Sedum adolphii  stem cutting 1-2 cm were sterilized and cultured on different media . The favorable medium for callus formation was Murashige and skoog (MS, 1962)supplemented with Banzylaminopurine ( BAP) plus Naphalene acetic acid (NAA) in106M  each . Whereas, the best medium for differentiation was MS ,1962 supplemented with BAP 10-7M and NAA10-7M . The formed shoots were transferred to media without Auxin (control) or with different (NAA) concentrations (10-6and 10-7M) . The best rooted shoots were on control, transferred successfully to jeffy 7  discs and to the green house after 3 weeks.

The object of the presented study was to monitor the changes that had happened
in the main features (water, vegetation, and soil) of Al-Hammar Marsh region. To
fulfill this goal, different satellite images had been used in different times, MSS
1973, TM 1990, ETM+ 2000 and MODIS 2010. K-Means which is unsupervised
classification and Neural Net which is supervised classification was used to classify
the satellite images 0Tand finally by use 0Tadaptive classification 0Twhich is0T3T 0T3Tapply
s0Tupervised classification on the unsupervised classification. ENVI soft where used
in this study.

Low back pain a major causes of morbidity throughout the world and it is a most debilitating condition ,and can lead to decreased physical function ,compromised quality of life, and psychological distress. Obesity is nowadays a pandemic condition. Obese subjects are commonly characterized by musculoskeletal disorders and particularly by non-specific LBP. However, the relationship between obesity and LBP remain to date unsupported by objective measurements of mechanical behavior of spine and it is morphology in obese subjects.                                     Key words: obesity, low back pain,  

A new de-blurring technique was proposed in order to reduced or remove the blur in the images. The proposed filter was designed from the Lagrange interpolation calculation with adjusted by fuzzy rules and supported by wavelet decomposing technique. The proposed Wavelet Lagrange Fuzzy filter gives good results for fully and partially blurring region in images.
 

Although the Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case when the blurring filter is singular, the Wiener filtering actually amplify the noise. This suggests that a denoising step is needed to remove the amplified noise .Wavelet-based denoising scheme provides a natural technique for this purpose .

                In this paper  a new image restoration scheme is proposed, the scheme contains two separate steps : Fourier-domain inverse filtering  and wavelet-domain image denoising. The first stage is Wiener filtering of the input image , the filtered image is inputted to adaptive threshold wavelet

With the quick grow of multimedia contents, from among this content, face recognition has got a lot of significant, specifically in latest little years. The face as object formed of various recognition characteristics for detect; so, it is still the most challenge research domain for researchers in area of image processing and computer vision. In this survey article, tried to solve the most demanding facial features like illuminations, aging, pose variation, partial occlusion and facial expression. Therefore, it indispensable factors in the system of facial recognition when performed on facial pictures. This paper study the most advanced facial detection techniques too, approaches: Hidden Markov Models, Principal Component Analysis (PCA)

Face recognition is a type of biometric software application that can identify a specific
individual in a digital image by analyzing and comparing patterns. It is the process of
identifying an individual using their facial features and expressions.
In this paper we proposed a face recognition system using Stationary Wavelet Transform
(SWT) with Neural Network, the SWT are applied into five levels for feature facial
extraction with probabilistic Neural Network (PNN) , the system produced good results
and then we improved the system by using two manner in Neural Network (PNN) and
Support Vector Machine(SVM) so we find that the system performance is more better
after using SVM where the result shows the performance o