FPGA-based digital Filter Design for Biomedical Signal

Özpolat, Erman; Karakaya, Barış; Gülten, Arif

doi:10.1109/memstech.2016.7507523

Cited by 12 publications

(3 citation statements)

References 2 publications

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“…Ozpolat et al (2006) has developed the FIR-Finite Impulse Response filter design. The FIR filter was anticipated due to the smooth structural characteristic and design properties on filtering the EEG signal.…”

Section: Literature Surveymentioning

confidence: 99%

A field programmable gate array-based biomedical noise reduction framework using advanced trilateral filter

Raja

Naveen

Palaniswamy

et al. 2021

Transactions of the Institute of Measurement and Control

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Filtering is one of the essential tools utilized to remove undesirable features in biomedical images. Most biomedical image denoising systems are used for clinical diagnosis. So, in this paper, we use the advanced trilateral filter in the field programmable gate array (FPGA) for removing noise in the biomedical image. Generally, the trilateral filter is used as an edge preserving smoothing filter. This advanced approach of trilateral filter gives the best noise diminution and enhances the image quality. This paper also proposes the hardware implementation of an efficient FPGA-based advanced trilateral filter on real time execution. In this manuscript, we intend to design and implement the FPGA architecture using an advanced trilateral filter. Biomedical images with different noises are used during implementation and compared with the existing bilateral and trilateral architecture to assess the proposed architecture performance. For evaluating the performance metrics of the proposed advanced trilateral filter on MATLAB platform, peak signal-to-noise ratio (PSNR), mean squared error (MSE) and structured similarity index (SSIM) are calculated for different biomedical images – such as brain (MRI), chest (x-ray) and lungs (CT) – with different noises – such as salt and pepper, Gaussian, Poisson and Speckle noises – compared with existing bilateral filter and trilateral filter, respectively. The proposed advanced trilateral filter implementations are checked on Virtex-6, Virtex-7 and Zynq FPGA development board using Verilog programming language in Xilinx ISE 14.5 design tools. The simulation outcomes display that the FPGA execution of the advanced trilateral filter contains better noise removal efficiency in biomedical images compared with the existing bilateral and trilateral filter.

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Section: Literature Surveymentioning

confidence: 99%

A field programmable gate array-based biomedical noise reduction framework using advanced trilateral filter

Raja

Naveen

Palaniswamy

et al. 2021

Transactions of the Institute of Measurement and Control

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show abstract

“…The process of digital filtering is extensively used in many applications in communications, signal processing, electrical and biomedical engineering, and control [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]; for example, coding and compression, signal augmentation, denoising, amplitude and frequency demodulation, analog-to-digital conversions, shape detection, and extraction [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. For some applications, nonlinearity is tailored to a specific purpose [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Some Remarks about Entropy of Digital Filtered Signals

Borges

Nepomuceno

Duque

et al. 2020

Entropy

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The finite numerical resolution of digital number representation has an impact on the properties of filters. Much effort has been done to develop efficient digital filters investigating the effects in the frequency response. However, it seems that there is less attention to the influence in the entropy by digital filtered signals due to the finite precision. To contribute in such a direction, this manuscript presents some remarks about the entropy of filtered signals. Three types of filters are investigated: Butterworth, Chebyshev, and elliptic. Using a boundary technique, the parameters of the filters are evaluated according to the word length of 16 or 32 bits. It has been shown that filtered signals have their entropy increased even if the filters are linear. A significant positive correlation (p < 0.05) was observed between order and Shannon entropy of the filtered signal using the elliptic filter. Comparing to signal-to-noise ratio, entropy seems more efficient at detecting the increasing of noise in a filtered signal. Such knowledge can be used as an additional condition for designing digital filters.

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“…The arithmetic operators required more area which is the main limitations for designing a digital filter in the DSP system [7]. The FIR filter is always stable because it has no feedback and linear phase response [8]. Traditionally, the fixed point coefficients designed and presented in binary forms and integer forms.…”

Section: Introductionmentioning

confidence: 99%

Low Area VLSI implementation of CSLA for FIR Filter Design

Kumar¹,

Gangappa²

2019

IJIES

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Finite Impulse Response (FIR) filter is a major building block in Digital Signal Processing (DSP) system. The conventional FIR filters are implemented by utilizing a normal adder, which consumes more area. Minimizing the hardware utilization of the adder is a significant challenge in FIR filter design. Generally, complexity of the FIR filter is dominated by the adder. So, an efficient FIR filter was designed with the help of Carry Select Adder (CSLA) to reduce area and hardware complexity of the accumulation block. The area has been reduced by using CSLA that is very significant idea while designing a less area filter. The proposed method named as Low Area-CSLA-FIR (LA-CSLA-FIR) filter. The LA-CSLA-FIR filter was implemented in Xilinx Field Programmable Gate Array (FPGA) on different devices such as Virtex-4, Virtex-5 and Virtex-6 by using Verilog code. The experimental results of the LA-CSLA-FIR filter design reduced average the FPGA device utilization: 15.38 % of LUTs, 8% of flip flops and 8% of slices on Virtex-5 compared to existing filter designs.

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FPGA-based digital Filter Design for Biomedical Signal

Cited by 12 publications

References 2 publications

A field programmable gate array-based biomedical noise reduction framework using advanced trilateral filter

A field programmable gate array-based biomedical noise reduction framework using advanced trilateral filter

Some Remarks about Entropy of Digital Filtered Signals

Low Area VLSI implementation of CSLA for FIR Filter Design

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