In this paper, we proposed a data-hiding scheme based on Run Length matrix. In a previously proposed method, a technique based on texture classification was introduced where four statically features extracted from Run Length matrix; then best cover images are selected based on these features. Using appropriate features for comparing images from undetectability viewpoint, guarantees, less detectability of stego images and consequently, enhances security of the steganography algorithms. Based on this idea, in this paper, more features are extracted from Run Length matrix to select the best covers. Our method is examined with feature based and wavelet based steganalysis algorithms. The results illustrate the effectiveness and benefits of the proposed method.

A novel approach for steganography cover selection is proposed, based on image texture features and human visual system. Our proposed algorithm employs Run Length matrix to select a set of appropriate images from an image database and creates their stego version after embedding process. Then, it computes similarity between original images and their stego versions by using structural similarity as image quality metric to select, as the best cover, one image with maximum similarity with its stego. According to the results of comparing our new proposed cover selection algorithm with other steganography methods, it is confirmed that the proposed algorithm is able to increase the stego quality. We also evaluated the robustness of our algorithm over steganalysis methods such as Wavelet based and Block based steganalyses, the experimental results show that the proposed approach decreases the risk of message hiding detection.

We propose a new approach to monitor an overall mean shift of a bi-variate quality control system. To do this, we first define beliefs on deciding whether the quality characteristics are in out-of-control state. Then, by taking new observations, in an iterative approach we update the belief of each quality characteristics being out-of-control. This task is performed using a recursive method and prior beliefs. Finally, we introduce a statistics in combination with Bivariate Exponentially Weighted Moving Average (BEWMA) statistics to improve the performance of the proposed method. In order to understand the proposed methodology and to evaluate its performance, we perform a simulation study. Moreover, we compare in- and out-of-control Average Run Lengths of the proposed method with the ones from the well-known MCUSUM and MEWMA procedures in different scenarios of mean shifts. The results of the simulation study show that the proposed methodology performs better than the other methods for small shifts of the process mean.

If the quality of a process is described using a linear functional relationship betweenthe response variable and independent variables, such a relationship is called the profile. Today, with the development of manufacturing technologies, multistage processes have found a special position in manufacturing companies and industriesIn the present paper, we consider a multistage process with AR(1) auto-correlatedsimple linear profile in each stage and address the effect of both auto-correlationand cascade property on the efficiency of common monitoring procedures. Toeliminate the effect of auto-correlation, we used a transformation method as a remedial measure at first. Then, an approach based on the U statistic is applied toeliminate the cascade effect. Next, a modified T 2 control chart is proposed to monitor the process in the second stage. The performance of the proposed controchart is evaluated in terms of the Average Run Length criterion. The simulationstudies show that the proposed control chart perform satisfactorily.

Background and Objective: Improved and modified automation will require the development of smart process control systems that provide on-line decisions to release patients’ test results based on high analytical quality assurance formula.Materials and Methods: We collected patients’ test results from 10840 healthy subjects based on 1.96z as tRuncation limit for 29 common haematochemical analytes at a regional reference laboratory. Computer simulation studies by EZ rulesTM and EZ RunsTM software were performed to generate operating specification charts (OPSpces) that consider tRuncation limits set at 3(Spop) and control limits set at 3 Spop/n1/2 and number of patient subgroups which varied from 10 to 480 depending on the spop/smvsa ratio that varied from 1.58 to 19.75.Results: On the basis of the test parameters defined and the workload expected in our regional laboratory, Average of patients (AOP) algorithms would be expected to be useful for monitoring Run Length on analytical systems that test for ALP, ALT, AST, total bilirubin, calcium, creatinine, glucose, hematocrit, hemoglobin, potassium, sodium, TSH and urea. These tests provide high potential capability indicating low Pfr, high Ped and high analytical quality assurance (AQA) with low control observations for applying AOP algorithms to monitor Run Length.Conclusion: Our investigation revealed that approximately fifty percent of commonly requested haematochemical tests could achieve high capability in order to establish AOP method to maximize analytical Run Length.

Background: Early detection and reliable diagnosis of breast cancer could lead to improved cure rates and reduce mortality and management costs. Objectives: To explore the potential of texture analysis based on Run-Length matrix features for classifying benign and malignant breast tumors in ultrasound imaging. Methods: A total of 70 breast tumors (38 benign and 32 malignant) have used in the proposed computer-aided diagnosis system. Twenty Run-Length matrix features have extracted for texture analysis in three normalizations (default, 3sigma, and 1%-99%). Linear discriminant analysis and principal component analysis have employed to transform raw data to lower-dimensional spaces and increase discriminative power. The features have classified by the first nearest neighbor classifier. Results: The features under 3sigma normalization have designed via Linear discriminant analysis indicated high performance in classifying benign and malignant breast tumors with a sensitivity of 96. 87%, specificity of 100%, accuracy of 98. 57%, positive predictive value of 100%, and negative predictive value of 97. 43%. The area under receiver operating characteristic curve was 0. 992. Conclusions: Run-Length matrix featureshada high potential to characterizeandcould help radiologist to diagnosis breast tumors.

This study aims to introduce a new multivariate ‎‎p‎-chart using a structure of the multivariate Bernoulli distribution‎. ‎In this structure‎, ‎we consider a class of dependent Bernoulli variables where the conditional success probability is a linear combination of the last few trials and the original success probability‎. ‎The efficiency of the proposed control chart is investigated in terms of the Average Run Length criterion with two approaches‎. ‎Also‎, ‎the issue of misclassification and measurement error in binary random variables is discussed, and ‎another control chart based on the corrected ‎proportion of non-conforming products is proposed‎.

The Shewhart np control chart is often used to monitor the quantity of nonconforming, but it is slow in detecting small deviations. This paper proposes an efficient approach to monitor the quantity of nonconforming. The novelty of the paper is utilization of an initial belief to construct an analytic variable limit np control chart. The proposed method uses all gathered data, sequentially. This approach is significantly faster than some existent effective approaches in detecting small deviations. These charts are mainly used for evaluation of the initial setup in the process. The simulated results for the Average Run  Length profiles demonstrate the superiority of the new approach against the standard np chart, binomial CUSUM, binomial EWMA and moving Average approach.