scholarly journals Coefficient quantization effects on new filters based on Chebyshev fourth-kind polynomials

2021 ◽  
Vol 34 (2) ◽  
pp. 291-305
Author(s):  
Biljana Stosic
Keyword(s):  
Digital Filters ◽  
Digital Signal ◽  
Linear Phase ◽  
Response Characteristics ◽  
Recursive Filters ◽  
Filter Characteristic ◽  
Low Pass ◽  
Frequency Response Characteristics ◽  
Low Pass Filters ◽  
Fourth Kind

The aim of this paper is to construct non-recursive filters, extensively used type of digital filters in digital signal processing applications, based on Chebyshev orthogonal polynomials. The paper proposes the use of the fourth-kind Chebyshev polynomials as functions in generating new filters. In this kind, low-pass filters with linear phase responses are obtained. Comprenhansive study of the frequency response characteristics of the generated filter functions is presented. The effects of coefficient quantization as one type of quantization that influences a filter characteristic are investigated here also. The quantized-coefficient errors are considered based on the number of bits and the implementation algorithms.

Analytic Techniques for Designing Digital Non-Recursive Filters

1977 ◽  
Vol 14 (3) ◽  
pp. 251-267 ◽  
Author(s):  
J. Attikiouzel ◽  
R. Bennett
Keyword(s):  
Power Series ◽  
Digital Filters ◽  
Stop Band ◽  
Pass Band ◽  
Linear Phase ◽  
Recursive Filters ◽  
Low Pass ◽  
Power Series Technique ◽  
Recursive Digital Filters ◽  
Series Technique

Non-iterative analytic techniques are presented which employ orthogonal polynomials in the design of linear phase non-recursive digital/filters. Pass band and stop band transformations are desired to approximate an ideal low pass digital filter. Also the economization of power series technique is employed to derive near optimum responses.

scholarly journals Improving Efficiency of NPP Software Based I&C

2016 ◽  
pp. 71-76
Author(s):  
H. Ukhina ◽  
A. Bilenko ◽  
V. Sytnikov
Keyword(s):  
Transfer Function ◽  
Frequency Response ◽  
Digital Filters ◽  
First Order ◽  
Response Characteristics ◽  
Function Coefficient ◽  
Low Pass ◽  
Frequency Response Characteristics ◽  
Digital Components ◽  
High Pass

The paper considers improving efficiency of NPP software based I&C during adjustment and readjustment of its characteristics. The research analyzes impact of transfer function coefficient of digital components on features of frequency-response characteristics, which shall be considered during design of software based I&C. The paper objective was to determine the numerator and denominator dependencies of transfer function of first order high-pass and low-pass digital filters of cut-off frequency, and also to determine dependencies on pulsation coefficient.

Novel Methods to Design Low-Complexity Digital Finite Impulse Response (FIR) Filters

2018 ◽  
pp. 6234-6244
Author(s):  
David Ernesto Troncoso Romero ◽  
Gordana Jovanovic Dolecek
Keyword(s):  
Impulse Response ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
Digital Signal ◽  
Low Complexity ◽  
Fir Filters ◽  
Linear Phase ◽  
Guaranteed Stability ◽  
Important Challenge ◽  
Dsp Systems

Digital filters play a central role in modern Digital Signal Processing (DSP) systems. Finite Impulse Response (FIR) filters can provide solutions with guaranteed stability and linear phase. However, the main disadvantage of conventional FIR filter designs is that they become computationally complex, especially in applications demanding narrow transition bandwidths. Therefore, designing FIR filters with very stringent specifications and a low complexity is currently an important challenge. In this chapter, a review of the recent methods to efficiently design low-complexity linear-phase FIR filters is presented. The chapter starts with an introduction to linear-phase FIR digital filters. Then, an overview of the design methods that have been developed in literature to design low-complexity FIR filters is presented. Finally, the most common and recent of these methods along with their corresponding special structures are explained.

The design of linear-phase low-pass filters

1953 ◽  
Vol 1953 (4) ◽  
pp. 184-185
Author(s):  
C.F. Floyd ◽  
R.L. Corke ◽  
H. Lewis
Keyword(s):  
Linear Phase ◽  
Low Pass ◽  
Low Pass Filters

scholarly journals Effective resolution concepts for lidar observations

2015 ◽  
Vol 8 (5) ◽  
pp. 5363-5424
Author(s):  
M. Iarlori ◽  
F. Madonna ◽  
V. Rizi ◽  
T. Trickl ◽  
A. Amodeo
Keyword(s):  
Digital Signal ◽  
High Spectral Resolution ◽  
Lidar Data ◽  
Vertical Profiles ◽  
Range Resolution ◽  
True Profile ◽  
Low Pass ◽  
Low Pass Filters ◽  
The Impact ◽  
Aerosol Properties

Abstract. Since its first establishment in 2000, EARLINET (European Aerosol Research Lidar NETwork) has been devoted to providing, through its database, exclusively quantitative aerosol properties, such as aerosol backscatter and aerosol extinction coefficients, the latter only for stations able to retrieve it independently (from Raman or High Spectral Resolution Lidars). As these coefficients are provided in terms of vertical profiles, EARLINET database must also include the details on the range resolution of the submitted data. In fact, the algorithms used in the lidar data analysis often alter the spectral content of the data, mainly working as low pass filters with the purpose of noise damping. Low pass filters are mathematically described by the Digital Signal Processing (DSP) theory as a convolution sum. As a consequence, this implies that each filter's output, at a given range (or time) in our case, will be the result of a linear combination of several lidar input data relative to different ranges (times) before and after the given range (time): a first hint of loss of resolution of the output signal. The application of filtering processes will also always distort the underlying true profile whose relevant features, like aerosol layers, will then be affected both in magnitude and in spatial extension. Thus, both the removal of noise and the spatial distortion of the true profile produce a reduction of the range resolution. This paper provides the determination of the effective resolution (ERes) of the vertical profiles of aerosol properties retrieved starting from lidar data. Large attention has been addressed to provide an assessment of the impact of low-pass filtering on the effective range resolution in the retrieval procedure.

Novel Methods to Design Low-Complexity Digital Finite Impulse Response (FIR) Filters

2019 ◽  
pp. 1053-1066
Author(s):  
David Ernesto Troncoso Romero ◽  
Gordana Jovanovic Dolecek
Keyword(s):  
Impulse Response ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
Digital Signal ◽  
Low Complexity ◽  
Fir Filters ◽  
Linear Phase ◽  
Guaranteed Stability ◽  
Important Challenge ◽  
Dsp Systems

Digital filters play a central role in modern digital signal processing (DSP) systems. Finite impulse response (FIR) filters can provide solutions with guaranteed stability and linear phase. However, the main disadvantage of conventional FIR filter designs is that they become computationally complex, especially in applications demanding narrow transition bandwidths. Therefore, designing FIR filters with very stringent specifications and a low complexity is currently an important challenge. In this chapter, a review of the recent methods to efficiently design low-complexity linear-phase FIR filters is presented. The chapter starts with an introduction to linear-phase FIR digital filters. Then, an overview of the design methods that have been developed in literature to design low-complexity FIR filters is presented. Finally, the most common and recent of these methods along with their corresponding special structures are explained.

Design of Linear Phase FIR Low Pass Filter Using Mutation-Based Particle Swarm Optimization Technique

2020 ◽  
pp. 344-358
Author(s):  
Taranjit Kaur ◽  
Balwinder Singh Dhaliwal
Keyword(s):  
Particle Swarm Optimization ◽  
Filter Design ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Linear Phase ◽  
Swarm Optimization ◽  
Local Optima ◽  
Response Characteristics ◽  
Low Pass ◽  
Particle Swarm Optimization Technique

This chapter presents a mutation-based particle swarm optimization (PSO) approach for designing a linear phase digital low pass FIR filter (LPF). Since conventional gradient-based methods are susceptible to being trapped in local optima, the stochastic search methods have proven to be effective in a multi-dimensional non-linear environment. In this chapter, LPF with 20 coefficients has been designed. Since filter design is a multidimensional optimization problem, the concept of mutation helps in maintaining diversity in the swarm population and thereby efficiently controlling the local search and convergence to the global optimum solution. Given the filter specifications to be realized, the Mutation PSO (MPSO) tries to meet the ideal frequency response characteristics by generating an optimal set of filter coefficients. The simulation results have been compared with basic PSO and state of artworks on filter design. The results justify that the proposed technique outperforms not only in convergence speed but also in the quality of the solution obtained.

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