Minimal structures for the implementation of digital rational lossless systems

Digital lossless transfer matrices and vectors (power-complementary vectors) are discussed for applications in digital filter bank systems, both single rate and multirate. Two structures for the implementation of rational lossless systems are presented. The first structure represents a characterization of single-input, multioutput lossless systems in terms of complex planar rotations, whereas the second structure offers a representation of M-input, M-output lossless systems in terms of unit-norm vectors. This property makes the second structure desirable in applications that involve optimization of the parameters. Modifications of the second structure for implementing single-input, multioutput, and lossless bounded real (LBR) systems are also included. The main importance of the structures is that they are completely general, i.e. they span the entire set of M×1 and M×M lossless systems. This is demonstrated by showing that any such system can be synthesized using these structures. The structures are also minimal in the sense that they use the smallest number of scalar delays and parameters to implement a lossless system of given degree and dimensions. A design example to demonstrate the main results is included

On one-multiplier implementations of FIR lattice structures

One-multiplier realizations for certain recently reported FIR lossless lattice structures are investigated. The multiplier extraction approach is used to show that there does not exist a real one-multiplier realization whereas it is possible to get complex one-multiplier realizations. This is unlike the situation in conventional linear-prediction FIR lattice structures, where real one-multiplier realizations are possible

The role of lossless systems in modern digital signal processing: a tutorial

A self-contained discussion of discrete-time lossless systems and their properties and relevance in digital signal processing is presented. The basic concept of losslessness is introduced, and several algebraic properties of lossless systems are studied. An understanding of these properties is crucial in order to exploit the rich usefulness of lossless systems in digital signal processing. Since lossless systems typically have many input and output terminals, a brief review of multiinput multioutput systems is included. The most general form of a rational lossless transfer matrix is presented along with synthesis procedures for the FIR (finite impulse response) case. Some applications of lossless systems in signal processing are presented

Improved technique for design of perfect reconstruction FIR QMF banks with lossless polyphase matrices

A technique is developed for the design of analysis filters in an M-channel maximally decimated, perfect reconstruction, finite-impulse-response quadrature mirror filter (FIR QMF) bank that has a lossless polyphase-component matrix E(z). The aim is to optimize the parameters characterizing E(z) until the sum of the stopband energies of the analysis filters is minimized. There are four novel elements in the procedure reported here. The first is a technique for efficient initialization of one of the M analysis filters, as a spectral factor of an Mth band filter. The factorization itself is done in an efficient manner using the eigenfilters approach, without the need for root-finding techniques. The second element is the initialization of the internal parameters which characterize E(z), based on the above spectral factor. The third element is a modified characterization, mostly free from rotation angles, of the FIR E(z). The fourth is the incorporation of symmetry among the analysis filters, so as to minimize the number of unknown parameters being optimized. The resulting design procedure always gives better filter responses than earlier ones (for a given filter length) and converges much faste

General Structural Representations for Multi-Input Multi-Output Discrete-Time FIR and IIR Lossless Systems

Discrete-time lossless systems have been found to be of great importance in many signal processing applications. However, a representation for lossless transfer matrices that spans all such matrices with the smallest possible number of parameters has not been proposed earlier. Existing representations are usually for special cases and therefore not general enough. In this study, two general and minimal representations are presented for multi-input, multi-output FIR and IIR lossless systems. The first representation is in terms of planar rotations and it leads to multi-input, multi-output lattice structures. The second representation is in terms of unit-norm vectors and it enables shorter convergence times in optimization applications. A simple modification of this representation leads to structures that remain lossless under quantization. The structures that follow from these representations share some properties such as the orthogonality of the implementations, and minimality of the number of parameters and scalar delays they are. Since all lossless transfer matrices can be spanned by appropriately adjusting their parameters, these structures can be particularly useful in applications that involve optimization under the constraint of losslessness. Some examples of such applications are included.</p