We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach.
First, we examined how well the mutual information estimated from the decoding approach matched the actual mutual information.
No smoothing was applied in the time dimension to accurately estimate the latency of decoding.
At the decoding stage, the most likely image sequence of vowels is estimated by decoding the HMM-GMM state posterior probabilities using the Viterbi algorithm.
MC simulations are used to estimate the performance of ML bit decoding quantified by SDR (i.e., the simulated probability of successful decoding from tests).
We roughly estimated the size of this effect for the range of codon decoding speeds we observed.
In the proposed algorithm, the decoding complexity is decreased by reducing the number of candidate codewords and, an efficient method to estimate the extrinsic information without competing codeword is designed, too.
To test this, we varied phase locking k and decoded the path length following the method of Maurer et al. (2012), which decodes the location represented by the population at each time bin in a theta cycle to estimate the encoded trajectory.
Since the receiver can estimate the channel gain of all the users (either by receiving known pilots or by using initial data received), the receiver can use successive decoding strategy to decode all the users.
Since the receiver can estimate the channel gain of all the users (either by receiving known pilots or by using initial data received), the receiver can use successive cancellation decoding strategy to decode all the users.
