Application of VDVTA as Grounded Lossy Series and Parallel RL Circuit

This paper presents two simple topologies of novel grounded lossy series and parallel RL circuit employing VDVTA. Inductor and resistor are simulated employing one VDVTA and one passive component. Both the proposed topologies for the immittance simulation have been realized using a simple circuit. There is little deviation in the values of theoretically calculated and practically simulated series and parallel RL circuit. To study the performance of the realized grounded lossy series and parallel RL circuit, a second order high pass filter has been constructed using one of the circuits. To verify the presented theoretical analysis, the PSPICE simulation results are given using CMOS technology.

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Publication Details

Journal
Transactions on Machine Learning and Artificial Intelligence
Published
2025-04-25
DOI
https://doi.org/10.14738/tmlai.1302.18693
Citations
1
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
2.48
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article

Application of VDVTA as Grounded Lossy Series and Parallel RL Circuit

1 citations
Transactions on Machine Learning and Artificial Intelligence
Neural Networks and Applications
2.48
article

Application of VDVTA as Grounded Lossy Series and Parallel RL Circuit

article en
1 citations

Abstract

This paper presents two simple topologies of novel grounded lossy series and parallel RL circuit employing VDVTA. Inductor and resistor are simulated employing one VDVTA and one passive component. Both the proposed topologies for the immittance simulation have been realized using a simple circuit. There is little deviation in the values of theoretically calculated and practically simulated series and parallel RL circuit. To study the performance of the realized grounded lossy series and parallel RL circuit, a second order high pass filter has been constructed using one of the circuits. To verify the presented theoretical analysis, the PSPICE simulation results are given using CMOS technology.

Transactions on Machine Learning and Artificial IntelligenceVol. 13(02)
Jamia Millia Islamia (IN)
Openalex Percentile: Top 10%
Neural Networks and Applications
2.48
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