Many cognitive, sensory and motor processes have correlates in oscillatory neural sources, which are embedded as a subspace into the recorded brain signals. Decoding such processes from noisy magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires the use of data-driven analysis methods. The objective evaluation of such decoding algorithms on experimental raw signals, however...

An improved method for the fast correlation attack on certain stream ciphers is presented. The proposed algorithm employs the following decoding approaches: list decoding in which a candidate is assigned to the list based on the most reliable information sets, and minimum distance decoding based on Hamming distance. Performance and complexity of the proposed algorithm are considered. A desirabl...

Syndrome decoding is a more efficient method of decoding linear codes over finite fields over a noisy channel [5]. Thus, in this paper we investigate the generalization of the syndrome decoding to linear codes over finite commutative rings. A first generalization was given in [1] via Pontryagin duality. In the same direction we give another generalization using linear functional-based duality. ...

Sphere decoding for multiple antenna systems has been shown to achieve near-ML performance with low complexity. However, the achievement of such an excellent performance-complexity tradeoff is highly dependent on the initial choice of sphere radius. In this paper, we present a new sphere decoding algorithm which is even less computationally complex than the original sphere decoder. Moreover, th...