Sentence examples for information decoder from inspiring English sources

This universal decoder is shown to be closely related to the Lagrange-optimized decoder of Oosterwijk et al. and the empirical mutual information decoder of Moulin.

In particular, as shown in Figure 1, the received signal at each user is split to the information decoder and the energy harvester by a power spitter.

In between, Section 3.4 discusses a completely different approach to obtain a universal simple decoder (based on the empirical mutual information decoder of Moulin [25]) and shows how in the end the result is again quite similar.

We also showed how the proposed universal decoder is very similar to both Oosterwijk et al.'s decoder h and an approximation of Moulin's empirical mutual information decoder m for 0≪p≪1 by begin{array}{*{20}l} c cdot g x,y,p) sim h x,y,p) sim n cdot m x,y,p), end{array}.

To construct this decoder, we start with the empirical mutual information decoder previously proposed by Moulin [25], and for now, let us assume p i ≡p is fixed.4 With this decoder, a user is assigned a score of the form begin{array}{*{20}l} S_{j} &= sum_{x,y} hat{f}_{X,Y|P} x,y|p) lnleft(frac{hat{f}_{X,Y|P} x,y|p)}{hat{f}_{X|P}(x|p)hat{f}_{Y|P} y|p)}right) end{array}.

For the interleaving attack strategy, the Bayesian approximation of the empirical mutual information decoder of (11) satisfies begin{array}{*{20}l} m x,y,p) = left{begin{array}{cc} lnleft(1 + frac{p}{n(1 - p)}right) & text{if}~ x = y = 0; !!!!!!!!!!!!!lnleft(1 - frac{1}{n}right) & !!!!!!!!!!!!text{if}~ x neq y; !!!!!!lnleft(1 + frac{1 - p}{np}right) & text{if}~ x = y = 1.

Using input symbols and a priori decoding information, each SISO decoder computes extrinsic information.

Error resilience modes are used in the JPEG2000 source codec making it possible to provide useful source decoded information to the channel decoder.

The simulation results suggest that, with no access to the watermark amplitude information at the decoder side, the sub-optimal decoders in the DCT domain are more reliable than their counterparts in the DFT magnitude domain.

Additionally, the inserted 0 bits will also yield a faster convergence of channel decoding due to the exactly correct side information at the decoder provided by the received BMMs (see next subsection).

Even for the joint processing, they are jointly processed only through the extrinsic information from the decoder just as the conventional joint detection and decoding structure, e.g, the iterative MMSE scheme [8] which is verified to be optimal.