Vehicular ad-hoc networks (VANETs) form when vehicles are equipped with devices capable of short-range wireless communication. Vehicular Ad hoc networks involve motion of nodes depending on the mobility model chosen. Three important considerations in simulation of VANETs are mobility models, network simulator and the routing protocols. Selection of appropriate mobility model for evaluating routing protocol leads to efficient simulation results. Performance of routing protocols in VANETs can be measured using four metrics; bandwidth, packet loss, throughput and scalability. This research work is based on the simulation based analysis of Vehicular Ad- hoc networks using NS-2 as the network simulator. Performance evaluation of the protocols is conducted on the basis of four defined metrics and as conclusion is made according to the simulation results.

No doubt software plays an important role in improvement of our lives. Great demand of software makes software architecture more complex. Flaws in any software have direct impact on diverse fields of life (such as business, science, engineering etc). The main reason of any software failure is due to poor software architecture or quality attributes. This paper focuses on factors that affect the quality of software architectures and highlighted the major reason of the defects through questionnaire and survey. In the light of this survey a technique is proposed to improve the quality of any software architecture. The proposed architecture is Enhanced-Pattern driven architecture (EPDA). This architecture focuses on the improvement of design phase in any architecture. This will also help in resolving lots of problems which arise due to usage of different traditional architectural styles.

During past few years, brain tumor segmentation in magnetic resonance imaging (MRI) has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this paper for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image.