A Novel Way to Design and Implement Statistical Operations based on FPGA

Ismael, Sarmad F.; Mahmood, Basil Shukr

doi:10.5120/ijca2017914359

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…In skewness and kurtosis the ratio error value is slightly higher, this is reasonable because it has a longer calculation path so it is very susceptible to rounding, considering that this implementation is done at a fixed point. In comparison to previous studies [37] the implementation presented in this research demonstrates significantly improved accuracy in calculating the mean and variance. However, it should be noted that the accuracy of the standard deviation is relatively lower.…”

Section: Resultsmentioning

confidence: 76%

Implementation of first order statistical processor on FPGA for feature extraction

Hadiyoso,

Ramdani,

Irawati

et al. 2024

IJRES

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Statistical calculations on signals commonly used in feature extraction. In software processing, statistical computation is an easy task. However, providing a computer requires high costs for simple statistical processing. Another consideration is the need for implementation with real-time and portable processing. Therefore, an alternative device is needed, one of which is the field programmable gate array (FPGA). FPGA is a logic circuit board that can be reconfigured according to computing needs. FPGA can also be used as a prototyping of electronic chips. However, implementing statistical formulas in FPGA is interesting in developing its architecture. Therefore, this research proposes a logic circuit design that can be used for first-order statistical calculations. Statistical parameters include the mean, variance, standard deviation, skewness, and kurtosis. The validation test was performed on the electrocardiogram (ECG) signal series and compared with manual calculations. Validation shows that the mean and variance has very high accuracy with an average error of less than 0.06%.

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Section: Resultsmentioning

confidence: 76%

Implementation of first order statistical processor on FPGA for feature extraction

Hadiyoso,

Ramdani,

Irawati

et al. 2024

IJRES

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FPGA based Implementation of Image Quality Measures: A Comprehensive Approach

Ismael¹,

Qassim²

2017

IJCA

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This paper presents the design and implementation of six Image quality measures used to investigate the similarity between reconstructed images (after Denoising process) and the original ones on Spartan 3E XC3S500E FPGA. Since the visual comparison is too subjective, objective measure of image quality is required. The objective measure takes advantage of the variance in the statistical distribution of the coefficients in the image. The designed architecture was tested using five gray scale (128 x 128) images and it runs properly at a maximum clock rate of 70.74 MHz. By employing a parallel architecture, the speed performance has increased to 1064 times in comparison to Matlab running time. General TermsComputer architecture, computational technology.

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A Novel Way to Design and Implement Statistical Operations based on FPGA

Cited by 2 publications

References 4 publications

Implementation of first order statistical processor on FPGA for feature extraction

Implementation of first order statistical processor on FPGA for feature extraction

FPGA based Implementation of Image Quality Measures: A Comprehensive Approach

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