Fifth generation (5G) mobile networks demand large bandwidth with the explosive growth of data driven applications. This necessitates enormous amount of spectrum in the Millimeter wave (mmWave) bands to greatly enhance the communication capacity. The mmWave band offers the potential for high-bandwidth communication channels in cellular networks. Relative to conventional networks, dense mmWave networks can achieve both higher data rates and comparable coverage. The paper presents the performance analysis of mobile networks in terms of propagation path loss, coverage probability and data rates for different mm wave operating frequencies of 28GHz and 73GHz. A scenario of multi-users in a micro cell is considered in different environments i.e. rural, sub urban and urban regions and the performance parameters in each case are analyzed. Millimeter wave cellular networks at 28GHz offer less rain attenuation compared to 73GHz and is useful for next generation communications with enhanced data rates and coverage.

Extraction of flower regions from complex background is a difficult task, it is an important part of flower image retrieval, and recognition .Image segmentation denotes a process of partitioning an image into distinct regions. A large variety of different segmentation approaches for images have been developed. Image segmentation plays an important role in image analysis. According to several authors, segmentation terminates when the observer's goal is satisfied. For this reason, a unique method that can be applied to all possible cases does not yet exist. This paper studies the flower image segmentation in complex background. Based on the visual characteristics differences of the flower and the surrounding objects, the flower from different backgrounds are separated into a single set of flower image pixels. The segmentation methodology on flower images consists of five steps. Firstly, the original image of RGB space is transformed into Lab color space. In the second step 'a' component of Lab color space is extracted. Then segmentation by two-dimension OTSU of automatic threshold in 'a-channel' is performed. Based on the color segmentation result, and the texture differences between the background image and the required object, we extract the object by the gray level co-occurrence matrix for texture segmentation. The GLCMs essentially represent the joint probability of occurrence of grey-levels for pixels with a given spatial relationship in a defined region. Finally, the segmentation result is corrected by mathematical morphology methods. The algorithm was tested on plague image database and the results prove to be satisfactory. The algorithm was also tested on medical images for nucleus segmentation.