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Journal articles on the topic 'Recursive digital filters'

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Published: 6 September 2023

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1

Siwczyński, M., A. Drwal, and S. Żaba. "The digital function filters – algorithms and applications." Bulletin of the Polish Academy of Sciences: Technical Sciences 61, no. 2 (June 1, 2013): 371–77. http://dx.doi.org/10.2478/bpasts-2013-0036.

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Abstract The simple digital filters are not sufficient for digital modeling of systems with distributed parameters. It is necessary to apply more complex digital filters. In this work, a set of filters, called the digital function filters, is proposed. It consists of digital filters, which are obtained from causal and stable filters through some function transformation. In this paper, for several basic functions: exponential, logarithm, square root and the real power of input filter, the recursive algorithms of the digital function filters have been determined The digital function filters of exponential type can be obtained from direct recursive formulas. Whereas, the other function filters, such as the logarithm, the square root and the real power, require using the implicit recursive formulas. Some applications of the digital function filters for the analysis and synthesis of systems with lumped and distributed parameters (a long line, phase shifters, infinite ladder circuits) are given as well.

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2

Porsani, Milton J., and Bjørn Ursin. "Direct multichannel predictive deconvolution." GEOPHYSICS 72, no. 2 (March 2007): H11—H27. http://dx.doi.org/10.1190/1.2432260.

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The Levinson principle generally can be used to compute recursively the solution of linear equations. It can also be used to update the error terms directly. This is used to do single-channel deconvolution directly on seismic data without computing or applying a digital filter. Multichannel predictive deconvolution is used for seismic multiple attenuation. In a standard procedure, the prediction-error filter matrices are computed with a Levinson recursive algorithm, using a covariance matrix of the input data. The filtered output is the prediction errors or the nonpredictable part of the data. Starting with the classical Levinson recursion,wehave derived new algorithms for direct recursive calculationof the prediction errors without computing the data covariance-matrix or computing the prediction-error filters. One algorithm generates recursively the one-step forward and backward predic-tion errors and the L-step forward prediction error, computing only the filter matrices with the highest index. A numerically more stable algorithm uses reduced QR decomposition or singular-value decomposition (SVD) in a direct recursive computation of the prediction errors without computing any filter matrix. The new, stable, predictive algorithms require more arithmetic opera-tions in the computer, but the computer programs and data flow are much simpler than for standard predictive deconvolution.

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3

Kaplun, Dmitry, Denis Butusov, Valerii Ostrovskii, Alexander Veligosha, and Vyacheslav Gulvanskii. "Optimization of the FIR Filter Structure in Finite Residue Field Algebra." Electronics 7, no. 12 (December 2, 2018): 372. http://dx.doi.org/10.3390/electronics7120372.

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This paper introduces a method for optimizing non-recursive filtering algorithms. A mathematical model of a non-recursive digital filter is proposed and a performance estimation is given. A method for optimizing the structural implementation of the modular digital filter is described. The essence of the optimization is that by using the property of the residue ring and the properties of the symmetric impulse response of the filter, it is possible to obtain a filter having almost a half the length of the impulse response compared to the traditional modular filter. A difference equation is given by calculating the output sample of modules p1 … pn in the modified modular digital filter. The performance of the modular filters was compared with the performance of positional non-recursive filters implemented on a digital signal processor. An example of the estimation of the hardware costs is shown to be required for implementing a modular digital filter with a modified structure. This paper substantiates the expediency of applying the natural redundancy of finite field algebra codes on the example of the possibility to reduce hardware costs by a factor of two. It is demonstrated that the accuracy of data processing in the modular digital filter is higher than the accuracy achieved with the implementation of filters on digital processors. The accuracy advantage of the proposed approach is shown experimentally by the construction of the frequency response of the non-recursive low-pass filters.

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4

Deng, Tian-Bo. "Generalized Stability-Triangle for Guaranteeing the Stability-Margin of the Second-Order Digital Filter." Journal of Circuits, Systems and Computers 25, no. 08 (May 17, 2016): 1650094. http://dx.doi.org/10.1142/s0218126616500948.

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In the design of recursive digital filters, the stability of the recursive digital filters must be guaranteed. Furthermore, it is desirable to add a certain amount of margin to the stability so as to avoid the violation of stability due to some uncertain perturbations of the filter coefficients. This paper extends the well-known stability-triangle of the second-order digital filter into more general cases, which results in dented stability-triangles and generalized stability-triangle. The generalized stability-triangle can be viewed as a special case of the dented stability-triangles if the two upper bounds on the radii of the two poles are the same, which is a generalized version of the existing conventional stability-triangle and can guarantee the radii of the two poles of the second-order recursive digital filter below some prescribed upper bound. That is, it is able to provide a prescribed stability-margin in terms of the upper bound of the pole radii. As a result, the generalized stability-triangle increases the flexibility for guaranteeing a prescribed stability-margin. Since the generalized stability-triangle is parameterized by using the upper bound of pole radii, i.e., the stability-margin is parameterized as a function of the upper bound, the proposed generalized stability-triangle facilitates the stability-margin guarantee in the design of the second-order as well as high-order recursive digital filters.

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5

Çetin, A. Enis, and Rashid Ansari. "Digital interpolation beamforming using recursive filters." Journal of the Acoustical Society of America 85, no. 1 (January 1989): 493–95. http://dx.doi.org/10.1121/1.397701.

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6

NIKOLIC, SASA V., and VIDOSAV S. STOJANOVIC. "Transitional Butterworth-Chebyshev recursive digital filters." International Journal of Electronics 80, no. 1 (January 1996): 13–20. http://dx.doi.org/10.1080/002072196137552.

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7

Murthy, H., Daka Reddy, and P. Reddy. "Stabilization of Multidimensional Recursive Digital Filters." IEEE Transactions on Geoscience and Remote Sensing GE-23, no. 2 (March 1985): 158–63. http://dx.doi.org/10.1109/tgrs.1985.289413.

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8

Stancic, Goran, and Sasa Nikolic. "Design of narrow stopband recursive digital filter." Facta universitatis - series: Electronics and Energetics 24, no. 1 (2011): 119–30. http://dx.doi.org/10.2298/fuee1101119s.

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The procedure for design of narrow stopband recursive digital filter realized through parallel connections of two allpass sub-filters is described in this paper. This solution also allows realization of complementary filter, using only one additional adder, and exhibit low sensitivity on coefficients quantization. The method is based on phase approximation of allpass sub-filter. The procedure is very efficient and solution can be obtained within only a few iterations even for large filter order n. Every stopband provides two more equations, one at notch frequency and the other at passband boundary. It is not possible to control attenuation at both passband boundaries, but described procedure provides that achieved attenuations are less or equal to prescribed values. Using this algorithm full control of passband edges is obtained comparing with existing methods where it is not possible.

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9

Deng, Tian-Bo. "Stability-Guaranteed Two-Phase Design of Odd-Order Variable-Magnitude Digital Filters." Journal of Circuits, Systems and Computers 26, no. 02 (November 3, 2016): 1750033. http://dx.doi.org/10.1142/s0218126617500335.

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Guaranteeing the stability is one of the most critical issues in designing a variable recursive digital filter. In this paper, we first present an odd-order recursive variable model (transfer function) that is used for designing an odd-order variable-magnitude (VM) digital filter, and then we replace the original coefficients of the denominator of the odd-order transfer function with a set of new parameters. These new parameters can ensure that they can take arbitrary values without incurring instability of the designed odd-order VM filter. To make the VM filter coefficients variable, we find all the VM filter coefficients as polynomial functions of the tuning parameter, which includes two phases. The first phase designs a set of recursive digital filters with fixed coefficients (constant filters), and the second phase utilizes a curve-fitting scheme to represent each coefficient as a polynomial function. As a result, the VM filter coefficients become variable, and the proposed parameter-substitution-based denominator coefficients ensure the filter stability. This is the most important contribution of the parameter-substitution-based design scheme. This paper uses the fifth-order demonstrative example to verify the stability guarantee as well as the design accuracy of the obtained the fifth-order VM filter.

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10

Feng, Z., and R. Unbehauen. "Synthesis of recursive digital filters by state feedback of non-recursive cascaded lattice filters." International Journal of Circuit Theory and Applications 14, no. 2 (April 1986): 147–52. http://dx.doi.org/10.1002/cta.4490140204.

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11

ZABALAWI, I. H., and B. Z. KAHHALEH. "Phase (delay) equalization of recursive digital filters." International Journal of Electronics 66, no. 1 (January 1989): 121–33. http://dx.doi.org/10.1080/00207218908925368.

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12

Ferrari, L. A., P. V. Sankar, S. Shinnaka, and J. Sklansky. "Recursive Algorithms for Implementing Digital Image Filters." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9, no. 3 (May 1987): 461–66. http://dx.doi.org/10.1109/tpami.1987.4767929.

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13

Soderstrand, M. A., and A. E. de la Serna. "Minimum denominator-multiplier pipelined recursive digital filters." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 42, no. 10 (1995): 666–72. http://dx.doi.org/10.1109/82.471395.

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14

Kaneko, M. "Alternative implementation of systolic recursive digital filters." Electronics Letters 25, no. 15 (1989): 982. http://dx.doi.org/10.1049/el:19890657.

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15

YEH, KUO-HSIEN, HUNG-CHING LU, and JIE-CHERNG LIU. "On approximated linear-phase recursive digital filters." International Journal of Electronics 78, no. 3 (March 1995): 493–500. http://dx.doi.org/10.1080/00207219508926180.

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16

Fettweis, Alfred. "On assessing robustness of recursive digital filters." European Transactions on Telecommunications 1, no. 2 (March 1990): 103–9. http://dx.doi.org/10.1002/ett.4460010205.

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17

Stosic, Biljana. "Coefficient quantization effects on new filters based on Chebyshev fourth-kind polynomials." Facta universitatis - series: Electronics and Energetics 34, no. 2 (2021): 291–305. http://dx.doi.org/10.2298/fuee2102291s.

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The aim of this paper is to construct non-recursive filters, extensively used type of digital filters in digital signal processing applications, based on Chebyshev orthogonal polynomials. The paper proposes the use of the fourth-kind Chebyshev polynomials as functions in generating new filters. In this kind, low-pass filters with linear phase responses are obtained. Comprenhansive study of the frequency response characteristics of the generated filter functions is presented. The effects of coefficient quantization as one type of quantization that influences a filter characteristic are investigated here also. The quantized-coefficient errors are considered based on the number of bits and the implementation algorithms.

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18

Kennedy, Hugh L. "Digital Filter Designs for Recursive Frequency Analysis." Journal of Circuits, Systems and Computers 25, no. 02 (December 23, 2015): 1630001. http://dx.doi.org/10.1142/s0218126616300014.

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Digital filters for recursively computing the discrete Fourier transform (DFT) and estimating the frequency spectrum of sampled signals are examined, with an emphasis on magnitude-response and numerical stability. In this tutorial-style treatment, existing recursive techniques are reviewed, explained and compared within a coherent framework; some fresh insights are provided and new enhancements/modifications are proposed. It is shown that the replacement of resonators by (non-recursive) modulators in sliding DFT (SDFT) analyzers with either a finite impulse response (FIR), or an infinite impulse response (IIR), does improve performance somewhat; however, stability is not guaranteed as the cancellation of marginally stable poles by zeros is still involved. The FIR deadbeat observer is shown to be more reliable than the SDFT methods, an IIR variant is presented, and ways of fine-tuning its response are discussed. A novel technique for stabilizing IIR SDFT analyzers with a fading memory, so that all poles are inside the unit circle, is also derived. Slepian and sum-of-cosine windows are adapted to improve the frequency responses for the various FIR and IIR DFT methods.

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19

S. Khanande, Sachin, and S. J. Honade. "Design and Implementation of Recursive Least Square Adaptive Filter Using Block DCD approach." International Journal of Reconfigurable and Embedded Systems (IJRES) 4, no. 3 (November 1, 2015): 209. http://dx.doi.org/10.11591/ijres.v4.i3.pp209-212.

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<p>Due to the explosive growth of multimedia application and tremendous demands in Very Large Scale Integrated (VLSI), there is a need of high speed and low power digital filters for digital signal processing applications. In Digital Signal Processing (DSP) systems, Finite Impulse Response (FIR) filters are one of the most common components which is used, by convolving the input data samples with the desired unit sample response of the filter. The proposed work deals with the design and implementation of RLS adaptive filter using block DCD approach. The evaluation of speed, area and power for proposed work will be done. Also, the comparison of the proposed design with the existing will be carried out for various input combinations.</p>

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20

Zhong, Meisu, Yongsheng Yang, Yamin Zhou, M. Octavian Postolache, M. Chandrasekar, G. Venkat Babu, C. Manikandan, V. S. Balaji, S. Saravanan, and V. Elamaran. "Advanced Digital Signal Processing Techniques on the Classification of the Heart Sound Signals." Journal of Medical Imaging and Health Informatics 10, no. 9 (August 1, 2020): 2010–15. http://dx.doi.org/10.1166/jmihi.2020.3127.

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Speech processing subject primarily depends on the digital signal processing (DSP) methods, such as convolution, discrete Fourier transform (DFT), fast Fourier transforms (FFT), finite impulse response (FIR) and infinite impulse response (IIR) filters, FFT recursive and non-recursive digital filters, FFT processing, random signal theory, adaptive filters, upsampling and downsampling, etc. Recursive and non-recursive digital filters are primarily deployed to absorb the signal of interest signals and to block the unwanted signals (noise). Broadly, low-pass, high-pass, band-pass, and band-stop filters are implemented for filtering functions. In frequent, the DSP theories can be used for further biomedical engineering domains like biomedical imaging (MRI, ultrasound, CT, X-ray, PET) and genetic signal analysis-cum-processing too. In this article, the experiments such as voiced/unvoiced detection, formants estimation using FFT and spectrograms, pitch estimation and tracking and yes/no sound classification are used. Also, the analysis of normal/abnormal heart sound signals using simple energy computation and the zero-crossing rate and their results are obtained. For the entire study, the Matlab R2018a tool is used to obtain the simulation results. At last, the criticism, feedbacks, comments, reactions from the student are detailed for the exceptional development of the course.

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21

Raghuramireddy, D., and Rolf Unbehauen. "On the Stabilization of Multidimensional Recursive Digital Filters." IEEE Transactions on Geoscience and Remote Sensing GE-25, no. 4 (July 1987): 521–23. http://dx.doi.org/10.1109/tgrs.1987.289724.

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22

SID-AHMED, M. A. "Hardware architectures for two-dimensional recursive digital filters." International Journal of Electronics 67, no. 1 (July 1989): 87–99. http://dx.doi.org/10.1080/00207218908921059.

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23

Kyung Hi Chang and W. G. Bliss. "Limit cycle behavior of pipelined recursive digital filters." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 41, no. 5 (May 1994): 351–55. http://dx.doi.org/10.1109/82.287006.

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24

Kwan, H. K., and Y. C. Lui. "Lattice implementation of two-dimensional recursive digital filters." IEEE Transactions on Circuits and Systems 36, no. 3 (March 1989): 383–86. http://dx.doi.org/10.1109/31.17584.

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25

Prasad, K. S., and C. Eswaran. "Limit-cycle free complex biquad recursive digital filters." IEEE Transactions on Circuits and Systems 36, no. 2 (1989): 280–85. http://dx.doi.org/10.1109/31.20206.

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26

Peceli, G., and B. Feher. "Digital filters based on recursive Walsh-Hadamard transformation." IEEE Transactions on Circuits and Systems 37, no. 1 (1990): 150–52. http://dx.doi.org/10.1109/31.45706.

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27

SOLTIS, J. J., and M. A. SID-AHMED. "Direct design of Chebyshev-type recursive digital filters." International Journal of Electronics 70, no. 2 (February 1991): 413–19. http://dx.doi.org/10.1080/00207219108921289.

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28

Liu, Yin, and Keshab K. Parhi. "Architectures for Recursive Digital Filters Using Stochastic Computing." IEEE Transactions on Signal Processing 64, no. 14 (July 15, 2016): 3705–18. http://dx.doi.org/10.1109/tsp.2016.2552513.

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29

Mitra, Sanjit K., Yrjö Neuvo, and Hannu Roivainen. "Design of recursive digital filters with variable characteristics." International Journal of Circuit Theory and Applications 18, no. 2 (March 1990): 107–19. http://dx.doi.org/10.1002/cta.4490180202.

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30

Werter, Michael J. "Recursive digital filters which are free from subharmonics." International Journal of Circuit Theory and Applications 19, no. 4 (July 1991): 365–73. http://dx.doi.org/10.1002/cta.4490190404.

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31

Ferber, R. G. "Recursive deconvolution filters for seismograph systems." Bulletin of the Seismological Society of America 79, no. 5 (October 1, 1989): 1629–41. http://dx.doi.org/10.1785/bssa0790051629.

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Abstract A time-domain solution of the deconvolution problem for digital recordings from seismograph systems is given. Compensation for the signal distortion caused by the seismograph can be done by numerical data processing of the recorded seismograms using recursive filters which are designed from the analog transfer function using the bilinear z-transform. An application of the method is worked out for the seismograph system of the Central Seismological Observatory Gräfenberg, F. R. Germany.

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32

Dehghani, M. J., R. Aravind, and K. M. M. Prabhu. "Design ofM-Channel IIR Uniform DFT Filter Banks Using Recursive Digital Filters." ETRI Journal 25, no. 5 (October 14, 2003): 345–55. http://dx.doi.org/10.4218/etrij.03.0102.0501.

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33

Sukhotin, V. V., A. S. Tikhtenko, A. V. Zhgun, and V. A. Sidorin. "Digital filter for device of determining coordinates of radio signal source in satellite telecommunications systems." Spacecrafts & Technologies 4, no. 4 (December 4, 2020): 226–32. http://dx.doi.org/10.26732/j.st.2020.4.05.

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Work is devoted research of signal power dependence that will be used in the measurements and noise components of the signal from the digital filter type, filter parameters for various signal-to-noise ratios. The substantiation of the relevance of research is given, which consists in finding the possibility of using digital filters for a device for determining the coordinates of a radio signal source in satellite telecommunications, providing a minimum error in measuring the phase difference. The structure of the system for determining the coordinates of the radio signal source is given. The developed computer model and its description are presented. The researchers are considered for digital recursive band-pass filters of the Butterworth, Chebyshev type 1, Chebyshev type 2, Zolotarev-Cauer type, which are specified using a discrete linear system. Appropriate conclusions are drawn about the applicability of digital filters in the device for determining coordinates. The types of filters are determined and requirements are presented to ensure the minimum error in measuring the phase difference.

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34

Ramnarayan, R., and F. Taylor. "On large moduli residue number system recursive digital filters." IEEE Transactions on Circuits and Systems 32, no. 4 (April 1985): 349–59. http://dx.doi.org/10.1109/tcs.1985.1085716.

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35

TAM, Y. H., and P. C. CHING. "Sequential adaptation of recursive digital filters in cascade form." International Journal of Electronics 61, no. 4 (October 1986): 441–48. http://dx.doi.org/10.1080/00207218608920886.

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36

Hinamoto, T., S. Karino, N. Kuroda, and T. Kuma. "Error spectrum shaping in two-dimensional recursive digital filters." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 46, no. 10 (1999): 1203–15. http://dx.doi.org/10.1109/81.795833.

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37

Kyung Hi Chang and W. G. Bliss. "Finite word-length effects of pipelined recursive digital filters." IEEE Transactions on Signal Processing 42, no. 8 (1994): 1983–95. http://dx.doi.org/10.1109/78.301837.

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38

Raymond, D. M., and M. M. Fahmy. "Spatial-domain design of two-dimensional recursive digital filters." IEEE Transactions on Circuits and Systems 36, no. 6 (June 1989): 901–5. http://dx.doi.org/10.1109/31.90414.

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39

Deng, Tian-Bo. "New method for designing stable recursive variable digital filters." Signal Processing 64, no. 2 (January 1998): 197–207. http://dx.doi.org/10.1016/s0165-1684(97)00188-6.

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40

Goodall, RM, and MG Goodall. "The efficiency of computational procedures for recursive digital filters." Microprocessors and Microsystems 17, no. 10 (January 1993): 587–96. http://dx.doi.org/10.1016/s0141-9331(05)80003-1.

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41

IBRAHIM, F., A. HANAFY, and S. GHONIEMY. "STATISTICAL DESIGN OF RECURSIVE DIGITAL FILTERS USING ITERATIVE TECHNIQUES." International Conference on Aerospace Sciences and Aviation Technology 1, CONFERENCE (May 1, 1985): 1–17. http://dx.doi.org/10.21608/asat.1985.26583.

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42

Kwan, H. K. "New form of delayed N-path recursive digital filters." Electronics Letters 29, no. 9 (1993): 736. http://dx.doi.org/10.1049/el:19930494.

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43

Stojanović, V. S., and S. V. Nikolić. "Direct design of transitional Butterworth-Chebyshev recursive digital filters." Electronics Letters 29, no. 3 (1993): 286. http://dx.doi.org/10.1049/el:19930195.

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44

Domanski, Marek, and Alfred Fettweis. "Pseudopassive two-dimensional recursive digital filters for image processing." International Journal of Circuit Theory and Applications 17, no. 1 (January 1989): 103–14. http://dx.doi.org/10.1002/cta.4490170110.

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45

Hinamoto, Takao, Akihiro Nakatsuji, and Sadao Maekawa. "Design of three-dimensional recursive digital filters with symmetry." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 72, no. 8 (1989): 22–31. http://dx.doi.org/10.1002/ecjc.4430720803.

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46

Eckhardt, K. "How to construct recursive digital filters for baseflow separation." Hydrological Processes 19, no. 2 (2005): 507–15. http://dx.doi.org/10.1002/hyp.5675.

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47

Deng, Tian-Bo. "The Lp-Norm-Minimization Design of Stable Variable-Bandwidth Digital Filters." Journal of Circuits, Systems and Computers 27, no. 07 (March 26, 2018): 1850102. http://dx.doi.org/10.1142/s0218126618501025.

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This paper proposes a two-step strategy for designing a variable-bandwidth (VBW) digital filter through minimizing the [Formula: see text]-norm of the magnitude-response error. This [Formula: see text]-norm design can be regarded as a generalized version of the existing weighted-least-squares (WLS) design. Equivalently, the WLS design is a special case of the [Formula: see text]-norm-minimization design for [Formula: see text]. This paper discusses the design of the recursive VBW filter with the transfer function whose denominator is expressed as the product of the second-order sections. As long as all the second-order sections are stable, the recursive VBW filter is also stable. To ensure that the designed recursive VBW filter is stable, we adopt the coefficient-conversion strategy that constrains all the denominator-parameter pairs of the second-order sections within the stability triangle. This paper also proposes a novel conversion function for performing the coefficient conversion. As a consequence, the designed VBW filter is definitely stable. A bandpass VBW filter is designed for showing the feasibility of the [Formula: see text]-norm-minimization-based design and verifying the stability guarantee.

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48

Yuan-Hau Yang and Ju-Hong Lee. "Design Of 2-D Recursive Digital Filters Using Nonsymmetric Half-Plane Allpass Filters." IEEE Transactions on Signal Processing 55, no. 12 (December 2007): 5604–18. http://dx.doi.org/10.1109/tsp.2007.900172.

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49

Novosyadly, S. P., and R. V. Valter. "Computer Modeling of Second-Order Recursive Digital Filters of the Automated Design Signaling System." Фізика і хімія твердого тіла 18, no. 4 (December 27, 2017): 484–86. http://dx.doi.org/10.15330/pcss.18.4.486.

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In the article the analytical method of modeling software recursive digital filters of the second order with zeros on the circle of the single radius is presented. The corresponding algorithm of scaling of this composition of filters for signal CAD is developed.

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50

Stojanovic, Vidosav, and Sinisa Minic. "Finite impulse response digital filters with integer multipliers." Serbian Journal of Electrical Engineering 1, no. 1 (2003): 131–41. http://dx.doi.org/10.2298/sjee0301131s.

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In this paper, a family of non-recursive digital filters will be described in which all multipliers are small integers. It is shown that this practical advantage is only available if some rather severe restrictions on the locations of z-plane poles and zeros are accepted. These restrictions have the further advantage that all filters of the family display pure linear-phase characteristics, imposing a pure transmission delay on all frequency components of an input signal.

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