DOCSLIB.ORG

Why Study ISR?

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Why Study ISR? Introduction to ISR Signal Processing Joshua Semeter, Boston University Why study ISR? Requires that you learn about a great many useful and fascinating subjects in substantial depth. • Plasma physics • Radar • Coding (information theory) • Electronics (Power, RF, DSP) • Signal Processing • Inverse theory Outline • Mathematical toolbox • Review of basic radar concepts • Ionospheric Doppler spectrum • Range resolution and matched filtering • I/Q demodulation • Autocorrelation function (ACF) and Power Spectral Density (PSD) Euler identity and the complex plane Imaginary Time Real is the “angular velocity” (radians/s) of the spinning arrow is the number of complete rotations ( radians) in one second (1/s or Hz) We need a signal that tells us how fast and in which direction the arrow is spinning. This signal is the complex exponential. Invoking the Euler identity, I = in-phase component Q = in-quadrature component Essential mathematical operations Fourier Transform: Expresses a function as a weighted sum of harmonic functions (i.e., complex exponentials) Convolution: Expresses the action of a linear, time-invariant system on a function. Correlation: A measure of the degree to which two functions look alike at a given offset. Autocorrelation, Convolution, Power Spectral Density, Wiener-Khinchin Theorem Dirac Delta Function &$ +", x = 0 !(t) = % '& 0, x # 0 The Fourier Transform of a train of delta functions is a train of delta functions. Harmonic Functions Gate function ISR spectrum Autocorrelation function (ACF) Increasing Te Not surprisingly, the ISR ACF looks like a sinc function… How it all hangs together. • Duality: - Gate function in the time domain represents amplitude modulation - Gate function in the frequency domain represents filtering • Limiting cases: τ - Gate function approaches delta function as width goes to 0 with constant area - A constant function in time domain is a special case of harmonic function where frequency = 0. - A constant function in time domain is a special case of a gate function where width = infinity. frequency τ f0 f0 – ()1 ⁄ τ f0 + ()1 ⁄ τ frequency Figure 5.3. Amplitude spectrum for a single pulse, or a train of f0 non-coherent pulses. τ f0 – ()1 ⁄ τ f0 + ()1 ⁄ τ Now consider the coherent gated CW waveform f3()t given by Figure 5.3. Amplitude spectrum for a single pulse, or a train of How many cycles are in a typical ISR pulse? non-coherent pulses. PFISR frequency: 449∞ MHz 215,520 cycles! Typical long-pulse length: 480 s (5.15) f3()t = f2()tnT– µ Now consider the coherent gated CW waveform f3()t given by ∑ frequency n = –∞ ∞ f Clearly f ()t is periodic, where T0 is the period (recall that f = 1 ⁄ T is the 3 r f3()t = ∑ f2()tnT– (5.15) PRF). Using the complex exponential Fourier series we can rewrite f3()t as f0 – ()1 ⁄ τ f0 + ()1 ⁄ τ n = –∞ Clearly is periodic, where is the period (recall that = 1 is the Bandwidth∞ off3()t a pulsedT signalfr ⁄ T j--------------2πnt PRF). Using the complex exponential Fourier series we can rewrite f ()t as T 3 Figure 5.3. Amplitudef3()t spectrum= F forne a single pulse, or a train of (5.16) ∑ ∞ non-coherent pulses. --------------j2πnt Spectrum of receivern = –∞ output has sinc shape, with sidelobes halfT the width of the f3()t = Fne (5.16) where the Fouriercentral series coefficients lobe and F continuously are given by diminishing in amplitude ∑above and below main lobe n n = –∞ Now consider the coherent gated CW waveform f3()t given by where the Fourier series coefficients F are given by Aτ nτπ n F = ------ ∞Sinc--------- (5.17) n T T Aτ nτπ Fn = ------ Sinc --------- (5.17) f3()t = f2()tnT– (5.15) T T It follows that the FT of f3()t is ∑ n = –∞ It follows that the FT of f3()t is ∞ A 1 microsecond pulse has a null- ∞ Clearly f3()t is periodic, where T is the period (recall that fr = 1Two⁄ T ispossible the bandwidth measures: F3()ω = 2π Fnδω()– 2nπfr (5.18) PRF). Using the complexto-null exponentialbandwidth∑ Fourier of the series central we can rewrite f ()tF ()asω = 2π F δω()– 2nπf (5.18) 3 3 ∑ n r lobe = 2 MHzn = –∞ 2 “null to null” bandwidthn = –∞ ∞ Bnn = --------------j2πnt T ! © 2000 by Chapman & Hall/CRC = © 2000 by Chapman & Hall/CRC(5.16) f3()t ∑ Fne n = –∞ “3dB” bandwidth where the Fourier series coefficients Fn are given by Aτ nτπ F = ------ Sinc--------- (5.17) n T T Unless otherwise specified, assume bandwidth refers to 3 dB bandwidth It follows that the FT of f3()t is ∞ 10 = 2 – 2 (5.18) F3()ω π ∑ Fnδω()nπfr n = –∞ © 2000 by Chapman & Hall/CRC Pulse-Bandwidth Connection frequency fo Shorter pulse Larger bandwidth Faster sampling rate Larger bandwidth Components of a Pulsed Doppler Radar + _ _ + _ + Plasma density (Ne) Ion temperature (T ) Model Fit i Electron temperature (Te) Bulk velocity (Vi) 12 The deciBel (dB) The relative value of two quantities expressed on a logarithmic scale P1 V1 SNR = 10 log10 = 20 log10 2 P2 V2 (Power Voltage ) Scientific Other forms used in radar: Factor of: Notation dB -1 0.1 10 -10 dBW dB relative to 1 Watt 0.5 100.3 -3 0 dBm dB relative to 1 mW 1 10 0 2 2 100.3 3 dBsm dB relative to 1 m of 10 101 10 radar cross section 100 102 20 dBi dB relative to isotropic 1000 103 30 . radiation 1,000,000 106 60 Pulsed Radar Pulse length 100 µsec 1 Mega-Watt Peak power Power Target Return 10-14 Watt Inter-pulse period (IPP) 1 msec Time Pulse length Duty cycle = 10% Pulse repetition interval Average power = Peak power * Duty cycle 100 kWatt Pulse repetition frequency (PRF) = 1/(IPP) 1 kHz Continuous wave (CW) radar: Duty cycle = 100% (always on) Doppler Frequency Shift Transmitted signal: After return from target: To measure frequency, we need to observe signal for at least one cycle. So we will need a model of how R changes with time. Assume constant velocity: Substituting: constant ! f D By convention, positive Doppler shift Target and radar are “closing” Two key concepts Two key concepts: e- Distant Time e- e- R = c!t 2 R How many cycles are in a typical pulse? Velocity Frequency PFISR frequency: 449 MHz v = ! fD"0 2 Typical long-pulse length: 480 µs A Doppler radar measures backscattered power as a function range and velocity. Velocity is manifested as a Doppler frequency shift in the received signal. Two key concepts Two key concepts: Distant Time R = c!t 2 Velocity Frequency v = ! fD"0 2 A Doppler radar measures backscattered power as a function range and velocity. Velocity is manifested as a Doppler frequency shift in the received signal. ENG SC700 Radar Remote Sensing J. Semeter, Boston University Concept of a “Doppler Spectrum” Some Other Doppler Spectra NEXRAD WSR-88D Two key concepts: Distant Time ENG SC700 RadarR = Remotec!t 2 Sensing J. Semeter, Boston University Velocity FrequencySome Other Doppler Spectra v = ! f " 2 D 0 Power (dB) NEXRAD WSR-88D Tennis ball, birds, aircraft engine --examples of solid objects that give Doppler spectrum Velocity (m/s)S. Bachman, MS Thesis If there is a distribution of targets moving at different velocities (e.g., electrons in the ionosphere) then there is no single Doppler shift but, rather, a Doppler spectrum. What is the Doppler spectrum of the ionosphere at UHF ( ! of 10 to 30 cm)? Tennis ball, birds, aircraft engine Incoherent Scatter Radar --examples of solid objects that give Doppler spectrum S. Bachman, MS Thesis --random thermal motion of plasma results in Doppler spectrum ..more on that in the next 2 lectures. Incoherent Scatter Radar --random thermal motion of plasma results in Doppler spectrum ..more on that in the next 2 lectures. DIAZ ET AL.: BEAM-PLASMA INSTABILITY EFFECTS ON IS SPECTRA X-5 55 can account for the simultaneous enhancement in the two ion lines, and the simultaneous 56 ion and plasma line enhancement. 57 This purpose of this paper is to provide a unified theoretical model of modes expected in 58 the ISR spectrum in the presence of field-aligned electron beams. The work is motivated 59 by the phenomenological studies summarized above, in addition to recent theoretical 60 results–in particular, those of Yoon et al. [2003], and references therein, which suggest 61 that Langmuir harmonics should arise as a natural consequence of the same conditions 62 producing NEIALs. Although these effects have been treated in considerable detail in the 63 plasma physics literature, their implications for the field of ionospheric radio science (and 64 ISR in particular) have not yet been discussed. The conditions to detect all the modes 65 present within the IS spectrum within the same ISR is also presented in this work. 2. Plasma in Thermal Equilibrium 66 There exist two natural electrostatic longitudinal modes in a plasma in thermal equilib- 67 rium: the ion acoustic mode, which is the main mode detected by ISRs, and the Langmuir 19 68 mode [Boyd and Sanderson, 2003]. Using a linear approach to solve the Vlasov-Poisson X-6 DIAZ ET AL.: BEAM-PLASMA INSTABILITY EFFECTS ON IS SPECTRA When the plasma is warm, which meansX-6 that the thermalDIAZ velocityET AL.: ofBEAM-PLASMA the particles INSTABILITY is EFFECTS ON IS SPECTRA 69 system of equations, the dispersion relation of these modes is obtained. The real part of 19 Longitudinal1 3 Modes in a Thermal Plasma important,X-6 it can be describedDIAZET as previously AL.: BEAM-PLASMA with a2 force-balance INSTABILITY2 motion EFFECTS equation ON IS SPECTRA but this π me Te Te 3 70 the ion acoustic dispersion relation readsωsi = + exp ωs 1 3 (2) − 8 m T − 2T − 2 2 2 timeWhen with the a plasma term that is warm, accounts which for means the thermal that thei velocity thermal ofi velocity the particles, of theiπ a particles pressureme is term Te Te 3 ωsi = + exp ωs (2) Ion-acoustic 1 3 2 2 − 8 mi Ti − 2Ti − 2 important,(γpTj n itj).
Load more

Recommended publications
  • On Singularity Properties of Convolutions of Algebraic Morphisms
    ON SINGULARITY PROPERTIES OF CONVOLUTIONS OF ALGEBRAIC MORPHISMS ITAY GLAZER AND YOTAM I. HENDEL Abstract. Let K be a field of characteristic zero, X and Y be smooth K-varieties, and let V be a finite dimensional K-vector space. For two algebraic morphisms ϕ : X → V and ψ : Y → V we define a convolution operation, ϕ ∗ ψ : X × Y → V , by ϕ ∗ ψ(x,y)= ϕ(x)+ ψ(y). We then study the singularity properties of the resulting morphism, and show that as in the case of convolution in analysis, it has improved smoothness properties. Explicitly, we show that for any morphism ϕ : X → V which is dominant when restricted to each irreducible component of X, there exists N ∈ N such that for any n > N the n-th convolution power ϕn := ϕ ∗···∗ ϕ is a flat morphism with reduced geometric fibers of rational singularities (this property is abbreviated (FRS)). By a theorem of Aizenbud and Avni, for K = Q, this is equivalent to good asymptotic behavior of the size of the Z/pkZ-fibers of ϕn when ranging over both p and k. More generally, we show that given a family of morphisms {ϕi : Xi → V } of complexity D ∈ N (i.e. that the number of variables and the degrees of the polynomials defining Xi and ϕi are bounded by D), there exists N(D) ∈ N such that for any n > N(D), the morphism ϕ1 ∗···∗ ϕn is (FRS). Contents 1. Introduction 2 1.1. Motivation 2 1.2. Main results 3 1.3. A brief discussion of the (FRS) property 4 1.4.
    [Show full text]
  • A Fourier Restriction Theorem Based on Convolution Powers
    PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 142, Number 11, November 2014, Pages 3897–3901 S 0002-9939(2014)12148-4 Article electronically published on July 21, 2014 A FOURIER RESTRICTION THEOREM BASED ON CONVOLUTION POWERS XIANGHONG CHEN (Communicated by Alexander Iosevich) Abstract. We prove a Fourier restriction estimate under the assumption that certain convolution power of the measure admits an r-integrable density. Introduction Let F be the Fourier transform defined on the Schwartz space by fˆ(ξ)= e−2πiξ,xf(x)dx Rd where ξ,x is the Euclidean inner product. We are interested in Borel measures μ defined on Rd for which F maps Lp(Rd) boundedly to L2(μ), i.e. ˆ d (1) fL2(μ) fLp(Rd), ∀f ∈S(R ). Here “” means the left-hand side is bounded by the right-hand side multiplied by a positive constant that is independent of f. If μ is a singular measure, then such a result can be interpreted as a restriction property of the Fourier transform. Such restriction estimates for singular measures were first obtained by Stein in the 1960’s. If μ is the surface measure on the ≤ ≤ 2(d+1) sphere, the Stein-Tomas theorem [12], [13] states that (1) holds for 1 p d+3 . Mockenhaupt [10] and Mitsis [9] have shown that Tomas’s argument in [12] can be used to obtain an L2-Fourier restriction theorem for a general class of finite Borel measures satisfying (2) |μˆ(ξ)|2 |ξ|−β, ∀ξ ∈ Rd, (3) μ(B(x, r)) rα, ∀x ∈ Rd,r >0, ≤ 4(d−α)+2β where 0 <α,β<d; they showed that (1) holds for 1 p<p0 = 4(d−α)+β .Bak and Seeger [2] proved the same result for the endpoint p0 and further strengthened it by replacing the Lp0 -norm with the Lp0,2-Lorentz norm.
    [Show full text]
  • 7. Transformations of Variables
    Virtual Laboratories > 2. Distributions > 1 2 3 4 5 6 7 8 7. Transformations of Variables Basic Theory The Problem As usual, we start with a random experiment with probability measure ℙ on an underlying sample space. Suppose that we have a random variable X for the experiment, taking values in S, and a function r:S→T . Then Y=r(X) is a new random variabl e taking values in T . If the distribution of X is known, how do we fin d the distribution of Y ? This is a very basic and important question, and in a superficial sense, the solution is easy. 1. Show that ℙ(Y∈B) = ℙ ( X∈r −1( B)) for B⊆T. However, frequently the distribution of X is known either through its distribution function F or its density function f , and we would similarly like to find the distribution function or density function of Y . This is a difficult problem in general, because as we will see, even simple transformations of variables with simple distributions can lead to variables with complex distributions. We will solve the problem in various special cases. Transformed Variables with Discrete Distributions 2. Suppose that X has a discrete distribution with probability density function f (and hence S is countable). Show that Y has a discrete distribution with probability density function g given by g(y)=∑ f(x), y∈T x∈r −1( y) 3. Suppose that X has a continuous distribution on a subset S⊆ℝ n with probability density function f , and that T is countable.
    [Show full text]
  • Finitary Isomorphisms of Renewal Point Processes and Continuous-Time Regenerative Processes
    FINITARY ISOMORPHISMS OF RENEWAL POINT PROCESSES AND CONTINUOUS-TIME REGENERATIVE PROCESSES YINON SPINKA Abstract. We show that a large class of stationary continuous-time regenerative processes are finitarily isomorphic to one another. The key is showing that any stationary renewal point process whose jump distribution is absolutely continuous with exponential tails is finitarily isomorphic to a Poisson point process. We further give simple necessary and sufficient conditions for a renewal point process to be finitarily isomorphic to a Poisson point process. This improves results and answers several questions of Soo [33] and of Kosloff and Soo [19]. 1. Introduction and main results An important problem in ergodic theory is to understand which measure-preserving systems are isomorphic to which other measure-preserving systems. The measure-preserving systems considered here are the flows associated to continuous-time processes and point processes, equipped with the action of R by translations. The simplest and most canonical of these processes are the Poisson point processes.1 It is therefore of particular interest to identify those processes which are isomorphic to a Poisson point process. A consequence of Ornstein theory [25] is that any two Poisson point processes are isomorphic to one another. In fact, Poisson point processes are examples of infinite-entropy Bernoulli flows, and any two infinite-entropy Bernoulli flows are isomorphic. The factor maps provided by Ornstein theory are obtained through abstract existence results. Consequently, such maps are non-explicit and one usually has no control on the coding window, i.e., the size of the window one needs to observe in the source process in order to be able to determine the target process on a fixed interval.
    [Show full text]
  • Dynamics of Markov Chains for Undergraduates
    Dynamics of Markov Chains for Undergraduates Ursula Porod February 19, 2021 Contents Preface 6 1 Markov chains 8 1.1 Introduction . .8 1.2 Construction of a Markov chain . .9 1.2.1 Finite-length trajectories . .9 1.2.2 From finite to infinite-length trajectories . 11 1.3 Basic computations for Markov chains . 16 1.4 Strong Markov Property . 21 1.5 Examples of Markov Chains . 23 1.6 Functions of Markov chains . 35 1.7 Irreducibility and class structure of the state space . 42 1.8 Transience and Recurrence . 44 1.9 Absorbing chains . 53 1.9.1 First step analysis . 53 1.9.2 Finite number of transient states . 55 1.9.3 Infinite number of transient states . 61 1.10 Stationary distributions . 66 1.10.1 Existence and uniqueness of an invariant measure . 68 1.10.2 Positive recurrence versus null recurrence . 73 1.10.3 Stationary distributions for reducible chains . 74 1.10.4 Steady state distributions . 76 1.11 Periodicity . 77 1.12 Exercises . 85 2 Limit Theorems for Markov Chains 87 2.1 The Ergodic Theorem . 87 2.2 Convergence . 93 2.3 Long-run behavior of reducible chains . 98 1 CONTENTS 2 2.4 Exercises . 100 3 Random Walks on Z 101 3.1 Basics . 101 3.2 Wald's Equations . 103 3.3 Gambler's Ruin . 105 3.4 P´olya's Random Walk Theorem . 111 3.5 Reflection Principle and Duality . 115 3.5.1 The ballot problem . 120 3.5.2 Dual walks . 121 3.5.3 Maximum and minimum .
    [Show full text]
  • Approximation of a Free Poisson Process by Systems of Freely Independent Particles
    Approximation of a free Poisson process by systems of freely independent particles Article Accepted Version Boż ejko, M., da Silva, J. L., Kuna, T. and Lytvynov, E. (2018) Approximation of a free Poisson process by systems of freely independent particles. Infinite Dimensional Analysis, Quantum Probability and Related Topics, 21 (3). 1850020. ISSN 1793- 6306 doi: https://doi.org/10.1142/s0219025718500200 Available at http://centaur.reading.ac.uk/78509/ It is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing . To link to this article DOI: http://dx.doi.org/10.1142/s0219025718500200 Publisher: World Scientific Publishing All outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders. Terms and conditions for use of this material are defined in the End User Agreement . www.reading.ac.uk/centaur CentAUR Central Archive at the University of Reading Reading’s research outputs online Approximation of a free Poisson process by systems of freely independent particles Marek Bo_zejko Instytut Matematyczny, Uniwersytet Wroc lawski, Pl. Grunwaldzki 2/4, 50-384 Wroc law, Poland; e-mail: [email protected] Jos´eLu´ısda Silva Universidade da Madeira, Edif´ıcioda Penteada, Caminho da Penteada, 9020-105, Fun- chal, Madeira, Portugal; e-mail: [email protected] Tobias Kuna University of Reading, Department of Mathematics, Whiteknights, PO Box 220, Read- ing RG6 6AX, U.K. e-mail: [email protected] Eugene Lytvynov Department of Mathematics, Swansea University, Singleton Park, Swansea SA2 8PP, U.K.; e-mail: [email protected] Abstract d Let σ be a non-atomic, infinite Radon measure on R , for example, dσ(x) = z dx where z > 0.
    [Show full text]
  • Convolution Semigroups Kevin W
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Louisiana State University Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 2009 Convolution semigroups Kevin W. Zito Louisiana State University and Agricultural and Mechanical College, [email protected] Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Applied Mathematics Commons Recommended Citation Zito, Kevin W., "Convolution semigroups" (2009). LSU Doctoral Dissertations. 2990. https://digitalcommons.lsu.edu/gradschool_dissertations/2990 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. Convolution Semigroups A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Mathematics by Kevin W. Zito B.S. in Mathematics, Louisiana State University, 2001 M.Ed. in Mathematics Education, Louisiana State University, 2002 M.S. in Mathematics, Louisiana State University, 2005 August 2009 Dedication This dissertation is dedicated to the memories of Carl Wayne “Bubba” Belello, Tony Wayne Zito, & Frank Zito ii Acknowledgments It is a great pleasure to thank my advisor Professor Frank Neubrander. This de- gree, and a couple of the other ones, would not have been possible without his guidance and support. I would also like to thank Lee Windsperger, Fareed Hawwa, Dr.
    [Show full text]
  • A Note on the Closure of Convolution Power Mixtures (Random Sums) of Exponential Distributions
    J. Aust. Math. Soc. 84 (2008), 1–7 doi:10.1017/S1446788708000104 A NOTE ON THE CLOSURE OF CONVOLUTION POWER MIXTURES (RANDOM SUMS) OF EXPONENTIAL DISTRIBUTIONS J. M. P. ALBIN (Received 11 April 2007; revised 21 July 2007) Communicated by V. T. Stefanov Abstract We make a correction to an important result by Cline [D. B. H. Cline, ‘Convolutions of distributions with exponential tails’, J. Austral. Math. Soc. (Series A) 43 (1987), 347–365; D. B. H. Cline, ‘Convolutions of distributions with exponential tails: corrigendum’, J. Austral. Math. Soc. (Series A) 48 (1990), 152–153] on the closure of the exponential class L(α) under convolution power mixtures (random summation). 2000 Mathematics subject classification: primary 60F99, 62E20; secondary 44A10, 60E07. Keywords and phrases: compound Poisson distribution, convolution power, exponential distribution, random sum, Tauberian theorem. 1. Introduction We say that a cumulative probability distribution function H on the real line belongs to the exponential class L(α), α > 0, if H(x + t) 1 − H(x + t) −αt lim = lim = e for t ∈ R. (1) x→∞ H(x) x→∞ 1 − H(x) (Some authors require members of L(α) to be supported on [0, ∞) to avoid some technicalities. However, because of the applications we have in mind (see, for example, Section 4 below) we do not want to make this restriction.) Note the elementary fact that (1) holds for t ∈ R if and only if it holds for t > 0. Furthermore, H ∈ L(α) if and only if H ◦ log is regularly varying at infinity with index −α. When (1) holds with α = 0 we get the class of long-tailed distributions L(0) = L introduced by Pitman [10].
    [Show full text]
  • Limit Theorems for Monotone Convolution
    数理解析研究所講究録 第 1820 巻 2012 年 118-129 118 LIMIT THEOREMS FOR MONOTONE CONVOLUTION JIUN-CHAU WANG ABSTRACT. We survey some recent progress in limit theorems for monotone convolution. This note is based on the author’s lecture at the RIMS workshop. 1. STATEMENT OF THE PROBLEM $\mathcal{M}$ The monotone convolution $\triangleright$ is an associative binary operation on , the set of Borel probability measures on the real line $\mathbb{R}$ . Introduced by Muraki in [21, 22], this operation is based on his notion of monotonic independence, which is one of the five natural quantum stochastic independences coming from universal products [27, 23]. (The others are tensor, free, Boolean, and antimonotonic independences.) We begin by reviewing the construction of $\triangleright.$ Consider $\mathcal{B}(H)$ the $C^{*}$ -algebra of bounded linear operators on a separable Hilbert space $\xi$ $H$ and a unit vector $\xi\in H$ . Let $\varphi$ be the vector state associated with the vector ; i.e., $\mathcal{A}_{1}$ $\mathcal{A}_{2}$ $\mathcal{B}(H)$ $\varphi(a)=\langle a\xi,$ $\xi\rangle$ for each $a\in \mathcal{B}(H)$ . $Two*$-subalgebras and of are said to be monotonically independent (with respect to $\xi$ ) if for every mixed moment $\varphi(a_{1}a_{2}\cdots a_{n})$ $(i.e., a_{j}\in \mathcal{A}_{\eta_{J}}\cdot, i_{j}\in\{1,2\}, and i_{1}\neq i_{2}\neq\cdots\neq i_{n})$ , one hae that (1.1) $\varphi(a_{1}a_{2}\cdots a_{n})=\varphi(a_{j})\varphi(a_{1}\cdots a_{j-1}a_{j+1}\cdots a_{n})$ whenever $a_{j}\in \mathcal{A}_{2}.$ Remark 1.
    [Show full text]
  • Pharmacokinetic Simulations Involving Convolution Approaches
    PPhhaarrmmaaccookkiinneettiicc SSiimmuullaattiioonnss IInnvvoollvviinngg CCoonnvvoolluuttiioonn AApppprrooaacchheess By Abdul Hakim Abdullah Ahmed Khaled A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mathematics May 2014 Department of Mathematics The Islamia University of Bahawalpur Bahawalpur 63100, Pakistan Pharmacokinetic Simulations Involving Convolution Approaches By Abdul Hakim Abdullah Ahmed khaled A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mathematics May 2014 Supervised by Dr. Khalid Pervaiz Akhter Dr. Ghulam Murtaza Department of Mathematics The Islamia University of Bahawalpur, Pakistan Bahawalpur 63100, Pakistan In The Name Of Allah, The Most Beneficent, The Most Merciful Dedicated To My respectful mother, my lovely wife, sons, and my daughter, who always pray, love, support and encourage me. I II Approval It is hereby certified that the work presented by Abdul Hakim Abdullah Ahmed Khaled S/O Abdullah Ahmed in the dissertation entitled “Pharmacokinetic Simulations Involving Convolution Approaches” has been successfully carried out under our supervision in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mathematics, at the Department of Mathematics, Faculty of Science, The Islamia University of Bahawalpur, Pakistan. [email protected] Mobile: +92-3468783832 [email protected] Mobile: + 92-3142082826; fax: + 92-62925556 III IV Acknowledgement All praises and gratitude to my Lord Allah, the most gracious, the most merciful for guiding me out of darkness, helping in difficulties and bestowing upon me with the courage to accomplish this dissertation. All respect to the holy prophet Muhammad peace be upon him for showing the right path to humanity.
    [Show full text]
  • Convolution Series and the Generalized Convolution Taylor
    CONVOLUTION SERIES AND THE GENERALIZED CONVOLUTION TAYLOR FORMULA YURI LUCHKO Abstract. In this paper, we deal with the convolution series that are a far reach- ing generalization of the conventional power series and the power series with the fractional exponents including the Mittag-Leffler type functions. Special attention is given to the most interesting case of the convolution series generated by the So- nine kernels. In this paper, we first formulate and prove the second fundamental theorem for the general fractional integrals and the n-fold general sequential frac- tional derivatives of both the Riemann-Liouville and the Caputo types. These results are then employed for derivation of two different forms of a generalized convolution Taylor formula for representation of a function as a convolution poly- nomial with a remainder in form of a composition of the n-fold general fractional integral and the n-fold general sequential fractional derivative of the Riemann- Liouville and the Caputo types, respectively. We also discuss the generalized Tay- lor series in form of convolution series and deduce the formulas for its coefficients in terms of the n-fold general sequential fractional derivatives. 1. Introduction The power series are a very important instrument both in mathematics and its applications. Without any loss of generality, a power series can be represented in the form +∞ Σ(x)= aj hj (x), aj R, (1) +1 ∈ j=0 X where with hβ we denote the following power function xβ−1 h (x)= , β> 0. (2) β Γ(β) n−1 arXiv:2107.10198v3 [math.CA] 2 Aug 2021 A given function f (f C ( δ, δ), δ > 0) can be approximated by its Taylor polynomial through the∈ well-known− Taylor formula n−1 (j) f(x)= aj hj (x) + rn(x), aj = f (0), j =0, 1,...,n 1 (3) +1 − j=0 X with the remainder rn = rn(x) in different forms including the one suggested by Lagrange (for the functions f Cn( δ, δ)): ∈ − (n) rn(x)= f (ξ) hn (x), ξ (0, x).
    [Show full text]
  • Convolution Powers of Complex Functions on Zd
    Rev. Mat. Iberoam. 33 (2017), no. 3, 1045–1121 c European Mathematical Society doi 10.4171/rmi/964 Convolution powers of complex functions on Zd Evan Randles and Laurent Saloff-Coste Abstract. The study of convolution powers of a finitely supported prob- ability distribution φ on the d-dimensional square lattice is central to ran- dom walk theory. For instance, the nth convolution power φ(n) is the dis- tribution of the nth step of the associated random walk and is described by the classical local limit theorem. Following previous work of P. Diaco- nis and the authors, we explore the more general setting in which φ takes on complex values. This problem, originally motivated by the problem of Erastus L. De Forest in data smoothing, has found applications to the theory of stability of numerical difference schemes in partial differential equations. For a complex valued function φ on Zd, we ask and address four basic and fundamental questions about the convolution powers φ(n) which concern sup-norm estimates, generalized local limit theorems, point- wise estimates, and stability. This work extends one-dimensional results of I. J. Schoenberg, T. N. E. Greville, P. Diaconis and the second author and, in the context of stability theory, results by V. Thom´ee and M. V. Fe- doryuk. 1. Introduction We denote by 1(Zd) the space of complex valued functions φ: Zd → C such that φ1 = |φ(x)| < ∞. x∈Zd For ψ, φ ∈ 1(Zd), the convolution product ψ ∗ φ ∈ 1(Zd) is defined by ψ ∗ φ(x)= ψ(x − y) φ(y) y∈Zd for x ∈ Zd.Givenφ ∈ 1(Zd), we are interested in the convolution powers (n) 1 d (n) (n−1) (1) φ ∈ (Z ) defined iteratively by φ = φ ∗ φ for n ∈ N+ =: {1, 2,...}, Mathematics Subject Classification (2010): Primary 42A85; Secondary 60F99.
    [Show full text]