Tropical cyclone intensity estimation is a challenging task as it required domain knowledge while extracting features, significant pre-processing, various sets of parameters obtained from satellites, and human intervention for analysis. The inconsistency of results, significant pre-processing of data, complexity of the problem domain, and problems on generalizability are some of the issues related to intensity estimation. In this study, we design a deep convolutional neural network architecture for categorizing hurricanes based on intensity using graphics processing unit. Our model has achieved better accuracy and lower root-mean-square error by just using satellite images than 'state-of-the-art' techniques. Visualizations of learned features at various layers and their deconvolutions are also presented for understanding the learning process.

Routing in ad-hoc networks is a complicated task because of many reasons. The nodes are low-memory, low-powered, and they cannot maintain routing tables large enough for well-known routing protocols. Because of that, greedy forwarding at intermediate nodes is desirable in ad-hoc networks. Also, for traffic engineering, multi-path capabilities are important. So, it is desirable to define routes at the source like in source based routing (SBR) while performing greedy forwarding at intermediate nodes. In this paper, we investigate trajectory-based routing (TBR) which was proposed as a middle-ground between SBR and greedy forwarding techniques. We address various issues regarding implementation of TBR. We also provide techniques to efficiently forward packets along a trajectory defined as a parametric curve.