Reducing Complexity of FIR Filter Implementations for Low Power Applications

DeBrunner, L.S.

doi:10.1109/acssc.2007.4487460

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The working principle of linear filters depends on the addition and multiplication [6]. There are many designs for filters that have been proposed by researchers which can be classified according to the mechanism of its work into two categories [7]: first one is multiplier based filters which takes a set of multipliers and implement multiplications through them such as in [8]- [10]. While the second one is multiplerless filters which depend on avoiding using costly multipliers through representations and various arithmetic transformations such as works in [11]- [14].…”

Section: Related Workmentioning

confidence: 99%

Detection of brain stroke in the MRI image using FPGA

2021

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One of the most important difficulties which doctors face in diagnosing is the analysis and diagnosis of brain stroke in magnetic resonance imaging (MRI) images. Brain stroke is the interruption of blood flow to parts of the brain that causes cell death. To make the diagnosis easier for doctors, many researchers have treated MRI images with some filters by using Matlab program to improve the images and make them more obvious to facilitate diagnosis by doctors. This paper introduces a digital system using hardware concepts to clarify the brain stroke in MRI image. Field programmable gate arrays (FPGA) is used to implement the system which is divided into four phases: preprocessing, adjust image, median filter, and morphological filters alternately. The entire system has been implemented based on Zynq FPGA evaluation board. The design has been tested on two MRI images and the results are compared with the Matlab to determine the efficiency of the proposed system. The proposed hardware system has achieved an overall good accuracy compared to Matlab where it ranged between 90.00% and 99.48%.

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Section: Related Workmentioning

confidence: 99%

Detection of brain stroke in the MRI image using FPGA

2021

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“…It seems clear that much research has been devoted to investigating the influence of finite precision on digital filters [ 32 , 34 , 36 , 42 , 43 ]. In those investigations, there are many cases where the quality of filter is measured using the filter response or signal-to-noise ratio (SNR) [ 43 ].…”

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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“…A number of approaches to efficient design of filters have been proposed. Generally, filter designs can be split into two categories [6]: multiplier-based filters, which implement multiplications explicitly through a set of multipliers as in [7], [8], [9], and multiplierless filters which avoid using expensive multipliers through various arithmetic transformations and representations as in [10], [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

High Throughput 2D Spatial Image Filters on FPGAs

Al-Dujaili,

Fahmy

2017

Preprint

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FPGAs are well established in the signal processing domain, where their fine-grained programmable nature allows the inherent parallelism in these applications to be exploited for enhanced performance. As architectures have evolved, FPGA vendors have added more heterogeneous resources to allow oftenused functions to be implemented with higher performance, at lower power and using less area. DSP blocks, for example, have evolved from basic multipliers to support the multiplyaccumulate operations that are the core of many signal processing tasks. While more features were added to DSP blocks, their structure and connectivity has been optimised primarily for onedimensional signal processing. Basic operations in image processing are similar, but performed in a two-dimensional structure, and hence, many of the optimisations in newer DSP blocks are not exploited when mapping image processing algorithms to them. We present a detailed study of two-dimensional spatial filter implementation on FPGAs, showing how to maximise performance through exploitation of DSP block capabilities, while also presenting a lean border pixel management policy.

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Reducing Complexity of FIR Filter Implementations for Low Power Applications

Cited by 4 publications

References 8 publications

Detection of brain stroke in the MRI image using FPGA

Detection of brain stroke in the MRI image using FPGA

Some Remarks about Entropy of Digital Filtered Signals

High Throughput 2D Spatial Image Filters on FPGAs

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