm the possibility of creating chaos-based communication systems based on symmetry modulation.

DOCEST-fast and accurate estimator of human NGS sequencing depth and error rate.

Accurate estimation of next-generation sequencing depth of coverage is needed for detecting the copy number of repeated elements in the human genome. The common methods for estimating sequencing depth are based on counting the number of reads mapped to the genome or subgenomic regions. Such methods are sensitive to the mapping quality. The presence of contamination or the large deviance of an individual genome from the reference may introduce bias in depth estimation. Here, we present an algorithm and implementation for estimating both the sequencing depth and error rate from unmapped reads using a uniquely filtered k-mer set. On simulated reads with 20× coverage, the margin of error was less than 0.01%. At 0.01× coverage and the presence of 10-fold contamination, the precision was within 2% for depth and within 10% for error rate. DOCEST program and database can be downloaded from https://bioinfo.ut.ee/docest/. Supplementary data are available at Bioinformatics Advances online.

Supplemental Material for Estimation of Eyewitness Error Rates in Fair and Biased Lineups

Supplemental Material for Estimation of Eyewitness Error Rates in Fair and Biased Lineups

Visible-light wireless communication system performance signature improvement for high bit rate transmission system

Abstract This paper deals with the visible-light communication (VLC) network system improvement for high bit rate transmission system. The previous model use the visualizer analyzer for the estimate of bit error rate in which the maxi. Q factor is reached to 2.51. The previous study is used free space channel over along length of 200 km by the optical wireless communication (OWC) channel with available data rates of 4 Gb/s. This presented model has a transmission rate of 10 Gb/s which has a maximum Q factor of 6.34 at the same operating parameters by using CW laser as an optical source and Mach–Zehnder modulator with two cascades optical wireless communication (OWC) channel.

Finding haplotypic signatures in proteins.

The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples and detectable by mass spectrometry, but they are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches and the discoverability of peptides specific to haplotypes remain unknown. Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42% of the discoverable amino acid substitutions encoded by common haplotypes, 2 or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multivariant peptides, and out of the 4,582 amino acid substitutions identified, 6.37% were covered by multivariant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches. As these procedures become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.

Asymmetric Adaptive LDPC-Based Information Reconciliation for Industrial Quantum Key Distribution.

We develop a new approach for asymmetric LDPC-based information reconciliation in order to adapt to the current channel state and achieve better performance and scalability in practical resource-constrained QKD systems. The new scheme combines the advantages of LDPC codes, a priori error rate estimation, rate-adaptive and blind information reconciliation techniques. We compare the performance of several asymmetric and symmetric error correction schemes using a real industrial QKD setup. The proposed asymmetric algorithm achieves significantly higher throughput, providing a secret key rate that is close to the symmetric one in a wide range of error rates. Thus, our approach is found to be particularly efficient for applications with high key rates, limited classical channel capacity and asymmetric computational resource allocation.

Quantifying Valve Regurgitation Using 3-D Doppler Ultrasound Images and Deep Learning.

Accurate quantification of cardiac valve regurgitation jets is fundamental for guiding treatment. Cardiac ultrasound is the preferred diagnostic tool, but current methods for measuring the regurgitant volume (RVol) are limited by low accuracy and high interobserver variability. Following recent research, quantitative estimators of orifice size and RVol based on high frame rate 3-D ultrasound have been proposed, but measurement accuracy is limited by the wide point spread function (PSF) relative to the orifice size. The aim of this article was to investigate the use of deep learning to estimate both the orifice size and the RVol. A simulation model was developed to simulate the power-Doppler images of blood flow through orifices with different geometries. A convolutional neural network (CNN) was trained on 30 000 image pairs. The network was used to reconstruct orifices from power-Doppler data, which facilitated estimators for regurgitant orifice areas and flow volumes. We demonstrate that the network improves orifice shape reconstruction, as well as the accuracy of orifice area and flow volume estimation, compared with a previous approach based on thresholding of the power-Doppler signal (THD), and compared with spatially invariant deconvolution (DC). Our approach reduces the area estimation error on simulations: (THD: 13.2 ± 9.9 mm2, DC: 12.8 ± 15.8 mm2, and ours: 3.5 ± 3.2 mm2). In a phantom experiment, our approach reduces both area estimation error (THD: 10.4 ± 8.4 mm2, DC: 10.98 ± 8.17, and ours: 9.9 ± 6.0 mm2) and flow rate estimation error (THD: 20.3 ± 9.9 ml/s, DC: 18.14 ± 13.01 ml/s, and ours: 7.1 ± 10.6 ml/s). We also demonstrate in vivo feasibility for six patients with aortic insufficiency, compared with standard echocardiography and magnetic resonance references.

Soft Error Effects on Arm Microprocessors: Early Estimations versus Chip Measurements

Extensive research efforts are being carried out to evaluate and improve the reliability of computing devices either through beam experiments or simulation-based fault injection. Unfortunately, it is still largely unclear to which extend fault injection can provide an accurate error rate estimation at early stages and if beam experiments can be used to identify the weakest resources in a device. The importance and challenges associated with a timely, but yet realistic reliability evaluation grow with the increase of complexity in both the hardware domain, with the integration of different types of cores in an SoC (System-on-Chip), and the software domain, with the OS (operating system) required to take full advantage of the available resources. In this paper, we combine and analyze data gathered with extensive beam experiments (on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">final</i> physical CPU hardware) and microarchitectural fault injections (on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">early</i> microarchitectural CPU models). We target a standalone Arm Cortex-A5 CPU and an Arm Cortex-A9 CPU integrated into an SoC and evaluate their reliability in bare-metal and Linux-based configurations. Combining experimental data that covers more than 18 million years of device time with the result of more than 176,000 injections we find that both the SoC integration and the presence of the OS increase the system DUEs (Detected Unrecoverable Errors) rate (for different reasons) but do not significantly impact the SDCs (Silent Data Corruptions) rate which is solely attributed to the CPU core. Our reliability analysis demonstrates that even considering SoC integration and OS inclusion, early, pre-silicon microarchitecture-level fault injection delivers accurate SDC rates estimations and lower bounds for the DUE rates.

Surrogate Scoring Rules

Strictly proper scoring rules (SPSR) are incentive compatible for eliciting information about random variables from strategic agents when the principal can reward agents after the realization of the random variables. They also quantify the quality of elicited information, with more accurate predictions receiving higher scores in expectation. In this article, we extend such scoring rules to settings in which a principal elicits private probabilistic beliefs but only has access to agents’ reports. We name our solution Surrogate Scoring Rules (SSR). SSR is built on a bias correction step and an error rate estimation procedure for a reference answer defined using agents’ reports. We show that, with a little information about the prior distribution of the random variables, SSR in a multi-task setting recover SPSR in expectation, as if having access to the ground truth. Therefore, a salient feature of SSR is that they quantify the quality of information despite the lack of ground truth, just as SPSR do for the setting with ground truth. As a by-product, SSR induce dominant uniform strategy truthfulness in reporting. Our method is verified both theoretically and empirically using data collected from real human forecasters.

Probabilistic linkage of multiple data sources for estimating prevalence of problem drug users in England in 2018/19.

ObjectivesProblem drug use (PDU) prevalence is an essential part of evidence base to formulate policy, inform service provision, and assess interventions. Opiate and crack use, particularly injecting, is associated with infectious diseases. Multiple data sources, where PDU are observed, are linked to estimate the hidden population of PDU using capture-recapture. ApproachDatasets consisted of (1) community treatment, (2) prison treatment, (3) probation (Ministry of Justice), and (4) drug-related deaths, for financial year 2018/19. The following offenders’ attributes and geography are used: first and second initials, date of birth, gender, multiple areas of residence and area of prison release. Geography information consists of local authority code and region name. Probabilistic linkage approach of Fellegi–Sunter is applied through FastLink package in R, where pairs of records are classified as match, possible match, or non-match at 85% threshold. Estimates of error rates, sensitivity and specificity are used to test the results. ResultsThere were 138,341 records in community treatment, 41,700 records in prison, 18,849 in probation and 2,368 deaths from opiate or cocaine. The total number of individuals observed in at least one linked dataset is estimated at 170,307 records at 85% threshold. The number of exact and possible matches overlapping between community treatment and prison is estimated at 17,544 records, from which 13,731 (78.3%) are exact matches, and 3,813 (21.7%) are possible matches at 85% threshold. The number of exact and possible matches overlapping between linked community treatment/prison and probation is estimated at 12,586 records, from which 9,872 (78.4%) are exact and 2,714 (21.6%) are possible matches at 85% threshold. False discovery rate was estimated at 0.3%, sensitivity and specificity were 99.8%. ConclusionsInformation on PDUs attending community treatment is poor and relying on exact matches underestimate the overlap between data sources. This tend to bias hidden population estimates derived from capture-recapture. Probabilistic approaches, and use of multiple candidate geographic areas for matching, maximise linkages between individuals and thus likely to improve estimates.

A Novel Morphological Feature Extraction Approach for ECG Signal Analysis Based on Generalized Synchrosqueezing Transform, Correntropy Function and Adaptive Heuristic Framework in FPGA

Nowadays, a computer-aided diagnosis system is required to monitor the cardiac patients continuously and detecting the heart diseases automatically. In this paper, a new field programmable gate array-based morphological feature extraction approach is proposed for electrocardiogram signal analysis. The proposed architecture is mainly based on the Generalized Synchrosqueezing transform but a detrended fluctuation analyzer is applied in the reconstruction stage for capturing the maximum information of QRS complexes and P-waves by eliminating a set of noisy intrinsic modes. Then, a correntropy envelope is determined from the QRS enhanced signal for localizing the QRS region accurately. Also, an adaptive heuristic framework is introduced to detect the true P-wave from the P-wave enhanced reconstructed signal by analyzing both the positive and negative amplitudes. In addition, a root mean square Error estimation-based adaptive thresholding approach is used to estimate the T-wave after removing the P-QRS complexes. The proposed architecture has been implemented on field programmable gate array using the Xilinx Vertex 7 platform. The performance of the proposed architecture is validated by performing a comparative study between the resultant performances and those attained with state-of-the-art feature descriptors, in terms of Sensitivity, accuracy, positive prediction, error rate and field programmable gate array resources estimation. The proposed sensitivity, accuracy and positive prediction are 99.84%, 99.85% and 99.86% for QRS detection approach. The proposed sensitivity, accuracy and positive prediction are 99.45%, 99.23% and 99.78% for P-wave detection approach. The proposed sensitivity, accuracy and positive prediction are 99.58%, 99.65% and 100% for T-wave detection approach. The simulation results show that the proposed architecture overtakes existing designs and minimizes hardware complexity, which proves the suitability of this approach on real-time applications of electrocardiogram signals.

Development of a Plastic Scintillator-Based Active Shield for the ICARE-NG Radiation Monitor

An active shield using a scintillator and silicon photo-multipliers (SiPMs) has been developed to operate with space environment particle detectors sensitive to both protons and electrons, such as Influence sur les Composants Avancés des Radiations de l’Espace-Nouvelle Génération (ICARE-NG). The method shows a reduction in electron contamination through the sides of the detector, thus increasing energy resolution. Two geometries are studied, one working in coincidence mode with the main detector and the other in anti-coincidence. Performances of both the geometries are simulated in proton and electron environments in energy ranges typical of a space environment. Experimental measurements then aimed to validate the Monte Carlo simulation framework for scintillating materials. Finally, a case study of electron decontamination is carried out, as well as an error rate estimation in flux reconstruction.

MLPNN‐RF: Software fault prediction based on robust weight based optimization and Jacobian adaptive neural network

SummarySoftware fault prediction (SFP) is a vital objective in software engineering. This might permit effective resource allocation and also enhance informed decisions about the release quality. SFP is being a critical issue for software professionals as well as the tech industry. Thus, SFP is necessary. The study intends to perform efficient error rate estimation using the proposed hybrid robust weight based optimization and Jacobian adaptive neural network (RWO‐JANN). It also aims to classify the software faults in an efficient way using multi‐layer perceptron neural network‐random forest (MLPNN‐RF). Various processes are involved to accomplish SFP. At first, the dataset is taken as input. After this, data preprocessing is performed. Subsequently, weights are initialized using the proposed RWO‐JANN. Weight initialization is performed through a series of steps. Then, the position and the weight parameter are updated to perform weight initialization. After this, the error rate is estimated and the weight is updated on the basis of the learning rate and Jacobian matrix calculation. Lastly, the decay rate is analyzed. If the error rate extends beyond the threshold value, the process repeats from weight initialization. If not, the testing process is performed and lastly the classified output for SFP is obtained by the proposed MLPNN‐RF. The proposed system is comparatively analyzed with the existing methods in terms of accuracy, precision, recall, F1 score, sensitivity, specificity, and error rate. The analytical results revealed effective outcomes of proposed system than the existing techniques with accuracy of 99.01%.

Current Approaches to Vaccine Safety Using Observational Data: A Rationale for the EUMAEUS (Evaluating Use of Methods for Adverse Events Under Surveillance-for Vaccines) Study Design.

Post-marketing vaccine safety surveillance aims to detect adverse events following immunization in a population. Whether certain methods of surveillance are more precise and unbiased in generating safety signals is unclear. Here, we synthesized information from existing literature to provide an overview of the strengths, weaknesses, and clinical applications of epidemiologic and analytical methods used in vaccine monitoring, focusing on cohort, case-control and self-controlled designs. These designs are proposed to be evaluated in the EUMAEUS (Evaluating Use of Methods for Adverse Event Under Surveillance–for vaccines) study because of their widespread use and potential utility. Over the past decades, there have been an increasing number of epidemiological study designs used for vaccine safety surveillance. While traditional cohort and case-control study designs remain widely used, newer, novel designs such as the self-controlled case series and self-controlled risk intervals have been developed. Each study design comes with its strengths and limitations, and the most appropriate study design will depend on availability of resources, access to records, number and distribution of cases, and availability of population coverage data. Several assumptions have to be made while using the various study designs, and while the goal is to mitigate any biases, violations of these assumptions are often still present to varying degrees. In our review, we discussed some of the potential biases (i.e., selection bias, misclassification bias and confounding bias), and ways to mitigate them. While the types of epidemiological study designs are well established, a comprehensive comparison of the analytical aspects (including method evaluation and performance metrics) of these study designs are relatively less well studied. We summarized the literature, reporting on two simulation studies, which compared the detection time, empirical power, error rate and risk estimate bias across the above-mentioned study designs. While these simulation studies provided insights on the analytic performance of each of the study designs, its applicability to real-world data remains unclear. To bridge that gap, we provided the rationale of the EUMAEUS study, with a brief description of the study design; and how the use of real-world multi-database networks can provide insights into better methods evaluation and vaccine safety surveillance.

Non-inferiority test for a continuous variable with a flexible margin in an active controlled trial: an application to the \u201cStratall ANRS 12110 / ESTHER\u201d trial

BackgroundNon-inferiority trials are becoming increasingly popular in public health and clinical research. The choice of the non-inferiority margin is the cornerstone of such trials. Most of the time, the non-inferiority margin is fixed and constant, determined from historical trials as a fraction of the effect of the reference intervention. But in some circumstances, there may some uncertainty around the reference treatment that one would like to account for when performing the hypothesis testing. In this case, the non-inferiority margin is not fixed in advance and depends on the reference intervention estimate. Hence, the uncertainty surrounding the non-inferiority margin should be accounted for in statistical tests. In this work, we explore how to perform the non-inferiority test for a continuous variable with a flexible margin.MethodsWe have proposed in this study, two procedures for the non-inferiority test with a flexible margin for continuous endpoints. The proposed test procedures are based on a test statistic and confidence interval approaches respectively. Simulations have been used to assess the performances and properties of the proposed test procedures. An application was done on a real-world clinical data, to assess the efficacy of clinical monitoring alone versus laboratory and clinical monitoring in HIV-infected adult patients.ResultsBasically, for both proposed methods, the type I error estimate was not dependent on the values of the reference treatment. In the test statistic approach, the type 1 error rate estimate was approximatively equal to the nominal value. It has been found that the confidence interval level determined approximatively the level of significance. For a given nominal type I error α, the appropriate one- and two-sided confidence intervals should be with levels 1−α and 1−2α, respectively.ConclusionsBased on the type I error rate and power estimates, the proposed non-inferiority hypothesis test procedures had good performances and were applicable in practice.Trial registrationClinicalTrials.gov NCT00301561. Registered on March 13, 2006, url: https://clinicaltrials.gov/ct2/show/NCT00301561.