Multipath routing protocols for Mobile Ad hoc NETwork (MANET) address the problem of scalability, security (confidentiality and integrity), lifetime of networks, instability of wireless transmissions, and their adaptation to applications.Our protocol, called MP-OLSR (MultiPath OLSR), is a multipath routing protocol based on OLSR [1]. The Multipath Dijkstra Algorithm is proposed to obtain multiple paths. The algorithm gains great flexibility and extensibility by employing different link metrics and cost functions. In addition, route recovery and loop detection are implemented in MP-OLSR in order to improve quality of service regarding OLSR. The backward compatibility with OLSR based on IP source routing is also studied. Simulation based on Qualnet simulator is performed in different scenarios. A testbed is also set up to validate the protocol in real world. The results reveal that MP-OLSR is suitable for mobile, large and dense networks with large traffic, and could satisfy critical multimedia applications with high on time constraints.

A color fundus image is a photograph obtained using a fundus camera of the inner wall of the eyeball. In the image, doctors may see changes in the retinal vessels, which can be used to diagnose various dangerous disorders such as arteriosclerosis, some macular degeneration related to age, and glaucoma. To diagnose certain disorders as early as possible, automatic segmentation of retinal arteries is used to help the doctors. Also, it is a challenge for the medical community to analyze the image with the right procedure to diagnose the disorders with high accuracy. Furthermore, this will help the doctor to make the right decision on effective treatment. Hence, the authors have implemented an enhanced architecture called U-Net to segment retinal vessels in this paper. The proposed conventional U-Net permits using all the accessible spatial setting information by adding the multiscale input layer and a thick square to the conventional U-Net in terms of improving the accuracy level of image segmentation. It achieved 95.6% accuracy with a comparatively traditional U-Net model. Moreover, the segmentation results have proved that the proposed approach outperformed in detecting most complex low-contrast blood vessels even when they are very thin. The task of segmenting vessels in retinal images is known as retinal vessel segmentation. Blood vessel density can be assessed using dense pixel values. Data augmentation and analytics play a major role in building the true value of eye blood vessels for medical diagnosis. The proposed method is very promising in the automatic segmentation of retinal arteries.

Vehicular Ad-hoc NETworks (VANETs) are typically termed as a wireless ad-hoc network that contains extreme node mobility and also the network carries a great significance in various traffic-oriented commercial applications and safety services. Due to its high mobility, routing in VANET has been a challenging work and also proving a higher rate of packet delivery ratio with reduced packet loss has been more important to be considered in route formations. With that note, this paper contributes to developing a clustering model called Middle-Order Vehicle-based Clustering (MOVC) model for managing the frequent topological change and high vehicle mobility, and efficiently handling the typical road traffic scenario. Moreover, the algorithm is intended to maintain the cluster to be constant for managing the vehicles in effective ways and also to provide uninterrupted communication between the vehicles. An algorithm for Effective Cluster Head Election (ECHE) is also derived in this paper for proficiently handling the frequency variation on the highways. Further, the model is simulated and evaluated on the basis of various metrics of VANET routing, specifically packet loss, packet delivery ratio, network lifetime and throughput. The results show that the proposed mechanism outperforms the results of existing models.

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