Memristor-Based Approximation of Gaussian Filter
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Moving Average Filters
CHAPTER 15 Moving Average Filters The moving average is the most common filter in DSP, mainly because it is the easiest digital filter to understand and use. In spite of its simplicity, the moving average filter is optimal for a common task: reducing random noise while retaining a sharp step response. This makes it the premier filter for time domain encoded signals. However, the moving average is the worst filter for frequency domain encoded signals, with little ability to separate one band of frequencies from another. Relatives of the moving average filter include the Gaussian, Blackman, and multiple- pass moving average. These have slightly better performance in the frequency domain, at the expense of increased computation time. Implementation by Convolution As the name implies, the moving average filter operates by averaging a number of points from the input signal to produce each point in the output signal. In equation form, this is written: EQUATION 15-1 Equation of the moving average filter. In M &1 this equation, x[ ] is the input signal, y[ ] is ' 1 % y[i] j x [i j ] the output signal, and M is the number of M j'0 points used in the moving average. This equation only uses points on one side of the output sample being calculated. Where x[ ] is the input signal, y[ ] is the output signal, and M is the number of points in the average. For example, in a 5 point moving average filter, point 80 in the output signal is given by: x [80] % x [81] % x [82] % x [83] % x [84] y [80] ' 5 277 278 The Scientist and Engineer's Guide to Digital Signal Processing As an alternative, the group of points from the input signal can be chosen symmetrically around the output point: x[78] % x[79] % x[80] % x[81] % x[82] y[80] ' 5 This corresponds to changing the summation in Eq. -
Comparative Analysis of Gaussian Filter with Wavelet Denoising for Various Noises Present in Images
ISSN (Print) : 0974-6846 Indian Journal of Science and Technology, Vol 9(47), DOI: 10.17485/ijst/2016/v9i47/106843, December 2016 ISSN (Online) : 0974-5645 Comparative Analysis of Gaussian Filter with Wavelet Denoising for Various Noises Present in Images Amanjot Singh1,2* and Jagroop Singh3 1I.K.G. P.T.U., Jalandhar – 144603,Punjab, India; 2School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara - 144411, Punjab, India; [email protected] 3Department of Electronics and Communication Engineering, DAVIET, Jalandhar – 144008, Punjab, India; [email protected] Abstract Objectives: This paper is providing a comparative performance analysis of wavelet denoising with Gaussian filter applied variouson images noises contaminated usually present with various in images. noises. Wavelet Gaussian transform filter is ais basic used filter to convert used in the image images processing. to wavelet Its response domain. isBased varying on with its kernel sizes that have also been shown in analysis. Wavelet based de-noisingMethods/Analysis: is also one of the way of removing thresholding operations in wavelet domain noise could be removed from images. In this paper, image quality matrices like PSNR andFindings MSE have been compared for the various types of noises in images for different denoising methods. Moreover, the behavior of different methods for image denoising have been graphically shown in paper with MATLAB based simulations. : In the end wavelet based de-noising methods has been compared with Gaussian basedKeywords: filter. The Denoising, paper provides Gaussian a review Filter, MSE,of filters PSNR, and SNR, their Thresholding, denoising analysis Wavelet under Transform different noise conditions. 1. Introduction is of bell shaped. -
MPA15-16 a Baseband Pulse Shaping Filter for Gaussian Minimum Shift Keying
A BASEBAND PULSE SHAPING FILTER FOR GAUSSIAN MINIMUM SHIFT KEYING 1 2 3 3 N. Krishnapura , S. Pavan , C. Mathiazhagan ,B.Ramamurthi 1 Department of Electrical Engineering, Columbia University, New York, NY 10027, USA 2 Texas Instruments, Edison, NJ 08837, USA 3 Department of Electrical Engineering, Indian Institute of Technology, Chennai, 600036, India Email: [email protected] measurement results. ABSTRACT A quadrature mo dulation scheme to realize the Gaussian pulse shaping is used in digital commu- same function as Fig. 1 can be derived. In this pap er, nication systems like DECT, GSM, WLAN to min- we consider only the scheme shown in Fig. 1. imize the out of band sp ectral energy. The base- band rectangular pulse stream is passed through a 2. GAUSSIAN FREQUENCY SHIFT lter with a Gaussian impulse resp onse b efore fre- KEYING GFSK quency mo dulating the carrier. Traditionally this The output of the system shown in Fig. 1 can b e describ ed is done by storing the values of the pulse shap e by in a ROM and converting it to an analog wave- Z t form with a DAC followed by a smo othing lter. g d 1 y t = cos 2f t +2k c f This pap er explores a fully analog implementation 1 of an integrated Gaussian pulse shap er, which can where f is the unmo dulated carrier frequency, k is the c f result in a reduced power consumption and chip mo dulating index k =0:25 for Gaussian Minimum Shift f area. Keying|GMSK[1] and g denotes the convolution of the rectangular bit stream bt with values in f1; 1g 1. -
A Simplified Realization for the Gaussian Filter in Surface Metrology
In X. International Colloquium on Surfaces, Chemnitz (Germany), Jan. 31 - Feb. 02, 2000, M. Dietzsch, H. Trumpold, eds. (Shaker Verlag GmbH, Aachen, 2000), p. 133. A Simplified Realization for the Gaussian Filter in Surface Metrology Y. B. Yuan(1), T.V. Vorburger(2), J. F. Song(2), T. B. Renegar(2) 1 Guest Researcher, NIST; Harbin Institute of Technology (HIT), Harbin, China, 150001; 2 National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899 USA. Abstract A simplified realization for the Gaussian filter in surface metrology is presented in this paper. The sampling function sinu u is used for simplifying the Gaussian function. According to the central limit theorem, when n approaches infinity, the function (sinu u)n approaches the form of a Gaussian function. So designed, the Gaussian filter is easily realized with high accuracy, high efficiency and without phase distortion. The relationship between the Gaussian filtered mean line and the mid-point locus (or moving average) mean line is also discussed. Key Words: surface roughness, mean line, sampling function, Gaussian filter 1. Introduction The Gaussian filter has been recommended by ISO 11562-1996 and ASME B46-1995 standards for determining the mean line in surface metrology [1-2]. Its weighting function is given by 1 2 h(t) = e−π(t / αλc ) , (1) αλc where α = 0.4697 , t is the independent variable in the spatial domain, and λc is the cut-off wavelength of the filter (in the units of t). If we use x(t) to stand for the primary unfiltered profile, m(t) for the Gaussian filtered mean line, and r(t) for the roughness profile, then m(t) = x(t)∗ h(t) (2) and r(t) = x(t) − m(t), (3) where the * represents a convolution of two functions. -
Chapter 3 FILTERS
Chapter 3 FILTERS Most images are a®ected to some extent by noise, that is unexplained variation in data: disturbances in image intensity which are either uninterpretable or not of interest. Image analysis is often simpli¯ed if this noise can be ¯ltered out. In an analogous way ¯lters are used in chemistry to free liquids from suspended impurities by passing them through a layer of sand or charcoal. Engineers working in signal processing have extended the meaning of the term ¯lter to include operations which accentuate features of interest in data. Employing this broader de¯nition, image ¯lters may be used to emphasise edges | that is, boundaries between objects or parts of objects in images. Filters provide an aid to visual interpretation of images, and can also be used as a precursor to further digital processing, such as segmentation (chapter 4). Most of the methods considered in chapter 2 operated on each pixel separately. Filters change a pixel's value taking into account the values of neighbouring pixels too. They may either be applied directly to recorded images, such as those in chapter 1, or after transformation of pixel values as discussed in chapter 2. To take a simple example, Figs 3.1(b){(d) show the results of applying three ¯lters to the cashmere ¯bres image, which has been redisplayed in Fig 3.1(a). ² Fig 3.1(b) is a display of the output from a 5 £ 5 moving average ¯lter. Each pixel has been replaced by the average of pixel values in a 5 £ 5 square, or window centred on that pixel. -
Ballistic Electron Transport in Graphene Nanodevices and Billiards
Ballistic Electron Transport in Graphene Nanodevices and Billiards Dissertation (Cumulative Thesis) for the award of the degree Doctor rerum naturalium of the Georg-August-Universität Göttingen within the doctoral program Physics of Biological and Complex Systems of the Georg-August University School of Science submitted by George Datseris from Athens, Greece Göttingen 2019 Thesis advisory committee Prof. Dr. Theo Geisel Department of Nonlinear Dynamics Max Planck Institute for Dynamics and Self-Organization Prof. Dr. Stephan Herminghaus Department of Dynamics of Complex Fluids Max Planck Institute for Dynamics and Self-Organization Dr. Michael Wilczek Turbulence, Complex Flows & Active Matter Max Planck Institute for Dynamics and Self-Organization Examination board Prof. Dr. Theo Geisel (Reviewer) Department of Nonlinear Dynamics Max Planck Institute for Dynamics and Self-Organization Prof. Dr. Stephan Herminghaus (Second Reviewer) Department of Dynamics of Complex Fluids Max Planck Institute for Dynamics and Self-Organization Dr. Michael Wilczek Turbulence, Complex Flows & Active Matter Max Planck Institute for Dynamics and Self-Organization Prof. Dr. Ulrich Parlitz Biomedical Physics Max Planck Institute for Dynamics and Self-Organization Prof. Dr. Stefan Kehrein Condensed Matter Theory, Physics Department Georg-August-Universität Göttingen Prof. Dr. Jörg Enderlein Biophysics / Complex Systems, Physics Department Georg-August-Universität Göttingen Date of the oral examination: September 13th, 2019 Contents Abstract 3 1 Introduction 5 1.1 Thesis synopsis and outline.............................. 10 2 Fundamental Concepts 13 2.1 Nonlinear dynamics of antidot superlattices..................... 13 2.2 Lyapunov exponents in billiards............................ 20 2.3 Fundamental properties of graphene......................... 22 2.4 Why quantum?..................................... 31 2.5 Transport simulations and scattering wavefunctions................ -
Electronic Filters Design Tutorial - 3
Electronic filters design tutorial - 3 High pass, low pass and notch passive filters In the first and second part of this tutorial we visited the band pass filters, with lumped and distributed elements. In this third part we will discuss about low-pass, high-pass and notch filters. The approach will be without mathematics, the goal will be to introduce readers to a physical knowledge of filters. People interested in a mathematical analysis will find in the appendix some books on the topic. Fig.2 ∗ The constant K low-pass filter: it was invented in 1922 by George Campbell and the Running the simulation we can see the response meaning of constant K is the expression: of the filter in fig.3 2 ZL* ZC = K = R ZL and ZC are the impedances of inductors and capacitors in the filter, while R is the terminating impedance. A look at fig.1 will be clarifying. Fig.3 It is clear that the sharpness of the response increase as the order of the filter increase. The ripple near the edge of the cutoff moves from Fig 1 monotonic in 3 rd order to ringing of about 1.7 dB for the 9 th order. This due to the mismatch of the The two filter configurations, at T and π are various sections that are connected to a 50 Ω displayed, all the reactance are 50 Ω and the impedance at the edges of the filter and filter cells are all equal. In practice the two series connected to reactive impedances between cells. -
Weierstrass Transform on Howell's Space-A New Theory
IOSR Journal of Mathematics (IOSR-JM) e-ISSN: 2278-5728, p-ISSN: 2319-765X. Volume 16, Issue 6 Ser. IV (Nov. – Dec. 2020), PP 57-62 www.iosrjournals.org Weierstrass Transform on Howell’s Space-A New Theory 1 2* 3 V. N. Mahalle , S.S. Mathurkar , R. D. Taywade 1(Associate Professor, Dept. of Mathematics, Bar. R.D.I.K.N.K.D. College, Badnera Railway (M.S), India.) 2*(Assistant Professor, Dept. of Mathematics, Govt. College of Engineering, Amravati, (M.S), India.) 3(Associate Professor, Dept. of Mathematics, Prof. Ram Meghe Institute of Technology & Research, Badnera, Amravati, (M.S), India.) Abstract: In this paper we have explained a new theory of Weierstrass transform defined on Howell’s space. The definition of the Weierstrass transform on this space of test functions is discussed. Some Fundamental theorems are proved and some results are developed. Weierstrass transform defined on Howell’s space is linear, continuous and one-one mapping from G to G c is also discussed. Key Word: Weierstrass transform, Howell’s Space, Fundamental theorem. --------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: 10-12-2020 Date of Acceptance: 25-12-2020 --------------------------------------------------------------------------------------------------------------------------------------- I. Introduction A new theory of Fourier analysis developed by Howell, presented in an elegant series of papers [2-6] n supporting the Fourier transformation of the functions on R . This theory is similar to the theory for tempered distributions and to some extent, can be viewed as an extension of the Fourier theory. Bhosale [1] had extended Fractional Fourier transform on Howell’s space. Mahalle et. al. [7, 8] described a new theory of Mellin and Laplace transform defined on Howell’s space. -
Sampling and Reconstruction
Sampling and reconstruction COMP 575/COMP 770 Spring 2016 • 1 Sampled representations • How to store and compute with continuous functions? • Common scheme for representation: samples write down the function’s values at many points [FvDFH fig.14.14b / Wolberg] [FvDFH fig.14.14b / • 2 Reconstruction • Making samples back into a continuous function for output (need realizable method) for analysis or processing (need mathematical method) amounts to “guessing” what the function did in between [FvDFH fig.14.14b / Wolberg] [FvDFH fig.14.14b / • 3 Filtering • Processing done on a function can be executed in continuous form (e.g. analog circuit) but can also be executed using sampled representation • Simple example: smoothing by averaging • 4 Roots of sampling • Nyquist 1928; Shannon 1949 famous results in information theory • 1940s: first practical uses in telecommunications • 1960s: first digital audio systems • 1970s: commercialization of digital audio • 1982: introduction of the Compact Disc the first high-profile consumer application • This is why all the terminology has a communications or audio “flavor” early applications are 1D; for us 2D (images) is important • 5 Sampling in digital audio • Recording: sound to analog to samples to disc • Playback: disc to samples to analog to sound again how can we be sure we are filling in the gaps correctly? • 6 Undersampling • What if we “missed” things between the samples? • Simple example: undersampling a sine wave unsurprising result: information is lost surprising result: indistinguishable from -
Eigenfunctions and Eigenoperators of Cyclic Integral Transforms with Application to Gaussian Beam Propagation
NORTHWESTERN UNIVERSITY Eigenfunctions and Eigenoperators of Cyclic Integral Transforms with Application to Gaussian Beam Propagation A DISSERTATION SUBMITTED TO THE GRADUATE SCHOOL IN PARTIAL FULFILLMENT OF THE REQUIREMENTS for the degree DOCTOR OF PHILOSOPHY Field of Applied Mathematics By Matthew S. McCallum EVANSTON, ILLINOIS December 2008 2 c Copyright by Matthew S. McCallum 2008 All Rights Reserved 3 ABSTRACT Eigenfunctions and Eigenoperators of Cyclic Integral Transforms with Application to Gaussian Beam Propagation Matthew S. McCallum An integral transform which reproduces a transformable input function after a finite number N of successive applications is known as a cyclic transform. Of course, such a transform will reproduce an arbitrary transformable input after N applications, but it also admits eigenfunction inputs which will be reproduced after a single application of the transform. These transforms and their eigenfunctions appear in various applications, and the systematic determination of eigenfunctions of cyclic integral transforms has been a problem of interest to mathematicians since at least the early twentieth century. In this work we review the various approaches to this problem, providing generalizations of published expressions from previous approaches. We then develop a new formalism, differential eigenoperators, that reduces the eigenfunction problem for a cyclic transform to an eigenfunction problem for a corresponding ordinary differential equation. In this way we are able to relate eigenfunctions of integral equations to boundary-value problems, 4 which are typically easier to analyze. We give extensive examples and discussion via the specific case of the Fourier transform. We also relate this approach to two formalisms that have been of interest to the math- ematical physics community – hyperdifferential operators and linear canonical transforms. -
Deconvolution
Pulses and Parameters in the Time and Frequency Domain David A Humphreys National Physical Laboratory ([email protected]) Signal Processing Seminar 30 November 2005 Structure • Introduction • Waveforms and parameters • Instruments • Waveform metrology and correction Waveforms and parameters Parameters for a pulse waveform Signal Time Why use parameters? • Convenient way to compare information • Reduce data complexity • Specification of instruments • IEEE Standard on Transitions, Pulses, and Related Waveforms, IEEE Std 181, 2003 • These documents standardise how to characterise a waveform but do not specify units Measuring and characterising a signal Determine parameters that describe a signal or response I. Instrument capabilities II. Waveform metrology and correction III. Parameter analysis and extraction Assumptions • In practice we want to define a response in terms of single parameters such as width or risetime or bandwidth • Assume oscilloscope response has smooth bell-shaped e.g. Gaussian Simple analytically Can calculate frequency response, convolution simply, reasonably realistic Only 3 parameters – width/risetime; amplitude and position Instruments Instrumentation • Time-domain Frequency Real-time oscilloscope Time High-speed A/D converter Memory subsystem e.g. 32 Msamples X(t) A/D Memory Instrumentation • Time-domain Frequency Real-time oscilloscope Time Sample rates up to 40Gsamples/s Multiple high-speed A/D converters X(t) A/D Memory Fast memory subsystem A/D Memory Memory architecture may limit the length of trace A/D Memory e.g. 1 Msamples A/D Memory Resolution typically 8 bits Instrumentation • Time-domain Frequency Real-time oscilloscope Time Sampling oscilloscope Requires a repetitive waveform Sampling X(t) High-speed >70 GHz Gate Short trace length (e.g. -
A Unified Framework for the Fourier, Laplace, Mellin and Z Transforms
The Generalized Fourier Transform: A Unified Framework for the Fourier, Laplace, Mellin and Z Transforms Pushpendra Singha,∗, Anubha Guptab, Shiv Dutt Joshic aDepartment of ECE, National Institute of Technology Hamirpur, Hamirpur, India. bSBILab, Department of ECE, Indraprastha Institute of Information Technology Delhi, Delhi, India cDepartment of EE, Indian Institute of Technology Delhi, Delhi, India Abstract This paper introduces Generalized Fourier transform (GFT) that is an extension or the generalization of the Fourier transform (FT). The Unilateral Laplace transform (LT) is observed to be the special case of GFT. GFT, as proposed in this work, contributes significantly to the scholarly literature. There are many salient contribution of this work. Firstly, GFT is applicable to a much larger class of signals, some of which cannot be analyzed with FT and LT. For example, we have shown the applicability of GFT on the polynomially decaying functions and super exponentials. Secondly, we demonstrate the efficacy of GFT in solving the initial value problems (IVPs). Thirdly, the generalization presented for FT is extended for other integral transforms with examples shown for wavelet transform and cosine transform. Likewise, generalized Gamma function is also presented. One interesting application of GFT is the computation of generalized moments, for the otherwise non-finite moments, of any random variable such as the Cauchy random variable. Fourthly, we introduce Fourier scale transform (FST) that utilizes GFT with the topological isomorphism of an exponential map. Lastly, we propose Generalized Discrete-Time Fourier transform (GDTFT). The DTFT and unilateral z-transform are shown to be the special cases of the proposed GDTFT. The properties of GFT and GDTFT have also been discussed.