lec1-intro fall 2014

7/24/2019 Lec1-Intro Fall 2014

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Dr. Shoab Khan

Adv Digital Signal Processing

Lecture 1

Introduction: DSP is Everywhere

Source: Internet CARE/EME Projects


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Outline

Introduction of DSP Applications

Projects

DSP in Biomedical Engineering DSP in Radars

DSP in Communication Systems

Course Outline, Text Book, Grading


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INTRODUCTION TO DSP

3

Adv DSP:Focus on DSP Software Desi n


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DSP

Application of mathematical operations (linearand non-linear) to digitally represented signals

IN OUT

A/D D/ADSP

-3 -2 -1 0 1 2 3 4

x[0]x[1]

n


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General IntroductionDiscrete Time Signal

sequence x[n]

- as opposed to continuous-timesignals x(t)

- time = independent variable


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Example- 1D Signal

Sampled continuous-time (analog) signals

- Speech


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DSP

Roots in 17-th and 18-th centurymathematics

An important modern tool in a

multitude of fields of science andtechnology

Techniques and Applications of DSP

As old as Newton and Gauss As new as digital computers and integrated

circuits


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Purpose

To estimate characteristic parametersof signals

Statistical Signal Processing

To transform a signal into a moredesirable form

Fourier, Wavelet etc

Classical numerical analysis formulasare also DSP

interpolation, integration, anddifferentiation


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2-D Array: A Digital Image


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10

Typical Scenario

Step 1: Analog sensor picking analog signal (e.g., microphone picking sound)

Step 2: Analog to Digital Converter

Step 3: DSP processes the digital signals (e.g., compression, noise suppression)

Step 4: Digital to analog converter to recover the analog signal


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Real-Time DSP

Example: Processor clocked at 120 MHz and can

perform 120MIPS Sampling rate = 48KHz (Digital Audio Tape - DAT)

number of instructions per sample = (120 x106)/(48 x 103) = 2500.

Sampling rate = 8KHz (voice-band, telephony)number of instructions per sample = 15000.

Sampling rate = 75MHz (CIF 360x288 Video at 30frames per second) number of instructions persample = 1.6.

Real-TimeDigital Processing

Digital Signal in Digital Signal out

Time-constrained Operation or Transformationperformed on digitalsignals within a required period

of time to maintain synchronization with occurring events.


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DSP Targets: Cell Phone

-Speech Coders

-Speech Recognition

- Equalizers

- Antenna noise cancellation

-Image enhancement techniques

DSP

Chip

RF

Codec

Voice

Codec

RF

Receiver

Microprocessor

Chip

Cell

Peripherals

Controlled by Power Management Unit


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DSP Targets: Cell Phone


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Purpose

DSP SW Design: Availability of high-speed digital computers has fostereddevelopment of increasingly complexand sophisticated signal processingalgorithms

Digital Design of DSP: Advances in VLSItechnology have made possible

economical implementations of verycomplex digital signal processingalgorithms


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DSP APPLICATIONS

15Adv DSP:Focus on DSP Software Desi n


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DSP is Everywhere

Sound applications

Compression, enhancement, special effects, synthesis, recognition, echocancellation,

Cell Phones, MP3 Players, Movies, Dictation, Text-to-speech,

Communication Modulation, coding, detection, equalization, echo cancellation,

Cell Phones, dial-up modem, DSL modem, Satellite Receiver,

Automotive ABS, GPS, Active Noise Cancellation, Cruise Control, Parking,

Medical Magnetic Resonance, Tomography, Electrocardiogram,

Military Radar, Sonar, Space photographs, remote sensing,

Image and Video Applications DVD, JPEG, Movie special effects, video conferencing,

Mechanical Motor control, process control, oil and mineral prospecting,

l b dd d


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Digital

Radiographic

Imaging

Ultrasound

Medical

Imaging

SpySatellite

Imaging

Military

Appls

Real-Time

Video-Camera

Cell-Phones

Video

Communications

Space

Imaging

Appls

Optical

Wearable

Computers

Web wireless

technology

Data Storage

& Transmission

Car Awakewarning system

Real- Time DSP

Speech

Recognition

DSP in Real Time Embedded Systems
http://www.euspaceimaging.com/content/Downloads/samples/madrid/madrid_(1280x960).jpghttp://www.ieee.com/


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Digital Signal Processing Represent signals by a sequence of numbers

Sampling or analog-to-digital conversions

Perform processing on these numbers with a program or HW

Digital signal processing

Reconstruct analog signal from processed numbers

Reconstruction or digital-to-analog conversion

A/D DSP D/Aanalogsignal

analogsignal

digitalsignal

digitalsignal

Analog input analog output

Speech in Mobile Phone

Analog input digital output

Speech to text

Digital input analog output

Text to speech

Digital input digital output

Compression of a file on computer

Data minning


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EEG electroencephalogram signal

Records the electrical activity of the brain obtained from

electrodes placed on the scalp.


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A computer aided system capable of processing biological signals of learners in real-time to

monitor their level of attention, cognition and engagement.

d l


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Intra-cardiac Signals

f


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fMRI

fMRI measures brain activity by detecting associated changes in

blood flow


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3-D Accelerometers

Powergloves: body pose estimation using a network of 3D

accelerometers


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Thallium scans


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Fundus Image

The fundus of the eyeis the interior surface of the eye, opposite the lens, and

includes the retina, optic disc, maculaand fovea,
http://en.wikipedia.org/wiki/Human_eyehttp://en.wikipedia.org/wiki/Lens_(anatomy)http://en.wikipedia.org/wiki/Retinahttp://en.wikipedia.org/wiki/Optic_dischttp://en.wikipedia.org/wiki/Maculahttp://en.wikipedia.org/wiki/Foveahttp://en.wikipedia.org/wiki/Foveahttp://en.wikipedia.org/wiki/Maculahttp://en.wikipedia.org/wiki/Optic_dischttp://en.wikipedia.org/wiki/Retinahttp://en.wikipedia.org/wiki/Lens_(anatomy)http://en.wikipedia.org/wiki/Human_eye


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Projects related to signal processing

DSP in CARE / EME

I t lli t M di l E i t b d U ifi d N t k d


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i-Hospital

Cardiac ECG

Intra Cardiac

Holter Monitor

Nuclear

Cardiac & CT

Angiography

Diabetic

Retinopathy,

Maculopathy,OCT

Hess

Charting

Gastroenterology

National

Repository &

Analytics

Use ICT as a Catalyst

Intelligent Medical Equipment based Unified Networked

Hospital

Digitization and net-enabling of medical equipment

Incorporation of intelligent diagnostics

Image and signal processing & artificial intelligence

The virtual hospital and workflows in SW

Need to add payment system in the SW

National Storage for Decision Aiding


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Eye Care Diabetic Retinopathy (DR) Diabetic Maculopathy (DM)

Detection of AMD using OCT

Glaucoma Detection

Diagnosis of Paralytic Strabismus using HessScreening

CASE/College of EME

Dr Shafaat A Bazaz

Dr Usman Akram

Dr Waheed

Dr Shahzad

AFIO

Dr Mazhar Ishaq

Dr Ubaidullah Yasin

Dr Yasir

Funded by

Human Retina and Fundus Image


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Human Retina and Fundus Image

The retina is the layer of tissue at the back of the inner eye

Optic Disc - brightest circular spot

Macula - main central part of retina responsible for fine

details and sharp vision


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Retinal Image

Blood Vessels

Optic Disc

Candidate Lesions

FeatureExtraction

Microaneurysms

Soft Exudates

Haemorrhage

Hard Exudates

C

L

A

SS

I

F

I

E

R

G

R

A

D

I

N

G

Normal

Severe

Mild

Moderate

Feature Selection

Feature Set


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AFIC / NIHD

National ICT R&D Fund

Corporate Social Responsibility

A Tele-Cardiac &

i-Diagnostic System

Deployed System in AFIC

Doctor on the move Tele-cardiac node at


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p y y

49

ONT

ONT

Doctors

TerminalOperatorPatient

Ambulance

WirelessInternet

WiFi Router

PDA

District hospital

WebSerices

and NMSServer

Local Area Network


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ECG Wave

50


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ECG View

52


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Advanced ECG View

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Advanced ECG View

54


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ECG Streaming View

55


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Doctors Comment View

56


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Decision Aiding Tools

57


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ECG Wave

58

ECG i l


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Total

Power

VLF

ULF

LF10m

HF

LF

HF10m

Frequency Domain

Parameters

LF/ HF

Alpha

ECG signal

Morphology Comprehension and location of fiducial points

SDNN

SDANNRMSSD SDNN

index

NN50

countpNN50

HRV

Triangular

index

TINNDifferent

ial index

Logarithm

ic indexDispersion

Time Domain Parameters

ApEn MSE

SEn

TWA

DFA MFA

IBSI

Nonlinear Analysis

Parameters

MKLT

HRT

Input Space Classifier 3rdStage

Output Space

RR ST QT PP PR P QRS T HR

1 2 3 4 5 6 NMembership functions

StartPerform Analysis


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60

Acquire Digital ECG dataEctopic Beat

Calculate RR

intervals (Heart

Rate Variability)

Calculate QT

interval

Calculate ST

segment duration

Calculate P, QRS and

T wave Statistical

parameters (onset, Offset,

duration, amplitude etc)

Calculate T wave

alternans

Calculate Heart

Rate Turbulence

Perform Analysis Perform Analysis Perform Analysis Perform Analysis Perform Analysis Perform Analysis

Morphology

Analysis

A Hybrid Classifier

Compute Abnormality Factor

Generate Report

Abnormality

Found? Local

Database

End

Classify Ectopic Beat

Get total no. of

Ectopic beats

Eliminate Ectopic Beats

Repopulate signal

of normal beats

yesSet sampling and bit resolution parameters

ECG preprocessing and noise removal

Locate fiducial points

Locate P wave, QRS complex and T wave

Beat Classification

no

yes

no

Transmit

Data

y

Perform Time Domain Analysis

Perform Frequency Domain Analysis

Perform Nonlinear Analysis

Template

Matching


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Acquire ECG Data

Bandpass Filter

0.5/1 Hz < fs < 40Hz

Bandpass Filter

0.05 Hz < fs < 150Hz

Signal Smoothing through

polynomial fitting

Signal Smoothing through

higher order polynomial fitting

Gaussian Modeling for

high quality filtering

Monitoring Mode ECG Raw ECG Diagnostic Quality ECG

Remove Baseline Wandering

Remove Powerline Interference

Remove Wideband Noise (AWGN)

Remove Muscle Artifacts such as EMG signals


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Start

ECG Signal

Baseline wandering free signal

Overall Median Calculation

Sample by Sample shifting in

accordance with median

Fourth Degree Polynomial Fit

Calculate deviation of the signal

with respect to zero line

Measure discrepancy

and eliminate

R waves and RR intervals

are detected

Median corrected in

each RR interval


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Start

Acquire ECG Signal

Powerline Interfaceremoved signal

Compute power spectral density

Slide a 1Hz window on power

spectrum ranging from 40Hz to 70Hz

Compute frequency component

fnotch with highest energy

in the sliding window

Filter ECG

Create a digital notch filter with

sampling frequency fs, notch

frequency fnotch and attenuationconstant of at least 10dBs


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Start

Acquire ECG data

Diagnosed ECG signal

Select a mother wavelet e.g. db6

Decompose into subbands

Modify the coefficients by

applying a threshold function

Reconstruct the signal


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Start

Acquire Digital ECG data

Diagnosed ECG

Pilot Signal EstimationQRS Detection

Inverse Transform

Beats Splitting

and Assignment

Inter-beat

decorrelating transform

Intra-beat

decorrelating transform

Noise Estimation

Transform Domain

Wiener Filter

Inverse TransformBeats Merging

Start


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Digital ECG data

Multiscale Zero-crossing

point and edge detection

QRS onsetand Q point

QRS offsetand S point

Detection of

Q point

Detection of

S point

Detection of

QRS onset

Detection of

QRS offset

Calculation of Time

Window before R peak

Calculation of Time

Window after R peak





Detection of P

wave onset

Detection of P

wave offset

Detection of T

wave onset

Detection of T

wave offset

Detection of P pointDetection of

T point

Detection of

Modulus Maxima Pair

Detection of

Modulus Maxima Pair

P point T pointP wave Onset P wave Offset T wave Onset T wave OffsetR point

Multiscale Wavelet Transform

using a Quadratic Spline wavelet

Determination of Modulus

Maximum Lines of R waves

Elimination of Redundant andIsolation Modulus Maximum Lines

Detection of R peak

Calculation of

Singular Degree

Selection ofCharacteristic Scales


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ECG signal with fiducial points marked with cross. P

wave in pink, QRS in blue and S wave in red color 67


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AR coefficients

Calculate Y1 APC/

PVC/ NSR/ SVT or VT/ VF

APC/ PVC/ NSR/ SVT VT/ VF

Calculate Y3 APC/ PVC/ NSR/ SVT Calculate Y2 VT/ VF

APC/ PVC/ NSRSVT VT VF

Calculate Y4 APC or PVC

APC/ NSR PVC/ NSR

Calculate Y5 APC or NSR Calculate Y6 PVC or NSR

APC NSR PVC NSR


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RR intervals

Clean RR intervals

HRV Preprocessing

DWT

Soft/ hard Thresholding

iDWT


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P

Q

S

R

T

P

Q

S

R

TP

Q

S

R

T

P

Q

S

R

TT1T2 T2T4

T1T3T3T4

If (|T1 T3| < |T1 T2|) and (|T2 T4| < |T3 T4|) and (|T2 T4| < |T1 T2|) and (|T1 T3| < |T3 T4|)

Then T wave Alternans in this window is present

Here Tn is the nth T wave in a Four Beat Window (n=1,2,3,4)

Type PPInterval

Variation

(s)

PRInterval

Variation

(s)

PPInterval

Duration

(s)

RRInterval

Duration

(s)

AtrialRate

(1/s)

VentricularRate (1/s)

P-wave PRInterval

Duration

(s)

QRSInterval

Duration

(s)

T wave QTInterval

Duration

(s)

STsegment

shift

(mV)


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(s) (s) (s) (s) (s) (s) (s) (mV)

Normal 0.16 0.33

0.6

0.33

0.6

Normal Normal Normal >0.2 Normal Normal Normal -

TDB - - Normal >1 Normal 1

Ischemia - - - - - - - - - - - 1 >1 0.16 >0.16 >0.33 >0.33 Normal Normal Normal Normal Normal Normal Normal -

SB Normal Normal >1 >1 0.1 - - -

VA - - - > 1.5 - - - - - - - -

PAC > 0.16 > 0.16 0.33

0.6

0.33

0.6

- - - - Normal - Normal -

PVC - - - - - - Absent - >0.1 Opposite in

direction to

QRS

- -

AT Normal Normal 0.24

0.4

- 150 -

250

- - - Normal - Normal -

AFr Normal Normal - Normal 250 -

400

Normal Abnorma

l

- Normal - - -

AFn - - - 0.33 >400 100 - 180 Abnorma - Normal - - -


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STM: Coherent Burst Satellite Receiver

72

32 kbps 512 Kbps BPSK/QPSK

DSP Algorithms

Burst Receiver

FEC

Carrier recovery and PLL

DSP Software

Embedded Software

Glue Logic in Xilinx

System Board

Integration and testing

HSP52014

B-bit DAC & LFP

68332

A/D

DDS

Flash

SRAM

Amplifier &

squarer

SBSRAM

TMS320C6201

Output

Bitsream

Square

wave outpt

49.162 MHz Sine

wave clock

Xlllnx 4062

From RF

Board

To RF

Board

73


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A Pakistani Engineering Team Making History

Voice processing @ 32 ms LEC

VZM1004L, 256 x G.711 or 168 x G.726

VAD, CNG & LPC (Lost Packet Compensation)

Voice band signaling: CAS, DTMF, MF

~6 mw/channel in .18m Standard Cell

World Highest DensityMedia Processing Chip

Developed in Pakistan in 2000

RTOS Abstraction Layer

Media

Processing

Apps &

Devices

Cell &

Packet

Processing

Apps &

Devices

Call

Control &

Signaling

Protocols

& Apps

AVAZ

SNMP

Agent

& MIB

Utilities

User Applications

AVAZ System Components & Resources Manager

74


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VoIPPlatform for PTCL

VoIP platform with multiple applications in

telecom like call centers, IVR, CTI, ACD,

PBX etc


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Passive Navigation System

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Universal Radio Receiver COMINT System

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SDPROLIANT

1850R

DRSE DemodulatorLoad BatteryLine

O n O nBattery

SmartBoost

ReplaceBattery

Test

SD

DATAX

iZ 9200

SD R D

PORT ASD R D

PORTBA

ONLINEB

BWD - ENTER

POR T SEL D ISC D AT A

+

DRSE Demodulator

Universal Demodulator

Universal Demultiplexer

Ethernet

Control & Operator

Console Computer

IF up

to 70

MHz

Multi Frame Generation Channel Activity Status


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Software Defined Radio


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SDR

Th T h l


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The Technology

Analog

input

1 0 1 0 0 1 0

Analog to

DigitalConverter Bits Encoded

Bits

SourceEncode

1 0 1 1 0

Encrypt

Encrypted

Data

0 1 1 0 1

Bit to Sym.& Pulse

Modulate

Pulse

modulated

waveform

Digital Bandpass

waveformBandpassmodulate

Multiplex

0 1 1 0 1

0 1 0 1 0

1 0 1 0 1

From Other

Channels

Multiplexed Data

ChannelEncode

Channel

Encoded

Data

1 0 0 1 1 0 1

Scrambler

Scrambled

data

1 0 0 0 1

Equalizer,

Timing andSym. to Bits

Bits

Decrypted

Bits

1 0 1 1 0DecryptAnalog

output

D/A

De-modulate

Digital

Baseband

waveform

Digital

Bandpass

waveformChannel

Decode

Channel

Decoded

Data

0 1 1 0 1

Source

Decoded

Bits

1 0 1 0 0 1 0

SourceDecode

De-Multiplex

To otherChannels

De-

multiplexed

Bits

Descrambled Bits

1 0 0 0 1

De-scramble


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Software Defined Radio (Frequency

Hopping)

80


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Software Defined Radio (Equilizer)

81


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SDR (Equalizer)

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Speaker Identification System: Speech Playback


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Selected Speech File

for Playback and

Spectrum Analysis

Speech playback with

Spectrum Analysis

Speaker Identification System: Speech Playback

KLT (PCA)

Ei i l


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Eigenimagesexamples:

3 eigenimages and the individual variations on those components

Facial

image

set

Corresponding

eigenfaces

Face

aproximation,

from rough to

detailed, as more

coefficients areadded

COURSE OUTLINE


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COURSE OUTLINE

&LEARNING OUTCOMES

85

Adv DSP:

Focus on DSP Software Desi n

Course Learning Outcomes


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1. To review basic concepts of DSPa. Use basic concepts of convolution, system analysis,

transformations and design in solution of DSP Problems2. To build advanced concepts in DSP

a. Learn advanced topics in DSP relating to Multi-rate systems,Bandpass sampling, Statistical Signal Processing, Wavelettransform

b. Use these advanced concepts in designing signal processingsystems

3. To learn key areas in DSP Software Designa. To design DSP SW Systems relating to 1 and 2

b. To develop software relating to 1 and 2 for mapping it on DSP

Processors

Course Outline


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Basic Concepts Fundamentals of discrete-time, linear, shift-invariant signals

and systems in Representation and Analysis:sampling, quantization, Fourier and

z-transform;

Implementation:filtering and transform techniques;

Design:filter & processing algorithm design. Efficient computational algorithms for FFT and their implementation.

Course Outline


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Advanced Topics

Sampling rate conversion in multirate systems,multirate signal processing, bandpass samplingand advanced transforms

Signal Modeling, Least Square Method, PadeApproximation, Pronys Method, Finite DataRecord and Stochastic Models

Levinson Recursion

Course Outline


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DSP SW Design Hybrid System: DSPs, GPPs and FPGAs

Fixed Point & Floating Point Arithmetic

DSP Architectures and HW interfaces

DSP Processor Programming

DSP BIOS, Programming DMA & Interrupts DSP Software Engineering Processes

DSP Software Architecture Design

Course Outline


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DSP Fundamentals

Band-pass

sampling

Multi-rate

SignalProcessing

Signal

Modeling

DSP SWDesign

Prerequisite


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A fundamental course in signalprocessing

Liner System analysis and transformanalysis

convolution and filtering Fourier transforms

Laplace and z transforms

Textbook


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Oppenheim, Schafer and

Buck, Discrete-TimeSignal Processing, 2ndedition (Prentice-Hall,

1999) Refrences: Hayes, Digital Signal

Processing (Schaums

Outlines Series), 1999 McClellan, Schafer, &

Yoder, DSP First

Text


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Monson H. Hayes

Statistical Digital Signal Processing andModeling

John Wiley & Sons, Inc

J. G. Proakis, C. M. Rader, F. Ling, & L. NikiasAdvanced Digital Signal Processing

eferences

Ifeachor JervisDigital Signal Processing- A Practical Approach

Prentice Hall

Marks Distribution


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Grading Sessional #1: 20%

Sessional #2: 20% Quizzes 5%

Assignments 5%

Term Paper 2%

Term Project: 5%-10%

Final: 40-45%

Homework: Due at the beginning of next week class fromthe date of issuing of assignment Problems from the book / previous exams

MATLAB simulations Code of DSP Processors

DSP FUNDAMENTALS


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DSP FUNDAMENTALS

95

Adv DSP:

Focus on DSP Software Desi n


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Signals

Basic Types

Discrete-Time Signals: Sequences


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Discrete-time signals are represented by sequence of numbers The nthnumber in the sequence is represented with x[n]

Sampling of continuous time signal x[n]is value of the analog signal at xc(nT)

Where T is the sampling period

0 20 40 60 80 100-10

0

10

t (ms)

0 10 20 30 40 50-10

0

10

n (samples)

Basic Signals


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Basic Signals

Unit sample (impulse) sequence

Unit step sequence

Exponential sequences

0n1

0n0]n[

0n1

0n0]n[u

nanx ][

-10 -5 0 5 100

0.5

1

1.5

-10 -5 0 5 100

0.5

1

1.5

-10 -5 0 5 100

0.5

1

Sinusoidal Sequences


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Sinusoidal Sequences

Sinusoid

A complex exponential

nAnx ocos

][

nj oAenx

njAnAnx oo sincos

sindemo

Sine and Exp Using Matlab


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% sine generation: A*sin(omega*n+theta)

% exponential generation: A^n

n = 0: 1: 50;

% amplitude

A = 0.87;

% phase

theta = 0.4;

% frequency

omega = 2*pi / 20;

% sin generation

xn1 = A*sin(omega*n+theta);

% exp generation

xn2 = A.^n;

Basic Operations


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operations

Operations in Matlab


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xn1 = [1 0 3 2 -1 0 0 0 0 0];

xn2 = [1 3 -1 1 0 0 1 2 0 0];

yn = xn1 + xn2;


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x[n] via impulse functions


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Input: sum of weighted shifted impulses


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Time Domain Analysis

Linear Time-Invariant Systems


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Linear


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linearity


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Linear Time-Invariant SystemsLinear Time-Invariant System


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Linear Time-Invariant System


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Input: sum of weighted shifted impulses


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Using Linearity and Time-Invariance for the impulses


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Sum of wt. Shifted impulses sum of wt. Shifted impulse responses


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LTI System


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The Spatial Filtering Process


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j k lm n o

p q r

Origin x

y Image f (x, y)

eprocessed= n* e +

j* a + k* b + l* c +

m* d + o* f +

p* g + q* h + r* i

Filter (w)Simple 3*3

Neighbourhoode 3* 3 Fil ter

a b cd e f

g h i

Original Image

Pixels

*

The above is repeated for every pixel in the

original image to generate the filtered image


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lec1-intro fall 2014

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