Nowadays, using state of the art multivariate machine learning approaches, researchers are able to classify brain states from brain data. One of the applications of this technique is decoding phonemes that are being produced from brain data in order to decode produced words. However, this approach has been only moderately successful. Instead, decoding articulatory features from brain data may b...

Multivariate decoding methods, such as multivoxel pattern analysis (MVPA), are highly effective at extracting information from brain imaging data. Yet, the precise nature of the information that MVPA draws upon remains controversial. Most current theories emphasize the enhanced sensitivity imparted by aggregating across voxels that have mixed and weak selectivity. However, beyond the selectivit...

Although the decoding rules have been largely elucidated, the physical-chemical reasons for the "correctness" of codon:anticodon duplexes have never been clear. In this work, on the basis of the available data, we propose that the correct codon:anticodon duplexes are those whose formation and interaction with the ribosomal decoding center are not accompanied by uncompensated losses of hydrogen ...

The boundaries between block and convolutional codes have become diffused after recent advances in the understanding of the trellis structure of block codes and the tail-biting structure of some convolutional codes. Therefore, decoding algorithms traditionally proposed for decoding convolutional codes have been applied for decoding certain classes of block codes. This paper presents the decodin...

We consider a signaling format where information is modulated via a superposition of independent data streams. Each data stream is formed by error-correction encoding, constellation mapping, replication and permutation of symbols, and application of signature sequences. The relations between data bits and the modulation symbols transmitted over the channel can be represented in the form of a sp...