The theories of signal sampling, filter banks, wavelets and "overcomplete wavelets" are well-established for the Euclidean spaces and are widely used in the processing a...
We propose a neural architecture that estimates the speed of motion. The basis is a two-dimensional map made of locally connected integrate-and-fire neurons, that propagates and i...
We consider the problem of approximating a regular function f(t) from its samples, f(nT), taken in a uniform grid. Quasi-interpolation schemes approximate f(t) with a dilated versi...
Over the last years, particle filters have been applied with great success to a variety of state estimation problems. We present a statistical approach to increasing the efficienc...
Particle filtering (PF) for dynamic Bayesian networks (DBNs) with discrete-state spaces includes a resampling step which concentrates samples according to their relative weight in ...