Software Defined Network (SDN) is more dynamic, manageable, adaptive and programmable network architecture. This architecture separates the control plane from the forwarding plane that enables the network to become directly programmable. The programmable features of SDN technology has dramatically improved network efficiency and simplify the network configuration and resource management. SDN supports Open-Flow technology as forwarding function and centralized control successfully. Wireless environment has recently added to the SDN infrastructure that has rapidly emerged with Open-Flow protocol. To achieve more deterministic network behaviors, QoS provisioning is a necessary consideration. In this paper, the Spanning Tree Protocol (STP) has applied on a SDWN and then analyzed the Quality of Service (QoS) using Mininet-Wifi. STP protocol is used to suppress the occurrence of broadcast streams and observe the performance of the QoS parameters. Various parameters that determine QoS, such as, bandwidth utilization, packet transmission rate, round trip time, maximum obtained throughput, packet loss ratio, delay time is analyzed for different base stations defined in the SDWN architecture.

Medical image segmentation is a fundamental task in the medical imaging field. Optimal segmentation is required for the accurate judgment or appropriate clinical diagnosis. In this paper, we proposed automatically gradient threshold estimator of anisotropic diffusion for Meyer’s Watershed algorithm based optimal segmentation. The Meyer’s Watershed algorithm is the most significant for a large number of regions separations but the over segmentation is the major drawback of the Meyer’s Watershed algorithm. We are able to remove over segmentation after using anisotropic diffusion as a preprocessing step of segmentation in the Meyer’s Watershed algorithm. We used a fixed window size for dynamically gradient threshold estimation. The gradient threshold is the most important parameter of the anisotropic diffusion for image smoothing. The proposed method is able to segment medical image accurately because of obtaining the enhancement image. The introducing method demonstrates better performance without loss of any clinical information while preserving edges. Our investigated method is more efficient and effective in order to segment the region of interests in the medical images indeed.