DSP and Filters

Prof. Nagendra GajjarAssistant Professor

Electronics & Communication Engineering Department

Nirma University, Ahmedabad

Agenda Introduction to DSP

Applications Digital Signals and Processing Block Diagram Advantages Disadvantages

DSP Systems

Filters Classifications Analog Filters Digital Filters Design of Digital Filters

Introduction to DSP

Applications Digital Signals and Processing Block Diagram Advantages Disadvantages

Digital Signal Processing

Digital Signal Processing means Processing signals in digital domain, which includes Modifying signal characteristics Multiplying two signals( Modulation, correlation

etc) Filtering Averaging etc..

DSP can extract one signal from another DSP can analyze a signal to extract the

characteristics

Digital Signal Processing

Applications of DSP

Space -- Remote Sensing-- Space photograph enhancement

Medicine -- Diagnostic Imaging-- EEG,ECG, Patient Monitoring

Communication-- Voice and Data Compression – Signal Multiplexing -- Filtering, Telecommunication

Defense

-- RADAR , SONAR -- Secure Communication,

-- Missile Guidance

Applications of DSP ( Contd..)

Speech Audio --Speech Recognition / Synthesis-- TTS, Digital Audio

Image Processing

--Robotic Vision

-- Animation, Image Recognition

Instrumentation / Control

-- Spectrum Analysis – Position and Rate Control -- Noise Reduction, Automotive Applications

Consumer Applications

--Digital, Cellular Mobile Phoes, Digital TVs, Digital Cameras, Voice Mail Systems, Active Suspension in the cars

Signals

DSP Systems(LTI)

Signal Transforms

System Transforms

Filter Design

Qunatization

Advanced Topics

Signals

Analog Signal x = f(t) Continuous function of independent variable Present at each and every instant of time

Digital Signal x[n]=f(nT) , T Sampling Interval Discrete function of time Present at discrete interval of time ( sampling

period)

Discrete signal Continuous signal

Converter

Sampling

These are numbers indicating amplitude at that instant.

Sample Signals

Types of Signals

Continuous Signals and Discrete Signals Analog Signals and Digital Signals Periodic Signal and Aperiodic Signals Natural Signals and Synthetic Signals 1-D, 2-D, Multi Dimensional Signals Multi Channel Signals Deterministic and Random Signals Real Valued and Complex Valued Signals Scalar and Vector Signals

Signals

Basic Digital Signals

Impulse Signal Step Signal Ramp Signal Exponential Signal

Sinusoidal Signal

Block Diagram of DSP System

Digital Processing

ADC DAC

Analog Filter Analog Filter( Antialias Filter ) ( Reconstruction

Filter )

Components of DSP system

Components of DSP System

Components of DSP System

Another Example

Basic DSP operations

Addition Subtraction Delay Multiplication

Key DSP operations

ConvolutionY(n) = Σk x(k) h(n-k)

Correlation Filtering Multiplexing Demultiplexing Modulation Demodulation Transforms

Filtering

Filtering

Modulation - DeModulation

Advantages of DSP

High Performance Guaranteed Accuracy Stability Uniformity

High Reliability Time and Temp have no effect

Flexibility Software Controlled

Time sharing of Components

Advantages of DSP ( Contd..)

No loading of Circuit Exact Linear Phase Multirate Signal Processing Easy Storage for large amount of data Very Low frequency Processing Reconfigurable Processing

Disadvantages of DSP

Speed and Cost ADC/ DAC Frequency Range

Design Time Increased Complexity Knowledge of DSP techniques

Power Dissipation Finite word length problems

DSP- When to use ?

Real Time Processing ( Processing completed within the sampling duration)

Pseudo Real-time Processing Off-Line Processing

Sampling Duration T

T= 1/f

Digitization of Analog Signals

Sampling Lossless Process Done at Nyquist Rate

Quantization Lossy Process More nits improve resolution and reduce

quantization noise

DSP Systems

“A system is defined as a process that produces an output signal in response to an input signal.”

SYSTEM

x[n] y[n]

System Response of OPAMP

system, in which the required information is stored, either as IMPULSE Response, FREQUENCY Response, or the Coefficients of the systems equation.

VinVout

BLACK BOX

Types of systems

.

CONTINUOUS TIME SYSTEM

DISCRETE TIME SYSTEM

••••••

•••

••••

•••

X(t) Y(t)

X(n) Y(n)

Values defined at all points

Values defined only at certain points values in between are not defined.

Systems are basically divided in two categories. CONTINUOUS TIME SYSTEMS. DISCRETE TIME SYSTEMS.

System Characteristics

Linearity Super Position Homogeneous

Time Invariance Causality Stability

Such Systems are called as LTI- Causal Systems

Digital System Equation

Recursive System Output of the system depends upon the

current input and its weighed previous input as well as its weighted previous outputs

Closed Loop systems Non Recursive Systems

Output of the system depends upon the current input and its weighed previous input

Open Loop systems Always Stable

Types of Digital Systems

FIR – Finite Impulse Response Filter IIR – Infinite Impulse Response Filter

Filters

An electrical device which retains certain frequency components and rejects certain frequency components

It amplifies/attenuates certain frequency components

Frequency

Magnitude

0

Classification of FiltersBased on Frequency Characteristics Low Pass Filter High Pass Filter Band Pass Filter Band Reject Filter Notch Filter Multi Pass filter ( Comb Filter )

Filter Specifications

Pass Band Frequency Stopband Frequency Passband Ripple Stopband Ripple Sampling Frequency

Computation of Order

N= -10 log(delp * dels) -15 + 1

14[ ( ws –wp)/2 *pi ]

In MATLAB

Fir1 :

In MATLAB: fir2

Fir2 : FIR arbitrary filter design using the frequency sampling method

B=fir2(N,F,am,NPT,window) N- Order of the Filter F- Frequency sampling Points A- Amplitude NPT, No of Points for frequency response Window : Type of window

Analog Filter Design

FIR Advantages

Linear Phase Multi band is possible Simple structure Always stable and no limit cycle Easy to get high speed and pipeline design Low coefficient arithmetic and round off error

and well defined quantization noise

FIR Disadvantages

Recursive FIR may be unstalbe because of imperfact pole/zero annihilation

High Filter length/ order requires high implementation cost

IIR Advantages

Standard Design using analog prototpyes Highly selective filter using low order design Design using tables and pocket calculators Good tolerance scheme Closed Loop Design Algorithms can be used.

IIR Filter Disadvantages

Non Linear Phase response Limit cycle may occur for integer

implementation Multiband design is difficult Feedback can introduce instabilities Difficult to get high Speed, pipelines design

Summary of Important IIR Design

Butterworth: Maximally –flat passband, flat stopband, wide

transitionband : Filter order highest Chebyshev-I

Equiripple passband, flat stopband, moderate transition band

Chebyshev II Flat passband, equiripple stopband, moderate

transition band : Filter Order Medium Elliptic:

Equiripple passband, equiripple stop band narrow transition bnad : Filter order : Lowest

Thank Younpgajjar@yahoo.co

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