And Bandlimiting IMAHA, November 14, 2009, 11:00–11:40

Total Page:16

File Type:pdf, Size:1020Kb

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: PΣf (x) = (bf 1Σ) (x) Paley-Wiener space : 2 PWΣ = PΣ(L (R)) Bandlimiting Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting _ Bandlimiting: PΣf (x) = (bf 1Σ) (x) Paley-Wiener space : 2 PWΣ = PΣ(L (R)) Bandlimiting R −2πitξ Fourier transform: bf (ξ) = f (t) e dt R Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Paley-Wiener space : 2 PWΣ = PΣ(L (R)) Bandlimiting R −2πitξ Fourier transform: bf (ξ) = f (t) e dt R _ Bandlimiting: PΣf (x) = (bf 1Σ) (x) Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Bandlimiting R −2πitξ Fourier transform: bf (ξ) = f (t) e dt R _ Bandlimiting: PΣf (x) = (bf 1Σ) (x) Paley-Wiener space : 2 PWΣ = PΣ(L (R)) Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting _ Bandlimiting: Pf (x) = (bf 1[−1=2; 1=2]) (x) 2 Paley-Wiener space : PW = P(L (R)) 2 Sampling theorem: If f 2 PW then, with convergence in L (R), 1 X sin π(t − k) f (t) = f (k) π(t − k) k=−∞ The Shannon sampling theorem Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting 2 Paley-Wiener space : PW = P(L (R)) 2 Sampling theorem: If f 2 PW then, with convergence in L (R), 1 X sin π(t − k) f (t) = f (k) π(t − k) k=−∞ The Shannon sampling theorem _ Bandlimiting: Pf (x) = (bf 1[−1=2; 1=2]) (x) Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting The Shannon sampling theorem _ Bandlimiting: Pf (x) = (bf 1[−1=2; 1=2]) (x) 2 Paley-Wiener space : PW = P(L (R)) 2 Sampling theorem: If f 2 PW then, with convergence in L (R), 1 X sin π(t − k) f (t) = f (k) π(t − k) k=−∞ Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting 0.04 0.03 0.02 0.01 0 −0.01 −0.02 −0.03 10 20 30 40 50 60 70 80 90 100 The problem: sinc not well localized 1 0.8 0.6 0.4 0.2 0 −0.2 −0.4 −10 −8 −6 −4 −2 0 2 4 6 8 10 Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting 0.04 0.03 0.02 0.01 0 −0.01 −0.02 −0.03 10 20 30 40 50 60 70 80 90 100 The problem: sinc not well localized 1 0.8 0.6 0.4 0.2 0 −0.2 −0.4 −10 −8 −6 −4 −2 0 2 4 6 8 10 Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting 0.04 0.03 0.02 0.01 0 −0.01 −0.02 −0.03 10 20 30 40 50 60 70 80 90 100 The problem: sinc not well localized 1 0.8 0.6 0.4 0.2 0 −0.2 −0.4 −10 −8 −6 −4 −2 0 2 4 6 8 10 Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Bandlimited functions can be recovered from samples Sinc function not well localized Find a subspace of PW whose elements can be approximated locally by finite sinc series The problem: how many samples Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Sinc function not well localized Find a subspace of PW whose elements can be approximated locally by finite sinc series The problem: how many samples Bandlimited functions can be recovered from samples Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Find a subspace of PW whose elements can be approximated locally by finite sinc series The problem: how many samples Bandlimited functions can be recovered from samples Sinc function not well localized Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting The problem: how many samples Bandlimited functions can be recovered from samples Sinc function not well localized Find a subspace of PW whose elements can be approximated locally by finite sinc series Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting I (QT f )(x) = 1[−T ;T ](x) f (x) I PQT P: self-adjoint, I 2 λmax = λ0 = kPQT k = sup kQT (f )k f 2PW; kf k=1 I Uncertainty principle: λmax < 1 I PΩQT commutes with certain differential operator ::: d2 d (4T 2 − t2) − 2t − Ω2t2 dt2 dt I Eigenfunctions: Prolate Spheroidal Wave Functions Time- and bandlimiting: Bell Labs Theory _ I Pf (x) = (bf 1[−1=2; 1=2]) (x) Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting I PQT P: self-adjoint, I 2 λmax = λ0 = kPQT k = sup kQT (f )k f 2PW; kf k=1 I Uncertainty principle: λmax < 1 I PΩQT commutes with certain differential operator ::: d2 d (4T 2 − t2) − 2t − Ω2t2 dt2 dt I Eigenfunctions: Prolate Spheroidal Wave Functions Time- and bandlimiting: Bell Labs Theory _ I Pf (x) = (bf 1[−1=2; 1=2]) (x) I (QT f )(x) = 1[−T ;T ](x) f (x) Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting I 2 λmax = λ0 = kPQT k = sup kQT (f )k f 2PW; kf k=1 I Uncertainty principle: λmax < 1 I PΩQT commutes with certain differential operator ::: d2 d (4T 2 − t2) − 2t − Ω2t2 dt2 dt I Eigenfunctions: Prolate Spheroidal Wave Functions Time- and bandlimiting: Bell Labs Theory _ I Pf (x) = (bf 1[−1=2; 1=2]) (x) I (QT f )(x) = 1[−T ;T ](x) f (x) I PQT P: self-adjoint, Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting I Uncertainty principle: λmax < 1 I PΩQT commutes with certain differential operator ::: d2 d (4T 2 − t2) − 2t − Ω2t2 dt2 dt I Eigenfunctions: Prolate Spheroidal Wave Functions Time- and bandlimiting: Bell Labs Theory _ I Pf (x) = (bf 1[−1=2; 1=2]) (x) I (QT f )(x) = 1[−T ;T ](x) f (x) I PQT P: self-adjoint, I 2 λmax = λ0 = kPQT k = sup kQT (f )k f 2PW; kf k=1 Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting I PΩQT commutes with certain differential operator ::: d2 d (4T 2 − t2) − 2t − Ω2t2 dt2 dt I Eigenfunctions: Prolate Spheroidal Wave Functions Time- and bandlimiting: Bell Labs Theory _ I Pf (x) = (bf 1[−1=2; 1=2]) (x) I (QT f )(x) = 1[−T ;T ](x) f (x) I PQT P: self-adjoint, I 2 λmax = λ0 = kPQT k = sup kQT (f )k f 2PW; kf k=1 I Uncertainty principle: λmax < 1 Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting I Eigenfunctions: Prolate Spheroidal Wave Functions Time- and bandlimiting: Bell Labs Theory _ I Pf (x) = (bf 1[−1=2; 1=2]) (x) I (QT f )(x) = 1[−T ;T ](x) f (x) I PQT P: self-adjoint, I 2 λmax = λ0 = kPQT k = sup kQT (f )k f 2PW; kf k=1 I Uncertainty principle: λmax < 1 I PΩQT commutes with certain differential operator ::: d2 d (4T 2 − t2) − 2t − Ω2t2 dt2 dt Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Time- and bandlimiting: Bell Labs Theory _ I Pf (x) = (bf 1[−1=2; 1=2]) (x) I (QT f )(x) = 1[−T ;T ](x) f (x) I PQT P: self-adjoint, I 2 λmax = λ0 = kPQT k = sup kQT (f )k f 2PW; kf k=1 I Uncertainty principle: λmax < 1 I PΩQT commutes with certain differential operator ::: d2 d (4T 2 − t2) − 2t − Ω2t2 dt2 dt I Eigenfunctions: Prolate Spheroidal Wave Functions Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Eigenvalue properties Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Graphs ::: coming later Study of heat flow (C. Niven, 1879) Relationships to special functions (Legendre, Bessel) Stratton, 1935 Tridiagonal method: expands PSFWs locally in Legendre polynomials (Bouwkamp, 1946, tables) 'n has n zeros in [−1; 1] (Kellogg, 1916 for general principle) Eigenfunction properties I Solutions: Prolate spheroidal wave functions '0;'1;::: Joe Lakey (w Scott Izu and Jeff Hogan) Time- and bandlimiting Study of heat flow (C.
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]
  • 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]
  • 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]
  • 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]
  • 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.
    [Show full text]
  • Bandlimited Signal Extrapolation Using Prolate Spheroidal Wave Functions
    IAENG International Journal of Computer Science, 40:4, IJCS_40_4_09 ______________________________________________________________________________________ Bandlimited Signal Extrapolation Using Prolate Spheroidal Wave Functions Amal Devasia 1 and Michael Cada 2 reconstruction of signals. Further to this, in [11,12], they Abstract— A simple, efficient and reliable method to showed signal reconstruction using non-uniform sampling extrapolate bandlimited signals varying from lower to higher and level-crossing sampling with Slepian functions. frequencies is proposed. The orthogonal properties of linear Since the primary focus of this paper is on bandlimited prolate spheroidal wave functions (PSWFs) are exploited to signal extrapolation and not just reconstruction or form an orthogonal basis set needed for synthesis. A significant step in the process is the higher order piecewise polynomial interpolation, we will concentrate more on the recent approximation of the overlap integral required for obtaining advancements in this regard. While we consider the signals the expansion coefficients accurately with very high precision. to be bandlimited in the Fourier transform domain, much Two sets of PSWFs, one relatively with lower Slepian attention has recently been on extrapolation of signals frequency and the other with higher Slepian frequency, are bandlimited in linear canonical transform (LCT) domain, considered. Numerical results of extrapolation of some this being a four-parameter family of linear integral standard test signals using the two sets are presented, compared, discussed, and some interesting inferences are transform [13,14] that generalizes Fourier transform as one made. of its special cases. For extrapolation of LCT bandlimited signals, several iterative and non-iterative algorithms have Index Terms—Signal extrapolation; Slepian series; been proposed [15-18].
    [Show full text]
  • Alias-Free Digital Synthesis of Classic Analog Waveforms
    Alias-Free Digital Synthesis of Classic Analog Waveforms Tim [email protected] rd.edu Julius Smith [email protected] u CCRMA http://www-ccrma.stanford.edu/ Music Department, Stanford University Abstract Techniques are reviewed and presented for alias-free digital synthesis of classical analog synthesizer waveforms such as pulse train and sawtooth waves. Techniquesdescribed include summation of bandlim- ited primitive waveforms as well as table-lookup techniques. Bandlimited pulse and triangle waveforms are obtained by integrating the difference of two out-of-phase bandlimited impulse trains. Bandlimited impulse trains are generated as a superposition of windowed sinc functions. Methods for more general bandlimited waveform synthesis are also reviewed. Methods are evaluated from the perspectives of sound quality, computational economy, and ease of control. 1 Introduction impulse train Any analog signal with a discontinuity in the wave- 1 form (such as pulse train or sawtooth) or in the wave- 0.8 form slope (such as triangle wave) must be bandlim- 0.6 ited to less than half the sampling rate before sampling 0.4 to obtain a corresponding discrete-time signal. Simple methods of generating these waveforms digitally con- 0.2 tain aliasing due to having to round off the discontinuity 0 0 5 10 15 20 time to the nearest available sampling instant. The sig- nals primarily addressed here are the impulse train, rect- Box Train, and Sample Positions angular pulse, and sawtooth waveforms. Because the 1 latter two signals can be derived from the ®rst by inte- 0.8 gration, only the algorithm for the impulse train is de- 0.6 veloped in detail.
    [Show full text]
  • Analytical Model for GNSS Receiver Implementation Losses*
    © The MITRE Corporation. All rights reserved. Analytical Model for GNSS Receiver Implementation Losses* Christopher J. Hegarty, The MITRE Corporation intensive and do not provide much insight into the various BIOGRAPHY1 loss mechanisms. Dr. Christopher J. Hegarty is a director with The MITRE Earlier analytical studies of DSSS receiver SNR losses Corporation, where he has worked mainly on aviation due to quantization include derivations in [1] for applications of GNSS since 1992. He is currently the quantization losses of 1.96 dB and 0.55 dB, respectively, Chair of the Program Management Committee of RTCA, for 1- and 2-bit uniform quantizers. These derivations are Inc., and co-chairs RTCA Special Committee 159 simplified in that they assume that the received signal is (GNSS). He served as editor of NAVIGATION: The sampled, but with independent noise upon each sample Journal of the Institute of Navigation from 1997 – 2006 and ignoring bandlimiting effects on the desired signal and as president of the Institute of Navigation in 2008. He component. A more thorough treatment of bandlimiting, was a recipient of the ION Early Achievement Award in sampling, and quantization effects in digital matched 1998, the U.S. Department of State Superior Honor filters is provided in [2]. The results in [2] were later Award in 2005, the ION Kepler Award in 2005, and the inferred to apply to GNSS receiver losses in [3, 4]. As Worcester Polytechnic Institute Hobart Newell Award in detailed later in this paper, however, the results in [2] are 2006. He is a Fellow of the ION, and co-editor/co-author truly not directly applicable to GNSS or other DSSS of the textbook Understanding GPS: Principles and receivers because the losses derived therein presume Applications, 2nd Ed.
    [Show full text]
  • 1 the Fourier Transform of Discrete Time Sequences
    Sampling and Reconstruction Supplement 1 The Fourier Transform of Discrete Time Sequences Wehave found that x t, the analog signal sampled by a train of Dirac delta functions with s spacing T =1=f , had aFourier transform related to Ffxtg = X f by s 1 X X f mf 1 X f = f s s s m=1 This Fourier transform is p erio dic in f , with p erio d f , as is easily shown: s 1 X X f kf = f X f kf mf s s s s s m=1 1 X = f X f m + k f s s m=1 1 X X f lf ; l = m + k = f s s l =1 = X f : 2 s We de ned X f as b eing identical to X f . Since this function is p erio dic, it can be d s represented by aFourier series, which led us to the inverse DTFT Z f =2 s 1 j 2nT f x n = X f e df T d f f =2 s s Z 1=2T j 2nT f = T X f e df 3 d 1=2T This view tends to leave the reader with the impression that without the ideal impulsive sampling representation, we would be at a loss to deal with the DTFT. Actually, we can do nicely without resorting to impulsive sampling, if we like, for generating the DTFT. Consider the sequence x n, and supp ose that there is some continuous time signal xt, T whose instantaneous values at multiples of time T constitute the values of x n, as in Fig.
    [Show full text]