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EECE 301 Signals & Systems Prof. Mark Fowler Note Set #5 • Basic Properties of Systems • Reading Assignment: Section 1.5 of Kamen and Heck 1/9 Course Flow Diagram The arrows here show conceptual flow between ideas. Note the parallel structure between the pink blocks (C-T Freq. Analysis) and the blue blocks (D-T Freq. Analysis). Ch. 3: CT Fourier Ch. 5: CT Fourier Ch. 6 & 8: Laplace Signal Models System Models Models for CT New Signal Signals & Systems Models Fourier Series Frequency Response Transfer Function Periodic Signals Based on Fourier Transform Fourier Transform (CTFT) Ch. 1 Intro Non-Periodic Signals New System Model New System Model C-T Signal Model Functions on Real Line Ch. 2 Diff Eqs C-T System Model System Properties Differential Equations Ch. 2 Convolution LTI D-T Signal Model Causal Difference Equations New System Model C-T System Model Etc Zero-State Response Convolution Integral D-T Signal Model Zero-Input Response D-T System Model Functions on Integers Characteristic Eq. Convolution Sum Ch. 7: Z Trans. Ch. 4: DT Fourier Ch. 5: DT Fourier Models for DT New Signal Signal Models System Models Signals & Systems Model DTFT Freq. Response for DT Transfer Function (for “Hand” Analysis) Based on DTFT DFT & FFT Powerful New System (for Computer Analysis) New System Model Analysis Tool Model 2/9 1.5 Basic System Properties There are some fundamental properties that many (but not all!) systems share regardless if they are C-T or D-T and regardless if they are electrical, mechanical, etc. An understanding of these fundamental properties allows an engineer to develop tools that can be widely applied… rather than attacking each seemingly different problem anew!! The three main fundamental properties we will study are: 1. Causality 2. Linearity 3. Time-Invariance 3/9 Causality: A causal (or non-anticipatory) system’s output at a time t1 does not depend on values of the input x(t) for t > t1 The “future input” cannot impact the “now output” ⇒ A Causal system (with zero initial conditions) cannot have a non-zero output until a non-zero input is applied. Causal System Non-Causal System Input Input t t Output Output t t Most systems in nature are causal But… we need to understand non-causal systems because theory shows that the “best” systems are non-causal! So we need to find causal systems that are as close to the best non-causal systems!!! 4/9 Linearity: A system is linear if superposition holds: x1(t) y1(t) Linear System x2(t) y2(t) Linear System x(t) = a1 x1(t)+ a2 x2(t) y(t) = a1 y1(t)+ a2 y2(t) Linear System x1(t) y1(t) Non-Linear x2(t) y2(t) Non-Linear x(t) = a1 x1(t)+ a2 x2(t) y(t) ≠ a1 y1(t)+ a2 y2(t) Non-Linear 5/9 If a system is linear: “a scaled x(t) y(t) input gives a scaled output” Linear System Same scaling factor kx(t) ky(t) Linear System When superposition holds, it makes our life easier! We then can decompose complicated signals into a sum of simpler signals… and then find out how each of these simple signals goes through the system!! This is exactly what the so-called Frequency Domain Methods of later chapters do!!! Systems with only R, L, and C are linear systems! Systems with electronics (diodes, transistors, op-amps, etc.) may be non-linear, but they could be linear… at least for inputs that do not exceed a certain range of inputs. 6/9 Time-Invariance Physical View: The system itself does not change with time Ex. A circuit with fixed R,L,C is time invariant. Actually, R,L,C values change slightly over time due to temperature & aging effects. A circuit with, say, a variable R is time variant (assuming that someone or something is changing the R value) Technical View: A system is time invariant (TI) if: x(t) y(t) system x(t-t0) y(t-t0) x(t) y(t) t t x(t-t0) y(t-t0) t t t0 t0 7/9 Systems described by Linear, Constant-Coefficient Differential Equations are Continuous-Time, Linear Time-Invariant (LTI) Systems Differential equations like this are LTI dn () y t dn−1() y t dm () x t dx() t a + a +...a + y ( t ) = b +... +b b +() x t n dt n n−1 dt n−1 0 m dt m 1 dt 0 - coefficients (a’s & b’s) are constants ⇒ TI - o nonlinear terms N⇒ Linear Examples Of Nonlinear terms k p k p n d⎡ ()() x⎤ td⎡ x⎤ t n d⎡ ()() y⎤ td⎡ y⎤ t x( t ), ⎢ k ⎥⎢ ⎥,(),y t ⎢ k ⎥⎢ p ⎥ ,.etc ⎣ dt ⎦⎣ dtp ⎦ ⎣ dt ⎦⎣ dt ⎦ 8/9 Systems described by Linear, Constant-Coefficient Difference Equations are Discrete-Time, Linear Time-Invariant (LTI) Systems Difference equations like this are LTI [ ]a y [0 n+ 1 ]1 aa y − ... y n ++ nN [ N− ]= b0 x [+ ] n1 − b [+ xb 1+ n xM ] n− ... M [ ] - coefficients (a’s & b’s) are constants ⇒ TI - o nonlinear terms N⇒ Linear Examples Of Nonlinear terms x n[ x ], nn [p x ] n− [ m ] − , yn [ n ],− y [ n − p] [ y n ] m , . etc 9/9.
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