International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020


IJSTR >> Volume 9 - Issue 4, April 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Implementation Of Least Mean Square Algorithm Using Arduino & Simulink

[Full Text]



Mohcin Mekhfioui, Rachid Elgouri, Amal Satif, Meryem Moumouh, Laamari Hlou



Least Mean Square, Arduino Due, Matlab/Simulink, Real Time Workshop.



In this work, the least mean square (LMS) filter module is modeled, implemented and verified on a low-cost microcontroller to eliminate acoustic noise, which is a problem in voice communications. The main objective of this paper is to implement the module on an autonomous Arduino Due board in real-time, taking advantage of the low computational cost and ease of implementation through Matlab/Simulink programming. In the experimental application, the results of the implementation phase verify that the behavior of the implemented module is similar to the Simulink model.



[1] P. Regalia, Adaptive IIR Filtering in Signal Processing and Control. Routledge, 2018.
[2] C. Paleologu, J. Benesty, and S. Ciochină, “Adaptive filtering for the identification of bilinear forms,” Digital Signal Processing, vol. 75, pp. 153–167, Apr. 2018, doi: 10.1016/j.dsp.2018.01.010.
[3] J. Dhiman, S. Ahmad, and K. Gulia, “Comparison between Adaptive filter Algorithms (LMS, NLMS and RLS),” 2013.
[4] P. Kumar, H. S. Bhadauriya, A. R. Verma, and Y. Kumar, “Design Spline Adaptive Filter with Fractional Order Adaptive Technique for ECG Signal Enhancement,” Augment Hum Res, vol. 5, no. 1, p. 4, Oct. 2019, doi: 10.1007/s41133-019-0022-5.
[5] C. Venkatesan, P. Karthigaikumar, and R. Varatharajan, “FPGA implementation of modified error normalized LMS adaptive filter for ECG noise removal,” Cluster Comput, vol. 22, no. 5, pp. 12233–12241, Sep. 2019, doi: 10.1007/s10586-017-1602-0.
[6] D. Esposito, D. De Caro, G. Di Meo, E. Napoli, and A. G. M. Strollo, “Low-Power Hardware Implementation of Least-Mean-Square Adaptive Filters Using Approximate Arithmetic,” Circuits Syst Signal Process, vol. 38, no. 12, pp. 5606–5622, Dec. 2019, doi: 10.1007/s00034-019-01132-y.
[7] S. Dixit and D. Nagaria, “Hardware Reduction in Cascaded LMS Adaptive Filter for Noise Cancellation Using Feedback,” Circuits Syst Signal Process, vol. 38, no. 2, pp. 930–945, Feb. 2019, doi: 10.1007/s00034-018-0896-3.
[8] D. K. Gupta, V. K. Gupta, M. Chandra, A. N. Mishra, and P. K. Srivastava, “Hardware Co-Simulation of Adaptive Noise Cancellation System using LMS and Leaky LMS Algorithms,” in 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), 2019, pp. 1–6, doi: 10.1109/IoT-SIU.2019.8777658.
[9] “Arduino Due | Arduino Official Store.” [Online]. Available: https://store.arduino.cc/arduino-due. [Accessed: 16-Feb-2020].
[10] “Arduino Support from MATLAB.” [Online]. Available: https://fr.mathworks.com/hardware-support/arduino-matlab.html. [Accessed: 16-Feb-2020].
[11] M. Mekhfioui, R. Elgouri, A. Satif and L. Hlou, « Arduino Due Implementation of an Algorithm for Blind Source Separation using Matlab Simulink», International Journal of Innovative Technology and Exploring Engineerin, vol. 9, no 2, p. 3692-3696, dec. 2019, doi: 10.35940/ijitee.B7385.129219.