This discussion sparse representations of signals in R. The sparsity of a signal is quantified by the number of nonzero components in its representation. Such representations of signals are useful in signal processing, lossy source coding, image processing, etc. We first speak of an uncertainty principle regarding the sparsity of any two different orthonormal basis representations of a signal S...

Binary tree-structured filter banks have been employed in the past to generate wavelet bases. While the relation between paraunitary filter banks and orthonormal bases is known to some extent, there are some extensions which are either not known, or not published so far. In particular it is known that a binary tree-structured filter bank with the same paraunitary polyphase matrix on all levels ...

We establish a one-to-one correspondence between (1) quantum teleportation schemes, (2) dense coding schemes, (3) orthonormal bases of maximally entangled vectors, (4) orthonormal bases of unitary operators with respect to the Hilbert-Schmidt scalar product, and (5) depolarizing operations, whose Kraus operators can be chosen to be unitary. The teleportation and dense coding schemes are assumed...

There has been recent interest in using orthonormalised forms of xed denominator model structures for system identiication. However, modulo numerical conditioning considerations, the transfer function estimates obtained by using these sometimes complex to implement structures are identical to that obtained by simply pre-ltering the input with an all pole denominator (with poles the same as the ...

We construct a uniformly bounded orthonormal almost greedy basis for L p ([0, 1]), 1 < p < ∞. The example shows that it is not possible to extend Orlicz's theorem, stating that there are no uniformly bounded orthonormal unconditional bases for L p ([0, 1]), p = 2, to the class of almost greedy bases.