Lecture 41

CSE 331Dec 10, 2010

HW 10 due today

Q1 in one pile and Q 3+4 in another

I will not take any HW after 1:15pm

Finals

3:35-6:05pm

KNOX 104

Tue, Dec 14

Blog post on the

finals up

Blog post on the

finals up

On Friday, Dec 10

hours-a-thon

Atri: 2:00-3:30 (Bell 123)

Jeff: 4:00-5:00 (Bell 224)Alex: 5:00-6:30 (Bell 242)

Reminder

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High level view of CSE 331Problem StatementProblem Statement

AlgorithmAlgorithm

Problem DefinitionProblem Definition

“Implementation”“Implementation”

AnalysisAnalysis Correctness+Runtime Analysis

Data Structures

Three general techniques

Three general techniques

If you are curious for more

CSE431: Algorithms

CSE 396: Theory of Computation

Another course of interest (S 11)

CSE 443: Compilers

Pre-req: 396

Offered infrequently!

HW 10 due today

Q1 in one pile and Q 2+3 in another

I will not take any HW after 1:15pm

Now relax…

12

Coding Theory

13

The setupC(x)

x

y = C(x)+error

x Give up

Mapping C Error-correcting code or just code Encoding: x C(x) Decoding: y X C(x) is a codeword

14

Different Channels and Codes• Internet

– Checksum used in multiple layers of TCP/IP stack

• Cell phones• Satellite broadcast

– TV• Deep space

telecommunications– Mars Rover

15

“Unusual” Channels

• Data Storage– CDs and DVDs– RAID– ECC memory

• Paper bar codes– UPS (MaxiCode)

Codes are all around us

16

Redundancy vs. Error-correction

• Repetition code: Repeat every bit say 100 times– Good error correcting properties– Too much redundancy

• Parity code: Add a parity bit– Minimum amount of redundancy– Bad error correcting properties

• Two errors go completely undetected

• Neither of these codes are satisfactory

1 1 1 0 0 1

1 0 0 0 0 1

17

Two main challenges in coding theory

• Problem with parity example– Messages mapped to codewords which do not

differ in many places• Need to pick a lot of codewords that differ a

lot from each other

• Efficient decoding– Naive algorithm: check received word with all

codewords

18

The fundamental tradeoff

• Correct as many errors as possible with as little redundancy as possible

Can one achieve the “optimal” tradeoff with efficient encoding and decoding ?

Interested in more?

CSE 545, Spring 2011

20

Datastream AlgorithmsSingle pass over the input Poly-log “scratch” space

21

Data Streams (another application)

• Databases are huge– Fully reside in disk memory

• Main memory– Fast, not much of it

• Disk memory– Slow, lots of it– Random access is expensive– Sequential scan is reasonably

cheap

Main memory

Disk Memory

22

Data Streams (another application)• Given a restriction on number

of random accesses to disk memory

• How much main memory is required ?

• For computations such as join of tables

Main memory

Disk memory

Group Testing Overview

Test soldier for a disease

WWII example: syphillis

Group Testing Overview

Test an army for a disease

WWII example: syphillis

What if only one soldier has the

disease?

What if only one soldier has the

disease?

Can pool blood samples and

check if at least one soldier has

the disease

Can pool blood samples and

check if at least one soldier has

the disease

Whatever your impression of the 331

IT WASIT WAS

Hopefully it was fun!

Thanks!