scholarly journals Mathematical Modelling and Simulation of Band Pass Filters using the Floating Admittance Matrix Method

2021 ◽  
Vol 20 ◽  
pp. 208-214
Author(s):  
Sanjay Kumar Roy ◽  
Kamal Kumar Sharma ◽  
Cherry Bhargava ◽  
Brahmadeo Prasad Singh
Keyword(s):  
Mathematical Modeling ◽  
Matrix Method ◽  
Transfer Functions ◽  
Large Network ◽  
Admittance Matrix ◽  
The Matrix ◽  
Band Pass ◽  
Time And Energy ◽  
Mathematical Modelling And Simulation ◽  
Partitioning Technique

This article aims to develop a band pass filter's mathematical model using the Floating Admittance Matrix (FAM) method. The use of the conventional methods of analysis based KCL, KVL, Thevenin's, Norton's depends on the type of the particular circuit. The proposed mathematical modeling using the floating admittance matrix method is unique, and the same can be used for all types of circuits. This method uses the partitioning technique for large network. The sum property of all the elements of any row or any column equal to zero provides the assurance to proceed further for analysis or re-observe the very first equation. This saves time and energy. The FAM method presented here is so simple that anybody with slight knowledge of electronics but understating the matrix maneuvering, can analyze any circuit to derive all types of transfer functions. The mathematical modeling using the FAM method provides leverage to the designer to comfortably adjust their design at any stage of analysis. These statements provide compelling reasons for the adoption of the proposed process and demonstrate its benefits

scholarly journals Unique Analysis Approach to Bridge-T Network using Floating Admittance Matrix Method”

2021 ◽  
Vol 15 ◽  
pp. 1297-1304
Author(s):  
Sanjay Kumar Roy ◽  
Brahmadeo Prasad Singh ◽  
Kamal Kumar Sharma ◽  
Cherry Bhargava
Keyword(s):  
Mathematical Modeling ◽  
Tuning Range ◽  
Transfer Functions ◽  
Large Network ◽  
Wide Tuning Range ◽  
Admittance Matrix ◽  
The Matrix ◽  
Band Pass ◽  
Time And Energy ◽  
Network Form

The RC bridge-T Circuit are sometimes preferred for radio frequency applications as it does not require transformer (inductive coupling). The uses of the resistance-capacitance form of the network permits a wide tuning range. The article aims to develop a band pass filter's mathematical model using the Floating Admittance Matrix (FAM) approach. Both types of RC bridge-T network form the band-pass filters. The use of the conventional methods of analysis such as KCL, KVL, Thevenin's, Norton's depends on its suitability for the type of the particular circuit. The proposed mathematical modeling scheme using the floating admittance matrix approach is unique, and the same can be used for all types of circuits. This method is suitable to use the partitioning technique for large network. The sum property of all the elements of any row or any column equal to zero provides the assurance to proceed further for analysis or re-observe the very first equation. This saves time and energy. The FAM method presented here is so simple that anybody with slight knowledge of electronics but understating the matrix maneuvering, can analyze any circuit to derive all types of its transfer functions. The mathematical modeling using the FAM approach provides leverage to the designer to comfortably adjust their design at any stage of analysis. These statements provide compelling reasons for the adoption of the proposed process and demonstrate its benefits. The theoretically obtained equations meet the expected result for the RC bridge-T network. Its response peaks at the theoretically obtained value of the frequency. The simulated results are in agreement with the topological explanations and expectations.

scholarly journals Mathematical Modelling of Twin -T Notch Filter using Floating Admittance Matrix

2022 ◽  
Vol 16 ◽  
pp. 88-93
Author(s):  
Sanjay Kumar Roy ◽  
Kamal Kumar Sharma ◽  
Brahmadeo Prasad Singh
Keyword(s):  
Mathematical Modelling ◽  
Transfer Functions ◽  
Notch Filter ◽  
Large Network ◽  
Electronic Circuits ◽  
Filter Function ◽  
Admittance Matrix ◽  
The Matrix ◽  
Time And Energy ◽  
Partitioning Technique

A novel article presents the RC-notch filter function using the floating admittance matrix approach. The main advantages of the approach underlined the easy implementation and effective computation. The proposed floating admittance matrix (FAM) method is unique, and the same can be used for all types of electronic circuits. This method takes advantage of the partitioning technique for a large network. The sum property of all the elements of any row or any column equal to zero provides the assurance to proceed further for analysis or re-observe the very first equation at the first instant itself. This saves time and energy. The FAM method presented here is so simple that anybody with slight knowledge of electronics but understating the matrix maneuvering can analyze any circuit to derive all types of transfer functions. The mathematical modelling using the FAM method allows the designer to adjust their design at any stage of analysis comfortably. These statements provide compelling reasons for the adoption of the proposed process and demonstrate its benefits.

scholarly journals Mathematical Modelling and Simulation of Band Pass Filters Using The Floating Admittance Matrix Method.

2021 ◽  
Author(s):  
SANJAY KUMAR ROY ◽  
Cherry Bhargava ◽  
Kamal Kumar Sharma ◽  
Brahmadeo Prasad Singh
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Modelling ◽  
Matrix Method ◽  
Circuit Analysis ◽  
Modelling And Simulation ◽  
Notch Filters ◽  
Admittance Matrix ◽  
New Strategy ◽  
Band Pass ◽  
Mathematical Modelling And Simulation

Abstract This article describes the Band Pass Filters mathematical modeling, focusing on solutions using the Floating Admittance matrix method (FAM). The solution using the FAM Method looks superior for any circuit analysis. We are introducing a new strategy resulting in one of the best designs of the Bandpass and the Notch Filters. This document provides compelling reasons for the proposed Process, demonstrates its benefits and many valuable extensions and resources.

Fast programs for layered half-space problems

1967 ◽  
Vol 57 (6) ◽  
pp. 1299-1315
Author(s):  
M. J. Randall
Keyword(s):  
Half Space ◽  
Matrix Method ◽  
Transfer Functions ◽  
Synthetic Seismograms ◽  
Time Function ◽  
Source Time Function ◽  
Fast Computer ◽  
The Matrix ◽  
Crustal Reflection ◽  
Layered Half Space

Abstract Knopoff's matrix method for the solution of P-SV problems has been somewhat simplified and modified to take account of oceanic structures. Advantage has been taken of a method of separating the frequency-dependent operations from the matrix multiplications to obtain very fast computer programs for calculating Rayleigh dispersion, crustal reflection functions, and crustal transfer functions. Applications include Rayleigh dispersion inversion, QitR, inversion, crustal investigations using pP, crustal transfer corrections to amplitude observations, and the construction of synthetic seismograms for investigation of the source time-function.

Selecting optimal SpMV realizations for GPUs via machine learning

2021 ◽  
pp. 109434202199073
Author(s):  
Ernesto Dufrechou ◽  
Pablo Ezzatti ◽  
Enrique S Quintana-Ortí
Keyword(s):  
Machine Learning ◽  
Sparse Matrix ◽  
Machine Learning Techniques ◽  
Optimal Method ◽  
Learning Techniques ◽  
General Rules ◽  
Machine Learning Approach ◽  
The Matrix ◽  
Time And Energy ◽  
Matrix Vector

More than 10 years of research related to the development of efficient GPU routines for the sparse matrix-vector product (SpMV) have led to several realizations, each with its own strengths and weaknesses. In this work, we review some of the most relevant efforts on the subject, evaluate a few prominent routines that are publicly available using more than 3000 matrices from different applications, and apply machine learning techniques to anticipate which SpMV realization will perform best for each sparse matrix on a given parallel platform. Our numerical experiments confirm the methods offer such varied behaviors depending on the matrix structure that the identification of general rules to select the optimal method for a given matrix becomes extremely difficult, though some useful strategies (heuristics) can be defined. Using a machine learning approach, we show that it is possible to obtain unexpensive classifiers that predict the best method for a given sparse matrix with over 80% accuracy, demonstrating that this approach can deliver important reductions in both execution time and energy consumption.

scholarly journals EIGENVECTOR LOCALIZATION AS A TOOL TO STUDY SMALL COMMUNITIES IN ONLINE SOCIAL NETWORKS

2010 ◽  
Vol 13 (06) ◽  
pp. 699-723 ◽  
Author(s):  
FRANTIŠEK SLANINA ◽  
ZDENĚK KONOPÁSEK
Keyword(s):  
Online Social Networks ◽  
Large Network ◽  
Bipartite Networks ◽  
Small Communities ◽  
Complementary Approach ◽  
Matrix Encoding ◽  
The Matrix ◽  
Network Modules ◽  
Principal Tool ◽  
Eigenvector Localization

We present and discuss a mathematical procedure for identification of small "communities" or segments within large bipartite networks. The procedure is based on spectral analysis of the matrix encoding network structure. The principal tool here is localization of eigenvectors of the matrix, by means of which the relevant network segments become visible. We exemplified our approach by analyzing the data related to product reviewing on Amazon.com. We found several segments, a kind of hybrid communities of densely interlinked reviewers and products, which we were able to meaningfully interpret in terms of the type and thematic categorization of reviewed items. The method provides a complementary approach to other ways of community detection, typically aiming at identification of large network modules.

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