1. Introduction
In this paper, we develop a novel and effective algorithm for symmetric Laurent polynomial matrix extension (SLPME) and apply it in the construction of the symmetric multi-band perfect reconstruction filter bank (SPRFB). The paper is a continuative study of [
1].
To describe the SLPME problem clearly, we first give some notions and notations. For a given matrix 
A, we denote by 
 the 
j-th column of 
A and by 
 its 
i-th row. Let 
 be the ring of all Laurent polynomials (LPs) with real coefficients and 
 an integer. An LP vector 
 is called prime if there is 
 such that 
. In this case, we call 
 a dual of 
 and call 
 a dual pair. An invertible LP matrix 
 is called 
-invertible if 
, as well. We will denote by 
 the group of all 
 -invertible matrices. We write an 
s-dimensional column vector 
 as 
 and write its transpose as 
. The symmetry of LP vectors and matrices is defined as follows:
      
Definition 1. An LP vector  is called polar-symmetric (or -symmetric), if , and called polar-antisymmetric (or -symmetric), if . An LP matrix  is called vertically symmetric (or -symmetric), if each of its columns is either -symmetric or -symmetric.
 In the paper, we employ 
 for the sign notation: 
 or −. Thus, an LP vector is said to be 
-symmetric if it is either 
-symmetric or 
-symmetric. When the sign is not stressed, we simplify 
 to 
. We now define an SLPME of an LP vector as follows:
      
Definition 2. Let  be a given -symmetric prime vector. An LP matrix  is called an SLPME of  if  is -symmetric and . Furthermore,  is called an SLPME of a -symmetric dual pair  if  is an SLPME of  and .
 It is worth pointing out that the construction of a dual pair with or without the symmetry property is also a key ingredient in LPME and SLPME. This problem has been completely resolved in [
2].
The study of the Laurent polynomial matrix extension (LPME) has a long history. In the early 1990s, the two-band LPME arose in the study of the construction of compactly-supported wavelets [
3,
4,
5,
6,
7]. In the construction of multi-wavelets, the LPME problems arise [
8,
9,
10,
11].
It has become well known that LPME is the core in the construction of multi-band prefect reconstruction filter banks (PRFB) and multi-band wavelets [
12,
13,
14,
15,
16]. If a PRFB is represented by the polyphase form, then constructing the polyphase matrices of PRFB is essentially identical with LPME. The general study of multi-band PRFB is referred to [
1,
17,
18,
19,
20,
21]. We mention that the algorithm proposed in [
1] was based on Euclidean division in the ring 
. The author revealed the relation between Euclidean division in 
 and 
-elementary matrices, then developed the algorithm for LPME using 
-elementary matrix factorization.
Unfortunately, the algorithm for LPME cannot be applied for SLPME because it does not preserve symmetry in the factorization. A special case of SLPME was given in Theorem 4.3 of [
22]. However, the development of effective algorithms for SLPME is still desirable. Recently, Chui, Han and Zhuang in [
17] introduced a bottom-up algorithm to construct SPRFB for a given dual pair of symmetric filters. Their algorithm consists of a forward (or top-down) phase and a backward (or down-top) phase. In the top-down phase, the algorithm gradually reduces the filters in the dual pair to the simplest ones, keeping the symmetry in the process. Thus, an SPRFB is first constructed for the simplest dual pair. Then, in the down-top phase, the algorithm builds the SLPME for the original dual pair. Their method does not employ the polyphase forms of filters. Hence, it is not directly linked to SLPME.
In this paper, we develop an SLPME algorithm in the framework of the Laurent polynomial algebra. We first introduce the Euclidean -symmetric division algorithm, which keeps the symmetry of LPs in the division. Then, we introduce the symmetric -elementary matrices in the Laurent polynomial ring and reveal the relation between the Euclidean -symmetric division and the symmetric -elementary transformation. Our SLPME algorithm essentially is based on the symmetric -elementary transformations on the -symmetric matrices in the group .
The paper is organized as follows. In 
Section 2, we introduce 
-symmetric vectors and matrices and their properties. In 
Section 3, we first develop the Euclidean symmetric division algorithms in the Laurent polynomial ring, introduce symmetric 
-elementary matrices and reveal the relation between the Euclidean symmetric division and the symmetric 
-elementary transformation. Then, at the end of the section, we present the Euclidean symmetric division algorithm for SLPME. In 
Section 4, we apply our SLPME algorithm in the construction of multi-band SPRFBs. In 
Section 5, we present several illustrative examples for the construction of symmetric multi-band SPRFBs and SLPMEs.
  2. Symmetries of LP Vectors and Matrices
In this section, we study the symmetric properties of 
-symmetric vectors and 
-symmetric matrices. For 
, we write 
. Then, 
 is 
-symmetric if and only if 
. Define:
Then, ,  and . Later, if no confusion arises, we will simplify  to ,  to M, and so on.
We denote by  the set of all -symmetric vectors in . Particularly, when , the vector  is reduced to a Laurent polynomial, say, . Thus,  if and only if .
Lemma 1. Let  be a symmetric dual pair. Then, they have the same symmetry, i.e., if  is -symmetric, so is .
 Proof.  We have 
 so that 
. Therefore, if 
, by 
, we have:
      
      which yields 
, i.e., 
 is 
-symmetric. The lemma is proven.  ☐
 Definition 3. A matrix  is called centrally polar symmetric, denoted by -symmetric, if .
 All 
-symmetric matrices in 
 form a semigroup of 
, denoted by 
; and all 
-invertible, 
-symmetric matrices in 
 form a subgroup of 
, denoted by 
. By Definition 3, we have the following:
      
Proposition 1. A matrix  is -symmetric if and only if:Therefore,  and .  We say that  is a -symmetric matrix if all columns of  are -symmetric.
Lemma 2. For any , there exists a non-singular -symmetric matrix and a non-singular -symmetric one.
 Proof.  We first prove the lemma for the 
-symmetric case by mathematical induction. For 
 the matrices 
 and 
 are non-singular 
-symmetric matrices because their determinants are not zero. Assume that the statement is true for each 
. We prove that the statement is also true for 
. Let 
 be a 
 non-singular 
-symmetric matrix. Then, so is the following 
 matrix:
      
 The proof is completed. For the -symmetric case,  and  are non-singular and -symmetric. The remainder of the proof is similar.  ☐
The following proposition describes the role of -symmetric matrices.
Proposition 2. Any matrix in  represents a linear transformation from  to . Conversely, any linear transformation from  to  is realized by a matrix in .
 Proof.  We first prove the proposition for the case of 
. If 
, then for any 
, writing 
, we have:
      
Hence, 
. On the other hand, if for any 
, 
, then we have:
      which yields that the equality:
      holds for any 
-symmetric matrix. By Lemma 2, we can choose a non-singular matrix 
 in (
2), which yields 
, i.e., 
. The proposition is proven. For the case of 
, the proof is similar.  ☐
 Since 
, by Proposition 2, 
 is a group of linear transformations on the set 
. For the matrices in 
, we have the following:
      
Proposition 3. Assume . Then, for any prime vector , the vector  is also prime.
 Proof.  Assume that  is a -symmetric prime vector. Then, there is a -symmetric vector , such that . Therefore, we have , which indicates that  is a -symmetric prime vector. The proof is similar for .  ☐
 In linear algebra, a well-known result is that each invertible matrix can be written as a product of elementary matrices. To produce the similar factorization of a matrix in , we introduce the -symmetric elementary matrices. We first define the -elementary matrices (that may not be -symmetric).
Definition 4. Let I be the  identity matrix. An  -elementary matrix is obtained by performing one of the following -elementary row operations on I:
- (1) 
 Interchanging two rows, e.g., .
- (2) 
 Multiplying a row by a non-zero real number c, e.g., .
- (3) 
 Replacing a row by itself plus a multiple  of another row, e.g., .
 For convenience, we denote by 
, 
 and 
 for the 
-elementary matrices in (1), (2) and (3), and call them Types 1, 2 and 3, respectively. Since 
, we agree that 
 in 
. It is clear that an 
-elementary matrix is 
-invertible, and its inverse is of the same type. Indeed, we have the following:
Later, when the type of an -elementary matrix is not stressed, we simply denote it by E. On the other hand, if the dimension of an -elementary matrix needs to be stressed, then we write it as , , etc. For developing our SLPME algorithm, we define the -symmetric elementary matrix based on Definition 4.
Definition 5. Let  be an integer. Write . When , the matrices:are called -symmetric elementary matrices of Type 1, 2 or 3, respectively. When , the matrices:are called -symmetric elementary matrices of Type 1, 2 or 3, respectively.  We denote by  the set of all -symmetric elementary matrices in  and by  the set of all matrices of type i in .
We can verify that the inverses of 
-symmetric elementary matrices are given by the following:
	  If we do not stress the type of 
-symmetric elementary matrix, we will simply denote it by 
. On the other hand, if we need to stress the dimension of an 
 -symmetric elementary matrix, we write it as 
, 
, and so on.
Example 1. Let  and . All -symmetric elementary matrices in  are: By (4), their inverses are: All -symmetric elementary matrices in  are:    3. Euclidean Algorithm for SLPME
For simplification, in the paper, we only discuss LPs with real coefficients. Readers will find that our results can be trivially generalized to the LPs with coefficients in the complex field or other number fields. First, we recall some notations and notions used in [
1]. We denote by 
 the ring of all (real) polynomials and write 
. We also write 
 and denote by 
 the group of all nonzero Laurent monomials: 
. If 
, writing 
, where 
 and 
, we define its highest degree as 
, its lowest degree as 
 and its support length as 
. When 
, we agree that 
, 
 and 
.
Let the semi-group 
 be defined by 
 Then, the power mapping 
,
      
      defines an equivalent relation “∽” in 
, i.e., 
 if and only if 
. For convenience, we agree that 
. It is obvious that 
. In [
1], we established the following Euclid’s division theorem for Laurent polynomials.
Theorem 1 (
-Euclid’s division theorem) 
Let . Then, there exists a unique pair  such that:where, if , Furthermore, if , then:  Remark 1. In [1], we defined  for  and  for . In this paper, the definition of the support length is slightly changed so that it is up to the standard. Therefore, the inequality in (8) is updated according to the new definition.  In [
1], we already developed a Euclidean algorithm for LPME based on Theorem 1. We now develop a Euclidean algorithm for SLPME. For this purpose, we introduce two lemmas.
Lemma 3. Let  and  be an integer satisfying  and . Then, there exists a unique pair  such that:where  and:  Proof.  In the case that 
, we have 
. Hence, the lemma is identical to Theorem 1. We now assume 
. Define 
. Then, 
 and 
. By Theorem 1, there is a unique pair 
 such that:
      
      where, if 
,
      
Let 
. We have:
If 
, then 
. In this case, it is clear that there exists a unique 
 such that (
9) holds. We now consider the case that 
. If 
, then 
, so that 
 and 
. In this case, we must have 
. Indeed, if 
, then 
. Setting 
 in (
9), we get 
, which leads to a contradiction with 
. Hence, we have 
 so that (
10) holds. Finally, if 
 is neither zero nor 
, by 
, we have 
 and 
 so that (
10) holds. The proof is completed.  ☐
 For a real number x, we denote by  the integer part of x, denote by  the nearest integer of x that is no less than x and denote by  the nearest integer of x that is no greater than x. For instance, for ,  and .
Lemma 4. Assume ,  and . Define . Then, there is a pair  such that:where ,  and .  Proof.  By 
, we may write 
, where 
 and 
 so that 
. Define:
      
Then, 
 and 
. Write 
. By 
, we have 
. Applying Lemma 3 to 
 and 
 by setting 
, we obtain a unique pair 
 such that:
     where 
 and 
. Since 
, we have:
Writing , we have  and . The proof is completed.  ☐
 The proof suggests the following Euclidean -symmetric division algorithm for computing  and  in the division of  described by Lemma 4.
Algorithm 1 (Euclidean 
-symmetric division algorithm). 
- 1.
 Compute .
- 2.
 Construct  and .
- 3.
 Perform polynomial division  to produce .
- 4.
 Output .
 For , we define  as the number of nonzero entries in  and define . The following theorem describes the relation between the Euclidean -symmetric division and the -symmetric elementary transformation on .
Theorem 2. Assume that  with . Then, there is a -symmetric elementary matrix  of Type 3, such that  with  and .
 Proof.  Write . We first consider the case of . Since , by the -symmetry of , there are at least two nonzero entries in , say , where  and . By Theorem 1, there is a pair  such that  and , where r possibly vanishes. Let . Then, , , and the other entries are unchanged. Hence,  and .
We now consider the case of 
. If there are at least two nonzero entries in 
, the proof is similar to what we have done for 
. Otherwise, 
, so that there is a nonzero entry 
 and 
. If 
, applying Theorem 1, we produce the pair 
 such that 
. Let 
. Then, in 
, 
, 
, and the other entries are unchanged. Else, if 
, by Lemma 4, there is 
 and 
 such that:
      where 
. Let 
. Then, 
, and the other entries are unchanged. In both cases, we have 
, 
 and 
. The proof is completed.  ☐
 Definition 6. Let . A -symmetric prime vector  is called the smallest one if it is given as follows:
- (1) 
 , where  is the -th coordinate basis vector of .
- (2) 
  with only two nonzero entries  and .
- (3) 
  with only two nonzero entries:  and .
Particularly, we call  normalized if  in (1) and  in (2) and (3).
 In Definition 6, because  is prime,  in (2) and (3) satisfies . Besides, we may normalize the smallest -symmetric prime vector as follows: In (1), if , then  is normalized. In (2) and (3), if , then  is the normalized one. Repeating the -symmetric elementary transformations in Theorem 2, we may transform a -symmetric prime vector to the smallest one.
Corollary 1. Assume that  is a -symmetric prime vector. Then, there are final -symmetric elementary matrices  of Type 3 such that  is the smallest -symmetric prime vector.
 Proof.  We first assume that the prime vector 
, and it is not the smallest one. Then, 
. By Theorem 2, applying the mathematical induction, we can construct final 
-symmetric elementary matrices 
 such that 
 has only one nonzero entry in 
. If 
, then 
. Otherwise, there is 
 such that 
. Writing 
, by the 
-symmetry of 
, we have 
 and 
. By the extended Euclidean algorithm in [
1], we can find a LP pair 
 such that 
 Let 
. Then:
      
Defining  and , we have . The proof for the case of  is completed.
We now consider the case of . Similar to the proof above, we can construct  such that  has only one nonzero entry in . If , because  is prime, . By , we would have , which yields . Therefore, the only nonzero entry in  cannot be . Assume now . Then,  and  is smallest. The proof for  is completed. The proof for the case of  is similar.  ☐
 When the vector 
 is prime, we choose 
 in (
12) and define:
Then, 
 is an SLPME of the vector 
. The inverse of 
 is:
In (
13), if we set 
, then 
 is reduced to:
      which will be used in the construction of SLPMEs. In the following content, the submatrix of 
M, which contains all elements 
 in 
M with 
 and 
, is denoted by 
.
Lemma 5. Let  and  be a normalized smallest -symmetric prime vector. Let  be the matrix in (13). Assume that ,  and  are arbitrary. Write:and: Then, an SLPME  of  is constructed as follows.
- (i) 
 For , we define  as the following:and the other entries are zero. Its inverse  is the following:and the other entries vanish. - (ii) 
 For ,and the other entries vanish. Its inverse  is the following:and the other entries vanish. - (iii) 
 For ,and the other entries vanish. Its inverse  is the following:and the other entries vanish. 
 Proof.  Recall that 
. By computation, we claim that 
 in (i), (ii) or (iii) is 
-symmetric and 
-invertible, and 
 is given by (
18), (
20) or (
22), respectively. The proof is completed.  ☐
 The SLPME of the smallest 
-symmetric prime vector is not unique because 
 and 
 can be arbitrary. Besides, each 
 can be replaced by 
 in (
13), where 
 and 
 can also be freely chosen.
We show the SLPMEs of some smallest -symmetric prime vectors in the following example.
Example 2. - (i) 
 An SLPME of  is given by:whose inverse is: - (ii) 
 An SLPME of  is given by:whose inverse is: - (iii) 
 An SLPME of  is:whose inverse is: 
 We now give the main theorem for SLPME.
Theorem 3 (Euclidean symmetric division algorithm for SLPME). 
Let  be a -symmetric prime vector. Then, the following Euclidean symmetric division algorithm realizes its SLPME:
- 1.
 Apply Euclidean symmetric division to construct the -symmetric elementary matrices  such that  is a normalized smallest -symmetric prime vector.
- 2.
 Apply Lemma 5 to construct an SLPME  of  and its inverse  by choosing ,  and  at random, say .
- 3.
 Construct the SLPME for  by: 
Then,  is an SLPME of .
If a dual pair  is given, then Step (2) is replaced by the following to compute .
- 2.a 
 Compute .
- 2.b 
 If , set: If  in  or in , set:and also set  if . - 2.c 
 Construct the SLPME  as in Lemma 5 using ,  and .
Then,  is an SLPME of the dual pair .
 Proof.  By the construction of , its first column  is the smallest -symmetric prime vector. Since , we have  and  is -symmetric and -invertible, whose inverse can be computed by . Hence,  is an SLPME of . Assume now the dual pair  is given. By the computation in Step (2.a) and Step (2.b), we claim that . Since , . Hence,  is an SPLME of the pair . The proof is completed.  ☐
   4. Application in the Construction of Symmetric Multi-Band Perfect Reconstruction Filter Banks
In this section, we use the results in the previous section to construct symmetric 
M-band perfect reconstruction filter banks (SPRFBs). We adopt the standard notions and notations of digital signals, filters, the 
M-downsampling operator and the 
M-upsampling operator in signal processing (see [
6,
7]). In this paper, we restrict our study to real digital signals and simply call them signals.
Mathematically, a signal 
 is defined as a bi-infinite real sequence, whose 
n-th term is denoted by 
 or 
. A finite signal is a sequence that has only finite nonzero terms. All signals form a linear space, denoted by 
l. A filter 
 can be represented as a signal 
 that makes 
 well defined, where ∗ denotes the convolution operator:
A finite filter 
H is called a finite impulse response (FIR). Otherwise, it is called an infinite impulse response (IIR). In this paper, we only study FIR. The 
z-transform of a signal 
 is the Laurent series 
 where 
 resides on the unit circle of the complex plane 
. Hence, 
. Similarly, the 
z-transform of an FIR 
H is the Laurent polynomial:
We define the support length of an FIR as the support length of its z-transform: . By the convolution theorem, if , then .
PRFBs have been widely used in many areas such as signal and image processing, data mining, feature extraction and compressive sensing [
12,
13,
14,
15,
16]. The readers can find an introduction to PRFB from many references on signal processing and wavelets, say [
6,
7]. A PRFB consists of two sub-filter banks: an analysis filter bank, which decomposes a signal into different bands, and a synthesis filter bank, which composes a signal from its different band components. Assume that an analysis filter bank consists of the band-pass filter set 
 and a synthesis one consists of the band-pass filter set 
, where 
 and 
 are low-pass filters. They form an 
M-band PRFB if and only if the following condition holds:
      where 
 is the 
M-downsampling operator, 
 is the 
M-upsampling operator, 
I is the identity operator and 
 denotes the conjugate filter of 
. Here, the conjugate of a real filter 
 is 
. Therefore, the 
z-transform of 
 is 
.
The polyphase form of a signal is defined as follows:
      
Definition 7. Let  be the z-transform of a signal  and  an integer. The Laurent series:is called the k-th M-phase of , and the vector of Laurent series  is called an M-polyphase of .  Since a filter can be identical with a signal, we define its polyphase in the same way. For instance, let 
 be the 
z-transform of an FIR filter 
F. We call:
      the 
k-th 
M-phase of 
F and call the LP vector 
 the 
M-polyphase of 
F. We will abbreviate 
 to 
 if the band number 
M is not stressed. It is clear that 
. Since, for any filter 
F,
      
      the 
M-polyphase of 
F can be generalized to 
 with 
. Then, in general,
      
For a filter bank 
, we define its 
M-polyphase matrix as:
The characterization identity (
24) for PRFB now can be written as the following:
      where 
 is the Hermitian adjoint matrix of 
 and 
I is the identity matrix.
A pair of low-pass filters 
 is called a conjugate pair if their 
M-polyphase forms satisfy:
We write 
 and 
. Then, the vector form of (
28) is:
Recall that, in the previous section, we call 
 a dual pair, if 
. Therefore, 
 in (
29) is a conjugate pair if and only if 
 is a dual pair.
The PRFB construction problem is the following: Assume that a conjugate pair of low-pass filters 
 is given. Find the filter sets 
 and 
 such that the pair of filter banks 
 and 
 forms an 
M-band PRFB. The problem can be presented in the polyphase form: Let 
 be the 
M-polyphase of 
. Then, 
 is an LP dual pair. The PRFB construction problem becomes to find an LPME 
 of 
 such that 
 and 
. Once the pair 
 is constructed, then the polyphase matrices for the PRFB are 
 and 
. Hence, the PRFB construction problem essentially is identical to the LPME one, which we have studied thoroughly in [
1].
The symmetric PRFB (SPRFB) plays an important role in signal processing because it has the linear phase. An FIR  is said to be -symmetric (with respect to the symmetric center ) if . It is clear that if c is even, then  is odd, else if c is odd, then  is even. In applications, we usually shift a given -symmetric filter to the center zero if  is odd or to one if  is even. We abbreviate -symmetric to symmetric if the symmetric center c and type (characterized by ) are not stressed. For convenience, we will call the z-transform of  the -symmetric if  is so. It is easy to verify that a -symmetric LP is -symmetric, a -symmetric  satisfies  and a -symmetric  satisfies . We will denote by  and  the sets of all -symmetric and -symmetric LPs, respectively. It is clear that, if , so is . If , then .
Assume that a conjugate pair of symmetric low-pass filters 
 is given. An SPRFB construction problem is to find two symmetric filter sets 
 and 
 such that the pair of symmetric filter banks 
 and 
 forms an 
M-band SPRFB. Because the filters in a conjugate dual pair 
 have the same symmetric type and center (see Lemma 1 or [
17]), without loss of generality, we will assume that the given conjugate pair is 
-symmetric (if 
 is odd) or 
-symmetric (if 
 is even). Although the construction of PRFB has been well studied, the development of the algorithms for SPRFB is relatively new. The authors of [
17] introduced a bottom-up algorithm to construct SPRFB for a given symmetric conjugate pair, without using SLPME. Our purpose in this section is to develop a novel algorithm based on the symmetric Euclidean SLPME algorithm introduced in the previous section. We want to put the algorithm in the framework of the matrix algebra on the Laurent polynomial ring to make it more constructive. The PRFB algorithm in [
1] does not work for the construction of SPRFB. The new development is required.
To develop the SPRFB algorithm based on M-polyphase representation, we need to characterize the M-polyphase of a symmetric filter. By computation, we can verify that the k-th M-phase of a symmetric filter satisfies the following:
- (1)
 If 
F is 
-symmetric, then:
          
- (2)
 If 
F is 
-symmetric, then:
          
- (3)
 If 
F is 
-symmetric, then:
          
- (4)
 If 
F is 
-symmetric, then:
          
We call a vector in 
 -symmetric if it satisfies (
32) and call a vector in 
 -symmetric if it satisfies (
33). We denote by 
 and 
 the sets of all 
-symmetric vectors in 
 and 
-symmetric vectors in 
, respectively. By computation, we have the following:
      
Proposition 4. Let  be a  -symmetric elementary matrix and . Then, . Let  be a  -symmetric elementary matrix and . Then, .
 We now characterize the 
M-polyphase of a symmetric filter 
F as follows:
      
Lemma 6. Let , and  be the M-polyphase of a filter F.
- 1.
 If M is odd and F is -symmetric, or M is even and F is -symmetric, then  is -symmetric.
- 2.
 Assume . If F is -symmetric, then (30) holds for  and:else if F is -symmetric, then (32) holds for  and: - 3.
 Assume . If F is -symmetric, then (31) holds for  and:else if F is -symmetric, then (32) holds for  and: 
 Proof.  We obtain Part 1 directly from (
30) and (
31). To prove Parts 2 and 3, according to (
30)–(
33), we only need to verify (
34)–(
37).
If 
 and 
F is 
-symmetric, by (
26) and (
30), 
 , which yields (
34); and if 
F is 
-symmetric, then 
, which yields (
35).
If 
 and 
F is 
-symmetric, then 
, which yields (
36); and if 
F is 
-symmetric, then 
, which yields (
37). The lemma is proven.  ☐
 We call a vector in 
 -symmetric if it satisfies (
30) for 
 and (
34) and call it 
-symmetric if it satisfies (
32) for 
 and (
35). Similarly, we call a vector in 
 -symmetric, if it satisfies (
31) for 
 and (
36), and call it 
-symmetric if it satisfies (
33) for 
 and (
37). We denote by 
, 
, 
 and 
, the sets of all 
-symmetric, 
-symmetric, 
-symmetric and 
-symmetric vectors, respectively. All of these symmetric vectors (other than 
-symmetric) will be called 
-symmetric ones.
Example 3. The vector  is -symmetric, but not -symmetric; and the vector  is -symmetric, but not -symmetric.
 By Part 1 of Lemma 6, we have the following SPRFB construction algorithm:
      
Theorem 4. Let  be a conjugate pair of symmetric filters and  the M-polyphase of the pair. Assume that M is odd and  is -symmetric, or M is even and  is -symmetric. Write  and . Let  be an SLPME of the dual pair  computed by the Euclidean division algorithm in Theorem 3. Write  and . Then,  is the M-polyphase form of the M-band SPRFB, in which  is a filter in the analysis filter bank and  is in the synthesis bank.
 Proof.  By 
, we have:
      
Hence,  is a symmetric LP dual pair. By Theorem 3, ,  and . The theorem is proven.  ☐
 Lemma 6 shows that, when the odevityof the band number M mismatches the odevity of the support length of the conjugate pair , then their M-polyphase forms are not -symmetric. Thus, we cannot apply Theorem 3 to solve the SPRFB constriction problem for . To employ the results we already obtained in the previous section, we establish a relation between - symmetry and -symmetry.
Definition 8. The  matrix:is called the symmetrizer for the vectors in . The  matrix:is called the symmetrizer for the vectors in .  Recall 
. It is easy to verify that the left inverse of 
 is:
      and the left inverse of 
 is:
Lemma 7. We have the following.
- (a) 
 If , then . Conversely, if , then .
- (b) 
 If , then . Conversely, if , then .
- (c) 
 If , then . Conversely, if , then .
- (d) 
 If , then . Conversely, if , then .
 Proof.  Let 
. Writing 
 and applying (
30) and (
34), we have:
      
      which show that 
 is 
-symmetric. On the other hand, if 
 is 
-symmetric, writing 
, for 
, we have 
, so that 
. We also have:
By 
 and the identify above, we have:
Hence, 
. The proof for Part (a) is completed. By the similar computation and applying (
31) and (
36), (
32) and (
35), (
33) and (
37), respectively, we can prove Parts (b), (c) and (d) of the lemma.  ☐
 Similar to Definition 6, we define the smallest -symmetric prime vector in the sets  and .
Definition 9. The smallest -symmetric prime vector is defined as follows:
- (1) 
 The vector  is called the smallest -symmetric prime vector in .
- (2) 
 Let  be the smallest -symmetric prime vector in (2) of Definition 6 with . Then,  is called the smallest -symmetric prime vector in .
- (3) 
 Let  be the smallest -symmetric prime vector in (3) of Definition 6 with . Then,  is called the smallest -symmetric prime vector in .
 By Definition 9, we immediately have the following:
      
Proposition 5. The smallest -symmetric prime vector has the following form:
- (1) 
 The smallest -symmetric prime vector in  has the form , where  is the smallest -symmetric prime vector in . Therefore, if  in the smallest -symmetric prime vector, then  is the smallest -symmetric prime vector.
- (2) 
 The smallest -symmetric prime vector in  has the form , where  is the smallest -symmetric prime vector in . Therefore, if  in the smallest -symmetric prime vector, then  is the smallest -symmetric prime vector.
 Example 4. Assume  satisfies . The smallest -symmetric prime vector in  has the form  The smallest -symmetric prime vector in  has the form  The smallest -symmetric prime vector in  has the form  or .
 At the next step, we define 
-symmetric elementary matrices for transforming a 
-symmetric prime vector to the smallest one. By Lemma 7, we immediately have the following:
      
Lemma 8. For any ,  and , we have  and . For any ,  and , we have  and .
 We also have the following:
      
Lemma 9. Let  be an  -symmetric elementary matrix of Type 3.
- (1) 
 If , then .
- (2) 
 If , then .
 Proof.  In the case of 
, we have 
 and:
      
      where
      
      satisfies 
 and 
. Therefore,
      
If , then , which yields . If , then ; else if , then . By  and , in both cases, we have  The lemma is proven for odd M. The proof for even M is similar.  ☐
 By Lemmas 8 and 9, we define the -symmetric elementary matrices as follows.
Definition 10. The matrix:is called a -symmetric elementary matrix. We denote by  the set of all  -symmetric elementary matrices.  As before, when the indices of  are not stressed, we simply write it as . If we need to stress the dimension of an  -symmetric elementary matrix, we write it as .
Proposition 6.  and .
 Proof.  The first identity is derived from:
      
The second one is derived from  and Lemma 9.  ☐
 To derive the explicit expressions of 
-symmetric elementary matrices, we write:
When 
 by (
38) and:
      we have:
Similar computation yields:
For even-dimensional cases, we have:
Example 5. All elements of  are derived from , where  and . By Corollary 6, we only need to present  for , and . By the formulas above, we obtain:where , and: All elements of  are derived from . Similarly, we only need to present  for  and . By the formulas above,  We now generalize Lemma 4 to the sets  and .
Lemma 10. Assume that , ,  and .
- (1) 
 If , then there is  and  with  such that . If , then there is  and  with  such that .
- (2) 
 If , then there is  and  with  such that . If , then there is  and  with  such that .
- (3) 
 If , there is a  and  with  such that: - (4) 
 If , there is a  and  with  such that: 
 Proof.  We first prove (1). If 
, applying Lemma 4 to 
 and 
, we have 
 and 
 with 
 such that:
      
Since 
, 
, so that:
      where 
, which leads to 
. Because 
 is even and 
 is odd, 
.
If 
, writing 
 and 
, we have 
. Similar to the proof of Lemma 4, we write 
, where 
 and 
. Define:
Then, 
 and 
. Applying Lemma 3 to 
 and 
, we obtain a unique pair 
 such that:
      where 
 and 
. Since 
, we have:
Writing 
 and 
, we have:
      where 
 and 
. The proof for (1) is completed. The proof of (2) is similar to that for (1), and the proofs for (3) and (4) are similar to that for Lemma 4.  ☐
 The algorithms that perform the divisions in Lemma 10 are similar to Algorithm 1. We present the algorithm that performs the divisions in Parts (1) and (2) of Lemma 10 in the following:
      
Algorithm 2 (Euclidean 
-symmetric division algorithm I). 
- 1.
 If , apply Algorithm 1 to  to produce  and output  where  and 
- 2.
 Else, if , set  and: Apply the Euclidean division algorithm  in the polynomial ring to obtain  and output , where:and: 
 The algorithm that performs the divisions in Parts (3) and (4) of Lemma 10 is the following:
      
Algorithm 3 (Euclidean 
-symmetric division algorithm II). 
Assume , , and: - 1.
 Compute .
- 2.
 - 3.
 Apply the Euclidean division algorithm  to obtain .
- 4.
 
 Similar to Theorem 2, we have the following:
      
Theorem 5. Assume  or  with . Then, there is a -symmetric elementary matrix  such that  or  with  and .
 Proof.  We first consider 
 with 
. Recall that 
 is 
-symmetric. If 
, by Theorem 2, we obtain the conclusion. Assume 
. Then, 
. Without loss of generality, we may assume 
 so that 
. If 
, by Theorem 1, there is a pair 
 such that 
 with 
. Therefore,
      
Set , where . Then, , , and other terms are unchanged. Hence,  and . If , applying the division in (3) of Lemma 10, we obtain the conclusion. The proof for  is completed. The proof for  is similar.  ☐
 Corollary 2. Assume that the prime vector  (or in ) is not the smallest one. Then, there are final -symmetric elementary matrices such that  is the smallest -symmetric (or -symmetric) prime vector.
 Proof.  The proof is almost identical with that for Corollary 1. Assume . First, we find final -elementary matrix, say , such that  with . If , the only possible nonzero term is either  or . By , the nonzero term must have the form of . we must have . However,  is -symmetric; if , it must be zero. Therefore, we have ; if , but . Therefore, we must have  Applying the division scheme in (1) of Lemma 10, we find  such that  has only one nonzero term. As we proved before, we have . If  and , then there is  such that  and  and . Similar to the proof of Corollary 1, we can find  and  such that . The proof for  is completed. For the cases that  and in , the proofs are similar.  ☐
 For convenience, an LPME  of a -symmetric dual pair  will be called an SLPME, if  represents the polyphase form of an SPRFB.
Lemma 11. Let  be the smallest -symmetric prime vector in  and  be its dual so that . Write  and . Let  be an SLPME of  such that  and . Then:is the SLPME of  and:in which . Similarly, let  be the smallest -symmetric prime vector in  and  be its dual so that . Write  and . Let  be an SLPME of  such that  and . Then:is the SLPME of  and:in which .  Proof.  We consider 
. Since 
 is the smallest prime vector, we have 
, and 
 is the smallest 
-symmetric prime vector in 
. We find the SLPME 
 for 
 by Lemma 4. Because any column of 
, say 
, is 
-symmetric, all columns of 
 except the last one are 
-symmetric. Recall that 
 is in 
. Hence, 
. Therefore, the last column of 
, 
, is 
-symmetric. Thus, 
 is an SLPME of 
. By computation,
      
Hence, (
40) gives 
, whose first row is 
. It is also easy to verify that the last column of 
 is 
-symmetric, and others are 
-symmetric. The proof for 
 is completed. The proof for 
 is similar.  ☐
 Combining the results above, we develop the algorithm for the construction of the SPRFB of a given -symmetric conjugate pair of filters.
Theorem 6. Let  be a given -symmetric (-symmetric) conjugate pair of filters and the LP vector pair  be the -polyphase (-polyphase) form of . Then, an SPRFB  of  can be constructed as follows:
- (1) 
 [Normalizing the dual pair] Set  and , where .
- (2) 
 [Reducing  to the smallest prime one] Use the Euclidean -symmetric division algorithm to construct the -symmetric elementary matrices  such that  is the smallest -symmetric prime vector.
- (3) 
 [Computing the dual of ] Set .
- (4) 
 [Constructing SLPME for the smallest dual pair] Apply (39)–(42) to construct the SLPME  for the dual pair . - (5) 
 [Computing the polyphase matrices of SPRFB] Set:and . Then,  is the M-polyphase form of the SPRFB for . 
 Proof.  The proof is similar to that for Theorem 3. We skip its details here.  ☐