The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was t

Electrocardiogram (ECG) is an important physiological signal for cardiac disease diagnosis. With the increasing use of modern electrocardiogram monitoring devices that generate vast amount of data requiring huge storage capacity. In order to decrease storage costs or make ECG signals suitable and ready for transmission through common communication channels, the ECG data
volume must be reduced. So an effective data compression method is required. This paper presents an efficient technique for the compression of ECG signals. In this technique, different transforms have been used to compress the ECG signals. At first, a 1-D ECG data was segmented and aligned to a 2-D data array, then 2-D mixed transform was implemented to compress the

In this paper, introduce a proposed multi-level pseudo-random sequence generator (MLPN). Characterized by its flexibility in changing generated pseudo noise (PN) sequence according to a key between transmitter and receiver. Also, introduce derive of the mathematical model for the MLPN generator. This method is called multi-level because it uses more than PN sequence arranged as levels to generation the pseudo-random sequence. This work introduces a graphical method describe the data processing through MLPN generation. This MLPN sequence can be changed according to changing the key between transmitter and receiver. The MLPN provides different pseudo-random sequence lengths. This work provides the ability to implement MLPN practically

The world's renewable energy sources have taken on great importance, for its cleanness and its environmental effects as well as being a renewable source, Increased demand for fossil energy sources is also causing global warming and climate change. Iraq is an appropriate area for renewable energy This study shows that renewable alternative energy has not been used sufficiently enough at present. But this energy can play an important role in the future of renewable energy in Iraq. This research aims to study the renewable energy in Iraq (solar energy) and it is appropriate to develop this alternative energy for crude oil, which is characterized by the use of the most appropriate and less economical and more environmentally friendly. Solar

Autism is considered as one of the most developmental  problems  in the world that interfere with children growth and affect their social ,emotional and cognitive development child with autism used to be normal in his growth but in his development parents started to notice that their child characterize by loneliness and withdrawal himself from the surrounding world with some mannerism behaviors these characteristics used to be manifested children during the 1^st  three year of their life .  It appears, one in every 500 birth (The American International Institution for child health 1997. and it would be less in females than makes at 1/4 percent .

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          The research aims to build a communication apprehention scale for student (females) from preparatory schools .research sample included (400)students (females) were selected from the preparatory . to build a tool for the researchers are several steps , todetermine the meaning of communication apprehention and formulation of the items of the seale according to the linkert method .

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it