Abstract: This paper explores an integration of compressive sensing, curvelet transform, and Principal Component Analysis to develop a robust gender recognition method from face images. Compressive measurements of face images leading to a significant reduction in feature space. Here curvelet transform has been used to represent the face images with prominent edges, curvatures, boundaries and to offer sparse representation to apply compressive measurements on detailed subband. To extract the feature vector, Principal Component Analysis is applied on the reconstructed detailed subband. Performance of the proposed method is evaluated by employing different classifiers. The proposed method efficiently handles the effect of Gaussian noise maintaining high accuracy on gender recognition. Extensive experiments on FERET database, is conducted to substantiate our claim.

• Publication Date: 29-Apr-2018
• DOI: 10.15224/978-1-63248-149-8-17
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Abstract: Delay Tolerant Networks (DTNs) are characterized by the absence of end-to-end path. One potential application of such networks is post-disaster environment, where conventional network infrastructure is fully or partially destroyed. Routing is a challenging issue in DTNs because of intermittent connectivity. Most of the traditional DTN routing schemes do not consider the contact history of the nodes for data delivery. The history of encounter of a node with the other nodes gives valuable information about the network topology. In this paper, a set of potential relay nodes is primarily chosen based on available buffer space and residual energy of the nodes in the network. Among the potential relays, one is selected considering the value of a metric which is designed based on the encounter history and residual energy. The node with the highest value of this metric is finally selected as a relay. The performance of our proposed scheme is evaluated in ONE simulator considering a post-disast

• Publication Date: 29-Apr-2018
• DOI: 10.15224/978-1-63248-149-8-18
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