EEWeb Pulse - Issue 3, 2011

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PULSEEEWeb.c

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July 19, 2

Ben CoughlanUnmanned Aircraft

Electrical Engineering Commun

EEWeb

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TABLE OF C ONTENTS

Ben Coughlan 4PHD SCHOLAR, AUSTRALIAN NATIONAL UNIVERSITYInterview with Ben Coughlan, consultant/engineer, working on unmanned aircraft.

Unmanned Aerial Vehicles 7BY BEN COUGHLAN

A look into the study of aircraft behavior.

Accuracy of the Computational 9

Experiements called Time DomainSimulationBY MICHAEL STEINBERGER WITH SISOFT

Advantages of Packaging a Proximity

Sensor with an Ambient Light SensorBY TAMARA SCHMITZ WITH INTERSIL

RTZ - Return to Zero Comic 17

Consumer devices like cell phones are using more and more sensors to save power and

enhance our interaction with them. It is a natural question for cell phone manufacturers to ask if

any of these sensors can be co-packaged to save power, space, and cost.

Time domain simulations of high speed serial channels are really computational experiments

rather than mathematical evaluations. They have confidence limits just like any physical

experiment, and users should determine what those confidence limits are.

14

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INTERVIEW

Ben CoughlanUnmanned Aerial VehiclesHow did you originally get into

electrical engineering and

electronics?

My interest in electronics can

go back as far as playing with

Funway into Electronics kits from

Dick Smith. My background since

then has been mostly software.I completed my Bachelors

degree in Software Engineering

at the Australian National

University in 2009 while working

at Codarra Advanced Systems.

After getting a taste for embedded

software development on a few

projects, I jumped at the chance to

return to university to complete a

PhD focused on Unmanned Aerial

Vehicles. So far this has takenme well outside of my software

comfort zone involving a lot of

electronic and mechanical design.

My interest in

electronics can go

back as far as playingwith Funway into

Electronics kits

from Dick Smith.

How do you nd working inother disciplines given your

software background?

I touched on a number of other

disciplines during my degree

including basic electronics and

mechanics. The things I find

most useful are the abstract

concepts required for systems

engineering. These concepts are

very familiar after learning about

software architecture and design.

The thing I find the

most useful is the

abstract concepts

required for systems

engineering.

Ben Coughlan - PhD Student, The Australian National University

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INTERVIEW

When I approach a new discipline,

its easy to map the required

system knowledge. Its then just

a matter of learning the specifics

of design and implementation

for what Im trying to build.

What are your favorite

hardware tools that you use?

The tool I use most often would

easily be my callipers. Simple

yes, but whenever I need to build

a model, which is pretty often,

my callipers are invaluable.

I should probably also mention my

cast-iron frying pan. Its the easiestway for me to reflow a board with

surface mount components and

it makes pretty great pancakes.

What are your favorite

software tools that you use?

I think my two favorite pieces of

software would be Altium Designer

and Solid Works. Between these

two products I can design and

model just about everything Iwant to build. Being able to create

virtual prototypes is invaluable

when money for physical

prototypes is hard to come by.

What is on your bookshelf?

There are a lot of textbooks.

The two most relevant/recent

additions areFeedback Control

of Dynamic Systems by Franklin

Powell andProbabilistic Roboticsby Thrun, Burgard, and Fox.

On the fiction side Ive been

enjoying theBook of the New

Sun series on audio book. At the

moment Im listening to Catch-22.

Do you have any tricks

up your sleeve?

Nothing specific. My usual

approach always involves doing

things the hard way, or from scratchmyself. Often I learn why I shouldnt

be doing it myself from scratch

but it does leave me with a better

understanding of how something

works. As the quote goes: Aim

for the moon; even if you miss

youll land among the stars.

It always helps to surround yourself

with people that know things.

Im lucky to have experienced

colleagues that can easily answer

all my silly questions. Otherwise I

can always turn to online forums.

Its important to involve yourself

and your work with the world.

Do you have any note-worthy

engineering experiences?

My most noteworthy

accomplishment would be

an award for innovation my

team and I won in 2009 at the

Australian National iAwards for a

software framework supporting

the development of robotic

applications on Linux platforms.

The Linux Robotics Framework

was my final year project for my

Bachelors degree. I managed

a team of five other students

to produce the framework for

our sponsor Nias Digital. Theframework was intended to

provide a collection of software

components and accompanying

design concepts to simplify the

development of robots running

Linux. This included a hardware

abstraction layer with drivers for

a few interface devices like the

Pololu TReX motor controllers and

serial servo controllers, as well

as some higher level functions

like steering, throttle, and a

controller for a 3 DOF arm.

We built a robotic vehicle named

Buzz as a demonstration for our

project. Starting with a 4WD RC

truck, we constructed a chassis

to mount the extra hardware we

wanted. This included a pan/tilt

CMOS camera, a 3DOF arm with

a gripper, various controller boards

and transceivers for 2.4GHz Wi-Fi

and video. The main processor

was a 32bit AVR on an AtmtelNGW-100. This was a conveniently

sized, low-powered board that our

sponsor was using at the time.

More recently, the first prototype

of Asity, the avionics board Ive

developed, came off of the

frying pan and actually worked

on the first try. It being my first

significant electronic design,

I was pretty happy with this.

What are you currently

working on?

My PhD is investigating

energy usage in unmanned

aerial vehicles. The goal is

to monitor energy levels and

consumption onboard the aircraft

in real time and try to develop

behaviors that optimize these.

Including solar and wind energy,I hope this will lead to extreme-

endurance aircraft that maintain

the capabilities required in

the growing UAV industry.

Can you tell us more about

your UAV research ?

My research is investigating the

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INTERVIEW

energy usage of onboard UAVs.

This includes monitoring the

total energy stored in the system,

including the aircrafts velocity

and altitude, in addition to the

battery. The goal is to develop

flight behaviors that optimize

energy usage in reaction to air

conditions and energy inputs

(e.g., solar). In suitable aircraft

or use cases, this will hopefully

increase the endurance of the

system. Simply put, I would like

to show that the most efficient

behavior for an aircraft is not

necessarily straight and level.

This is a highly experimental

project so I have had the

opportunity to develop many

custom hardware components.

The main avionics is a custom

board Ive named Asity. This

is a processor, inertial sensor

pack, and radio in a small

package to fit in the slim fuselage

of the gliders I work with.

The main processor is actually anFPGA to allow for high integrity,

interrupt-free, and flexible design

of the avionics firmware. FPGAs

are notoriously power hungry, so

I have used the Actel ProAsic3

series of chip. Being flashed

based, in contrast to their SRAM

based competitors, they have a

much lower current draw and dont

require any configuration memory.

The current Asity prototype has1M system gates; time will tell

if this is sufficient. I am avoiding

soft-core processors for as long as

I can, and I believe I can build a

complete avionics system in HDL.

While Im developing an

experimentation platform, Ive

decided to include capabilities

for the Outback Rescue

Challenge. I hope to compete

in 2012 with my 4 meter glider.

What has been your

favorite project?

My current one, hands down.

I was into model aircraft as a

kid and now I get to play with

them for a living. Given this is a

research project, I enjoy a lot of

freedom with what I work on.

What direction do you see

your business heading

in the next few years?

I still have a few years in the

comfort of academia. Between

now and then I hope to develop

something that can support

further research. My main goal

is just to keep working on the

same or similar projects.

What challenges do you

foresee in our industry?

The biggest challenge in theUAV industry specifically is

mostly legislative, although this

is driven by quite reasonable,

technical short-comings.

Aircraft are not currently

permitted to fly truly unmanned

without constant supervision

from someone who can take

control. This does limit the range

and utility of such aircraft.

The challenge for engineers in

this field is to develop systems

that are safe, reliable, and

capable of sensing and reacting

to abnormal situations.

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Mike SteinbergerLead Architect

Serial Channel ProductsAccuracy of theComputational

Experiments Called

Time Domain

SimulationsEvaluation vs. Experimentation

Were used to thinking of resultsthat come from computers as being

completely accurate and much

more precise than we need. Many

times, this leads to a false sense of

security due to any of three possible

problems:

1. Wrong Computation: The

computation performed wasnt

the correct one to begin with. For

example, the boundary conditions

imposed were unrealistic (3Dfield solver users beware) or the

equations chosen did not apply to

the problem at hand.

2. Numerical Inaccuracy: The

algorithms used to solve the

equations were not perfect (See [1]

for the definitive practical treatment

of this subject).

3. Incomplete Coverage: Not all

relevant cases were considered.

If none of these problems occurred,

then we could call that computation

an evaluation. Otherwise, we

should consider the computation

to be a computational experiment

subject to the same uncertainties

as a physical experiment. That is,

the computational experiment can

have sources of both random and

systematic error, and there are

confidence limits which apply to the

results. One should be able to draw

the error bars around the results and

account for these error bars when

making engineering decisions.

This article considers time

domain simulations of high speed

serial channels as computational

experiments, and explores the

confidence limits that should be

applied to such experiments. For the

experiments considered here, the

most critical problem is incomplete

coverage. Serial channelperformance is strongly affected

by intersymbol interference and, as

demonstrated in [2], all messages

of length 64 or longer should be

included in the experiment in order

to obtain consistently accurate

results. Suffice it to say that no time

domain simulation will ever come

close to the more than 10^19 bits

required.

While the results shown in this

article may be of some direct

value, the goal is to demonstrate

some techniques that can be used

to determine confidence limits for

time domain simulations in general.

While the results shown in this

article may be of some direct

value, the goal is to demonstrate

some techniques that can be used

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TECHNICAL ARTICLE

to determine confidence limits for

time domain simulations in general.

Experimental Approach

The channel simulated was 5 Gb/sdata transmitted over 1.5m of PC

board trace in a low loss dielectric.

There was no equalization at the

transmitter and linear equalization

at the receiver.

The experimental approach taken

was to vary the data pattern used in

the time domain simulation as well

as the length of the time domain

simulation. To make sure that the

data patterns were independent,they were drawn from different

starting positions in the same

263-1 linear feedback shift register

(LFSR) pattern. This LFSR pattern

has the advantage that it is much

longer than any of the time domain

simulations in the experiment. If a

data pattern were to be repeated

over the course of a simulation, then

the data patterns would no longer

be independent.

Rather than choosing different

seeds for the same LFSR pattern,

we could have chosen different

LFSR patterns. If the different data

patterns were long enough to

produce a representative sample

of the intersymbol interference, that

would have been a valid choice.

An alternating 1/0 pattern or a 27-1

LFSR would not have provided an

adequate sample of the intersymbol

interference, however.

This approach was applied to

simulations of three different

lengths: one million bits, ten million

bits, and one hundred millionbits. These results can be used to

estimate how much the confidence

limits can be improved by running

longer time domain simulations.

Statistical analysis was also applied

to the same channel. Statistical

analysis is entirely different from

time domain simulation in that

it computes the statistics of the

eye diagram directly rather than

compiling them from samples ofa time domain waveform. This

computation has the advantage that

it directly accounts for a statistically

significant sample of the intersymbol

interference, and the disadvantage

that it is only rigorously applicable

to linear, time invariant channels.

Since the channel used in this

study was truly linear and time

invariant, this statistical analysis can

be considered to be an evaluation

rather than a computational

experiment, and its results are what

the average of the time domain

simulation results should be. For the

purposes of this study, the statistical

analysis results are the right

answer.

Results

A performance analysis of a high

speed serial link produces a lot

of results offering many different

ways to look at the behavior of thechannel. It is not the goal of this

article to explore the many ways

in which channel performance

can be presented. Rather, the goal

is to show how the results of time

domain simulations vary. We will

therefore use three different outputs

as examples:

1. Inner eye contours: The shape

of the inside of the eye diagram

at a particular probability. The

probabilities shown are 10-3, 10-6,

10-9, and 10-12.

2. Bathtub curves: Plots of the

probability of error as a function of

sampling time. These curves are

called bathtub curves because

they often resemble the cross

section of a bathtub.

3. Eye width: The width of the open

portion of the eye diagram. This

value loosely correlates with timing

margin.

Figure 1 is an example eye diagram

for the channel. All the eye diagrams

in this study look very similar to

each other.

The following figures show the

inner eye contours for the three

different lengths of time domain

simulation. Note that as the length

of the time domain simulation

progresses from one million bits

to one hundred million bits, the

10-12 contour becomes clearly

distinct from the 10^-6 contour,

and its almost possible to discern

the 10^-9 contour. Notice also that

the lower probability contours have

Table 1: The data patterns used.

Data Pattern Denitions

Pattern Number Pattern Seed

1 2^63-1 LFSR 8191

2 2^63-1 LFSR 8291

3 2^63-1 LFSR 8391

4 2^63-1 LFSR 8491

5 2^63-1 LFSR 8591

6 2^63-1 LFSR 8691

7 2^63-1 LFSR 8791

8 2^63-1 LFSR 8891

9 2^63-1 LFSR 8991

10 2^63-1 LFSR 8091

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TECHNICAL ARTICLE

Volts(v)

Probability

100.0

Persistent Eye Diagram1.5m low loss PCB trace

Time ( s)

-50.0 0.0 50.0 100.0

0.60

0.40

0.20

0.0

-0.20

-0.40

-0.60

_ 2.4E-2

_ 7.1E-3

_ 6E-4

_ 3.7E-7

_ 4.8E-12

Volts(mV)

Probability

100.0

Eye Diagram ContoursOne million bit simulations with ten different data patterns

Time ( s)

-50.0 0.0 50.0 100.0

150.0

100.0

50.0

0.0

-50.0

-100.00

-150.00

_ 1E-3

_ 1E-4

_ 1E-6

_ 1.1E-12

_ 1E-12

Figure 1: Example eye diagram.

Figure 2: Inner eye contours for one million bit simulations.

Volts(mV)

Probability

100.0

Eye Diagram ContoursTen million bit simulations with ten different data patterns

Time ( s)

-50.0 0.0 50.0 100.0

150.0

100.0

50.0

0.0

-50.0

-100.00

-150.00

_ 1E-3

_ 1E-4

_ 1E-6

_ 1.1E-12

_ 1E-12

Volts(v)

Probability

100.0

Inner Eye Diagram ContoursOne hundred million bit simulations with ten different data patterns

Time ( s)

-50.0 0.0 50.0

+Sensitivity 25 0mV

Sensitivity 25 0mV

100.0

150.0

100.0

50.0

0.0

-50.0

-100.00

-150.00

_ 1E-3

_ 1E-4

_ 1E-6

_ 1.1E-12

_ 1E-12

considerably more variance than

the higher probability contours.

The following figures show the

bathtub curves for the same sets of

simulations, along with the bathtub

curve for the statistical analysis

(shown in red) and the clock PDFs

for the time domain simulations.

Note that this way of viewing the

data makes it much easier to see

the variation due to the different

data patterns.

Figure 8 is an expanded view of

Figure 5, Bathtub curves for one

million bit simulations and statistical

analysis, on page 5, showing how

the bathtub curves diverge for the

ten different data patterns. Note

that the bathtub curves are nearly

the same for the higher error

probabilities, but then diverge for

the lower probabilities.

Finally, Table 2 summarizes the

mean and standard deviation of

the eye width for the time domain

simulations and statistical analysis.

Note that as the time domain

simulation gets longer, the eye width

approaches the statistical analysis

result. Note also that increasing

Figure 3: Inner eye contours for ten million bit simulations.

Figure 4: Inner eye contours for one hundred million bit simulations.

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TECHNICAL ARTICLE

Probability

100.0

Bathtub Curve Set

BLACK: Ten million bit time domain simulations using ten different data parameters RED: Statistical Analysis

Time ( s)

-50.0 0.0 50.0 100.0

1E0

1E-2

1E-4

1E-6

1E-81E-10

1E-12

1E-14

1E-16

1E-18

1E-20

Eye Width

x1: (-62.150ps)x2: (65.040ps)dx: 127.20ps

Probability

100.0

Bathtub Curve Set

BLACK: Ten million bit time domain simulations using ten different data parameters RED: Statistical Analysis

Time ( s)

-50.0 0.0 50.0 100.0

1E0

1E-2

1E-4

1E-6

1E-8

1E-10

1E-12

1E-14

1E-16

1E-18

1E-20

Probability

100.0

1E0

1E-2

1E-4

1E-6

1E-8

1E-10

1E-12

1E-14

1E-16

1E-18

1E-20

Bathtub Curve SetBLACK: One hundred million bit time domain simulations uisng ten different data patterns RED: Statistical Analysis

Time ( s)

-50.0 0.0 50.0 100.0

Probability

131.0 132.0 133.0 134.0 135.0 136.0 137.0 138.0 139.0

1E-4

1E-6

1E-8

Expanded Bathtub Curve SetBLACK: One million bit simulations with ten different data patterns RED: Statistical Analysis

Time ( s)

the length of the simulation doesnt

reduce the standard deviation very

much.

Discussion and Conclusions

The accumulation of a persistent

eye from a time domain simulation

is an event counting experiment

very much like counting radioactive

particles with a Gieger counter.

That is, for any particular bin in the

eye diagram, the expected number

of events is equal to the probability

density for that particular bin times

the number of bits simulated.

Also, as in the Gieger counter

experiment, the variance of the even

count is equal to the square root

of the number of events counted

[3]. Therefore, as the number of

expected events goes down, the

variance of the count becomes a

larger percentage of the count.

In the limit that only one event is

expected (for example, along the

inner contour of the eye diagram),

the variance is also one, meaning

that maybe there will be an eventcounted and maybe there wont.

One simple conclusion from the

above reasoning is that the number

of bits in a time domain simulation

should be greater than the reciprocal

of the probability of error. That is, if

the target bit error rate is 10-12, the

time domain simulations should be

at least 10-12 bits long. Thats not an

experiment Im anxious to try.

The more important conclusion,

however, is that there is a statistical

variation associated with the results

of any time domain simulation

of a high speed serial channel.

Its important that the user has

a reasonable estimate of that

variance so that they can use the

Figure 5: Bathtub curves for one million bit simulations and statistical analysis.

Figure 6: Bathtub curves for ten million bit simulations and statistical analysis.

Figure 7: Bathtub curves for one hundred million bit simulations and analysis.

Figure 8: Expanded view of one million bit bathtub curves.

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Ambient LightSensor Tamara SchmitzSenior Principal Applications Engineer

and Global Training Coordinator

Advantages of Packaging a

Proximity

Sensorwith an

Consumer devices like cell

phones are using more andmore sensors to save power

and enhance our interaction with

them. Some of the latest devices

have more than ten sensors. It is

a natural question for cell phone

manufacturers to ask if any of

these sensors can be co-packaged

to save power, space, and cost.

There are many good reasons for

co-packaging a Proximity Sensor

with an Ambient Light Sensor. After

clarifying their roles, their operationsand some simple differences, these

reasons will be discussed.

An ambient light sensor acts like

an eye for a system that measures

the surrounding light. If the device

is indoors, it is the light in a room.

If the device is outside, it could be

bright from sunlight or less in the

shade. The measurement of this

amount of light is made by a lightemitting diode (LED) and quantified

to enable a system to adjust its own

display. If the surrounding light is

bright, the backlight of the display

is run at full power. If the area is

darker, the backlight is reduced,

saving power. Incidentally, this is

also pleasing to the user. Have

you ever tried looking directly into

a bright light in a dark room? Eyes

can tire quite quickly from this

overstimulation, so the dimmingfunction provided by the ambient

light sensor is a welcome addition.

The challenge is that silicon diodes

naturally react to a wide spectrum

of wavelengths. An ambient light

sensor must be designed to mimic

the human eye. This filtering is one

of the quality measurements of the

sensor, especially since the majority

of light sources have energy in the

infrared wavelengths (think about which light sources also give off

heat). To demonstrate this filtering,

see the plot in Figure 1. The

ISL29028A from Intersil provides

the best match of filtering in its

ambient light sensor compared to

the response of the human eye.

A proximity sensor measures an

infrared signal. Instead of the signal

coming from the surrounding area,

the proximity sensor drives an

external infrared LED. The signal

from this LED is directed out above

the proximity sensor. If something

enters the

path of the infrared emission, some

will be reflected back toward the

sensor. There is another LED within

the proximity sensor ready to pick

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TECHNICAL ARTICLE

The next reason is slightly more

subtle: location. Both the proximity

sensor and ambient light sensor

need access to the outside world for

proper function, so their placement

within a system is strongly relatedto their sensitivity and their correct

operation. In some cases where an

ambient light sensor is packaged

alone, it has been placed deeper

within a systembehind a speaker

screen or further down a printed

circuit board from a nearby external

access point. This practice has

pushed ambient light sensors to

be more and more sensitive to

this indirect light. Light intensityis

measured in lux. While sunlightexceeds 100,000 lux, these

ambient light sensors can detect

0.001 lux! Thats a tiny fraction of a

candles light. For a practical array

of the lux levels of various light

sources, see Figure 2.

A final and compelling reason to

house the proximity sensor and

ambient light sensor in the same

package is that it enables quick

and undisturbed communication

between the two. Remember in the

beginning during the explanation

of the operation of the ambient

light sensor that we explained how

its sensor must mimic the human

eye. The human eye does not see

infrared light, so the ambient light

sensor is specifically designed

to remove as much energy in the

Direct Sunlight 100,000 to 130,000 Lux

Full Daylight 10,000 to 20,000 Lux

Cloudy Day 1,000 Lux

Office Lights 300-500 Lux

Candle Light/Dark 10-15 Lux

Lux - Measure of light density within the visible spectrum.

Wavelength (nm)

NormalizedResponse

300

1.2

1.0

0.8

0.6

0.4

0.2

0

-0.2400

IR AndProximitySensing

HumanEyeResponse

AmbientLightSensing

500 600 700 800 900 1000 1100

Figure 1: Human eye response, ambient light sensor spectrum

and proximity sensing spectrum of the ISL9028A

Figure 2: Table of lux values

up this reflected light. This allows

a system to react to someone or

something coming close. A great

example of this is on many cell

phones. The user doesnt want their

cheek to be pressing buttons or

hanging up on a call while they have

the phone up to their ear. It would beconvenient if the phone could turn

off the touch screen whenever the

phone is brought up to a users ear.

This is exactly what the proximity

sensor allows the phone to do.

These two separate systems are

now being offered in one package.

Are semiconductor companies

overexcited by their drive to integrate

more features and systems, or

are there real advantages in co-

packaging the proximity sensor

with the ambient light sensor?

While it is true that they are two

separate systems, they are both

optical systems utilizing a sensing

LED. They collect information from

the outside world, quantify it, and

provide it to the system. Currently,

the system predominantly uses the

information to adjust the backlight

of the display. The information could

just as easily be used to control

more system features in the future.

Of course, it is convenient to save

space, to share supplies, and to

combine power supply bypassing.

The size of the solution is a critical

parameter in many systems,

especially portable ones. The co-

packaging of the proximity sensor

and ambient light sensor is an

enabling step in the development

of more compact, yet feature

enhanced, cell phones.

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TECHNICAL ARTICLE

both the ambient light sensor and

the proximity sensor.

Locating the ambient light sensorand proximity sensor in the same

package provides a number of

advantages. They both enable

power savings through the dimming

or shutdown of the backlight and

interface with the same system

blocks. Co-packaging saves

space and reduces complexity.

Both sensors need access to the

outside of the system and would

likely be located in similar places.

And since interference from theproximity sensor system can

disturb the ambient light sensor,

coordination between these two

features is paramount. It is for all of

these reasons that there is a huge

advantage in co-packaging the

proximity sensor and ambient light

sensor.

About the Author

Tamara Schmitz is a Senior Principal

Applications Engineer and Global

Technical Training Coordinator

at Intersil Corporation, where she

has been employed since 2007.

Tamara holds a BSEE and MSEE

in electrical engineering and a PhD

in RF CMOS Circuit Design from

Stanford University. From 1997 until

2002 she was a lecturer in electrical

engineering at Stanford; from 2002

until 2007, she served as assistant

professor of electrical engineering

at San Jose State University.

infrared wavelengths as possible.

Remember also that the proximity

sensor operates precisely within

the infrared spectrum. Wheneverthe proximity sensor is attempting

the make a measurement, it is

simultaneously sending out infrared

light in the hope of bouncing off of a

nearby object. This infrared energy

could easily swamp the ambient

light sensors input and cause

false positive measurements, an

instance in which the ambient light

sensor measures more light energy

than is actually in the surrounding

area. It is for this reason that it is vital to coordinate the operation

of the ambient light sensor with

the proximity sensor. While this

can be accomplished with a

microcontroller, it is easier and

a much smaller footprint to have

this coordination within a single

package. That one package houses

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