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

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.

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

WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS ◽

10.37394/23201.2021.20.24 ◽

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

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

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2022.16.12 ◽

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.

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Wide tuning range MEMS band-pass filter with inductance change

The 13th International Conference on Solid-State Sensors, Actuators and Microsystems, 2005. Digest of Technical Papers. TRANSDUCERS '05. ◽

10.1109/sensor.2005.1497508 ◽

2005 ◽

Cited By ~ 1

Author(s):

A.B. Yu ◽

A.Q. Liu ◽

Q.X. Zhang

Keyword(s):

Tuning Range ◽

Band Pass Filter ◽

Wide Tuning Range ◽

Pass Filter ◽

Band Pass

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Design of a Compact Electrically Tunable Band-Pass Filter with Wide Tuning Range

Advances in Intelligent Systems and Computing - Recent Developments in Mechatronics and Intelligent Robotics ◽

10.1007/978-3-319-70990-1_19 ◽

2017 ◽

pp. 133-139

Author(s):

Chun Li ◽

Xiaofa Zhang ◽

Naichang Yuan

Keyword(s):

Tuning Range ◽

Band Pass Filter ◽

Wide Tuning Range ◽

Pass Filter ◽

Band Pass

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

10.21203/rs.3.rs-340422/v1 ◽

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.

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A wide tuning range low-pass Gm-C filter for multi-mode wireless receivers with automatic frequency calibration

2011 9th IEEE International Conference on ASIC ◽

10.1109/asicon.2011.6157393 ◽

2011 ◽

Author(s):

Yu Wang ◽

Na Yan ◽

Hao Min

Keyword(s):

Tuning Range ◽

Wireless Receivers ◽

Automatic Frequency ◽

Wide Tuning Range ◽

Low Pass ◽

Frequency Calibration ◽

Multi Mode

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A Novel Fast-Locking ADPLL Based on Bisection Method

Electronics ◽

10.3390/electronics10121382 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1382

Author(s):

Xiaoying Deng ◽

Huazhang Li ◽

Mingcheng Zhu

Keyword(s):

High Performance ◽

Tuning Range ◽

Frequency Control ◽

Control Word ◽

Cmos Process ◽

Bisection Method ◽

Reference Frequency ◽

Wide Tuning Range ◽

Cascade Structure ◽

And Control

Based on the idea of bisection method, a new structure of All-Digital Phased-Locked Loop (ADPLL) with fast-locking is proposed. The structure and locking method are different from the traditional ADPLLs. The Control Circuit consists of frequency compare module, mode-adjust module and control module, which is responsible for adjusting the frequency control word of digital-controlled-oscillator (DCO) by Bisection method according to the result of the frequency compare between reference clock and restructure clock. With a high frequency cascade structure, the DCO achieves wide tuning range and high resolution. The proposed ADPLL was designed in SMIC 180 nm CMOS process. The measured results show a lock range of 640-to-1920 MHz with a 40 MHz reference frequency. The ADPLL core occupies 0.04 mm2, and the power consumption is 29.48 mW, with a 1.8 V supply. The longest locking time is 23 reference cycles, 575 ns, at 1.92 GHz. When the ADPLL operates at 1.28 GHz–1.6 GHz, the locking time is the shortest, only 9 reference cycles, 225 ns. Compared with the recent high-performance ADPLLs, our design shows advantages of small area, short locking time, and wide tuning range.

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Selecting optimal SpMV realizations for GPUs via machine learning

The International Journal of High Performance Computing Applications ◽

10.1177/1094342021990738 ◽

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.

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