Response Analysis of Fir High Pass Filter Design Using Window Methods

Shehu, N.M.; Gidado, A.S.; Faruk, M.I.

doi:10.33564/ijeast.2019.v04i05.005

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Two low-pass filters, one with a Blackman window and the other with a flat top window, were designed in MATLAB Simulink [18]. The filters were 34th order and the normalized cutoff frequency was 0.2.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Linear Digital Filters Applied to Improve the Quality of the Measured Acoustic Field

Rodrigues,

Santos,

Buiochi

2023

Eng

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The measurement of acoustic fields generated by ultrasonic transducers is important for determining the focal length, lateral resolution, and amplitudes of the lateral and grating lobes. The acoustic field is commonly characterized by a set of scans using a needle hydrophone. The output of the hydrophone can be connected to an analog filter to enhance the signal. However, the analog filter might not be sufficient to avoid the noises that distort the signals. Alternatively, linear digital filters can be advantageous to improving the acoustic-field characterization. In this work, three filters were investigated: moving average (MA), band-pass Hamming window (HW), and band-pass Blackman window (BW). The filters were implemented and evaluated in terms of the root-mean-square error (RMSE) of the measured sound field, which was filtered, in relation to the simulated acoustic field (gold standard). As a compromise between effective filtering and signal non-distortion, a method to model the MA kernel length was proposed. All the filters reduced the noise of the measured acoustic field. The HW and the BW filters were more effective (RMSE = 4.01%) than the MA filter (RMSE = 4.28%). In spite of the small quantitative difference, acoustic field comparisons showed qualitative improvements.

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Section: Introductionmentioning

confidence: 99%

Comparison of Three Linear Digital Filters Applied to Improve the Quality of the Measured Acoustic Field

Rodrigues,

Santos,

Buiochi

2023

Eng

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“…However, there is still a need to improve and overcome some of the challenges associated with existing filters that makes such filters to converge very fast with ripples but sometimes gets trap in local minima, and has low band pass and stop band ripples. Moreover, techniques such as the one proposed by [7] that used Blackman and Flat Top Window method has slow convergence and high ripples. Another downside of the existing filters lower ripples and fitness values such as the FIR filters using improved PSO designed by [8].…”

Section: Introductionmentioning

confidence: 99%

Moth Flame Optimization Algorithm for Optimal FIR Filter Design

Adamu¹,

Dada²,

Bassi³

2021

IJISA

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This paper presents the application of Moth Flame optimization (MFO) algorithm to determine the best impulse response coefficients of FIR low pass, high pass, band pass and band stop filters. MFO was inspired by observing the navigation strategy of moths in nature called transverse orientation composed of three mathematical submodels. The performance of the proposed technique was compared to those of other well-known high performing optimization techniques like techniques like Particle Swarm Optimization (PSO), Novel Particle Swarm Optimization (NPSO), Improved Novel Particle Swarm Optimization (INPSO), Genetic Algorithm (GA), Parks and McClellan (PM) Algorithm. The performances of the MFO based designed optimized FIR filters have proved to be superior as compared to those obtained by PSO, NPSO, INPSO, GA, and PM Algorithm. Simulation results indicated that the maximum stop band ripples 0.057326, transition width 0.079 and fitness value 1.3682 obtained by MFO is better than that of PSO, NPSO, INPSO, GA, and PM Algorithms. The value of stop band ripples indicated the ripples or fluctuations obtained at the range which signals are attenuated is very low. The reduced value of transition width is the rate at which a signal changes from either stop band to pass band of a filter or vice versa is very good. Also, small fitness value in an indication that the values of the control variable of MFO are very near to its optimum solutions. The proposed design technique in this work generates excellent solution with high computational efficiency. This shows that MFO algorithm is an outstanding technique for FIR filter design.

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“…In some high performance applications, an FPGA or ASIC is used instead of a general purpose microprocessor, or a specialized DSP with specific paralleled architecture for expediting operations such as filtering. (Shehu et al, 2016) FIR Filter: Finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. A finite impulse response (FIR) filter is a filter structure that can be used to implement almost any sort of frequency response digitally.…”

mentioning

confidence: 99%

Comparative analysis of a high pass Finite Impulse Response (FIR) filter designed and simulated by window methods

Shehu¹,

Baraya²,

Sani³

2019

Journal of Applied Sciences and Environmental Management

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The design of digital filters is a deceptively complex topic. Although filters are easily understood and calculated, the practical challenges of their design and implementation are significant and are the subject of much advanced research. In this research paper, a high pass FIR filter is designed and simulated using Gaussian, Taylor and Hanning windows. The three windows are compared on the basis of their magnitude responses, step responses, impulse responses and phase responses and the results are presented. The comparative result shows that the proposed design of Taylor window is the most efficient of the three windows.

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Response Analysis of Fir High Pass Filter Design Using Window Methods

Cited by 3 publications

References 7 publications

Comparison of Three Linear Digital Filters Applied to Improve the Quality of the Measured Acoustic Field

Comparison of Three Linear Digital Filters Applied to Improve the Quality of the Measured Acoustic Field

Moth Flame Optimization Algorithm for Optimal FIR Filter Design

Comparative analysis of a high pass Finite Impulse Response (FIR) filter designed and simulated by window methods

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