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
=
n
T
x(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(n
T
s
T_s
Ts), 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)=
x
e
x_e
xe(t)+
x
o
x_o
xo(t) where
x
e
x_e
xe(-t) =
x
e
x_e
xe(t),
x
o
x_o
xo(-t) = -
x
o
x_o
xo(t)
→
\rightarrow
→
x
e
x_e
xe(t)=
1
2
\frac{1}{2}
21[x(t)+x(-t)]
→
\rightarrow
→
x
o
x_o
xo(t)=
1
2
\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
∫
−
T
T
x
(
t
)
d
t
\int_{-T}^Tx(t)dt
∫−TTx(t)dt=0 always of x(t) is ODD
=0 sometimes if x(t) is EVEN
∫
−
T
T
x
(
t
)
d
t
\int_{-T}^Tx(t)dt
∫−TTx(t)dt=2
∫
0
T
x
(
t
)
d
t
\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-
t
0
t_0
t0)
→
\rightarrow
→
t
0
t_0
t0>0, 右移(shift towards right) ;
t
0
t_0
t0<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) =
x
1
x_1
x1(t) +
x
2
x_2
x2(t)
y[n] =
x
1
x_1
x1[n] +
x
2
x_2
x2[n]
y(t) =
x
1
x_1
x1(t)
x
2
x_2
x2(t)
y[n] =
x
1
x_1
x1[n]
x
2
x_2
x2[n]
y(t) = d d t \frac{d}{dt} dtdx(t)
y(t) = ∫ − ∞ t x ( τ ) d τ \int_{-∞}^tx(τ)dτ ∫−∞tx(τ)dτ
f(t)
→
\rightarrow
→f(t+
β
\beta
β)
→
\rightarrow
→f(
α
\alpha
αt+
β
\beta
β)
→
\rightarrow
→ f(-
α
\alpha
αt+
β
\beta
β)
平移
→
\rightarrow
→ 展缩
→
\rightarrow
→ 反转
f(-
α
\alpha
αt +
β
\beta
β)
→
\rightarrow
→ f(
α
\alpha
αt+
β
\beta
β)
→
\rightarrow
→ f(t+
β
\beta
β)
→
\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=
2
Π
ω
\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=
2
Π
m
ω
\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>0
0,t<0
Rectangular pulse脉冲信号:p(t)=u(t+ 1 2 \frac{1}{2} 21)-u(t- 1 2 \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) = ∫ − ∞ t u ( τ ) d τ \int_{-∞}^tu(τ)dτ ∫−∞tu(τ)dτ=tu(t)=r(t) → \rightarrow → 斜坡信号
![[n]=1, n=0; 0, n≠0](https://1000bd.com/contentImg/2024/04/17/33db4f9420f8cde0.png)
δ
\delta
δ(t)=0 for t ≠0
∫
−
∞
∞
δ
(
t
)
d
t
\int_{-∞}^∞δ(t)dt
∫−∞∞δ(t)dt=1
δ \delta δ(-t)= δ \delta δ(t)
δ
\delta
δ(t-
t
0
t_0
t0) = 0, t ≠
t
0
t_0
t0
∫
−
∞
∞
δ
(
t
−
t
o
)
d
t
\int_{-∞}^∞δ(t-to)dt
∫−∞∞δ(t−to)dt=1
δ \delta δ(at+b)= 1 a \frac{1}{a} a1 δ \delta δ(t+ b a \frac{b}{a} ab)
∫ − ∞ ∞ x ( τ ) δ ( t ) d t \int_{-∞}^∞x(τ)δ(t)dt ∫−∞∞x(τ)δ(t)dt=x(0)
x(t)* δ \delta δ(t- t 0 t_0 t0)= ∫ − ∞ ∞ x ( t ) δ ( t − t o ) d t \int_{-∞}^∞x(t)δ(t-to)dt ∫−∞∞x(t)δ(t−to)dt=x( t 0 t_0 t0)
x ( t ) δ ( t − t o ) x(t)δ(t-to) x(t)δ(t−to)=x( t 0 t_0 t0) δ \delta δ(t- t 0 t_0 t0)
∑ i = − ∞ ∞ \sum_{i=-∞}^∞ ∑i=−∞∞x(t) δ \delta δ(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
−
t
o
)
x(t)δ(t-to)
x(t)δ(t−to)=
x
(
t
o
)
δ
(
t
−
t
o
)
x(to)δ(t-to)
x(to)δ(t−to)

δ(t) is the derivative of u(t): δ(t)= d d t u ( t ) \frac{d}{dt}u(t) dtdu(t)
u(t) is the integral of δ(t): u(t) = ∫ − ∞ t δ ( τ ) d τ \int_{-∞}^tδ(τ)dτ ∫−∞tδ(τ)dτ
u[n] = δ[n]+δ[n-1]+…= ∑ i = 0 ∞ \sum_{i=0}^∞ ∑i=0∞ δ \delta δ[n-k]= ∑ i = − ∞ n \sum_{i=-∞}^n ∑i=−∞n δ \delta δ[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|≤
k
1
k_1
k1
The corresponding output y is also bounded defined by |y|≤
k
2
k_2
k2 where
k
1
k_1
k1 and
k
2
k_2
k2 are finite real constants

x(t) = input; y(t) = output
H = first system operator; H
i
n
v
_{inv}
inv = second system operator

H
i
n
v
_{inv}
inv=inverse operator
H
i
n
v
_{inv}
inv H= I
I = identity operator (单位算符)