Output Impedance Mismatch Effects on the Linearity Performance Of
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Automatic Linearity Detection
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Mathematical Institute Eprints Archive Report no. [13/04] Automatic linearity detection Asgeir Birkisson a Tobin A. Driscoll b aMathematical Institute, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK. [email protected]. Supported for this work by UK EPSRC Grant EP/E045847 and a Sloane Robinson Foundation Graduate Award associated with Lincoln College, Oxford. bDepartment of Mathematical Sciences, University of Delaware, Newark, DE 19716, USA. [email protected]. Supported for this work by UK EPSRC Grant EP/E045847. Given a function, or more generally an operator, the question \Is it lin- ear?" seems simple to answer. In many applications of scientific computing it might be worth determining the answer to this question in an automated way; some functionality, such as operator exponentiation, is only defined for linear operators, and in other problems, time saving is available if it is known that the problem being solved is linear. Linearity detection is closely connected to sparsity detection of Hessians, so for large-scale applications, memory savings can be made if linearity information is known. However, implementing such an automated detection is not as straightforward as one might expect. This paper describes how automatic linearity detection can be implemented in combination with automatic differentiation, both for stan- dard scientific computing software, and within the Chebfun software system. The key ingredients for the method are the observation that linear operators have constant derivatives, and the propagation of two logical vectors, ` and c, as computations are carried out. -
Transformations, Polynomial Fitting, and Interaction Terms
FEEG6017 lecture: Transformations, polynomial fitting, and interaction terms [email protected] The linearity assumption • Regression models are both powerful and useful. • But they assume that a predictor variable and an outcome variable are related linearly. • This assumption can be wrong in a variety of ways. The linearity assumption • Some real data showing a non- linear connection between life expectancy and doctors per million population. The linearity assumption • The Y and X variables here are clearly related, but the correlation coefficient is close to zero. • Linear regression would miss the relationship. The independence assumption • Simple regression models also assume that if a predictor variable affects the outcome variable, it does so in a way that is independent of all the other predictor variables. • The assumed linear relationship between Y and X1 is supposed to hold no matter what the value of X2 may be. The independence assumption • Suppose we're trying to predict happiness. • For men, happiness increases with years of marriage. For women, happiness decreases with years of marriage. • The relationship between happiness and time may be linear, but it would not be independent of sex. Can regression models deal with these problems? • Fortunately they can. • We deal with non-linearity by transforming the predictor variables, or by fitting a polynomial relationship instead of a straight line. • We deal with non-independence of predictors by including interaction terms in our models. Dealing with non-linearity • Transformation is the simplest method for dealing with this problem. • We work not with the raw values of X, but with some arbitrary function that transforms the X values such that the relationship between Y and f(X) is now (closer to) linear. -
Glossary: a Dictionary for Linear Algebra
GLOSSARY: A DICTIONARY FOR LINEAR ALGEBRA Adjacency matrix of a graph. Square matrix with aij = 1 when there is an edge from node T i to node j; otherwise aij = 0. A = A for an undirected graph. Affine transformation T (v) = Av + v 0 = linear transformation plus shift. Associative Law (AB)C = A(BC). Parentheses can be removed to leave ABC. Augmented matrix [ A b ]. Ax = b is solvable when b is in the column space of A; then [ A b ] has the same rank as A. Elimination on [ A b ] keeps equations correct. Back substitution. Upper triangular systems are solved in reverse order xn to x1. Basis for V . Independent vectors v 1,..., v d whose linear combinations give every v in V . A vector space has many bases! Big formula for n by n determinants. Det(A) is a sum of n! terms, one term for each permutation P of the columns. That term is the product a1α ··· anω down the diagonal of the reordered matrix, times det(P ) = ±1. Block matrix. A matrix can be partitioned into matrix blocks, by cuts between rows and/or between columns. Block multiplication of AB is allowed if the block shapes permit (the columns of A and rows of B must be in matching blocks). Cayley-Hamilton Theorem. p(λ) = det(A − λI) has p(A) = zero matrix. P Change of basis matrix M. The old basis vectors v j are combinations mijw i of the new basis vectors. The coordinates of c1v 1 +···+cnv n = d1w 1 +···+dnw n are related by d = Mc. -
Chapter 1: Linearity: Basic Concepts and Examples
CHAPTER I LINEARITY: BASIC CONCEPTS AND EXAMPLES In this chapter we start with the concept of general linear spaces with elements in it called vectors, for “setting up the stage”. Then we introduce “actors” called linear mappings, which act upon vectors. In the mathematical literature, “vector spaces” is synonymous to “linear spaces” and these words will be used exchangeably. Also, “linear transformations” and “linear mappings” or simply “linear maps”, are also synonymous. 1. Linear Spaces and Linear Maps § 1.1. A vector space is an entity containing objects called vectors. A vector is usually conceived to be something which has a magnitude and direction, so that it can be drawn as an arrow: You can add or subtract two vectors: You can also multiply vectors by scalars: Such things should be familiar to you. However, we should not be so narrow-minded to think that only those objects repre- 1 sented geometrically by arrows in a 2D or 3D space can be regarded as vectors. As long as we have a collection of objects among which two algebraic operations called addition and scalar multiplication can be performed, so that certain rules of such operations are obeyed, we may regard this collection as a vector space and call the objects in this col- lection vectors. Our definition of vector spaces should be so general that we encounter vector spaces almost everyday and almost everywhere. Examples of vector spaces include many spaces of functions, spaces of polynomials, spaces of sequences etc. (in addition to the well-known 3D space in which vectors are represented as arrows.) The universality and the omnipresence of vector spaces is one good reason for placing linear algebra in the position of paramount importance in basic mathematics. -
Linearity in Calibration: How to Test for Non-Linearity Previous Methods for Linearity Testing Discussed in This Series Contain Certain Shortcom- Ings
Chemometrics in Spectroscopy Linearity in Calibration: How to Test for Non-linearity Previous methods for linearity testing discussed in this series contain certain shortcom- ings. In this installment, the authors describe a method they believe is superior to others. Howard Mark and Jerome Workman Jr. n the previous installment of “Chemometrics in Spec- instrumental methods have to produce a number, represent- troscopy” (1), we promised we would present a descrip- ing the final answer for that instrument’s quantitative assess- I tion of what we believe is the best way to test for linearity ment of the concentration, and that is the test result from (or non-linearity, depending upon your point of view). In the that instrument. This is a univariate concept to be sure, but first three installments of this column series (1–3) we exam- the same concept that applies to all other analytical methods. ined the Durbin–Watson (DW) statistic along with other Things may change in the future, but this is currently the way methods of testing for non-linearity. We found that while the analytical results are reported and evaluated. So the question Durbin–Watson statistic is a step in the right direction, but we to be answered is, for any given method of analysis: Is the also saw that it had shortcomings, including the fact that it relationship between the instrument readings (test results) could be fooled by data that had the right (or wrong!) charac- and the actual concentration linear? teristics. The method we present here is mathematically This method of determining non-linearity can be viewed sound, more subject to statistical validity testing, based upon from a number of different perspectives, and can be consid- well-known mathematical principles, consists of much higher ered as coming from several sources. -
Loudspeaker Parameters
Loudspeaker Parameters D. G. Meyer School of Electrical & Computer Engineering Outline • Review of How Loudspeakers Work • Small Signal Loudspeaker Parameters • Effect of Loudspeaker Cable • Sample Loudspeaker • Electrical Power Needed • Sealed Box Design Example How Loudspeakers Work How Loudspeakers Are Made Fundamental Small Signal Mechanical Parameters 2 • Sd – projected area of driver diaphragm (m ) • Mms – mass of diaphragm (kg) • Cms – compliance of driver’s suspension (m/N) • Rms – mechanical resistance of driver’s suspension (N•s/m) • Le – voice coil inductance (mH) • Re – DC resistance of voice coil ( Ω) • Bl – product of magnetic field strength in voice coil gap and length of wire in magnetic field (T•m) Small Signal Parameters These values can be determined by measuring the input impedance of the driver, near the resonance frequency, at small input levels for which the mechanical behavior of the driver is effectively linear. • Fs – (free air) resonance frequency of driver (Hz) – frequency at which the combination of the energy stored in the moving mass and suspension compliance is maximum, which results in maximum cone velocity – usually it is less efficient to produce output frequencies below F s – input signals significantly below F s can result in large excursions – typical factory tolerance for F s spec is ±15% Measurement of Loudspeaker Free-Air Resonance Small Signal Parameters These values can be determined by measuring the input impedance of the driver, near the resonance frequency, at small input levels for which the -
Characteristic Impedance of Lines on Printed Boards by TDR 03/04
Number 2.5.5.7 ASSOCIATION CONNECTING Subject ELECTRONICS INDUSTRIES ® Characteristic Impedance of Lines on Printed 2215 Sanders Road Boards by TDR Northbrook, IL 60062-6135 Date Revision 03/04 A IPC-TM-650 Originating Task Group TEST METHODS MANUAL TDR Test Method Task Group (D-24a) 1 Scope This document describes time domain reflectom- b. The value of characteristic impedance obtained from TDR etry (TDR) methods for measuring and calculating the charac- measurements is traceable to a national metrology insti- teristic impedance, Z0, of a transmission line on a printed cir- tute, such as the National Institute of Standards and Tech- cuit board (PCB). In TDR, a signal, usually a step pulse, is nology (NIST), through coaxial air line standards. The char- injected onto a transmission line and the Z0 of the transmis- acteristic impedance of these transmission line standards sion line is determined from the amplitude of the pulse is calculated from their measured dimensional and material reflected at the TDR/transmission line interface. The incident parameters. step and the time delayed reflected step are superimposed at the point of measurement to produce a voltage versus time c. A variety of methods for TDR measurements each have waveform. This waveform is the TDR waveform and contains different accuracies and repeatabilities. information on the Z0 of the transmission line connected to the d. If the nominal impedance of the line(s) being measured is TDR unit. significantly different from the nominal impedance of the Note: The signals used in the TDR system are actually rect- measurement system (typically 50 Ω), the accuracy and angular pulses but, because the duration of the TDR wave- repeatability of the measured numerical valued will be form is much less than pulse duration, the TDR pulse appears degraded. -
Student Difficulties with Linearity and Linear Functions and Teachers
Student Difficulties with Linearity and Linear Functions and Teachers' Understanding of Student Difficulties by Valentina Postelnicu A Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Approved April 2011 by the Graduate Supervisory Committee: Carole Greenes, Chair Victor Pambuccian Finbarr Sloane ARIZONA STATE UNIVERSITY May 2011 ABSTRACT The focus of the study was to identify secondary school students' difficulties with aspects of linearity and linear functions, and to assess their teachers' understanding of the nature of the difficulties experienced by their students. A cross-sectional study with 1561 Grades 8-10 students enrolled in mathematics courses from Pre-Algebra to Algebra II, and their 26 mathematics teachers was employed. All participants completed the Mini-Diagnostic Test (MDT) on aspects of linearity and linear functions, ranked the MDT problems by perceived difficulty, and commented on the nature of the difficulties. Interviews were conducted with 40 students and 20 teachers. A cluster analysis revealed the existence of two groups of students, Group 0 enrolled in courses below or at their grade level, and Group 1 enrolled in courses above their grade level. A factor analysis confirmed the importance of slope and the Cartesian connection for student understanding of linearity and linear functions. There was little variation in student performance on the MDT across grades. Student performance on the MDT increased with more advanced courses, mainly due to Group 1 student performance. The most difficult problems were those requiring identification of slope from the graph of a line. That difficulty persisted across grades, mathematics courses, and performance groups (Group 0, and 1). -
Unit – I Filters
EC6503 Transmission lines and Waveguides Department of ECE 2018 - 2019 EC6503 TRANSMISSION LINES AND WAVEGUIDES UNIT –I TRANSMISSION LINE THEORY PART A - C303.1 1. Distinguish lumped parameters and distributed parameters. Lumped parameters are individually concentrated or lumped at discrete points in the circuit and can be identified definitely as representing a particular parameter. Distributed parameters are distributed along the circuit, each elemental length of the circuit having its own values and concentration of the individual parameters is not possible. 2. What are the different types of transmission lines? The different types of transmission lines are 1.Open wire line 2. Cable 3. Coaxial line 4. Waveguide 3. Describe the different parameters of a transmission line. (1)The parameters include resistance, which is uniformly distributed along the length of the conductors. Since current will be present, the conductors will be surrounded and linked by magnetic flux, and this phenomena will demonstrate its effect in distributed inductance along the line.(2)The conductors are separated by insulating dielectric, so that capacitance will be distributed along the conductor length.(3)The dielectric or the insulators of the open wire line may not be perfect, a leakage current will flow, and leakage conductance will exist between the conductors. 4. Give the general equations of a transmission line. The general equations are, E = ERcosh√ZY s + IRZosinh √ZY s I = IRcosh√ZY s + ( ER / Zo ) sinh √ZY s where Z= R + jωL = series impedance, ohms per unit length of line and Y = G + j ωC = shunt admittance, mhos per unit length of line. 5. -
LINEARITY Definition
SECTION 1.1 – LINEARITY Definition (Total Change) What that means: Algebraically Geometrically Example 1 At the beginning of the year, the price of gas was $3.19 per gallon. At the end of the year, the price of gas was $1.52 per gallon. What is the total change in the price of gas? Algebraically Geometrically 1 Definition (Average Rate of Change) What that means: Algebraically: Geometrically: Example 2 John collects marbles. After one year, he had 60 marbles and after 5 years, he had 140 marbles. What is the average rate of change of marbles with respect to time? Algebraically Geometrically 2 Definition (Linearly Related, Linear Model) Example 3 On an average summer day in a large city, the pollution index at 8:00 A.M. is 20 parts per million, and it increases linearly by 15 parts per million each hour until 3:00 P.M. Let P be the amount of pollutants in the air x hours after 8:00 A.M. a.) Write the linear model that expresses P in terms of x. b.) What is the air pollution index at 1:00 P.M.? c.) Graph the equation P for 0 ≤ x ≤ 7 . 3 SECTION 1.2 – GEOMETRIC & ALGEBRAIC PROPERTIES OF A LINE Definition (Slope) What that means: Algebraically Geometrically Definition (Graph of an Equation) 4 Example 1 Graph the line containing the point P with slope m. a.) P = ( ,1 1) & m = −3 c.) P = − ,1 −3 & m = 3 ( ) 5 b.) P = ,2 1 & m = 1 ( ) 2 d.) P = ,0 −2 & m = − 2 ( ) 3 5 Definition (Slope -Intercept Form) Definition (Point -Slope Form) 6 Example 2 Find the equation of the line through the points P and Q. -
The Gradient System. Understanding Gradients from an EM Perspective: (Gradient Linearity, Eddy Currents, Maxwell Terms & Peripheral Nerve Stimulation)
The Gradient System. Understanding Gradients from an EM Perspective: (Gradient Linearity, Eddy Currents, Maxwell Terms & Peripheral Nerve Stimulation) Franz Schmitt Siemens Healthcare, Business Unit MR Karl Schall Str. 6 91054 Erlangen, Germany Email: [email protected] 1 Introduction Since the introduction of magnetic resonance imaging (MRI) as a commercially available diagnostic tool in 1984, dramatic improvements have been achieved in all features defining image quality, such as resolution, signal to noise ratio (SNR), and speed. Initially, spin echo (SE)(1) images containing 128x128 pixels were measured in several minutes. Nowadays, standard matrix sizes for musculo skeletal and neuro studies using SE based techniques is 512x512 with similar imaging times. Additionally, the introduction of echo planar imaging (EPI)(2,3) techniques had made it possible to acquire 128x128 images as produced with earlier MRI scanners in roughly 100 ms. This im- provement in resolution and speed is only possible through improvements in gradient hardware of the recent MRI scanner generations. In 1984, typical values for the gradients were in the range of 1 - 2 mT/m at rise times of 1-2 ms. Over the last decade, amplitudes and rise times have changed in the order of magnitudes. Present gradient technology allows the application of gradient pulses up to 50 mT/m (for whole-body applications) with rise times down to 100 µs. Using dedicated gradient coils, such as head-insert gradient coils, even higher amplitudes and shorter rise times are feasible. In this presentation we demonstrate how modern high-performance gradient systems look like and how components of an MR system have to correlate with one another to guarantee maximum performance. -
Chapter 4. Linear Input/Output Systems
Chapter 4 Linear Input/Output Systems This chapter provides an introduction to linear input/output systems, a class of models that is the focus of the remainder of the text. 4.1 Introduction In Chapters ?? and ?? we consider construction and analysis of di®eren- tial equation models for physical systems. We placed very few restrictions on these systems other than basic requirements of smoothness and well- posedness. In this chapter we specialize our results to the case of linear, time-invariant, input/output systems. This important class of systems is one for which a wealth of analysis and synthesis tools are avilable, and hence it has found great utility in a wide variety of applications. What is a linear system? Recall that a function F : Rp ! Rq is said to be linear if it satis¯es the following property: F (®x + ¯y) = ®F (x) + ¯F (y) x; y 2 Rp; ®; ¯ 2 R: (4.1) This equation implies that the function applied to the sum of two vectors is the sum of the function applied to the individual vectors, and that the results of applying F to a scaled vector is given by scaling the result of applying F to the original vector. 53 54 CHAPTER 4. LINEAR INPUT/OUTPUT SYSTEMS Input/output systems are described in a similar manner. Namely, we wish to capture the notion that if we apply two inputs u1 and u2 to a dynamical system and obtain outputs y1 and ys, then the result of applying the sum, u1 + u2, would give y1 + y2 as the output.