One of the simple approaches to handle the measurement error in the regression analysis is the geometric mean (GM) functional relationship, initially proposed by Teissier (1948) and later by Barker et al. (1988) (cf Draper and Yang, 1997). This estimator has frequently been mentioned in the literature for two reasons. First, when there is no basis for distinguishing between the response and explanatory variables. Second, to handle the measurement error when no prior information is available. The geometric mean method has received much attention from the experts, and some have suggested that it is more useful than the ordinary least squares method (see Sprent and Dolby, 1980).
18 Read more
The moving-mean method is one of the conventional approaches for trend-extraction from a data set. It is usually applied in an empirical way. The smoothing degree of the trend depends on the selections of window length and weighted coefficients, which are associated with the change pattern of the data. Are there any uniform criteria for determining them? The present ar- ticle is a reaction to this fundamental problem. By investigating many kinds of data, the results show that: 1) Within a certain range, the more points which participate in moving-mean, the better the trend function. However, in case the window length is too long, the trend function may tend to the ordi- nary global mean. 2) For a given window length, what matters is the choice of weighted coefficients. As the five-point case concerned, the local-midpoint, local-mean and global-mean criteria hold. Among these three criteria, the lo- cal-mean one has the strongest adaptability, which is suggested for your usage.
10 Read more
 Eman Abdul-Jabar Saad “Speeding Up Fractal Image Compression by Reducing Image Size” Electronic Computer Center, Mustansiriyah University, Baghdad, Iraq, Diyala Journal for pure sciences , Vol: 6 No: 4, October 2010 ISSN: 1992-0784  George, L., "IFS Coding for Zero-Mean Image Blocks", Iraqi
The guarantee that the test packages are equal can be confirmed both theoretically and empirically. This confirmation is related to the concept equating or concordance of test score . The equating can be performed using the classical approach (classical test theory approach) and the modern approach (item response theory approach) [6, 7]. The equating using the modern approach basically calculates the students’ ablities and level of difficulty into certain scores with a linear equation [2, 8]. In order to perform this calculation, parameter of index discriminant (a), level of difficulty (b), and pseudo-guessing (c) should be estimated first. The estimation that involves these parameters in the item response theory is known as the 3PL Model. On the other hand, several methods that can be implemented in order to perform equating are namely mean and mean method, mean and sigma method, and item characteristics curve method which includes the Stocking and Lord method [9, 10].
Abstract— Estimation of missing data is essential in the meteorological, climatologically and hydrology analyses. This study employed the arithmetic mean method, normal ratio method, the modified normal ratio method, and correlation coefficient weighting method. The performance of these methods are then compared using correlation coefficient, the S-index, the root mean squared error and mean absolute error methods. The objective of this study is to determine the best estimation method for missing data for four precipitation stations in Makassar city. The results show that the modified usual ratio method is suitable to estimate the missing precipitation data in Makassar city. This study result could be useful information for climate research to complete the missing precipitation data, especially for rain gauge stations in Makassar city.
Due to the complexity of algorithms and the large dif- ferences between the segmentation results and the real- ity, the current image segmentation research results limit the application of image segmentation results. The main reason is that there is a large loss of information between the continuous expression of the image and the discrete expression of the segmentation. This loss is often due to the generation of boundary information during the classification process. As a clustering method, K-mean has been successfully applied to the classifica- tion research of many studies. For example, Kang S H  and others proposed a data clustering model based on a variational approach. This model is an extension of the classical K-mean method, a regularized K-mean method, by selecting a parameter that automatically gives a reasonable number of clusters. The Walvoort D J J  team chose the mean squared shortest distance (MSSD) as an objective function to minimize it using K-mean clustering. The results describe two K-mean methods: one for unequal areas and the other for equal-area-segmentation; the results of simulation exper- iments on soil samples show that the algorithm gives satisfactory results within reasonable calculations. Frigg- stad Z  described how to solve better worst-case ap- proximation guarantee problem in the results. Friggstad Z and others settle this problem by showing that a sim- ple local search algorithm provides a polynomial time approximation scheme (PTAS) for K-means for a Euclid- ean space for any fixed point. Due to the advantage of K-mean in clustering, clustering studies in many fields today use K-mean as a classification tool and achieved good results [16 – 20].
Implementing the clustering methods to the gene data of chromosome1 we conclude that the 8 clusters (see sheet 2) which has been obtained after implementing the statistical approach through STATISTICA in which Cluster no.3 having many gene involved which can be concluded in it as on the basis of %coverage 0.97, number of cases 246, Centroids percentage % 77.35, also graph frequencies (see figure1) of cluster confirms the information of cluster 3, also chi square test gave the confirmation 1673.41 (see figure 2), also confirmation done by the normal distribution(figure3), frequency (figure4), Dendogram (figure5).In this paper goal was to find out the some interesting mathematical and important clusters in chromosome1 gene data which has been achieved. Conclusion is pulled that mathematical equations algorithms are helpful to find out the interesting information of data, and can implement those methods to smaller and larger scale for analysis. In future plan is to proceed to check the functions and structure of each cluster and this k-mean method can be implemented to get the clusters on large scale data of complete genome of Homo sapiens and other organisms.
Turbocharging an engine boosts its power by increasing the amount of input air. This task is accomplished by using the exhaust gas to power a turbine which is engaged with a compressor. The Variable Geometry Turbocharger, VGT is a unique turbocharger that the diffuser vane angle can be changed over a wide range of positions. The mathematics of turbomachinery flow analysis is intensive and uses iterative methods. Most of the flow analyses in the area of turbochargers are either experimental or numerical. Three-dimensional Computational Fluid Dynamics (CFD), two- dimensional multiple streamline and one dimensional mean line is the three primary numerically available methods. In this paper a mean line method has been used for predicting the performance of a centrifugal compressor with variable diffuser vane angle position at subcritical Mach numbers. The calculation is based on common thermodynamic and aerodynamic principles, and empirical correlations for losses in a mean line analyses. The model calculates the velocities, pressures, temperatures, pressure losses, work consumption, and efficiencies for a specified set of turbocharger geometry, atmospheric conditions, rotational speed, and fluid mass flow rate. The obtained numerical results are validated with the in house measured experimental data and good agreement observed. The purpose for compressor model analysis is to generate overall characteristic map and identify the impact of the diffuser vane angles on the performance. The overall characteristic map is generated by this method demonstrate very good agreement and the effect of variable vane angle in pressure ratio and operating range observed.
The dimension of image feature vector is too high, the data volume is large, and there is a large amount of information redundancy between the features. Principal Component Analysis (PCA) is one of the common and effective method of dimensionality reduction of feature level data. By means of principal component, the method presented in this paper not only eliminates the redundant information between features, but also reduces the dimension of feature space, and retains the required identification information . PCA is a well-known algorithm for data dimensionality reduction and feature extraction which can transform high- dimensional original data vector into a low dimensional vector with uncorrelated components. It is an unsupervised method based on statistical analysis without prior knowledge. 
Figure 4 shows the segmentation results of four histological images selected from the dataset by using k-Means++, GMM-EM, KGC, HMRF-EM, Mean Shift and our proposed FMShift respectively. Fibrosis, vessels (including sinusoids) and other tissues are represented in light blue, white and lavender respectively. Table 1 illustrates the average accuracies with standard deviation over segmentation results obtained from twenty histological images using different methods. The perform- ance of six segmentation methods is quantified by Rand Index and Variation of Information for global evaluations. And the segmentation results of fibrosis and vessels are also measured by Dice Index independently at the same time. Table 2 summaries the average computation times for each method. All the above results are obtained with a standard Windows computer equipped with a 2.4 GHz Intel Core i5 processor and 8 GB RAM.
12 Read more
Abstract :- Smart CCTV (Closed-Circuit Television) technology has increasingly been developed in the last few years to judge the situation and notify the administer or take immediate action for security and surveillance motives. Earlier, the Difference Method (FDM),Background Subtraction Method (BSM), and Adaptive Background Subtraction Method (ABSM) is used for motion object detection but these methods could not recognize rapid scene changes or an object does not move relatively for a long time. To solve such problem , we proposed a novel moving object detection method which showed high performance with regard to the MSE(Mean Squared Error ) and the accuracy of detecting the moving object contours compared to other existing methods. It also reduces the time complexity and provides the accuracy .It is also good for observation of many places at the same time with only a single CCTV system.
The rest of this paper is organized as follows. In Section 2, an iterative formula for computing matrix sign and its acceleration through a com- bination with Newton’s iteration (see e.g.  and the references therein) are presented. We also provide some discussions and illustrate how the new scheme could be constructed and imple- mented. An error analysis for computing ma- trix sign function is brought forward in Section 3. Note that the idea of computing the geometric mean using the sign function can also be found in [9, page 131] and has recently been revived in . In Section 4, we show the numerical results and highlight the benefit of the technique. Fi- nally, several concluding comments are collected in Section 5.
range corresponding to 98% positive internal T-cells was used as the threshold marker. The threshold for positivity for ZAP-70 was 20%. 2) The geometric mean fluorescence intensity (geo MFI) index method: this approach was based on the evaluation of ZAP-70 expression levels in terms of geo MFI index. A two-tube method was used to calculate the ZAP-70 geo MFI index obtained from T-lymphocytes, CLL cells, and PE-conjugated isotype controls from CLL cells. Each program permits the analysis of ZAP-70 or PE- conjugated isotype control histograms with 256-channel resolution. Nonspecific staining was evaluated on gated CLL cells in a CD19/PE-isotype control plot, setting the electric voltage and compensation so that the geo mean of CLL cells was 10 ± 1 (tube 2). Subsequently, ZAP-70 was measured on a histogram, utilizing the “geo mean” parameter, on gated T-lymphocytes (T-geo mean), or CLL cells (B-geo mean) as defined in the CD3/CD5 or CD5/CD19 dot plot (tube 1). Values of the isotype control and CLL cells were determined from the CD5 + CD19 + gate and a minimum of 20,000 cells
so that the equation (2) is also known as the third order Runge-Kutta method based on arithmetic mean. Many reseachers follow this idea see [1, 4, 7] for examples. Wazwaz  replaces the arithmetic mean in equation (3) with contra-harmonic mean. Then Abadneh and Rosita  proposed a weighted third order Runge-Kutta method based on contra-harmonic mean.
13 Read more
Abstract: This paper aims at presenting a new voting function which is obtained in Balinski- Laraki's framework and benefits mean and median advantages. The so-called Mean-Median Comprise Method (MMCM) has fulfilled criteria such as unanimity, neutrality, anonymity, monotonicity, and Arrow's independence of irrelevant alternatives. It also generalizes approval voting system.
Background Information on Side Weirs ROWLINGS (2010) Muslu (2001) conducted a numerical analysis of side weirs which examined them by using a variation of De Marchi‟s (1934) integral solution method. This slight alteration takes the stance that the weir coefficient and discharge angle are not in fact constant, as De Marchi assumed, but functions of several parameters. Doing this complicated the problem to such an extent that it could only be solved using an iterative process.
129 Read more
Keywords Harmonic Mean, Runge-Kutta method, stability, error control. Abstrak Dalam kertas ini kami rumuskan satu kaedah terbenam berperingkat 4 yang berasaskan min harmonik dan min aritmetik. Kaedah ini berserta de- ngan skema RK-Harmonik boleh digunakan bagi menganggarkan penyelesaian kepada masalah nilai awal. Rantau kestabilan untuk skema ini juga dikaji dan kita simpulkan dapatan ini dengan satu contoh masalah sebagai mengesahkan keberkesanan kaedah ini.
C ), contra-harmonic mean Newton’s method ( ), cen- troidal mean Newton’s method ( ), logarithmic mean Newton’s method (LMNM) respectively to com- pute the extrememum of the function given in Table 1. We use the same functions as Kahya [6, 7]. The results are summarized in Table 2. We use as toler- ance. Computations have been performed using
In most cases the region of significance is split into two parts: The upper part ( μ is large) describes the region where a huge RTM effect is expected, larger than the actual difference of means, and a negative treatment effect ( τ < 0) can be confirmed. For example, assuming a correlation of r = 0.5 in Provencher's trial the region of significance includes all values above 481 meters, saying that Bosentan has a significantly (p < 0.05) negative effect on the patient's 6MWD if only the true mean 6MWD is above this value in the population of interest. This part of the region is of no further interest in our example, because here we are only interested in the one-sided hypothesis whether Bosentan can increase the patient's 6MWD. In other situations however a two-sided hypothesis might be more appropriate.
When we are using Min –Max and Mean-Mean algorithm there is a lack of clarity when we view the image, where as when we are using fuzzy logic which gives clarity of image which aids radiologist to diagnose the disease accurately. This technique gives the high quality image. In the fused image, the relative position of the functional information with respect to the anatomic landmarks is clearly displayed. This information may be very useful for physicians in medical diagnosis.