2.004 Dynamics and Control II Spring 2008
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Frequency Response and Bode Plots
1 Frequency Response and Bode Plots 1.1 Preliminaries The steady-state sinusoidal frequency-response of a circuit is described by the phasor transfer function Hj( ) . A Bode plot is a graph of the magnitude (in dB) or phase of the transfer function versus frequency. Of course we can easily program the transfer function into a computer to make such plots, and for very complicated transfer functions this may be our only recourse. But in many cases the key features of the plot can be quickly sketched by hand using some simple rules that identify the impact of the poles and zeroes in shaping the frequency response. The advantage of this approach is the insight it provides on how the circuit elements influence the frequency response. This is especially important in the design of frequency-selective circuits. We will first consider how to generate Bode plots for simple poles, and then discuss how to handle the general second-order response. Before doing this, however, it may be helpful to review some properties of transfer functions, the decibel scale, and properties of the log function. Poles, Zeroes, and Stability The s-domain transfer function is always a rational polynomial function of the form Ns() smm as12 a s m asa Hs() K K mm12 10 (1.1) nn12 n Ds() s bsnn12 b s bsb 10 As we have seen already, the polynomials in the numerator and denominator are factored to find the poles and zeroes; these are the values of s that make the numerator or denominator zero. If we write the zeroes as zz123,, zetc., and similarly write the poles as pp123,, p , then Hs( ) can be written in factored form as ()()()s zsz sz Hs() K 12 m (1.2) ()()()s psp12 sp n 1 © Bob York 2009 2 Frequency Response and Bode Plots The pole and zero locations can be real or complex. -
Standard for Terminology V2
Joint Monitoring Programme for Ambient Noise North Sea 2018 – 2020 Standard for Terminology WP 3 Deliverable/Task: 3.1 Authors: L. Wang, S Robinson Affiliations: NPL Version 2.0 Date: March 2020 INTERREG North Sea Region Jomopans Project Full Title Joint Monitoring Programme for Ambient Noise North Sea Project Acronym Jomopans Programme Interreg North Region Programme Programme Priority Priority 3 Sustainable North Sea Region Colophon Name Niels Kinneging (Project Manager) Organization Name Rijkswaterstaat Email [email protected] Phone +31 6 5321 5242 This report should be cited: JOMOPANS standard: Terminology for ambient ocean noise monitoring Cover picture: UK Crown copyright 2 INTERREG North Sea Region Jomopans Table of contents 1 General acoustic terms ......................................................................................................... 5 1.1 Definitions of basic quantities and metrics ............................................................................ 5 1.2 Levels used in underwater acoustics ...................................................................................11 2 Metrics for ambient noise monitoring in JOMOPANS ..........................................................14 2.1 Metrics for use in JOMOPANS ............................................................................................14 2.2 Other common metrics not used in JOMOPANS .................................................................14 3 Terminology used in acoustic modelling ..............................................................................15 -
Computational Entropy and Information Leakage∗
Computational Entropy and Information Leakage∗ Benjamin Fuller Leonid Reyzin Boston University fbfuller,[email protected] February 10, 2011 Abstract We investigate how information leakage reduces computational entropy of a random variable X. Recall that HILL and metric computational entropy are parameterized by quality (how distinguishable is X from a variable Z that has true entropy) and quantity (how much true entropy is there in Z). We prove an intuitively natural result: conditioning on an event of probability p reduces the quality of metric entropy by a factor of p and the quantity of metric entropy by log2 1=p (note that this means that the reduction in quantity and quality is the same, because the quantity of entropy is measured on logarithmic scale). Our result improves previous bounds of Dziembowski and Pietrzak (FOCS 2008), where the loss in the quantity of entropy was related to its original quality. The use of metric entropy simplifies the analogous the result of Reingold et. al. (FOCS 2008) for HILL entropy. Further, we simplify dealing with information leakage by investigating conditional metric entropy. We show that, conditioned on leakage of λ bits, metric entropy gets reduced by a factor 2λ in quality and λ in quantity. ∗Most of the results of this paper have been incorporated into [FOR12a] (conference version in [FOR12b]), where they are applied to the problem of building deterministic encryption. This paper contains a more focused exposition of the results on computational entropy, including some results that do not appear in [FOR12a]: namely, Theorem 3.6, Theorem 3.10, proof of Theorem 3.2, and results in Appendix A. -
A Weakly Informative Default Prior Distribution for Logistic and Other
The Annals of Applied Statistics 2008, Vol. 2, No. 4, 1360–1383 DOI: 10.1214/08-AOAS191 c Institute of Mathematical Statistics, 2008 A WEAKLY INFORMATIVE DEFAULT PRIOR DISTRIBUTION FOR LOGISTIC AND OTHER REGRESSION MODELS By Andrew Gelman, Aleks Jakulin, Maria Grazia Pittau and Yu-Sung Su Columbia University, Columbia University, University of Rome, and City University of New York We propose a new prior distribution for classical (nonhierarchi- cal) logistic regression models, constructed by first scaling all nonbi- nary variables to have mean 0 and standard deviation 0.5, and then placing independent Student-t prior distributions on the coefficients. As a default choice, we recommend the Cauchy distribution with cen- ter 0 and scale 2.5, which in the simplest setting is a longer-tailed version of the distribution attained by assuming one-half additional success and one-half additional failure in a logistic regression. Cross- validation on a corpus of datasets shows the Cauchy class of prior dis- tributions to outperform existing implementations of Gaussian and Laplace priors. We recommend this prior distribution as a default choice for rou- tine applied use. It has the advantage of always giving answers, even when there is complete separation in logistic regression (a common problem, even when the sample size is large and the number of pre- dictors is small), and also automatically applying more shrinkage to higher-order interactions. This can be useful in routine data analy- sis as well as in automated procedures such as chained equations for missing-data imputation. We implement a procedure to fit generalized linear models in R with the Student-t prior distribution by incorporating an approxi- mate EM algorithm into the usual iteratively weighted least squares. -
1 1. Data Transformations
260 Archives ofDisease in Childhood 1993; 69: 260-264 STATISTICS FROM THE INSIDE Arch Dis Child: first published as 10.1136/adc.69.2.260 on 1 August 1993. Downloaded from 1 1. Data transformations M J R Healy Additive and multiplicative effects adding a constant quantity to the correspond- In the statistical analyses for comparing two ing before readings. Exactly the same is true of groups of continuous observations which I the unpaired situation, as when we compare have so far considered, certain assumptions independent samples of treated and control have been made about the data being analysed. patients. Here the assumption is that we can One of these is Normality of distribution; in derive the distribution ofpatient readings from both the paired and the unpaired situation, the that of control readings by shifting the latter mathematical theory underlying the signifi- bodily along the axis, and this again amounts cance probabilities attached to different values to adding a constant amount to each of the of t is based on the assumption that the obser- control variate values (fig 1). vations are drawn from Normal distributions. This is not the only way in which two groups In the unpaired situation, we make the further of readings can be related in practice. Suppose assumption that the distributions in the two I asked you to guess the size of the effect of groups which are being compared have equal some treatment for (say) increasing forced standard deviations - this assumption allows us expiratory volume in one second in asthmatic to simplify the analysis and to gain a certain children. -
Towards a New Enlightenment? a Transcendent Decade
Towards a New Enlightenment? A Transcendent Decade Preface This book, Towards a New Enlightenment? A Transcendent Decade, is the eleventh in an annual series that BBVA’s OpenMind project dedicates to disseminating greater knowledge on the key questions of our time. We began this series in 2008, with the first book, Frontiers of Knowledge, that celebrated the launching of the prizes of the same name awarded annually by the BBVA Foundation. Since then, these awards have achieved worldwide renown. In that first book, over twenty major scientists and experts used language accessible to the general public to rigorously review the most relevant recent advances and perspectives in the different scientific and artistic fields recognized by those prizes. Since then, we have published a new book each year, always following the same model: collections of articles by key figures in their respective fields that address different aspects or perspectives on the fundamental questions that affect our lives and determine our futu- re: from globalization to the impact of exponential technologies, and on to include today’s major ethical problems, the evolution of business in the digital era, and the future of Europe. The excellent reaction to the first books in this series led us, in 2011, to create OpenMind (www.bbvaopenmind.com), an online community for debate and the dissemination of knowle- dge. Since then, OpenMind has thrived, and today it addresses a broad spectrum of scientific, technological, social, and humanistic subjects in different formats, including our books, as well as articles, posts, reportage, infographics, videos, and podcasts, with a growing focus on audiovisual materials. -
C-17Bpages Pdf It
SPECIFICATION DEFINITIONS FOR LOGARITHMIC AMPLIFIERS This application note is presented to engineers who may use logarithmic amplifiers in a variety of system applica- tions. It is intended to help engineers understand logarithmic amplifiers, how to specify them, and how the loga- rithmic amplifiers perform in a system environment. A similar paper addressing the accuracy and error contributing elements in logarithmic amplifiers will follow this application note. INTRODUCTION The need to process high-density pulses with narrow pulse widths and large amplitude variations necessitates the use of logarithmic amplifiers in modern receiving systems. In general, the purpose of this class of amplifier is to condense a large input dynamic range into a much smaller, manageable one through a logarithmic transfer func- tion. As a result of this transfer function, the output voltage swing of a logarithmic amplifier is proportional to the input signal power range in dB. In most cases, logarithmic amplifiers are used as amplitude detectors. Since output voltage (in mV) is proportion- al to the input signal power (in dB), the amplitude information is displayed in a much more usable format than accomplished by so-called linear detectors. LOGARITHMIC TRANSFER FUNCTION INPUT RF POWER VS. DETECTED OUTPUT VOLTAGE 0V 50 MILLIVOLTS/DIV. 250 MILLIVOLTS/DIV. 0V 50.0 ns/DIV. 50.0 ns/DIV. There are three basic types of logarithmic amplifiers. These are: • Detector Log Video Amplifiers • Successive Detection Logarithmic Amplifiers • True Log Amplifiers DETECTOR LOG VIDEO AMPLIFIER (DLVA) is a type of logarithmic amplifier in which the envelope of the input RF signal is detected with a standard "linear" diode detector. -
Linear and Logarithmic Scales
Linear and logarithmic scales. Defining Scale You may have thought of a scale as something to weigh yourself with or the outer layer on the bodies of fish and reptiles. For this lesson, we're using a different definition of a scale. A scale, in this sense, is a leveled range of values/numbers from lowest to highest that measures something at regular intervals. A great example is the classic number line that has numbers lined up at consistent intervals along a line. What Is a Linear Scale? A linear scale is much like the number line described above. They key to this type of scale is that the value between two consecutive points on the line does not change no matter how high or low you are on it. For instance, on the number line, the distance between the numbers 0 and 1 is 1 unit. The same distance of one unit is between the numbers 100 and 101, or -100 and -101. However you look at it, the distance between the points is constant (unchanging) regardless of the location on the line. A great way to visualize this is by looking at one of those old school intro to Geometry or mid- level Algebra examples of how to graph a line. One of the properties of a line is that it is the shortest distance between two points. Another is that it is has a constant slope. Having a constant slope means that the change in x and y from one point to another point on the line doesn't change. -
A Dark Energy Timeline for the Universe Thomas Ethan Lowery Advisor: Marc Sher
A Dark Energy Timeline for the Universe Thomas Ethan Lowery Advisor: Marc Sher April 2013 1 Abstract Dark energy is a form of vacuum energy that generates a negative pressure in space and is described by the cosmological constant, . This vacuum pressure serves to generate a negative gravitational field pointed radially outward. The purpose of this project is to calculate and compare the relative effects on dynamical relaxation of large cosmological structures due to gravity and dark energy negative pressure. We find that the relative effects of dark energy and gravity are comparable to within a few orders of magnitude over long enough time frames and at large enough size scales with the relative contribution of dark energy increasing for more vast structures. 1. Introduction Our universe is an unfathomably large and expanding place and is still in its infancy after its Big Bang birth. In only about its thirteen billionth year ( ) of existence, the universe is expected to live on until the one hundredth cosmological decade (this is a form of standard shorthand used in cosmology to discuss time scales over many orders of magnitude. The “ cosmological decade” refers to the year so the one hundredth cosmological decade is the year ) or even beyond where it will face its effective end by the Big Crunch or Cosmological Heat Death or the Universe Phase Transition. Suffice it to say, the lifespan of our universe is very long and there is a lot about our universe that we are only just beginning to understand, including a recently proposed form of energy called dark energy. -
The Indefinite Logarithm, Logarithmic Units, and the Nature of Entropy
The Indefinite Logarithm, Logarithmic Units, and the Nature of Entropy Michael P. Frank FAMU-FSU College of Engineering Dept. of Electrical & Computer Engineering 2525 Pottsdamer St., Rm. 341 Tallahassee, FL 32310 [email protected] August 20, 2017 Abstract We define the indefinite logarithm [log x] of a real number x> 0 to be a mathematical object representing the abstract concept of the logarithm of x with an indeterminate base (i.e., not specifically e, 10, 2, or any fixed number). The resulting indefinite logarithmic quantities naturally play a mathematical role that is closely analogous to that of dimensional physi- cal quantities (such as length) in that, although these quantities have no definite interpretation as ordinary numbers, nevertheless the ratio of two of these entities is naturally well-defined as a specific, ordinary number, just like the ratio of two lengths. As a result, indefinite logarithm objects can serve as the basis for logarithmic spaces, which are natural systems of logarithmic units suitable for measuring any quantity defined on a log- arithmic scale. We illustrate how logarithmic units provide a convenient language for explaining the complete conceptual unification of the dis- parate systems of units that are presently used for a variety of quantities that are conventionally considered distinct, such as, in particular, physical entropy and information-theoretic entropy. 1 Introduction The goal of this paper is to help clear up what is perceived to be a widespread arXiv:physics/0506128v1 [physics.gen-ph] 15 Jun 2005 confusion that can found in many popular sources (websites, popular books, etc.) regarding the proper mathematical status of a variety of physical quantities that are conventionally defined on logarithmic scales. -
Logarithmic Amplifier for Ultrasonic Sensor Signal Conditioning
Application Report SLDA053–March 2020 Logarithmic Amplifier for Ultrasonic Sensor Signal Conditioning Akeem Whitehead, Kemal Demirci ABSTRACT This document explains the basic operation of a logarithmic amplifier, how a logarithmic amplifier works in an ultrasonic front end, the advantages and disadvantages of using a logarithmic amplifier versus a linear time-varying gain amplifier, and compares the performance of a logarithmic amplifier versus a linear amplifier. These topics apply to the TUSS44x0 device family (TUSS4440 and TUSS4470), which includes Texas Instrument’s latest ultrasonic driver and logarithmic amplifier-based receiver front end integrated circuits. The receive signal path of the TUSS44x0 devices includes a low-noise linear amplifier, a band pass filter, followed by a logarithmic amplifier for input-level dependent amplification. The logarithmic amplifier allows for high sensitivity for weak echo signals and offers a wide input dynamic range over full range of reflected echoes. Contents 1 Introduction to Logarithmic Amplifier (Log Amp)......................................................................... 2 2 Logarithmic Amplifier in Ultrasonic Sensing.............................................................................. 8 3 Logarithmic versus Time Varying Linear Amplifier..................................................................... 10 4 Performance of Log Amp in Ultrasonic Systems....................................................................... 11 List of Figures 1 Linear Input and Logarithmic -
Arxiv:1712.03037V1 [Cs.CV] 8 Dec 2017 Community
A Frequency Domain Neural Network for Fast Image Super-resolution Junxuan Li1∗ Shaodi You1;2 Antonio Robles-Kelly1;2 1College of Eng. and Comp. Sci., Australian National University, Canberra, ACT 2601, Australia 2Datat61-CSIRO, Black Mountain Laboratories, Acton, ACT 2601, Australia Abstract by aggregating multiple frames with complementary spa- tial information or by relating the higher-resolved image to In this paper, we present a frequency domain neural net- the lower resolution one by a sparse linear system. For in- work for image super-resolution. The network employs the stance, Baker and Kanade [1] formulated the problem in a convolution theorem so as to cast convolutions in the spatial regularization setting where the examples are constructed domain as products in the frequency domain. Moreover, the using a pyramid approach. Protter et al. [22] used block non-linearity in deep nets, often achieved by a rectifier unit, matching to estimate a motion model and use exemplars to is here cast as a convolution in the frequency domain. This recover super-resolved videos. Yang et al. [31] used sparse not only yields a network which is very computationally ef- coding to perform super-resolution by learning a dictionary ficient at testing but also one whose parameters can all be that can then be used to produce the output image, by lin- learnt accordingly. The network can be trained using back early combining learned exemplars. propagation and is devoid of complex numbers due to the Note that, the idea of super-solution “by example” can use of the Hartley transform as an alternative to the Fourier be viewed as hinging on the idea of learning functions so transform.