Specifically we discuss both continuous time and discretetime sinusoidal signals as well as real and complex expo nentials Sinusoidal signals for both continuous time and discrete time will be come important building blocks for more general signals ID: 22190 Download Pdf

In this lecture we discuss these signals and then proceed to a discussion of sys tems first in general and then in terms of various classes of systems defined by specific system properties The unit step both for continuous and discrete time is zero

Unit impulse function Unit step function Their relation in both continuous and discrete domain We shall even look at the Sifting property of the unit impulse function Basic Signals in detail We now introduce formally some of the basic signals namely

Examples of signals:. Voltage output of a RLC circuit, stock market, ECG, speech, sequences of bases in a gene, MRI or CT scan. Examples of systems:. RLC circuit, an algorithm for predicting future of stock market, an algorithm for detecting abnormal heart rhythms, speech understanding systems, edge detection algorithm for medical images..

brPage 1br EEE3086F Signals and Systems II 212 Page April 14 2014 EEE3086F Signals and Systems II 212 Page April 14 2014 brPage 2br EEE3086F Signals and Systems II 212 Page April 14 201

Instructor: . Dr. Ghazi Al Sukkar. Dept. of Electrical Engineering. The . University of Jordan. Email: . ghazi.alsukkar@ju.edu.jo. 1. Fall 2014. 2. Course Details. Objective. Establish a background in Signal and System Analysis.

We shall look at some of the basic signals namely . Unit impulse function Unit step function Their relation in both continuous and discrete domain We shall even look at the Sifting property of the uni

They are also referred to as Linear TimeInvariant systems in case the independent variable for the input and output signals is time Remember that linearity means that is t and t are responses of the system to signals t and t respectively then the re

Molecular Structure. by Nuclear Magnetic Resonance (NMR). 2. Nuclear Magnetic Resonance Spectroscopy exploits a property called Nuclear Spin. Any atomic nucleus that has an odd mass, an odd atomic number, or both is a nucleus .

(Part One). Biology 10(A). Learning Objectives. Identify major organ systems in animals. Describe the interactions that occur among systems to carry out vital animal functions. Interactions Among Animal Systems.

e LSI Linear shift invariant systems We shall define the term Impulse response in context to LSI systems We shall learn Convolution an operation which helps us find the output of the LTI system given the impulse response and the input signal NOTE I

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Specifically we discuss both continuous time and discretetime sinusoidal signals as well as real and complex expo nentials Sinusoidal signals for both continuous time and discrete time will be come important building blocks for more general signals

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2 Signals and Systems: Part I In this lecture, we consider a number of basic signals that will be important building blocks later in the course. Specifically, we discuss both continuous- time and discrete-time sinusoidal signals as well as real and complex expo- nentials. number of distinctions between continuous-time and discrete-time sinusoidal signals. For example, continuous-time sinusoids are always periodic. Further- more, a time shift corresponds to a phase change and vice versa. Finally, if we consider the family of continuous-time sinusoids of the form ferent values of wo, the corresponding signals are distinct. The situation is considerably different for discrete-time sinusoids. Not all discrete-time sinu- soids are periodic. Furthermore, while a time shift can be related to a change in phase, changing the phase cannot time shift for discrete-time sinusoids. Finally, as the parameter flo is varied in the discrete-time sinusoidal sequence Acos(flon + 4), two sequences for which the frequency flo differs by an integer multiple of 27r are in fact indistin- time and discrete-time complex exponentials. In both cases the complex ex- ponential can be expressed through Euler's relation in the form of a real and an imaginary part, both of which are sinusoidal with a phase difference of 'N/2 and with an envelope that Signals and Systems 2-2 time the complex exponential may or may not be periodic depending on whether the sinusoidal real and imaginary components are periodic. In addition to the basic signals discussed in this lecture, a number of ad- ditional signals play an important role as building blocks. These are intro- duced in Lecture 3. Suggested Reading Section 2.2, Transformations of the Independent Variable, pages 12-16 Section 2.3.1, Continuous-Time Complex Exponential and Sinusoidal Signals, pages 17-22 Section 2.4.2, Discrete-Time Complex Exponential and Sinusoidal Signals, pages 27-31 Section 2.4.3, Periodicity Properties of Discrete-Time Complex Exponentials, pages 31-35 Signals and Systems: Part I 2-3 IDAL SIGNAL TRANSPARENCY 2.1 Continuous-time sinusoidal signal indicating the definition of ampli- tude, frequency, 27 and phase. 0W0 t iallest To TRANSPARENCY 2.2 Relationship between a time shift and a change in phase for a continuous-time sinusoidal signal. Signals and Systems TRANSPARENCY 2.3 Illustration of the signal A cos wot as an even signal. x(t) = A cos wot A Periodic: Even: 2r 0 WO A x(t) = x(-t) TRANSPARENCY 2.4 Illustration of the signal A sin wot as an odd signal. A cos (w t --ff) -x(t) = A sin wot A cos [ w(t- )] Periodic: x(t) = x(t + TO) x(t) =-x(-t) Odd: ----------- r V Signals and Systems: Part I Time Shift �= Phase Change = A cos [Mnn + E20n0] TRANSPARENCY 2.5 Illustration of discrete-time sinusoidal signals. TRANSPARENCY 2.6 Relationship between a time shift and a phase change for discrete-time sinusoidal signals. In discrete time, a time shift always implies a phase change. A cos [920(n + no)] Signals and Systems 2-6 TRANSPARENCY 2.7 The sequence A cos flon illustrating the symmetry of an even sequence. p = 0 x[n] =A cos 0n .00 even: x[n] = x[-n] TRANSPARENCY 2.8 The sequence A sin flon illustrating the antisymmetric property of an odd sequence. A cos (En -) x[n] = A sin E2 n A cos [W2(n -no)] n =I 0 000 odd: x[n] = -x [-n] 0ee n 71' 90 8 0 8 Signals and Systems: Part I 2-7 Time Shift �= Phase Change A cos [20(n + no)] Time Shift = A cos [Mon + 90n0] A cos [920(n + no)] = Phase Change A cos [2 n ++J TRANSPARENCY 2.9 For a discrete-time sinusoidal sequence a time shift always implies a change in phase, but a change in phase might not imply a time shift. x[n] = A cos (Wn + #) Periodic? x[n] = x [n + N] A cos [20(n + N) + #] smallest integer N = A cos [20 = period n + 2 N + #] integer multiple of 27r ? Periodic = � 920N 27rm 27rm 0 N,m must be integers smallest N (if any) = period TRANSPARENCY 2.10 The requirement onne for a discrete-time sinusoidal signal to be periodic. Signals and Systems TRANSPARENCY 2.11 Several sinusoidal sequences illustrating the issue of periodicity. 12 060 S31 0 0 6 p= 0 TI I 00T0I 1IT,. TRANSPARENCY 2.12 Some important distinctions between continuous-time and discrete-time sinusoidal signals. A cos(oot + #) Distinct signals for distinct values of wo Periodic for any choice of o A cos(E0n + G) Identical signals for values of Eo separated by 27r Periodic only if _ 21rm o N for some integers N � 0 and m 'T TI TI I ~TI H, .TT~ Go* n 000 ese " 1 0 1 -a In I 1 0 1 Signals and Systems: Part I SINUSOIDAL SIGNALS AT DISTINCT FREQUENCIES: Continuous time: x 1 (t) = A cos(w 1 t +) x2(t) = A cos (w2t +) Discrete time: x, [n] = A cos [M, n + ] x2[n] = A cos[22n + #] If w2 1 Then x2(t) # x1(t) If 22 1 + 27rm Then x2[n] = x, [n] TRANSPARENCY 2.13 Continuous-time sinusoidal signals are distinct at distinct frequencies. Discrete- time sinusoidal signals are distinct only over a frequency range of 2,. REAL EXPONENTIAL: CONTINUOUS-TIME x(t) = Ceat C and a are real numbers X (t) C a �0 a TRANSPARENCY 2.14 Illustration of continuous-time real exponential signals. Time Shift &#x=000;Scale Change Cea(t + to) = Ceato eat Signals and Systems 2-10 TRANSPARENCY 2.15 Illustration of discrete-time real exponential sequences. REAL EXPONENTIAL: DISCRETE-TIME x[n] = Ceon = Can C,a are real numbers a n a l TRANSPARENCY 2.16 Continuous-time complex exponential signals and their relationship to sinusoidal signals. COMPLEX EXPONENTIAL: CONTINUOUS-TIME x(t) = Ceat C and a are complex numbers C= ICI ej6 a = r + jo x(t) = 0 e jo e (r + jcoo)t = ICI ert ej(wot + 0) Euler's Relation: cos( 0t + 0) + j sin(w ot + 0) = ej(wot + 0) x(t) = IC i ert cos(cot + 0) + jl |C ert sin(wot+ 0) L a �0 �a0 n Jal Signals and Systems: Part I 2-11 TRANSPARENCY 2.17 Sinusoidal signals with exponentially growing and exponentially decaying envelopes. COMPLEX EXPONENTIAL: DISCRETE-TIME x[n] = Can C and a are complex numbers C = ICI eji a= 1al ei x [n] = C e j (lal ejo) n = IC 1al n e j( on + 0) Euler's Relation: cos(20n + 0) + j sin(&20n + 0) x[n] = ICI lal n cos(92on + 0) + j IC I|al n sin(92on + 0) |al = 1 �= sinusoidal real and imaginary parts Ce jon periodic ? TRANSPARENCY 2.18 Discrete-time complex exponential signals and their relationship to sinusoidal signals. Signals and Systems 2-12 TRANSPARENCY 2.19 Sinusoidal sequences with geometrically growing and geometrically decaying envelopes. |al � I lal The following may not correspond to a particular course on MIT OpenCourseWare, but has been provided by the author as an individual learning resource. For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms

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