The work includes fabrication of undoped and silver-doped nanostructured nickel oxide in form thin films, which use for applications such as gas sensors. Pulsed-laser deposition (PLD) technique was used to fabricate the films on a glass substrate. The structure of films is studied by using techniques of x-ray diffraction, SEM, and EDX. Thermal annealing was performed on these films at 450°C to introduce its effect on the characteristics of these films. The films were doped with a silver element at different doping levels and both electrical and gas sensing characteristics were studied and compared to those of the undoped films. Reasonable enhancements in these characteristics were observed and attributed to the effects of thermal annealing

 The gas sensing properties of undoped Co₃O₄ and doped with Y₂O₃ nanostructures were investigated. The films were synthesized using the hydrothermal method on a seeded layer. The XRD, SEM analysis and gas sensing properties were investigated for the prepared thin films. XRD analysis showed that all films were polycrystalline, of a cubic structure with crystallite size of (12.6) nm for cobalt oxide and (12.3) nm for the Co₃O₄:6% Y₂O₃. The SEM analysis of thin films indicated that all films undoped Co₃O₄ and doped possessed a nanosphere-like structure.

The sensi

The Ge0.4Te0.6 alloy has been prepared. Thin films of Ge0.4Te0.6 has been prepared via a thermal evaporation method with 4000A thickness, and rate of deposition (4.2) A/sec at pressure 2x10-6 Torr. The A.C electrical conductivity of a-Ge0.4Te0.6 thin films has been studied as a function of frequency for annealing temperature within the range (423-623) K, the deduced exponent s values, was found to decrease with increasing of annealing temperature through the frequency of the range (102-106) Hz. It was found that, the correlated barrier hopping (CBH) is the dominant conduction mechanism. Values of dielectric constant ε1 and dielectric loss ε2 were found to decrease with frequency and increase with temperature. The activation energies have

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.

In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.

In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected  from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.