Abstract: Signal processing structures for providing direct prediction coefficients and direct Least Square-Finite Impulse Response (LS-FIR) filter coefficients. The structures include one or more processors, and a storage and retrieval structure for selectively storing predictor and filter coefficients and intermediate variables, to thereby allow the one or more real processors to emulate a plurality of virtual processors, which take the form of a side fed superlattice structure, in the case of linear prediction, and a side-fed superlattice-superladder structure, in the case of direct LS-FIR filtering.

Abstract: A signal processor, which receives autocorrelation and cross-correlation coefficients of a system under study, provides predictor and filter coefficients of the system in an optimal manner, and allows the use of any number of available parallel processing units. The signal processor may be implemented in a fully parallel or fully sequential manner, or in a "parallel-partitioned" implementation.

Abstract: An optimal parametric signal processor, which receives autocorrelation coefficients, provides lattice coefficients in an optimal manner, and allows the use of any number of available parallel processing units. The signal processor may be implemented in a fully parallel or fully sequential manner, or in a "parallel-partitioned" implementation which provides the benefits of parallel processing, manageable hardware complexity, and optimal signal processing for a given number of available processors. By using a "lattice basic cell" structure, hardware complexity is minimized.

Abstract: A signal processor, which receives autocorrelation coefficients, provides lattice coefficients in an optimal manner, and allows the use of any number of available parallel processing units. The signal processor may be implemented in a fully parallel or fully sequential manner, or in a "parallel-partitioned" implementation which provides the benefits of parallel processing, manageable hardware complexity, and optimal signal processing for a given number of available processors.

Abstract: A parametric signal processing structure, which receives autocorrelation coefficients, produces, in an additive sparse superlattice embodiment, quantities .lambda..sub.i.sup.+, defined as (1-k.sub.m) (k.sub.m-1 +1), and in a subtractive sparse superlattice embodiment, quantities .lambda..sub.m.sup.-, defined as (1+k.sub.m) (1-k.sub.m-1), where the quantities k.sub.i correspond to the lattice predictor coefficients, or PARCORS of the signal under study. The additive and subtractive sparse superlattice processing structures are strictly optimal in terms of hardware and complexity, yet lend themselves to implementation in a fully parallel, fully sequential, or parallel partitioned manner.