Sentence examples for information equalization from inspiring English sources

We model the subjectively estimated perceived motion direction as an information equalization process.

The channel information and equalization strategy are same as that in Fig. 6.

The estimates of the equivalent channels are used in the following N b data frames for self-information removal and equalization.

Estimates of the channel gains are required to perform self-information removal and equalization given by (4) and (5), respectively.

It is shown, with the aid of a representative set of simulation results, that the proposed methodologies are capable of leading to significant performance improvements in the context of relevant information processing tasks – channel equalization and chaotic time series prediction – particularly when the input data suits well a cluster-based profile.

At the first iteration, since no a priori information is available, the equalization process is reduced to the classical MMSE solution: tilde{s}_{k} = left[textbf{H}^{H}textbf{H} + frac{{sigma_{n}^{2}}}{{sigma_{s}^{2}}}textbf{I}_{M}right]^{-1}textbf{H}^{H}textbf{y}.

In the case of MIMO iterative receiver, the MIMO equalizer and the channel decoder exchange soft information according to the turbo equalization principle [4,6].

Notice that in the initial equalization iteration, the information symbols are not available and the equalization is performed without the IBI cancellation.

The equalized ZP, however, contains information that can help to improve the equalization of the useful signal.

At the first iteration I out=1 (Figure 7a), since no a priori information is available at the equalizer, a classical MMSE equalization is performed.

Thus, the effect of a severe ISI channel is mitigated by the equalization process and then the equalized information passes through the MAP algorithm-based decoders which decode the two encoded streams by exchanging the soft decisions.