Multivariate decoding models are increasingly being applied to functional magnetic imaging (fMRI) data to interpret the distributed neural activity in the human brain. These models are typically formulated to optimize an objective function that maximizes decoding accuracy. For decoding models trained on full-brain data, this can result in multiple models that yield the same classification accur...

In this paper we propose a hybrid maximum likelihood decoding (MLD) for linear block codes. For the reliable data transmission over noisy channels, convolutional and block codes are widely used in most digital communication systems. Much more efficient algorithms have been found for using channel measurement information in the decoding of convolutional codes than in the decoding of block codes....

Reed-Solomon (RS) codes have been used for a long time in many data storage systems for error correction. Popularity of RS codes is due to their powerful burst error correction capability and well-established decoding algorithm with efficient hardware implementation providing high throughput. Conventional hard-decision decoding algorithms are able to correct up to half the minimum distance. To ...

In this study we consider rateless coding over discrete memoryless channels (DMC) with feedback. Unlike traditional fixed-rate codes, in rateless codes each codeword in infinitely long, and the decoding time depends on the confidence level of the decoder. Using rateless codes along with sequential decoding, and allowing a fixed probability of error at the decoder, we obtain results for several ...

Algebraic soft-decision decoding of Reed-Solomon codes delivers promising coding gains over conventional hard-decision decoding. The main computational steps in algebraic soft-decoding (as well as Sudan-type list-decoding) are bivariate interpolation and factorization. We discuss a new computational technique, based upon re-encoding and coordinate transformation, that significantly reduces the ...