Very Large Scale Integration (VLSI) implementation of the Delayed Error Normalized LMS (DENLMS) adaptive filter using a pipelined architecture is proposed and it can be used for biomedical applications. The proposed pipelined VLSI architecture increases the performance of adaptive filters by lowering the amount of time spent on critical path calculation. When compared to the current Error Normalized Least Mean Square (ENLMS) method for pipelining purposes, the DENLMS technique requires an additional delay element, and because of the delay element, the algorithm functions faster and more effective in low-power applications. The proposed pipelined architecture of the DENLMS filter, on the other hand, improves the efficiency while consuming less power overall. The cell leakage power reduction and total dynamic power reduction obtained by using the DENLMS filter are 31.8% and 33.5%, respectively, although the overall area of the filter has increased by 20.4%.

Keywords: DENMLS technique, Adaptive filter, Dynamic power, Pipelined architecture.

[1] Venkatesan, C., Karthigaikumar, P., & Varatharajan, R. (2018). FPGA implementation of modified Error Normalized LMS adaptive filter for ECG noise removal. Cluster Computing, 22(S5): 12233-12241, https://doi. org/10.1007/s10586-017-1602-0.

[2] T.Thamaraimanalan, S.P.Vivekk, G.Satheeshkumar and P.Saravanan (2018). Smart Garden Monitoring System Using IOT. Asian Journal of Applied Science and Technology, 2(2): 186-192.

[3] Rajagopal, L. (2016). Power and area efficient decimation filter architectures of wireless receivers. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 87(1): 83-96,

[4] Venkatesan, C., Karthigaikumar, P., & Satheeskumaran, S. (2018). Mobile cloud computing for ECG telemonitoring and real-time coronary heart disease risk detection. Biomedical Signal Processing and Control, 44: 38-145,

[5] Niranjan, L., Venkatesan, C., Suhas, A. R., Satheeskumaran, S., & Nawaz, S. A. (2021). Design and implementation of chicken egg incubator for hatching using IoT. International Journal of Computational Science and Engineering, 24(4): 363,

[6] Jubairahmed, L., Satheeskumaran, S., & Venkatesan, C. (2017). Contourlet transform based adaptive nonlinear Diffusion filtering for speckle noise removal in ultrasound images. Cluster Computing, 22(S5): 11237-11246,

[7] Thamaraimanalan, T., RA, L., & RM, K. (2021). Multi Biometric Authentication using SVM and ANN Classifiers. Irish Interdisciplinary Journal of Science & Research, 5(1): 118-130.

[8] Venkatesan, C., Karthigaikumar, P., & Varatharajan, R. (2018). A novel LMS algorithm for ECG Signal preprocessing and KNN classifier Based abnormality detection. Multimedia Tools and Applications, 77(8): 10365-10374,

[9] Thamaraimanalan T, Sampath P (2019). A low power fuzzy logic based variable resolution ADC for wireless ECG monitoring systems. Cogn Syst Res., 57: 236-245,

[10] Balachander, K., Venkatesan, C. and R., K. (2021). Safety driven intelligent autonomous vehicle for smart cities using IoT. International Journal of Pervasive Computing and Communications, 17(5): 563-582.

[11] Chellappan, R., Satheeskumaran, S., Venkatesan, C., & Saravanan, S. (2021). Discrete stationary wavelet transform and SVD-based digital image watermarking for improved security. International Journal of Computational Science and Engineering, 24(4): 354,

[12] T. Thamaraimanalan and P. Sampath (2019). Leakage Power Reduction in Deep Submicron VLSI Circuits using Delay based Power Gating. National Academy Science Letters, 43(3): 229-232.

[13] Venkatesan, C., & Karthigaikumar, P. (2018). An efficient noise removal technique using modified error normalized LMS algorithm. National Academy Science Letters, 41(3): 155-159, 1007/s40009-018-0635-0.

[14] Thamaraimanalan, T., Mohankumar, M., Dhanasekaran, S., & Anandakumar, H. (2021). Experimental analysis of intelligent vehicle monitoring system using Internet of Things (IoT).

[15] K Kavitha, T Thamaraimanalan, M Suresh Kumar (2014). An Optimized Heal Algorithm for Hole Detection and Healing in Wireless Sensor Networks. International Journal of Advanced Engineering Research and Technology, 2(3): 243-249.

[16] Venkatesan, C., Saravanan, S., & Satheeskumaran, S. (2021). Real-time ECG Signal pre-processing and Neuro fuzzy-based CHD risk prediction. International Journal of Computational Science and Engineering, 24(4): 323.

[17] T.Thamaraimanalan, D.Naveena, M.Ramya & M.Madhubala (2020). Prediction and Classification of Fouls in Soccer Game using Deep Learning. Irish Interdisciplinary Journal of Science & Research, 4(3): 66-78.

[18] Venkatesan, C., Karthigaikumar, P., Paul, A., Satheeskumaran, S., & Kumar, R. (2018). ECG signal preprocessing and SVM Classifier-based abnormality detection in Remote healthcare applications. IEEE Access, 6: 9767-9773.

[19] R. Santhiya and M. T. Thamaraimanalan, (2015). Power Gating Based Low Power 32 Bit BCD Adder using DVT. Int. J. Sci. Res. Dev., 3(2): 802-805.

[20] Latha, R., & Vanathi, P. T. (2015). Design of digital filters for multi-standard transceivers. International Journal on Electrical Engineering and Informatics, 7(3): 517-530.

[21] Madasamy Raja, G., Thaha, M., Latha, R., & Karthikeyan, A. (2019). Texture classification using optimized local ternary patterns with nonlinear diffusion as pre-processing. Multimedia Tools and Applications, 79(5-6): 3831-3846.