A signal is formally defined as a function of one or more variables that conveys information on the
nature of a physical phenomenon.
A system is formally defined as an entity that manipulates one or more signals to accomplish a function, thereby yielding new signals.
A continuous-time signal is defined for all time t, except at some discontinuous point.
A continuous-time signal is defined only at discrete instants of time.
· A discrete-time signal is often derived from a continuous-time signal by sampling (抽样) it at a uniform rate (nT)
x[n]= x(t)∣t=nTx(t)|_{t=nT}x(t)∣t=nT=x(nT)
T: sampling period, n: an integer
Continuous-time signals: x(t)
Discrete-time signals: x[n]=x(nTsT_sTs), n=0, ±\pm± 1, ±\pm± 2, …\ldots…
Symmetric about vertical axis: x (-t) = x (t), x [-n] = x [n] for all t
Antisymmetric about origin: x (-t) = - x (t), x [-n] = x [n] for all t
x (t)=xex_exe(t)+xox_oxo(t) where xex_exe(-t) = xex_exe(t), xox_oxo(-t) = -xox_oxo(t)
→\rightarrow→ xex_exe(t)=12\frac{1}{2}21[x(t)+x(-t)]
→\rightarrow→ xox_oxo(t)=12\frac{1}{2}21[x(t)-x(-t)]
ODD ×\times× ODD →\rightarrow→ EVEN
EVEN ×\times× EVEN →\rightarrow→ EVEN
EVEN ×\times× ODD →\rightarrow→ ODD
ODD ×\times× EVEN →\rightarrow→ ODD
∫−TTx(t)dt\int_{-T}^Tx(t)dt∫−TTx(t)dt=0 always of x(t) is ODD
=0 sometimes if x(t) is EVEN
∫−TTx(t)dt\int_{-T}^Tx(t)dt∫−TTx(t)dt=2∫0Tx(t)dt\int_{0}^Tx(t)dt∫0Tx(t)dt for x(t) EVEN
y(t) = x (at) →\rightarrow→ a>1, compressed; 0 y[n] =x [kn] , k>0, k is an integer→\rightarrow→some values lost
y(t)=x(-t)→\rightarrow→The signal y(t) represents a reflected version of x(t) about t=0
y(t)=x(t-t0t_0t0) →\rightarrow→ t0t_0t0>0, 右移(shift towards right) ;t0t_0t0<0, 左移(shift towards left)
y[n]=x[n-m] →\rightarrow→ m>0, 右移(shift towards right) ;m<0, 左移(shift towards left)
x(t) →\rightarrow→ y(t)=cx(t)
x[n] →\rightarrow→ y[n]=cx[n]
y(t) = x1x_1x1(t) + x2x_2x2(t)
y[n] = x1x_1x1[n] + x2x_2x2[n]
y(t) = x1x_1x1(t) x2x_2x2(t)
y[n] = x1x_1x1[n] x2x_2x2[n]
y(t) = ddt\frac{d}{dt}dtdx(t)
y(t) =∫−∞tx(τ)dτ\int_{-∞}^tx(τ)dτ∫−∞tx(τ)dτ
f(t)→\rightarrow→f(t+β\betaβ)→\rightarrow→f(α\alphaαt+β\betaβ) →\rightarrow→ f(-α\alphaαt+β\betaβ)
平移 →\rightarrow→ 展缩 →\rightarrow→ 反转
f(- α\alphaαt +β\betaβ) →\rightarrow→ f(α\alphaαt+β\betaβ) →\rightarrow→ f(t+β\betaβ) →\rightarrow→ f(t)
反转 →\rightarrow→ 展缩 →\rightarrow→ 平移
x(t) = Beαt, B and a are real parameters
a. Decaying exponential, for which α < 0
b. Growing exponential, for which α > 0
x[n]=Brn , r=e α
a. Decaying exponential, for which α < 0
b. Growing exponential, for which α > 0
x (t)=A cos (ωt+φ), T=2Πω\frac{2Π}{ω}ω2Π
x (t +T) = x(t)
x [n] =A cos (Ωn+φ)
Periodic condition: x [n + N] =A cos (Ωn+ΩN+φ)
→\rightarrow→ ΩN=2Πm or Ω=2Πmω\frac{2Πm}{ω}ω2Πm
Euler’s identity:ejθ=cosθ+jsinθ
Complex exponential signal: Bejωt= A ejφejωt=A cos (ωt+φ)+j Asin (ωt+φ)
A cos (ωt+φ)= Re {Bejωt}
A sin (ωt+φ) = Im {Bejωt}
A cos (Ωn+φ) = Re {BejΩn}
A sin (Ωn+φ) = Im {BejΩn}
x(t)= A e-αt sin (ωt+φ), α>0
x(t)u(t)= { x(t) ,t>00,t<0
Rectangular pulse脉冲信号:p(t)=u(t+12\frac{1}{2}21)-u(t-12\frac{1}{2}21)
sgn(t) function符号函数
sgn(t)={1,t>0-1, t<0=u(t)-u(-t)
y(t) =∫−∞tu(τ)dτ\int_{-∞}^tu(τ)dτ∫−∞tu(τ)dτ=tu(t)=r(t)→\rightarrow→ 斜坡信号
δ\deltaδ(t)=0 for t ≠0
∫−∞∞δ(t)dt\int_{-∞}^∞δ(t)dt∫−∞∞δ(t)dt=1
δ\deltaδ(-t)=δ\deltaδ(t)
δ\deltaδ(t-t0t_0t0) = 0, t ≠ t0t_0t0
∫−∞∞δ(t−to)dt\int_{-∞}^∞δ(t-to)dt∫−∞∞δ(t−to)dt=1
δ\deltaδ(at+b)=1a\frac{1}{a}a1δ\deltaδ(t+ba\frac{b}{a}ab)
∫−∞∞x(τ)δ(t)dt\int_{-∞}^∞x(τ)δ(t)dt∫−∞∞x(τ)δ(t)dt=x(0)
x(t)*δ\deltaδ(t-t0t_0t0)=∫−∞∞x(t)δ(t−to)dt\int_{-∞}^∞x(t)δ(t-to)dt∫−∞∞x(t)δ(t−to)dt=x(t0t_0t0)
x(t)δ(t−to)x(t)δ(t-to)x(t)δ(t−to)=x(t0t_0t0)δ\deltaδ(t-t0t_0t0)
∑i=−∞∞\sum_{i=-∞}^∞∑i=−∞∞x(t) δ\deltaδ(k)= x (0)
x(t)δ(t)x(t)δ(t)x(t)δ(t)=x(0)δ(t)x(0)δ(t)x(0)δ(t)
x(t)δ(t−to)x(t)δ(t-to)x(t)δ(t−to)=x(to)δ(t−to)x(to)δ(t-to)x(to)δ(t−to)
δ(t) is the derivative of u(t): δ(t)=ddtu(t)\frac{d}{dt}u(t)dtdu(t)
u(t) is the integral of δ(t): u(t) =∫−∞tδ(τ)dτ\int_{-∞}^tδ(τ)dτ∫−∞tδ(τ)dτ
u[n] = δ[n]+δ[n-1]+…=∑i=0∞\sum_{i=0}^∞∑i=0∞ δ\deltaδ[n-k]=∑i=−∞n\sum_{i=-∞}^n∑i=−∞n δ\deltaδ[m]
δ[n]=u[n]-u[n-1]
y(t)=H{x(t)}
y[n]=H{x[n]}
A system is said to be memoryless if the output at any time depends on only the input at that same time.
A system is said to be memory if the output at any time depends on only the input at past or in the future.
A system is said to be causal if its present value of the output signal depends only on the present or past values of the input signal.
A system is said to be noncausal if its output signal depends on one or more future values of the input signal.
A system is bounded-input/bounded-output (BIBO,有界输入有界输出) stable if for any bounded input x defined by |x|≤ k1k_1k1
The corresponding output y is also bounded defined by |y|≤ k2k_2k2 where k1k_1k1 and k2k_2k2 are finite real constants
x(t) = input; y(t) = output
H = first system operator; H inv_{inv}inv = second system operator
H inv_{inv}inv=inverse operator
H inv_{inv}inv H= I
I = identity operator (单位算符)