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Systematic method to convert of analog filters to digital filters
SiPS 2013 Proceedings, 2013The analog filters are usually converted to IIR digital filters using appropriate transformation of system function from s-domain to z-domain. Recently another approach of converting an analog filter described by a diagram or a netlist to a digital filter has been proposed.
Alexandru Lodin +3 more
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Soft morphological filters: a robust morphological filtering method
Journal of Electronic Imaging, 1994We introduce new morphological filters, called soft morphological filters. They maintain most of the desirable properties of standard morphological operations yet are less sensitive to additive noise and to small variations in the shapes of the objects to be filtered.
Lasse Koskinen, Jaakko Astola
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A rapid method for digital filtering
Communications of the ACM, 1964Since much of the computer time spent in time-series analysis is used for multiplications, a minimum multiplication method was devised for digital filtering, with the expectation that it would be useful in the online, real-time analysis of biological data.
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1990
Non-recursive digital filters have a finite impulse response (FIR) sequence, and they are inherently stable. Furthermore, a digital filter with a symmetrical impulse response has a linear phase characteristic, and therefore in this case there is no phase distortion imposed by the filter.
T. J. Terrell, E. T. Powner
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Non-recursive digital filters have a finite impulse response (FIR) sequence, and they are inherently stable. Furthermore, a digital filter with a symmetrical impulse response has a linear phase characteristic, and therefore in this case there is no phase distortion imposed by the filter.
T. J. Terrell, E. T. Powner
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Applied Optics, 1988
An image reconstruction method of filtering backprojection is compared with the filtered backprojection method for cases with and without Poisson (signal-dependent) noise in the projection data. The case with noise is especially interesting since the result reveals the noise sensitivity of the two methods.
S, Suzuki, S, Yamaguchi
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An image reconstruction method of filtering backprojection is compared with the filtered backprojection method for cases with and without Poisson (signal-dependent) noise in the projection data. The case with noise is especially interesting since the result reveals the noise sensitivity of the two methods.
S, Suzuki, S, Yamaguchi
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Method combination for document filtering
Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '96, 1996David A. Hull +2 more
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A time filter method for solving the double-diffusive natural convection model
Computers and Fluids, 2022Pengzhan Huang, Yinnian He
exaly
2004
As it was stated in Sect. 4.2, basic formula of digital filtering a signal defined by its samples { a k , k = 0,1 N a − 1} by a filter defined by its discrete PSF { h n n = 0,1..., N h − 1} is $$ {b_k} = \sum\limits_{n = 0}^{{N_h} - 1} {{h_n}{a_{k - n}}} $$ (5.1.1)
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As it was stated in Sect. 4.2, basic formula of digital filtering a signal defined by its samples { a k , k = 0,1 N a − 1} by a filter defined by its discrete PSF { h n n = 0,1..., N h − 1} is $$ {b_k} = \sum\limits_{n = 0}^{{N_h} - 1} {{h_n}{a_{k - n}}} $$ (5.1.1)
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2011
The filter error method is the most general approach to parameter estimation that accounts for both the process and the measurement noise. The method was first studied in Reference 1 and since then, various applications of the techniques to estimate parameters from measurements with turbulence (accounting for process noise) have been reported.
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The filter error method is the most general approach to parameter estimation that accounts for both the process and the measurement noise. The method was first studied in Reference 1 and since then, various applications of the techniques to estimate parameters from measurements with turbulence (accounting for process noise) have been reported.
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2013
Filtering is essential for surface texture characterisation. Filtration separates the measured data into different scales of interest. The extracted scales can be characterised by parameters given in ISO 25178 part 2. In this chapter the two draft areal filtering standards, ISO 16610 part 61 (areal Gaussian filter) and ISO 16610 part 71 (robust areal ...
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Filtering is essential for surface texture characterisation. Filtration separates the measured data into different scales of interest. The extracted scales can be characterised by parameters given in ISO 25178 part 2. In this chapter the two draft areal filtering standards, ISO 16610 part 61 (areal Gaussian filter) and ISO 16610 part 71 (robust areal ...
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