Operator Theory-based Computation of Linear Canonical Transforms

The paper entitled “Operator Theory-based Computation of Linear Canonical Transforms” published in Signal Processing by Dr. Koç and his collaborators applied the operator theory to the computation of linear canonical transforms.

By exploiting hyperdifferential operators and through a systematic investigation of possible parameters and design choices, the proposed algorithm achieves a discrete linear canonical transform (DLCT) definition that is both theoretically satisfying and highly accurate.

The advantage and elegance of this approach lie in the fact that it reduces the problem of defining sophisticated discrete transforms to much simpler problem of defining discrete coordinate multiplication and differentiation operations. The proposed approach may lead to further possible research directions in the theory of discrete transforms.

The paper can be accessed [here].

 

Abstract:

Linear canonical transforms (LCTs) are extensively used in many areas of science and engineering with many applications, which requires a satisfactory discrete implementation. Recently, hyperdifferential operators have been proposed as a novel way of defining the discrete LCT (DLCT). Here we first focus on improving the accuracy of this approach by considering alternative discrete coordinate multiplication and differentiation operations. We also consider canonical decompositions of LCTs and compare them with the originally proposed Iwasawa decomposition. We show that accuracy of the approximation of the continuous LCT with the DLCT can be drastically improved. The advantage and elegance of this approach lie in the fact that it reduces the problem of defining sophisticated discrete transforms to merely defining discrete coordinate multiplication and differentiation operations, by reducing the transforms to these operations. As a result of systematic investigation of possible parameters and design choices, we achieve a DLCT that is both theoretically satisfying and highly accurate.