Data Research, Vol. 4, Issue 6, Dec  2020, Pages 1-8; DOI:

Noise Cancellation System Based On IIR Low Pass Digital Filter Design by Using LabVIEW

Data Research, Vol. 4, Issue 6, Dec  2020, Pages 1-8.


Aye Than Mon 1* , Su Mon Aye 1 , Htet Htet Lin Zaw 1 , Phyoe Sandar Win 2 , Khaing Wai Pyone 3

1 Department of Electronic Engineering, Technological University (Pathein), Pathein, Myanmar

2 Department of Electronic Engineering, Technological University (Myeik), Myeik, Myanmar

3 Department of Electronic Engineering, Technological University (Loikaw), Loikaw, Myanmar

Received: 1 June 2020; Accepted: 22 September 2020; Published: 12 October 2020

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Aiming at importance of virtual instruments in the field of Digital Signal Processing, a digital IIR Filter system is developed using National Instruments (NI) data LabVIEW software package. All the types of IIR filters like Butterworth filters, Chebyshev filters, inverse Chebyshev filters, and Elliptic filters are designed to generate their magnitude response and filter coefficients. The LabVIEW software is used to develop virtual instrument (VI) that includes a front panel and a functional diagram. The VI reads the desired parameters of the filters entered by the user on the front panel and determines its magnitude response and filter coefficients.


Digital IIR Filter, LabVIEW, Virtual Instruments, Low Pass Filter, Digital Signal Processing


© 2017 by the authors. Licensee International Technology and Science Press Limited. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


[1] Mitra, S.K.; Kaiser, J. Handbook for Digital Signal Processing, 1993 John Wiley and Sons, Inc.

[2] Kodosky, J.; McCrisken, J.; Rymar, G. Visual Programming Using Structured Dataflow, Proceedings of 1991 IEEE Workshop on Visual Languages, October 8-11, 1991/Kobe, Japan.

[3] Jamal, R.; Wenzel, L. The Applicability of the Visual Programming Language LabVIEW to Large Real-World Applications, Proceedings of 1995 IEEE Symposium on Visual Languages, September 4-8, 1995/Darmstadt, Germany.

[4] Kodosky, J.; Perez, E. Linear Systems in LabVIEW, National Instruments Application Note 08, January 1991.

[5] Licata, W.H.; Principal, S.; Engineer, S. Automatic Target Recognition (ATR). Beyond the Year 2000, 2001.

[6] Bhanu, B.; Lin, Y. Genetic algorithm based feature selection for target detection in SAR images. Image and Vision Computing, 2003, 21, 591-608.

[7] Li, B.; Chellappa, R.; Zheng, Q. Experimental Evaluation of FLIR ATR Approaches—A Comparative Study. Computer Vision and Image Understanding, 2001, 24, 5-24.

[8] Pasquariello, G.; Satalino, G.; Spilotros, F. Automatic target recognition for naval traffic control using neural networks. Image and Vision Computing, 1998, 16(2), 67-73.

[9] Choras, R.S. Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems. International Journal of Biology and Biomedical Engineering, 2007, 1, 6-16.

[10] Howe, N.R.; Science, C. Silhouette Lookup for Automatic Pose Tracking. Pattern Recognition, 2004.

[11] Mangasarian, O.L. A Feature Selection Newton Method for Support Vector Machine Classification. Sciences-New York, 2004, pp. 185-202.

[12] Mundy J.L.; Zisserman, A. Geometric Invariance in Computer Vision, The MIT press, Cambridge, Massachusetts, 1992.

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