wireless sensor system design

Wireless Sensor System Design

A Joint Course of the University of South Florida

and Tennessee Technological University

Spring 2002

Lecture 3 - Signal Processing Techniques

(as applicable to the project)

Tennessee TechUNIVERSITY

USF EEL 4935/TTU ECE 4720SPECIAL TOPIC: WIRELESS SENSOR SYSTEMS


Weekly Lecture Topics Course Introduction Analog and Digital Modulation Methods (1/11) Fundamentals of Antennas and Propagation (1/18)

Signal Processing Techniques (1/25) Microwave Systems: Communications Hardware, Noise, Linearity (2/1) System Test, Evaluation and Documentation / Effective Presentation Styles (2/8) Preliminary Design Review (student presentations*) (2/15) Microwave Sensor Technology (2/22) @ TTU TBD (3/1) TBD (3/8) Critical Design Review (student presentations*) (3/22) Microelectromechanical Systems (MEMS) Sensors (4/5) Modern Wireless Communication Systems (4/12) @ USF Review / Course Wrap-up (4/19)

* On-site internal reviews/preparation will precede inter-university presentations.



The BIG Picture

Sensor: Temperature, Light Intensity, Humidity & Location

Data for 01APR01

Time Sensor °C Intensity Humidity1532 26 24 70% 50%1533 14 25 65% 55%1544 29 30 0% 60%… … … … …… … … … …… … … … …

Historical Data:By timeBy sensorBy location

Sensor 14: °C

Date/time

Sensor n

http://www.ece.tntech.edu/472s02/dsp/



GPS

How are we going to get there?

Baseband

DC Power

TempIntensityHumidity

RF TX Antenna

Baseband

DC Power

SignalProcessing /

WEBRF RXAntenna

ENVIRONMENT

USER



USF Team Project Areas : TX

•Baseband input•500 MHz VCO•500 MHz Filter

•Lenny•Brad•TTU TX

•1.9 GHz VCO•2.45 GHz mixer•2.45 GHz filter

•Anand•Hugo

•2.45 GHz dualdual amplifier•2.45 GHz antenna

•Glenn•Rob

Red Team Green Team Blue Team

SENSOR / TRANSMITTER



USF Team Project Areas : RX

•500 MHz VCO•500 MHz Filter•500 MHz amplifiers•Baseband output

•Lenny•Brad•TTU RX

•1.9 GHz VCO•2.45 GHz mixer•2.45 GHz filter

•Anand•Hugo

•2.45 GHz receive network (with amplifiers)•2.45 GHz antenna

•Glenn•Rob

Red Team Green Team Blue Team

RECEIVER

TTU and USF need to talk ASAP!



GPS

How are we going to get there?

Baseband

DC Power

TempIntensityHumidity

RF TX Antenna

Baseband

DC Power

SignalProcessing /

WEBRF RXAntenna

ENVIRONMENT

USER



Outline for Today’s Tutorial

Some digital signal processing scenarios.

What are we receiving?

How can this data be processed?

Wrap up / What’s next!



Scenarios for Consideration

How can the data be manipulated to:

Improve transmission characteristics? Reduce the noise effects? Determine sharp changes in data? Extract frequency content vs. time (i.e., our

application)?



Scenario 1: Improving Transmission Characteristics

Scenario: Pulses are to be used to send data across a channel

Problem: Square waves produce harmonics which increase overall bandwidth

Solution: Filter output (simple pulse shaping)



0 20 40 60 80 100-0.2

0

0.2

0.4

0.6

0.8

1

1.2Square wave vs. time

time

mag

nitu

de

0 1 2 3 4 5 6 7 8 9 100

5

10

15

20

25

30

35Square wave vs. harmonic

harmonic

mag

nitu

de

Square Wave Characteristics

Recall Fourier Series

~20 V



0 20 40 60 80 100-0.2

0

0.2

0.4

0.6

0.8

1

1.2Filtered square wave vs. time

time

mag

nitu

de

0 1 2 3 4 5 6 7 8 9 100

5

10

15

20

25

30Filtered square wave vs. harmonic

harmonic

mag

nitu

de

“Filtered” Square Wave Characteristics

~ 25 V



Filtering Employed

Averaging over four values

Is this intuitive? In the limit, averaging over all values results in the

DC component Averaging = Low Pass Filtering

)]3()2()1()([41

)( nxnxnxnxny

3rd order filter



What is Digital Signal Processing?

This simple example illustrates the manipulation of discrete-time, discrete-amplitude data

DSP: Algorithms designed to extract specific information from or improve the characteristics of such signals.



Scenario 2: Reducing Noise Effects

0 20 40 60 80 100-0.2

0

0.2

0.4

0.6

0.8

1

1.2Noisy square wave vs. time

time

mag

nitu

de

0 1 2 3 4 5 6 7 8 9 100

5

10

15

20

25

30

35Noisy square wave vs. harmonic

harmonic

mag

nitu

de

SNR ~ 10 dB

Additive White Gaussian Noise



0 20 40 60 80 100-0.2

0

0.2

0.4

0.6

0.8

1

1.2Filtered noisy square wave vs. time

time

mag

nitu

de

0 1 2 3 4 5 6 7 8 9 100

5

10

15

20

25

30Filtered noisy square wave vs. harmonic

harmonic

mag

nitu

de

LPF Using 3rd-Order Digital Filter

SNR ~ 17 dB*

* as compared to filtered noise-free data



Scenario 3: Determining Sharp Changes in Data

DSP manipulation:

Is this intuitive? Large changes imply large slopes Recall the difference equation from Calculus Results in edge-detection Taking difference = High Pass Filtering

)1()()( nxnxny



0 20 40 60 80 100-0.2

0

0.2

0.4

0.6

0.8

1

1.2Square wave vs. time

time

mag

nitu

de

0 1 2 3 4 5 6 7 8 9 100

5

10

15

20

25

30

35Square wave vs. harmonic

harmonic

mag

nitu

de

Recall: Square Wave Characteristics

~20 V



“Edge-detected” Square Wave Characteristics

0 20 40 60 80 100

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

"edge-detected" square wave vs. time

time

mag

nitu

de

0 1 2 3 4 5 6 7 8 9 100

1

2

3

4

5

6

7

8

9"edge-detected" square wave vs. harmonic

harmonic

mag

nitu

de

Impulse train again Fourier series



Digital Image Processing

Apply similar ideas but into two-dimensions

LPF V-edge detect H-edge detect

)1,1()1,()1,1(),( 1,11,01,1 jixjixjixjiy

111

111

111

91

Average over 9 pixels

111

222

111

121

121

121

Note: diagonal edges can also be detected withappropriate weightings



Original Image LPF Image



Noisy Image Filtered Noisy Image



Horizontal Edge Detection Vertical Edge Detection



Scenarios 4 & 5: AW3 Baseband options

“Analog” Decimal data (i.e., 0-9) is encoded with baseband

tones (DTMF) Baseband signal drives VCO

FM modulation PLL receiver

Digital PCM encode information (i.e., analog to 0’s and 1’s)

Use binary signal and VCO to FSK modulate IF PLL and/or bit detector at receiver



FM/FSK



What are we receiving at 2.4 GHz?

Analog Spectrum Digital Spectrum

“0”

“1”

“0”

“9”



Analog FM Receiver/PLL

)(tFMfilter

VCO

X filter

synthesizer

< 20 kHz

550 MHz2.4 GHz

X filter )(tm

PLL: phase locked loop



Scenario 4: Extracting Frequency Content vs. Time

In the DTMF system, information is sent by using two tones

What tones are present at any point in time?

Note 7 distinct frequencies 697, 770, 852, 941, 1209, 1336,

1447

Info Tone 1 (Hz) Tone 2 (Hz)

1 697 1209

2 697 1336

3 697 1477

4 770 1209

5 770 1336

6 770 1477

7 852 1209

8 852 1336

9 852 1477

0 941 1209

* 941 1336

# 941 1477



Signal Construction

In general:

Dual tone: “7_a”

“7_b”

Analog System:

10 )2cos()(

nnnn tfCCty

)()()()( _7_7 tntytyty ba

1_7 )8522cos()( n na tnCty

1_7 )12092cos()( n nb tnBty

n>2: harmonics, since tones will not be pure



0 1 2 3

x 10-3

-1

-0.5

0

0.5

1Lane 1 vs. sec

0 1 2 3

x 10-3

-1

-0.5

0

0.5

1Lane 2 vs. sec

0 5000 100000

2

4

6x 10

4 Lane 1 vs. Hz

0 5000 100000

1

2

3

4x 10

4 Lane 2 vs. Hz

Time and Frequency Representation

tone 1 vs sec tone 1 vs Hz

tone 2 vs sec tone 2 vs Hz



0 1 2 3

x 10-3

-4

-2

0

2Clutter vs. sec

0 1 2 3

x 10-3

-4

-2

0

2

4Composite vs. sec

0 5000 100000

5

10x 10

4 Clutter vs. Hz

0 5000 100000

5

10x 10

4 Composite vs. Hz

Time and Frequency Representation

Problem:Only time orfrequency known.

Not both!

two tones vs sec two tones vs Hz

4 tones, what occurred when?



Short-Time Fourier Transform

Break time window into small chunks (of N samples each)

FFT each chunk Stack FFTs Represent as a time-

frequency image sample rate:

FFT FFT FFT FFT

FF

TF

FT

FF

TF

FT

time

freq

Time resolution:

sNTT

Frequency resolution:

Nff s /ss Tf /1



time in 0.1 secs

freq

uenc

y in

10

Hz

5 10 15 20 25 30 35 40 45 50

100

200

300

400

500

600

700

800

900

1000

Time-Frequency Map

Alwayspresent

Briefbursts

freq

uen

cy

time



Result on FM Data (NestWatch 2001)



Digital FSK Receiver

)(tFSKfilter

VCO

X filter

synthesizer

529 MHzor

571 MHz2.4 GHz

X

“1” filter detector

“0” filter detector

530 MHz (?)

41 MHz (?)

1 MHz (?)

compare

0 or 1



0 50 100 150-0.2

0

0.2

0.4

0.6

0.8

1

1.2FSK data [ 1 0 1] vs. time

time0 2000 4000 6000 8000 10000

0

5

10

15

20

25FSK data [ 1 0 1] vs. frequency

Hz

Scenario 5: FSK Data Detection*

Note: this is NOT exactly how our implementation will be done

1 0 1



OOK

time, ms

freq

uenc

y, k

Hz

100 200 300 400

5

10

15

20

25

30

FSK

time, ms

freq

uenc

y, k

Hz

100 200 300 400

5

10

15

20

25

30

Method 1: STFT on OOK and FSK data

Bit sequence: 1 0 1 1 0

time

freq

uen

cy

1 0 1 1 0



Method 2: Correlation Receiver: Time-Domain Processing

Correlate incoming signal with what you are looking for: “1” – higher frequency signal “0” – lower frequency signal

Take inner product of input with desired signal.

]10:1[]9:[][0 0xiiyicor T

]10:1[]9:[][1 1xiiyicor T



0 20 40 60 80 100 120 1400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Correlation with 2 kHz square wave vs. time

time0 20 40 60 80 100 120 140

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Correlation with 5 kHz square wave vs. time

time

Output of Correlation Receiver

threshold

1 0 11 0 1

Looking for “0s” Looking for “1s”



Conclusion

Signal processing is already widely used in telecommunication systems Algorithms improve system performance

e.g., error correction code, compression As processing power increases, DSP becomes even

more cost effective to implement

What’s next? Software Radios Chips running code perform typical radio functions Reconfigurable



Wrap Up

Need quick answer to the following:

USF/TTU: What are the RF and DC power requirements?

TTU: What signal voltages are expected at the baseband?



Two Month Course Schedule

January Week 1 (7-11) Week 2 (14-18)

Choose project Literature search

Week 3 (21-25) Literature search Submit project description (1 page;

references from literature search) and tentative schedule (design; fabrication; test; report)

February Week 4 (28-1)

First progress report Parts list due

Week 5 (4-8) Review peer reports

Week 6 (11-15) Preliminary Design Review

Week 7 (18-22) Weller at TTU

1. All inputs are due on Friday of the specified week - no exceptions2. Reviews of peer reports must be completed before the lecture on Friday



Coming Soon!

Tutorial from USF: Microwave Systems: Communications Hardware, Noise, Linearity (2/1)

Tutorial from TTU: System Test, Evaluation and Documentation / Effective Presentation Styles (2/8)

Initial parts order next week! (Wednesday at TTU)

Individual progress report next Friday (2/1) Hand in hardcopy to instructor



Format for Progress Reports

Brief project description and purpose (1-3 sentences) Objectives for the current reporting period (2-3 sentences) Progress for the current reporting period (2-3 paragraphs; use figures

and graphs where needed) Plans for the upcoming project period (2-3 sentences) Revised Project Schedule

Keep in mind that the peer(s) who review your report may not be intimately familiar with your project, so you need to clearly explain the objectives and outcomes.



Peer Review Process

Grading criteria: Clarity of report Level of progress made during reporting period Clear goals for upcoming project period

You are required to turn in reviews for two peer reports that will be assigned to you. You should include brief comments/suggestions and an overall grade of G (good), P (passing), and U (unsatisfactory). Hand-in the reports with your name attached, but NOT written on the report (the reviews are anonymous). Two “U” grades from peers will result in a loss of credit for the student, unless overridden by the instructor(s).



Final Comments

Need to think about subsystem interfaces Electrical (DC) Signal (frequency, voltages, etc.) Dimensions Packaging

Need TTU/USF contact to be made.

Keep up the good work!



Anything else?

wireless sensor system design

Documents

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projectusf eel

microwave sensor technology

historical data

microwave systems

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