Median based algorithm for sub-pixel estimation of extrema positions of diffuse light reflection signal

It has been proposed to utilize the median algorithm for determination of the extrema positions of diffuse light reflectance intensity distribution by a discrete signal of a photodiode linear array. The algorithm formula has been deduced on the base of piecewise-linear interpolation for signal representation by cumulative function. It has been shown that this formula is much simpler for implementation than known centroid algorithm and the noise immune Blais and Rioux detector algorithm. Also, the methodical systematic errors for zero noise as well as the random errors for full common mode additive noises and uncorrelated noises have been estimated and compared for mentioned algorithms. In these terms, the proposed median algorithm is proportionate to Blais and Rioux algorithm and considerably better then centroid algorithm.

Study of the Damage Evolution of Concrete with Different Initial Defect Rates under Uniaxial Compression with Acoustic Emission Technology

It is important to evaluate the internal damage of concrete under load conditions in order to evaluate its stability and usability for building applications. In this study, the uniaxial compression of concrete with initial defect was performed, and the internal damage of concrete was monitored by acoustic emission(AE) technology in real time to study the damage process and mechanism. The mechanical properties of concrete specimens with different initial defect were determined, and the cumulative impact count of AE was recorded. The response characteristics of AE in the process of concrete compression and damage were obtained. According to the analysis of the influence of the initial defect on the Kaiser effect and since the irreversibility of the AE process is related to the degree of damage caused by the material under the pre-load, it was determined that the initial defect will aggravate the damage inside the concrete under the same load level. Based on the statistics and analysis of the Weibull cumulative function, the correlation between AE parameters and damage variables was discussed.

Parameter Estimation of Poisson–Gaussian Signal-Dependent Noise from Single Image of CMOS/CCD Image Sensor Using Local Binary Cyclic Jumping

Since signal-dependent noise in a local weak texture region of a noisy image is approximated as additive noise, the corresponding noise parameters can be estimated from a given set of weakly textured image blocks. As a result, the meticulous selection of weakly textured image blocks plays a decisive role to estimate the noise parameters accurately. The existing methods consider the finite directions of the texture of image blocks or directly use the average value of an image block to select the weakly textured image block, which can result in errors. To overcome the drawbacks of the existing methods, this paper proposes a novel noise parameter estimation method using local binary cyclic jumping to aid in the selection of these weakly textured image blocks. The texture intensity of the image block is first defined by the cumulative average of the LBCJ information in the eight neighborhoods around the pixel, and, subsequently, the threshold is set for selecting weakly textured image blocks through texture intensity distribution of the image blocks and inverse binomial cumulative function. The experimental results reveal that the proposed method outperforms the existing alternative algorithms by 23% and 22% for the evaluative measures of MSE (a) and MSE (b), respectively.

Multiscale assessment of the degree of multifractality for physiological time series

Recent advancements in detrended fluctuation analysis (DFA) allow evaluating multifractal coefficients scale-by-scale, a promising approach for assessing the complexity of biomedical signals. The multifractality degree is typically quantified by the singularity spectrum width ( W SS ), a method that is critically unstable in multiscale applications. Thus, we aim to propose a robust multiscale index of multifractality, compare it with W SS and illustrate its performance on real biosignals. The proposed index is the cumulative function of squared increments between consecutive DFA coefficients at each scale n : α CF ( n ). We compared it with W SS calculated scale-by-scale considering monofractal/monoscale, monofractal/multiscale, multifractal/monoscale and multifractal/multiscale random processes. The two indices provided qualitatively similar descriptions of multifractality, but α CF ( n ) differentiated better the multifractal components from artefacts due to crossovers or detrending overfitting. Applied on 24 h heart rate recordings of 14 participants, the singularity spectrum failed to always satisfy the concavity requirement for providing meaningful W SS , while α CF ( n ) demonstrated a statistically significant heart rate multifractality at night in the scale ranges 16–100 and 256–680 s. Furthermore, α CF ( n ) did not reject the hypothesis of monofractality at daytime, coherently with previous reports of lower nonlinearity and monoscale multifractality during the day. Thus, α CF ( n ) appears a robust index of multiscale multifractality that is useful for quantifying complexity alterations of physiological series. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

Optimal Maintenance Probabilities and Preventive Replacement Maintenance Policy for Photocopy Machines

The need for suitable replacement policies are essential to minimize down time, maintenance cost and maximize the availability and reliability of equipment. On this premise, this work models the failure rate of Photocopy machines and obtain its optimal preventive maintenance policy that would prevent damage and its attendant losses to both users and end-product consumers. The failure distribution of the machine was shown to follow the Log-Logistic distribution with shape parameter, αˆ=1.723339368 and scale parameter, βˆ=763.9219635. Optimal probabilities of the distribution were obtained and utilized in both the cumulative failure function and cumulative hazard function-based replacement models to formulate a replacement maintenance policy for the machine. The failure cumulative function-based replacement model was found to be a better model which yields optimal replacement maintenance time of 166 hours at a minimum cost of 113 Naira for maintaining the machine per cycle time with 96% availability, 94% reliability and 0.07% chance of failure occurrence in the machine.

Structural health monitoring of bridge spans using Moment Cumulative Functions of Power Spectral Density (MCF-PSD) and deep learning

This article proposes a new parameter in evaluating mechanical behaviors of defected bridge spans. It is Moment Cumulative Function of Power Spectral Density (MCF-PSD) based on changes in shape of power spectrum and trained via cumulative function of spectral moment value by deep learning model. This new parameter allows evaluating stiffness attenuation along time, thereby helps to forecast the workability of bridge span. It can identify risky positions in not only a bridge span but also various spans of the same bridge, which proves its sensitivity to the structure’s behavior change over time. This study reveals that training MCF-PSD using cumulative function algorithm has gained outstanding results in comparison with previous studies in structural quality assessment. Therefore, it fulfills criteria of evaluating the damage level in a structure and also fosters new development of defect diagnosis and forecast. Conclusions from this study show that the change of this function is the basis to evaluate difference among measurement positions in the same span or among different spans of the same bridge and behaviors at different positions in the same span. Therefore, MCF-PSD is more sensitive than other parameters in evaluating the structure’s stiffness attenuation.

Smooth kNN Local Linear Estimation of the Conditional Distribution Function

Previous works were dedicated to the functional k-Nearest Neighbors (kNN) and the local linearity method estimations of a regression operator. In this paper, a sequence pair of (Xi,Yi)i=1,…,n of functional mixing observations are considered. We treat the local linear estimation of the cumulative function of Yi given functional input variable Xi. Precisely, we combine the kNN method with the local linear algorithm to construct a new and fast efficiency estimator of the conditional distribution function. The main purpose of this paper is to prove the strong convergence of the constructed estimator under mixing conditions. An application to the functional times series prediction is used to compare our proposed estimator with the existing competitive estimators, and show its efficiency and superiority.

Correction* Exponentiated quasi power Lindley power series distribution with applications in medical science

The present paper introduces an advanced five parameter lifetime model which is obtained by compounding exponentiated quasi power Lindley distribution with power series family of distributions. The EQPLPS family of distributions contains several lifetime sub-classes such as quasi power Lindley power series, power Lindley power series, quasi Lindley power series and Lindley power series. The proposed distribution exhibits decreasing, increasing and bathtub shaped hazard rate functions depending on its parameters. It is more flexible as it can generate new lifetime distributions as well as some existing distributions. Various statistical properties including closed form expressions for density function, cumulative function, limiting behaviour, moment generating function and moments of order statistics are brought forefront. The capability of the quantile measures in terms of Lambert W function is also discussed. Ultimately, the potentiality and the flexibility of the new class of distributions has been demonstrated by taking three real life data sets by comparing its sub-models.