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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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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
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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_eye7/24/2019 Lec1-Intro Fall 2014
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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
53
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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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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
Multiscale Zero-crossing
point and edge detection
Multiscale Zero-crossing
point and edge detection
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
75
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Universal Radio Receiver COMINT System
76
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)
82
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
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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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