The present work included new information about pollen of eighteen Spp. of Stachys  L. in Iraq ;showed their importance in diagnostsis.

Pollen shapes in equatorial view were ellipsoid in most spp .and spherical - subspherical ,  spherical - subprolate and  subprolate -

ellipsoid in others; while they were spherical - subspherical in polar

view . Pollen was tricolpate except St.iberica  M.B.georgica Rech.f. &

Ten. Which has tetracolpate and that was new for the genus.

The smallest pollen were seen in St.kotschyi  Boiss. & Hohen. but the biggest were in St.benthamiana Boiss.

Fraxinus ornus L. is considered as a special species that is frequently planted as a decorative tree in most of the country. The cross-sections of the root and stem are circular in shape and in the secondary growth stage, the vascular tissue in the root and stem consists of secondary xylem in radial rows and the type of vessels in the xylem are ring pours wood. Epidermal cells of leaves undulate on the upper and lower side, hairs are uniseriate and unicellular and the stomata appeared in the abaxial surface only is anomocytic type. The vertical-section of blade leaf includes upper epidermis and lower epidermis followed by the palisade layer and spongy layers. The cross-section of petiole horseshoe shape and the vascular bundles are cover

The concept of the active contour model has been extensively utilized in the segmentation and analysis of images. This technology has been effectively employed in identifying the contours in object recognition, computer graphics and vision, biomedical processing of images that is normal images or medical images such as Magnetic Resonance Images (MRI), X-rays, plus Ultrasound imaging. Three colleagues, Kass, Witkin and Terzopoulos developed this energy, lessening “Active Contour Models” (equally identified as Snake) back in 1987. Being curved in nature, snakes are characterized in an image field and are capable of being set in motion by external and internal forces within image data and the curve itself in that order. The present s

"Watermarking" is one method in which digital information is buried in a carrier signal;
the hidden information should be related to the carrier signal. There are many different types of
digital watermarking, including traditional watermarking that uses visible media (such as snaps,
images, or video), and a signal may be carrying many watermarks. Any signal that can tolerate
noise, such as audio, video, or picture data, can have a digital watermark implanted in it. A digital
watermark must be able to withstand changes that can be made to the carrier signal in order to
protect copyright information in media files. The goal of digital watermarking is to ensure the
integrity of data, whereas stegano

The emergence of staphylococci, either coagulase negative (CNS) or coagulase positive (CPS), as important human pathogens has implied that reliable methods for their identification are of large significance in understanding the diseases caused by them. The identification and characterization of staphylococci from biopsies taken from human breast tumors is reported here. Out of 32 tissue biopsies, a total of 12 suspected staphylococci grew on mannitol salt agar (MSA) medium, including 7 fermenters and 5 non-fermenter staphylococci based on traditional laboratory methods. Polymerase chain reaction (PCR) successfully identified seven isolates at the genus level as methicillin resistant Staphylococcus spp. by targeting a common region of the me

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor

Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s

  In this work, the theoretical study for designing of dielectric mirrors of high reflectance in the visible region of electromagnetic spectrum between wavelength of 400-700 nm is presented, and searching on the performance properties of the design, like there reflectance as a function to the wavelength, as beam incident in a normal form, for the materials of neglected absorbance, and scattering, in the form of thin film deposition, which are deposited on glass substrate, and by using matrix system in the study, which are used as computer simulation in MATLAB code.      The materials which are used in this study are represented by ( AlAs ), (TiO2 ),( SiC ), and  (Si3N4 ), which used in the designing mirrors alter

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.