Rescue1.asd.docx

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1.INTRODUCTION

Heart Rate

Heart rate is the speed of the heartbeat measured by the number of contractions of the heart per minute (bpm). The heart rate can vary according to the

body's physical needs, including the need to absorb oxygen and excrete carbon

dioxide. It is usually equal or close to the pulse measured at any peripheral point.

ctivities that can provo!e change include physical exercise, sleep, anxiety, stress,

illness, ingesting, and drugs.

Fig 1.1 Human heart

Heart rate, also !no"n as pulse, is the number of times a person's heart beats

per minute. normal heart rate depends on the individual, age, body si#e, heart

conditions, "hether the person is sitting or moving, medication use and even air

1

https://en.wikipedia.org/wiki/Heart_soundshttps://en.wikipedia.org/wiki/Human_bodyhttps://en.wikipedia.org/wiki/Oxygenhttps://en.wikipedia.org/wiki/Carbon_dioxidehttps://en.wikipedia.org/wiki/Carbon_dioxidehttps://en.wikipedia.org/wiki/Pulsehttps://en.wikipedia.org/wiki/Physical_exercisehttps://en.wikipedia.org/wiki/Sleephttps://en.wikipedia.org/wiki/Anxietyhttps://en.wikipedia.org/wiki/Drughttps://en.wikipedia.org/wiki/Human_bodyhttps://en.wikipedia.org/wiki/Oxygenhttps://en.wikipedia.org/wiki/Carbon_dioxidehttps://en.wikipedia.org/wiki/Carbon_dioxidehttps://en.wikipedia.org/wiki/Pulsehttps://en.wikipedia.org/wiki/Physical_exercisehttps://en.wikipedia.org/wiki/Sleephttps://en.wikipedia.org/wiki/Anxietyhttps://en.wikipedia.org/wiki/Drughttps://en.wikipedia.org/wiki/Heart_sounds


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temperature. $ven emotions can have an impact on heart rate. %or example,

getting excited or scared can increase the heart rate. &ut most importantly,

Table 1.1: Major factor affecting heart rate an! force of

contraction

Factor "ffect

ardioaccelerator

nerveselease of norepinephrine

roprioreceptors Increased rates of firing during exercise

hemoreceptors*ecreased levels of +- increased levels of H

, +, and

lactic acid

&aroreceptors*ecreased rates of firing, indicating falling blood

volume/pressure

0imbic system nticipation of physical exercise or strong emotions

atecholamines Increased epinephrine and norepinephrine

Thyroid hormones 1ariation in T2 and T3

alcium 1ariation in a

otassium 1ariation in 4

5odium 1ariation in 6a

&ody temperature Increased body temperature

6icotine and caffeine 5timulants, increasing heart rate

Meaurement

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https://en.wikipedia.org/wiki/Lactic_acidhttps://en.wikipedia.org/wiki/Lactic_acid


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1. Manual meaurement

Heart rate is measured by finding the pulse of the heart. This pulse rate can

be found at any point on the body "here the artery's pulsation is transmitted to the

surface by pressuring it "ith the index and middle fingers- often it is compressed

against an underlying structure li!e bone. good area is on the nec!, under thecorner of the 7a".

The radial artery is the easiest to use to chec! the heart rate. Ho"ever, in

emergency situations the most reliable arteries to measure heart rate are carotid

arteries.

ossible points for measuring the heart rate are8

9. The ventral aspect of the "rist on the side of the thumb (radial artery).

. The ulnar artery.

2. The nec! (carotid artery).

3. The inside of the elbo", or under the biceps muscle ( brachial artery).

:. The groin (femoral artery).

;. &ehind the medial malleolus on the feet ( posterior tibial artery).


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>. The chest (apex of the heart), "hich can be felt "ith one's hand or fingers. It

is also possible to auscultate the heart using a stethoscope.

9?.The temple (superficial temporal artery).

99.The lateral edge of the mandible (facial artery).

9.The side of the head near the ear ( posterior auricular artery).

#."lectronic meaurement

In obstetrics, heart rate can be measured by ultrasonography, ho"ever a more

precise method of determining heart rate involves the use of an electrocardiograph,

or $@. n $@ generates a pattern based on electrical activity of the heart,"hich closely follo"s heart function. ontinuous $@ monitoring is routinely

done in many clinical settings, especially in critical care medicine. +n the $@,

instantaneous heart rate is calculated using the "aveAtoA "ave () interval

and multiplying/dividing in order to derive heart rate in heartbeats/min.

Bultiple methods exist8

• H C 9,:??/( interval in millimeters)

• H C ;?/( interval in seconds)

• H C 2??/number of DlargeD squares bet"een successive "aves.

the monitors, used during sport, consist of a chest strap "ith electrodes. The

signal is transmitted to a "rist receiver for display.lternative methods of measurement include pulse oximetry and seismocardiography.

4

https://en.wikipedia.org/wiki/Apex_of_the_hearthttps://en.wikipedia.org/wiki/Auscultatehttps://en.wikipedia.org/wiki/Stethoscopehttps://en.wikipedia.org/wiki/Temple_(anatomy)https://en.wikipedia.org/wiki/Superficial_temporal_arteryhttps://en.wikipedia.org/wiki/Facial_arteryhttps://en.wikipedia.org/wiki/Posterior_auricular_arteryhttps://en.wikipedia.org/wiki/Obstetricshttps://en.wikipedia.org/wiki/Obstetric_ultrasonographyhttps://en.wikipedia.org/wiki/Electrocardiographhttps://en.wikipedia.org/wiki/Critical_care_medicinehttps://en.wikipedia.org/wiki/Electrodehttps://en.wikipedia.org/wiki/Pulse_oximetryhttps://en.wiktionary.org/wiki/seismocardiographyhttps://en.wikipedia.org/wiki/Apex_of_the_hearthttps://en.wikipedia.org/wiki/Auscultatehttps://en.wikipedia.org/wiki/Stethoscopehttps://en.wikipedia.org/wiki/Temple_(anatomy)https://en.wikipedia.org/wiki/Superficial_temporal_arteryhttps://en.wikipedia.org/wiki/Facial_arteryhttps://en.wikipedia.org/wiki/Posterior_auricular_arteryhttps://en.wikipedia.org/wiki/Obstetricshttps://en.wikipedia.org/wiki/Obstetric_ultrasonographyhttps://en.wikipedia.org/wiki/Electrocardiographhttps://en.wikipedia.org/wiki/Critical_care_medicinehttps://en.wikipedia.org/wiki/Electrodehttps://en.wikipedia.org/wiki/Pulse_oximetryhttps://en.wiktionary.org/wiki/seismocardiography


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Fig 1.# "C$ intrument Fig 1.% "C$ &a'e form

Heart Rate (ariabilit)

Heart rate variability (H1) is the physiological phenomenon of variation in

the time interval bet"een heartbeats. It is measured by the variation in the beatAtoA

beat interval.

Bethods used to detect beats include8 $@, blood pressure,

ballistocardiograms, and the pulse "ave signal derived from a

photoplethysmograph (@). $@ is considered superior because it provides a

clear "aveform, "hich ma!es it easier to exclude heartbeats not originating in the

sinoatrialnode.The main inputs are the sympathetic and the parasympathetic

nervous system (565) and humoral factors. %actors that affect the input are the

baroreflex, thermoregulation, hormones, sleepA"a!e cycle, meals, physical activity,

and stress.

5

https://en.wikipedia.org/wiki/Ballistocardiographyhttps://en.wikipedia.org/wiki/Photoplethysmographhttps://en.wikipedia.org/wiki/Sinoatrial_nodehttps://en.wikipedia.org/wiki/Sympathetic_nervous_systemhttps://en.wikipedia.org/wiki/Parasympathetic_nervous_systemhttps://en.wikipedia.org/wiki/Parasympathetic_nervous_systemhttps://en.wikipedia.org/wiki/Humoral_factorhttps://en.wikipedia.org/wiki/Baroreflexhttps://en.wikipedia.org/wiki/Thermoregulationhttps://en.wikipedia.org/wiki/Hormoneshttps://en.wikipedia.org/wiki/Sleep-wake_cyclehttps://en.wikipedia.org/wiki/Stress_(biology)https://en.wikipedia.org/wiki/Ballistocardiographyhttps://en.wikipedia.org/wiki/Photoplethysmographhttps://en.wikipedia.org/wiki/Sinoatrial_nodehttps://en.wikipedia.org/wiki/Sympathetic_nervous_systemhttps://en.wikipedia.org/wiki/Parasympathetic_nervous_systemhttps://en.wikipedia.org/wiki/Parasympathetic_nervous_systemhttps://en.wikipedia.org/wiki/Humoral_factorhttps://en.wikipedia.org/wiki/Baroreflexhttps://en.wikipedia.org/wiki/Thermoregulationhttps://en.wikipedia.org/wiki/Hormoneshttps://en.wikipedia.org/wiki/Sleep-wake_cyclehttps://en.wikipedia.org/wiki/Stress_(biology)


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HR( anal)i

The most "idely used methods can be grouped under timeAdomain and

frequencyAdomain. +ther methods have been proposed, such as nonAlinear

methods.

1.Time-domain methods

These are based on the beatAtoAbeat or 66 intervals, "hich are analysed to give

variables such as 5*66(the standard deviation of 66 intervals), B55*(root

mean square of successive differences),5*5*(standard deviation of successive

differences),$&(estimated breath cycle).

2.Frequency-domain methods

%requency domain methods assign bands of frequency and then count the

number of 66 intervals that match each band. The bands are typically high

frequency (H%) from ?.9: to ?.3 H#, lo" frequency (0%) from ?.?3 to ?.9: H#, and

the very lo" frequency (10%) from ?.??22 to ?.?3 H#.

Change of HR( relate! to *ecific *athologie

reduction of H1 has been reported in several cardiovascular and

noncardiovascular diseases.

Myocardial infarction

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https://en.wikipedia.org/wiki/Standard_deviationhttps://en.wikipedia.org/wiki/Standard_deviation


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*epressed H1 after BI may reflect a decrease in vagal activity directed to

the heart. H1 in patients surviving an acute BI reveal a reduction in total and in

the individual po"er of spectral components. The presence of an alteration in

neural control is also reflected in a blunting of dayAnight variations of interval.

Diabetic neuroathy

In neuropathy associated "ith diabetes mellitus characteri#ed by alteration in

small nerve fibers, a reduction in time domain parameters of H1 seems not only

to carry negative prognostic value but also to precede the clinical expression of

autonomic neuropathy.

M)ocar!ial !)function

reduced H1 has been observed consistently in patients "ith cardiac

failure. In this condition characteri#ed by signs of sympathetic activation such as

faster heart rates and high levels of circulating catecholamines, a relation bet"een

changes in H1 and the extent of left ventricular dysfunction "as reported. In

particular, in most patients "ith a very advanced phase of the disease and "ith a

drastic reduction in H1, an 0% component could not be detected despite the

clinical signs of sympathetic activation. This reflects that, as stated above, the 0%

may not accurately reflect cardiac sympathetic tone.

!i"er cirrhosis

0iver cirrhosis is associated "ith decreased H1. *ecreased H1 in patients

"ith cirrhosis has a prognostic value and predicts mortality. 0oss of H1 is also

#

https://en.wikipedia.org/wiki/Cirrhosishttps://en.wikipedia.org/wiki/Cirrhosis


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associated "ith higher plasma proAinflammatory cyto!ine levels and impaired

neurocognitive function in this patient population.

verage resting respiratory rates by age are8

• birth to ; "ee!s8 2?E;? breaths per minute

• ; months8 :E3? breaths per minute

• 2 years8 ?E2? breaths per minute

•

; years8 9=E: breaths per minute

• 9? years8 9


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and * conversion. esistors are available for only some specifications. Thus if

the required resistance does not match the available resistance thenit is

approximated to some available nearby values introducing very minute error

values "hich are resolved manually no"adays. %urther, analog to digital

conversion(uses an * "ith = input pins and 2 selection pins) involves 2 inputs

from sensors "hich leaves nearly 3 cycles unused "hich leads to "astage of

band"idth.

1.#Decri*tion of +ro*oe! ,)tem

The underlying source signal of interest is the &1 that propagates throughout the

body. *uring the cardiac cycle, volumetric changes in the facial blood vessels

modify the path length of the incident ambient light such that the subsequent

changes in amount of reflected light indicate the timing of cardiovascular events.

&y recording a video of the facial region "ith a "ebcam, the red, green, and blue

(@&) color sensors pic! up a mixture of the reflected plethysmographic signal

along "ith other sources of fluctuations in light due to artifacts.

@iven that hemoglobin absorptivity differs across the visible and nearA

infrared spectral range, each color sensor records a mixture of the original source

signals "ith slightly different "eights. These observed signals from the @& color

sensors are denoted by y9 (t), y (t), and y2 (t), respectively, "hich are the

amplitudes of the recorded signals at time point t. Ge assume three underlying

source signals, represented by x9 (t), x (t), and x2 (t).

Ca*turing (i!eo

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The experiments "ere conducted indoors and "ith a varying amount of

ambient sunlight entering through "indo"s as the only source of illumination.

articipants "ere seated at a table in front of a laptop at a distance of

approximately ?.: m from the builtAin "ebcam. *uring the experiment,

participants "ere as!ed to !eep still, breathe spontaneously, and face the "ebcam

"hile their video "as recorded for one minute. ll videos "ere recorded in color

(3Abit @& "ith three channels = bits/channel) at 9: frames per second (fps)

"ith pixel resolution of ;3? 3=? and saved in 1I format on the laptop.

Reco'er) of -(+ from ebcam Recor!ing

ll the video and physiological recordings "ere analy#ed offline using

custom soft"are "ritten in BT0&. It provides an overvie" of the stages

involved in our approach to recover the &1 from the "ebcam videos. to

automatically identify the coordinates of the face location in the first frame of the

video recording, Ge selected the center ;?J "idth and full height of the box as the

region of interest (+I) for our subsequent calculations.

1.% -enefit of *ro*oe! )tem

To achieve a robust evaluation, ensemble empirical mode decomposition of

the HilbertEHuang transform is used to acquire the primary heart rate signal "hile

reducing the effect of ambient light changes. The proposed approach is found to

outperform the current state of the art, providing greater measurement accuracy

"ith smaller variance and is sho"n to be feasible in realA"orld environments.

1&


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1./ Organi0ation of +roject Re*ort

The 6ext chapter deals "ith literature survey and follo"ed by specification

needed for system to run soft"are. %ourth chapter deals "ith architectural design,

data flo" diagram and activity diagram. %ifth chapter for testing, in this chapter it

discuss about taxonomy of testing and testing used particular for pro7ect.

#.IT"R2TUR" ,UR("3

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%. ,3,T"M ,+"CIFIC2TION

The 5ystem equirements 5pecification(55) document describes all data,

functional and behavioral requirements of the soft"are under production or

development. It is produced at the culmination of the analysis tas!. The function

and performance allocated to soft"are as part of system engineering are refined by

establishing a complete information description as functional representation of

system behavior, an indication of performance requirements and design constarints,

appropriate validation criteria.

H2RD2R" R"4UIR"M"NT ,+"CIFIC2TION

• rocessor 8 Intel entium III or 0ater

• Bain Bemory (B) 8 :; B&

• ache Bemory 8 :9 4&

• Bonitor 8 9< inch olor Bonitor

• 4eyboard 8 9?= 4eys

• Bouse 8 +ptical Bouse

• Hard *is! 8 9;? @&

,OFT2R" R"4UIR"M"NT ,+"CIFIC2TION

• %ront $nd/0anguage 8 Bat lab

• &ac! $nd/*atabase 8 6il

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• +perating 5ystem 8 Gindo"s K 5ervice ac! /Gindo"s

1ista/Gindo"s


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interpreting the labels of the boxes and lines. +ne must document the extent that a

components behavior influences ho" another component must be "ritten to

interact "ith it. 5tructures are important because they Lboil a"ayM details about the

soft"are that are independent of the concern reflected by the abstraction. $ach

structure provides a useful perspective of the system. 5ometimes the term is used

instead of structure.

5oft"are architectures are represented as graphs "here nodes represent

components8

• rocedures

• Bodules

• rocesses

• Tools

• *atabases

nd edges represent connectors8

• rocedure calls

• $vent broadcasts

• *atabase queries

• ipes

The design process starts by decomposing the soft"are into components.

The decomposition should be done topAdo"n, based on the functional

decomposition should be done topAdo"n, based on the functional decomposition in

the logical model. orrectness at each level can only be confirmed after

demonstrating feasibility of the next level do"n. 5uch demonstrations may require

prototyping. *esigners rely on their !no"ledge of the technology, and experience

of similar systems, to achieve a good design in 7ust a fe" iterations. This is the

lo"est level of the tas! hierarchy, and is the stage at "hich the control flo" has

been fully defined. It is usually unnecessary to describe the architecture do"n to

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the module level. Ho"ever some consideration of module level processing is

usually necessary if the functionality at higher levels is to be allocated correctly.

Fig /.1 ,)tem 2rchitecture

%igure 3.9 represents system architecture "here a video clip of patients face

is converted into frames. 0ater refined frames are converted into @& format.Then

the green signal is separated using I(Independent omponent nalysis). %urther

noises are eliminated and required parameters are extracted using N*$ algorithm.

Then extracted H,H1 and are validated by comparing "ith $@ results.

/.# Data Flo& Diagram

*ata %lo" *iagram(*%*) is a t"oAdimensional diagram that explains ho"

data is processed and transferred bet"een different processes in a system. It is a

graphical technique that depicts information flo" and the transforms that are

applied as data move from input to output. It provides a simple, intuitive method

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for describing business process "ithout focusing onthe details of computer for

describing business processes "ithout focusing on the details of computer systems.

The graphical depiction identifies each source of data and ho" it interacts "ith

other data sources to reach a common output. *%* are attractive technique because

they provide "hat users do rather than "hat computers do.

Com*onent of DFD

*%*s are constructed using four ma7or components

9.$xternal entitiesA represent the source of data as input to the system.

They are also the destination of system data. $xternal entities can be called data

stored outside the system. These are represented by squares.

. *ata stores represent stores of data "ithin the system, for example,

computer files or databases. n openAended box represents a data, "hich implies

stored data at rest or a temporary repository of data.

2. rocesses represent activities in "hich data is manipulated by being

stored or retrieved or transferred in some "ay. In other "ords, "e can say that

process transforms the input data into output data. ircles stand for a process that

converts data into information.

3. *ata flo" represents the movement of data from one component to

the other. n arro"( ) identifies data flo" i.e. data in motion. It is a pipeline

through "hich information flo"s. *ata flo"s are generally sho"n as oneA"ay only.

*ata flo"s bet"een external entities are sho"n as dotted lines(AAAAO).

Table 3.9 sho"s various symbols used for dra"ing *%* diagrams. *ata

%lo" *iagram(*%*) is a graphical representation of the Lflo"M of data through an

information system, modelling its process aspects. *%* is often used as a

preliminary step to create an overvie" of the system, "hich can later be elaborated.

1#


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hysical *%* offers the follo"ing advantages8

larifying "hich process are manual and "hich process are

automated

*escribing process in more detail than logical *%*s

5equencing process that has to be done in a particular order

Identifying temporary data stores

5pecifying actual names of files and printouts

dding controls to ensure the processes are done properly

e'el of DFD

0evel ?AHighest abstraction level *%* is !no"n as 0evel ? *%*, "hich depicts

the entire information system as one diagram concealing all the underlying details.

0evel ? *%*s are !no"n as context level *%*s.

0evel 9AThe 0evel ? *%* is bro!en do"n into more specific,0evel 9 *%*. 0evel

9 *%* depicts basic modules in the system and flo" of data among various

modules. 0evel 9 *%* also mentions basic processes and sources of information.

Higher level *%*s can be transformed into more specific lo"er level *%*s "ith

deeper level of understanding unless the desired level of specification is achieved.

e'el 5 DFD

%igure 3..9 depicts that image has the input for the system "hich is no"

given to the system. level ? *%*, also called a fundamental system model or a

1%


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context model, represents the entire soft"are element as a single bubble "ith input

and output data indicated by incoming and outgoing arro"s, respectively. It sho"s

ho" the system is divided into subAsystems(processes), each of "hich deals "ith

one or more of the data flo"s to or from an external agent, and "hich together

provide all of the functionality of the system as a "hole.

Fig /.#.1 le'el 5 DFD

e'el 1 DFD

2&


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Fig /.#.# e'el 1 DFD

%igure 3.. *%* diagram the three process of the sytem is explained

and the flo" is bee represented. The hybrid segmentation process is divided in

three processes. Initially the input T image is preprocessed to reduce noise and

the refined image is segmented by detecting visceral and pleural space through

initiali#ation. +n further iteration the pleural space gro"s by the edges to provide

segmented pleural space. The pleural liquid level "ill be determined based on the

segmented pixels. %inally, a set of segmented images are used for 2* deformable

surface.

/.% 2cti'it) Diagram

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ctivity diagrams are graphical representations of "or!flo"s of step"ise

activities and actions "ith support for choice, iteration and concurrency. In the

Pnified Bodeling 0anguage, activity diagrams are intended to model both

computational and organi#ational processes (i.e. "or!flo"s). ctivity diagrams

sho" the overall flo" of control.

ctivity diagrams are constructed from a limited number of shapes, connected

"ith arro"s.

rro"s run from the start to"ards the end and represent the order in "hich

activities happen. ctivity diagrams may be regarded as a form of flo"chart.

Typical flo"chart techniques lac! constructs for expressing concurrency. Ho"ever,

the 7oin and split symbols in activity diagrams only resolve this for simple cases-

the meaning of the model is not clear "hen they are arbitrarily combined "ith

decisions or loops.

Table 3. sho"s the various symbols used for dra"ing activity diagram.

ctivity diagrams are as simple to ma!e as an ordinary flo"chart. $ach symbol has

a meaning and context "here its use is appropriate. It focuses on the flo" of

activities involved in a single process. The ctivity diagram sho"s ho" these

singleAprocess activities depend on one another.

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Table /.# 2cti'it) )mbol

,3M-O, N2M" D",CRI+TION

ction The tas! need to be done

*ecision onditional flo" of

control

5plit or Berge &ar Berges concurrent

transitions into a single

target or splits single

transition into concurrent

targets.

Initial 5tate seudo state that

represents the start of the

event.

%inal 5tate $nd of state transitions.

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Face

Reflectance

Channels

Red/Green/Blue

Signals

Red/Green/Blue

Tranform the

Signals

Separated

Sources 1/2/3

Fig./.% 2cti'it) Diagram

%igure 3.2 represents the activity diagram- the video of the human face can be

recorded and split up into separate frames using +I. nd determine the 2

channels from the corresponding frames. If any error present means eliminate it by

using N*$ algorithm. %inally, the human heart rate, respiratory rate can be

evaluated.

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/./ Im*lementation

Implementation is the stage of the ob7ect "hen the theoretical design is

turned out into a "or!ing system. Thus it can be considered to be the most critical

stage in achieving a successful ne" system and in giving the user, confidence that

the ne" system "ill "or! and be effective. The implementation stage involves

careful planning, investigation of the existing system and its constrain on

implementation, designing of methods to achieve changeover and evolution of

changeover methods.

$ach program is tested individually at the time of development using the

data and has verified that this program lin!ed together in the "ay specified in the

program specification, the computer system and its environment is tested to the

satisfaction of the user. nd so the system is going to be implemented very soon.

simple operating procedure is included so that the user can understand the different

functions clearly and quic!ly.

Initially the desired tool is selected, then designing the system to get

required output. The final stage is to document the entire system "hich provides

components and the operating procedures of the system.

In this pro7ect first record the human face video and separate the frames

using +I. The +I "as then separated into the three @& channels and spatially

averaged over all pixels in the +I to yield a red, blue, and green measurement

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point for each frame and form the ra" signals. $ach trace "as 9 min long. nd

finding the three signals to demonstrate "hich is the best signal to calculate the

heart rate variation. Bost probably the green signal is the best one to determine the

difference signal propagation. To remove the environmental noise use ensembleA

empirical mode decomposition and then apply N*$ algorithm to find H,H1

and rates.

Mo!ule ue!

• apturing module

• &1 recovery module

• Quantification of physiological parameter module(H,H1,)

/./.1 C2+TURIN$ MODU"

The experiments "ere conducted indoors and "ith a varying

amount of ambient sunlight entering through "indo"s as the only source of

illumination. articipants "ere seated at a table in front of a laptop at a distance of

approximately ?.: m from the builtAin "ebcam. *uring the experiment,

participants "ere as!ed to !eep still, breathe spontaneously, and face the "ebcam

"hile their video "as recorded for one minute. ll videos "ere recorded in color

(3Abit @& "ith three channels = bits/channel) at 9: frames per second (fps)

"ith pixel resolution of ;3? 3=? and saved in 1I format.

/./.# -(+ R"CO("R3 MODU"

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ll the video and physiological recordings "ere analy#ed offline

using custom soft"are "ritten in BT0&. It provides an overvie" of the stages

involved in our approach to recover the &1 from the "ebcam videos. To

automatically identify the coordinates of the face location in the first frame of the

video recording, Ge selected the center ;?J "idth and full height of the box as the

region of interest (+I) for our subsequent calculations.

The +I "as then separated into the three @& channels and

spatially averaged over all pixels in the +I to yield a red, blue, and green

measurement point for each frame and form the ra" signals y9 (t), y (t), and y2

(t), respectively. $ach trace "as 9 min long. The ra" traces "ere detrended using a

procedure based on a smoothness priors approach "ith the smoothing parameter R

C 9? (cutoff frequency of ?.=> H#) and normali#ed as follo"s. To perform motionA

artifact removal by separating the fluctuations caused predominantly by the &1

from the observed ra" signals.

4U2TIFIC2TION OF +H3,IOO$IC2 +2R2M"T"R

MODU"

The separated source signal "as smoothed using a fiveApoint moving

average filter and band pass filtered (9=Apoint Hamming "indo", ?.


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6i7 HR D"T"CTION

The H detection can be performed by selecting the green signal

among the three signals. To avoid inclusion of artifacts, such as ectopic beats or

motion, the I&Is "ere filtered using the non causal of variable threshold algorithm

"ith a tolerance of 2?J. H "as calculated from the mean of the I&I time series as

;?/I&I.

6ii7H(R D"T"CTION

nalysis of H1 "as performed by po"er spectral density (5*)

estimation using the 0omb periodogram . The lo"frequency (0%) and high

frequency (H%) po"ers "ere measured as the area under the 5* curve

corresponding to ?.?3E?.9: and ?.9:E?.3 H#, respectively, and quantified in

normali#ed units (n.u.) to minimi#e the effect on the values of the changes in total

po"er.The 0% component is modulated by baroreflex activity and includes both

sympathetic and parasympathetic influences. The H% component reflects

parasympathetic influence on the heart through efferent vagal activity and isconnected to respiratory sinus arrhythmia (5), a cardio respiratory phenomenon

characteri#ed by I&I fluctuations that are in phase "ith inhalation and exhalation.

Ge also calculated the 0%/H% ratio, considered to mirror sympatho/vagal balance

or to reflect sympathetic modulations.

6iii7RR D"T"CTION

5ince the H% component is connected "ith breathing, the can be

estimated from the H1 po"er spectrum. Ghen the frequency of respiration

2$


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changes, the center frequency of the H% pea! shifts in accordance "ith

S?.Thus, "e calculated from the center frequency of the H% pea! fH%pea! in

the H1 5* derived from the "ebcam recordings as ;?/fH%pea! . The

respiratory rate measured using the chest belt sensor "as determined by the

frequency corresponding to the dominant pea! fresppea! in the 5* of the

recorded respiratory "aveform using ;?/fresppea!.

2$ORITHM :

,te* 1 : 5tart.

,te* # : onvert the given video into .avi format.

,te* % : alculate totalframe , totaltime , framerate for the given format.

,te* / : nd separate the three different frame "ith 2 signal(red,blue,green).

,te* 8 : rop the image into pixel resolution "hich only covers the face. nd also

calculate the mean value for the 2 signals "ith adopted crop image.

,te* 9 : %ind the determinant value for the separated signal,

detrUrCdetrend(rUsig)./sr-

detrUgCdetrend(gUsig)./sg-

detrUbCdetrend(bUsig)./sb-

,te* :nd plot the values.

,te* ; :ombine the detrU(r,g,b) signals and apply the N*$ algorithm.

,te*


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te* 15: %ind the pea! value for the signal.

,te* 11: alculate H, using the belo" formula

respUrateC;?Vfpea!

heartUrateC;?/mean(ibi).

,te* 1# : *isplay the corresponding value in the figure.

IM+"M"NT2TION +ROC"DUR":

-OC= DI2$R2M OF TH" "NTIR" ,3,T"M:

Fig /./ -loc> !iagram for im*lementation

In this I9;%=


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professionals. &ecause very easy using I9;%=


32/79

output is generally a signal that is converted to humanAreadable display at the

Fig /./.1 "a) *ule enor

The $asy ulse sensor is based on the principle of photoplethysmography

(@) "hich is a nonAinvasive method of measuring the variation in blood volume

in tissues using a light source and a detector. 5ince the change in blood volume is

synchronous to the heart beat, this technique can be used to calculate the heart rate.

Transmittance and reflectance are t"o basic types of photoplethysmography.The

transmittance @, a light source is emitted in to the tissue and a light detector is

placed in the opposite side of the tissue to measure the resultant light. &ecause of

the limited penetration depth of the light through organ tissue, the transmittance

@ is applicable to a restricted body part, such as the finger or the ear lobe.

Ho"ever, in the reflectance @, the light source and the light detector are both

placed on the same side of a body part. The light is emitted into the tissue and the

reflected light is measured by the detector. s the light doesnt have to penetrate

32


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the body, the reflectance @ can be applied to any parts of human body. In either

case, the detected light reflected from or transmitted through the body part "ill

fluctuate according to the pulsatile blood flo" caused by the beating of the heart.

The HBA:99$ sensor is manufactured by 4yoto $lectronic o., hina,

and operates in transmission mode. The sensor body is built "ith flexible 5ilicone

rubber material that helps to !eep the sensor tightly hold to the finger. Inside the

sensor case, an I 0$* and a photodetector are placed on t"o opposite sides and

are facing each other. Ghen a fingertip is plugged into the sensor, it is illuminated

by the I light coming from the 0$*. The photodetector diode receives the

transmitted light through the tissue on other side. Bore or less light is transmitted

depending on the tissue blood volume. onsequently, the transmitted light intensity

varies "ith the pulsing of the blood "ith heart beat. plot for this variation against

time is referred to be a hotoplethysmography or @ signal. The

follo"ing picture sho"s a basic transmittance @ probe setup to extract the pulse

signal from the fingertip.

Fig /./.# HRM?#811" a a tranmiion ++$ *robe

33


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The @ signal consists of a large * component, "hich is attributed to the total

blood volume of the examined tissue, and a pulsatile () component, "hich is

synchronous to the pumping action of the heart. The component, "hich carries

vital information including the heart rate, is much smaller in magnitude than the

* component. typical @ "aveform is sho"n in the figure belo" (not to

scale).

Fig /./.% ++$ com*onent

The t"o maxima observed in the @ are called 5ytolic and *iastolic pea!s, and

they can provide valuable information about the cardiovascular system (this topic

is outside the scope of this article). The time duration bet"een t"o consecutive

5ystolic pea!s gives the instantaneous heart rate.

Here are the features of $asy ulse 19.9 sensor module.

• Pses HBA:99$ transmission @ sensor for stable readings

• B;??3 +pamp "ith railAtoArail output capability for maximum signal

s"ing

• 5eparate analog and digital outputs

34


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• otentiometer gain control for the analog output

• ulse "idth control for the digital output

dditional test points on board for analy#ing signals at different stages of

instrumentation.

2DC:

&asic analogAtoAdigital converter terminology "ill be covered first, follo"ed

by configuration of the analogAtoAdigital converter peripheral. 6ext, information on

the usage of the peripheral "ill be presented, initially focusing on the =Abit analogA

todigital converter. Then the differences bet"een the =Abit and the 9?Aor 9Abit

converters "ill be discussed. %inally, some additional reference resources "ill be

highlighted.

Bicrocontrollers are very efficient at processing digital numbers, but they

cannot handle analog signals directly. n analogAtoAdigital converter, converts an

analog voltage level to a digital number. The microcontroller can then efficiently

process the digital representation of the original analog voltage. &y definition,

digital numbers are nonAfractional "hole numbers.

In this example, an input voltage of .232 volts is converted to =


36/79

inaccurate. The input range is set by high and lo" voltage references. These define

the upper and lo"er limits of the valid input range. In many cases, the high and

lo" voltage references are selected as the microcontroller supply voltage and

ground, at other times an external reference or references are used.In addition,

some devices have internal voltage references that can be used. The source or

sources for these voltage references are a configuration option "hen setting up the

analogAtoAdigital converter in the Imicro microcontroller (BP). 6ote that

there are restrictions on the voltage reference levels, for example8 the reference

voltages generally shouldnt be less than 1ss or greater than 1**. There is also a

minimum difference that is required bet"een the high and lo" reference voltages.

lease consult your data sheet for the voltage reference requirements.

The output of an analogAtoAdigital converter is a quanti#ed representation of

the original analog signal. The term quanti#ation refers to subdividing a range into

small but measurable increments. The total allo"able input range is divided into a

finite number of regions "ith a fixed increment. The analogAtoAdigital converter

determines the appropriate region to assign the given input voltage.

In this example, the step or increment is oneAtenth of a volt and the input

voltage is .232 volts. The appropriate result "ould be assigned as a digital value

of =


37/79

tenth of a volt. The maximum quanti#ation error in this case "ould be five

hundredths of a volt or oneAhalf of the increment si#e. It should be noted that the

minimum quanti#ation error for the analogAtoAdigital converter peripheral in the

Imicro devices is :?? micro volts. Therefore, the smallest step si#e for each

state cannot be less than one milliAvolt.

esolution defines the number of possible analogAtoAdigital converter output

states. s previously discussed, the result is a digital or "hole number, so for an =A

bit converter the possible states "ill be8 #ero, one, t"o, three and so on, "ith ::

as the maximum state. 9?Abit converter "ill have 9?2 as the maximum state,

and a 9A bit converter "ill have 3?>: as the maximum state. If the input range

remains constant, a higher resolution converter "ill have less quanti#ation error

because the range is divided into smaller steps. This is similar in concept to the process of rounding a number to the nearest hundredths, having potentially less

error than rounding to the nearest tenths.

cquisition time is the amount time required to charge the holding capacitor

on the front end of an analogAtoAdigital converter. The holding capacitor must be

given sufficient time to settle to the analog input voltage level before the actual

conversion is initiated. If sufficient time is not allo"ed for acquisition, the

conversion "ill be inaccurate. The required acquisition time is based on a number

of factors, t"o of them being the impedance of the internal analog multiplexer and

the output impedance of the analog source.

CD:

0* (0iquid rystal *isplay) screen is an electronic display module and

find a "ide range of applications. 9;x 0* display is very basic module and is

very commonly used in various devices and circuits. These modules are preferred

over seven segments and other multi segment 0$*s. The reasons being8 0*s are

3#

http://www.engineersgarage.com/content/seven-segment-displayhttp://www.engineersgarage.com/content/ledhttp://www.engineersgarage.com/content/seven-segment-displayhttp://www.engineersgarage.com/content/led


38/79

economical- easily programmable- have no limitation of displaying special W

even custom characters (unli!e in seven segments), animations.

9;x 0* means it can display 9; characters per line and there are such

lines. In this 0* each character is displayed in :x< pixel matrix. This 0* has

t"o registers, namely, ommand and *ata.The command register stores the

command instructions given to the 0*. command is an instruction given to

0* to do a predefined tas! li!e initiali#ing it, clearing its screen, setting the

cursor position, controlling display etc. The data register stores the data to be

displayed on the 0*. The data is the 5II value of the character to be displayed

on the 0*. lic! to learn more about internal structure of a 0*.The 0*

panel's $nable and egister 5elect is connected to the ontrol ort. The ontrol

ort is an open collector / open drain output. &y incorporating t"o 9?4 external pull up resistors, the circuit is made portable for a "ider range of computers. The

/G line of the 0* panel is hardA"ired into the "rite mode "hich "ill not cause

any bus conflicts on the data lines. Hence the 0*'s internal &usy %lag cannot tell

if the 0* has accepted and finished processing the last instruction or not. The 9?!

otentiometer controls the contrast of the 0* panel.

Table /.% +in Detail of CD

9 @6* @round

3$

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39/79

1cc 5upply 1oltage :1

2 1$$ ontrast ad7ustment

3 5

egister select 8?AOontrol

input,

9AO *ata input

: /G ead/ Grite

; $ $nable

< to 93 *? to *< I/+ *ata pins

+O"R ,U++3:

Intro!uction:

The input to the circuit is applied from the regulated po"er supply. The a.c.

input i.e., 2?1 from the mains supply is step do"n by the transformer to 91 and

is fed to a rectifier. The output obtained from the rectifier is a pulsating d.c voltage.

5o in order to get a pure d.c voltage, the output voltage from the rectifier is fed to a

filter to remove any a.c components present even after rectification. 6o", this

voltage is given to a voltage regulator to obtain a pure constant dc voltage.

-loc> Diagram:

3%


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Fig /././ -loc> !iagram for *o&er u**l)

Tranformer:

Psually, * voltages are required to operate various electronic equipment

and these voltages are :1, >1 or 91. &ut these voltages cannot be obtained

directly. Thus the a.c input available at the mains supply i.e., 2?1 is to be brought

do"n to the required voltage level. This is done by a transformer. Thus, a step

do"n transformer is employed to decrease the voltage to a required level.

Rectifier:

The output from the transformer is fed to the rectifier. It converts .. into

pulsating. *.. The rectifier may be a half "ave or a full "ave rectifier. In this

pro7ect, a bridge rectifier is used because of its merits li!e good stability and full

"ave rectification.

Filter:

4&


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apacitive filter is used in this pro7ect. It removes the ripples from the output of

rectifier and smoothens the *.. +utput received from this filter is constant until

the mains voltage and load is maintained constant. Ho"ever, if either of the t"o is

varied, *.. voltage received at this point changes. Therefore a regulator is applied

at the output stage.

(oltage Regulator:

s the name itself implies, it regulates the input applied to it. voltage regulator

is an electrical regulator designed to automatically maintain a constant voltage

level. In this pro7ect, po"er supply of :1 and 91 are required. In order to obtain

these voltage levels,


42/79

Testing is a process of executing a program "ith the intent of finding an

error. good test case is one that has a high probability of finding an as yet

undiscovered error. successful test is one that uncovers an as yet undiscovered

error. 5ystem testing is the stage of implementation, "hich is aimed at ensuring

that the system "or!s accurately and efficiently as expected before live operation

commences. It verifies that the "hole set of programs hang together. 5ystem

testing requires a test consists of several !ey activities steps for run program,

string, system and is important in adopting a successful ne" system. This is the last

chance to detect and correct errors before the system is installed for user

acceptance testing.

The soft"are testing process commences once the program is created and the

documentation and related data structures are designed. 5oft"are testing is

essential for correcting errors. +ther"ise the program or the pro7ect is not said to

be complete. 5oft"are testing is the critical element of soft"are quality assurance

and represents the ultimate the revie" of specification design and coding. Testing

is the process of executing the program "ith the intent of finding the error. goodtest case design is one that as a probability of finding an yet undiscovered error.

Testing is generally described as a group of procedures carried out to

evaluate some aspects of a piece of soft"are. It can be described as a process used

for revealing defects in the soft"are, and for establishing that the soft"are has

attained a specific degree of quality "ith respected to selected attributes. It is an

investigation "hich is conducted to provide sta!eholders "ith information aboutthe quality of the product or service under test. Testing can also provide an

42


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ob7ective, independent vie" of the soft"are to allo" the business to appreciate and

understand the ris!s of the soft"are implementation.

Testing is more than 7ust debugging. The purpose of testing can be quality

assurance, verification, and validation, or reliability estimation. Testing can be used

as a generic metric as "ell. orrectness testing and reliability are the t"o ma7or

areas of testing. 5oft"are testing is a tradeAoff bet"een budget, time and quality.

oor quality soft"are that can cause loss of life or property is no longer acceptable

to society. %ailures can result in catastrophic losses. onditions demand soft"are

development staffs "ith interest and training areas of soft"are product and process

quality. Highly qualified staff ensures that soft"are products are built on time,

"ithin budget, and are of the highest quality "ith respect to attributes such as

reliability, correctness, usability and the ability to meet all user requirements.

Testing helps in verifying and validating the soft"are to see if it is "or!ing as it is

intended to be "or!ing. Test techniques include, but are not limited to, the process

of executing a program or application "ith the intent of finding soft"are bugs

(errors or other defects).

5oft"are must definitely be tested before it is delivered to the users as

untested soft"are may contain faults, errors or failures. Hence, it is seen that

testing is an essential part of the process of developing soft"are or a soft"are

pro7ect. The necessity to test the soft"are and hence, the necessity to test the

pro7ect (need for testing), the taxonomy of testing, the types of testing, the levels of

testing and the test case design for the pro7ect are elucidated in this chapter.

8.1 N""D OF T",TIN$

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Ghen something is done, "e need to !no" "hy it is being done in order to

perform the process in a thorough and satisfactory manner. %rom this it is inferred

that !no"ing "hat testing is and does it enough- the need for testing also should be

!no"n. primary purpose of testing is to detect soft"are failures so that defects

may be discovered and corrected. Testing cannot establish that a product functions

properly under all conditions but can only establish that it does not function

properly under specific conditions. The scope of soft"are testing often includes

examination of code as "ell as execution of that code in various environments and

conditions as "ell as examining the aspects of code such as "hether it does "hat it

is supposed to do and "hether it does "hat it needs to do. The user "ill appreciate

it if a system is tested before it is delivered. It is good practice to include testing as

part of the development process in order to minimi#e the efforts prior toimplementation.

It is for this reason that a user representative is recommended to be on the

development team E they can test the system at its various stages of development.

This also assists "ith user training.

Ghile testing, care must be ta!en to not fall into the trap of re"riting large

parts of the system unnecessarily or even adding ne" coding. This comes about

"hen it is obvious that not of the required functionality has been implemented. It

can also happen "hen the user introduces ne" functionality "hich they had

omitted from the original specifications. Testing should, therefore, simply be

ensuring that the systems meets its original specifications and accurately performs

to that specification. Testing is not an easy phase of system development and

should not be treated lightly. 5ome organi#ations employ staff specifically to carry

44


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out the testing of the products prior to release to the user. *uring this outcome it is

required to8

9. Implement a test plan using a defined strategy8 Baintain test

documentation recording both the expected results of the test data and the actual

results. The ban! of test data should be sufficient to thoroughly test the

implemented solution in scope and range.

. $valuate the results of test runs8 mend coding as necessary8 "here there

are discrepancies bet"een the expected results and the actual results, the

application and documentation must be amended and corrected accordingly.

2. Testing is usually performed for the follo"ing purposes8

To im*ro'e @ualit)

s computers and soft"are are used in critical applications, the outcome of a

bug can be severe. &ugs can cause huge losses. &ugs in the critical systems have

caused airplane crashes, allo"ed space shuttle missions to go a"ry, halted trading

on the stoc! mar!et, and "orse. &ugs can !ill. &ugs can cause disasters. Quality is

the conformance of the specified design requirement. &eing correct, the minimum

requirement of quality, means performing as required under specified conditions.

*ebugging, a narro" vie" of soft"are testing, is performed heavily to find out

design defects by the programmer. The imperfection of human nature ma!es it

almost impossible to ma!e a moderately complex programs correct the first time.

%inding problems and get them fixed, is the purpose of debugging in the

programming phase.

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For 'erification an! 'ali!ation 6(A(7

nother important purpose of testing is verification and validation (1W1).

Testing can serve as metrics. It is heavily used as a tool in the 1W1 process.

Testers can ma!e claims based on interpretations of the testing results, "hich either

the product "or!s under certain situations, or it does not "or!. Ge can also

compare the quality among the different products under the same specifications,

based on results from the same test. Ge cannot test quality directly, but "e can test

related factors to ma!e quality visible. Quality has three sets of factors E

functionality, engineering and adaptability. These three sets of factors can be

thought of as dimensions in the soft"are quality space. $ach dimension may be

bro!en do"n into its component factors and considerations at successively lo"er

level of detail.

@ood testing provides measures for all relevant factors. The importance of

any particular factor varies from application to application. ny system "here

human lives are at sta!e must place an extreme emphasis on reliability and

integrity. In the typical business system usability and maintainability are the !ey

factors, "hile for a oneAtime scientific program neither may be significant. +ur

testing, to be fully effective, must be fully effective, must be geared to measuring

each relevant factor and thus forcing quality to become tangible and visible.

For reliabilit) etimation

5oft"are reliability has important relations "ith many aspects of the

soft"are, including the structure, and the amount of testing it has been sub7ected

to.

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8.# Teting Objecti'e

The main set of testing ob7ectives is

1. Testing is a process of executing a program "ith the intent of finding an

error.

#. good test case is one that has a high probability of finding an

undiscovered error.

%. successful test is one that uncovers an asAyetAundiscovered error.

8.% T)*e of Teting

4#


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Fig 8.1 Teting t)*e

hite -oB Teting

Ghite &ox Testing is a testing in "hich the soft"are tester has !no"ledge of

the inner "or!ings, structure and language of the soft"are, or at least its purpose. It

is used to test areas that cannot be reached from a blac! box level. To design test

cases using this inner structure of the soft"are !no"ledge of that structure. The

code or a suitable pseudo code li!e representation must be available. These testing

4$


49/79

methods are especially useful for revealing design and code based control, logic

and sequence defects, initiali#ation defects and data flo" defects.

ma7or Ghite box testing technique is Co!e Co'erage anal)i. ode

overage analysis, eliminates gaps in a test case suite. It identifies areas of a

program that are not exercised by a set of test cases. +nce gaps are identified, youcreate test cases to verify untested parts of code, thereby increase the quality of the

soft"are product. There are automated tools available to perform ode coverage

analysis. &elo" are a fe" coverage analysis techniques

,tatement Co'erage8 This technique requires e'er) *oible tatement in the

co!e to be tete! at leat once during the testing process

-ranch Co'erage: Thi technique chec> e'er) *oible *ath (ifAelse and other

conditional loops) of a soft"are application.

part from above, there are numerou co'erage t)*e uch a Con!ition

Co'erage Multi*le Con!ition Co'erage +ath Co'erage Function Co'erage

etc. $ach technique has its o"n merits and attempts to test (cover) all parts of

soft"are code. Psing ,tatement an! -ranch co'erage )ou generall) attain ;5?


50/79

a blac! box you cannot LseeM into it. The test provides inputs and responds to

outputs "ithout considering ho" the soft"are "or!s. It exploits specifications to

generate test cases in a methodical "ay to avoid redundancy and to provide better

coverage.

&y applying blac!Abox techniques, "e derive a set of test cases that satisfy

the follo"ing criteria8 (9) test cases that reduce, by a count that is greater than one,

the number of additional test cases that must be designed to achieve reasonable

testing and () test cases that tell us something about the presence or absence of

classes of errors, rather than an error associated only "ith the specific test at hand.

$ra*h?-ae! Teting:

The first step in blac!Abox testing is to understand the ob7ects; that are

modeled in soft"are and the relationships that connect these ob7ects. +nce this has

been accomplished, the next step is to define a series of tests that verify Lall ob7ects

have the expected relationship to one another S&$I>:.M 5tated in another "ay,

soft"are testing begins by creating a graph of important ob7ects and their

relationships and then devising a series of tests that "ill cover the graph so that

each ob7ect and relationship is exercised and errors are uncovered.

"@ui'alence +artitioning:

It is a blac!Abox testing method that divides the input domain of a program

into classes of data from "hich test cases can be derived. n ideal test case singleA

handedly uncovers a class of errors (e.g., incorrect processing of all character data)

that might other"ise require many cases to be executed before the general error is

observed. $quivalence partitioning strives to define a test case that uncovers

5&


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classes of errors, thereby reducing the total number of test cases that must be

developed.

-oun!ar) (alue 2nal)i:

%or reasons that are not completely clear, a greater number of errors tend to

occur at the boundaries of the input domain rather than in the Dcenter.D It is for this

reason that boundary value analysis (&1) has been developed as a testing

technique. &oundary value analysis leads to a selection of test cases that exercise

bounding values. &oundary value analysis is a test case design technique that

complements equivalence partitioning. ather than selecting any element of an

equivalence class, &1 leads to the selection of test cases at the DedgesD of the

class. ather than focusing solely on input conditions, &1 derives test cases from

the output domain as "ell.

Com*arion Teting:

Ghen multiple implementations of the same specification have been

produced, test cases designed using other blac!Abox techniques (e.g., equivalence partitioning) are provided as input to each version of the soft"are. If the output

from each version is the same, it is assumed that all implementations are correct. If

the output is different, each of the applications is investigated to determine if a

defect in one or more versions is responsible for the difference. In most cases, the

comparison of outputs can be performed by an automated tool. omparison testing

is not foolproof. If the specification from "hich all versions have been developed

is in error, all versions "ill li!ely reflect the error. In addition, if each of the

51


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independent versions produces identical but incorrect results, condition testing "ill

fail to detect the error.

Unit Teting

Pnit testing focuses verification effort on the smallest unit of soft"are

design the soft"are component or module. Psing the componentAlevel design

description as a guide, important control paths are tested to uncover errors "ithin

the boundary of the module. The relative complexity of tests and uncovered errors

is limited by the constrained scope established for unit testing. The unit test is

"hiteAbox oriented, and the step can be conducted in parallel for multiple

components.

The module interface is tested to ensure that information properly flo"s into

and out of the program unit under test. The local data structure is examined to

ensure that data stored temporarily maintains its integrity during all steps in an

algorithm's execution. &oundary conditions are tested to ensure that the module

operates properly at boundaries established to limit or restrict processing. ll

independent paths (basis paths) through the control structure are exercised to

ensure that all statements in a module have been executed at least once. nd

finally, all error handling paths are tested.

2cce*tance Teting

cceptance of the system is !ey factor for the success of any system. It is a

critical phase of any pro7ect and requires significant participation by the end user.

It also ensures that the system meets the functional requirements.

52


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The system under consideration is tested for user acceptance by constantly !eeping

in touch "ith prospective system and user at the time of developing and ma!ing

changes "henever required. This is done in regarding to the follo"ing points.

• Input screen design.

• +utput screen design.

Integration Teting

Integration testing is a systematic technique for constructing the program

structure "hile at the same time conducting tests to uncover errors associated "ith

interfacing. The ob7ective is to ta!e unit tested components and build a program

structure that has been dictated by design.. ll components are combined in

advance. The entire program is tested as a "hole. Psually a set of errors is

encountered. orrection is difficult because isolation of causes is complicated by

the vast expanse of the entire program. +nce these errors are corrected, ne" ones

appear and the process continues in a seemingly endless loop.

Teting +roce

aterfall !e'elo*ment mo!el

common practice of soft"are testing is that testing is performed by an

independent group of testers after the functionality is developed, before it is

shipped to the customer. This practice often results in the testing phase being used

as a pro7ect buffer to compensate for pro7ect delays, thereby compromising the

time devoted to testing.

53


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2gile !e'elo*ment mo!el

In contrast, some emerging soft"are disciplines such as extreme

programming and the agile soft"are development movement, adhere to a LtestA

driven soft"are developmentM model. In this process, unit tests are "ritten first, by

the soft"are engineers (often "ith pair programming in the extreme programming

methodology). +f course these tests fail initially- as they are expected to. Then as

code is "ritten it passes incrementally larger portions of the test suites. The test

suites are continuously updated as ne" failure conditions and corner cases are

discovered, and they are integrated "ith any regression tests that are developed.

The ultimate goal of this test process is to achieve continuous integration "here

soft"are updates can be published to the public frequently.

This methodology increases the testing effort done by development, before

reaching any formal testing team. In some other development models, most of the

test execution occurs after the requirements have been defined and the coding

process has been completed.

To*?!o&n an! bottom?u*

&ottom up Testing is an approach to integrated testing "here the lo"est level

components (modules, procedures, and functions) are tested first, then integrated

and used to facilitate the testing of higher level components. fter the integration

testing of lo"er level integrated modules, the next level of modules "ill be formed

and can be used for integration testing. This method also helps to determine the

levels of soft"are developed and ma!es it easier to report testing progress in the

form of a percentage.

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(ali!ation Teting

5oft"are validation is achieved through a series of blac!Abox tests that

demonstrate conformity "ith requirements. test plan outlines the classes of tests

to be conducted and a test procedure defines specific test cases that "ill be used to

demonstrate conformity "ith requirements. &oth the plan and procedure are

designed to ensure that all functional requirements are satisfied, all behavioral

characteristics are achieved, all performance requirements are attained.

Functional Teting

%unctional testing provide systematic demonstrations that functions testedare available as specified by the business and technical requirements, system

documentation, and user manuals.

%unctional testing is centered on the follo"ing items8

1alid Input 8 identified classes of valid input must be accepted.

Invalid Input 8 identified classes of invalid input must be re7ected.

%unctions 8 identified functions must be exercised.

+utput 8 identified classes of application outputs must be exercised.

5ystems/rocedures8 interfacing systems or procedures must be invo!ed.

+rgani#ation and preparation of functional tests is focused on requirements,

!ey functions, or special test cases. In addition, systematic coverage pertaining to

identify &usiness process flo"s- data fields, predefined processes, and successive

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processes must be considered for testing. &efore functional testing is complete,

additional tests are identified and the effective value of current tests is determined.

Three types of tests in %unctional test8

• erformance Test

• 5tress Test

• 5tructure Test

+erformance Tet: It determines the amount of execution time spent in various

parts of the unit, program throughput, and response time and device utili#ation bythe program unit.

,tre Tet: It designed to intentionally brea! the unit. @reat deal can be

learned about the strength and limitations of a program by examining the manner

in "hich a programmer in "hich a program unit brea!s.

,tructure! Tet: 5tructure Tests are concerned "ith exercising the internal logic

of a program and traversing particular execution paths. The "ay in "hich GhiteA

&ox test strategy "as employed to ensure that the test cases could guarantee that

all independent paths "ithin a module have been exercised at least once.

• $xercise all logical decisions on their true or false sides.

• $xecute all loops at their boundaries and "ithin their operational bounds.

• $xercise internal data structures to assure their validity.

• hec!ing attributes for their correctness.

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8./ Teting In +articular

5ystem testing of soft"are or hard"are is testing conducted on a complete,

integrated system to evaluate the system's compliance "ith its specified

requirements. 5ystem testing falls "ithin the scope of blac! box testing, and as

such, should require no !no"ledge of the inner design of the code or logic.

s a rule, system testing ta!es, as its input, all of the DintegratedD soft"are

components that have successfully passed integration testing and also the soft"are

system itself integrated "ith any applicable hard"are system(s). The purpose of

integration testing is to detect any inconsistencies bet"een the soft"are units that

are integrated together (called assemblages) or bet"een any of the assemblages and

the hard"are. 5ystem testing is a more limited type of testing- it see!s to detect

defects both "ithin the DinterAassemblagesD and also "ithin the system as a "hole.

Teting the &hole )tem

5ystem testing is performed on the entire system in the context of a

%unctional equirement 5pecification(s) (%5) and/or a 5ystem equirement

5pecification (55). 5ystem testing tests not only the design, but also the behavior

and even the believed expectations of the customer. It is also intended to test up to

and beyond the bounds defined in the soft"are/hard"are requirements

specification(s)

,)tem Teting

5ystem testing ensures that the entire integrated soft"are system meets

requirements. It tests a configuration to ensure !no"n and predictable results. n

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example of system testing is the configuration oriented system integration test.

5ystem testing is based on process descriptions and flo"s, emphasi#ing preAdriven

process lin!s and integration points. 5ystem testing of soft"are or hard"are is

testing conducted on a complete, integrated system to evaluate the system's

compliance "ith its specified requirements. 5ystem testing falls "ithin the scope of

blac! box testing, and as such, should require no !no"ledge of the inner design of

the code or logic.

Tet Cae Deign? Integration teting

Input is to record the human video by age "ise.

valid test is to find the H,H1, "aves from recorded human face video and

chec! it "ith $@ reports.

Invalid test is to not able to find the expected output.

Test case T98 Input is belo" 9: year and expected output is to find the equali#ed

range of output.

Test case T8 Input is belo" : year and expected output is to find the equali#ed

range of output.

.

Test case T28 Input is above 3? year and expected output is to find the equali#ed

range of output.

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8.8 Tet Re*ort

roduct 8 &lac! box testing

Table 8.1 Tet cae !eign

5%

Tet ID 2ge limit "B*ecte!

out*ut

+aEFail

T9 ;? ccurate

output

ass

T 9 ccurate

output

ass

T2 < ccurate

output

ass

T3 ?

but changing

the seating

arrangements

6ot exact

output

%ail


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9."+"RIM"NT2 R",UT

BT0& is a highAperformance language for technical computing. It integrates

computation, visuali#ation, and programming in an easyAtoAuse environment "here

problems and solutions are expressed in familiar mathematical notation.

Typical uses include8

Bath and computation

lgorithm development

Bodeling, simulation, and prototyping

*ata analysis, exploration, and visuali#ation

5cientific and engineering graphics

pplication development, including @raphical Pser Interface building

BT0& is an interactive system "hose basic data element is an array that does

not require dimensioning. This allo"s you to solve many technical computing

problems, especially those "ith matrix and vector formulations, in a fraction of the

time it "ould ta!e to "rite a program in a scalar nonAinteractive language such as

or %+T6.

M2T2- ha e'eral a!'antage o'er other metho! or language:

Its basic data element is the matrix. simple integer is considered an

matrix of one ro" and one column. 5everal mathematical operations that "or! on

arrays or matrices are builtAin to the Batlab environment. %or example, crossA

products, dotAproducts, determinants, inverse matrices.

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• 1ectori#ed operations. dding t"o arrays together needs only one command,

instead of a for or "hile loop.

• The graphical output is optimi#ed for interaction. Xou can plot your data

very easily, and then change colors, si#es, scales, etc, by using the graphical

interactive tools.

• BT0&s functionality can be greatly expanded by the addition of toolboxes.

These are sets of specific functions that provided more speciali#ed

functionality.$xample8 $xcel lin! allo"s data to be "ritten in a format recogni#ed

by $xcel, 5tatistics Toolbox allo"s more speciali#ed statistical manipulation of data (nova, &asic %its, etc)

M2T2- ,)tem:

The BT0& system consists of five main parts8

• De'elo*ment "n'ironment. This is the set of tools and facilities that help

you use BT0& functions and files. Bany of these tools are graphical

user interfaces. It includes the BT0& des!top and ommand Gindo", a

command history, and bro"sers for vie"ing help, the "or!space, files, and

the search path.

• The M2T2- Mathematical Function ibrar). This is a vast collection

of computational algorithms ranging from elementary functions li!e sum,

sine, cosine, and complex arithmetic, to more sophisticated functions li!ematrix inverse, matrix eigenvalues, &essel functions, and fast %ourier

transforms.

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• The M2T2- anguage. This is a highAlevel matrix/array language "ith

control flo" statements, functions, data structures, input/output, and ob7ectA

oriented programming features. It allo"s both Dprogramming in the smallD to

9.1 "(2U2TION OF F2C" R"F"CT2NC"

The frame rate "as set 2? fps (frames per second) and a total of >??

frames "ere selected for each heart rate evaluation. The testing data set included

9video clips recorded from the participants.actually the face reflectance is already

measured using HilbertAHuang transform but in this concept only heart rate should

be measured.

Fig 9.1 com*are the -lan!?2ltman *lot for Hilbert?Huang tranform

frame&or>

%or fair comparison "ith the results, the detection range of heart rate is set

bet"een :? and >?. +ur proposed %rame"or! provides more robust evaluation

"ith a smaller degree of deviation. The performance evaluation, the precision for

different ! settings is measured. The highest precision (about =3J) is achieved

"hen k is set at 9??.

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Table 9.1 Com*aring the !ifferent age *eron an! !etermine their heart rate

'alue

IN+UT C2+TURIN$

IM2$"

ROI

,"+"R2TION

OUT+UT

R2N$"6HRRRH(

R7

@$ 8O :?

&PT Y


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normal. The typical respiratory rate for a healthy adult at rest is 9E?

breaths per minute.

. ,CR""N ,HOT,

OUT+UT:

Fig .1 out*ut creen

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Fig .# ,e*erating three ignal6re!bluegreen7

Fig .% Rectif)ing the noie in the three ignal

65


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Fig ./ !etermining the 0ero *ea> in the three ignal an! fin! the

green ignal

Fig .8 Di*la)ing the heart rate

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;. CONCU,ION 2ND FUTUR" "NCH2NC"M"NT

;.1 CONCU,ION

In this pro7ect, "e use a "ebcam to record the human face video

for 9 minutes and convert into .avi format. lready this procedure is done in the

HilbertAHuang Transform method, they supposed to detect only the heart rate from

the separated green signal. &ecause in the green signal the pea! value is nearly

seems to be #ero. $ven though, at that method eliminate the noise but it cannot

loo! as accurate.

5o that "e perform N*$ algorithm for this same face

reflectance procedure. In this technique "e find the ranges for heart rate, heart rate

variability and respiratory rate "hich is more or less same to the result of $@

result. To achieve a robust evaluation, ensemble empirical mode decomposition of

the N*$ algorithm is used to acquire the primary heart rate signal "hile reducing

the effect of ambient light changes. +ur proposed approach is found to outperform

the current state of the art, providing greater measurement accuracy "ith smaller

variance and is sho"n to be feasible in realA"orld environments.

;.# FUTUR" "NH2NC"M"NT

The program "or!s in the hospital by recording the face even

though it ta!es some time to get the result .

6#


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mgCmean(green)-

mbCmean(blue)-

rUsig(i)Cmr-

gUsig(i)Cmg-

bUsig(i)Cmb-

end

figure

subplot(2,9,9)

plot(rUsig,'r'),grid on

subplot(2,9,)

plot(gUsig,'g'),grid onsubplot(2,9,2)

plot(bUsig,'b'),grid on

srCstd(rUsig)-

sgCstd(gUsig)-

sbCstd(bUsig)-

meanrCmean(rUsig)-

meangCmean(gUsig)-

meanbCmean(bUsig)-

detrUrCdetrend(rUsig)./sr-

detrUgCdetrend(gUsig)./sg-

detrUbCdetrend(bUsig)./sb-

figure-

subplot(2,9,9)

plot(detrUr,'r'),grid on

subplot(2,9,)

6%


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plot(detrUg,'g'),grid on

subplot(2,9,2)

plot(detrUb,'b'),grid on

combUsigCSdetrUr-detrUg-detrUb-

&CNade(combUsig)-

sourceUsigC&VcombUsig-

gsourceCsourceUsig(,8)-

avgUfiltCones(9,:)/:-

smoothedUsig C convn(gsource,avgUfilt,'same')-

figure-

subplot(2,9,9)- plot(timestamp,smoothedUsig,'g')-

grid on-

%s C framerate-

6 C 9=-

%c9 C ?.3-

%c C 3-

flag C 'scale'-

"in C hamming(69)-

b C fir9(6, S%c9 %c/(%s/), 'bandpass', "in, flag)-

bandpassCconvn(smoothedUsig,b,'same')-

subplot(2,9,)

plot(timestamp,bandpass),grid on-

xxC?8?.?


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interpolateCspline(xx,bandpass,sampledata)-

subplot(2,9,2)

plot(interpolate),grid on-

Sp!s,locCfindpea!s(interpolate,'minpea!distance',9??)-

hold on

plot(loc,p!s,'Vr')-

hold off

temp9CS? loc-

tempCSloc ?-

tempCtempAtemp9-

ibiCtemp(9,98si#e(loc,))/:;-timeibiCloc/:;-

ibisignalCdetrend(ibi)-

figure,subplot(2,9,9)

plot(timeibi,ibisignal,'AAVb'),grid on

Sf,xx,prob C lomb(timeibi,ibisignal,3,9)-

Spsdpea!,psdlocCfindpea!s(xx)-

Spea!value,indCmax(psdpea!)-

fpea!Cf(psdloc(ind))-

subplot(2,9,)

plot(f,xx,'b'),grid on-

hold on

plot(fpea!,pea!value,'Vr')

hold off

respUrateC;?Vfpea!

heartUrateC;?/mean(ibi)

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lomb.m

function Sf,,prob C lomb(t,h,ofac,hifac)

hCh'-tCt'-

6 C length(h)-

T C max(t) A min(t)-

mu C mean(h)-

s C var(h)-

f C (9/(TVofac)89/(TVofac)8hifacV6/(VT)).'-

" C VpiVf-

tau C atan(sum(sin(V"Vt.'),),sum(cos(V"Vt.'),))./(V")-

cterm C cos("Vt.' A repmat(".Vtau,9,length(t)))-sterm C sin("Vt.' A repmat(".Vtau,9,length(t)))-

C (sum(ctermVdiag(hAmu),).Z./sum(cterm.Z,) ...

sum(stermVdiag(hAmu),).Z./sum(sterm.Z,))/(Vs)-

BCVlength(f)/ofac-

prob C BVexp(A)-

inds C prob O ?.?9-

prob(inds) C 9A(9Aexp(A(inds))).ZB-

Ga!er.m

function & C Nade(K,m)

verbose C ? -

Sn,T C si#e(K)-

if narginCC9, mCn - end-

if mOn , fprintf('7ade AO *o not as! more sources than sensors here[[[\n'),

return,end

if verbose, fprintf('7ade AO 0oo!ing for Jd sources\n',m)- end -

#2


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if verbose, fprintf('7ade AO emoving the mean value\n')- end

K C K A mean(K')' V ones(9,T)-

if verbose, fprintf('7ade AO Ghitening the data\n')- end

SP,* C eig((KVK')/T)-

Spuiss,! C sort(diag(*))-

rangeG C nAm98n-

scales C sqrt(puiss(rangeG)) -

G C diag(9./scales) V P(98n,!(rangeG))'-

iG C P(98n,!(rangeG)) V diag(scales)-

K C GVK-

if verbose, fprintf('7ade AO $stimating cumulant matrices\n')- enddimsymm C (mV(m9))/-

nbcm C dimsymm -

B C #eros(m,mVnbcm)-

C eye(m)-

Qi7 C #eros(m)-

Kim C #eros(9,m)-

K7m C #eros(9,m)-

scale C ones(m,9)/T -

ange C 98m -

for im C 98m

Kim C K(im,8) -

Qi7 C ((scaleV (Kim.VKim)) .V K ) V K' A A V (8,im)V(8,im)' -

B(8,ange) C Qi7 -

ange C ange m -

for 7m C 98imA9

#3


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K7m C K(7m,8) -

Qi7 C ((scale V (Kim.VK7m) ) .VK ) V K' A (8,im)V(8,7m)' A

(8,7m)V(8,im)' -

B(8,ange) C sqrt()VQi7 -

ange C ange m -

end -

end-

JJ

if 9,

if verbose, fprintf('7ade AO Initiali#ation of the diagonali#ation\n')- end

S1,* C eig(B(8,98m))-

for uC98m8mVnbcm,B(8,u8umA9) C B(8,u8umA9)V1 -

end-

B C 1'VB-

else,

1 C eye(m) -

end-

seuil C 9/sqrt(T)/9??-

encore C 9-

s"eepC ?-

updates C ?-

g C #eros(,nbcm)-

gg C #eros(,)-

@ C #eros(,)-

c C ? -

s C ? -

#4


75/79

ton C ? -

toff C ? -

theta C ? -

JJ Noint diagonali#ation

if verbose, fprintf('7ade AO ontrast optimi#ation by 7oint diagonali#ation\n')- end

"hile encore, encoreC?-

if verbose, fprintf('7ade AO 5"eep ]Jd\n',s"eep)- end

s"eepCs"eep9-

for pC98mA9,

for qCp98m,

Ip C p8m8mVnbcm -

Iq C q8m8mVnbcm - g C S B(p,Ip)AB(q,Iq) - B(p,Iq)B(q,Ip) -

gg C gVg'-

ton C gg(9,9)Agg(,)-

toff C gg(9,)gg(,9)-

theta C ?.:Vatan( toff , tonsqrt(tonVtontoffVtoff) )-

if abs(theta) O seuil, encore C 9 -

updates C updates 9-

c C cos(theta)-

s C sin(theta)-

@ C S c As - s c -

pair C Sp-q -

1(8,pair) C 1(8,pair)V@ -

B(pair,8) C @' V B(pair,8) -

B(8,SIp Iq) C S cVB(8,Ip)sVB(8,Iq) AsVB(8,Ip)

cVB(8,Iq) -

#5


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end

end

end

end

if verbose, fprintf('7ade AO Total of Jd @ivens rotations\n',updates)- end

& C 1'VG -

if verbose, fprintf('7ade AO 5orting the components\n',updates)- end

C iGV1 -

Svars,!eys C sort(sum(.V)) -

& C &(!eys,8)-

& C &(m8A989,8) -

if verbose, fprintf('7ade AO %ixing the signs\n',updates)- end b C &(8,9) -

signs C sign(sign(b)?.9) -

& C diag(signs)V& -

return -

b*hamming.m

function Hd C bphamming

%s C 93.992-

6 C 9=-

%c9 C ?.


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S; aolo Belillo, et.al,M Heart ate 1ariability and renal organ damage in

hypertensive patientsM, International onference of the I$$$ $B&5,?9.

S>9.

#$


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S9; . 6. &elhumeur, N. . Hespanha, and *. 4riegman, L$igenfaces vs.

%isherfaces8 ecognition using class specific linear pro7ection,M IEEE Trans.

Pattern Anal. Mach. Intell., vol. 9>, no. >=.

S9= . 5. @eorghiades, *. 4riegman, and . 6. &elhumeur, L%rom fe" to many8

@enerative models for recognition under variable pose and illumination,M in Proc.

IEEE PAMI , ???.

S9> T. i!linAaviv and . 5hashua, LThe quotient image8 lassAbased reArendering and recognition "ith varying illumination conditions,M IEEE Trans.

Pattern Anal. Mach. Intell.,??9

S? . 5. @eorghiades, *. 4riegman, and . 6. &elhumeur, LIllumination cones

for recognition under variable lighting8 %aces,M in Proc. IEEE Con". C!P#, 9>>=.

S9 1. &lan#, 5. omdhani, and T. 1etter, L%ace identification across different

poses and illuminations "ith a 2* morphable model,M in Proc. IEEE Con". Atom.

$ace %estre #ecognit., ??.

S . @ross and 1. &ra7ovic, Ln image preprocessing algorithm for

illumination invariant face recognition,M in Proc. &th Int. Con". Adio'!ideo'(ased

(iometric Person Athentication )A!(PA*, ??2.

S2 ^. Gu and 6. $. Huang, L$nsemble empirical mode decomposition8 noiseA

assisted data analysis method,M Centre Ocean'+and'Atmos. Std., Tech. #ep. Ser.,

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