Optimal design of window functions for filter window bank

Xueling Zhou; Bingo Wing-Kuen Ling; Hai Huyen Dam; Kok-Lay Teo

doi:10.3934/jimo.2020014

Article Contents

2021, Volume 17, Issue 3: 1119-1145. Doi: 10.3934/jimo.2020014

This issue Previous Article Effect of warranty and quantity discounts on a deteriorating production system with a Markovian production process and allowable shortages Next Article Mean-variance investment and contribution decisions for defined benefit pension plans in a stochastic framework

Optimal design of window functions for filter window bank

Faculty of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China

^* Corresponding author: Bingo Wing-Kuen Ling
^* Corresponding author: Bingo Wing-Kuen Ling

Received: December 31, 2018

Revised: April 30, 2019

Early access: January 2020

Published: May 2021

This paper was supported partly by the National Nature Science Foundation of China

Abstract Full Text(HTML) Figure(4) / Table(1) Related Papers Cited by

Abstract

This paper considers the designs of the periodic window functions in the filter window banks. First, the filter window bank with the constant synthesis periodic window functions is considered. The total number of the nonzero coefficients in the impulse responses of the analysis periodic window functions is minimized subject to the near perfect reconstruction condition. This is an $ L_0 $ norm optimization problem. To find its solution, the $ L_0 $ norm optimization problem is approximated by the $ L_1 $ norm optimization problem. Then, the column of the constraint matrix corresponding to the element in the solution with the smallest magnitude is removed. Next, it is tested whether the feasible set corresponding to the new $ L_0 $ norm optimization problem is empty or not. By repeating the above procedures, a solution of the $ L_0 $ norm optimization problem is obtained. Second, the filter window bank with the time varying synthesis periodic window functions is considered. Likewise, the design of the periodic window functions in both the analysis periodic window functions and the synthesis periodic window functions is formulated as an $ L_0 $ optimization problem. However, this $ L_0 $ norm optimization problem is subject to a quadratic matrix inequality constraint. To find its solution, the set of the synthesis periodic window functions is initialized. Then, the set of the analysis periodic window functions is optimized based on the initialized set of the synthesis periodic window functions. Next, the set of the synthesis periodic window functions is optimized based on the found set of the analysis periodic window functions. Finally, these two procedures are iterated. It is shown that the iterative algorithm converges. A design example of a filter window bank with the constant synthesis periodic window functions and a design example of a filter window bank with the time varying synthesis periodic window functions are illustrated. It is shown that the near perfect reconstruction condition is satisfied, whereas this is not the cases for the nonuniform filter banks with the conventional samplers and the conventional block samplers.

Keywords:

Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

Citation:

Full Text(HTML)

Figure 1. Block diagram of a filter window bank

Download: Full-size image PowerPoint slide

Figure 2. Nonuniform filter bank with the conventional samplers

Download: Full-size image PowerPoint slide

Figure 3. Nonuniform block filter bank with the block samplers

Download: Full-size image PowerPoint slide

Figure 4. (a) The transfer functional distortions, (b) the first aliasing distortion components, (c) the second aliasing distortion components, (d) the third aliasing distortion components, (e) the fourth aliasing distortion components and (f) the fifth aliasing distortion components of both the filter window bank and the block filter bank

Download: Full-size image PowerPoint slide

Table 1. Both the transfer functional distortions and the aliasing distortions of both the filter window bank and the block filter bank

	Filter window bank	Block filter bank	Improvement
The maximum absolute value of the transfer functional distortion	$-17.3157dB$	$0.9315dB$	$18.2472dB$
The maximum absolute value of the first aliasing distortion	$-24.8282dB$	$-2.0172dB$	$22.8110dB$
The maximum absolute value of the second aliasing distortion	$-18.6761dB$	$-5.4446dB$	$13.2316dB$
The maximum absolute value of the third aliasing distortion	$-27.5018dB$	$-9.3399dB$	$18.1618dB$
The maximum absolute value of the fourth aliasing distortion	$-18.7578dB$	$-5.1906$	$13.5672dB$
The maximum absolute value of the fifth aliasing distortion	$-23.9515dB$	$-0.7191dB$	$23.2325dB$

| Show Table

DownLoad: CSV

Related Papers

Cited by

References

[1]	K. D. Abdesselam, Design of stable, causal, perfect reconstruction, IIR uniform DFT filter banks, IEEE Transactions on Signal Processing, 48 (2000), 1110-1119.
[2]	T. S. Bindiya and E. Elias, Design of totally multiplier-less sharp transition width tree structured filter banks for non-uniform discrete multitone system, AEU-International Journal of Electronics and Communications, 69 (2015), 655-665. doi: 10.1016/j.aeue.2014.12.004.
[3]	M. Blanco-Velasco, F. Cruz-Roldán, E. Moreno-Martínez, J. I. Godino-Llorente and K. E. Barner, Embedded filter bank-based algorithm for ECG compression, Signal Processing, 88 (2008), 1402-1412. doi: 10.1016/j.sigpro.2007.12.006.
[4]	G. F. Choueiter and J. R. Glass, An implementation of rational wavelets and filter design for phonetic classification, IEEE Transactions on Audio, Speech, and Language Processing, 15 (2007), 939-948. doi: 10.1109/TASL.2006.889793.
[5]	F. Cruz-Roldán, P. Martín-Martín, J. Sáez-Landete, M. Blanco-Velasco and T. Saramäki, A fast windowing-based technique exploiting spline functions for designing modulated filter banks, IEEE Transactions on Circuits and Systems I: Regular Papers, 56 (2009), 168-178. doi: 10.1109/TCSI.2008.925350.
[6]	H. H. Dam, S. Nordholm and A. Cantoni, Uniform FIR filter bank optimization with gGroup delay specifications, IEEE Transactions on Signal Processing, 53 (2005), 4249-4260. doi: 10.1109/TSP.2005.857008.
[7]	G. Doblinger, A fast design method for perfect-reconstruction uniform cosine-modulated filter banks, IEEE Transactions on Signal Processing, 60 (2012), 6693-6697. doi: 10.1109/TSP.2012.2217139.
[8]	B. Farhang-Boroujeny, Filter bank spectrum sensing for cognitive radios, IEEE Transactions on Signal Processing, 56 (2008), 1801-1811. doi: 10.1109/TSP.2007.911490.
[9]	C. Gu, J. Zhao, W. Xu and D. Sun, Design of linear-phase notch filters based on the OMP scheme and the chebyshev window, IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 59 (2012), 592-596.
[10]	C. Y. F. Ho, B. W. K. Ling and P. K. S. Tam, Representations of linear dual-rate system via single SISO LTI filter, conventional sampler and block sampler, IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 55 (2008), 168-172. doi: 10.1109/TCSII.2007.910803.
[11]	A. Kumar, G. K. Singh and S. Anurag, An optimized cosine-modulated nonuniform filter bank design for subband coding of ECG signal, Journal of King Saud University-Engineering Science, 27 (2015), 158-169. doi: 10.1016/j.jksues.2013.10.001.
[12]	B. W. K. Ling, C. Y. F. Ho, J. Cao and Q. Dai, Necessary and sufficient condition for a set of maximally decimated integers to be incompatible, Necessary and Sufficient Condition for a Set of Maximally Decimated Integers to be Incompatible, 9 (2013), 564-566.
[13]	B. W. K. Ling, C. Y.-F. Ho, K. L. Teo, W. C. Siu, J. Z. Cao and Q. Y. Dai, Optimal design of cosine modulated nonuniform linear phase FIR filter bank via both stretching and shifting frequency response of single prototype filter, IEEE Transactions on Signal Processing, 62 (2014), 2517-2530. doi: 10.1109/TSP.2014.2312326.
[14]	Q. Liu, X. Z. Zhang, W. K. Ling, M. Wang and Q. Dai, Exact perfect reconstruction of filter window bank with application to incompatible nonuniform filter banks, IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP, (2016), 20–22.
[15]	M. Narendar, A. P. Vinod, A. S. Madhukumar and A. K. Krishna, A tree-structured DFT filter bank based spectrum detector for estimation of radio channel edge frequencies in cognitive radios, Physical Communication, 9 (2013), 45-60. doi: 10.1016/j.phycom.2013.06.001.
[16]	R. C. Nongpiur and D. J. Shpak, Maximizing the signal-to-alias ratio in non-uniform filter banks for acoustic echo cancellation, IEEE Transactions on Circuits and Systems Ⅰ: Regular Papers, 59 (2012), 2315-2325. doi: 10.1109/TCSI.2012.2185333.
[17]	G. W. Ou, D. P. K. Lun and B. W. K. Ling, Compressive sensing of images based on discrete periodic Radon transform, IET Electronics Letters, 50 (2014), 591-593. doi: 10.1049/el.2014.0770.
[18]	A. Pandharipande and S. Dasgupta, On biorthogonal nonuniform filter banks and tree structures, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 49 (2002), 1457-1467. doi: 10.1109/TCSI.2002.803248.
[19]	S. Rahimi and B. Champagne, Oversampled perfect reconstruction DFT modulated filter banks for multi-carrier transceiver systems, Signal Processing, 93 (2013), 2942-2955. doi: 10.1016/j.sigpro.2013.05.003.
[20]	A. K. Soman and P. P. Vaidyanathan, On orthonormal wavelets and paraunitary filter banks, IEEE Transactions on Signal Processing, 41 (1993), 1170-1183. doi: 10.1109/78.205722.
[21]	R. Soni, A. Jain and R. Saxena, An optimized design of nonuniform filter bank using variable-combinational window function, AEU-International Journal of Electronics and Communications, 67 (2013), 595-601. doi: 10.1016/j.aeue.2013.01.003.
[22]	K. Swaminathan and P. Vaidyanathan, Theory and design of uniform DFT, parallel, quadrature mirror filter banks, IEEE Transactions on Circuits and Systems, 33 (1986), 1170-1191. doi: 10.1109/TCS.1986.1085876.
[23]	G. Wang, Time-varying discrete-time signal expansions as time-varying filter banks, IET Signal Processing, 3 (2009), 353-367. doi: 10.1049/iet-spr.2008.0049.
[24]	X. G. Xia and B. W. Suter, Multirate filter banks with block sampling, IEEE Transactions on Signal Processing, 44 (1996), 484-496.
[25]	X. M. Xie, S. C. Chan and T. I. Yuk, Design of perfect-reconstruction nonuniform recombination filter banks with flexible rational sampling factors, IEEE Transactions on Circuits and Systems Ⅰ: Regular Papers, 52 (2005), 1965-1981. doi: 10.1109/TCSI.2005.852009.
[26]	H. Xiong, L. Zhu, N. Ma and Y. F. Zheng, Scalable video compression framework with adaptive orientational multiresolution transform and nonuniform directional filterbank design, IEEE Transactions on Circuits and Systems for Video Technology, 21 (2011), 1085-1099.
[27]	Z. Xiong, K. Ramchandran, C. Herley and M. T. Orchard, Flexible tree-structured signal expansions using time-varying wavelet packets, IEEE Transactions on Signal Processing, 45 (1997), 333-345.
[28]	W. Xu, J. X. Zhao and C. Gu, Design of linear-phase FIR multiple-notch filters via an iterative reweighted OMP scheme, IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 61 (2014), 813-817. doi: 10.1109/TCSII.2014.2345299.
[29]	C. Q. Yang, J. Xiao, Y. F. Zeng, B. W. Deng and W.-K. Ling, Design of periodic window functions in filter window filter banks for harsh environment, International Conference on Industrial Informatics, INDIN, (2016), 18–21. doi: 10.1109/INDIN.2016.7819305.
[30]	Z. Yang, B. W. K. Ling and C. Bingham, Approximate affine linear relationship between $L_1$ norm objective function values and $L_2$ norm constraint bounds, IET Signal Processing, 9 (2015), 670-680.
[31]	K. C. Zangi and R. D. Koilpillai, Software radio issues in cellular base stations, IEEE Journal on Selected Areas in Communications, 17 (1999), 561-573. doi: 10.1109/49.761036.
[32]	Y. Zhang, S. Negahdaripour and Q. Z. Li, Low bit-rate compression of underwater imagery based on adaptive hybrid Wavelets and directional filter banks, Signal Processing: Image Communication, 47 (2016), 96-114. doi: 10.1016/j.image.2016.06.001.