Electrocardiography (ECG or EKG) is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. The main idea is how to detect activity of the heart from skin that appears in video without using electrodes. This paper, proposes an algorithm that works on analyzing video frames to detect heartbeats from tiny changes that happen in a skin color luminance (brightness) and then using them to amplifying heartbeat and drawing ECG. The results show that the heartbeat was detected and amplified and ECG was drawing from any part of the human body in different situations and from different video.

In this paper, the proposes secure system to improving security of ID card and passports is by generating cubic spline co-occurrence code (CCO code) for each ID card. The authentication part, begins passing ID card through the checkpoint then the checkpoint will check the information of card or passport by also extracting features in order to generate the cubic spline co-occurrence code (CCO code), finally comparison is made between extracted CCO code at the checkpoint and CCO code that has been printed on ID card or passport (type of fraud like change personal picture or fraud it’s information). Several tests were conducted to evaluate the performance of the proposed security system. Furthermore, the experiment results reveal that the

This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as com

This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as

Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a

Phenotypic And genotypic characteristics of Salmonella enterica serotype Typhi have been determined for 29 isolates, from Baghdad in 2007. Conventional typing methods were performed by biochemical tests, and antimicrobial susceptibility test. Molecular typing performed by analysis plasmid DNA beside using the Random Amplified Polymorphic DNA (RAPD-PCR). For the latter, two universal primers that have selected for the high discriminatory power were used for RAPD analysis. All isolates were belong one biotype according to the differention by their ability to decarboxylat lysine, 29(100%) were lysine (+). All the isolates were susceptible to the Antibiotics used. However, all the strains free of plasmids. RAPD was capable of grouping the strai

THE Schiff base reaction played an important role of the condensation reaction between 2-aminophenol and Glyoxylic acid in the presence of calculated amounts of KOH as a catalyst. The reaction has been carried out in ethanol under reflux and stirring condition for 3.5 hrs. All syntheses were carried out under hydrogen gas forming a new potassium (E)-1-hydroxy-2-(2-hydroxyphenylimino)ethanolate ligand type [NO2]. The ligand of the general formula K2[Mn(L2)] type and its Mnп complex K2[Mn(N2O4)] type, has been characterized by spectroscopic methods (F.T-I.R. and U.V-Vis.), elemental analysis (C.H.N) metal content, magnetic susceptibility measurement, Thin-layer chromatography (T.L.C), X-RD powder diffraction, 1H-NMR, 13C-NMR molar conductanc

In this paper, an algorithm for reconstruction of a completely lost blocks using Modified
Hybrid Transform. The algorithms examined in this paper do not require a DC estimation
method or interpolation. The reconstruction achieved using matrix manipulation based on
Modified Hybrid transform. Also adopted in this paper smart matrix (Detection Matrix) to detect
the missing blocks for the purpose of rebuilding it. We further asses the performance of the
Modified Hybrid Transform in lost block reconstruction application. Also this paper discusses
the effect of using multiwavelet and 3D Radon in lost block reconstruction.

In this research, a group of gray texture images of the Brodatz database was studied by building the features database of the images using the gray level co-occurrence matrix (GLCM), where the distance between the pixels was one unit and for four angles (0, 45, 90, 135). The k-means classifier was used to classify the images into a group of classes, starting from two to eight classes, and for all angles used in the co-occurrence matrix. The distribution of the images on the classes was compared by comparing every two methods (projection of one class onto another where the distribution of images was uneven, with one category being the dominant one. The classification results were studied for all cases using the confusion matrix between every

The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of