background: municipal solid waste (msw) is the natural result of human activities. msw generation modeling is of prime im­portance in designing and programming municipal solid waste management system. this study tests the short-term pre­diction of waste generation by artificial neural network (ann) and principal component-regression analysis. methods: two forecasting techniques are presented in...

Fault detection and process monitoring using principal component analysis (PCA) have been studied intensively and applied to industrial processes. PCA is used to deene an orthogonal partition of the measurement space into two orthogonal subspaces: a principal component sub-space (PCS), and a residual subspace (RS). In this paper, each process fault is also described by a subspace and the fundam...

background: fetal electrocardiography is a developing field that provides valuable information on the fetal health during pregnancy. by early diagnosis and treatment of fetal heart problems, more survival chance is given to the infant. objective: here, we extract fetal ecg from maternal abdominal recordings and detect r-peaks in order to recognize fetal heart rate. on the next step, we find a b...

Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impac...

background: in order to check the practicality of classification of universities of medical sciences (umss) based on their infrastructures, and scientific contributions, this study explored the most appropriate indicators to measure the size and productivity of umss. methods: in the first phase, we approached a group of experts who had a deep experience in the management of umss and in the mini...

Large datasets can be analyzed through different linear and nonlinear methods. Most frequently used linear method is Principal Component Analysis (PCA) known also as EOF (Empirical Orthogonal Function) analysis, permitting both clustering and visualizing high-dimensional data items. However, many problems are nonlinear in nature, so, for analyzing such a problems some nonlinear methods will be ...

remote sensing techniques are known as very useful methods for studying land use management in arid and semi arid areas, where undeveloped soils are dominant. these techniques can be used for determining different environmental characteristics including soil and vegetations. in this study etm+ band ratios and principal component analysis (pca) were used for monitoring of vegetation cover and it...