Basic concepts
Signals and Systems
Signal: a function of one or more independent variables (e.g., time and spatial variables); typically contains information about the behavior or nature of some physical phenomena
System: responds to a particular signal input by producing another signal(output)
Transformation
Time reflection: x(t) ←→ x(−t), x[n] ←→ x[−n]
Time scaling: x(t) ←→ x(ct)
Time shift: x(t) ←→ x(t − t0), x[n] ←→ x[n − n0]
Usually do shift then scaling to avoid complex mathematics
Even and Odd functions
Every signal function is x(t)=Even(x)+Odd(t) ,then
x(−t)=Even(−t)+Odd(−t)=Even(t)−Odd(t)
Even(t)=21(x(t)+x(−t))
Odd(t)=21(x(t)−x(−t))
Periodic
Preodic: x(t)=x(t+mT) or x[t]=x[t+mT]
Fundamental period T: Smallest positive T
如果是合成的 signal, 其 Fundamental Period 是最小公倍数
Eular's Formula
ejω0t=cos(ω0t)+j⋅sin(ω0t)
Fundamental period T0=2π/∣ω0∣
Acos(ω0t+ϕ)=2Aejϕejω0t+2Ae−jϕe−jω0t
只要基础频率 ω0 一样, 指数和三角函数可以互相转化
\begin{equation} \mathrm{e}^{-\mathrm{j} \omega t}=\cos (\omega t)-\mathrm{j} \sin (\omega t) \end{equation}
\begin{equation} \cos (\omega t)=\frac{1}{2}\left(\mathrm{e}^{\mathrm{j} \omega t}+\mathrm{e}^{-\mathrm{j} \omega t}\right) \end{equation}
\begin{equation} \sin (\omega t)=\frac{1}{2 \mathrm{j}}\left(\mathrm{e}^{\mathrm{j} \omega t}-\mathrm{e}^{-\mathrm{j} \omega t}\right) \end{equation}
Sin
x(t)=Acos(ω0t+ϕ)
phase: ω0t+ϕ
Discrete Time Unit Step and Unit Impulse Sequence
u[n]相当于 δ[n]的积分
常见信号与周期判断
功率与能量
Other concepts
Energy and Power of Periodic Signals: 积分与积分后的处理
Properties of System
Memory / Memoryless
Output only depends on input at the same time
Invertibility and Inverse System
Distinct input leads to distinct output
Causality
Output only depends on input at the same time or before
Stability
Bounded input gives Bounded output
Time-Invariance
A time-shift in the input causes a same time-shift in the output
x[n]→y[n] then x[n−n0]→y[n−n0]
Example:
Linearity
Additivity and Scaling
If x[n]→y[n] then ax1[n]+bx2[n]→ay1[n]+by2[n]
Convolution
Begin
We can construct any signal by discrete function y[n]=x[k]δ[n−k], so that y[n]can be valued only at k and get x[k]. By accumulation, the signal can be piled up. This is convolution, defined by x[n]=Σk=−∞∞x[k]δ[n−k]=x[n]∗h[n], which is called convolution sum.
Example:
Properties of Convolution
Commutative: x(t)∗h(t)=h(t)∗x(t)
Bi-linear: (ax1(t)+bx2(t))∗h(t)=a(x1∗h)+b(x2∗h),x∗(ah1+bh2)=a(x∗h1)+b(x∗h2)
Shift: x(t−τ)∗h(t)=x(t)∗h(t−τ)
Identity: δ(t) is the identity signal, x∗δ=x=δ∗x
Identity is unique: i(t)=i(t)∗δ(t)=δ(t)
Associative: x1∗(x2∗x3)=(x1∗x2)∗x3
Smooth derivative: y(n)′=x(n)′∗h(n)=x(n)∗h(n)′
Properties of L.T.I System
Memoryless: x[n]=0 when n=0
Invertibility: A system is invertible only if an inverse system exists.
Causality: h[n]=0 when n<0
y[n]=∑k=0∞h[k]×[n−k]
\begin{equation} y[n]=\sum_{k=-\infty}^{n} x[k] h[n-k] \end{equation}
Stability:
(∑k=−∞∞∣h[k]∣<∞)
Convolving δ(t) with itself
(δ(t)∗δ(t))=δ(t)
Calculation
Sliding window:
Reverse the simpler one x(t)
Record reversed x(t) 's jumping points
Slide reversed x(t), for each g(t), is integral of multiplication
g(t)=∫dtdx∗h(t)dt=…
\begin{equation} g(t)=\frac{d}{d t} \int x * h(t) d t=\ldots \end{equation}
逆变换
Eigen-function of L.T.I
Eigen-functions
A signal for which the system's output is just a constant (possibly complex) times the input.
Eigen Basis: If an input signal can be decomposed to a weighted sum of eigenfunctions (eigen basis), then the output can be easily found.
So goal: Getting an Eigen basis of an L.T.I system.
e^{st} as eigenfunction of L.T.I
Consider the input to be x(t)=est, then the output is y(t)=∫h(τ)es(t−τ)dτ=est∫h(τ)e−sτdτ
\begin{equation} H(s)=\int h(\tau) e^{-s \tau} d \tau \end{equation} is just a constant, i.e. eigenvalue for function est
Orthonormal Basis
When s purely imaginary \begin{equation} j k \omega_{0} \end{equation}, \begin{equation} e^{j k \omega_{0} t} \end{equation} is orthonormal and standard among different .
Definition of inner-product of perioidic functions: \begin{equation} <x_{1}(t), x_{2}(t)>=\frac{1}{T_{0}} \int_{T_{0}} x_{1}(t) x_{2}^{*}(t) d t \end{equation}
Fourier Analysis
CT & DT
CT: ejωt
\begin{equation} \mathrm{e}^{\mathrm{j} \omega \mathrm{t}} \longrightarrow \mathrm{H}(\mathrm{j} \omega) \mathrm{e}^{\mathrm{j} \omega \mathrm{t}} \end{equation}
DT: ejωn
\begin{equation} \mathrm{e}^{j \omega n} \rightarrow H\left(\mathrm{e}^{j \omega}\right) \mathrm{e}^{j \omega n} \end{equation}
Periodic signals & Fourier Series Expansion
x(t) may be expressed as a Fourier series:
\begin{equation} x(t)=\sum_{k=-\infty}^{\infty} a_{k} \cdot e^{j k \omega_{0} t} \end{equation}, \begin{equation} x(t) \leftarrow{ }^{F . S .} \rightarrow a_{k} \end{equation}
sum of sinusoids whose frequencies are multiple of ω0, the “fundamental frequency”.
Where ak can be obtained by
\begin{equation} a_{k}=\frac{1}{T_{0}} \int_{T_{0}} x(\tau) e^{-j k \omega_{0} \tau} d \tau \end{equation}
a0 controls a constant
And,
x(t)=k=−∞∑∞akejkω0t=k=−∞∑∞(akcos(kω0t)+jaksin(kω0t))=a0+k>0∑((ak+a−k)cos(kω0t)+j(ak−a−k)sin(kω0t)) Odd / Even
Approximation
Linearity
z(t)=αx(t)+βy(t)⟷⟵S⟶αak+βbk Time-shift
x(t−t0)⟷FSe−jkω0t0ak Time-reverse
x(−t)⟵FSa−k Time-scaling
x(αt)=k=−∞∑∞akejk(αω0)t Multiplication
x(t)y(t)⟷⟷S,hk=l=−∞∑∞albk−1 which is Convolution
conjugation & conjugate symmetry
7
dtdx(t)⟷FS,jkω0ak ∫−∞tx(τ)dτ⟷ FS ,jkω0ak Parseval's identity
T1∫T∣x(t)∣2dt=k=−∞∑∞∣ak∣2 Proof:
T1∫T∣x(t)∣2dt=T1∫Tk1,k2∑ak1ak2∗ej(k1−k2)ω0tdt=k1,k2∑ak1ak2∗δ[k1−k2]=k∑∣ak∣2 Continuous-Time Fourier Transform (CTFT)
变换与逆变换
Fourier series: \begin{equation} x(t)=\sum_{k=-\infty}^{\infty} a_{k} \mathrm{e}^{j k \omega_{0} t} \end{equation}
Inverse fourier transform: x(t)=2π1∫∞∞X(jω)ejωt dω
Fourier Transform Pair
Fourier Transform: \begin{equation} \mathcal{F}: X(j \omega)=\int_{-\infty}^{\infty} x(t) e^{-j \omega t} d t \end{equation}
For periodic signals, \begin{equation} X(j \omega)=2 \pi \sum_{-\infty}^{\infty} a_{k} \delta\left(\omega-k \omega_{0}\right) \end{equation}, i.e. the Fourier Series
\begin{equation} X(j \omega) \end{equation}is called the "Spectrum" of x(t)
Inverse F.T. \begin{equation} \mathcal{F}^{-1}: x(t)=\frac{1}{2 \pi} \int_{-\infty}^{\infty} X(j \omega) e^{j \omega t} d \omega \end{equation}
Dual Porperty
\begin{equation} \mathcal{F}(\mathcal{F}(x(t)))=2 \pi \cdot x(-t) \end{equation}
Means that when we put the \begin{equation} X(j \omega) \end{equation} in time domain, it will produce 2πtimes x(−t) waveform in freq domain
Properties
Normal CT Fourier Pairs
REF