Aliasing Reduction in Clipped Signals

Total Page:16

File Type:pdf, Size:1020Kb

Aliasing Reduction in Clipped Signals This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Author(s): Fabián Esqueda, Stefan Bilbao and Vesa Välimäki Title: Aliasing reduction in clipped signals Year: 2016 Version: Author accepted / Post print version Please cite the original version: Fabián Esqueda, Stefan Bilbao and Vesa Välimäki. Aliasing reduction in clipped signals. IEEE Transactions on Signal Processing, Vol. 64, No. 20, pp. 5255-5267, October 2016. DOI: 10.1109/TSP.2016.2585091 Rights: © 2016 IEEE. Reprinted with permission. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Aalto University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. This publication is included in the electronic version of the article dissertation: Esqueda, Fabián. Aliasing Reduction in Nonlinear Audio Signal Processing. Aalto University publication series DOCTORAL DISSERTATIONS, 74/2018. All material supplied via Aaltodoc is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Powered by TCPDF (www.tcpdf.org) This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication. The final version of record is available at http://dx.doi.org/10.1109/TSP.2016.2585091 REVISED MANUSCRIPT, JUNE 2016. 1 Aliasing Reduction in Clipped Signals Fabian´ Esqueda, Stefan Bilbao, Senior Member, IEEE and Vesa Valim¨ aki,¨ Fellow, IEEE Abstract—An aliasing reduction method for hard-clipped sam- the aliased components are sufficiently attenuated, their effects pled signals is proposed. Clipping in the digital domain causes a become inaudible and can therefore be neglected [1], [2]. large amount of harmonic distortion, which is not bandlimited, A large share of earlier research on clipped signals has so spectral components generated above the Nyquist limit are reflected to the baseband and mixed with the signal. A model focused on declipping or the reconstruction of the underlying for an ideal bandlimited ramp function is derived, which leads original unclipped signal. Abel and Smith [3] introduced to a post-processing method to reduce aliasing. A number of optimization methods to reconstruct the clipped samples based samples in the neighborhood of a clipping point in the waveform on constraints. Recent work on declipping has considered are modified to simulate the Gibbs phenomenon. This novel methods based on matching pursuits [4], compressed sensing method requires estimation of the fractional delay of the clipping point between samples and the first derivative of the original [5], social sparsity [6], sparse and co-sparse regularization signal at that point. Two polynomial approximations of the [7], and non-negative matrix factorization [8]. Declipping can bandlimited ramp function are suggested for practical implemen- improve the audio quality of nonlinearly-compressed sound tation. Validation tests using sinusoidal, triangular, and harmonic files [9] and help speaker recognition [10], for example. signals show that the proposed method achieves high accuracy in The purpose of this work is not signal reconstruction but aliasing reduction. The proposed 2-point and 4-point polynomial correction methods can improve the signal-to-noise ratio by 12 dB enhancement, by allowing clipping to occur and by sup- and 20 dB in average, respectively, and are more computationally pressing the aliasing introduced. Practical applications for the efficient and cause less latency than oversampling, which is the proposed approach are found in systems in which clipping is standard approach to aliasing reduction. An additional advantage implemented digitally. One application in radio broadcasting of the polynomial correction methods over oversampling is that and music production is the limiting of the maximum values they do not introduce overshoot beyond the clipping level in the waveform. The proposed techniques are useful in audio and of the signal, such as in dynamic range compressors and other fields of signal processing where digital signal values must limiters, which are known to introduce distortion and aliasing be clipped but aliasing cannot be tolerated. [11]–[13]. In such applications, declipping is out of question, Index Terms—Antialiasing, interpolation, nonlinear distortion, because it would cancel the limiting effect, which is necessary signal denoising, signal sampling. for maximizing the signal level. However, the antialiasing method proposed in this work can be useful, since it cleans the clipped signal by suppressing the aliasing, and restores the I. INTRODUCTION limited distribution of sample values, as required. LIPPING is a form of distortion that limits the values Other practical applications involving digital clipping are C of a signal that lie above or below certain threshold. simulations of analog and physical systems in which signal In practice, signal clipping may be necessary due to system values are naturally limited. In digital simulation of analog fil- limitations, e.g. to avoid overmodulating an audio transmitter. ters, hard clipping is used as a simple model for the saturation In discrete systems, it can be caused unintentionally due to of large signals inside analog filters [14], [15]. In the digital data resolution constraints, such as when a sample exceeds modeling of vacuum-tube amplifiers and guitar effects, the the maximum value that can be represented, or intentionally saturating characteristics must also be implemented digitally as when simulating a process in which signal values are [16]–[18]. Often hard clipping is used in connection with soft constrained. Clipping is a nonlinear operation and introduces clipping so that the latter works at small signal values while the frequency components not present in the original signal. In the former saturates (clips) the large signal values to a maximum digital domain, when the frequencies of these new components or minimum value. Antialiasing for the combination of a soft exceed the Nyquist limit, the components are reflected back and a hard clipper in this context was the first application into the baseband, causing aliasing. This paper proposes a for the method discussed in this paper [19]. Similar saturating novel method to reduce aliasing in clipped signals. modules appear in physical models of musical wind and string Aliasing can severely affect the quality of a digital signal instruments in which a saturating waveshaper simulates the by corrupting the data it represents. For example, in audio nonlinear interaction between the excitation and state of an applications, aliasing can cause severe audible effects such as acoustic resonator, such as a tube or a string [20], [21]. beating, inharmonicity and heterodyning [1]. Nevertheless, if Full-wave and half-wave rectification are special cases of hard clipping. Thus, the proposed method can enhance such This work was supported by the CIMO Center for International Mobility digitally rectified signals, which have applications in various and the Aalto ELEC Doctoral School. F. Esqueda and V. Valim¨ aki¨ are with the Department of Signal Pro- fields. For example in music processing, full-wave rectification cessing and Acoustics, Aalto University School of Electrical Engineer- offers an easy implementation of an octaver, a device which ing, FI-00076 AALTO, Espoo, Finland (e-mail: fabian.esqueda@aalto.fi; doubles the fundamental frequency of a signal [13]. vesa.valimaki@aalto.fi). S. Bilbao is with the Acoustics and Audio Group, University of Edinburgh, The only previous general method for reducing aliasing in Edinburgh EH9 3JZ, UK (e-mail: [email protected]). digitally-clipped signals is oversampling [11], [16], [18], [22]– Copyright (c) 2016 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected]. This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication. The final version of record is available at http://dx.doi.org/10.1109/TSP.2016.2585091 REVISED MANUSCRIPT, JUNE 2016. 2 [24]. Its use is limited to cases where the input signal of a ideal bandlimited case. To do so, we begin by considering the nonlinear device can be accessed. In oversampling, the input simple case of a continuous-time sinewave signal of the nonlinear device is upsampled (e.g. by factor of eight) so that most of the spectral components generated by s(t) = cos (2πf0t) ; (1) the clipper can be suppressed using a digital lowpass filter. where t is time and f0 is fundamental frequency in Hz. Next, Then, the output signal is downsampled back to the original we define c(t), a time-domain expression for this signal after rate. The level of aliasing reduction will then depend on the bipolar (positive and negative) clipping: oversampling factor and order of the antialiasing lowpass filter, which will together also determine the computational cost of c(t) = sgn [s(t)] min (js(t)j;L) : (2) the process as a whole. Here, 0 < L < 1 is the clipping threshold relative to a The concept of aliasing suppression has been previously normalized peak amplitude of 1 and sgn(·) is the sign function. studied in the design of digital oscillators such as those Now, in order to generate a digital bandlimited clipped used in music synthesis [25].
Recommended publications
  • Sampling Signals on Graphs from Theory to Applications
    1 Sampling Signals on Graphs From Theory to Applications Yuichi Tanaka, Yonina C. Eldar, Antonio Ortega, and Gene Cheung Abstract The study of sampling signals on graphs, with the goal of building an analog of sampling for standard signals in the time and spatial domains, has attracted considerable attention recently. Beyond adding to the growing theory on graph signal processing (GSP), sampling on graphs has various promising applications. In this article, we review current progress on sampling over graphs focusing on theory and potential applications. Although most methodologies used in graph signal sampling are designed to parallel those used in sampling for standard signals, sampling theory for graph signals significantly differs from the theory of Shannon–Nyquist and shift-invariant sampling. This is due in part to the fact that the definitions of several important properties, such as shift invariance and bandlimitedness, are different in GSP systems. Throughout this review, we discuss similarities and differences between standard and graph signal sampling and highlight open problems and challenges. I. INTRODUCTION Sampling is one of the fundamental tenets of digital signal processing (see [1] and references therein). As such, it has been studied extensively for decades and continues to draw considerable research efforts. Standard sampling theory relies on concepts of frequency domain analysis, shift invariant (SI) signals, and bandlimitedness [1]. Sampling of time and spatial domain signals in shift-invariant spaces is one of the most important building blocks of digital signal processing systems. However, in the big data era, the signals we need to process often have other types of connections and structure, such as network signals described by graphs.
    [Show full text]
  • Lecture19.Pptx
    Outline Foundations of Computer Graphics (Fall 2012) §. Basic ideas of sampling, reconstruction, aliasing CS 184, Lectures 19: Sampling and Reconstruction §. Signal processing and Fourier analysis §. http://inst.eecs.berkeley.edu /~cs184 Implementation of digital filters §. Section 14.10 of FvDFH (you really should read) §. Post-raytracing lectures more advanced topics §. No programming assignment §. But can be tested (at high level) in final Acknowledgements: Thomas Funkhouser and Pat Hanrahan Some slides courtesy Tom Funkhouser Sampling and Reconstruction Sampling and Reconstruction §. An image is a 2D array of samples §. Discrete samples from real-world continuous signal (Spatial) Aliasing (Spatial) Aliasing §. Jaggies probably biggest aliasing problem 1 Sampling and Aliasing Image Processing pipeline §. Artifacts due to undersampling or poor reconstruction §. Formally, high frequencies masquerading as low §. E.g. high frequency line as low freq jaggies Outline Motivation §. Basic ideas of sampling, reconstruction, aliasing §. Formal analysis of sampling and reconstruction §. Signal processing and Fourier analysis §. Important theory (signal-processing) for graphics §. Implementation of digital filters §. Also relevant in rendering, modeling, animation §. Section 14.10 of FvDFH Ideas Sampling Theory Analysis in the frequency (not spatial) domain §. Signal (function of time generally, here of space) §. Sum of sine waves, with possibly different offsets (phase) §. Each wave different frequency, amplitude §. Continuous: defined at all points; discrete: on a grid §. High frequency: rapid variation; Low Freq: slow variation §. Images are converting continuous to discrete. Do this sampling as best as possible. §. Signal processing theory tells us how best to do this §. Based on concept of frequency domain Fourier analysis 2 Fourier Transform Fourier Transform §. Tool for converting from spatial to frequency domain §.
    [Show full text]
  • And Bandlimiting IMAHA, November 14, 2009, 11:00–11:40
    Time- and bandlimiting IMAHA, November 14, 2009, 11:00{11:40 Joe Lakey (w Scott Izu and Jeff Hogan)1 November 16, 2009 Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting sampling theory and history Time- and bandlimiting, history, definitions and basic properties connecting sampling and time- and bandlimiting The multiband case Outline Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Time- and bandlimiting, history, definitions and basic properties connecting sampling and time- and bandlimiting The multiband case Outline sampling theory and history Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting connecting sampling and time- and bandlimiting The multiband case Outline sampling theory and history Time- and bandlimiting, history, definitions and basic properties Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting The multiband case Outline sampling theory and history Time- and bandlimiting, history, definitions and basic properties connecting sampling and time- and bandlimiting Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Outline sampling theory and history Time- and bandlimiting, history, definitions and basic properties connecting sampling and time- and bandlimiting The multiband case Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting R −2πitξ Fourier transform: bf (ξ) = f (t) e dt R _ Bandlimiting:
    [Show full text]
  • “WW 2,873,387 United States Patent Rice Patented Feb
    Feb. 10, 1959 M. c. KIDD 2,873,387 CONTROLLABLE.‘ TRANSISTOR CLIPPING CIRCUIT Filed Dec. 17, '1956 INVENTOR. v MARSHALL [.KIDD “WW 2,873,387 United States Patent rice Patented Feb. 10, 1959. 2 tap 34 on a second source of energizing potential, here illustrated as a battery 30, through a load resistor 32. 2,873,387 The battery 30 has a ground tap 31 at an intermediate point thereon, and the variable tap 34 allows the ener CONTROLLABLE TRANSISTOR CLIPPING gizing potential supplied to the collector electrode 14 to cmcurr be varied from a positive to a negative value. Output Marshall vC. Kidd, Haddon Heights, N. J., assignor to signals are derived between an output terminal 36, which Radio Corporation of America, a corporation of Dela is connected directly to the collector electrode 14 of the ware ‘ ‘ transistor 16, and a ground terminal 37. One type of 10 clipped or limited output signal that is available at the Application December 17, 1956, Serial No. 628,807 output terminals 36 is illustrated by the waveform 38, 5 ‘Claims. (Cl. 307-885) and the manner in which it is derived is hereinafter de scribed. , In order to describe the operation of the circuit, as This invention relates to signal translating circuits and 15 sume that the variable tap 34 on the battery 30 is set more particularly to transistor circuits for limiting or so that a small positive voltage, negative, however, with clipping a translated signal; - respect to base electrode voltage, appears on the collector In many types of electronic equipment, such as tele electrode 14, and that a sine wave is applied to the input vision, radar, computer and like equipment, it may be terminal 10, as illustrated by the waveform 18.
    [Show full text]
  • Tektronix Cookbook of Standard Audio Tests
    ( Copyright © 1975, Tektronix, Inc. AI! rig hts re served P ri nt ed· in U .S.A. ForeIg n and U .S.A. Products of Tektronix , Inc. are covered by Fore ign and U .S.A . Patents and /o r Patents Pending. Inform ation in thi s publi ca tion supersedes all previously published material. Specification and price c hange pr ivileges reserve d . TEKTRON I X, SCOPE-MOBILE, TELEOU IPMENT, and @ are registered trademarks of Tekt ro nix, Inc., P. O. Box 500, Beaverlon, Oregon 97077, Phone : (Area Code 503) 644-0161, TWX : 910-467-8708, Cabl e : TEKTRON IX. Overseas Di stributors in over 40 Counlries. 1 STANDARD AUDIO TESTS BY CLIFFORD SCHROCK ACKNOWLEDGEMENTS The author would like to thank Linley Gumm and Gordon Long for their excellent technical assistance in the prepara­ tion of this paper. In addition, I would like to thank Joyce Lekas for her editorial assistance and Jeanne Galick for the illustrations and layout. CONTENTS PRELIMINARY INFORMATION Test Setups ________________ page 2 Input- Output Load Matching ________ page 3 TESTS Power Output _______________ page 4 Frequency Response ____________ page 5 Harmonic Distortion _____________ page 7 Intermodulation Distortion __________ page 9 Distortion vs Output _____________ page 11 Power Bandwidth page 11 Damping Factor page 12 Signal to Noise Ratio page 12 Square Wave Response page 15 Crosstalk page 16 Sensitivity page 16 Transient Intermodulation Distortion page 17 SERVICING HINTS___________ page19 PRELIMINARY INFORMATION Maintaining a modern High-Fidelity-Stereo system to day requires much more than a "trained ear." The high specifications of receivers and amplifiers can only be maintained by performing some of the standard measure­ ments such as : 1.
    [Show full text]
  • Information Theory
    Information Theory Professor John Daugman University of Cambridge Computer Science Tripos, Part II Michaelmas Term 2016/17 H(X,Y) I(X;Y) H(X|Y) H(Y|X) H(X) H(Y) 1 / 149 Outline of Lectures 1. Foundations: probability, uncertainty, information. 2. Entropies defined, and why they are measures of information. 3. Source coding theorem; prefix, variable-, and fixed-length codes. 4. Discrete channel properties, noise, and channel capacity. 5. Spectral properties of continuous-time signals and channels. 6. Continuous information; density; noisy channel coding theorem. 7. Signal coding and transmission schemes using Fourier theorems. 8. The quantised degrees-of-freedom in a continuous signal. 9. Gabor-Heisenberg-Weyl uncertainty relation. Optimal \Logons". 10. Data compression codes and protocols. 11. Kolmogorov complexity. Minimal description length. 12. Applications of information theory in other sciences. Reference book (*) Cover, T. & Thomas, J. Elements of Information Theory (second edition). Wiley-Interscience, 2006 2 / 149 Overview: what is information theory? Key idea: The movements and transformations of information, just like those of a fluid, are constrained by mathematical and physical laws. These laws have deep connections with: I probability theory, statistics, and combinatorics I thermodynamics (statistical physics) I spectral analysis, Fourier (and other) transforms I sampling theory, prediction, estimation theory I electrical engineering (bandwidth; signal-to-noise ratio) I complexity theory (minimal description length) I signal processing, representation, compressibility As such, information theory addresses and answers the two fundamental questions which limit all data encoding and communication systems: 1. What is the ultimate data compression? (answer: the entropy of the data, H, is its compression limit.) 2.
    [Show full text]
  • Lecture 16 Bandlimiting and Nyquist Criterion
    ELG3175 Introduction to Communication Systems Lecture 16 Bandlimiting and Nyquist Criterion Bandlimiting and ISI • Real systems are usually bandlimited. • When a signal is bandlimited in the frequency domain, it is usually smeared in the time domain. This smearing results in intersymbol interference (ISI). • The only way to avoid ISI is to satisfy the 1st Nyquist criterion. • For an impulse response this means at sampling instants having only one nonzero sample. Lecture 12 Band-limited Channels and Intersymbol Interference • Rectangular pulses are suitable for infinite-bandwidth channels (practically – wideband). • Practical channels are band-limited -> pulses spread in time and are smeared into adjacent slots. This is intersymbol interference (ISI). Input binary waveform Individual pulse response Received waveform Eye Diagram • Convenient way to observe the effect of ISI and channel noise on an oscilloscope. Eye Diagram • Oscilloscope presentations of a signal with multiple sweeps (triggered by a clock signal!), each is slightly larger than symbol interval. • Quality of a received signal may be estimated. • Normal operating conditions (no ISI, no noise) -> eye is open. • Large ISI or noise -> eye is closed. • Timing error allowed – width of the eye, called eye opening (preferred sampling time – at the largest vertical eye opening). • Sensitivity to timing error -> slope of the open eye evaluated at the zero crossing point. • Noise margin -> the height of the eye opening. Pulse shapes and bandwidth • For PAM: L L sPAM (t) = ∑ai p(t − iTs ) = p(t)*∑aiδ (t − iTs ) i=0 i=0 L Let ∑aiδ (t − iTs ) = y(t) i=0 Then SPAM ( f ) = P( f )Y( f ) BPAM = Bp.
    [Show full text]
  • Topaz Sr10 / Sr20 Stereo Receivers Top-Tips
    TOPAZ SR10 / SR20 STEREO RECEIVERS TOP-TIPS Incredible performance, connectivity and value for money… The most powerful amplifiers in the Topaz range are designed to on the back allow you to connect traditional sources such as be the heart of your separates hi-fi system. The SR10 offers a CD players and even Blu-ray players. Thanks to a high quality room-filling 85 watts per channel and is backed by a dedicated Wolfson DAC, the SR20 goes one step further and provides three subwoofer output as well as two sets of speaker outputs - so you additional digital audio inputs, allowing the connection of digital can listen in two rooms at once or bi-wire your main speakers for sources such as streamers, TVs or set top boxes. a true audiophile performance. The SR20 steps things up a notch The SR10 and SR20’s playback capabilities also include a built- by offering the same flexible connectivity, but with an even more in FM/AM tuner, giving you access to all of your favourite radio powerful 100 watts per channel, driving virtually any loudspeaker stations with ease. We only use pure audiophile components, including discreet However you listen to your music, the SR10 and SR20 have it amplifiers, full metal chassis’ and high-performance toroidal covered. They boast a built-in phono stage so you can instantly transformers. The Topaz SR10 and SR20 are a winning connect a turntable, and a direct front panel input for iPods, combination of power, connectivity and purity, putting far more smart phones or MP3 players.
    [Show full text]
  • Frames and Other Bases in Abstract and Function Spaces Novel Methods in Harmonic Analysis, Volume 1
    Applied and Numerical Harmonic Analysis Isaac Pesenson Quoc Thong Le Gia Azita Mayeli Hrushikesh Mhaskar Ding-Xuan Zhou Editors Frames and Other Bases in Abstract and Function Spaces Novel Methods in Harmonic Analysis, Volume 1 Applied and Numerical Harmonic Analysis Series Editor John J. Benedetto University of Maryland College Park, MD, USA Editorial Advisory Board Akram Aldroubi Gitta Kutyniok Vanderbilt University Technische Universität Berlin Nashville, TN, USA Berlin, Germany Douglas Cochran Mauro Maggioni Arizona State University Duke University Phoenix, AZ, USA Durham, NC, USA Hans G. Feichtinger Zuowei Shen University of Vienna National University of Singapore Vienna, Austria Singapore, Singapore Christopher Heil Thomas Strohmer Georgia Institute of Technology University of California Atlanta, GA, USA Davis, CA, USA Stéphane Jaffard Yang Wang University of Paris XII Michigan State University Paris, France East Lansing, MI, USA Jelena Kovaceviˇ c´ Carnegie Mellon University Pittsburgh, PA, USA More information about this series at http://www.springer.com/series/4968 Isaac Pesenson • Quoc Thong Le Gia Azita Mayeli • Hrushikesh Mhaskar Ding-Xuan Zhou Editors Frames and Other Bases in Abstract and Function Spaces Novel Methods in Harmonic Analysis, Volume 1 Editors Isaac Pesenson Quoc Thong Le Gia Department of Mathematics School of Mathematics and Statistics Temple University University of New South Wales Philadelphia, PA, USA Sydney, NSW, Australia Azita Mayeli Hrushikesh Mhaskar Department of Mathematics Institute of Mathematical
    [Show full text]
  • Amplifier Clipping
    Amplifier clipping Amplifier clipping – What is it? How is it caused and how can we prevent itit???? What does it do? We shall deal with each of these topics one at a time. What is it: Almost everyone assumes that amplifier clipping is the sole domain of power amplifiers? This is not true. A preamplifier is just as prone to clipping as power amplifiers. If the level of signal is high enough to cause the preamplifier to clip, the power amplifier being a faithful servant will just amplify the clipped signal it receives. For the purposes of this discussion we will assume that our amplifier/preamplifier models use a bi-polar power supply (as almost all audio electronics does today) and therefore the signal swings from a level of zero to either the positive or negative supply rails (“rail” is a commonly used term which we use to describe a power supply output). We shall also assume that the electronic building blocks are in the form of operational amplifiers WITH negative feedback. Op-amps as they are called, are TWO input ONE output building blocks. Input is to either positive or negative ports and feedback is taken from the output and returned to the (-) input. I shall show the op-amps as in the diagram below even though it is not the standard schematic symbol for an op-amp. Note: A power amplifier (that is one which can drive a loudspeaker) is nothing less than just a high current preamplifier. Preamplifiers can and do run off very high rail (there we go again with the term “rail”) voltages – they just do not have the capability to source lots of current.
    [Show full text]
  • Ideal C/D Converter
    Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.341: Discrete-Time Signal Processing OpenCourseWare 2006 Lecture 4 DT Processing of CT Signals & CT Processing of DT Signals: Fractional Delay Reading: Sections 4.1 - 4.5 in Oppenheim, Schafer & Buck (OSB). A typical discrete-time system used to process a continuous-time signal is shown in OSB Figure 4.15. T is the sampling/reconstruction period for the C/D and the D/C converters. When the input signal is bandlimited, the effective system in Figure 4.15 (b) is equivalent to the continuous-time system shown in Figure 4.15 (a). Ideal C/D Converter The relationship between the input and output signals in an ideal C/D converter as depicted in OSB Figure 4.1 is: Time Domain: x[n] = xc(nT ) j! 1 P1 ! 2¼k Frequency Domain: X(e ) = T k=¡1 Xc(j( T ¡ T )); j! where X(e ) and Xc(jΩ) are the DTFT and CTFT of x[n], xc(t). These relationships are illustrated in the figures below. See Section 4.2 of OSB for their derivations and more detailed descriptions of C/D converters. Time/Frequency domain representation of C/D conversion 1 In the frequency domain, a C/D converter can be thought of in three stages: periodic 1 repetition of the spectrum at 2¼, normalization of frequency by , and scaling of the amplitude T 1 ! by . The CT frequency Ω is related to the DT frequency ! by Ω = . If x (t) is not T T c ­s ­s bandlimited to ¡ 2 < Ωmax < 2 , repetition at 2¼ will cause aliasing.
    [Show full text]
  • Focm 2017 Foundations of Computational Mathematics Barcelona, July 10Th-19Th, 2017 Organized in Partnership With
    FoCM 2017 Foundations of Computational Mathematics Barcelona, July 10th-19th, 2017 http://www.ub.edu/focm2017 Organized in partnership with Workshops Approximation Theory Computational Algebraic Geometry Computational Dynamics Computational Harmonic Analysis and Compressive Sensing Computational Mathematical Biology with emphasis on the Genome Computational Number Theory Computational Geometry and Topology Continuous Optimization Foundations of Numerical PDEs Geometric Integration and Computational Mechanics Graph Theory and Combinatorics Information-Based Complexity Learning Theory Plenary Speakers Mathematical Foundations of Data Assimilation and Inverse Problems Multiresolution and Adaptivity in Numerical PDEs Numerical Linear Algebra Karim Adiprasito Random Matrices Jean-David Benamou Real-Number Complexity Alexei Borodin Special Functions and Orthogonal Polynomials Mireille Bousquet-Mélou Stochastic Computation Symbolic Analysis Mark Braverman Claudio Canuto Martin Hairer Pierre Lairez Monique Laurent Melvin Leok Lek-Heng Lim Gábor Lugosi Bruno Salvy Sylvia Serfaty Steve Smale Andrew Stuart Joel Tropp Sponsors Shmuel Weinberger 2 FoCM 2017 Foundations of Computational Mathematics Barcelona, July 10th{19th, 2017 Books of abstracts 4 FoCM 2017 Contents Presentation . .7 Governance of FoCM . .9 Local Organizing Committee . .9 Administrative and logistic support . .9 Technical support . 10 Volunteers . 10 Workshops Committee . 10 Plenary Speakers Committee . 10 Smale Prize Committee . 11 Funding Committee . 11 Plenary talks . 13 Workshops . 21 A1 { Approximation Theory Organizers: Albert Cohen { Ron Devore { Peter Binev . 21 A2 { Computational Algebraic Geometry Organizers: Marta Casanellas { Agnes Szanto { Thorsten Theobald . 36 A3 { Computational Number Theory Organizers: Christophe Ritzenhaler { Enric Nart { Tanja Lange . 50 A4 { Computational Geometry and Topology Organizers: Joel Hass { Herbert Edelsbrunner { Gunnar Carlsson . 56 A5 { Geometric Integration and Computational Mechanics Organizers: Fernando Casas { Elena Celledoni { David Martin de Diego .
    [Show full text]