Alias-Free Digital Synthesis of Classic Analog Waveforms
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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. -
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 §. -
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: -
Lab 4: Using the Digital-To-Analog Converter
ECE2049 – Embedded Computing in Engineering Design Lab 4: Using the Digital-to-Analog Converter In this lab, you will gain experience with SPI by using the MSP430 and the MSP4921 DAC to create a simple function generator. Specifically, your function generator will be capable of generating different DC values as well as a square wave, a sawtooth wave, and a triangle wave. Pre-lab Assignment There is no pre-lab for this lab, yay! Instead, you will use some of the topics covered in recent lectures and HW 5. Lab Requirements To implement this lab, you are required to complete each of the following tasks. You do not need to complete the tasks in the order listed. Be sure to answer all questions fully where indicated. As always, if you have conceptual questions about the requirements or about specific C please feel free to as us—we are happy to help! Note: There is no report for this lab! Instead, take notes on the answers to the questions and save screenshots where indicated—have these ready when you ask for a signoff. When you are done, you will submit your code as well as your answers and screenshots online as usual. System Requirements 1. Start this lab by downloading the template on the course website and importing it into CCS—this lab has a slightly different template than previous labs to include the functionality for the DAC. Look over the example code to see how to use the DAC functions. 2. Your function generator will support at least four types of waveforms: DC (a constant voltage), a square wave, sawtooth wave, and a triangle wave, as described in the requirements below. -
Fourier Series
Chapter 10 Fourier Series 10.1 Periodic Functions and Orthogonality Relations The differential equation ′′ y + 2y = F cos !t models a mass-spring system with natural frequency with a pure cosine forcing function of frequency !. If 2 = !2 a particular solution is easily found by undetermined coefficients (or by∕ using Laplace transforms) to be F y = cos !t. p 2 !2 − If the forcing function is a linear combination of simple cosine functions, so that the differential equation is N ′′ 2 y + y = Fn cos !nt n=1 X 2 2 where = !n for any n, then, by linearity, a particular solution is obtained as a sum ∕ N F y (t)= n cos ! t. p 2 !2 n n=1 n X − This simple procedure can be extended to any function that can be repre- sented as a sum of cosine (and sine) functions, even if that summation is not a finite sum. It turns out that the functions that can be represented as sums in this form are very general, and include most of the periodic functions that are usually encountered in applications. 723 724 10 Fourier Series Periodic Functions A function f is said to be periodic with period p> 0 if f(t + p)= f(t) for all t in the domain of f. This means that the graph of f repeats in successive intervals of length p, as can be seen in the graph in Figure 10.1. y p 2p 3p 4p 5p Fig. 10.1 An example of a periodic function with period p. -
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. -
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. -
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 -
Tektronix Signal Generator
Signal Generator Fundamentals Signal Generator Fundamentals Table of Contents The Complete Measurement System · · · · · · · · · · · · · · · 5 Complex Waves · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 15 The Signal Generator · · · · · · · · · · · · · · · · · · · · · · · · · · · · 6 Signal Modulation · · · · · · · · · · · · · · · · · · · · · · · · · · · 15 Analog or Digital? · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 7 Analog Modulation · · · · · · · · · · · · · · · · · · · · · · · · · 15 Basic Signal Generator Applications· · · · · · · · · · · · · · · · 8 Digital Modulation · · · · · · · · · · · · · · · · · · · · · · · · · · 15 Verification · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 8 Frequency Sweep · · · · · · · · · · · · · · · · · · · · · · · · · · · 16 Testing Digital Modulator Transmitters and Receivers · · 8 Quadrature Modulation · · · · · · · · · · · · · · · · · · · · · 16 Characterization · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 8 Digital Patterns and Formats · · · · · · · · · · · · · · · · · · · 16 Testing D/A and A/D Converters · · · · · · · · · · · · · · · · · 8 Bit Streams · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 17 Stress/Margin Testing · · · · · · · · · · · · · · · · · · · · · · · · · · · 9 Types of Signal Generators · · · · · · · · · · · · · · · · · · · · · · 17 Stressing Communication Receivers · · · · · · · · · · · · · · 9 Analog and Mixed Signal Generators · · · · · · · · · · · · · · 18 Signal Generation Techniques -
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. -
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 . -
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.