Development

and

validation

of

an

epitope

prediction

tool

for

swine

(PigMatrix)

based

on

the

pocket

profile

method

Background:

T

cell

epitope

prediction

tools

and

associated

vaccine

design

algorithms

have

accelerated

the

development

of

vaccines

for

humans.

Predictive

tools

for

swine

and

other

food

animals

are

not

as

well

developed,

primarily

because

the

data

required

to

develop

the

tools

are

lacking.

Here,

we

overcome

a

lack

of

T

cell

epitope

data

to

construct

swine

epitope

predictors

by

systematically

leveraging

available

human

information.

Applying

the

"pocket

profile

method",

we

use

sequence

and

structural

similarities

in

the

binding

pockets

of

human

and

swine

major

histocompatibility

complex

proteins

to

infer

Swine

Leukocyte

Antigen

(SLA)

peptide

binding

preferences.

We

developed

epitope-prediction

matrices

(PigMatrices),

for

three

SLA

class

I

alleles

and

and

one

class

II

allele

based

on

the

binding

preferences

of

the

best-matched

Human

Leukocyte

Antigen

(HLA)

pocket

for

each

SLA

pocket.

The

contact

residues

involved

in

the

binding

pockets

were

defined

for

class

I

based

on

crystal

structures

of

either

SLA

(SLA-specific

contacts,

Ssc)

or

HLA

supertype

alleles

(HLA

contacts,

Hc);

for

class

II,

only

Hc

was

possible.

Different

substitution

matrices

were

evaluated

(PAM

and

BLOSUM)

for

scoring

pocket

similarity

and

identifying

the

best

human

match.

The

accuracy

of

the

PigMatrices

was

compared

to

available

online

swine

epitope

prediction

tools

such

as

PickPocket

and

NetMHCpan.Results:

PigMatrices

that

used

Ssc

to

define

the

pocket

sequences

and

to

score

pocket

similarity

demonstrated

the

best

predictive

performance

and

were

able

to

accurately

separate

binders

from

random

peptides.

For

and

PigMatrix

achieved

area

under

the

receiver

operating

characteristic

curves

(AUC)

of

and

respectively,

which

were

equivalent

or

better

than

PickPocket

and

and

NetMHCpan

version

and

and

version

and

In

addition,

we

developed

the

first

predictive

SLA

class

II

matrix,

obtaining

an

AUC

of

for

existing

epitopes.

Notably,

PigMatrix

achieved

this

level

of

predictive

power

without

training

on

SLA

binding

data.

Conclusion:

Overall,

the

pocket

profile

method

combined

with

binding

preferences

from

HLA

binding

data

shows

significant

promise

for

developing

T

cell

epitope

prediction

tools

for

pigs.

When

combined

with

existing

vaccine

design

algorithms,

PigMatrix

will

be

useful

for

developing

genome-derived

vaccines

for

a

range

of

pig

pathogens

for

which

no

effective

vaccines

currently

exist

(e.g.

porcine

reproductive

and

respiratory

syndrome,

influenza

and

porcine

epidemic

diarrhea).

Immunoinformatics

tools

have

accelerated

the

identification

of

epitopes

and

design

of

human

vaccines.

However,

comparable

tools

have

not

been

applied

extensively

to

pigs.

During

the

last

two

decades,

swine

T

cell

epitope

discovery

has

been

based

on

experimental

studies

of

numerous

overlapping

peptides

While

these

studies

are

essential

for

validating

T

cell

epitope

prediction

tools,

they

can

be

expensive

and

time

consuming.

To

reduce

experimental

effort

and

expedite

the

process,

algorithms

developed

for

human

T

cell

epitope

prediction

have

also

been

used

to

identify

porcine

epitopes

However,

the

substitution

of

HLA

predictions

for

SLA

predictions

may

not

reflect

the

fine

specificity

of

SLA

binding,

which

limits

the

efficacy

of

this

oversimplified

approach.

To

overcome

this,

we

have

developed

PigMatrix,

a

simple

yet

effective

method

that

leverages

available

data

(SLA-binding

peptides,

SLA

structures

and

HLA

binding

data)

and

pocket

profiles

already

constructed

for

HLA-based

epitope

prediction

in

EpiMatrix

to

predict

potential

T

cell

epitopes

for

SLA

class

I

and

II

alleles.

Using

the

pocket

profile

method

and

the

concept

that

pockets

that

have

similar

amino

acids

will

share

similar

binding

preferences,

we

built

and

validated

matrices

that

were

able

to

separate

SLA-restricted

peptides

from

random

peptides

and

non-binders.

Fig.

Matrix

performance

comparison.

AUCs

of

the

matrices

built

for

SLA

class

I

and

II

alleles

are

shown.

The

highest

AUC

for

each

method

is

shown

above

the

bars.

For

AUC

of

Ssc

ModB

a

matrix

with

a

pocket

B

profile

different

than

the

best

human

match,

is

shown

in

a

dashed

rectangle

to

illustrate

the

impact

of

pocket

B.

If

one

or

more

matrices

for

the

same

approach

(Ssc

or

Hc)

have

equal

AUC,

it

is

indicated

with

Human

pan-specific

tools

based

on

the

pocket

profile

method

have

been

described

for

prediction

of

class

I

(PickPocket)

and

class

II

T

cell

epitopes

(TEPITOPEpan)

These

methods

defined

the

amino

acids

in

the

pockets

from

HLA

crystal

structures.

Similarly,

we

defined

SLA-specific

contacts

(Ssc)

from

two

crystal

structures

available

for

Additionally,

we

extrapolated

the

pocket

residues

from

crystal

structures

of

HLA

class

I

and

II

crystal

structures

(Hc).

Both

approaches

assume

for

a

given

pocket

that

all

contact

residues

are

conserved

across

all

class-specific

MHC

molecules.

However,

because

there

are

differences

in

the

pocket

residues

between

MHC

alleles

and

even

between

the

same

allele

structures

depending

on

the

ligand

this

simplification

is

a

limitation

of

the

peptide:MHC

modeling

approach.

Even

so,

it

is

a

reasonable

approximation

when

structural

information

is

limited.

For

class

I,

differences

in

the

pocket

residues

using

the

Hc

and

the

Ssc

approaches

were

noticeable

and

impacted

the

subsequent

selection

of

the

best

human

match

to

build

the

prediction

matrices.

Matrices

based

on

pockets

defined

from

SLA

structure-specific

contacts

performed

better

than

HLA-derived

pockets.

While

a

definition

of

the

contact

residues

based

on

several

HLA

structures

account

for

the

intra-and

inter-allelic

variability

of

binding

pockets,

it

also

dilutes

the

importance

of

key

residues

in

the

peptide:MHC

interaction.

We

speculate

that

more

allele-specific

pockets

could

potentially

improve

the

selection

of

the

best

human

match

and

therefore

the

predictions.

Selection

might

be

also

improved

by

weighting

the

similarity

score

by

conservation

of

key

contact

residues..

PickPocket

and

TEPITOPEpan

use

a

method

based

on

to

calculate

a

weighted

score

of

specificity

to

define

the

most

similar

HLA-derived

pocket.

For

Pig-Matrix,

in

addition

to

we

used

which

is

considered

equivalent

to

for

comparison

of

distantly

related

proteins

to

calculate

pocket

similarity.

We

also

included

and

which

are

both

designed

to

score

similarity

between

closely

related

protein

sequences.

The

SLA

matrices

with

highest

AUC

were

based

on

using

both

Hc

and

Ssc,

with

only

one

exception.

If

we

consider

the

pocket

contact

residues

as

short

pseudo-sequences,

the

better

performance

of

matrices

might

be

explained

because

low-numbered

PAM

matrices

are

more

efficient

for

searches

involving

short

sequences.

on

the

other

hand,

performs

better

to

identify

distant

homologs

using

longer

sequences.

like

is

used

for

closely

related

sequences;

however,

it

is

not

recommended

for

short

peptides

and

unsurprisingly

did

not

perform

as

well

as

in

these

studies..

Predictive

methods

for

porcine

T-cell

epitopes

are

limited,

and

existed

previously

for

SLA

class

II..

PigMatrix

is

the

first

tool

to

make

binding

predictions

for

an

SLA-DR

allele.

Class

II

predictions

were

limited

to

the

Hc

method

because

no

SLA-DR

molecule

has

been

crystallized.

Since

SLA-DR-specific

binding

data

are

scarce,

predictions

require

further

prospective

validation.

While

it

is

not

possible

at

this

time

to

benchmark

the

SLA-DR

matrix

against

other

predictors,

a

comparison

can

be

made

for

the

SLA

class

I

matrices

developed

here.

NetMHCpan

has

been

used

for

SLA

binding

predictions.

PickPocket,

which

is

also

based

on

the

pocket

profile

method,

has

been

described

primarily

for

HLA

class

I

but

predictions

are

also

available

for

SLA

alleles.

In

this

study,

for

an

existing

set

of

published

peptides,

PigMatrix

performed

equally

or

better

than

two

versions

of

NetMHCpan

and

PickPocket

for

and

While

PigMatrix

and

PickPocket

derive

SLA

binding

preferences

from

HLA

binding

data,

NetMHCpan

artificial

neural

networks

are

trained

using

information

derived

from

available

binding

data

as

well

as

peptide

sequences

and

MHC

sequence

information

It

was

previously

demonstrated

that

in

a

scenario

where

the

quantitative

binding

data

were

limited

for

human

and

non-human

MHC

alleles,

PickPocket

performed

better

than

NetMHCpan

This

is

also

evident

when

NetMHCpan

results

are

compared

to

NetMHCpan

predictions.

NetMHCpan

was

trained

with

a

limited

set

of

SLA

binders

and

its

predictions

were

equivalent

to

random

selection

(average

AUC

Version

on

the

other

hand,

was

trained

with

more

data

and

its

performance

improved

for

the

alleles

we

evaluated

(average

AUC

Conversely,

PigMatrix

was

not

trained

with

SLA-specific

binding

data

and

performed

similarly

or

better

than

NetMHCpan

predictions

for

two

of

three

class

I

alleles

we

tested.

Moreover,

because

the

number

of

published

peptides

is

limited,

we

were

not

able

to

compile

a

test

dataset

of

peptides

known

to

be

different

from

the

training

set

used

by

NetMHCpan

Hence,

it

is

possible

that

NetMHCpan

performance

was

overestimated..

For

PickPocket

and

NetMHCpan

outperformed

PigMatrix.

Upon

closer

analysis,

these

results

provided

an

example

of

how

PigMatrix

could

be

improved.

We

were

able

to

build

a

better

performing

model

by

modifying

pocket

B

in

the

matrix

constructed

using

This

might

be

explained

by

the

role

of

the

amino

acid

in

position

two

of

the

peptide

as

a

binding

anchor

and

its

specific

interaction

with

this

pocket.

It

is

also

worth

noting

that

the

HLA

pocket

library

we

used

was

limited

to

class

I

and

eight

class

II

supertypes

alleles

available

in

EpiMatrix.

It

is

possible

that

pocket

sequences

from

other

HLA

alleles

and

their

profiles

are

more

similar

to

SLA

pockets.

Therefore,

if

the

number

of

HLA

alleles

in

the

library

is

increased,

we

might

find

better

human

matches

for

SLA

pockets,

which

could

potentially

improve

matrix

performance..

So

as

to

illustrate

the

PigMatrix

approach,

we

built

initially

matrices

for

only

three

SLA

class

I

alleles

and

one

class

II

SLA

allele

for

which

quantitative

binding

data

were

available.

These

alleles

are

commonly

expressed

in

different

porcine

breeds

and

cell

lines

for

in

vitro

culture

However,

like

HLA,

SLA

diversity

is

considerable.

These

results

demonstrate

the

potential

of

the

approach

to

be

extended

to

SLA

alleles

with

limited

or

nonexistent

epitope

binding

data.

Thus,

future

versions

of

PigMatrix

will

include

a

more

comprehensive

and

representative

set

of

matrices

for

SLA

alleles

expressed

in

outbred

porcine

populations.

Moreover,

prospective

in

vitro

and

in

vivo

evaluation

of

PigMatrix

predictions

will

help

to

refine

the

matrices..

We

developed

the

PigMatrix

tool

with

the

intent

to

integrate

it

into

the

iVAX

toolkit,

which

is

a

comprehensive

set

of

tools

for

computational

vaccine

design

that

includes

EpiMatrix,

Conservatrix,

ClustiMer,

EpiAssembler,

Janus-Matrix,

and

VaccineCAD

When

the

PigMatrices

are

used

to

substitute

for

HLA

matrices

(EpiMatrix)

in

iVAX,

all

of

the

existing

suite

of

iVAX

vaccine

design

tools

can

be

used

with

the

SLA

epitope

predictions,

which

makes

it

possible

to

envision

accelerated

development

of

novel

T

cell

epitope-based

vaccines

or

whole

subunit

vaccines

optimized

for

epitope

content

that

protect

against

infectious

disease

in

swine.

The

interaction

of

Major

Histocompatibility

Complex

(MHC)

proteins

with

peptides

derived

from

protein

antigens

plays

a

key

role

in

the

adaptive

immune

response

mediated

by

T

cells.

The

MHC:peptide

complex

presented

on

the

surface

of

a

cell

is

recognized

by

the

T

cell

receptor

(TCR),

which

activates

the

T

cell

and

drives

the

immune

response.

There

are

two

classes

of

MHC

molecules:

MHC

class

I

presents

peptides

of

intracellular

origin

to

T

cells

(cytotoxic

T

cells,

or

CTL)

and

MHC

class

II

presents

peptides

of

extracellular

origin

to

T

cells

(T-helper

cells,

or

Th).

Both

classes

of

molecules

have

similar

tertiary

structure.

Class

I

molecules

have

a

transmembrane

(α)

chain

noncovalently

associated

with

β

-microglobulin

where

the

α

and

α

domains

form

the

peptide-binding

groove;

class

II

molecules

have

two

transmembrane

chains

(α

and

β)

where

the

α

and

β

domains

form

the

peptide-binding

groove.

The

MHC

class

I

binding

groove

is

closed,

which

restricts

the

length

of

bound

peptides

to

residues;

the

MHC

class

II

binding

groove

on

the

other

hand,

is

open,

and

peptides

can

extend

beyond

the

ends

of

the

groove,

allowing

binding

of

longer

and

more

flexible

peptides

of

variable

lengths

(typically

amino

acids)

The

tertiary

structure

of

MHC

molecules

is

relatively

conserved,

even

across

species.

For

example,

crystallographic

studies

have

shown

similarity

between

Human

Leukocyte

Antigen

(HLA;

human

MHC)

and

Swine

Leukocyte

Antigen

(SLA;

swine

MHC)

molecules

The

class

I

allele

has

been

crystallized

in

complex

with

peptides

derived

from

swine-origin

influenza

A

virus

(S-OIV)

and

Ebola

virus.

A

structural

comparison

revealed

that

the

SLA

class

I

molecule,

contains

six

pockets

in

its

binding

groove,

similar

to

HLA

class

I

molecules.

The

root-mean-squared

deviation

(RMSD)

for

all

of

the

Cα

atoms

in

and

which

has

the

highest

identity

with

was

Å

indicating

a

similar

arrangement

of

their

backbones.

Furthermore,

three

out

of

influenza

binders

were

identical

to

previously

defined

peptides

presented

by

For

SLA

class

II,

no

crystal

structures

are

available,

but

amino

acid

SLA-DR

sequences

are

highly

similar

to

their

human

counterparts.

For

example,

the

amino

acid

sequences

of

and

are

identical..

Due

to

the

importance

of

peptide

binding

to

MHC

molecules

in

the

immune

response,

human

T

cell

epitope

prediction

tools

have

been

developed

based

on

a

range

of

approaches

and

are

widely

used

in

vaccine

development

and

experimental

immunology

The

availability

of

a

large

and

expanding

database

of

validated

MHC

ligands

has

contributed

to

the

development

of

more

accurate

algorithms.

Epitope

predictions

using

these

tools

reduce

the

time

and

effort

required

to

identify

T

cell

epitopes

The

number

of

epitope

prediction

tools

is

more

limited

for

pigs

due

to

the

paucity

of

experimental

data

available.

To

overcome

the

lack

of

quantitative

measurements

of

MHC

interaction

for

a

large

number

of

HLA

alleles,

'pan-specific'

methods

have

been

implemented

for

prediction

of

T

cell

epitopes

that

bind

to

MHC

for

which

experimental

data

are

limited

or

not

available.

Pan-specific

methods

use

experimental

binding

data

and

amino

acid

sequences

of

multiple

MHC

alleles

to

infer

binding

preferences

to

uncharacterized

MHC

molecules.

These

methods

have

been

used

for

development

of

prediction

tools

for

MHC

class

I

and

II

alleles

but

only

NetMHCpan

has

been

used

for

prediction

of

SLA

class

I-restricted

peptides

This

method

is

based

on

artificial

neural

networks

(ANN)

trained

using

as

input

a

pseudosequence

composed

of

the

polymorphic

residues

in

the

binding

groove

of

a

given

MHC,

a

peptide

sequence

and

the

experimental

affinity

data.

To

our

knowledge,

there

are

no

in

silico

tools

that

are

available

for

SLA

class

II..

Sturniolo

et

al.

first

described

a

method

for

using

existing

data

to

develop

new

epitope

predictors,

the

pocket

profile

method,

in

It

has

been

used

to

develop

pan-specific

methods

for

predicting

binding

of

peptides

to

HLA

class

I

and

II

alleles

The

approach

depends

on

the

identification

of

certain

polymorphic

regions

within

HLA

molecules

that

are

known

to

be

the

areas

of

contact

between

peptides

and

the

binding

groove

of

HLA

Contact

residues

from

the

HLA

molecule

that

bind

the

R

group

(side

chain)

of

a

specific

amino

acid

within

a

linear

peptide

can

be

considered

to

form

a

pocket

for

that

R

group.

Thus,

each

'pocket'

can

also

be

described

in

terms

of

its

amino

acid

binding

preferences

('pocket

profile').

The

pocket

profiles

are

nearly

independent

of

the

remaining

binding

groove.

So

this

method

assumes

that

two

MHC

alleles

with

identical

pocket

residues

will

have

the

same

pocket

profile.

Therefore,

given

sufficient

information

about

the

contact

residues

of

the

set

of

pockets

in

the

binding

groove

of

an

MHC

and

experimentally

determined

pocket

profiles,

it

is

possible

to

compose

predictive

matrices

in

silico.

The

method

was

originally

applied

to

develop

TEPITOPE,

an

algorithm

for

prediction

of

peptide

ligands

to

HLA

class

II

alleles

with

known

pocket

residues

and

then

extended

to

any

HLA-DR

molecules

with

similar

pockets

(TEPITOPEpan)

A

similar

method

has

also

been

used

in

the

PickPocket

algorithm

for

MHC

class

I

prediction

Whereas

TEPITO-PEpan

uses

pocket

profiles

from

TEPITOPE,

PickPocket

generates

binding

preferences

using

position-specific

scoring

matrices

(PSSMs)

from

binding

data

directly.

Although

no

publications

exist

using

these

algorithms

for

SLA

binding

predictions,

SLA

alleles

are

available

for

use

in

the

PickPocket

server

(www.cbs.dtu.dk/services/Pick-Pocket/)..

EpiMatrix

is

a

matrix-based

algorithm

that

uses

the

pocket

profile

method

to

predict

potential

HLA

class

I

and

II

T

cell

epitopes.

The

first

version

of

this

algorithm

was

developed

in

and

newer

versions

have

been

extensively

validated

in

vitro

in

HLA

binding

assay

and

human

T

cell

assays

and

in

animal

studies

using

HLA

transgenic

mouse

models

For

common

class

II

alleles,

EpiMatrix

appears

to

predict

more

accurately

than

many

available

epitope-mapping

algorithms

Comparative

performance

for

EpiMatrix

class

I

predictions

has

not

been

published;

however,

the

tools

have

been

successfully

applied

to

identify

class

I-restricted

T

cell

epitopes

in

human

pathogens

The

pocket

profile

method

was

used

to

develop

a

matrix

for

a

bovine

MHC

class

I

allele

and

in

the

early

this

method

was

also

used

to

derive

SLA

class

II

prediction

matrices

from

EpiMatrix,

but

this

work

was

not

published..

This

paper

describes

the

development

and

retrospective

validation

of

predictive

matrices

to

map

T

cell

epitopes

for

SLA

class

I

alleles

and

a

class

II

allele.

"PigMatrix"

matrices

are

built

based

on

the

pocket

profile

method

using

EpiMatrix

pocket

profiles

for

HLA

epitope

prediction.

While

these

alleles

represent

a

small

subset

of

commonly

expressed

alleles

in

pigs

they

were

selected

for

their

available

peptide

binding

data

As

before,

we

assumed

that

predictive

matrices

developed

for

HLA

alleles

should

function

as

reasonable

proxies

for

the

prediction

of

ligands

to

SLA

molecules

with

similar

pocket

profiles.

Thus,

we

developed

'composite

matrices'

by

selecting

the

most

similar

HLA

pocket

(best

human

match)

for

each

SLA

pocket,

and

built

matrices

composed

of

the

corresponding

HLA

binding

preferences

Fig.

Two

methods

were

used

to

define

the

pocket

contact

residues

considering

different

scenarios

of

availability

of

SLA

crystal

structures.

In

the

first

scenario,

SLA

crystal

structures

were

available,

so

pockets

were

defined

from

these

structures.

In

the

second,

no

SLA

crystal

structures

were

available;

therefore,

contact

residues

were

selected

based

on

crystal

structures

of

HLA.

We

also

tested

different

substitution

matrices

(PAM

and

BLO-SUM)

to

score

pocket

similarity

to

define

the

best

human

match.

PigMatrix

was

benchmarked

against

existing

SLA

prediction

tools

for

class

I

alleles.

Benchmarking

against

other

SLA

class

II

predictors

was

not

possible

as

no

other

prediction

algorithms

are

available.

The

results

demonstrate

the

potential

of

this

approach

to

develop

matrices

to

make

accurate

predictions

for

both

SLA

class

I

and

II

alleles

for

which

experimental

binding

data

are

limited

or

even

non-existent.

O O O O O B I O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B B I O O O O O O O B I O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O B I O O O O O O B O O O O B O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O B I O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O B O O B O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O B O O O O O O O O O B I O B O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B B I B O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O B O O O O O O O O O O O O O O O O O B B I O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O B O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B I O O B I O O O O B O O O O O O O O B O O O O O O O O B O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O B B I O O O B I B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O B O O O O B O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B B I O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O B O O O O B O B O O O O O B I O O O O O O O O O O O O O O B O O O O O O O O O O O O B B I O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O B O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O B I O B O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O B B I O O O B O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O B I O O B O O O O O O O O O O B O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O B O O O B I O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O


An Algorithm for the Estimation of the Initial Text Skew Abstract. The paper presents a methodology for the estimation of the initial skew rate of text lines. Firstly, it splits text into groups according to the bounding boxes. Linked bounding boxes establish the bigger objects called connected components. After applying mathematical morphology operations, the enlarged group of the connected components is formed. The longest connected component is extracted by the longest common subsequence method. Inside the longest connected component, the gravity centers are determined for each bounding box. They represent the reference points, which are used for the calculation of the initial skew rate. Calculation is made by the moment based method. The comparative analysis of the origin and estimated skew rate is used to evaluate the algorithm. Hence, the proposed algorithm is examined with different printed text samples. It showed robustness for the skew estimation in the wide range of resolutions. The old documents have been saved mainly in the printed form. Those documents have to be converted from the printed to electronic form. This process is done by scanning. However, the obtained electronic form is not completely editable. Hence, the main role is the transformation of the document image into a computer editable form. For this purpose, the optical character recognition (OCR) system is used. If in document image the skew exists, then OCR will fail. Hence, the skew makes this process more complex. That's why the skew identification has an important role in the preprocessing stage of the OCR [1] .. Printed text represents a pretty well formed text type. It has a strong regularity in shape, which is characterized with the uniform skewness. Decent spacing between nearby text lines with descendant and ascendant characters enables the constant separation of them. Hence, the distance between them is a quite sufficient. At the end, the printed text is considered as a predictable one.. The known methods for the skew estimation are classified as follows [1] : Histogram analysis method, K-nearest neighbor clustering method, Crosscorrelation method, Hough transforms method, Fourier transformation method, and other methods.. Histogram analysis represents the widely used method for the skew estimation in printed text. It is based on the identification of the minima in the horizontal pixel density histogram [2] . However, this method is suited for the uniform skew only. Hence, it failed to identify the skew in multi-column or multiline text.. K-nearest neighbor clustering method is based on the page layout analysis [3] . This method forms the connected components by nearest neighbors clustering, which represents the characters themselves. Still, it is characterized with the poor text line segmentation. Hence, its application is suitable primarily for Roman languages.. The cross-correlation method calculates the similarity between the original (document text) and referent image. It forms the histogram of crosscorrelation. Furthermore, it compares the shift of the interline cross-correlation to determine the skew rate. However, the method is suited for small skew angles up to 10° [4] .. The Hough transform is a technique used to find shapes in a binary digital image [5] . In the circumstances of printed text, detecting of lines is a prerequisite. Hence, the image is transformed in the Hough domain. Potential alignments are hypothesized in Hough domain. Further, they are validated in the image domain. If scattered text is under consideration, then the difficulties in choosing a peak in Hough space are one of major limitation of the method. In addition, the method is complex and computer time intensive.. The Fourier transform method is a Fourier domain representation of the projected profile method in the pixel domain. This way, it is just a similar methodology as projected profile, but in different domains [6] .. The techniques classified as other methods are based mostly on geometrical transformation. These algorithms are typically computationally inexpensive.. The simplest method based on the polygon gravity centers for skew estimation has been proposed in [7] . The simplicity and global approach represent its advantage. However, its accuracy is lower than 87%.. Another method based on boundary growing approach is proposed in [8] . It is a simple and computationally inexpensive method. However, it contains few constraints based on text elements pre assumptions. Hence, the modified method, which solves some of these disadvantages, is given in [9] . Compared with other methods, the obtained results are promising.. The paper describes a modification of the method based on the bounding boxes, and theirs gravity center points [9] . The initial method is extended with the binary dilatation. It creates the connected component objects. Further, the longest connected component is selected. In addition, some constraints of the original algorithm have been overcome. The proposed algorithm shows the skew estimation accuracy of the document images in standard and low resolutions. Hence, it is characterized as robust method.. Organization of this paper is following. Section 2 describes the proposed algorithm. Section 3 defines text experiments. Section 4 compares and discusses the results. Section 5 makes conclusions. The paper describes the method for the skew estimation of the printed text based on the longest connected component. Firstly, it establishes the boundary growing area around text based on the filled bounding boxes. This way, the connected components have been created. The algorithm chooses the longest connected component by LCS method. Furthermore, the gravity centers of the bounding boxes are extracted. This set of points represents the input for the text skew estimation of the least square (LSM) and moment-based methods (MBM). Based on that, the initial text skew is estimated. Also, the initial skew rate is estimated from the longest connected component contour by the moment-based method (MBOM), too. Different methods are subjected to the single and multi-line text experiments. The experiment results confirm a low absolute deviation and good RMSE values in the tested angle range. Hence, all method proves theirs correctness for the estimation of the printed text skew. In addition, the extension of the algorithm with dilatation is examined. It is suitable for the multi-line and multi-column printed text without any segmentation. As an additional advantage, the extended method is computationally inexpensive. Due to its correctness, the method can be integrated partly or completely in the algorithms for the parameter extraction of the handwritten text. It is especially true for the moment-based object method. Future work should point out the efficient methodology for omitting the ascender and descender elements in bounding boxes of the longest connected component. It can be expected that these steps will further improve the accuracy of the initial skew rate method. where matrix Z is defined by vectors A and B and resulted object is defined by matrix CC. Table 1 . Absolute error (Δβ) and RLHR for the bounding boxes gravity center algorithm (LSM or MBM -identical results) with the text sample 2 in different resolutions 25 dpi 50 dpi 300 dpi

O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Segmenting Shape Using Deformation Information To segment a shape into parts is an important problem in shape representation and analysis. We propose in this paper a novel framework of shape segmentation using deformation models learned from multiple shapes. The deformation model from the target image to every other image is then estimated. Finally, normalized-cut graph partition is applied to the graph constructed based on the similarity of local patches in the target image, and a segmentation of the shape is carried out. Experimental results for images from MPEG7 shape database show the effectiveness of the proposed method. A significant problem in many computer vision and computer graphics applications is the retrieval of a wellstructured shape model by segmenting it into a set of simpler shape segments, such that each segment can be semantically meaningful and easier to model. Complex shapes are often characterized as a collection of parts. Many of these objects undergo articulated motion in which each part moves independently and rigidly in its own motion model, stretching or rotating with respect to the other parts of the object. Shape segmentation finds its uses in many fields of applications. Such techniques are employed in medical imaging for segmenting shape of an organ, in computer-aided design for identifying segments of mechanical parts, reverse engineering of CAD model and in computer graphics for constructing geometric models, etc. Also, a proper segmentation of a shape can be useful in shape matching, shape analysis and object detection in that it provides a representation with semantic contexts.. In this paper, we propose a novel framework of shape segmentation using deformation models learned from multiple shapes. Our framework contains three steps. Firstly, we roughly divide a target shape into a number of patches. This is done by randomly sampling points in the internal of the target shape. Then they are clustered with k-means method. Next, correspondences between the target and reference images are found using the Shape Context shape matching [1] , then the Thin Plate Spline [2] transformation model from the target shape to each of the different reference shapes is estimated. Using the transformation models, the deformational pattern of each patch under the transformation is calculated. Finally, a graph is created, with patches as vertices and the affine-error between two patches as the weight of edges between them. This graph captures the relation of local patches by evaluation of pair-wise similarity between them. Normalized cut graph partition algorithm is then applied to the graph by taking the graph into several subsets. By merging the patches that belong to a same subset, the final segmentation is computed. The complete framework is shown in Fig. 1 . To address the effectiveness and the application of the proposed method, shape segmentation experiment with a number of shape categories in MPEG7 database is shown in the latter part of the paper. There are literatures related to the shape segmentation method. Current approaches using geometry, topology and dynamic analysis models had been established and studied. Siddiqi et al. proposed a theory for representing shapes by shocks which is called shock graph model [3] . They also applied the theory to a shape matching problem by introducing a novel tree matching algorithm. Wu and Levine proposed a physics-based segmentation method [4] . An object is viewed as a charged conductor and the part boundaries are detected based on the electrical charge density distribution. A survey article [5] gives more detailed information on this issue. In more recent works, Dey et al. proposed a method for defining features of shapes by stabilizing shape induced flows [6] . They also developed a shape matching algorithm based on the features. Gorelick et al. proposed a segmentation method by thresholding Poisson equation solution [7] as a product of Poisson equation based shape representation. Reniers and Telea proposed a hierarchical shape segmentation method by skeletonizing the shape [8] . Kageyama et al. proposed a method to decompose a shape into parts with a hierarchical structure by categorizing the edges of the graph obtained by the medial axis transform of the shape [9] .. All of these methods try to segment a shape using one single image. Unfortunately, however, the ideal segmentation containing rich information from one single image can be hard. The most concerning problem of the shape segmentation lies in setting a proper criterion of geometric segmentation, which is often ambiguous and sometimes can only be assumed routinely. Utilizing only the geometric features from one static, isolated shape, these frameworks are inherently incapable of processing semantic information that can be extracted from multiple shapes of a category.. Many of shape analysis tasks that involve perceptual shape grouping and segmentation deal with huge amount of shapes. Thus, it is reasonable to assume that multiple instances of a prototype exist for the same shape category. For example, when segmenting the shape of an organ, it is highly possible that at least one reference shape of the organ is available and useful information can be extracted from the comparison between the target shape and the reference ones. Motivated by this idea, the proposed method utilizes the deformation information with multiple images to help segmenting the target shape. We aim at segmentation of a shape into a number of parts, of which the dissimilarity between each component is most significant. We used for statistic shape segmentation experiment shapes from MPEG-7 Shape Silhouette Database CE-Shape-1 part B [13] , which provides 20 samples per category. For a shape to be segmented, we prepared 9 images in a category as reference and one target image, forming a test set, as shown in Fig. 4 . Next, the method proposed in the previous section was applied. In the experiment, we used similarity graphs based on k nearest neighbor criterion. Experiment with 15 categories of images was carried out. Some results are shown in Fig. 5 .. In the experiment, the number of nearest neighbor was k = 10 and the initial number of clusters was K = 60, which were determined experimentally. The parameters need not to be exact and usually a large K and k produce better result since this will allow the method to generate a more subtle similarity graph. However, since K is the number of nodes in the similarity graph, computation speed should also be considered when choosing K. Solving eigenvectors for the Laplacian of the similarity graph will become expensive if a large value of K is used. The value of σ for Gaussian similarity function was set to 10% of the total range of distance function ε(P i , P j ), the same as it was recommended in [12] .. While some segmentation results may contain faults in the sense of human perception, they do not disprove the validity of the proposed method. The general problem of unsupervised shape segmentation is not trivial and it is highly possible that there exist multiple grouping from different semantic hierarchical level that the segmentation can take cue from. In the experiment, except simple shapes such as "key," "cup" and "hammer" in Fig. 5 , in which segmentation is straight-forward, many complex objects can be segmented in different ways. It is possible that two parts which should be grouped together in geometric point of view may be separated since a shape share dissimilar deformational patterns. It should be noted that error on shape segmentation can somewhat be eliminated by tweaking parameters K, k and σ. However, we used uniform parameters to show that the proposed method does not heavily depend on parameter settings.. In the experiment with MPEG-7 database, we visualized the eigenvalues and eigenvectors of the Laplacian of the similarity graph, as illustrated. To visualize an eigenvector u i , we assigned every patch a grayscale value, based on its value in the eigenvector, that is, the color of P j in u i is assigned based on u i ( j), with negative values represented by brighter colors and positive values represented by darker colors. Figure 6 (b) and (c) show the effect of similarity graphs of k-NN and fully connected.. Segmentation of different target images in the same category of shapes are similar in nature in that the eigenvalues and corresponding eigenvectors of their similarity graphs are alike. In Fig. 6 (e) and (f), two target images from the same category, although different in shape, share similar eigenvectors, which indicates the robustness of the proposed method to variances. However, we observed that in cases where reference images contain such drastic difference (e.g. reference images are taken from other categories of shapes) that its TPS deformation model is virtually invalid and meaningless, the algorithm fails to produce informative segmentation, and the result become resemble to what directly applying k-means to data points gives.. Several important practical details affects the performance of Ncut clustering algorithm, one of which is choosing the appropriate number S of components. For the proposed method, the number of components S can either be assigned manually or automatically detected using eigengap heuristic. This method observes the magnitude of eigenvalues of graph Laplacian, and chooses the number of clusters to be k, such that k + 1-th smallest eigenvalue λ k+1 is significantly larger than λ 1 , . . . , λ k , or, the gap between k-th and k + 1-th smallest eigenvalue is relatively large, such as that plotted in Fig. 6 (e) and (f). Each eigenvector carries information about the clusters. We observed that the eigenvectors starting from that corresponds the smallest eigenvalue, namely u 2 , . . . , u k , each eigenvector roughly serves to separate two cluster from each other. For example, in Fig. 6 (e) , eigenvector 2 discriminate the bird's head while eigenvector 3 discriminate the bird's tail from the rest of the body. However, as the eigenvalue grows larger, its eigenvector becomes less informative and therefore should not be used in segmentation.. The eigengap approach finds its root in perturbation theory [14] : the number of 0 eigenvalue in a graph with k disconnected components is k, and its λ k+1 is greater than 0. For the above reason, we can almost always find meaningful and stable segmentation if eigengap heuristic is followed. The segmentation number S in the proposed method is found by setting the least number of clusters and then detect the eigengap where |λ k+1 − λ k | > γ|λ k − λ k−1 | occurs, and γ is a parameter usually set to 2. However, we find in practice that the number of clusters for the most stable segmentation usually falls below k when k is large since there are cases where one eigenvector separates more than two clusters. In the experiment, we used the number of clusters k − 1 for k greater than 5.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A Sensor Network Protocol for Automatic Meter Reading in an Apartment Building Abstract. A wireless sensor network for automatic meter reading needs to satisfy two contradicting requirements, i.e., long lifetime and prompt detection and notification of emergency. We propose a sensor network protocol for this purpose, in which sensor nodes operate on a low duty cycle while the latency of transmission is guaranteed to be less than a certain bound. In this protocol, each node is assigned a time slot in which it receives messages from other nodes. To accomplish slot assignment where nodes further from a BS are assigned earlier time slots for a packet to be transmitted to the BS in one cycle, we propose a slot assignment function with which a node can determine its own slot in a distributed way. We explore several slot assignment functions to find one which gives low and homogeneous contention over a grid network. The simulation results show that our protocol performs well close to the optimal case. Wireless sensor network (WSN) is a key technology to realize our safe, secure, and comfortable future life. Among its wide variety of applications, we focus our attention on automatic meter reading (AMR) in a large-scale apartment building which consists of hundreds of apartments. In such a building, meters are attached to pipes and cables in each apartment to monitor consumption of water, gas, electricity, and so on. A water, gas, or electricity company hires and sends personnel to collect meter readings once a month in most cases. In these days, those companies consider to adopt a WSN for meter reading, since it is costly to hire many personnel to cover all houses and buildings of customers and it becomes difficult for outsiders to enter modern apartment buildings for security reasons.. Such a WSN for AMR consists of meters equipped with a radio transceiver operated on battery power supply and a gateway server, i.e., base station (BS), connected to a monitoring station of a company through a regional wired or wireless network. Since a meter is usually stored in a meter box or pipe shaft, radio signal is heavily disturbed and the range of transmission is relatively small. Therefore, a WSN is sparse, where the average number of neighboring nodes, i.e., nodes in the range of radio signals, is a few. Monthly meter reading does not put too strict restriction on the delay requirement as far as meter reading is collected from all meters in a building. On the contrary, urgent information such as detection of gas leakage must be transmitted to the BS immediately, e.g., within 10 seconds, once it happens.. In this paper, we consider a network protocol for AMR, which can collect meter reading from all meters at a predetermined interval, e.g., once a month, and transmit urgent information from a meter to a BS within the specified delay bound. We assume that a contention-based MAC is adopted and that timers of nodes are synchronized by a certain synchronization protocol, such as proposed in [5, 6, 11] . In addition, a WSN is maintained by a certain topology control protocol, together with a health check mechanism conducted once a day, for example. Under these assumptions, we consider sleep scheduling for energy-efficient operation of a WSN.. The trade-off between latency and energy-efficiency has been discussed in some literatures [2] [3] [4] 9] . Sleep scheduling to reduce latency is also proposed in [1, 8, 10] . However, they do not consider reliability of data transmission. Both loss for unreliable wireless communication and latency caused by sleeping nodes are considered in the dynamic switch-based forwarding [7] , which optimizes the expected delivery ratio, expected communication delay, or expected energy consumption. However, this forwarding method does not provide any guarantee on end-to-end delay. We aim to satisfy both requirements on bounded end-to-end delay and low duty cycle by waking up nodes from the edge of a WSN to the BS adopting a distributed and self-organizing slot assignment mechanism.. The rest of this paper is organized as follows. First we describe the details of our protocol in Sect. 2. In Sect. 3, we define the contention degree, which represents the intensity of contention at each node, and expected contention degree in a grid network is presented. Next we evaluate four slot assignment functions in Sect. 4 and the best one is further tuned for a grid network in Sect. 5. We conclude this paper in Sect. 6. In this paper, a WSN protocol for AMR in a large-scale apartment building was proposed. For accomplishing both a low duty cycle and delay-bounded transmission, the operation interval is set at the delay bound and divided into time slots. A node determines its time slot for packet reception in a distributed and stochastic manner, so that every node has a next-hop node with a larger slotID. It was shown that EXPONEN-TIAL SAPDF enabled less and more identical contention degree at all level nodes in a grid network and the results were near-optimal. Our future work includes evaluation of energy efficiency of our protocol in practical experiments, including comparison to a TDMA-type protocol, which we will report in near future.

O O O O O B I I O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Capability curve based enhanced reactive power control strategy for stability enhancement and network voltage management Reactive power has become a vital resource in modern electricity networks due to increased penetration of distributed generation. This paper examines the extended reactive power capability of DFIGs to improve network stability and capability to manage network voltage profile during transient faults and dynamic operating conditions. A coordinated reactive power controller is designed by considering the reactive power capabilities of the rotor-side converter (RSC) and the grid-side converter (GSC) of the DFIG in order to maximise the reactive power support from DFIGs. The study has illustrated that, a significant reactive power contribution can be obtained from partially loaded DFIG wind farms for stability enhancement by using the proposed capability curve based reactive power controller; hence DFIG wind farms can function as vital dynamic reactive power resources for power utilities without commissioning additional dynamic reactive power devices. Several network adaptive droop control schemes are also proposed for network voltage management and their performance has been investigated during variable wind conditions. Furthermore, the influence of reactive power capability on network adaptive droop control strategies has been investigated and it has also been shown that enhanced reactive power capability of DFIGs can substantially improve the voltage control performance. © 2013 Published by Elsevier Ltd. Reactive power has become a vital resource in modern electricity networks due to increased penetration of distributed generation. This paper examines the extended reactive power capability of DFIGs to improve network stability and capability to manage network voltage profile during transient faults and dynamic operating conditions. A coordinated reactive power controller is designed by considering the reactive power capabilities of the rotor-side converter (RSC) and the grid-side converter (GSC) of the DFIG in order to maximise the reactive power support from DFIGs. The study has illustrated that, a significant reactive power contribution can be obtained from partially loaded DFIG wind farms for stability enhancement by using the proposed capability curve based reactive power controller; hence DFIG wind farms can function as vital dynamic reactive power resources for power utilities without commissioning additional dynamic reactive power devices. Several network adaptive droop control schemes are also proposed for network voltage management and their performance has been investigated during variable wind conditions. Furthermore, the influence of reactive power capability on network adaptive droop control strategies has been investigated and it has also been shown that enhanced reactive power capability of DFIGs can substantially improve the voltage control performance. Wind power has become the most attractive renewable power source at present to achieve future renewable energy targets in power generation. Wind energy conversion technology has rapidly evolved during the last two decades and with the development of power electronic converter systems conventional induction generator based fixed-speed wind generators (FSWGs) have been superseded by power electronic based wind generation systems (e.g. doubly-fed induction generator (DFIG) and full-converter wind generator (FCWG)). The ability to control both active and reactive power and superior fault-ride through (FRT) capability are the main driving factors responsible for the popularity of power electronic based wind generator systems.. Power electronic based wind generators inherit substantial reactive power capability within their generator and converter systems. In particular, many researchers have shed light on DFIG reactive power capability characteristics and its reactive power limitations [1] [2] . In the published literature a number of studies have proposed using the DFIG reactive power capability for transient stability enhancement [3] [4] , FRT improvement [4] [5] , system loss reduction [6] [7] , and to mitigate voltage fluctuations [8] . However, none of these studies have considered the DFIG reactive power capability characteristics within their control schemes and simple approximations have been made to determine the available reactive power capability from the DFIG. Consequently the reactive power capabilities of the DFIG have been underutilised for network performance enhancements.. Voltage rise issue has been considered as one of the major issues faced by the distribution network operators (DNOs) due to high penetration of renewable power generators (RPGs) in distribution networks [9] [10] . Reactive power control has been proposed by many researchers as the main control approach to mitigate voltage rise issue in distribution networks. However, the extended reactive power capability of the RPGs has received less emphasis in these studies [9] [10] .. The droop control based approaches have been proposed in a number of studies; however these studies have assumed static capability curves for the RPGs [11] . Therefore, the importance of reactive power capability of the RPGs for voltage control must be further investigated in the context of the DFIG.. The novel contributions made in this study can be summarised as follows: A capability curve based reactive power controller is proposed for the DFIG, which can maximise the reactive power contribution from DFIG wind farms during dynamic operating conditions by generating dynamic capability characteristics for the DFIG. In particular, during partially loaded conditions a capability curve based reactive power controller can be effectively utilised as a vital dynamic reactive power resource for network stability enhancement. Moreover, the generator constraints of the DFIG have implicitly incorporated into the DFIG capability characteristics; hence it evades additional protection requirements to avert potential overloading conditions in the DFIG. In addition, a number of network adaptive droop control strategies have been developed using feeder characteristics, thus enabling effective use of the existing reactive power capability of the DFIG for The extended reactive power capability of the DFIG was examined in this paper in order to improve the network stability and voltage management during transient faults and dynamic operating conditions. A novel coordinated reactive power controller was designed for the DFIG considering RSC and GSC reactive power capability characteristics. This study has shown that a substantial improvement can be achieved in voltage recovery during transient disturbances by using the extended reactive power capability of the DFIG. In particular, partially loaded DFIGs are capable of providing a higher reactive power output during system contingencies for network stability enhancement. Moreover, a number of droop control strategies were also investigated, and it was been shown that droop control strategy highly influences the reactive power requirement and feeder voltage management. The proposed feeder adaptive droop control method can be used to determine the droop settings for the distribution feeder, since it requires minimum feeder information and ultimately reactive power burden can be effectively distributed among the generator units installed along the distribution feeder. Furthermore, the study has shown that a lesser reactive power capability for the DFIG will significantly impact on voltage control performance of the network adaptive droop control strategy.. At present, commercially available wind generator systems provide limited reactive power support based on RSC capability, however extended reactive power support has not been effectively utilised in many DFIGs. Therefore, it is essential to incorporate these extended reactive power capabilities for DFIG wind generators in the future in order to provide enhanced network support.

B I O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O B I O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O B I O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O B I O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O


A Multilevel Mixture-of-Experts Framework for Pedestrian Classification Abstract-Notwithstanding many years of progress, pedestrian recognition is still a difficult but important problem. We present a novel multilevel Mixture-of-Experts approach to combine information from multiple features and cues with the objective of improved pedestrian classification. On pose-level, shape cues based on Chamfer shape matching provide sample-dependent priors for a certain pedestrian view. On modality-level, we represent each data sample in terms of image intensity, (dense) depth, and (dense) flow. On feature-level, we consider histograms of oriented gradients (HOG) and local binary patterns (LBP). Multilayer perceptrons (MLP) and linear support vector machines (linSVM) are used as expert classifiers.Experiments are performed on a unique real-world multimodality dataset captured from a moving vehicle in urban traffic. This dataset has been made public for research purposes. Our results show a significant performance boost of up to a factor of 42 in reduction of false positives at constant detection rates of our approach compared to a baseline intensity-only HOG/linSVM approach. We obtained a significant boost in pedestrian classification performance from the use of multiple modalities and features in a Mixture-of-Experts setting. Our experiments show that the largest performance gain stems from the combination of intensity features with depth and motion features. We expect the use of additional modalities, e.g., far-infrared (FIR) [31] , to further increase performance. Multimodality classifiers particularly outperform multifeature classifiers in a single modality. However, modalities and features are orthogonal, so that a combined multimodality/multifeature approach can further boost performance.. In this work, we did not heavily optimize the feature sets with regard to the different modalities. Instead, we transferred general knowledge and experience from the behavior of features and classifiers in the intensity domain to the depth and motion domains. At this point, it is not clear if (and how) additional modification and adaptation of the feature sets to the different characteristics found in depth and motion data (see Section IV-A) can further improve performance. While the HOG/MLP classifier outperforms the LBP/MLP classifier in all modalities in our experiments, this may not be generally true. See, for example, [43] , where the relative order of feature/classifier performance reverses with respect to intensity and depth.. Orthogonal to the improvements presented in this paper are benefits resulting from an increased training set [10] , [36] . In the intensity domain, feature-classifier combinations respond differently to an increased training set (in both size and dimensionality), e.g., in terms of classifier complexity, discriminative power, practical feasibility and saturation effects [10] , [36] . It is currently unknown to what extent similar (or different) effects are present for features and classifiers in other modalities.. Recent work analyzed the dependence of classification performance and pedestrian image size (as a proxy for distance to the camera) in the intensity domain [7] . Results show significant relative performance differences of the evaluated classifiers across multiple scales. Similar effects may also be found in depth and motion features, particularly since depth and motion measurements tend to get noisy at larger distances to the camera. In case of stereo vision, the range of measurements is further limited by the camera setup.. Certainly, more research is necessary to fully explore the benefits of multimodality/multifeature classification. For that purpose, we provide our multicue dataset not only as a means for benchmarking but also to stimulate further research on the issues mentioned above.

O O B O O B I O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Technological support for the enactment of collaborative scripted learning activities across multiple spatial locations Computer Supported Collaborative Blended Learning (CSCBL) scripts are innovative practices that benefit from interactive devices to combine and coordinate activities occurring in different spatial locations. However, the adoption of CSCBL scripts is hindered by the difficulties in orchestration that they entail for practitioners. As observed in a concrete experiment, these orchestration problems include: adapting group formation according to students' actions in previous activities, supporting transitions between activities and artifacts across locations using diverse technologies, or displaying the appropriate tools to students depending on their group and assigned task. This paper describes the technological support designed to alleviate this complexity. The result is a Computer Supported Collaborative Blended Learning (CSCBL) script and its associated orchestration system that allows the replication of these practices at a minimum cost. The CSCBL script has been evaluated in a case study with 35 students and 5 teachers. Based on this orchestration system and the findings of the experiment, we also propose an architecture based on IMS Learning Design and Generic Service Integration in combination with other web based tools to support the enactment of other similar CSCBL scripts. The findings of this experiment offer interesting insights into the extend to which different technologies and multiple spaces can be combined for orchestrating integrated complex collaborative practices. This paper has presented an in-depth analysis of the issues that appear when Computer Supported Collaborative Blended Learning scripts are deployed in real life scenarios. The work focused on how to take an already existing experience which was proven to be pedagogically positive, but expensive in terms of the time required by the teaching staff, and re-designing it to be deployed in a real life scenario with the support of a technology-based orchestration system.. We presented as a solution an orchestration system where the collaborative learning flow is managed and delivered to the participants by IMS Learning Design, and the activities are executed with the help of case specific tools and hardware. Generic Service Integration allows the adaptation of scripts based on the information obtained from these case specific tools, being such information exchanged with the IMS LD player during the enactment of the script. We also described a scenario in which these external services included mobile devices and Google Spreadsheets: the former were used by the students to interact with NFC tags, so that they received contextualized multimedia information of the environment; the latter were used to capture the data generated by the students and to create the students' groups. These applications allowed an intuitive and simple manipulation of numerous aspects of the activity flow.. We also presented an experiment. First, a detailed account of the limitations found regarding the enactment of the CSCBL script was documented. Second, we presented the requirements needed to overcome such limitations and the research questions addressed. The analysis of these research questions determined whether or not the requirements were fulfilled by the orchestration system. We considered several data sources for the mixed method adopted for the analysis. The experimental results sustain that the orchestration system properly provided guidance to the participants without restraining the flexibility of the script. Furthermore, the results also indicate that using IMS LD effectively supported the delivery of adaptive learning material and increased the reusability of the script. In summary, the system was able to enact the CSCBL script while keeping its cost, in terms of management tasks, at a minimum.. Another finding is that the enactment of the script with the support of the proposed orchestration system was verified to be similar to the original script version, in terms of learning benefits. More specifically, the analysis of the acquired learning outcomes reveals that: (i) the integration of formal and informal activities in the same flow enriched the whole experience, (ii) the CSCBL script properly supported the learners within the environment, and (iii) the script facilitated the exchange of personal views.. As a conclusion, in this paper we have presented a CSCBL script and a flexible architecture to support its enactment. On the one hand, we have shown that Generic Service Integration (GSI) enhances the flow-management capabilities of IMS Learning 13. Design. The orchestration system offers a framework that keeps management tasks at a minimum and increases the reusability of the script, while maintaining the effectiveness of a learning experience even in challenging CSCBL scripts. The generalization of the presented case of study is based on the script division into macro and micro levels, where the former level is supported by a flow manager and the latter by case-specific tools, being these two levels connected by GSI. The underlying architecture that emerges from this particular case study contributes to the support of collaborative activities across different spatial locations.. On the other hand, the results show that the architecture proposed dealt successfully with flexibility issues related to the group management typically related to the enactment of collaborative scripts.. Altogether might inspire other researchers to provide particularizations of this architecture to be used in other similar learning settings. Also, the CSCBL script design proposed can be adapted by any researcher and practitioner since it follows the structure defined by the 4SPPIces model [12] . The same design could be employed and re-adapted in other exploratory type of activities such as [46] in which students learn in a urban experience about the city of Barcelona. However, one of the difficulties to be addressed in this particularization process is to formalize the CSCBL script using IMS LD. Future work should facilitate this step with the creation of script templates and the provision of template based authoring tools such as Collage [33] . To increase the readability of the results, the findings of this experiment have been clustered according to the evaluation focuses explained in Section 4.2.1.

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A survey of fault tolerance mechanisms and checkpoint/restart implementations for high performance computing systems In recent years, High Performance Computing (HPC) systems have been shifting from expensive massively parallel architectures to clusters of commodity PCs to take advantage of cost and performance benefits. Fault tolerance in such systems is a growing concern for long-running applications. In this paper, we briefly review the failure rates of HPC systems and also survey the fault tolerance approaches for HPC systems and issues with these approaches. Rollback-recovery techniques which are most often used for long-running applications on HPC clusters are discussed because they are widely used for long-running applications on HPC systems. Specifically, the feature requirements of rollback-recovery are discussed and a taxonomy is developed for over twenty popular checkpoint/restart solutions. The intent of this paper is to aid researchers in the domain as well as to facilitate development of new checkpointing solutions. HPC systems continue to grow exponentially in scale; currently from petascale computing (10 15 floating point operations per second) to exascale computing (10 18 floating point operations per second) as well as in complexity due to the growing need to handle long-running computational problems with effective techniques. However, HPC systems come with their own technical challenges [67] . The total number of hardware components, the software complexity and overall system reliability, availability and serviceability (RAS) are factors to contend with in HPC systems, because hardware or software failure may occur while long-running parallel applications are being executed. The need for reliable fault tolerant HPC system has intensified because failure may result in a possible increase in execution time and cost of running the applications. Consequently, fault tolerance solutions are being incorporated into the HPC systems. Fault tolerant systems have the ability to contain failures when they occur, thereby minimizing the impact of failure. Hence, there is a need for further investigation of fault tolerance of HPC systems. We presented reliability and MTBF of HPC systems. Based on the analysis and published papers, we presented that reliability and MTBF of HPC systems with large number of components decreases as the number of components increases. We gave an overview of failure rates of HPC systems. Although it is difficult to determine the single root cause of failure, however, we presented that long-running applications are most frequently interrupted by human errors, hardware failures, or software failures. We conclude that a good fault tolerance mechanism should be able to handle all of these causes of failure. We have surveyed fault tolerance mechanisms (redundancy, migration, failure making, and recovery) for HPC and identified the pros and cons of each technique. Recovery techniques are discussed in detail, with over twenty checkpoint/restart facilities surveyed. The rollback feature requirements identified are used to evaluate them and the results are provided in a tabular format to aid researches on this area. The web site of each surveyed checkpoint/restart facility is also provided for further investigation.

O O O B I O O B O O O B O O O O O B I I I O O O O O O O O O O B O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O