abstract objective development is a complex process, completed over a specific period of time, through the maturation of the nervous system. it is affected by genetic, ethnic, nutritional, social, and economic factors; one of the environmental factors affecting the acquisition of motor skills in infants is the use of baby walkers. since this device is very commonly used for infants in our count...

OBJECTIVE The objective of this study is to compare the fast Walsh transform with autoregressive method for spectral analysis of heart rate variability signals based on evaluating the quantitative and dynamic differences between the results of Walsh and autoregressive analysis of heart rate variability signals for healthy patients and diabetic patients who have suffered from diabetes more than ...

In this paper, a hierarchical autoregressive model (HAR) method is proposed for image compression. The suggested techniques, looks at improving the compression ratio along with preserving the image quality by involving a multi-layered modeling concept. In comparison with traditional predictive coding or autoregressive model on a series of tested images it shows that the suggested method is bett...

This paper present a new automated detection method for cardiac arrhythmia. The detection system is implemented with integration of feature extraction and classification parts. In feature extraction phase of proposed method, the feature values for each arrhythmia are extracted using autoregressive (AR) and multivariate autoregressive (MVAR) modeling of one-lead and two-lead electrocardiogram si...

This paper presents a short-term electric load forecasting method based on Autoregressive Tree Algorithm and Rough Set Theory. Firstly, Rough Set Theory was used to reduce the testing properties of Autoregressive Tree. It can optimize the Autoregressive Tree Algorithm. Then, Autoregressive Tree Model of Short-term electric load forecasting is set up. Using Rough Set Theory, the attributes will ...

The vector autoregressive model is very popular for modeling multiple time series. Estimation of its parameters is typically done by a least squares procedure. However, this estimation method is unreliable when outliers are present in the data, and therefore we propose to estimate the vector autoregressive model by using a multivariate least trimmed squares estimator. We also show how the order...