Abstract
It has often been reported and is now well established that the traditional definition of Instantaneous Frequency (IFt) fails in the presence of multicomponent signals. New definitions of IFt have recently been proposed, in an attempt to cope with the difficulties of the traditional one. In this paper, we will address the reasons for failure of the traditional definition, show that it can also fail with monocomponent signals, and show how its behaviour can be predicted for any type of signal, thus generalizaing previous results. We will also analyse the performance in noise of one of the recently proposed definitions of IFt.

Abstract
A subspace-based on-line identification algorithm based on one specific technique, based on Van Overschee and De Moor's results, but can be adapted to other similar methods since they all recover from the state sequence and the observability matrix is presented. These results relate an estimated Kalman filter sequence with an oblique projection. With further improvements, the algorithm can adapt to the identification of time-variant systems.

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Higher-order statistics extend the analysis methods of non-linear systems and non-gaussian signals based on the autocorrelation and power spectrum. The main drawback of their use in real time applications is the high complexity of their estimation due to the large number of arithmetic operations. This paper presents an experimental vector architecture for the estimation of the higher-order moments. The processor's core is a pipelined multiply-accumulate unit that receives four data vectors and computes in parallel the moment taps up to the fourth-order. The design of custom cache memory organization and address generation circuits has led to more than 11 operations per clock cycle. The architecture was modeled and simulated in Verilog and is presently being implemented in XILINX field-programmable gate arrays (FPGAs) and one custom integrated circuit for the multiply-accumulate unit.