Multi-Objective Operator for Optimal Compression and De-compression of Random Signals

Pablo Soto-Quiros, A. Torokhti
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Abstract

New multi-objective operators of random signals are presented in this paper. The new operators improve, under a unrestrictive condition, the performance of known techniques: the generalized Karhunen-Loéve transform, the transform considered by Brillinger and the generalized Brillinger-like transform. This is obtained by particular design of new operators which have more parameters to optimize than that of other operators described in literature.
随机信号最优压缩解压缩的多目标算子
本文提出了一种新的随机信号多目标算子。在不受限制的条件下,新的算子改进了已知方法的性能:广义karhunen - losamade变换、Brillinger考虑的变换和广义Brillinger-like变换。这是通过特殊设计新的算子来实现的,这些算子比文献中描述的其他算子有更多的参数需要优化。
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