Introduction to Signals & Systems

Mrs. Swapna J. Jadhav

Introduction to Signals

• A Signal is the function of one or more independent variables that carries some information to represent a physical phenomenon.

• A continuous-time signal, also called an analog signal, is defined along a continuum of time.

Signals

• Signals are functions of independent variables; time (t) or space (x,y)

• A physical signal is modeled using mathematical functions.

• Examples:– Electrical signals: Voltages/currents in a circuit v(t),i(t)– Temperature (may vary with time/space)– Acoustic signals: audio/speech signals (varies with time)– Video (varies with time and space)– Biological signals: Heartbeat, EEG

Systems• A system is an entity that manipulates one or more signals

that accomplish a function, thereby yielding new signals. • The input/output relationship of a system is modeled using

mathematical equations.• We want to study the response of systems to signals.• A system may be made up of physical components

(electrical, mechanical, hydraulic) or may be an algorithm that computes an output from an input signal.

• Examples: – Circuits (Input: Voltage, Output: Current)

• Simple resistor circuit:– Mass Spring System (Input: Force, Output: displacement)– Automatic Speaker Recognition (Input: Speech, Output: Identity)

)()( tRitv

Applications of Signals and Systems

• Acoustics: Restore speech in a noisy environment such as cockpit

• Communications: Transmission in mobile phones, GPS, radar and sonar

• Multimedia: Compress signals to store data such as CDs, DVDs

• Biomedical: Extract information from biological signals:– Electrocardiogram (ECG) electrical signals generated by the heart – Electroencephalogram (EEG) electrical signals generated by the

brain– Medical Imaging

• Biometrics: Fingerprint identification, speaker recognition, iris recognition

Classification of Signals

Major classifications of the signal

• Continuous time signal

• Discrete time signal

• Continuous-time vs. discrete-time: – A signal is continuous time if it is defined for

all time, x(t). – A signal is discrete time if it is defined only at

discrete instants of time, x[n]. – A discrete time signal is derived from a

continuous time signal through sampling, i.e.:periodsamplingisTnTxnx ss ),(][

Classification of Signals

• One-dimensional vs. Multi-dimensional: The signal can be a function of a single variable or multiple variables.– Examples:

• Speech varies as a function of timeone-dimensional

• Image intensity varies as a function of (x,y) coordinatesmulti-dimensional

– In this course, we focus on one-dimensional signals.

• Analog vs. Digital:– A signal whose amplitude can take on any

value in a continuous range is an analog signal.– A digital signal is one whose amplitude can

take on only a finite number of values.– Example: Binary signals are digital signals.– An analog signal can be converted into a digital

signal through quantization.

• Deterministic vs. Random:– A signal is deterministic if we can define its

value at each time point as a mathematical function

– A signal is random if it cannot be described by a mathematical function (can only define statistics)

– Example:• Electrical noise generated in an amplifier of a

radio/TV receiver.

Deterministic & Non Deterministic Signals

Deterministic signals • Behavior of these signals is predictable w.r.t time• There is no uncertainty with respect to its value at any

time. • These signals can be expressed mathematically. For example x(t) = sin(3t) is deterministic signal.

Deterministic & Non Deterministic Signals Contd.

Non Deterministic or Random signals • Behavior of these signals is random i.e. not predictable

w.r.t time.• There is an uncertainty with respect to its value at any

time. • These signals can’t be expressed mathematically. • For example Thermal Noise generated is non

deterministic signal.

Classification of Signals

classifications of the signal in Continuous time and Discrete time signal

• Periodic and Aperiodic signal

• Even and Odd signal

• Energy and Power signal

• Causal and non-Causal

• Sinusoidal and Exponential

• Periodic vs. Aperiodic Signals:– A periodic signal x(t) is a function of time that satisfies

– The smallest T, that satisfies this relationship is called the fundamental period.

– is called the frequency of the signal (Hz).– Angular frequency, (radians/sec).– A signal is either periodic or aperiodic.– A periodic signal must continue forever.– Example: The voltage at an AC power source is

periodic.

)()( Ttxtx

Tf

1

0 0

0

)()()(Ta

a

Tb

b T

dttxdttxdttx

Tf

22

Periodic and Non-periodic Signals

• Given x(t) is a continuous-time signal • x (t) is periodic if x(t) = x(t+Tₒ) for any T and any integer n• Example

– x(t) = A cos(t)– x(t+Tₒ) = A cos[t+Tₒ)] = A cos(t+Tₒ)= A cos(t+2)

= A cos(t)– Note: Tₒ =1/fₒ ; fₒ

Periodic and Non-periodic Signals Contd.

• For non-periodic signals

x(t) ≠ x(t+Tₒ)• A non-periodic signal is assumed to have a

period T = ∞

• Example of non periodic signal is an exponential signal

Important Condition of Periodicity for Discrete Time Signals

• A discrete time signal is periodic if

x(n) = x(n+N)

• For satisfying the above condition the frequency of the discrete time signal should be ratio of two integers

i.e. fₒ = k/N

Sum of periodic Signals – may not always be periodic!

T1=(2)/()= 2; T2 =(2)/(sqrt(2));

T1/T2= sqrt(2);

– Note: T1/T2 = sqrt(2) is an irrational number

– X(t) is aperiodic

tttxtxtx 2sincos)()()( 21

Periodic Signals

Aperiodic Signals

• Even vs. Odd:

– A signal is even if x(t)=x(-t).– A signal is odd if x(t)=-x(-t)– Examples:

• Sin(t) is an odd signal.

• Cos(t) is an even signal.

– A signal can be even, odd or neither.– Any signal can be written as a combination of an

even and odd signal.

2

)()()(

2

)()()(

txtxtx

txtxtx

o

e

Properties of Even and Odd Functions

• Even x Odd = Odd

• Odd x Odd = Even

• Even x Even = Even

• Even + Even = Even

• Even + Odd = Neither

• Odd + Odd = Odd0)(

)(2)(0

dttx

dttxdttx

a

a

o

a

e

a

a

e

Even and Odd Parts of Functions

g gThe of a function is g

2e

t tt

even part

g gThe of a function is g

2o

t tt

odd part

A function whose even part is zero, is odd and a functionwhose odd part is zero, is even.

Various Combinations of even and odd functions

Function type Sum Difference Product Quotient

Both even Even Even Even Even

Both odd Odd Odd Even Even

Even and odd Neither Neither Odd Odd

Product of Two Even Functions

Product of Even and Odd Functions

Product of Even and Odd Functions Contd.

Product of an Even Function and an Odd Function

Product of an Even Function and an Odd Function

Product of Even and Odd Functions Contd.

Product of Two Odd Functions

Product of Even and Odd Functions Contd.

Derivatives and Integrals of Functions

Function type Derivative Integral

Even Odd Odd + constant

Odd Even Even

Discrete Time Even and Odd Signals

g gg

2e

n nn

g g

g2o

n nn

g gn n g gn n

Combination of even and odd function for DT Signals

Function type Sum Difference Product Quotient

Both even Even Even Even Even

Both odd Odd Odd Even Even

Even and odd Even or Odd Even or odd Odd Odd

Products of DT Even and Odd Functions

Two Even Functions

Products of DT Even and Odd Functions Contd.

An Even Function and an Odd Function

• Finite vs. Infinite Length:– X(t) is a finite length signal if it is nonzero over a

finite interval a<t<b– X(t) is infinite length signal if it is nonzero over all

real numbers.– Periodic signals are infinite length.

• Energy signals vs. power signals:– Consider a voltage v(t) developed across a

resistor R, producing a current i(t).– The instantaneous power: p(t)=v2(t)/R=Ri2(t)– In signal analysis, the instantaneous power of a

signal x(t) is equivalent to the instantaneous power over 1 resistor and is defined as x2(t).

– Total Energy: – Average Power:

2/

2/

2 )(1

limT

T

T dttxT

2/

2/

2 )(limT

T

T dttx

Energy and Power Signals Energy Signal• A signal with finite energy and zero power is called

Energy Signal i.e.for energy signal

0<E<∞ and P =0• Signal energy of a signal is defined as the area

under the square of the magnitude of the signal.

• The units of signal energy depends on the unit of the signal.

2

x xE t dt

Energy and Power Signals Contd.Power Signal• Some signals have infinite signal energy. In that

case it is more convenient to deal with average signal power.

• For power signals

0<P<∞ and E = ∞• Average power of the signal is given by

/ 2

2

x

/ 2

1lim x

T

TT

P t dtT

Energy and Power Signals Contd.

• For a periodic signal x(t) the average signal power is

• T is any period of the signal.

• Periodic signals are generally power signals.

2

x

1x

TP t dt

T

Signal Energy and Power for DT Signal

•The signal energy of a for a discrete time signal x[n] is

2

x xn

E n

•A discrete time signal with finite energy and zero power is called Energy Signal i.e.for energy signal

0<E<∞ and P =0

Signal Energy and Power for DT Signal Contd.

The average signal power of a discrete time power signal x[n] is

1

2

x

1lim x

2

N

Nn N

P nN

2

x

1x

n N

P nN

For a periodic signal x[n] the average signal power is

The notation means the sum over any set of

consecutive 's exactly in length.

n N

n N

• Energy vs. Power Signals:– A signal is an energy signal if its energy is finite, 0<E<∞.

– A signal is a power signal if its power is finite, 0<P<∞.

– An energy signal has zero power, and a power signal has infinite energy.

– Periodic signals and random signals are usually power signals.

– Signals that are both deterministic and aperiodic are usually energy signals.

– Finite length and finite amplitude signals are energy signals.

• Causal, Anticausal vs. Noncausal Signals:– A signal that does not start before t=0 is a

causal signal. x(t)=0, t<0– A signal that starts before t=0 is a noncausal

signal.– A signal that is zero for t>0 is called an

anticausal signal.

Elementary Signals

Sinusoidal & Exponential Signals• Sinusoids and exponentials are important in signal and

system analysis because they arise naturally in the solutions of the differential equations.

• Sinusoidal Signals can expressed in either of two ways :

cyclic frequency form- A sin 2Пfot = A sin(2П/To)t

radian frequency form- A sin ωot

ωo = 2Пfo = 2П/To

To = Time Period of the Sinusoidal Wave

Sinusoidal & Exponential Signals Contd.

x(t) = A sin (2Пfot+ θ)= A sin (ωot+ θ)

x(t) = Aeat Real Exponential

= Aejω̥�t = A[cos (ωot) +j sin (ωot)] Complex Exponential

θ = Phase of sinusoidal wave A = amplitude of a sinusoidal or exponential signal fo = fundamental cyclic frequency of sinusoidal signal ωo = radian frequency

Sinusoidal signal

Discrete Time Exponential and Sinusoidal Signals

• DT signals can be defined in a manner analogous to their continuous-time counter partx[n] = A sin (2Пn/No+θ)

= A sin (2ПFon+ θ)

x[n] = an n = the discrete time

A = amplitude θ = phase shifting radians, No = Discrete Period of the wave

1/N0 = Fo = Ωo/2 П = Discrete Frequency

Discrete Time Sinusoidal Signal

Discrete Time Exponential Signal

Discrete Time Sinusoidal Signals

Unit Step Function

1 , 0

u 1/ 2 , 0

0 , 0

t

t t

t

Precise Graph Commonly-Used Graph

Discrete Time Unit Step Function or Unit Sequence Function

1 , 0u

0 , 0

nn

n

Signum Function

1 , 0

sgn 0 , 0 2u 1

1 , 0

t

t t t

t

Precise Graph Commonly-Used Graph

The signum function, is closely related to the unit-step function.

Unit Ramp Function

, 0ramp u u

0 , 0

tt tt d t t

t

•The unit ramp function is the integral of the unit step function.•It is called the unit ramp function because for positive t, its slope is one amplitude unit per time.

Discrete Time Unit Ramp Function

, 0ramp u 1

0 , 0

n

m

n nn m

n

Rectangular Pulse or Gate Function

Rectangular pulse, 1/ , / 2

0 , / 2a

a t at

t a

The Unit Rectangle Function

The unit rectangle or gate signal can be represented as combination of two shifted unit step signals as shown

Unit Impulse Function

As approaches zero, g approaches a unit

step andg approaches a unit impulse

a t

t

So unit impulse function is the derivative of the unit step function or unit step is the integral of the unit impulse function

Functions that approach unit step and unit impulse

Representation of Impulse Function

The area under an impulse is called its strength or weight. It is represented graphically by a vertical arrow. An impulse with a strength of one is called a unit impulse.

Properties of the Impulse Function

0 0g gt t t dt t

The Sampling Property

0 0

1a t t t t

a

The Scaling Property

The Replication Property

g(t) ⊗ δ(t) = g (t)

Unit Impulse Train

The unit impulse train is a sum of infinitely uniformly-spaced impulses and is given by

, an integerTn

t t nT n

Discrete Time Unit Impulse Function or Unit Pulse Sequence

1 , 0

0 , 0

nn

n

for any non-zero, finite integer .n an a

Unit Pulse Sequence Contd.

• The discrete-time unit impulse is a function in the ordinary sense in contrast with the continuous-time unit impulse.

• It has a sampling property.• It has no scaling property i.e.

δ[n]= δ[an] for any non-zero finite integer ‘a’

Sinc Function

sinsinc

tt

t

The Unit Triangle Function

A triangular pulse whose height and area are both one but its base width is not, is called unit triangle function. The unit triangle is related to the unit rectangle through an operation called convolution.

Operations of Signals

• Sometime a given mathematical function may completely describe a signal .

• Different operations are required for different purposes of arbitrary signals.

• The operations on signals can be Time Shifting Time Scaling Time Inversion or Time Folding

Time Shifting• The original signal x(t) is shifted by an

amount tₒ.

• X(t)X(t-to) Signal Delayed Shift to the right

Time Shifting Contd.

• X(t)X(t+to) Signal Advanced Shift to the left

Time Scaling

• For the given function x(t), x(at) is the time scaled version of x(t)

• For a ˃ 1,period of function x(t) reduces and function speeds up. Graph of the function shrinks.

• For a ˂ 1, the period of the x(t) increases and the function slows down. Graph of the function expands.

Time scaling Contd.

Example: Given x(t) and we are to find y(t) = x(2t).

The period of x(t) is 2 and the period of y(t) is 1,

Time scaling Contd.

• Given y(t), – find w(t) = y(3t)

and v(t) = y(t/3).

Time Reversal

• Time reversal is also called time folding

• In Time reversal signal is reversed with respect to time i.e.

y(t) = x(-t) is obtained for the given function

Time reversal Contd.

0 0 , an integern n n n Time shifting

Operations of Discrete Time Functions

Operations of Discrete Functions Contd.

Scaling; Signal Compression

n Kn K an integer > 1

What is System?

• Systems process input signals to produce output signals

• A system is combination of elements that manipulates one or more signals to accomplish a function and produces some output.

system output signal

input signal

Examples of Systems– A circuit involving a capacitor can be viewed as a

system that transforms the source voltage (signal) to the voltage (signal) across the capacitor

– A communication system is generally composed of three sub-systems, the transmitter, the channel and the receiver. The channel typically attenuates and adds noise to the transmitted signal which must be processed by the receiver

– Biomedical system resulting in biomedical signal processing

– Control systems

System - Example

• Consider an RL series circuit– Using a first order equation:

dt

tdiLRtitVVtV

dt

tdiLtV

LR

L

)()()()(

)()(

LV(t)

R

Mathematical Modeling of Continuous Systems

Most continuous time systems represent how continuous signals are transformed via differential equations.

E.g. RC circuit

System indicating car velocity

)(1

)(1)(

tvRC

tvRCdt

tdvsc

c

)()()(

tftvdt

tdvm

Mathematical Modeling of Discrete Time Systems

Most discrete time systems represent how discrete signals are transformed via difference equations

e.g. bank account, discrete car velocity system

][]1[01.1][ nxnyny

][]1[][ nfm

nvm

mnv

Order of System

• Order of the Continuous System is the highest power of the derivative associated with the output in the differential equation

• For example the order of the system shown is 1.

)()()(

tftvdt

tdvm

Order of System Contd.

• Order of the Discrete Time system is the highest number in the difference equation by which the output is delayed

• For example the order of the system shown is 1.

][]1[01.1][ nxnyny

Interconnected Systems

notes

• Parallel

• Serial (cascaded)

• Feedback

LV(t)

R

L

C

Interconnected System Example• Consider the following systems with 4 subsystem

• Each subsystem transforms it input signal

• The result will be:– y3(t)=y1(t)+y2(t)=T1[x(t)]+T2[x(t)]

– y4(t)=T3[y3(t)]= T3(T1[x(t)]+T2[x(t)])

– y(t)= y4(t)* y5(t)= T3(T1[x(t)]+T2[x(t)])* T4[x(t)]

Feedback System• Used in automatic control

– e(t)=x(t)-y3(t)= x(t)-T3[y(t)]=

– y(t)= T2[m(t)]=T2(T1[e(t)]) y(t)=T2(T1[x(t)-y3(t)])= T2(T1( [x(t)] - T3[y(t)] ) ) =

– =T2(T1([x(t)] –T3[y(t)]))

Types of Systems

• Causal & Anticausal• Linear & Non Linear• Time Variant &Time-invariant• Stable & Unstable • Static & Dynamic• Invertible & Inverse Systems

Causal & Anticausal Systems

• Causal system : A system is said to be causal if the present value of the output signal depends only on the present and/or past values of the input signal.

• Example: y[n]=x[n]+1/2x[n-1]

Causal & Anticausal Systems Contd.

• Anticausal system : A system is said to be anticausal if the present value of the output signal depends only on the future values of the input signal.

• Example: y[n]=x[n+1]+1/2x[n-1]

Linear & Non Linear Systems

• A system is said to be linear if it satisfies the principle of superposition

• For checking the linearity of the given system, firstly we check the response due to linear combination of inputs

• Then we combine the two outputs linearly in the same manner as the inputs are combined and again total response is checked

• If response in step 2 and 3 are the same,the system is linear othewise it is non linear.

Time Invariant and Time Variant Systems

• A system is said to be time invariant if a time delay or time advance of the input signal leads to a identical time shift in the output signal.

Stable & Unstable Systems• A system is said to be bounded-input bounded-

output stable (BIBO stable) if every bounded input results in a bounded output.

Stable & Unstable Systems Contd.

Example

- y[n]=1/3(x[n]+x[n-1]+x[n-2])

1[ ] [ ] [ 1] [ 2]

31

(| [ ] | | [ 1] | | [ 2] |)31

( )3 x x x x

y n x n x n x n

x n x n x n

M M M M

Stable & Unstable Systems Contd.

Example: The system represented by

y(t) = A x(t) is unstable ; A˃1

Reason: let us assume x(t) = u(t), then at every instant u(t) will keep on multiplying with A and hence it will not be bonded.

Static & Dynamic Systems

• A static system is memoryless system• It has no storage devices• its output signal depends on present values of the

input signal• For example

Static & Dynamic Systems Contd.

• A dynamic system possesses memory• It has the storage devices• A system is said to possess memory if its output

signal depends on past values and future values of the input signal

Example: Static or Dynamic?

Example: Static or Dynamic?

Answer:

• The system shown above is RC circuit

• R is memoryless

• C is memory device as it stores charge because of which voltage across it can’t change immediately

• Hence given system is dynamic or memory system

Invertible & Inverse Systems

• If a system is invertible it has an Inverse System

• Example: y(t)=2x(t)– System is invertible must have inverse, that is:

– For any x(t) we get a distinct output y(t)

– Thus, the system must have an Inverse• x(t)=1/2 y(t)=z(t)

y(t)System

Inverse System

x(t) x(t)

y(t)=2x(t)System(multiplier)

Inverse System

(divider)

x(t) x(t)

LTI Systems

• LTI Systems are completely characterized by its unit sample response

• The output of any LTI System is a convolution of the input signal with the unit-impulse response, i.e.

Properties of Convolution

Commutative Property][*][][*][ nxnhnhnx

Distributive Property

])[*][(])[*][(

])[][(*][

21

21

nhnxnhnx

nhnhnx

Associative Property

][*])[*][(

][*])[*][(

][*][*][

12

21

21

nhnhnx

nhnhnx

nhnhnx

Useful Properties of (DT) LTI Systems•Causality:

• Stability:

Bounded Input ↔ Bounded Output

00][ nnh

k

kh ][

kk

knhxknhkxny

xnx

][][][][

][for

max

max