Alignment of the digital filter frequency axis with the bilinear transformation

1972 ◽  
Vol 60 (3) ◽  
pp. 341-342
Author(s):  
W.D. Stanley
Keyword(s):  
Digital Filter ◽  
Bilinear Transformation ◽  
Filter Frequency ◽  
Frequency Axis

A Demonstration of Digital Filter Design, Implementation and Testing

1977 ◽  
Vol 14 (3) ◽  
pp. 221-236 ◽  
Author(s):  
T. J. Terrell
Keyword(s):  
Digital Filter ◽  
Filter Design ◽  
Second Order ◽  
Bilinear Transformation ◽  
Digital Filter Design ◽  
Effective Implementation ◽  
Recursive Digital Filter ◽  
Low Pass

The paper presents simple illustrative examples of recursive digital filter design using the bilinear transformation. The effective implementation of the design examples using a digital mini-computer is described. Practical tests and the corresponding results applied to a second order Butterworth low-pass digital filter are presented.

scholarly journals Energy efficiency digital filter design for spectral analysis of primary electrical values

2019 ◽  
Vol 104 ◽  
pp. 02002
Author(s):  
Vladimir Mladenovic ◽  
Caslav Stefanovic ◽  
Sergey Makov
Keyword(s):  
Digital Filter ◽  
Filter Design ◽  
Point Of View ◽  
Electrical Networks ◽  
Analog Filter ◽  
Bilinear Transformation ◽  
Negative Sequence ◽  
Knowledge Based ◽  
Three Phase ◽  
Short Circuits

In this paper, the knowledge based design of digital filter for analysis of spectral components is illustrated. The primary electrical values are analyzed observing of the faults which appear by earth short circuits. The main point of view is negative sequence component and higher harmonics in distributed electrical networks. Method of symbolic processing is used to design digital filter that realizes negative sequence component. Designing of digital filter is based on active analog filter in the three-phase electrical networks using bilinear transformation. We use methods of computer algebra tools (Mathematica) to simulate processes in network. Also, we automatically derive properties of digital filter, and the knowledge embedded in symbolic expressions was used to simulate an example system. The characteristics of digital filter are given and annotated using different sampling frequency and entire cases of earth short circuits.

A MATLAB-based biomedical signal de-noising applied to digital signal processing course for third-year students

2018 ◽  
Vol 56 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Guo Luo
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Filter ◽  
Digital Signal ◽  
Band Pass Filter ◽  
Analog Filter ◽  
Bilinear Transformation ◽  
Pass Filter ◽  
Educational Method ◽  
Low Pass Filter

Signal de-noising is one of the major topics of engineering application covered in an undergraduate-level digital signal processing course. Generally speaking, it involves a number of tedious concepts that have intrinsic physical meaning, which is difficult for students to understand . In this paper, an educational method using diaphragmatic electromyographic (EMGdi) as the de-noising object, which runs on the MATLAB software, has been developed for the convenience of learning and understanding for three-years students in digital signal processing course. This method transforms the analog filter to a digital filter by applying bilinear transformation equations, which allows the students explore the various characteristics of digital filter, such as low pass filter, high pass filter, band pass filter and band stop filter. That means Laplace equation transformed by inductance, capacitance and resistance will be replaced by the z equation, which is used for deriving sequence of difference equations. In the case studies, the clinical EMGdi is used to show the features of the developed method. Furthermore, classroom experience in the Nanfang College of Sun Yat-sen University has shown that the developed method helps in consolidating a better understanding of signal de-noising processing in digital signal processing course.

Digital-filter frequency response

1969 ◽  
Vol 5 (10) ◽  
pp. 210-211
Author(s):  
A.C. Davies
Keyword(s):  
Frequency Response ◽  
Digital Filter ◽  
Filter Frequency

scholarly journals Optimization of digital filters performances simultaneously in frequency and time domains

2020 ◽  
Vol 8 (6) ◽  
pp. 63-77
Author(s):  
A. V. Smirnov
Keyword(s):  
Frequency Response ◽  
Digital Filter ◽  
Digital Filters ◽  
Simultaneous Optimization ◽  
Step Response ◽  
Analog Filters ◽  
Analog Filter ◽  
Bilinear Transformation ◽  
Time Domains ◽  
Filter Prototype

Wide used method of digital filters design consists in transformation of analog filter-prototype with required performances into digital filter. This method is applicable if the transformation preserves optimality of filter performances under specified set of quality indexes (QI). It was denoted earlier that such situation is possible when gain-frequency response (GFR) and phase-frequency response are optimized simultaneously. The task of simultaneous optimization of digital filters GFR and step response (SR) is also important but yet a little explored. Alternative method of this problem solving consists in search of digital filter transfer function (TF) which is optimal under GFR and SR QI’s. To investigate capabilities of the first method we found examples of analog filters Pareto-optimal under rise time and transient duration. Other QI’s of these filters fulfilled specified constraints. Then these filters were transformed into digital filters. Bilinear transformation and transformation with invariant impulse response were applied. Further we did the search of digital filters optimal under the same set of QI’s. In either method the hybrid heuristic algorithm was applied for search optimal solutions in the space of TF poles and zeroes coordinates. The results of investigation demonstrated that digital filters developed via search are superiorly under specified set of QI’s then digital filters developed via transformation of analog filters. Accordingly Pareto-optimality for QI of GFR and SR is not preserved during such transformation and direct search must be applied to optimized digital filters simultaneously in frequency and time domains. Further in some cases analog filters developed via reverse bilinear transformation of the found optimal digital filters are superiorly under the same set of QI’s then analog filters developed using search. In such cases using of digital filter-prototypes for design of analog filters is practical.

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