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Abstract—In the case of multicomponent signals with amplitude and frequency modulations, the idealized representation which consists of weighted trajectories on the time-frequency (TF) plane, is intrinsically sparse. Recent advances in optimal recovery from sparsity constraints thus suggest

Abstract--’We present a theory of quadratic time-frequency (TF) energy distributions that satisfy a covariance property and generalized marginal properties. The theory coincides with the characteristic function method of Cohen and Earaniuk in the special case of ‘‘conjugate operators.” 1

We introduce a framework for probabilistic modelling of timefrequency energy distributions based on correlated Gamma and inverse Gamma random variables. One advantage of the approach is that the resulting class of models are conjugate which makes inference easier. Moreover, both positivity

Gaussian signals play a very special role in classical time-frequency analysis because they are solutions of apparently unrelated problems such as minimum uncertainty or positivity and separability of Wigner-Ville distributions. We investigate here some of the logical connections which exist

Figure 5(b): time-frequency energy distribution of the m = 76 coherent structures of the noisy speech signal shown in Fig. 3. (b): signal reconstructed from the 76 coherent structures shown in (a). The white noise has been removed.

to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit defines an adaptive time-frequency transform. We derive a signal energy distribution in the time-frequency plane, which does not include interference terms, unlike Wigner and Cohen class distributions. A

We describe a distributed position-based network protocol optimized for minimum energy consumption in mobile wireless networks that support peer-to-peer communications. Given any number of randomly deployed nodes over an area, we illustrate that a simple local optimization scheme executed at each

proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads

motion mechanisms in which the first stage consists of linear filters that are oriented in space-time and tuned in spatial frequency. The outputs of quadrature pairs of such filters are squared and summed to give a measure of motion energy. These responses are then fed into an opponent stage. Energy