M. Lustig, EECS UC Berkeley

EE123Digital Signal Processing

Miki LustigElectrical Engineering and Computer Science, UC Berkeley, CA

1

M. Lustig, EECS UC Berkeley

Information

• Class webpage:– http://inst.eecs.berkeley.edu/~ee123/fa12/

2

M. Lustig, EECS UC Berkeley

My Research

3

M. Lustig, EECS UC Berkeley

Me - Exposed

4

M. Lustig, EECS UC Berkeley

MRI Raw-data (2D Fourier transform)

MRI Image of a Water/plastic phantom

5

M. Lustig, EECS UC Berkeley

MRI Raw-data (2D Fourier transform)

MRI Image of a Water/plastic phantom

6

M. Lustig, EECS UC Berkeley

“Aliasing”

7

M. Lustig, EECS UC Berkeley

“Aliasing”

8

M. Lustig, EECS UC Berkeley

Signal Processing in General

• Convert one signal to another(e.g. filter, generate control command, etc. )

• Interpretation and information extraction(e.g. speech recognition, machine learning)

9

M. Lustig, EECS UC Berkeley

Digital Signal Processing

• Discrete Samples• Discrete Representation (on a computer)

• Can be samples of a Continuous-Time signal: x[n] = X(nT)

• Inherently discrete (example?)

10

M. Lustig, EECS UC Berkeley

Why Learn DSP?

• Swiss-Army-Knife of modern EE• Impacts all aspects of modern life

– Communications (wireless, internet, GPS...)– Control and monitoring (cars, machines...)– Multimedia (mp3, cameras, videos, restoration ...)

– Health (medical devices, imaging....)– Economy (stock market, prediction)– More....

11

M. Lustig, EECS UC Berkeley

Advantages of DSP

• Flexibility• System/implementation does not age• “Easy” implementation• Reusable hardware• Sophisticated processing• Process on a computer • (Today) Computation is cheaper and better

12

M. Lustig, EECS UC Berkeley

Example I: Audio Compression

Error x10CD mp3

• Compress audio by 10x without perceptual loss of quality.

• Sophisticated processing based on models of human perception

• 3MB files instead of 30MB - Entire industry changed in less than 10 years!

13

M. Lustig, EECS UC Berkeley

Example II: Digital Camera

http://micro.magnet.fsu.edu/primer/digitalimaging/cmosimagesensors.html

Focus/exposure Control preprocessing white-balancing

demosaicColor transformPost-processing

Compression

14

M. Lustig, EECS UC Berkeley

Example II: Digital Camera

DSP

15

M. Lustig, EECS UC Berkeley

Example II: Digital Camera

• Compression of 40x without perceptual loss of quality.

• Example of slight overcompression:difference enables x60 compression!

DSP

16

M. Lustig, EECS UC Berkeley

Computational Photography

DSP

*www.hdrsoft.com

17

M. Lustig, EECS UC Berkeley

Example III: Computed Tomography

Sinogram cross-section

DSP

x-ray source

18

M. Lustig, EECS UC Berkeley

Example IV: MRI (again!)Fourier

k-space (Raw Data) Image

Discrete Fourier transform

19

M. Lustig, EECS UC Berkeley

Functional MRI Example

*Karla Miller, Oxford*Brian Wandell, Stanford

Sensitivity to blood oxygenation - response to brain activityConvert from one signal to another

20

M. Lustig, EECS UC Berkeley

Taking fMRI further

• fMRI decoding : “Mind Reading” Gallant Lab, UC Berkeley

• Interpretation of signals

21

M. Lustig, EECS UC Berkeley

Example V: Software Defined Radio

• Traditional radio:– Hardware receiver/demodulators/filtering – Outputs analog signals or digital bits

• Software Defined Radio:– Uses RF font end for baseband signal– High speed ADC digitizes samples– All processing chain done in software

22

M. Lustig, EECS UC Berkeley

Software Defined Radio

• Advantages:– Flexibility– Upgradable– Sophisticated processing– Ideal Processing chain - not approximate like in analog hardware

• Already used in consumer electronics– Cellphone baseband processors– Wifi, GPS, etc....

23

M. Lustig, EECS UC Berkeley

RTL-SDR

• Inexpensive TV dongle based on RTL2832U and E4000 chipset can be used as SDR

24

M. Lustig, EECS UC Berkeley

SDR & You

• Will provide easy interface to Matlab– > soc = rtl_sdr_connect;> rtl_sdr_setFreq(soc,94000000);> rtl_sdr_setRate(soc,24000000);> samps = rtl_sdr_getData(soc,4e6);

KPFA

25

M. Lustig, EECS UC Berkeley

In class

• Each student will be given a device• Homeworks/Labs based on the device• Final Project - implementation of:

– Police Scanner– FM stereo receiver– Decode digital weather information

26

M. Lustig, EECS UC Berkeley

@ Home

• Many Free available software to have fun! – GNU radio– SDR#– Airprobe (GSM receiver)– QtRadio– Many more.....

• Have a “Hack” of a time....– http://www.radioreference.com

27

M. Lustig, EECS UC Berkeley

GNU Radio Companion

28

M. Lustig, EECS UC Berkeley

Demo

29

M. Lustig, EECS UC Berkeley

Course Objective

• Develop skills for– Analyzing and synthesizing algorithms and systems that process discrete-time signals

– Emphasis on realization and implementation

30