Early classification of time series is important in time-sensitive applications. An approach is presented for early classification using generative classifiers with the dual objectives of providing a class label as early as possible while guaranteeing with high probability that the early class matches the class that would be assigned to a longer time series. We give a specific algorithm for early quadratic discriminant analysis (QDA), and demonstrate that this classifier meets the requirement of reliable early classification.

We address several inter-related aspects of underwater network design within the context of a cross-layer approach. We first highlight the impact of key characteristics of the acoustic propagation medium on the choice of link layer parameters; in turn, the consequences of these choices on design of a suitable MAC protocol and its performance are investigated. Specifically, the paper makes contributions on the following fronts: a) Based on accepted acoustic channel models, the pointto- point (link) capacity is numerically calculated, quantifying sensitivities to factors such as the sound speed profile, power spectral density of the (colored) additive background noise and the impact of boundary (surface) conditions for the acoustic channel; b) It provides an analysis of the Micromodem-like linklayer based on FH-FSK modulation; and finally c) it undertakes performance evaluation of a simple MAC protocol based on ALOHA with Random Backoff, that is shown to be particularly suitable for small underwater networks.