Session 1 Sig Rev

8/13/2019 Session 1 Sig Rev

1/68

PEMP

ESD2521

M.S. Ramaiah School of Advanced Studies, Bengaluru

Session 1 : Introduction to

Digital Signal Processing

Session delivered by:

Chandan N.


2/68

PEMP

ESD2521


Session Objective

To understand the basic concept of signals and digital signal

processing

To review on the basic architectures

To understand the concept of sampling

To understand the effects of under sampling and over sampling

2


3/68

PEMP

ESD2521


Session Topics

DSP Architecture evolution Types of Signals

Discrete time Systems

Sampling

3


4/68

PEMP

ESD2521


Three Markets Three Chips

General purpose

wide range of applications

performance matters

Microcontroller

one small app runs forever

possible real-time constraints

Digital Signal Processing

complicated processing of signals real-time constraints

4


5/68

PEMP

ESD2521


Hardware Differences

Microprocessor - processor datapath, register file, ALU,caches

Microcontroller microprocessor plus other peripherals, on-

chip memory

Digital Signal Processor microprocessor with ALUsoptimized for digital signal processing (MAC)

5


6/68

PEMP

ESD2521


Introduction to DSP Processors

Hardware

Algorithms

Image processing

Digital systems

Logical design

Control systems

Digital signal processing

DSP VLSI

Analog circuits

6


7/68

PEMP

ESD2521


DSP Introduction

Digital Signal Processing: application of mathematicaloperations to digitally represented signals

Signals represented digitally as sequences of samples

Digital signals obtained from physical signals via tranducers

(e.g., microphones) and analog-to-digital converters (ADC) Digital signals converted back to physical signals via digital-to-

analog converters (DAC)

Digital Signal Processor (DSP):

electronic system that processes digital signals

7


8/68

PEMP

ESD2521


Definitions

In DSP, digital signals often represent signals from physicalsystems, which are in relation to physical time

DSP systems are related to physical time

Systems where the correctness of the system behavior depends

not only an the logical results of the computations, but also onthe physical instant at which these results are produced, are real-

time systems

Real-time DSP system is a DSP system, where signal mapping

is performed in real-time and the real-time constraint is

determined by the repetition period of the algorithm

8


9/68

PEMP

ESD2521


What is DSP

Changing or analyzing information that is measured as discretesequences of numbers

The representation, transformation, and manipulation of signals

and the information they contain

9


10/68

PEMP

ESD2521


Unique Features of DSP

Signals come from the real world Need to react in real time

Need to measure signals and convert them to digital

numbers

Signals are discrete Information in between discrete samples is lost

10


11/68

PEMP

ESD2521


Processing Real Signals

Most of the signals in our environment are analog such as

sound, temperature and light To processes these signals with a computer, we must:

1. Convert the analog signals into electrical signals, e.g., usinga transducer such as a microphone to convert sound into

electrical signal2. Digitize these signals, or convert them from analog todigital, using an ADC (Analog to Digital Converter)

In digital form, signal can be manipulated

Processed signal may need to be converted back to an analogsignal before being passed to an actuator (e.g., a loudspeaker)

Digital to analog conversion can be done by a DAC (Digitalto Analog Converter)

11


12/68

PEMP

ESD2521


Typical DSP Components

Input lowpass filter (anti-aliasing filter) Analog to digital converter (ADC)

Digital computer or digital signal processor

Digital to analog converter (DAC)

Output lowpass filter (anti-imaging filter)

12


13/68

PEMP


14/68

PEMP

ESD2521


Advantage of DSP Versatility

Digital systems can be reprogrammed for other applications Digital systems can be ported to different hardware

Repeatability and stability

Digital systems can be easily duplicated

Digital systems do not depend on strict component tolerances

Digital system responses do not drift with temperature

Simplicity

Some things can be done more easily digitally than with analog systems

(e.g., linear phase filters)

Security can be introduced by encryption/scrambling Digital signals easily stored on magnetic media without deterioration

14

PEMP


15/68

PEMP

ESD2521


Disadvantages of DSP

DSP techniques are limited to signals with relatively low bandwidths

The point at which DSP becomes too expensive will depend on theapplication and the current state of conversion and digital processingtechnology

Currently DSP systems are used for signals up to video bandwidths(about 10 MHz)

The cost of high-speed ADCs and DACs and the amount of digitalcircuitry required to implement very high-speed designs (> 100 MHz)makes them impractical for many applications

As conversion and digital technology improve, the bandwidths forwhich DSP is economical continue to increase

The need for an ADC and DAC makes DSP not economical for simpleapplications (e.g., a simple filter)

Higher power consumption and size of a DSP implementation can makeit unsuitable for simple very low-power or small size applications

15

PEMP


16/68

PEMP

ESD2521


Applications of DSP

Noise Filtering

Coding 64 kbps-narrowband, 64 kbps-wideband

32 kbps-narrowband, 32 kbps-wideband

16 kbps-narrowband, 16 kbps-wideband

Compression

64 kbpsMu-Law PCM 32 kbps CCITT G.721 ADPCM

16 kbps LD-CELP

8 kbps CELP

4.8 kbps CELP for STU-3

2.4 kbps LPC-10E for STU-3 Recognition

Synthesis

Sampling Rate changes

16

PEMP


17/68

PEMP

ESD2521


Applications of DSP Image Processing: enhancement, coding, compression, pattern recognition

Multimedia: transmission of sound, still images, motion pictures, digital TV,video conferencing

Music: recording, playback and manipulation (mixing, special effects),

synthesis

Communication: encoding and decoding of digital communication signals,

detection, equalization, filtering, direction finding, echo cancellation Radar and Sonar: target detection, position and velocity estimation, tracking

Biomedical Engineering: analysis of biomedical signals, diagnosis, patient

monitoring, preventive health care, artificial organs

17

PEMP


18/68

PEMP

ESD2521


Real Time DSP System

18

PEMP


19/68

PEMP

ESD2521


Real Time DSP System

19

PEMP


20/68

PEMP

ESD2521


Why Go Digital?

Digital signal processing techniques are now so powerful that

sometimes it is extremely difficult, if not impossible, for analoguesignal processing to achieve similar performance.

Examples:

FIR filter with linear phase.

Adaptive filters.

Analogue signal processing is achieved by using analogue componentssuch as:

Resistors.

Capacitors.

Inductors.

The inherent tolerances associated with these components,temperature, voltage changes and mechanical vibrations candramatically affect the effectiveness of the analogue circuitry

20

PEMP


21/68

PEMP

ESD2521


Why Go Digital?

With DSP it is easy to: Change applications.

Correct applications.

Update applications.

Additionally DSP reduces:

Noise susceptibility.

Chip count.

Development time.

Cost.

Power consumption.

21

PEMP


22/68

PEMP

ESD2521


Why NOT Go Digital?

High frequency signals cannot be processed digitallybecause of two reasons:

Analog to Digital Converters, ADC cannot work fast

enough.

The application can be too complex to be performedin Real-time

22

PEMP


23/68

ESD2521


Use a DSP processor when the following are required: Cost saving.

Smaller size.

Low power consumption.

Processing of many high frequency signals in real-time.

Use a GPP processor when the following are required:

Large memory.

Advanced operating systems.

Need For DSP Processors

25

PEMP


24/68

ESD2521


Typical DSP Algorithms

Algorithm Equation

Finite Impulse Response Filter =

=M

k

k knxany

0

)()(

Infinite Impulse Response Filter ==

+=N

k

k

M

k

k knybknxany

10

)()()(

Convolution =

=N

k

knhkxny

0

)()()(

Discrete Fourier Transform

=

=1

0

])/2(exp[)()(

N

n

nkNjnxkX

Discrete Cosine Transform ( ) ( )

=

+=

1

0

122

cos).().(

N

x

xuN

xfucuF

The Sum of Products (SOP) is the key element in most

DSP algorithms:

26

PEMP


25/68

ESD2521


Hardware vs. Microcode Multiplication

DSP processors are optimised to perform multiplication andaddition operations.

Multiplication and addition are done in hardware and in one

cycle.

Example: 4-bit multiply (unsigned).

1011x 1110

1011x 1110

Hardware Mi cr ocode

10011010 00001011.

1011. .

1011. . .

10011010

Cycl e 1Cycl e 2

Cycl e 3Cycl e 4

Cycl e 5

27

PEMP


26/68

ESD2521


Floating vs. Fixed Point processors

Applications which require: High precision.

Wide dynamic range.

High signal-to-noise ratio.

Ease of use.Need a floating point processor.

Drawback of floating point processors:

Higher power consumption.

Can be higher cost. Can be slower than fixed-point counterparts and larger

in size.

28

PEMP


27/68

ESD2521


Floating vs. Fixed Point Processors

It is the application that dictates which device and platform touse in order to achieve optimum performance at a low cost.

For educational purposes, use the floating-point device

(C6711) as it can support both fixed and floating point

operations.

29

PEMP


28/68

ESD2521


General Purpose DSP vs. DSP in ASIC

Application Specific Integrated Circuits (ASICs) are

semiconductors designed for dedicated functions.

The advantages and disadvantages of using ASICs are

listed below:

Advantages

High throughput

Lower silicon area

Lower power consumption Improved reliability

Reduction in system noise

Low overall system cost

Disadvantages

High investment cost

Less flexibility

Long time from design tomarket

30

PEMP


29/68

ESD2521


Texas Instruments TMS320 Family

Different families and sub-families exist tosupport different markets.

Lowest CostControl Systems

Motor Control

Storage

Digital Ctrl Systems

C2000 C5000

EfficiencyBest MIPS per

Watt / Dollar / Size

Wireless phones

Internet audio players

Digital still cameras

Modems

Telephony

VoIP

C6000

Multi Channel and Multi

Function App's

Comm Infrastructure Wireless Base-stations

DSL

Imaging

Multi-media Servers

Video

Performance &Best Ease-of-Use

31

PEMP


30/68

ESD2521


DSP Architectures Evolution

Objective: fast computation of Z = X * Y

(one instruction and two operands)

Methods:

Von NeumannHarvard Architecture

Super Harvard Architecture

Modified Harvard Architecture

Cost Speed

32

PEMP


31/68

ESD2521


DSP Architectures Evolution:

Von Neumann

Data and instructions are stored in the same single bank.

One access to memory (1 piece of data or instruction) is

performed during each instruction cycle.

33

PEMP


32/68

ESD2521


DSP Architectures Evolution:

Harvard Architecture

Data and instructions are stored in two different memory

banks. One access to each of the banks is performed

simultaneously during each instruction cycle.

34

PEMP


33/68

ESD2521


DSP Architectures Evolution:Super Harvard Architecture

Data can be stored in the instructions block also. One access toeach of the banks is performed simultaneously to fetch

instruction+data or data+data. An instruction cache mechanism

is involved in the second option.

35

PEMP


34/68

ESD2521


DSP Architectures Evolution:Modified Harvard Architecture

One single-ported instruction block and one dual-ported data block

enable single-cycle Access of 2 Pieces of Data and 1 Instruction.

36

PEMP


35/68

ESD2521


DSP Speed

MIPS million instructions per second

MOPS million (mathematical) operation per second

MFLOPS million floating-point operation per second

MMACS million MACs per second

37

PEMP


36/68

ESD2521


DSP Development Flow

Simulation target (without a physical processor) enables you tobuild, edit, and debug your program, even before a processor is

manufactured. Your PC connects to the EZ-KIT Lite evaluation system via acable, enabling you to monitor processor behavior.

JTAG emulator enables application software to be downloadedand debugged from within VisualDSP++ or CCS

38

PEMP


37/68

ESD2521


DSP Development Flow

Mathematical algorithm

MATLAB code for Simulation/Validation

RT format MATLAB code + Validation with the former stage

DSP Simulation + Validation with the former stage

DSP Evaluation + Validation with the former stage DSP Emulation + Validation with the former stage

39

PEMP


38/68

ESD2521


C or Assembler

Assembler

Pros: Maximal efficiencyCons: Required core architecture knowledge Complicated for reading Complicated for writing Long development time Expensive development HR

CPros: Core architecture knowledge is not

required Easy for reading Easy for writing Short development time Cheap development HR Maximal efficiency

Cons: Limited efficiency (depends on the

optimizer)

40

PEMP

S 2 21


39/68

ESD2521


C or Assembler

Intensive code parts Assembler

Bureaucracy C/C++

Algorithms analysis by assembler expert.

41

PEMP

ESD2521


40/68

ESD2521


C and C++ Language Programming

Motivation: Portability, maintainability, time to market Full ANSI Language

plus: // C++ style comments

other general programmability extensions

Full-featured library full standard math function support

additional DSP functions

basic I/O: printf, simple file I/O

Extensions tailored for DSP

Highly effective optimizer

Fully integrated into programming environment

edit, build support runtime system in place

source-language debugging

42

PEMP

ESD2521


41/68

ESD2521


Types of Signals

Analog Signals (Continuous-Time Signals): Signals that are

continuous in both the dependant and independent variable(e.g., amplitude and time). Most environmental signals are

continuous-time signals.

Discrete Sequences (Discrete-Time Signals): Signals that are

continuous in the dependant variable (e.g., amplitude) butdiscrete in the independent variable (e.g., time). They are

typically associated with sampling of continuous-time signals.

Digital Signals: Signals that are discrete in both the dependant

and independent variable (e.g., amplitude and time) are digitalsignals. These are created by quantizing and sampling

continuous-time signals or as data signals (e.g., stock market

price fluctuations).

43

PEMP

ESD2521


42/68

ESD2521


Types of Signal

44

PEMP

ESD2521Discrete-Time Signals:


43/68

ESD2521


Discrete Time Signals:Time-Domain Representation

Signals represented as sequences of numbers, called samples

Sample value of a typical signal or sequence denoted asx[n] with nbeing

an integer in the range

x[n] defined only for integer values of nand undefined for non-integer

values of n

Discrete-time signal represented by {x[n]}

n

45

PEMP



44/68

ESD2521


Discrete-time signal may also be written as a

sequence of numbers inside braces:

In the above,

The arrow is placed under the sample at time

index n= 0

},9.2,7.3,2.0,1.1,2.2,2.0,{]}[{ =

nx

,2.0]1[ =x ,2.2]0[ =x ,1.1]1[ =x


46

PEMP



45/68

ESD2521


Graphical representation of a discrete-time signal with

real-valued samples:


47

PEMP



46/68

ESD2521


Discrete Time Signals:

Time-Domain Representation

In some applications, a discrete-time sequence {x[n]} may

be generated by periodically sampling a continuous-time

signal at uniform intervals of time)(txa

48

PEMP



47/68

ESD2521


Here, nthsample is given by

The spacing Tbetween two consecutive samples is called the

sampling intervalor sampling period

Reciprocal of sampling interval T, denoted as , is called

the sampling frequency:

),()(][ nTxtxnx anTta == =

TF

TFT1

=

g


49

PEMP



48/68

ESD2521


Discrete Time Signals:


Unit of sampling frequency is cycles per second, or Hertz

(Hz), if Tis in seconds

Whether or not the sequence {x[n]} has been obtained by

sampling, the quantityx[n] is called the n-th sample of the

sequence

{x[n]} is a real sequence, if the n-th samplex[n] is real for

all values of n

Otherwise, {x[n]} is a complex sequence

50

PEMP



49/68

S 5


g


A complex sequence {x[n]} can be written as

where

and are the real and imaginary parts ofx[n]

The complex conjugate sequence of {x[n]} is given by

Often the braces are ignored to denote a sequence if there is no ambiguity

][nxre

][nxim

]}[{]}[{]}[{ nxjnxnximre

+=

]}[{]}[{]}[*{ nxjnxnx imre =

51

PEMP



50/68


gTime-Domain Representation

Example - is a real sequence

is a complex sequence

We can write

where

}.{cos]}[{ nnx 250=

}{]}[{ . njeny 30=

}.sin.{cos]}[{ njnny 3030 +=

}.{sin}.{cos njn 3030 +=

}.{cos]}[{ nnyre 30=

}.{sin]}[{ nnyim 30=

52

PEMP



51/68


g


Example -

is the complex conjugate sequence of {y[n]}

That is,

}{}.{sin}.{cos]}[{ . njenjnnw 303030 ==

]}[*{]}[{ nynw =

53


52/68

PEMP



53/68



A discrete-time signal may be a finite-length or an infinite-

length sequence

Finite-length (also called finite-duration or finite-extent)

sequence is defined only for a finite time interval:

where and with

Lengthor durationof the above finite-length sequence is

21 NnN

1N


54/68

PEMP



55/68


g


Example -

is a finite-length sequence of length 12 obtained by zero-

padding with 4 zero-valued samples

= 850 432

nnnnxe ,,][

432 = nnnx ,][

57

PEMP



56/68



A right-sided sequencex[n] has zero-valued samples for

If a right-sided sequence is called a causal sequence

,01 N

1Nn

,02N

2Nn

A left-sided sequence

59

PEMP

ESD2521


58/68


Operations On Sequences

A single-input, single-output discrete-time system operates on a

sequence, called the input sequence, according to some prescribed

rules and develops another sequence, called the output sequence, with

more desirable properties

x[n] y[n]

Input sequence Output sequence

Discrete-time

system

60

PEMP

ESD2521


59/68


Operations On Sequences

For example, the input may be a signal corrupted with

additive noise Discrete-time system is designed to generate an output by

removing the noise component from the input

In most cases, the operation defining a particular discrete-

time system is composed of some basic operations

61

PEMP

ESD2521


60/68


Basic Operations

Product(modulation) operation:

Modulator

An application is in forming a finite-length sequence from

an infinite-length sequence by multiplying the latter with a

finite-length sequence called an window sequence

Process called windowing

x[n] y[n]

w[n]

][][][ nwnxny =

62

PEMP

ESD2521


61/68


Basic Operations

Additionoperation:

Adder

Multiplicationoperation

Multiplier

][][][ nwnxny +=

Ax[n] y[n] ][][ nxAny =

x[n] y[n]

w[n]

+

63

PEMP

ESD2521

B i O ti


62/68


Basic Operations

Time-shiftingoperation:

whereNis an integer

IfN> 0, it is delayingoperation Unit delay

IfN< 0, it is an advanceoperation

][][ Nnxny =

y[n]x[n]z

1z y[n]x[n] ][][ 1= nxny

][][ 1+= nxny

64

PEMP

ESD2521


63/68


Basic Operations

Time-reversal(folding) operation:

Branchingoperation: Used to providemultiple copies of a sequence

][][ nxny =

x[n] x[n]

x[n]

65

PEMP

ESD2521

Sampling Rate Alteration


64/68



Employed to generate a new sequence y[n] with a sampling

rate higher or lower than that of the sampling rate of

a given sequencex[n]

Sampling rate alteration ratiois

IfR> 1, the process called interpolation

IfR< 1, the process called decimation

TF'TF

T

T

F

FR

'

=

66

PEMP

ESD2521

S li R t Alt ti


65/68



In up-samplingby an integer factorL> 1,equidistant zero-valued samples are inserted by the up-sampler

between each two consecutive samples of the input sequencex[n]:1L

=

=otherwise,0

,2,,0],/[][

LLnLnxnxu

L][nx ][nxu

67

PEMP

ESD2521


66/68



An example of the up-sampling operation

0 10 20 30 40 50-1

-0.5

0

0.5

1Output sequence up-sampled by 3

Time index n

Amplitude

0 10 20 30 40 50-1

-0.5

0

0.5

1Input Sequence

Time index n

Amplitude

68

PEMP

ESD2521

S i A i


67/68



An example of the down-sampling

operation

0 10 20 30 40 50-1

-0.5

0

0.5

1Output sequence down-sampled by 3

Amplitude

Time index n

0 10 20 30 40 50-1

-0.5

0

0.5

1Input Sequence

Time index n

Amplitude

69

PEMP

ESD2521

S i S


68/68

Session Summary

An Embedded system is a special-purpose computer that

interacts with the real world through sensing and/or actuation

Embedded Systems are also extremely and diversely applied

in various areas Digital Signal Processing is an application ofmathematical operations to digitally represented signals

Session 1 Sig Rev

Documents

Transcript of Session 1 Sig Rev