scholarly journals Transformations for FIR and IIR Filters’ Design

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 533
Author(s):  
V. N. Stavrou ◽  
I. G. Tsoulos ◽  
Nikos E. Mastorakis
Keyword(s):  
Impulse Response ◽  
Transfer Functions ◽  
Finite Impulse Response ◽  
Transformation Method ◽  
Optimization Techniques ◽  
Infinite Impulse Response ◽  
Two Dimensional ◽  
Bilinear Transformation ◽  
One Dimensional ◽  
Iir Filters

In this paper, the transfer functions related to one-dimensional (1-D) and two-dimensional (2-D) filters have been theoretically and numerically investigated. The finite impulse response (FIR), as well as the infinite impulse response (IIR) are the main 2-D filters which have been investigated. More specifically, methods like the Windows method, the bilinear transformation method, the design of 2-D filters from appropriate 1-D functions and the design of 2-D filters using optimization techniques have been presented.

Theory of the Design, and Operation of Digital Filters

2021 ◽  
pp. 797-823
Author(s):  
Stevan Berber
Keyword(s):  
Impulse Response ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
Transformation Method ◽  
Infinite Impulse Response ◽  
Bilinear Transformation ◽  
Finite Impulse Response Filters ◽  
Infinite Impulse Response Filters ◽  
Basic Characteristics ◽  
Impulse Response Filters

Chapter 16 present the theoretical basis for digital filters, including issues related to their design. The basic characteristics and structures of finite impulse response and infinite impulse response filters are presented and discussed. In addition, the ideal and practical transfer characteristics of the digital filters are defined. The basic advantage of finite impulse response filters is that they can be designed to have an exact linear phase. However, infinite impulse response filters are generally more efficient computationally. The methods for filters design and related algorithms, which are based on the bilinear transformation method, windowed Fourier series, and algorithms based on iterative optimization, are also presented.

Design and Applications of Digital Filters

2011 ◽  
pp. 1016-1023
Author(s):  
Gordana Jovanovic Dolecek
Keyword(s):  
Impulse Response ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
High Reliability ◽  
Rapid Development ◽  
Digital Signal ◽  
Infinite Impulse Response ◽  
Iir Filters ◽  
Circuit Fabrication ◽  
Analog Systems

Digital signal processing (DSP) is an area of engineering that “has seen explosive growth during the past three decades” (Mitra, 2005). Its rapid development is a result of significant advances in digital computer technology and integrated circuit fabrication (Jovanovic Dolecek, 2002; Smith, 2002). Diniz, da Silva, and Netto (2002) state that “the main advantages of digital systems relative to analog systems are high reliability, suitability for modifying the system’s characteristics, and low cost”. The main DSP operation is digital signal filtering, that is, the change of the characteristics of an input digital signal into an output digital signal with more desirable properties. The systems that perform this task are called digital filters. The applications of digital filters include the removal of the noise or interference, passing of certain frequency components and rejection of others, shaping of the signal spectrum, and so forth (Ifeachor & Jervis, 2001; Lyons, 2004; White, 2000). Digital filters are divided into finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR digital filters are often preferred over IIR filters because of their attractive properties, such as linear phase, stability, and the absence of the limit cycle (Diniz, da Silva & Netto, 2002; Mitra, 2005). The main disadvantage of FIR filters is that they involve a higher degree of computational complexity compared to IIR filters with equivalent magnitude response (Mitra, 2005; Stein, 2000).

scholarly journals A Comparison of Evolutionary Computation Techniques for IIR Model Identification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Erik Cuevas ◽  
Jorge Gálvez ◽  
Salvador Hinojosa ◽  
Omar Avalos ◽  
Daniel Zaldívar ◽  
...  
Keyword(s):  
Evolutionary Computation ◽  
Impulse Response ◽  
Optimization Problems ◽  
Finite Impulse Response ◽  
Model Identification ◽  
Optimization Techniques ◽  
Infinite Impulse Response ◽  
Computation Techniques ◽  
Recent Developments ◽  
Complex Optimization

System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.

scholarly journals Design of One-Dimensional Linear Phase Digital IIR Filters Using Orthogonal Polynomials

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Vinay Kumar ◽  
Sunil Bhooshan
Keyword(s):  
Orthogonal Polynomials ◽  
Impulse Response ◽  
Phase 1 ◽  
Infinite Impulse Response ◽  
Linear Phase ◽  
One Dimensional ◽  
Iir Filters ◽  
Iir Filter ◽  
Amplitude Characteristics ◽  
Zeros And Poles

In the present paper, we discuss a method to design a linear phase 1-dimensional Infinite Impulse Response (IIR) filter using orthogonal polynomials. The filter is designed using a set of object functions. These object functions are realized using a set of orthogonal polynomials. The method includes placement of zeros and poles in such a way that the amplitude characteristics are not changed while we change the phase characteristics of the resulting IIR filter.

An Adaptive Feedback Noise Control Approach Using Q-Parameterization

2002 ◽  
Author(s):  
Mark A. McEver ◽  
Daniel G. Cole ◽  
Robert L. Clark
Keyword(s):  
Impulse Response ◽  
Gradient Descent ◽  
Noise Control ◽  
Finite Impulse Response ◽  
Open Loop ◽  
Infinite Impulse Response ◽  
Feedback Signal ◽  
Control Approach ◽  
Iir Filters ◽  
On Line

An algorithm is presented which uses adaptive Q-parameterized compensators for control of sound. All stabilizing feedback compensators can be described in terms of plant coprime factors and a free parameter, Q, which can be any stable function. By generating a feedback signal containing only disturbance information, the parameterized compensator allows Q to be designed in an open-loop fashion. The problem of designing Q to yield desired noise reduction is formulated as an on-line gradient descent-based adaptation process. Coefficient update equations are derived for different forms of Q, including digital finite impulse response (FIR) and lattice infinite impulse response (IIR) filters. Simulations predict good performance for both tonal and broadband disturbances, and a duct feedback noise control experiment results in a 37 dB tonal reduction.

Digital DC blocker filters

Frequenz ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Klaus Tittelbach-Helmrich
Keyword(s):  
Impulse Response ◽  
Transfer Functions ◽  
Cutoff Frequency ◽  
Special Kind ◽  
Infinite Impulse Response ◽  
Digital Signals ◽  
Low Frequencies ◽  
Iir Filters

Abstract This paper mathematically investigates a special kind of digital infinite-impulse response (IIR) filters, suitable for filtering out very low frequencies near zero from digital signals. We investigate the transfer functions of such filters from 1st to 3rd order and provide formulas to calculate the filter coefficients from the desired cutoff frequency.

scholarly journals A Phasor Measurement Unit Algorithm Using IIR Filters for FPGA Implementation

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1523
Author(s):  
Cornelis Jan Kikkert
Keyword(s):  
Impulse Response ◽  
Finite Impulse Response ◽  
Block Diagram ◽  
Rate Of Change ◽  
Measurement Unit ◽  
Infinite Impulse Response ◽  
Iir Filters ◽  
Iir Filter ◽  
Phasor Measurement ◽  
Ieee Standard

Phasor measurement units (PMU) are increasingly used in electrical power transmission networks, to maintain stability and protect the network. PMUs accurately measure voltage, phase, frequency, and rate of change of frequency (ROCOF). For reliability, it is desirable to implement a PMU using an FPGA. This paper describes a novel algorithm, suited to implementation in an FPGA and based on a simple PMU block diagram. A description of its realization using low hardware complexity infinite impulse response (IIR) filters is given. The IEC/IEEE standard 60255-118-1:2018 Part 118-1: Synchrophasor measurements for power systems, describes “reference” Finite Impulse Response (FIR) filters for implementing PMU hardware. At the 10 kHz sampling frequency used for our implementation, each “reference” FIR filter requires 100 multipliers, while an 8th order IIR filter only requires 12 multipliers. This paper compares the performance of different order IIR filter-based PMUs with the performance of the same PMU algorithm using the IEC/IEEE FIR reference filter. The IIR-based PMU easily satisfies all the requirements of IEC/IEEE standard and has a much better out of band signal rejection performance than a FIR-based PMU. Steady state errors for a rated voltage ± 10% and a rated frequency ± 5 Hz are < 0.000001% for total vector error (TVE) and < 1 µHz for frequency, with a latency of two mains cycles.

GOCE SGG filtering with FIR, IIR and wavelet MRA  

2021 ◽  
Author(s):  
Eleftherios A. Pitenis ◽  
Elisavet G. Mamagiannou ◽  
Dimitrios A. Natsiopoulos ◽  
Georgios S. Vergos ◽  
Ilias N. Tziavos
Keyword(s):  
Gravity Field ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Gravity Gradient ◽  
Satellite Gravity ◽  
Iir Filters ◽  
Field Spectrum ◽  
Lower Accuracy ◽  
Goce Satellite

&lt;p&gt;GOCE Satellite Gravity Gradiometry (SGG) data have been widely used in gravity field research in order to provide improved representations of the gravity field spectrum either in the form of Global Geopotential Models (GGMs) or grids at satellite altitude. One of the key points in utilizing SGG observations is their proper filtering, in order to remove noise and long-wavelength correlated error, while the signals in the GOCE measurement bandwidth (MBW) should be preserved. Due to the gradiometer&amp;#8217;s design, the GOCE satellite can achieve high accuracy and stable measurements in the MBW of 0.005 Hz to 0.1 Hz. The gravity gradient&amp;#160;in MBW are at an equivalent accuracy level, while &amp;#160; are of lower accuracy. Outside of the MBW, systematic errors, colored noise, and noise with sharp peaks are observed, especially in the frequencies lower than 0.005 Hz. With that in mind, the present work focuses on the investigation of various filtering options ranging from Finite Impulse Response (FIR) filters, Infinite Impulse Response (IIR) filters, and filtering based on Wavelets. The latter are employed given their inherent characteristic of being localized both in frequency and space, meaning that the signal can be decomposed at different levels, thus allowing multi-resolution approximation (MRA). The analysis is performed with one month of GOCE SGG data in order to conclude on the method that provides the overall best results. SGG observations are reduced to a GGM in order to account for the long- and medium-wavelengths of the gravity field spectrum. Then, various filter orders are investigated for the FIR and IIR filters, while selective reconstruction is employed for the WL-MRA. Evaluation of the results is performed in terms of the smoothness of the filtered fields and the Power Spectral Density (PSD) functions of the entire GOCE tensor.&lt;/p&gt;

scholarly journals On Using a Genetic Algorithm in the Designing of Linear Digital Filters

2020 ◽  
Vol 1 (346) ◽  
pp. 113-124
Author(s):  
Jacek Stelmach
Keyword(s):  
Genetic Algorithm ◽  
Impulse Response ◽  
Digital Filters ◽  
Noise Suppression ◽  
Transfer Functions ◽  
Finite Impulse Response ◽  
Optimal Parameter ◽  
Moving Average ◽  
Infinite Impulse Response ◽  
Input Signals

Digital filters, either as filters with moving average (Finite Impulse Response) or autoregressive filters (Infinite Impulse Response), are widely used in noise suppression, signal processing or extracting information from data streams. Although well‑known theory allows for optimal parameter selection, there still exist such real applications where requirements limit the use of digital filters. One of the most important limitations is the response time delay caused by too many used lagged input signals. The method proposed in the article allows us to estimate filter parameters with a genetic algorithm, decreasing its delay but keeping the requirements important for the user (e.g.: attenuation). Transfer functions of such filters were compared with transfer functions of the most known classical filters.

scholarly journals NANO-studio, the design environment of filter banks implemented in standard CMOS technology

2021 ◽  
Author(s):  
Andrzej Handkiewicz ◽  
Mariusz Naumowicz
Keyword(s):  
Impulse Response ◽  
Filter Banks ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Cmos Process ◽  
Digital Cmos ◽  
Additional Advantage ◽  
Analysis And Synthesis ◽  
Optimization Parameters ◽  
Synthesis Filter

AbstractThe paper presents a method of optimizing frequency characteristics of filter banks in terms of their implementation in digital CMOS technologies in nanoscale. Usability of such filters is demonstrated by frequency-interleaved (FI) analog-to-digital converters (ADC). An analysis filter present in these converters was designed in switched-current technique. However, due to huge technological pitch of standard digital CMOS process in nanoscale, its characteristics substantially deviate from the required ones. NANO-studio environment presented in the paper allows adjustment, with transistor channel sizes as optimization parameters. The same environment is used at designing a digital synthesis filter, whereas optimization parameters are input and output conductances, gyration transconductances and capacitances of a prototype circuit. Transition between analog s and digital z domains is done by means of bilinear transformation. Assuming a lossless gyrator-capacitor (gC) multiport network as a prototype circuit, both for analysis and synthesis filter banks in FI ADC, is an implementation of the strategy to design filters with low sensitivity to parameter changes. An additional advantage is designing the synthesis filter as stable infinite impulse response (IIR) instead of commonly used finite impulse response (FIR) filters. It provides several dozen-fold saving in the number of applied multipliers.. The analysis and synthesis filters in FI ADC are implemented as filter pairs. An additional example of three-filter bank demonstrates versatility of NANO-studio software.

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