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Journal articles on the topic 'Kaiser Window function'

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Published: 4 June 2021
Last updated: 15 February 2022

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1

Bai, Le Qiang, and Xue Wei Zhang. "Improved Window Function in the Application of MFCC Feature Parameter Extraction." Applied Mechanics and Materials 556-562 (May 2014): 3703–6. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3703.

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In view of spectrum leakage and the contradictory problem of spectrum accuracy of main lobe and reducing spectrum leakage, MFCC algorithm based on improved window function is proposed. Improved window function is based on the mathematical analysis of Kaiser window, and under the condition of finite sampling points minuses weighted impact function where is at the frequencies that side lobe peaks of correspond to. The amplitude of improved window compared with Kaiser window is smaller, and main lobe width is the same, solving the conflicting problem of main lobe width and side lobe amplitude and reducing spectrum leakage. The experimental results show that speech recognition rate of MFCC feature parameter extraction algorithm based on improved window function is better than Kaiser window and Hamming window.
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2

Samad, Md Abdus, Jia Uddin, and Md Razu Ahmed. "FIR Filter Design Using Modified Lanczos Window Function." Advanced Materials Research 566 (September 2012): 49–56. http://dx.doi.org/10.4028/www.scientific.net/amr.566.49.

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Attenuated side lobe peak in the range of around ~-45dB is required in many applications of signal processing and measurements. However, the problem is usual window based FIR filter design lies in its side lobes amplitudes that are higher than the requirement of application. We propose a modified Lanczos window function by heuristic by examining the Lanczos window, which has better performance like equiripple, minimum side lobe compared to the several commonly used windows. The proposed window has slightly larger main lobe width of the commonly used Hamming window, while featuring 5.1~18.5 dB smaller side lobe peak. The proposed modified Lanczos window maintains its maximum side lobe peak about -55.2~-51.9 dB compared to -39~-36.7 dB of Hamming window for M=10~14, while offering roughly equal main lobe width. Our simulated results also show significant performance upgrading of the proposed modified Lanczos window compared to the Kaiser, Gaussian, and Lanczos windows. The proposed modified Lanczos window also shows better performance than Dolph-Chebyshev window. Finally, the example of designed low pass FIR filter confirms the efficiency of the proposed modified Lanczos window.
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3

Kumar, Susheel, Munish Verma, Vijay K. Lamba, Avinash Kumar, and Sandeep Kumar. "COMPARATIVE ANALYSIS OF EXPONENTIAL WINDOW FUNCTION FOR DESIGNING OF FIR FILTERS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 2 (October 30, 2012): 329–34. http://dx.doi.org/10.24297/ijct.v3i2c.2895.

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Filters are very commonly found in everyday life and include examples such as water filters for water purification, mosquito nets that filter out bugs, bouncers at bars filtering the incoming guests according to age (and other criteria), and air filters found in air conditioners that we are sometimes a bit too lazy to change/clean periodically. Filters have two uses: signal separation and signal restoration. Signal separation is needed when a signal has been contaminated with interference, noise, or other signals. For example, imagine a device for measuring the electrical activity of a baby's heart (EKG) while still in the womb. The raw signal will likely be corrupted by the breathing and heartbeat of the mother. A filter might be used to separate these signals so that they can be individually analyzed. Signal restoration is used when a signal has been distorted in some way. For example, an audio recording made with poor equipment may be filtered to better represent the sound as it actually occurred [1, 2]. The main goal of this work is to study the exponential  window function and analyze a digital low pass FIR filter using the same in MATLAB. Properties of window functions is studied and frequeny domain responses of window functions is obtained. Then FIR filter is designed using widow design method and its characteristics have also been studied in frequency domain. The performace comparison between LPFs designed using other well known windows like Kaiser, Exponential, Cosh and modified kaiser window is done and it has been intuitively shown that for a given order and transition width, the filter designed using Exponential window provides the worse minimum stop band attenuation but better far end attenuation than filter designed by well known Kaiser Window.
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4

Mbachu, C. B. "Height Adjustable Triangular (HAT) Window Function for Impulse Response Modification of Signal Processing Systems." European Journal of Engineering Research and Science 5, no. 3 (March 27, 2020): 358–66. http://dx.doi.org/10.24018/ejers.2020.5.3.1442.

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A widow function, in signal processing and statistics, is a mathematical function that has zero values outside its chosen interval or limit of sequence, normally symmetric around the middle of the interval. Usually the middle of the window is either maximum or near maximum and tappers smoothly as it moves away to the sides. When another function or sequence of data is mathematically multiplied by the window function it forces the product to assume its nature of zero-value outside the interval and tapering from middle to the sides. Windows are finite functions and their main function is to modify an infinite impulse response sequence so as to make it finite within its chosen interval in system design. Several windows are in existence and they include Hamming, rectangular, Han, Kaiser, Triangular, Blackman, Sine, Blackman-Harris, Gaussian, Doph-Chebyshev and Lanczos, windows. Others are Parzen, Nuttall, flat top, Turkey, windows and many more. The window to apply in the design depends on the characteristics of the signal to be processed, types of system to be implemented and quality of output desired. In this paper, a new window called Height Adjustable triangular (HAT) window function is developed and added to the list of windows for signal processing system designs. The effectiveness of the window is tested by examining its characteristics. The adjustment parameter varies the amplitude or height of the window. Result shows that it is stable and linear.
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5

Mbachu, C. B. "Height Adjustable Sine (HAS) Window Function for Impulse Response Modification of Signal Processing Systems." European Journal of Engineering Research and Science 5, no. 3 (March 27, 2020): 367–74. http://dx.doi.org/10.24018/ejers.2020.5.3.1443.

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A widow function, in signal processing and statistics, is a mathematical function that has zero values outside its chosen interval or limit of sequence, normally symmetric around the middle of the interval. Usually the middle of the window is either maximum or near maximum and tappers smoothly as it moves away to the sides. When another function or sequence of data is mathematically multiplied by the window function the product assumes the appearance and characteristics of the window function. That is, the product is also zero-valued outside the interval and tapering from middle to the sides. The impulse response data of some systems used in signal processing truncate suddenly when being designed. Window functions convert the sudden truncation of these impulse response data to gradual truncation. Without this gradual truncation the processing system will degrade the integrity of complex signals when they are applied to them; the degradation appears in form of signal distortions. Windows are used to weight these impulse response data so as to reduce the degradation and distortions. Several windows are in existence and they include Hamming, rectangular, Han, Kaiser, Triangular, Blackman, Sine, Blackman-Harris, Gaussian, Doph-Chebyshev and Lanczos, windows. Others are Parzen, Nuttall, flat top, and Turkey, windows and others. The characteristics of the signal to be processed, types of systems to be implemented and quality of outputs desired determine the best window to weight any particular impulse response data. In this work a new window referred to as Height Adjustable Sine (HAS) window is developed to join the list of the existing windows. The quality of the window is tested by analysing its amplitude, magnitude and phase responses in frequency domain. Result shows that the window is stable and linear.
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6

Serreqi, Alessio, and Melvin B. Comisarow. "Frequency Interpolation of Discrete, Apodized, Magnitude Lineshapes." Applied Spectroscopy 41, no. 2 (February 1987): 288–95. http://dx.doi.org/10.1366/000370287774986985.

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All Fourier spectrometers have a residual error in frequency measurement arising from the discrete nature of the experimental Fourier spectrum. This residual error is a systematic error which has a maximum value of half the channel spacing in the discrete spectrum. This systematic error can be reduced by interpolation of values on the discrete lineshape. The residual error remaining after interpolation has not yet been determined for apodized Fourier spectra. In this work, a systematic study of frequency interpolation of discrete, apodized, magnitude-mode lineshapes is reported. Absolute maximum frequency errors as a percentage of the discrete channel spacing are reported in graphical and tabular form as a function of the type of apodization window, the type of function used for three-point frequency interpolation, the number of zero-fillings, and ( T/ r), the ratio of the acquisition time to the relaxation time of the time domain signal. The results allow independent choice of the window function most appropriate for the dynamic range of the spectrum and the interpolating function/zero-filling level which optimizes the accuracy of frequency measurement. General observations are (1) that the interpolation error is reduced by an order of magnitude for each additional level of zero-filling and (2) that the interpolation error is essentially independent of T/r. For the Hanning window, the Hamming window, the three-term Blackman-Harris window, and the Kaiser-Bessel window, the parabola is the interpolating function of choice.
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7

Lee, Judy P., and Melvin B. Comisarow. "Advantageous Apodization Functions for Magnitude-Mode Fourier Transform Spectroscopy." Applied Spectroscopy 41, no. 1 (January 1987): 93–98. http://dx.doi.org/10.1366/0003702874868016.

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A systematic examination of the efficacy of window functions for reducing the spectral skirt of magnitude-mode Fourier transform spectra is reported. The efficacy is examined for the general case of a damped time-domain signal, with specific cases ranging from undamped to essentially completely damped signals. The choice of the optimal window is dependent upon the required dynamic range and the amount of damping in the time-domain data. For a dynamic range of less than 100:1 and moderate damping, the Hamming window is the window of choice. For larger dynamic ranges or greater damping, the 3-term Blackman-Harris window and the Kaiser-Bessel window are the windows of choice. The 3-term Blackman-Harris window is preferred for a dynamic range of 1,000:1 and the Kaiser-Bessel window is preferred for a dynamic range of 10,000:1. The sensitivity (signal-to-noise ratio) reduction for windows is reported for a damping range from zero to essentially complete damping. All windows examined have the same sensitivity reduction within 25%.
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8

Burazer, Milenko, Milinko Grbović, and Vicko Žitko. "Magnetic data processing for hydrocarbon exploration in the Pannonian Basin, Yugoslavia." GEOPHYSICS 66, no. 6 (November 2001): 1669–79. http://dx.doi.org/10.1190/1.1486769.

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Because magnetic minerals may directly indicate the presence of oil and gas deposits, magnetic methods are applied to hydrocarbon exploration in oil‐bearing sedimentary basins. The basic problem in applying these methods is the isolation of weak magnetic anomalies sourced by low concentrations of the magnetic minerals present. These weak anomalies are often masked by much stronger magnetic anomalies caused by underlying magnetic rocks and/or by rocks in the basin sediments. Weak local anomalies can efficiently be isolated by applying selective 1‐D digital frequency filters. The method of filtering has been checked by data obtained using simple models of magnetic sources and using a model representative of the local geology in our study area in the southern Pannonian basin, Yugoslavia, The magnetic field frequency content was analyzed by applying the power spectral density estimation, using the maximum entropy method. The digital filters were designed using the window function method. The best results were obtained by the Kaiser window function for the chosen range of the band‐pass filter. In our study area, me isolated local magnetic anomalies have amplitudes of ±10 nT and trend in an east‐west direction parallel to the predominant structural grain. These anomalies correlate very well with the known oil and gas fields. As an example, filter processing of magnetic anomaly data, combined with the 3‐D seismic data gained in the filtered magnetic field, correlate well with one of the oil fields. The next phase of the project will concentrate on the anomalies occurring outside the established gas and oil fields.
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9

Lee, Judy P., and Melvin B. Comisarow. "Advantageous Apodization Functions for Absorption-Mode Fourier Transform Spectroscopy." Applied Spectroscopy 43, no. 4 (May 1989): 599–604. http://dx.doi.org/10.1366/0003702894202517.

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A systematic examination of the efficacy of window functions for reduction of the spectral skirt of absorption-mode Fourier transform spectra is reported. A window which has its maximum at the beginning and its minimum at the end of the time-domain data gives a satisfactory absorption line shape. In contrast, a symmetrical window with its minima at the ends and its maximum in the middle gives an unsatisfactory absorption line shape. These symmetrical windows are satisfactory for apodization of magnitude spectra. The efficacy for reducing the spectral skirt is examined for the general case of a damped time-domain signal, with specific cases ranging from undamped to essentially completely damped signals. The choice of the window is dependent upon the required dynamic range and the amount of damping in the time-domain data. For a dynamic range of 100:1, the Noest-Kort absorption window and the Norton-Beer F3 windows are the windows of choice. For a dynamic range of 1000:1, either the Filler E0.20 window or the Kaiser-Bessel window can be recommended. The sensitivity change accruing from windowing is reported for all windows examined, for a damping range from zero to essentially complete damping
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10

Cong, Weijian, Jian Yang, Yue Liu, and Yongtian Wang. "Fast and Automatic Ultrasound Simulation from CT Images." Computational and Mathematical Methods in Medicine 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/327613.

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Ultrasound is currently widely used in clinical diagnosis because of its fast and safe imaging principles. As the anatomical structures present in an ultrasound image are not as clear as CT or MRI. Physicians usually need advance clinical knowledge and experience to distinguish diseased tissues. Fast simulation of ultrasound provides a cost-effective way for the training and correlation of ultrasound and the anatomic structures. In this paper, a novel method is proposed for fast simulation of ultrasound from a CT image. A multiscale method is developed to enhance tubular structures so as to simulate the blood flow. The acoustic response of common tissues is generated by weighted integration of adjacent regions on the ultrasound propagation path in the CT image, from which parameters, including attenuation, reflection, scattering, and noise, are estimated simultaneously. The thin-plate spline interpolation method is employed to transform the simulation image between polar and rectangular coordinate systems. The Kaiser window function is utilized to produce integration and radial blurring effects of multiple transducer elements. Experimental results show that the developed method is very fast and effective, allowing realistic ultrasound to be fast generated. Given that the developed method is fully automatic, it can be utilized for ultrasound guided navigation in clinical practice and for training purpose.
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11

Pachauri, Rahul, Rajiv Saxena, and Sanjeev N. Sharma. "Studies on Z-Window Based FIR Filters." ISRN Signal Processing 2013 (September 1, 2013): 1–8. http://dx.doi.org/10.1155/2013/148646.

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As per classification of the window functions, the Z-windows are grouped in the category of steerable side-lobe dip (SSLD) windows. In this work, the application of these windows for the design of FIR filters with improved filter parameters has been explored. The numbers of dips with their respective positions in the side-lobe region have been compositely used to tailor the window shape. Filter design relationships have been established and included in this paper. Simultaneously, an application of these Z-window based FIR filters in designing two-channel quadrature mirror filter (QMF) bank has been presented. Better values of reconstruction and aliasing errors have been achieved in contrast to the Kaiser window based QMF bank.
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12

Nilchian, Masih, John Paul Ward, Cedric Vonesch, and Michael Unser. "Optimized Kaiser–Bessel Window Functions for Computed Tomography." IEEE Transactions on Image Processing 24, no. 11 (November 2015): 3826–33. http://dx.doi.org/10.1109/tip.2015.2451955.

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13

SUMATHI, M., MURALI C. KRISHNA, and R. MURUGESAN. "GA-BASED OPTIMIZATION OF TAPERING WINDOWS FOR ARTIFACT REDUCTION IN FOURIER ELECTRON MAGNETIC RESONANCE IMAGES." International Journal of Computational Intelligence and Applications 08, no. 02 (June 2009): 111–25. http://dx.doi.org/10.1142/s1469026809002503.

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Optimization of tapering windows for artifact reduction in two-dimensional (2D) Fourier electron magnetic resonance (EMR) tomography using genetic algorithm (GA) is presented. EMR imaging (EMRI) is a fast emerging functional imaging modality for studying free radicals in biological systems. EMRI by single point imaging (SPI) modality is a Fourier imaging technique. The bioclearance of the imaging agent as well as the need to minimize the radio frequency power deposition on the live animals, dictate reduced k-space sampling. This leads to ringing (Gibbs) artifacts in both directions of the 2D image, because, unlike the conventional MRI, SPI is phase encoding in both directions. To dampen the high-frequency components, data tapering windows are multiplicatively applied to provide tolerable blurred resultant image with reduced Gibbs ringing. To find a compromise between blur and ringing artifact, in this paper a method of optimizing the window functions by using GA is proposed. Our experiments suggest GA-based Kaiser window shows better performance by visual as well as quantitative evaluation.
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14

Lewitt, Robert M. "Multidimensional digital image representations using generalized Kaiser–Bessel window functions." Journal of the Optical Society of America A 7, no. 10 (October 1, 1990): 1834. http://dx.doi.org/10.1364/josaa.7.001834.

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15

Kanatov, Ivan, Dmitry Kaplun, Denis Butusov, Viacheslav Gulvanskii, and Aleksander Sinitca. "One Technique to Enhance the Resolution of Discrete Fourier Transform." Electronics 8, no. 3 (March 18, 2019): 330. http://dx.doi.org/10.3390/electronics8030330.

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Discrete Fourier transform (DFT) is a common analysis tool in digital signal processing. This transform is well studied and its shortcomings are known as well. Various window functions (e.g., Hanning, Blackman, Kaiser) are often used to reduce sidelobes and to spread the spectrum. In this paper, we introduce a transformation that allows removing the sidelobes of the Fourier transform and increasing the resolution of the DFT without changing the time sample. The proposed method is based on signal phase analysis. We give the comparison of the proposed approach with known methods based on window functions. The advantages and disadvantages of the proposed technique are explicitly shown. We also give a set of examples illustrating the application of our technique in some practical applications, including engine vibration analysis and a short-range radar system.
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16

Goel, Navdeep, and Jatinder Singh. "Analysis of Kaiser and Gaussian Window Functions in the Fractional Fourier Transform Domain and Its Application." Iranian Journal of Science and Technology, Transactions of Electrical Engineering 43, no. 2 (July 13, 2018): 181–88. http://dx.doi.org/10.1007/s40998-018-0100-6.

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17

"Proposing a new variable window for better side lobe reduction." International Journal of Innovative Technology and Exploring Engineering 8, no. 9 (July 10, 2019): 1107–12. http://dx.doi.org/10.35940/ijitee.i7792.078919.

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Digital signal processing is most widely used to process the signal. In digital signal processing filters are used to remove some unwanted constituents from aspired signal. Windowing is a scheme of finite impulse response filters. Present paper proposes a new versatile window function. It has two variable parameters first one is window span N and another changeable parameter is r. when the value of variable parameter r increases width of major lobe of window also increases with better side lobe reduction and vice versa. Gaussian window and Kaiser window are the well-known variable windows. This paper shows that the proposed window has more desirable results in comparison of Gaussian and Kaiser window with low power loss and better side lobe reduction. To achieve minimum power loss peak side lobe level should have to minimum. Proposed window has low peak side lobe level (-17.681dB) in comparison of Gaussian (-11.836dB) and Kaiser window (-6.9704dB). Proposed work shows that the proposed window has finer spectral characteristic then Gaussian and Kaiser window. FIR filter formed by applying proposed window has narrow -3dB bandwidth (2π×0.320 rad/sample) corresponding to FIR filter formed by using Gaussian and Kaiser window. Ripple ratio of FIR filter plotted by applying proposed window (-144.321dB) is less corresponding to FIR filter delineated by using Gaussian and Kaiser which indicates that the proposed window will give better side lobe rejection and reduce the aliasing problem. In the biomedical field noise present in ECG signal can also reduce by using proposed window.
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18

Potts, Daniel, and Manfred Tasche. "Continuous window functions for NFFT." Advances in Computational Mathematics 47, no. 4 (June 30, 2021). http://dx.doi.org/10.1007/s10444-021-09873-8.

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AbstractIn this paper, we study the error behavior of the nonequispaced fast Fourier transform (NFFT). This approximate algorithm is mainly based on the convenient choice of a compactly supported window function. Here, we consider the continuous Kaiser–Bessel, continuous exp-type, sinh-type, and continuous cosh-type window functions with the same support and same shape parameter. We present novel explicit error estimates for NFFT with such a window function and derive rules for the optimal choice of the parameters involved in NFFT. The error constant of a window function depends mainly on the oversampling factor and the truncation parameter. For the considered continuous window functions, the error constants have an exponential decay with respect to the truncation parameter.
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19

"Antenna Array Weight Synthesis for Low Side Lobe Levels using Window Functions." International Journal of Engineering and Advanced Technology 9, no. 1 (October 30, 2019): 3074–81. http://dx.doi.org/10.35940/ijeat.a1664.109119.

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A lot of research is being carried out to reduce side lobe levels (SSLs) in the radiation pattern of antenna arrays. A number of novel optimization techniques have been developed over the years and adapted for this purpose. In this paper, a number of window functions are applied to suppress the maximum side lobe level (MSLL) in linear antenna arrays. The window functions Bartlett, Taylor, Hanning, Barthann, Hamming, Gaussian, Blackman, Chebyshev, Blackman-Harris and Kaiser are considered in the simulation. The optimized pattern for a 10 element linear antenna array and corresponding normalized window tappers for every window are presented. Finally the efficiency of all windows is compared in terms of their computed parameters.
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