Bruin Innovation & Technology - Volume 1, Spring 2011

V O L U M E 1 | S P R I N G 2 0 1 1 | B R U I N I N N O VAT I O N & T E C H N O LO G Y

B I T

Neil TilleyMS Computer ScienceCo-Founder & Managing EditorBIT Magazine

FIRST PITCHThe Technocrat. Our world is becoming more dependent on the impact that their

innovations have on new and emerging technologies. Yet a great paradox has emerged: the

more dependent our world has become on the technocrat’s innovations, the more myste-

rious the technocrat has become to the masses. Part of that is the inherent complexity of

their creations. Part of that is also the complexity with which they convey how their crea-

tions were actualized. The Bruin campus is a microcosm of this world. Herein lay the need

for Bruin Innovation & Technology Magazine.

How do Bruin engineers and technologists think, and what is their workflow? What

are their specific research projects that will have a significant impact on new and emerg-

ing technologies? How can a “non-expert” be able to interact, assist, and collaborate with

these technocrats on specific projects, initiatives, and ventures? BIT Magazine serves to

answer all of these questions for each customized member of the Bruin Family. BIT is for

you, whether you are in another field, employed at UCLA, considering applying, involved

as an alumnus, or looking to invest in new, developing ideas.

BIT personifies the “True Bruin” values and, more specifically, epitomizes the mis-

sionary pillars of our great university: Access, Affordability, and Opportunity. BIT serves to

provide access to the technological developments happening on our campus in a simple,

comprehensible, and jargon-less manner. BIT has no subscription cost, is available online,

and is free to any reader. Finally, BIT is a specific mechanism providing opportunity for a

student-centric entrepreneurial culture and cross-disciplinary collaboration.

We look forward to BIT being embedded in Bruin culture for many years to come.

Let the dialogue begin.

Jamal A. MadniMS Electrical Engineering, MS Biomedical EngineeringCo-Founder & Managing EditorBIT Magazine

HTTP://ENGINEER.UCLA.EDU

[email protected]

[email protected]

SPONSORS

Vijay Dhir Dean, Henry Samueli School of Engineering & Applied Science

ENGINEERING GRADUATE STUDENTS ASSOCIATION

Asad MadniPresident, Engineering

Alumni Association

SPECIAL THANKS

Frank ChangChair, Electrical Engineering Department

Jiun-Shyan (JS) ChenChair, Civil & Environmental Engineering Department

Karl HolmesManagement Services Officer,

Civil & Environmental Engineering Department

Richard KorfVice Chair, Computer Science Department

Adrienne LavineChair, Mechanical & Aerospace Engineering Department

Janet LinAdministrative Assistant, Electrical Engineering Department

Harold MonbouquetteChair, Chemical & Biomolecular Engineering Department

Jens PalsbergChair, Computer Science Department

STAFF

Managing EditorsJamal Madni

Neil Tilley

Graphic & Layout DesignerMichelle Tu

Contributing WritersJong Hoon Ahnn

Navid AminiRahul Basava

Jeff ChangFang Gong

Derek KulinskiChenni Qian

Tanuj ThapliyalSarah Warren

Wenyao XuZhenkai Zhu

Photography DirectorPeyman Nazarian

Special AdvisorStacey Meeker

Sincerely,

CONTENTSA Brain-Computer Interface to Help the Elderly Reduce the Risk of Falling

The Dean’s Corner

Designing Applications Using Named Data Networking

Entrepreneurial Opportunities from the Other Side of Campus

A Wireless Home Automation System for Childhood Obesity Prevention

TEC: A Training Ground That Brings the Universe to our University

Innovative Design Method Creates New Varieties of Rotary Engines

The Social Network is Everywhere in Engineering—What to Read? With Whom to Work? Where to Publish?

Well...

Bruin Innovation & Technology (ISSN [TBA]), Volume 1, Spring 2011, is published two times a year at the University of California, Los Angeles, UCLA Henry Samueli School of Engineering and Applied Sci-ence, 7256 Boelter Hall, Box 951600, Los Angeles, California, 90095-1600. Annual subscription $10.00 domestic, $15.00 foreign air mail; single copies $5.00. Send subscriptions to UCLA Engineering, Office of External Affairs, 7256 Boelter Hall, Los Angeles, CA 90095. Third class postage paid at Los Angeles, CA. All rights reserved. No part of BIT may be printed without specific permission. Some parts of this publication are available electronically at http://www.engineer.ucla.edu/visitor-links/current-students/bit-magazine. As a publication of UCLA HSSEAS, BIT carries authorized advertisements about offerings of the school. The school assumes no responsibility for opinions of contributors on other subjects. © 2011, University of California, Los Angeles. Published by UCLA Engineering and the Engineering Alumni Association. Telephone 310-206-0678.

Editorial control is retained by the editorial staff of BIT.

Advertising in BIT does not indicate an endorsement by UCLA HSSEAS.

BY WENYAO XU AND FANG GONG

WITH VIJAY DHIR

BY RAHUL RAO BASAVA, DEREK KULINSKI,

CHENNI QIAN, AND ZHENKAI ZHU

BY JEFF CHANG

BY NAVID AMINI

BY TANUJ THAPLIYAL

BY SARAH WARREN

BY JONG HOON AHNN

04

10

14

22

24

34

36

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56

4 VOLUME 1 / SPRING 2011

Falls are reported by one third of all people

65 and older and have become a leading cause of

death. Recently, the number of elderly people with a

fall-induced injury has increased dramatically. The

corresponding health costs for fall-related medical

treatments such as fractures, open wounds, and

head traumas has risen from $20 billion in 2006

to $23 billion in 2010. As such, injuries due to falls

also account for large health care costs and have

become one of the most significant concerns in

geriatry. Therefore, a smart and low-cost solution

of fall prevention for the elderly is urgently sought.

The most promising approach for fall preven-

tion is a wearable assistive system which can detect

and forecast the risk of falling, given the fact that

over 4 million individuals in the United States pay

for health care support with such wearable assis-

tive devices in their daily lives. However, the use of

A BRAIN-COMPUTER INTERFACE TO HELP THE ELDERLY REDUCE

THE RISK OF FALLINGWenyao Xu, Computer Science, and Fang Gong, Electrical Engineering

MANY ELDER PEOPLE OVER 65 ARE AT HIGH RISK OF FALLING BECAUSE OF THEIR GENERAL FRAILTY AND MULTIPLE PATHOLOGIES.

these assistive devices also introduces a negative

influence of an additional cognitive burden for peo-

ple who suffer from cognitive disability because of

age or due to other causes. Fortunately, these limi-

tations are being overcome with the advancement

of sensing technology, especially with emerging,

wearable sensors. The novel sensing technology

can dramatically reduce the footprint of traditional

sensors and thereby integrate them into plastic, pa-

per, and even fabric materials. With wireless com-

munication and computing technology, these wear-

able assistive systems can be used by individuals

comfortably and non-invasively.

Moreover, recent studies empirically prove

that many mental and physiological signals such as

brain waves are also related to falls. They have dis-

covered surprisingly that brain waves, or brain sig-

nals, can offer a better solution for forecasting falls

5BRUIN INNOVATION & TECHNOLOGY

in terms of promptness and accuracy. As shown

in Figure 1, there is a significant anomaly in EEG

signals about 3 seconds prior to the fall [1]. There-

fore, it is possible to assess “fall risk” and activate

fall prevention measures through pre-warning pat-

terns from EEG signals.

Three UCLA graduate students, Wenyao Xu,

Fang Gong, and Ju-Yueh Lee, led by Professor Majid

Sarrafzadeh from the Wireless Health Institute and

Professor Lei He from the Electrical Engineering

Department, have invented a low-cost, lightweight,

wearable device to record the brain waves (so-

called “brain computer interface” or BCI) and fur-

ther interpret them for fall-prevention purposes.

OVERVIEW OF BRAIN COMPUTER INTERFACE SYSTEM

Brain waves were first scientifically observed

in 1912 through the use of Electroencephalograms,

or EEGs, as a kind of bioelectric potential related

to the thoughts of a human. In the 1970s, scientists

at UCLA began their research on a brain computer

interface (BCI). This project received a grant from

the National Science Foundation (NSF) and DARPA.

Through this grant program, several BCI systems

have been built to capture brain waves and inter-

pret them to meaningful thoughts.

In more recent decades, scientists have tried

various methods to decode these mysterious brain

signals. This is known to be a complicated and ex-

pensive task. Traditional BCI systems include two

parts: a signal acquisition module and a data min-

ing module. Conventional scalp BCI systems are

cumbersome and uncomfortable. Dozens of elec-

trodes corresponding to different channels need

to be deployed on the scalp with a conductive gel

or paste. Moreover, the scalp area needs to be pre-

pared ahead of time to remove dead skin cells, all of

FIGURE 1

BRAIN SIGNAL, INCLUDING:

ORIGINAL OUTPUT MEASURE-

MENTS IN A FALL OCCURRENCE

(TOP), EVOLUTION OF MEAN VAL-

UES (MIDDLE), TIME-FREQUENCY

PLOT SHOWING THE POWER

CHANGES DURING A TRANSITION-

TO-FALL STAGE (COLORED).

(COURTESY S. SLOBOUNOV [1])

THEREFORE, IT IS POSSIBLE TO ASSESS “FALL RISK” AND ACTIVATE FALL PREVENTION MEASURES THROUGH PRE-WARNING PATTERNS FROM EEG SIGNALS.


which is obtrusive and inapplicable for daily use in

fall prevention. Furthermore, for the sake of signal

integrity, sampling rates of conventional scalp BCI

systems can be up to 20 kHz in research applica-

tions, and the recorded EEG signal data can be as

large as 1 gigabyte per day. As such, it is highly chal-

lenging to process an EEG data stream for fall pre-

vention in real-time applications, and efficient sig-

nal processing algorithms are critical. Additionally,

conventional BCI systems are obtrusive and not

suitable for daily use, especially considering their

unaffordability for most people.

Fortunately, there has been good progress in

EEG signal acquisition methods. At UCLA, research-

ers have developed a new, wearable BCI system

(also called a “smart headset”) as shown in Figure

2 that addresses the risks associated with falling.

This new system looks like a normal Bluetooth

headset, but it can detect human brain waves and

function as well as a traditional BCI system.

Professor Majid Sarrafzadeh believes that the

new BCI system is easy to use and will not require

a complicated interface that would affect a user’s

normal daily life. Moreover its low cost can make it

accessible to more people, thus yielding many ap-

plications for this device.

The smart headset can be worn on the head

but does not restrict a user’s activity because it

uses wireless communication. After a user puts a

headset on, it acquires the user’s brain waves via

a sensor on the forehead, passing the captured

signals to a data analysis module where the brain

waves can be deciphered for the detection of mean-

ingful signals.

Most importantly, the headset is very light-

weight, weighing in at a mere ten grams. Addition-

ally, it is affordable, costing about $19. By contrast,

a conventional BCI system costs thousands of dol-

lars and weighs hundreds of kilograms. Therefore,

the new BCI device, properly marketed, will be

more accessible and affordable to more people.

SYSTEM FRAMEWORKIn general, the new BCI system is comprised of

two parts, a client end and a server end. The client

end goes with the user for bio-signal sensing, com-

puting, and transmitting. The server end is for min-

ing the received data. Figure 3 shows the stacked-

layer architecture of client and server, where Figure

3(a) is the structure of the client, and Figure 3(b) is

the structure of the server.

Client

The client part consists of four layers: the

sensing layer, actuating layer, middleware layer,

and communication layer. Sensing and actuating

layers are at the bottom level to interact with us-

FIGURE 2

ENGAGED WEARABLE BCI SYSTEM

MOREOVER, ITS LOW COST CAN MAKE IT ACCESSIBLE TO MORE PEOPLE, THUS YIELDING MANY APPLICATIONS FOR THIS DEVICE.


ers directly. Some sensors are included in the sens-

ing layer to acquire miscellaneous user bio-signals.

With the sensed data, middleware implemented

on a microcontroller will perform preliminary

processing such as signal filtering, sorting, com-

pression, and lightweight data analysis for feature

extraction. Some of the expected results are urgent

to report, and they are sent to the actuating layer

to notify users of a risk of falling. Otherwise, the

data will be sent through the communication layer

to the server end for further data mining. The most

common solution for the communication layer is

through Zigbee, Bluetooth, or WiFi, the choice of

which depends highly on the specific applications.

Commented Wenyao Xu, a third-year Ph.D.

student in UCLA’s Computer Science department

and who serves as the architect designer, “We inte-

grated BCI into a Bluetooth headset. Leveraging the

system-on-chips technology, the headset acquires

the brain wave via a simple but effective way. It is

complementary to existing assistive technologies

in terms of mental control.”

Server

For the server part, there are two layers. The

first layer is the communication layer for receiving

data from the client device, and the second layer

is the signal processing layer. A data mining algo-

rithm is implemented in the signal processing layer

to analyze the user’s risk of falling. After the calcu-

lation, the server end will send feedback to the cli-

ent side for actuating.

It is important to note that the data mining

procedure on the server is time-consuming, and

the response time is critical for fall prevention. To

mitigate this problem, researchers have invented

an innovative method for data processing and have

developed an efficient signal processing algorithm

based on a recently established theory of Com-

pressed Sensing (CS) [2][3].

“Decoding the brain wave is a difficult job and

very time-consuming. Perceptible delay can dra-

matically degrade the performance of our brain

control system. To handle this problem, we came

up with some novel algorithms to decode the brain

waves for real-time control purposes,” said Fang

Gong, who is a third-year Ph.D. student in UCLA’s

Electrical Engineering department and serves as

COMMUNICATION LAYER

MIDDLEWARE LAYER

ACTUATING LAYER SENSING LAYER

CLIENT

COMMUNICATION LAYER

SIGNAL PROCESSING LAYER

SERVER

FIGURE 3

(A) CLIENT STRUCTURE

(B) SERVER STRUCTURE

WE INTEGRATED BCI INTO

A BLUETOOTH HEADSET.

LEVERAGING THE SYSTEM-

ON-CHIPS TECHNOLOGY,

THE HEADSET ACQUIRES

THE BRAIN WAVE VIA A SIMPLE

BUT EFFECTIVE WAY.


the software designer for this project. “These more

efficient algorithms maximize computing resourc-

es, a chief reason why the unit can be so tiny.”

PROTOTYPEFigure 4(a) illustrates the acquisition system,

called Smart Headset. There are two main parts:

an ADC module (analog-to-digital converter) and a

data processing module. The ADC used in the sys-

tem is a kind of adaptive sampling module from

Neurosky Inc [4]. This ADC has a low-sampling

noise, high accuracy, and configurable data rate.

The other part is a data processing module. The key

chip on the module is an ultra low-power micro-

controller, MSP430 from Texas Instruments (TI).

The proposed computer science-based algorithm is

hosted on this module for compressed data analy-

sis. Additionally, Figure 4(b) shows that the whole

packaged system is very tiny when compared to the

size of a quarter (shown), yet the signal accuracy is

as accurate as those from a conventional scalp BCI

system. Figure 5 shows the researchers involved in

the project, analyzing the recorded brain signals in

their laboratory.

Currently, in order to prove empirically the

FIGURE 4

(A) PROTOTYPE CIRCUIT

(B) PACKAGED SMART HEADSET

IN ORDER TO PROVE

EMPIRICALLY THE FEASIBILITY

OF THE DESIGN FRAMEWORK,

THE GRADUATE RESEARCHERS

AT UCLA CONDUCTED A PILOT

STUDY WITH VOLUNTEERS

TO TEST THE SYSTEM.


feasibility of the design framework, the graduate

researchers at UCLA conducted a pilot study to test

the system using volunteers. The preliminary pilot

experiments show that a CS-based signal process-

ing strategy could not only preserve the fidelity of

the data but also dramatically reduce the size of the

raw data. In addition, a paper about the BCI sys-

tem has been accepted by HCMDSS/MDPnP 2011

for publication [5]. Aside from fall prevention, it is

possible to consider a number of other potential

healthcare related applications that can be en-

hanced by this BCI system, including for example

brain-control for the impaired and disabled.

REFERENCES[1] Slobounov S, Cao C, Jaiswal N, Newell KM, “Neural basis of pos-

tural instability identified by VTC and EEG”, Exp Brain Res.

2009 Oct;199(1):1–16.

[2] D. Donoho. “Compressed sensing”. IEEE Transactions on Bio-

medical Engineering, 52:1289–1306, April 2006.

[3] E. Candes, J. Romberg, and T. Tao. “Stable signal recovery from

incomplete and inaccurate measurements”. Communica-

tions on Pure and Applied Mathematics, 59:1207–1223,

August 2006.

[4] Neurosky Inc. http://www.neurosky.com/

[5] Wenyao Xu, Fang Gong, Lei He, Majid Sarrafzadeh, “Wearable

Assistive System Design for Fall Prevention”, HCMDSS/

MDPnP 2011, Chicago, US (accepted for publication).

FIGURE 5

RESEARCHERS

ANALYZING DATA

[email protected]

[email protected]

For further reading: http://bit.ly/kDPm8B (published in HCMDSS workshop 2011)



BIT: Let’s begin with graduate life. In-between go-

ing to classes and having papers to publish, what

do engineers do, and how do they work? In groups?

Alone? How many projects are done simultaneous-

ly? What is the workflow from concept to comple-

tion?

Dean Dhir: Most students are taking classes and

working with faculty members in labs by keeping

current of the latest published papers as well as

carrying out research to compose their own papers.

Research involves building an experimental setup,

testing it to get data, then analyzing the data, and

finally deducting conclusions that advance their

knowledge in a particular area. The above would

summarize the activities of the average engineer-

ing graduate student. A small, exceptional group of

students go beyond that level with more initiative

and pursue research unrelated to their graduate

topic, out of curiosity, and to advance knowledge

within a field. Some of these exceptional students

also find applications that are more entrepreneur-

ial, in that they go one step more and commercial-

ize the idea. In large measure, it depends on the

individual.

On the other hand, faculty is focused in an in-

dividual discipline or area of expertise. They are

seeking to find out the truly innovative problems,

what are the “show-stopper” problems in that par-

ticular discipline. They cultivate ideas by writing

proposals for federal grants, and if their ideas and

the packaging are superior, then they get funding,

in the hopes of taking a project to high social and

financial payoffs.

BIT: What do engineers do that isn’t so obvious, yet

relevant, important, and interesting for a non-tech-

nical reader? What sorts of things involve engineer-

ing from the standpoint of health, entertainment,

scholarship, intellectual development, and social

responsibility?

Dean Dhir: Engineers are problem solvers, crea-

tors, and master builders. They create new ideas

and concepts and steer those efforts in the direc-

tion of fabricating a device based on those ideas,

testing it, troubleshooting it, and adjusting it incre-

mentally toward a novel innovation.

Furthermore, engineers must pay attention to

the consequences on the public of a gadget or de-

BIT CO-FOUNDERS JAMAL MADNI AND NEIL TILLEY SAT DOWN WITH DR. VIJAY DHIR, DEAN OF THE UCLA HENRY SAMUELI SCHOOL OF ENGINEERING & APPLIED SCIENCE (HSSEAS), WHO CONVEYED A PICTURE OF THE WORKFLOW, RELEVANCE, AND IMPACT OF ENGINEERING RESEARCH AT UCLA, AS WELL AS TO DESCRIBE TODAY’S TYPICAL ENGINEER.

THE DEAN’S CORNER


vice of their design. Foremost this speaks of safety.

For example, take the discussion of cell phones:

there is the issue of microwaves from the antenna

in the phone and their effect on the brain. While

not entirely known, there are efforts exploring

whether they pose a tangible, albeit minimal, risk

on people. Engineers develop new technologies

and systems to the benefit of mankind and derive

a financial reward for these services. At the same

time they have a social and ethical responsibility

to be safe—a principle covered in the engineering

ethics classes offered in our school. There are peo-

ple who have made compromises in areas of safety;

these have come to haunt them. We’ve got to be

very careful at all times as to the consequences.

From an entertainment standpoint, we can

think of electronic arts: computer-aided design,

movie effects, sports arena jumbo screens, video

games—both standalone and within a smart phone,

for example. These are all part of everyday lives,

yet the underpinnings lie in computer science.

BIT: What makes engineers different people, and

what makes them people you might want to write

a movie about, such as The Social Network? If there

is a category called “engineer cool,” what comprises

that?

Dean Dhir: I think “engineer cool” is comprised of

people who go to an engineering school, acquire

an education in engineering principles, and then

use these principles to invent something that helps

the public do something faster and in a more eco-

nomical way than they are used to doing. You get so

much information on a cell phone now, for example,

that you can raise your productivity and have more

informed discussions.

Are engineers wired in a unique way? No, I

don’t think so. Engineering’s impact tends to be

more diffuse and generally affects many people,

more than the work of a medical specialist treating

a specific patient, for example. Broadly speaking,

engineers should go through a formal education—

this applies to 99% of the people in engineering;

perhaps 1% of the people are the exception, and

they may not need much formal training in princi-

ples, having an fundamental feel for it already. But

on the whole, good engineering is not limited to the

naturally gifted; rather it is much more fundamen-

tal than people think, because it is learnable.

BIT: Most of the great engineering pioneers in

recent memory dropped out of school to invent

a revolutionary idea in their garage. What ought

the school of engineering at UCLA offer (socially,

economically, politically, institutionally) to change

this paradigm and foster a fertile, attractive envi-

ronment benefiting the growth of the student and

school alike?

Dean Dhir: It’s the same thing you can find in ath-

letics—like Kareem Abdul-Jabbar, Bill Walton, or

Magic Johnson—relatively speaking, there are only

a few superstars—the outliers, if you will. However,

the majority of people, whether they are athletes or

engineers, need to have a good education so they

can thrive later in life. For example, the athletes

who aren’t capable of superstardom by skipping

college right away and directly going to the pros,

they cultivate their athletic skills in college for four

years in order to make a decent enough living in the

GOOD ENGINEERING IS NOT

LIMITED TO THE NATURALLY

GIFTED; RATHER IT IS MUCH

MORE FUNDAMENTAL THAN

PEOPLE THINK, BECAUSE

IT IS LEARNABLE.


pros after that. So I don’t think institutionally there

needs to be a fundamental change in the paradigm.

However, from a cultural and educational

standpoint, we’ve got to provide our engineering

students the fertile settings that give an outlet to

their ideas. If they have ideas, they should be fos-

tered within the school. At the student level, for ex-

ample, there is the Technical Entrepreneurs Com-

munity (TEC), and the organization’s members are

creating an environment where students can learn

about how to market their technology; but at the

same time the university needs to create environ-

ments for students to bring their ideas from their

basic research and grow it where it becomes of in-

terest to venture capitalists. The Institute for Tech-

nology Advancement (ITA) helps with the matu-

ration process of specific technologies developed

in-house at UCLA, where venture capitalists will

take an interest to acquire this technology.

Also, we have been trying to raise funds for a

new Creativity Center, slated to be in place by the

end of this summer. The first function would be to

let young kids, including our students, build some-

thing unique, with their hands. But it is their own

imagination they are exercising. It could be junior

high and high school kids who come in summer. We

provide them with hardware, software, and techni-

cal support, in the form of mentors, and then let

them loose and have their imagination run wild. If

it doesn’t work, they troubleshoot it, fix it, test it,

and try again.

It will cost about $1.5 million; we have raised

about half that at this time. It will be down on the

interior courtyard level of Boelter Hall, and we

hope to have the rest of the funds available by the

end of summer. To operate and administer the

center, we will look to hire someone part-time and

staff graduate students as mentors. This is on the

cutting edge of creating an infrastructure for stu-

dent imagination, and UCLA is the first engineering

school in the nation to provide a facility of this type.

During the academic year, student clubs can access

it, and then this will provide hands-on experience

during the summers, for groups of three to four in

order to stir their creative thinking.

BIT: How can our engineering school outreach to

the rest of the university to have a hand in campus-

related problems?

Dean Dhir: It’s a very good question. I’ve been say-

ing this very thing on the campus committees I’m

part of: Why don’t we utilize the resources we have

in the school? Faculty can be the consultants. At

the moment, we hire from outside, which is so iron-

ic. For example, some of our faculty are the fore-

most authorities in the field of cyber-security, and

yet, we hire “outside” experts to deal with cyber-

security issues. Students can definitely help in the

area of Information Technology. But this mentality

is not institutionalized. I don’t know the reasons

why. I think it should be done, but where possible,

we should advertise we have the resources and

they should be used.

BIT: Why would I want to hire an engineer for a

non-engineering job?

Dean Dhir: Engineers are very good at analyzing.

They know how to solve an open-ended problem

where the solution may not be unique, but they

come up with an optimal solution. Engineers get

training with ill-defined problems and find very

creative, elegant, and efficient solutions to these

“asymmetric” problems. This type of thinking is

needed in many functions and industries. But,

broadly speaking, this isn’t trained in very many

other fields of study.

[email protected]

http://bit.ly/kezzdQhttp://bit.ly/mvEBIU


You start watching the video. Behind the

scenes, the current Internet creates a link to one

of the YouTube servers, and the server streams the

video on this link. This happens with every single

person who watches the same video around the

globe, leading to a content distribution problem

that is solved today using a patchwork of Content

Distribution Networks.

Now suppose the router in your local area

network knew you were asking for the same video

your friend had requested a few minutes ago and

returned you a cached copy of the video it delivered

to your friend. Wouldn’t that be better? If we im-

plement this router intelligence on a global scale,

it forms an elegant way of solving the content dis-

tribution problem. This is but one of the major fea-

tures of a new Internet architecture called Named

Data Networking, or NDN, being developed here at

UCLA in association with PARC (a Xerox company)

and other partner institutions including the Univer-

sity of Arizona, UC Irvine, UC San Diego, Colorado

State University, the University of Illinois Urbana-

Champaign, the University of Memphis, Washing-

ton University, and Yale University.

DESIGNING APPLICATIONS USING NAMED DATA NETWORKING

Today’s Internet uses IP addresses, which are

number addresses to identify physical computers

at which the data can be found, in order to move

(route) data from one place to another. The vision

of NDN however is a new direction, to give names

to the data itself that applications and users care

about instead of addressing the machine where

they can be found. This allows applications simply

to ask the network for the data they want instead of

specifying where to find the data. It is the network’s

job to locate and deliver data to the requesting ap-

plication.

So how does NDN work? Applications send an

Interest packet, which carries the name of the de-

sired content (a name such as /parc.com/videos/

WidgetA.mpg as shown in Figure 1). The router that

receives the Interest packet remembers the inter-

face from which the request comes in, and forwards

the packet to other nodes (a node is a data source

or a router on the path to the data source) by look-

ing up the name in its Forwarding Information Base

(FIB). Once the Interest reaches a node that has the

requested data, a Data packet is sent back that car-

ries both the name and content of the data, togeth-

Rahul Rao Basava, Derek Kulinski, Chenni Qian, and Zhenkai Zhu, Computer Science

YOUR FRIEND ACROSS THE ROOM IMs YOU TO WATCH THE NEW YOUTUBE VIDEO THAT WENT VIRAL AN HOUR AGO.


er with a digital signature by the creator of the data.

The Interest packet leaves a breadcrumb trail

across the network (the breadcrumb is an entry in

the Pending Interest Table, or PIT, of a router) for

the Data packet to retrace the path to the applica-

tion that requested it. Neither Interest nor Data

packets carry IP addresses; Interest packets are

routed towards data producers based on the names

carried in the Interest packets, and Data packets

are returned based on the state information set up

by the Interest packets at each step along the route.

When multiple Interests requesting the same data

are received, the router forwards only the first In-

terest packet towards the data source. When the

Data packet returns along the path from the source,

the router sends out copies of the packet in all di-

rections from which Interest packets had arrived

earlier. Then the router clears the corresponding

PIT entry and caches the data in its Content Store

to satisfy future requests for the same data.

Three driver applications are being developed

at UCLA to drive and test the prototype implemen-

tation of the NDN architecture. These applications

leverage built-in features such as efficient content

distribution and packet authentication. Each of

these projects represents a broad area of applica-

tion design, and each is expected to yield further in-

sight into the requirements of the new architecture.

At the same time they demonstrate performance

and functional advantages of NDN in key areas and

show how NDN’s embedding of application names

in the routing system promotes efficient authoring

of sophisticated distributed applications with re-

duced complexity. An introduction to each of these

applications follows.

FIGURE 1

NAMED DATA

NETWORKING - OVERVIEW

THE INTEREST PACKET

LEAVES A BREADCRUMB

FOR THE DATA PACKET

TO RETRACE THE PATH

TO THE APPLICATION THAT

REQUESTED IT.


LIGHTING APPLICATION PROJECTBuildings of the future are likely to be con-

structed with digitally controlled and networked

facilities such as lighting, touch-activated displays,

sound systems, and video recording. The current

Internet architecture makes it cumbersome to

build applications for such systems with hetero-

geneous components, especially in the area of ad-

dressing schemes and security.

The Lighting Application project aims at cre-

ating a control system using NDN that will deal

with physical lights and building facilities. NDN’s

intrinsic support for naming data, broadcast, cach-

ing, and fine-grained authentication provide obvi-

ous advantages in this setting. Exploration of this

area expands on UCLA’s experience building in-

teractive spaces of the kinds found in museums,

physical simulation spaces, theme parks, and live

performances. The project will create an applica-

tion library to support naming of various devices;

a discovery mechanism for embedded devices; dis-

tributed, synchronized state management; effective

key management; and distribution for authentica-

tion of control system commands.

Figure 2 shows the various components of

the project. Fixtures are lighting facilities that are

used to illuminate or create lighting in an envi-

ronment. They can be a single luminary unit or a

lighting device that has multiple light units such as

lighting strings. Lighting Interfaces are an inter-

mediary between fixtures and the NDN network,

sending and receiving Interest and Data packets

from Building Services. From the perspective of

the network, the interfaces and their associated

fixtures are indistinguishable. The fixtures used in

our test project are Philips ColorBlast 12 LED wall-

washing lights connected to Gumstix - Overo Air

COM interfaces.

As shown in the diagram, Building Services

are applications that constitute the building con-

trol systems. The Configuration Manager Service

is responsible for assigning the names for fixtures,

maintaining cryptographic certificates and ac-

cess control lists (ACLs) for other services in the

network. Lighting interfaces request this service

for names using an Interest packet. The name is

FIGURE 2

LIGHTING APPLICATION

PROJECT – OVERVIEW


returned as data. Subsequently the service sends

Interest packets to the interfaces for a periodic

‘heartbeat’ message, for checking connectivity and

status.

The Architecture Lighting Service is respon-

sible for everyday building lighting control. This

service sends control commands as Interest pack-

ets to the Interfaces (for example, UCLA/boel-

ter/3551/lights/fixture/*/rgb-8bit-hex/FAF87F,

as shown in Figure 3). Here the Interest packet is

used as a signed command, while the returning

data packet carries a simple signed acknowledge-

ment of reception of the command. The command

carries the RBG (red-blue-green) color settings for

the fixture. This service may be extended to provide

real-time status of the lighting infrastructure. The

Fire Life Safety Service includes features like fire

prevention and emergency evacuation. This service

has highest priority, and its commands override all

other service commands. Environment Monitoring

Service allows building administrators to view fix-

ture status and compute statistics regarding energy

consumption of the building.

PERSONAL DATA CLOUD PROJECTIn a world filled with mobile devices where

activity monitoring sportswear (like the Nike+iPod

system*) is becoming more and more common,

personal data is also becoming ever more accessi-

ble. Data collected by applications on such devices

include your location, time of events in your day-to-

day life, pictures (geo-tagged and time stamped),

and other information that the individual is record-

ing (ranging from eating habits to daily memos).

The Personal Data Cloud (PDC) project aims

to build a secure data ecosystem that can be con-

trolled by a single user. The PDC is composed of

many entities such as mobile phones and sensing

devices, cloud-based secure repositories, and per-

sonal computers. The concept is illustrated in Fig-

ure 4. Creating the PDC will require the implemen-

tation of a distributed database on NDN with direct

name-based access of individual records, providing

a widely applicable test case for validating NDN-

based systems like data discovery, content distri-

bution, and diverse models for trusted communica-

tion among mobile users, PDCs, and applications.

FIGURE 3

COMMAND FLOW IN ARCHI-

TECTURE LIGHTING SERVICE

* see article on page 24


The major features of the PDC are disruption-toler-

ant uploads from sensors to the secure data store;

internal communication between nodes of the

PDC; and filtered, often anonymous data sharing

with authorized third-party applications providing

personal services to the individual. The PDC design

envisions all entities (mobile devices included) as

data repositories with varying capacities for stor-

age and processing.

A new protocol called the Stream Protocol has

been defined for communication between entities

in a PDC. A stream is an abstraction of a sequen-

tial set of data sent from one entity to another. The

Stream Protocol is designed to work over the NDN

and in practice replaces the Hypertext Transfer

Protocol Secure (HTTPS) modules of the applica-

tions on the current Internet (HTTPS being the

most common protocol on the Internet used for

any secure communication, for example, purchas-

ing goods online). Figure 5 shows a high-level com-

parison between the module structure of Stream

Protocol and HTTPS. An entity that sends data is

called a Publisher and the entity receiving the data

is called a Receiver. An entity can be both a Publish-

er and a Receiver. Every Publisher can be the source

of many such streams.

AndWellness is one such personal data collec-

tion system developed in the Center for Embedded

Network Sensing (CENS). It uses mobile phones to

collect and analyze data from active/triggered user

experience samples and passive logging of onboard

environmental sensors, for example the GPS locator

and accelerometer. The system includes an applica-

tion that runs on Android-based mobile phones,

server software that manages deployments and

acts as a central repository for data, and a dash-

board front-end for participants and researchers

alike to visualize incoming data in real time.

AndWellness will be the first framework to run

over the PDC architecture. The mobile application

component has been modified to behave as an in-

dependent entity capable of publishing informa-

tion to the user’s data cloud. The data receiver is

a cloud-based, secure repository that is considered

as a test destination for the data streams published

by the mobile application. While the mobile appli-

FIGURE 4

PERSONAL DATA CLOUD

PROJECT - OVERVIEW


cation does not have the functionality to receive

data and does not have filtering components, the

cloud-based repository will have both capabilities.

AUDIO CONFERENCING PROJECTCurrent audio conferencing tools rely on a

centralized system to discover ongoing conferenc-

es, the speakers participating in each conference

and transmission of voice data from individual

speakers. The Audio Conference Tool (ACT) project

explores multi-host real-time media streaming ap-

plications designed over NDN. ACT takes a named-

based approach, and the resulting design is com-

pletely distributed and robust against component

failures. ACT supports both conference data au-

thentication and participant control. The packets

generated by ACT are digitally signed using the

built-in security primitive in NDN, which guaran-

tees data integrity and provenance. An encryption-

based access control scheme is employed in the pri-

vate conference mode where a session key is used

to encrypt all data traffic of the conference and is

only distributed to legitimate users. ACT will test

the effectiveness of NDN in the context of time syn-

chronization, quality-of-service selection, security

of control / media streams, routing scalability, and

trust model.

Figure 6 shows an overview of the ACT design.

A separate module handles conference discovery—

both scheduled and ongoing—and facilitates the

task of joining a conference. The decoupling of con-

ference discovery from voice media delivery gives

the flexibility to extend ACT with other features

such as video, whiteboard, and text message. A user

who initiates a conference creates data that de-

scribes the various aspects of the conference, such

as estimated start time, duration, media type, pur-

pose, etc. Conference discovery requires propaga-

tion of Interest packets from participants in a given

conference for conference information across the

network. This is achieved by choosing the names

for conference information data from a broadcast

namespace, which is a dedicated set of names con-

figured on the routers for network broadcast. Once

conference discovery is completed, the speakers of

each ongoing conference are discovered. Partici-

pants in a conference should answer the Interests

for speaker information sent by listeners.

The Interest packets for voice data streams

need to be generated at frequencies orders of mag-

nitude higher than those needed for conference

setup and speaker management; thus broadcast

is not used for voice streaming. ACT uses speaker-

specific names for voice data, and this task is del-

egated to the Voice Data Distribution module. Voice

data is fetched by sending Interests directly to each

participant speaker in a conference. Pipelining of

Interests and flow balance are enforced to mitigate

the effects caused by the delay of round trips and

loss of packets, thus smoothing out the sound of the

voice transmission.

FIGURE 5

COMPARISON BETWEEN

HTTPS AND STREAMS


EXPLORATION OF THIS

AREA EXPANDS ON UCLA’S

EXPERIENCE BUILDING

INTERACTIVE SPACES

OF THE KINDS FOUND

IN MUSEUMS, PHYSICAL

SIMULATION SPACES,

THEME PARKS, AND LIVE

PERFORMANCES.


Media data processing and user interface de-

sign are necessary components of ACT but largely

decoupled from networking specifics. To focus our

effort on NDN-specific design issues, ACT adopted a

client-server based open-source audio application

package. It embeds the modules for speaker dis-

covery and voice data distribution in the original

server code, leaving the client intact. This modified

server runs on the same machine as the client and

communicates with the client using the standard IP

protocol stack. At the same time it communicates

with other participants (who may be using the

modified server) over NDN.

REFERENCES[1] Named Data Networking (NDN) Project. www.named-data.net.

[2] Named Data Networking (NDN) Project. PARC Technical Re-

port NDN-0001. L. Zhang, D. Estrin, J. Burke, V. Jacobson, J.

D. Thornton, D. K. Smetters, et al. http://www.named-data.

net/ndn-proj.pdf.

[3] AndWellness: An Open Mobile System for Activity and Expe-

rience Sampling. John Hicks, Nithya Ramanathan, Donnie

Kim, Mohamad Monibi, Joshua Selsky, Mark Hansen, De-

borah Estrin. Wireless Health ’10. http://openmhealth.org/

wp-content/uploads/wireless-health.pdf.

FIGURE 6

CONFERENCING

PROJECT - OVERVIEW

[email protected]

[email protected]

[email protected]

[email protected]

Many thanks to Jeff Burke, Lixia Zhang, Deborah Estrin, and Alessandro Marianantoni at UCLA and to the researchers of UCLA partner institutions for their support.



ENTREPRENEURIAL OPPORTUNITIES FROM THE OTHER SIDE OF CAMPUS

Jeff Chang, UCLA Anderson School of Management

WHAT IF YOU WANT TO BE IN A CREATIVE ROOM THAT SPAWNS MORE

IDEAS AND INSPIRES YOU? WHAT IF YOU WANT A CAREER AS A VENTURE

CAPITALIST? WHAT IF YOU HAVE AN IDEA AND WANT TO KNOW WHAT

TO DO NEXT?

Odds are, there is something in North Campus for you.

Enroll in TIP (Technology & Innovation Partners)

TIP is a yearlong course for accelerating research commercialization. It brings together students from vari-

ous UCLA graduate schools. TIP classes are taught by strong professors and guest speakers under the lead-

ership of Al Osborne, Jr., Senior Associate Dean and founder of the Price Center for Entrepreneurial Studies.

Become part of the UCLA Entrepreneur Association

The Anderson EA organizes nearly 150 events per year. Many of those events are open to all UCLA students.

A few examples are the following:

• eLabs – where students pitch their ideas and get feedback and support from the audience.

• Knapp Prep events get you ready to put together a business plan.

• eWeek – a week of seminars and keynotes with successful entrepreneurs.

• The EA Conference in mid-May – an entire day of panels, speakers, the fast-pitch competition, and op-

portunities to make contacts at breakfast, lunch, and happy hour.

You can also compete in a business plan competition any time of year. As a UCLA grad student, you are

encouraged to contact any of a hundred Anderson students through the site www.KnappComp.com if you have

an idea and need to put together a team. A feature on the site is a list of all one hundred worldwide, open-

format business plan competitions for the upcoming year – So stay tuned!

TIPhttp://bit.ly/lScAWQ

Entrepreneur Associationhttp://bit.ly/lbjspT

Knapphttp://bit.ly/ieDMVN


A WIRELESS HOME AUTOMATION SYSTEM FOR CHILDHOOD OBESITY

PREVENTION

Childhood obesity is recognized as a seri-

ous public health concern due to the rising preva-

lence of obesity in children (Troiano 1995). In the

United States, direct measurements of body mass

and height obtained by the National Health and

Nutrition Examination Survey indicate that about

15% of 6–19 year olds are classified as overweight

(Ogden 2002). As the children spend a significant

amount of their time at home, a sedentary lifestyle

accounts for the leading cause of childhood obes-

ity (Walker 1998). Many children fail to exercise

because they spend time doing stationary activities

such as playing video games or watching TV. Cer-

tain reports provide evidence that television view-

ing is a reason for increased body fatness and that

reducing television viewing is a promising strategy

for preventing childhood obesity (Andersen 1998,

Robinson 1998).

On the other hand, exercise would help chil-

dren control their weight. It also helps to reduce

the risk of some illnesses such as high blood pres-

sure, heart disease, sleep problems, and other simi-

lar disorders (Freedman 1993). Based on the men-

Navid Amini, Computer Science

PEOPLE ARE BECOMING MORE CONCERNED WITH THE PROBLEM OF CHILDHOOD OBE-SITY, WHEN EXCESS BODY FAT NEGATIVELY AFFECTS A CHILD’S HEALTH.

tioned facts, it is widely recommended that one

should moderately exercise for at least 30 minutes

five times a week (Sallis 1994, Pate 1998, Corbin

1994). Furthermore, it is reported that standards

for recommended pedometer-determined steps

per day for 6–12 year olds are 12,000 for girls and

15,000 for boys (Tudor-Locke 2004). This high-

lights the importance of physical activity for chil-

dren. Accordingly, many control units have been

implemented in games and appliances by parents

in an effort to encourage children to perform physi-

cal exercise. Using these units, parents can limit the

time that their children can spend playing compu-

ter or TV games by manually controlling the cor-

responding appliances using the GUI provided by

these units (Thompson 2006, Coshott 2007). How-

ever, the units are not capable of preventing chil-

dren from watching another channel or playing an-

other game that is not equipped with the parental

control option.

In this paper, the system proposed can miti-

gate the previously mentioned problems by con-

trolling power outlets instead of controlling certain


TV channels or particular games. It has been sev-

eral years since the introduction of smart homes. In

such homes, RF (radio frequency) signals from spe-

cial controllers are exploited to activate or deacti-

vate power outlets. In some cases, the controller is

able to regulate the intensity of room light as well.

A number of research projects have been carried

out based on these or similar controllers to build

smart homes (Cook 2003, Jiang 2000, Cole 2002).

Likewise, a certain type of RF power controller is

used in this project in order to acquire complete

control over the home’s power outlets. In addition,

a wireless sensor is utilized that can calculate the

travelled distance and average pace for a walk (or

a run) so as to monitor the daily activity level of

children. The recorded data can later be uploaded

to any computer via the USB interface. The goal of

the proposed system is to make a virtual connec-

tion between power outlets and a wireless activity

monitor sensor. This way, the children could be en-

couraged to do more exercise in exchange for more

time of home entertainment (e.g., watching TV).

The more exercise children perform, the more time

they secure to do stationary activities. The pro-

posed system, called No Pain No Game, is intended

to prevent childhood obesity disease and thus im-

prove childhood fitness levels.

The remainder of the paper is organized as

follows. Section 2 provides a brief overview of re-

lated work in the context of home automation and

childhood obesity prevention. Preliminary notions

followed by the structure of the No Pain No Game

system are covered in Section 3. The important fea-

tures of the system are summarized in Section 4. Fi-

nally, Section 5 concludes this study and highlights

future research directions.

RELATED WORKThere are several research projects based

on home automation systems. As an example,

MavHome smart home architecture developed at

the University of Texas at Arlington allows a home

to act as an intelligent agent (Cook 2003). In the

MavHome system, a sensor network including

light, humidity, temperature, smoke, gas, motion,

infrared, and switches is developed to keep track of

the home environment. Based on the data collected

from the sensor network, a server makes a decision

for resource management and executes the decision

through the smart actuators (e.g., X10 ActiveHome

Kit) with which the appliances in the home are

equipped. Generally, the MavHome aims at maxi-

mizing the inhabitant’s comfort and productivity

and minimizing costs. For elderly and disabled peo-

ple, the system can provide a fine health care and a

higher level of security. As another example, to help

people with disabilities, Jiang et al. has designed

a voice-activated environmental control system

which consists of a universal remote control with

X10 home automation capability, a Motorola 6811

microprocessor, and an off-the-shelf voice recogni-

tion circuit (Jiang 2000).

In the context of activity monitoring, in a

number of projects X10 components are integrated

into a toolkit which is intended to monitor the daily

activity of an individual (Cole 2002). In addition to

the home automation systems, certain appliances

are developed to encourage children to exercise.

For example, an interrupter system can be added to

an existing connection between a game console and

its controller (Coshott, 2007). The interrupter sys-

THE PROPOSED SYSTEM

CONTROLS POWER OUTLETS

INSTEAD OF CONTROLLING

CERTAIN TV CHANNELS OR

PARTICULAR GAMES.


tem is used selectively to enable and interrupt the

modified connection between the game controller

and the game console, dependent on the detected

operation of the exercise machine. Accordingly, the

parents can arrange it in such a way to block nor-

mal playing on the game console unless the child

performs an adequate amount of exercise on the

exercise machine.

In (Annavaram 2008), a wireless body area

network is developed for wearable monitoring ap-

plications, and the intended use of the system is to

avoid pediatric obesity. The network is composed

of heterogeneous sensors (e.g., heart-rate sensor

and accelerometer sensor), and the goal is to rec-

ognize, predict, and reason automatically about

human physical activity and behavior states by the

evaluation of multimodal sensing and interpreta-

tion.

THE PROPOSED SYSTEMNo Pain No Game is a home automation sys-

tem that encourages children’s daily exercise by

rewarding them with more home entertainment

time. In other words, the time children spend doing

stationary activities should be proportional to the

time they spend doing physical exercise. Aside from

the interface software, the system is composed of

three parts: a sport kit that monitors the exercise

records, a controller that commands the power

outlets, and a database that stores the health sta-

tistics and exercise records of children. The current

version of the system exploits Nike + iPod Sport Kit

as a means to monitor the physical exercise. It also

uses X10-based devices in the shape of controlla-

ble power outlet modules in order to acquire full

control over different entertainment appliances.

Microsoft Access databases are utilized to store the

relevant exercise data. The software that connects

these three parts is written in Visual Basic and C

Sharp and runs on a Windows platform.

X10 Standards

X10 is an international and open industry

standard for communication among electronic de-

vices used for home automation. It primarily uses

power line wiring for signaling and control, where

the signals involve brief radio frequency bursts

representing digital information. A wireless radio

protocol is also defined, where the data packets

are very similar to those used for power line wires.

The operating frequency of the wireless protocol

is 433 MHz and 310 MHz in the European systems

and U.S. systems, respectively. It should be noted

that the wireless protocol allows the operation of

keypad remote controllers on top of the underlying

wired X10 modules.

X10 is popular in the home environment, with

millions of units in use worldwide and new compo-

nents inexpensively available. Figure 1 illustrates a

typical configuration for an X10 network.

By using The ActiveHome Pro Scripting In-

terface provided by X10, one can create software,

FIGURE 1

A TYPICAL CONFIGURATION FOR

X10 CONTROLLED APPLIANCES.


web pages, and other tools that use the USB In-

terface to control and interact with X10 modules,

sensors, and remote controls (The ActiveHome Pro

SDK from X10). The address of each module is set

by the dials located on it. Accordingly, one can com-

mand the interface to turn on the intended module

by providing a valid address corresponding to the

module. Upon receiving a command (e.g., from a

remote control), the interface reports the action of

receiving the command which enables the user to

benefit from certain options. For the sake of more

reliability, each packet is sequentially sent twice to

make sure the receivers understand it even in the

presence of power line noise.

Nike + iPod Sport Kit

The Nike + iPod Sport Kit (Figure 2) consists

of a wireless sensor and a small wireless receiver

that plugs into an iPod. The sensor is a piezoelec-

tric accelerometer pedometer that fits inside spe-

cial Nike shoes and a wireless receiver that is con-

nected to an iPod. Each sensor has its unique serial

number that is used as an identification number for

each child in the proposed system. The personal

training application on the iPod can provide infor-

mation on distance and speed while a user is listen-

ing to music. By considering the number of steps

taken and the elapsed time, users (children) can

schedule their desired workout in the form a speci-

fied distance or a certain time period that can fit a

plan based on their goals and their previous per-

formance. The exercise records are stored in files

with XML format. The proposed system will upload

those files to the server and extract the useful data

from the files. Therefore, the data can be analyzed

to decide how much entertainment time the child

has earned.

The Proposed No Pain No Game System

In the proposed system, a server is main-

tained by parents while children will use remote

controls sending matching signals to trigger the

appliances. A child must register his or her sports

kit on the server, given that every sensor has a par-

ticular identification number. The proposed sys-

tem simultaneously analyzes the current exercise

records when children update the database with

new exercise records merely by plugging their iPod

into the client computer. This means that the server

automatically calculates the total time budget that

a child is allowed to use his/her intended applianc-

es (e.g., TV). A formula is applied to derive the time

budget based on the calories burned by the child:

where MET (Metabolic Equivalent of Task) is a

physiological concept that shows how intense the

exercise is (Dr Gily’s Health Portal), w is the child’s

FIGURE 2

APPLE NIKE + IPOD SPORT KIT.

THE SENSOR FITS INSIDE

SPECIAL NIKE SHOES AND

A WIRELESS RECEIVER IS

CONNECTED TO AN IPOD.

Calories METs w T=× ×

×3 5

200

.(1)


weight in kilograms, and T represents the total du-

ration of exercise (e.g., running or walking) in min-

utes.

MET expresses the energy cost of physical ac-

tivities as multiplies of the Resting Metabolic Rate

(RMR) and is defined as the ratio of metabolic rate

(and therefore, the rate of energy consumption)

during a certain physical activity to one’s metabolic

rate at rest (quiet sitting), set by convention to 3.5

ml O2 . kg-1 . min-1 or equivalently 1 kcal . kg-1 . hr-1. By

convention 1 MET is considered the resting meta-

bolic rate obtained during quiet sitting. MET values

of physical activities range from 0.9 (sleeping) to

18 (running at 17.5 km/h). It should be noted that

the MET value for watching television is 1 and for

walking at a speed of 3.2 km/h is about 2 (Meta-

bolic Equivalent 2009).

After the burned calories are derived, a ba-

sic requirement for this value is enforced. If the

amount of burned calories is less than a certain

quantity (called the basic requirement), no time

budget will be granted. However, if it exceeds the

basic requirement, the basic amount of time will be

given, plus extra time depending on the bonus rate

and the overhead. The required amount of calories,

the amounts of the basic time budget and extra

time can all be adjusted by the Account Manage-

ment Interface.

The total time budget for each child is stored

in a database by Microsoft Access. The time budget

gets verified and consumed every time a child tries

to activate an appliance. Moreover, after the time

budget is depleted, the server will automatically

power off the related appliance (e.g., TV). A child

has to perform more exercise and then plug in his/

her iPod into the client computer in order to in-

crease the time budget again. Figure 3 illustrates

the structure of the No Pain No Game system.

The implementation of the system is con-

structed with two ends, the client and the server

(Figure 3). On one end, the program on client com-

puters will parse iPod exercise records by creating

XML readers supported by the .NET Framework.

After validation, the files will be sent to the serv-

er unless they are expired. On the other end, the

server will increase the corresponding time budget

by an appropriate formula based on children’s

BMI and exercise records. The server also verifies

whether the sensor identification number of the

record is valid, and it makes sure that the record

is not outdated. Meanwhile, if one wants to regis-

ter more children (through Account Management)

or verify the status of all children, the server can

update or retrieve the data as required (see system

block diagram illustrated in Figure 4).

Moreover, the server handles the events when

the Power Control Module (ActiveHome Automa-

tion System interface) receives signals from remote

controls. Each received signal will be mapped to

a particular child and the module he/she has re-

quested. Therefore, the server can retrieve the cor-

responding data and determine if it should send a

command (sendrf in this case) to the automation

system in order to trigger the requested module.

In addition, for each appliance, there is a timer

in the server to update and monitor the available

time budget and, correspondingly, to turn off the

appliance when the time budget has exhausted. A

detailed block diagram of the server is depicted in

Figure 5.

IN THE PROPOSED SYSTEM,

A SERVER IS MAINTAINED BY

PARENTS WHILE CHILDREN

WILL USE REMOTE CONTROLS

SENDING MATCHING SIGNALS

TO TRIGGER THE APPLIANCES.


SYSTEM FEATURESSince each child has his/her own iPod + Nike

Sport Kit, the system can distinguish between dif-

ferent children by evaluating their identification

numbers. Furthermore, the system takes into ac-

count the date of exercise records to avoid copies

of previous exercise records from being taken into

consideration. This validation process prevents

children from cheating to some extent. For exam-

ple, a child will not gain any extra time budget by

plugging in the iPod twice with the same exercise

record.

As can be seen in Figure 6, a user-friendly GUI

on client computers is provided for children to use

so that they can easily learn how to update the ex-

ercise records without any difficulty. Furthermore,

the system allows children to watch TV or use other

entertainment appliances only if they have per-

formed an adequate amount of exercise throughout

the day. This gives the children a sense of achieve-

ment while the parents need not worry about their

kids being too inactive. Additionally, parents can

record the time and let the system monitor the ac-

tivity level of their children and the status of the

entertainment appliances. They can view earlier

data (e.g., how many calories have been burned)

and observe the progress of their child’s fitness

level. Figure 7 shows an example table containing

a child’s recent exercise data. This exercise table is

accessible from the server.

DISCUSSIONS AND FUTURE WORK In the proposed system’s current form, after

performing exercise, a child connects the iPod to

the client computer. This will update a database on

the server, and eventually a new schedule for the

related appliances will be sent from the server to

the ActiveHome Automation System. However, it is

more desirable to operate the system in real time,

where the updating process is done automatically

and wirelessly. This can be done by using a Blue-

tooth adapter connected to the iPod. This way, there

is no need for the child to connect the iPod to the

computer in order to extend his/her time budget.

Apart from Nike + iPod Sport Kit, other types

of sport kits can be added to the system in future to

FIGURE 3

STRUCTURE AND APPLICATIONS

OF NO PAIN NO GAME.


FIGURE 4

SYSTEM BLOCK DIAGRAM

FIGURE 5

DETAILED BLOCK

DIAGRAM OF THE SERVER.


provide the children with more options to perform

physical exercise. Also it should be noted that the

server side and client side of the system (Figure 3)

can run on two networked computers, or they can

run on a single machine.

A new method of gaming can be designed in

order to make players benefit from doing physi-

cal exercise in the real world. This can be achieved

by extending the system to a server maintained

by game companies. Similar to the design for the

Home Automation System, the system now rewards

children who have performed a sufficient amount

of exercise with the profit in the game. For example,

a child’s character in the game becomes stronger

and thus is able to carry better weapons. It is antic-

ipated that this idea gives encouraging incentives

for children in exchange for performing physical

exercise.

No Pain No Game can also cooperate with

a children’s health organization to advocate for-

mulating an exercise plan, encouraging children

by a competitive display record on the organiza-

tion’s website. A child can upload his/her exercise

progress and get ranked, accordingly. Further, the

exercise data collected from children can be a use-

ful resource for related research institutes. People

in health-related organizations can analyze how

the trend of health data progresses during the ex-

ercise.

CONCLUSIONSChildhood obesity has been a public concern

as people expend money and resources in order

to maintain their children’s physical fitness and

overall health. As such, it is necessary to build an

application that continually encourages children to

perform physical exercise. Several parental control

units have been designed, but few of them can ef-

fectively impact children. With home automation

system technology ever improving and with the

number of related research projects growing, the

proposed system is built on the idea of home au-

tomation systems. In this system, called No Pain No

Game, children have to perform exercise for enough

time in order to be able to activate any entertain-

ment appliance. The activity level of children is

monitored using a Nike + iPod Sport Kit, which is

becoming more popular. It records the number of

steps taken and time elapsed. Therefore, the appli-

cation can calculate calories and other related data

from the records. A database is utilized to store the

exercise records and the time budget for every reg-

istered child.

No Pain No Game lowers the risk of being

cheated by means of its validation process. Also,

its user-friendly GUI easily allows children to learn

how to extend their entertainment time. Children

can obtain a sense of achievement, while parents

can let the system automatically monitor the ac-

FIGURE 6 (TOP)

A TYPICAL SCREENSHOT

OF THE CLIENT GUI IN

NO PAIN NO GAME.

FIGURE 7 (BOTTOM)

AN EXAMPLE TABLE

OF THE EXERCISE DATA.


NO PAIN NO GAME CAN ALSO

COOPERATE WITH CHILDREN

HEALTH ORGANIZATIONS

TO ADVOCATE FORMULATING

AN EXERCISE PLAN…

A CHILD CAN UPLOAD HIS/HER

EXERCISE PROGRESS AND GET

RANKED ACCORDINGLY.


tivity level of their children and the status of the

appliances. It is believed that a complete No Pain

No Game product would enable the parents to su-

pervise their children’s exercise and entertainment

inexpensively and in a completely unobtrusive

fashion.

REFERENCES

[1] Troiano, R.P., Flegal, K.M., Kuczmarski, R.S., 1995. Overweight

prevalence and trends for children and adolescents. Arch

Pediatr Adolesc Med 1995;149:1085– 91.

[2] Ogden, C.L., Flegal, K.M., Carroll, M.D., Johnson, C.L., 2002. Prev-

alence and trends in overweight among US children and

adolescents, 1999 – 2000. JAMA 2002;288(14):1728– 32.

[3] Walker, A.R.P., Walker, B.F., 1998. Rises in schoolchildren’s an-

thropometry: what do they signify in developed and devel-

oping populations? J R Soc Health 1998;118(3):159– 66.

[4] Andersen, R.E., Crespo, C.J., Bartlett, S.J., Cheskin, L.J., Pratt,

M., 1998. Relationship of physical activity and television

watching with body weight and level of fatness among chil-

dren: results from the Third National Health and Nutrition

Examination Survey. JAMA 1998;279:938–42.

[5] Robinson, T.N., 1998. Does television cause childhood obesity?

JAMA 1998; 279: 959–60.

[6] Freedman, D.S., Dietz, W.H., Srinavisian, S.R., Berenson, G.S.,

1993. The relation of overweight to cardiovascular risk fac-

tors among children and adolescents: the Bogalusa heart

study. Pediatrics 1999; 103: 1175–1182.

[7] Sallis, J.F., Patrick, K., 1994. Physical activity guidelines for

adolescents: consensus statement. Pediatr Exerc Sci 1994;

6: 302 –314.

[8] Pate, R., Trost, S., Williams, C., 1998. Critique of existing guide-

lines for physical activity in young people. in Biddle, S., Sal-

lis, J., Cavill, N. (Eds), Young and Active? Young People and

Health-Enhancing Physical Activity-Evidence and Implica-

tions, Health Education Authority, London, pp. 162–76.

[9] Corbin, C.B., Pangrazi, R.P., Welk, G.J., 1994. Toward under-

standing of appropriate physical activity levels for youth.

President’s Council of Physical Activity and Sport. Phys Act

Fit Res Digest 1994;1(8):1– 8.

[10] Thompson, D.I., Cullen, K., Baranowski, T., 2006. Using Com-

puter Games and Other Media To Decrease Child Obesity.

Agricultural Research 2006 March.

[11] Coshott, R.J., 2007. Encouraging exercise whilst playing elec-

tronic games. USPTO Application number: 20090098979.

[12] Tudor-Locke, C., Pangrazi, R.P., Corbin, C.B., Rutherford, W.J.,

Vincent, S.D., Raustorp, A., et al, 2004. BMI-referenced

standards for recommended pedometer-determined

steps/day in children. Preventive Medicine, 2004.

[13] Cook, D.J., Youngblood, M., Heierman, E., Gopalratnam, K.,

Rao, S., Litvin, A., Khawaja, F., 2003. MavHome: An agent-

based smart home, in: Proceedings of the IEEE International

Conference on Pervasive Computing and Communications,

2003, pp. 521–524.

[14] Jiang, H., Han, Z., Scuccess, P., Robidoux, S., Sun, Y., 2000.

Voice-activated environmental control system for persons

with disabilities. Proc. of the IEEE 26th Annual Northeast

Bioengineering Conference, 2000, pp. 167–169.

[15] Cole, A., Tran, B., 2002. Home automation to promote inde-

pendent living in elderly populations. Presented at Proceed-

ings of the 2002 IEEE Engineering in Medicine and Biology

24th Annual Conference and the 2002 Fall Meeting of the

Biomedical Engineering Society (BMES / EMBS), Oct 23–26

2002, Houston, TX, United States, 2002.

[16] The ActiveHome Pro SDK from X10, http://www.x10.com/

activehomepro/sdk/

[17] Dr. Gily’s Health Portal, http://www.drgily.com/exercise-cal-

orie-counter.php

[18] Annavaram, M., Medvidovic, N., Mitra, U., Narayanan, S., Spru-

ijt-Metz, D., Sukhatme, G., Meng, Z., Qiu, S., Kumar, R., and

Thatte, G., 2008. Multimodal sensing for pediatric obesity

applications. In UrbanSense08 Workshop at SenSys Raleigh,

NC, USA, November 2008.

[19] Metabolic Equivalent of Task (MET) from Wikipedia, http://

en.wikipedia.org/wiki/Metabolic_equivalent

[email protected]

For further reading: http://bit.ly/kLTVD3


TEC: A TRAINING GROUND THAT BRINGS THE UNIVERSE TO OUR UNIVERSITY

At UCLA, the Technical Entrepreneurial Community

(TEC) is a group operating since 2005 with just that

aim. TEC actively fosters a culture of entrepreneur-

ship at UCLA—answering the question, “What will

I innovate or do with what I have learned?” TEC

believes that a business entrepreneur can come

from any background or direction. Its activities

help create environments where successful alumni

share with Bruins to discuss, educate, network, and

start companies.

How would you like to find out if you should start a

business with your degree?

Because TEC is open to all UCLA students, faculty,

alumni, and staff and is free of charge, its events

help connect the dots for people in all disciplines,

whether undergraduates, graduates, or post-docs.

TEC currently offers eight programs for the UCLA

community. Every year since its inception, the

number of programs and attendees has grown.

The organization takes part in several entrepre-

neurial programs and services as follows:

ITA Program

Participants work with the Institute for

Technology Advancement to help develop

their startup ideas.

Technology Assessment Group

Participants learn the process to evaluate

early-stage technology for commercial

feasibility through a seminar series, re-

port writing, and delivering a presenta-

tion on a UCLA patent. Participants who

complete the program obtain a signed

certificate from the Dean of the Engineer-

ing school and from the Vice Provost.

Tech Coast Angels Mentorship (TCA)

Startups receive guidance from angel in-

vestors.

Cross-Campus Entrepreneurship Mixer

A mixer is arranged where engineering

Tanuj Thapliyal, Electrical Engineering

LIFE AT COLLEGE LETS YOU LEARN ABOUT ONE WORLD. LIFE AFTER EARNING YOUR DEGREE REQUIRES AN ENORMOUS READJUSTMENT. IS THERE A WAY TO BRIDGE THE TRANSITION BETWEEN THE TWO?


students can network with business

school students. 93 students attended the

inaugural mixer in February.

Dinner with an Entrepreneur

Participants have a private dinner with a

successful alumnus.

In-House Legal Counsel

Participants obtain free legal counsel on

intellectual property.

Seminars

Successful entrepreneurs, investors, and

lawyers speak on a variety of issues.

TCA Screening Sessions

Participants are a “fly on the wall” as they

hear entrepreneurs pitch business plans

to investors.

TEC is entirely student-run and set up to incorpo-

rate new programs with people’s interests. Students

who have ideas for entrepreneurship activities that

are not currently offered are encouraged to become

a part of TEC and submit a proposal. Working in

collaboration with other members of TEC will defi-

nitely foster one’s own development of networking,

communication, and leadership skills—all impera-

tive when Bruins complete their training at UCLA

and begin their transition into industry.

WORKING IN COLLABORATION

WITH OTHER MEMBERS OF TEC

WILL DEFINITELY FOSTER ONE’S

OWN DEVELOPMENT OF NET-

WORKING, COMMUNICATION,

AND LEADERSHIP SKILLS—

ALL IMPERATIVE WHEN BRUINS

COMPLETE THEIR TRAINING AT

UCLA AND BEGIN THEIR TRAN-

SITION INTO INDUSTRY.

HTTP://WWW.TECBRUINS.ORG

[email protected]


FIGURE 1

MAZDA’S CROSS-SECTIONAL ROTARY ENGINE

DISPLAYED AT THE MAZDA RACEWAY IN MONTEREY, CA.


After seeing their first working prototype,

Americans Glenn Curtiss and the Wright broth-

ers recognized the potential of such a small, light-

weight, and powerful engine; they immediately

bought the North American rights and built their

own. Incredibly, the Wankel-type rotary engine

was about half the size, half the weight, half the

manufacturing cost, and had fewer than half the

moving parts, for the same horsepower as the well-

established piston engine. Because of the intrinsic

rotational motion, the rotary is a quieter engine, is

more compact, and has less vibration while being

higher revving. By 1975, nearly every major auto-

motive company in the world was anticipating the

eclipse of the reciprocating piston engine and ei-

ther held a license, or was in negotiations, to build

Wankel’s rotary engine [1].

Fifty years have passed since the debut of the

Wankel engine. NSU was eventually absorbed by

Volkswagen then coalesced into Audi, and the Wan-

kel disappeared from the auto manufacturing land-

INNOVATIVE DESIGN METHOD CREATES NEW VARIETIES OF ROTARY ENGINES

Sarah Warren, Mechanical & Aerospace Engineering

scape with the advent of the Iranian revolution and

the subsequent 1979 crude oil shortage. The rela-

tively new engine had been under limited develop-

ment for about ten years and had not yet achieved

the fuel efficiency that it commands today; it was

not even competitive with its centenarian rival, the

piston engine. This single deficiency nearly erased

the Wankel from automotive history. Now the only

manufacturer building a production rotary engine

is Mazda, for their RX-8 sports coupe.

Even today Wankel’s rotary engine does not

have the fuel efficiency of a comparable piston en-

gine, inhibiting any economical applications and in-

stead reserving it for performance purposes. Mazda

stunned the world in 1991 by winning the 24 Hours

of Le Mans with their rotary race car, prompting an

immediate rule change to make rotary engines in-

eligible against piston engines due to their inherent

and insurmountable advantage. Even so, the light-

weight powerhouse has proved itself repeatedly in

racing competition since then. The Wankel’s lower

THE ONLY ROTARY ENGINE DESIGN THAT HAS ENJOYED ANY MEASURE OF POPULARITY AND SUCCESS IS ATTRIBUTED TO ENGINEER FELIX WANKEL AND THE GERMAN AUTOMOTIVE COMPANY NSU.


fuel efficiency may be attributed to a few inher-

ent features, including the combustion chamber

and the apex seals. But optimizing the combustion

chamber and engine sealing—in fact, changing any

part of a Wankel engine profile—is not so easy.

One other remarkable and unique character-

istic of the rotary engine is its fuel versatility. The

engine design itself is inherently octane-flexible

and easily adaptable to alternative fuels. Mazda

has demonstrated this with gasoline and hydrogen

hybrid vehicles, designing a duel-fuel rotary engine

that can alternate between the two fuels at the flip

of a switch. It is conceivable that the rotary engine,

with improvements, could reduce fossil-fuel de-

pendency while allowing the defense industry, rac-

ing industry, and vehicle enthusiasts to indulge in

performance.

The scope of this research project includes

applying a relatively new method of conjugate pair

generation in order to design optimal rotary engine

alternatives to the Wankel. This method is called

the deviation-function (DF) method and was origi-

nally developed by Yang, Tong, and Lin and applied

to lobe pumps [9].

WANKEL’S ROTARY ENGINE The photograph in Figure 1 shows a cross-sec-

tion of Mazda’s Wankel engine. The triangular rotor

rotates eccentrically inside the oval-shaped engine

housing. The engine’s crankshaft is at the center of

the housing and rotates three times for every full

revolution of the rotor. During its orbit, the rotor is

always and only in contact with the housing at the

three apexes, never contacting the housing along

its flanks. This creates three chambers between the

rotor flanks and the housing, effectively sealed and

thus thermodynamically isolated from each other.

The chambers are constantly changing shape as the

rotor turns, allowing the four strokes of the Otto

cycle to occur sequentially for each chamber and

simultaneously on all sides of the rotor, similar to

a multi-piston internal combustion engine. The in-

take and exhaust breathing is through ports in the

housing, eliminating the need for valves and a valve

train. This is one reason why the rotary engine has

so many fewer parts than the reciprocating engine.

Thus the rotor and housing are analogous to a pis-

ton and cylinder, but one clear advantage of the

Wankel is the frequency of the power stroke. For a

one-cylinder 4-stroke engine, the power stroke oc-

curs once for two rotations of the crankshaft. For a

one-rotor Wankel engine, the power stroke occurs

on every rotation of the crankshaft, utilizing its dis-

placement twice as frequently.

Although not always credited by name, all pro-

duction rotary engines today are of Wankel’s origi-

nal design proportions, whether the application is

a sports car, compressor, model airplane, or out-

board motor. Since no other rotary engine design

has ever been in production, the name Wankel is

often thought to be synonymous with a rotary and

reciprocating combustion engine. In fact, the Wan-

kel engine has specific proportions for the 3-lobed

rotor and its conjugate housing profile. All design

variations are engendered by tweaking a single ra-

tio of rotor radius R to eccentricity e, called the K

factor, or trochoid constant.

ALL PRODUCTION ROTARY

ENGINES TODAY ARE OF

WANKEL’S ORIGINAL DESIGN

PROPORTIONS, WHETHER

THE APPLICATION IS A SPORTS

CAR, COMPRESSOR, MODEL

AIRPLANE, OR OUTBOARD

MOTOR.


The housing of a Wankel engine is very spe-

cifically defined by generating a particular epitro-

choidal curve. An epitrochoid is a type of trochoid

curve, and a trochoid is generated by the path of a

single point within a circle that rolls along the pe-

riphery of another (base) circle. The base circle is

stationary, the rolling circle is called the generating

circle, and the point that draws the path is called

the generating point. The two circles are related

in size because one must have a radius that is a

whole number multiple of the other. To create an

epitrochoid curve, the generating circle rolls on the

outside of the base circle, illustrated in Figure 2(a).

For the housing of a Wankel engine, the generating

circle of radius r2 is equal to half of the base circle

radius r1. In Figure 2(a), the stationary base circle

is large, the generating circle is small, and the gen-

erating point is identified by its radius, eccentricity

e, and is marked along its epitrochoidal path. The

generating circle rolls without slipping on the base

circle, and all of its points describe trochoids, ex-

cept the center whose path is a circle. A family of

epitrochoid curves is defined by choosing different

points (excluding the circumference) on the gener-

ating circle as shown in Figure 2(b). Once an epitro-

choid is defined for a potential engine housing, the

difference between the other curves in the same

family is the eccentricity. Thus, eccentricity is one

of the characteristics used to design Wankel engine

profiles, the other being rotor radius R. Rotor ra-

dius is the distance between the center of the ro-

tor and its apex, as indicated in Figure 3(b). The

profile of the 3-lobed rotor is found by rolling the

2-lobed epitrochoid around the rotor’s eccentric

center. Examining all incremental positions of the

epitrochoid during its orbit at the same time re-

veals two 3-lobed envelopes of the overall motion,

FIGURE 2

EPITROCHOID GEOMETRY.

(A) EPITROCHOID

(B) CHANGING ECCENTRICITY

ECCENTRICITY IS ONE

OF THE CHARACTERISTICS

USED TO DESIGN WANKEL

ENGINE PROFILES; THE OTHER

BEING ROTOR RADIUS.


shown in Figure 3(a). The silhouettes of the various

positions of the engine housing profile while roll-

ing around describe the conjugate inner envelope

and the conjugate outer envelope. A rotary engine’s

3-lobed rotor is the engine housing’s conjugate in-

ner envelope, correctly positioned together in Fig-

ure 3(b). The ratio of rotor radius R to eccentricity

e (the K factor) determines the engine compression

ratio, the relative volumetric displacement, and the

maximum leaning angle. There is a unique K factor

for all Wankel engine designs, so by deciding on a

single design criterion, such as maximum leaning

angle, the design of the engine profile is completed.

This method of designing a trochoidal mechanism

may recall the child’s toy Spirograph, and indeed

part of the success of Wankel’s design can be attrib-

uted to the easily understood geometry. However,

this simplistic methodology belies the profound

and powerful relations of trochoids and their con-

jugate envelopes [5] [8].

FIGURE 3

WANKEL ENGINE GEOMETRY.

(A) INNER AND OUTER ENVELOPES

(B) HOUSING AND ROTOR PROFILES

http://bit.ly/lFkRRP

http://bit.ly/lFkRRP


The main design considerations for the pro-

files of a Wankel engine are size (physical dimen-

sions), relative volumetric displacement, theoreti-

cal compression ratio, and maximum leaning angle.

The size of the profiles relative to the pitch circles

and the eccentricity are both manifest in the gear

set that positions the rotor relative to the housing.

The annular gear (for positioning, not power trans-

fer) is placed inside the rotor, and the eccentricity

of the designed profiles is sustained in the rotor’s

eccentric shaft. Theoretical compression ratio and

maximum leaning angle are indicators of horse-

power and efficiency, and both are limited by prac-

ticality. The leaning angle, also called angle of oscil-

lation or incidence, refers to the angle of contact that

the apex seals have with the housing as the rotor

orbits. The maximum leaning angle indicates how

well each chamber can be sealed from its neighbors

and thus serves as a sealing index. Manufacturers

of the Wankel engine have achieved optimal sealing

by using housings with fatter “waistlines.” This type

of chamber has lower maximum leaning angles, uti-

lizing smaller swaths of contact on their apex seals.

A higher maximum leaning angle, characteristic of

housings with high eccentricity (a sharply defined

waistline), means that the apex seals are in working

contact closer to their edges, which is detrimental

to sealing and will wear the seals out sooner [3].

DEVIATION-FUNCTION METHODIntroducing a new method to rotary engine

design demands adhering to certain industry

standards so that results of this project can be eas-

ily, fairly, and convincingly compared to the Wankel

engine. In accordance with this objective, the same

design and performance criteria of displacement,

compression ratio, leaning angle, and efficiency

are and will be calculated for DF-designed profiles.

The variety of rotary engine designs now available

for optimization is expected to generate interest

among design engineers and manufacturers. Ide-

ally, the complete development of the DF algorithm

as it applies to the rotary engine will appeal as an

accessible methodology for achieving previously

inaccessible results.

FIGURE 4

PITCH PAIR EXAMPLES.

(A) TWO GEARS WITH

THEIR PITCH CURVES

(B) NONCIRCULAR

EXTERNAL PITCH CURVES [7]

http://bit.ly/mBRETN

THE VARIETY OF ROTARY

ENGINE DESIGNS NOW

AVAILABLE FOR OPTIMIZATION

IS EXPECTED TO GENERATE

INTEREST AMONG DESIGN

ENGINEERS AND

MANUFACTURERS.


The possibilities of the DF method are wide-

ranging but cannot be described without first ap-

preciating conjugate pairs. Two rigid bodies that

are in contact along their profiles and have relative

motion, such as rolling or sliding, are called conju-

gate pairs. Two gears in mesh are an example of a

conjugate pair that have combined rolling and slid-

ing contact. When a conjugate pair is in pure roll-

ing contact, then the profiles are called centrodes,

and when the centrodes each have respective fixed

rotation centers, they are referred to as pitch pro-

files, or pitch curves. In general, the relative motion

of a conjugate pair is specified by its correspond-

ing pitch pair. Figure 4(a) shows two gears in mesh

(the conjugate pair) and the corresponding pitch

circles (the pitch pair) [6].

The design of conjugate pairs involves two steps:

1. Design the conjugate pitch pair.

2. Design a generated pair based on the

pitch pair.

The pitch pair has pure rolling contact and

defines the relative motion of the meshing con-

jugate pair. In generating the conjugate pair from

the pitch pair, the geometry of the pitch profile

is altered such that the required task can be per-

formed; for example, a pitch circle is transformed

to the toothed body of a gear. The generated conju-

gate pair in mesh has sliding contact, so the relative

motion is not pure rolling, but the speed ratio be-

tween them is the same as for the parent pitch pair.

The collective drawbacks to all current methods for

conjugate pair generation are

1. Limitation in the types of pitch pairs

they can be applied to.

2. Use of noncircular pitch curves requir-

ing solutions by high-order nonlinear

equations.

3. Lack of geometrically intuitive design of

profiles of the generated pairs.

4. Inability to assign analytical properties

to the designed conjugate profiles.

FIGURE 5

EXAMPLES OF CIRCULAR AND

NON-CIRCULAR INTERNAL

PITCH CURVES.

THE INEFFICIENCY OF THE

DESIGN PROCESS IMPEDES

THE INVENTION OF NEW

GEARS, ROTORS, AND OTHER

CONJUGATE MECHANISMS.


Because of these limitations, the design of

conjugate pairs is a blind trial-and-error process.

This makes it difficult to improve conjugate pair de-

sign and analyze resulting profiles. The inefficiency

of the design process impedes the invention of new

gears, rotors, and other conjugate mechanisms.

The deviation-function (DF) method was de-

veloped to address these problems; it works for all

pitch pairs, circular or noncircular, it enables geo-

metrically intuitive designing of profiles, and ana-

lytical properties can be assigned to the generated

conjugate pairs [6].

In practice, pitch curves are almost always a

pair of perfect circles, or circular solid objects, such

as cones or cylinders. Because of the limitations of

conventional conjugate pair generating methods,

only numerical methods are used to generate non-

circular pitch curves. The only known application in

industry for noncircular pitch curves is noncircular

gears. The large amount of computation required,

combined with poor understanding of their prop-

erties and how to design them, has limited their

use. The most frequent application has been for a

pair of identical elliptical gears [2]. Their potential

has been acknowledged, however, because of their

successful application in quick-return mechanisms,

speed matching on assembly lines, and stop-and-

dwell motion. Noncircular gears have been used to

improve the functionality and simplicity of mecha-

nisms, but their diversity has so far been limited

to ellipses [4]. Figure 4(b) shows a pair of external

noncircular (and nonelliptical) pitch curves that

were designed for use with the DF method as it ap-

plies to lobe pumps [7].

Internal pitch curves, like those in Figure 5,

are the type that apply to the rotary engine. The in-

novation, potential, and main objective of the work

described here is in the development of how the DF

method and noncircular pitch curves can optimize

rotary engine design. The DF method can be used

with pitch circles to design conventional Wankel

FIGURE 6

CONVENTIONAL WANKEL

ENGINE PROFILES WITH THEIR

CIRCULAR PITCH CURVES.

http://bit.ly/lVESVB

THE INNOVATION, POTENTIAL,

AND MAIN OBJECTIVE

OF THE WORK DESCRIBED

HERE IS IN THE DEVELOPMENT

OF HOW THE DF METHOD

AND NON-CIRCULAR PITCH

CURVES CAN OPTIMIZE

ROTARY ENGINE DESIGN.


engine profiles, and examples of these results are

shown in Figure 6, along with their calculated K fac-

tors.

In practice, noncircular internal pitch curves

are even more unusual than noncircular external

pitch curves. The acknowledged success of non-

circular gears in certain applications suggests that

mechanisms using internal pitch pairs may also be

improved by noncircular pitch curves. The devia-

tion-function method is a new solution to an inhib-

iting problem for noncircular internal pitch curves;

up until the DF method was developed there was

no systematic way of designing, and therefore uti-

lizing, noncircular internal pitch curves.

The DF method involves first and foremost

choosing a so-called deviation function, which in-

evitably contains parameters that are adjustable

for rendering different profiles. This is the crux of

using the DF method to design for particular me-

chanical properties. Even considering one type

of deviation function, say sinusoidal or quadratic

polynomial, the possible combinations of param-

eters are literally endless, encompassing a broad

range of design solutions that are inaccessible by

conventional design methods. Because of the sheer

number of possibilities, compounded by the variety

of monotonically increasing continuous functions,

a selection method to find the best solution is nec-

essary for practicality. The algorithms for applying

the DF method to circular and noncircular pitch

rotary engine design have been developed; all that

remains for the practical application of these equa-

tions is relating them and their parameters to the

standard design criteria for Wankel engines. One of

the ultimate goals of this research is to develop a

DF-based selection method for the rotary engine.

Figure 7 shows some of the possible rotary

engine profiles resulting from a single pair of non-

circular pitch curves, using different deviation

functions. The noncircular pitch pair is drawn in

thin lines, and the rotary engine housing and trian-

gular rotor are drawn in heavy lines. Only a single

parameter in the deviation function is changed to

generate the different profiles. Each deviation func-

tion may have four or more parameters that can

be manipulated to achieve different results. Figure

FIGURE 8 (ABOVE)

DF-DESIGNED ROTARY ENGINE

PROFILES USING DIFFERENT

NON-CIRCULAR PITCHES.

http://bit.ly/kmZxA8

FIGURE 7 (BELOW)

DF-DESIGNED ROTARY ENGINE

PROFILES USING THE SAME

NONCIRCULAR PITCH.

http://bit.ly/mwzl8w

http://bit.ly/mwzl8w


8 shows examples of using different noncircular

pitch curves with the same deviation function. The

pitch curves in Figure 8 were generated using the

same function, but increasing the noncircularity

from left to right.

To demonstrate possible design improve-

ments over the conventional Wankel engine solu-

tion, the relative volumetric displacement, maxi-

mum compression ratio, and maximum leaning

angle are calculated for a range of noncircular pitch

rotary engines.

WANKEL AND DF-DESIGNED ROTARY ENGINE COMPARISON

Felix Wankel’s original rotary engine design

was undoubtedly ingenious, but unforgiving and

limited in terms of modification and experimen-

tation. The complexity and calculations required

to manipulate and design other conjugate profiles

made Wankel’s simplicity ironclad. The plot in

Figure 9 shows the three main design criteria for

a Wankel engine on a single line. As mentioned pre-

viously, by selecting any of the three criteria specifi-

cally, or by selecting the K factor, the design of the

engine profile is completed. In the conventional de-

sign process the relative displacement and theoret-

ical compression ratio are limited by a range within

which the maximum leaning angle is minimized.

The reason for this is that the maximum leaning an-

gle is probably the most important criterion since it

serves as a sealing index and has the largest impact

on engine efficiency and longevity. Additionally, the

relative displacement and theoretical compression

ratio can be adjusted by the width of the rotor after

the two-dimensional profile has been determined.

The scattered dots in Figure 9 represent de-

viation-function-designed noncircular-pitch rotary

engine profile results. Only three of eight deviation-

FIGURE 9

ROTARY ENGINE DESIGN

CRITERIA CHART.


THE ROTARY ENGINES

POTENTIAL HAS NOT BEEN

REALIZED WITH ALTERNA-

TIVE FUELS; AND POSSIBLY

ITS PERFORMANCE COULD BE

IMPROVED FOR GASOLINE

AND DIESEL-POWERED

APPLICATIONS.


function parameters were manipulated to generate

this plot: pitch curve noncircularity, eccentricity,

and switch angle. Switch angle represents the con-

jugating range of the rotor and the housing and thus

affects the maximum leaning angle. Besides the DF

design results that are clinging to the conventional

Wankel engine line, clusters appear as certain fami-

lies of profiles emerge with similar results. This

suggests that a more exhaustive survey and smaller

increments of manipulation will produce distinct

blobs of heretofore inaccessible rotary engine so-

lutions. Dramatic changes to the engine profile are

not necessary, however, to improve the maximum

leaning angle. Many DF design results fall close to

the conventional Wankel engine design line, but

not directly on it. So a Wankel engine of certain dis-

placement and compression ratio can be changed

slightly but improved significantly by selecting a

DF-designed rotary engine with a lower maximum

leaning angle.

CONCLUSIONS AND FUTURE WORKThe ultimate goal of this research is to develop

a method of selection in which the DF parameters

can be manipulated to control performance crite-

ria. All results so far have been obtained computa-

tionally, so the next immediate step is to fully real-

ize the equations governing the noncircular-pitch

engine housing and rotor profiles, as well as the

resulting characteristics. Specifically applying the

DF algorithm to rotary engine design requires com-

putational geometry, especially envelope theory,

to define the housing and rotor profile equations.

After all profile equations have been established in

terms of DF parameters, the relationships between

the rotor geometry and the design criteria can be

determined. Performance indices to evaluate an

engine’s power efficiency and volumetric efficiency

will be defined so that the influence of pitch curve

noncircularity, and all other design parameters, is

clear. Then the new DF method of design can be ac-

cessible and utilized in a familiar and practical way.

More than likely, the rotary engine’s potential has

not yet been realized for use with alternative fuels;

and possibly its performance could be improved for

gasoline and diesel-powered applications. Explor-

ing the alternatives now available by using the DF

method and noncircular pitch curves makes opti-

mization and innovation possible.

REFERENCES[1] Cole, David E. “The Wankel Engine.” Scientific American Vol-

ume 227 No. 2 (August 1972) 14–23.

[2] Dooner, D. B. and A. A. Seireg. The Kinematics of Gearing. John

Wiley & Sons, Inc., 1995, 56–63.

[3] Hege, John B. The Wankel Rotary Engine. Jefferson, NC: McFar-

land & Company, Inc., 2001.

[4] Litvin, Faydor L., Alfonso Fuentes-Aznar, Ignacio Gonzalez-Pe-

rez, and Kenichi Hayasaka. Noncircular Gears: Design and

Generation. Cambridge University Press, 2009.

[5] Norbye, Jan. The Wankel Engine. Philadelphia; Chilton Book

Company, 1971.

[6] Tong, Shih-Hsi. New Conjugate Design Pair - Theory and Appli-

cation. Ph.D. thesis. University of California - Los Angeles,

1998.

[7] Tong, Shih-Hsi, and Daniel C. H. Yang. “Generation of Identical

Noncircular Pitch Curves.” Transactions of the ASME Jour-

nal of Mechanical Design 120 (1998) 337–341.

[8] Yamamoto, Kenichi. Rotary Engine. Toyo Kogyo Co., Ltd., 1981.

[9] Yang, Daniel C. H., Shih-Hsi Tong, and J. Lin. “Deviation-Func-

tion Based Pitch Curve Modification for Conjugate Pair

Design.” Transactions of the ASME Journal of Mechanical

Design 121 (1999) 579–586.

[email protected]

To see animated demos: http://bit.ly/j3OUTK


THE SOCIAL NETWORK ISEVERYWHERE IN ENGINEERING

WHAT TO READ?WITH WHOM TO WORK?WHERE TO PUBLISH?Jong Hoon Ahnn, Computer Science

SCIENTIFIC TECHNIQUES FOR ORGANIZING AND CONDUCTING ENGINEERING RESEARCH



In recent years, the growth of social networks

within engineering research has become wide-

spread. As no surprise, such a network is composed

of more than databases, relationships, and merely

the linking lots of information together. Research-

ers and scientists for example are commonly tied

by one or several specific types of interdepend-

ency, such as friendship, employment, mentorship,

discipleship, similarity of beliefs, and recognized

knowledge or prestige. From the relationships in

these networks, there have even arisen databases

that track academic published papers with authors:

Who has been published and is related to profes-

sors and other authors. Sifting out these patterns

among data—a kind of meta-database linking their

publishing relationships—has grown more impor-

tant as content contained in the World Wide Web

has grown. I have been fascinated with this type

of problem since 2009 and have developed some

harvesting techniques over the last two years, em-

ploying the world of academic citations mentioned

above as my model.

It was an interesting problem in the begin-

ning: my advisor in Computer Science Miodrag

Potkonjak asked me one day, “Can you imagine a

technique people could use to help them locate the

most productive, creative, and prolific new discov-

eries in a blogosphere of contributions?” To answer

FIGURE 1

A CITATION NETWORK FOR

DAVID E. CULLER AT UC BERKELEY

WHO HAS 308 PUBLICATIONS,

20,978 CITATIONS, AND 283 CO-

AUTHORS. THE CLOSER AN EDGE

LINKING A PEER TO SOMEONE,

THE MORE CITATIONS OF THE

PERSON’S WORK FROM THE PEER..

FOR EXAMPLE, THE CLOSEST CO-

AUTHOR, PHILIP ALEXANDER LEVIS

AT STANFORD, HAS CITED DAVID E.

CULLER 351 TIMES IN THE YEARS

1985–2010.


that, a data set with which to model and test this

seemed already at hand: as a graduate student, my

world revolves around academic papers—learning

others’ work and contributing my own. It’s a siz-

able population.

According to Microsoft Academic Search,

there are 10,869 researchers with at least twenty

citations in highly respected publications. Taking as

an example the social network of David E. Culler at

UC Berkeley, it is interesting to note he has worked

with 308 collaborators and garnered 20,978 cita-

tions, as shown in Figure 1. The number of cita-

tions is drastically different for different authors. It

is tempting to think that co-authoring papers with

most-cited authors or the most prolific authors

would be helpful for one’s own citation records.

Furthermore, there is a question whether it is bet-

ter to publish at highly specialized, smaller confer-

ences dedicated to an emerging topic or to target

the most prestigious and established venues. In

summary, each researcher must make a number of

important decisions that impact not just her count

of citations, but also the quality of her research.

The need for building such an author citation

database has become apparent: our practical objec-

tive is to address some of the essential productiv-

ity questions for persons in engineering, including

such questions as What papers to read? Where to

publish? and With whom to collaborate? In analyz-

ing publication databases, the scientific objective

is to develop statistically sound techniques for de-

scribing what guides the actions of highly success-

ful contributors to engineering research, whom we

define for the purposes of this article as ones who

author the most cited papers. It is evident such

techniques for establishing relationships (a “Who’s

who” of activity) can be applied to any number of

other kinds of data sets: sports, foreign languages,

the spread of epidemics, patents, and many others.

What we crafted over the two years has the

following characteristics. At the core of our ap-

proach is analysis and prediction of the expected

number of citations for a paper starting at the mo-

ment of its publication. Essentially, for the purpose

of our analysis we assume that the goal of a re-

searcher is to publish highly cited papers. Of course

that is not an altogether correct assumption; how-

ever we postulate that there is a strong correlation

between the number of citations and the actual

impact of the paper. We also assume a strong cor-

relation between the authors of highly cited papers

and their impact. One additional line of reasoning is

that one can benefit from reading papers that will

be highly cited following their publication. There-

fore, our goal is to predict the number of citations

a given paper will receive over a specified period

of time, as well as the likelihood that it will receive

that number of citations, by only using data that is

available at the time of publishing. This informa-

tion includes several citation indices, the average

number of citations for papers at a given database,

and the citation data for co-authors.

In developing this analysis method, we had to

wrestle with questions and technical challenges.

We found first of all that the vast majority of papers

have very few citations. Therefore, to take that into

account, any accurate prediction algorithm for the

majority of papers must predict that a given paper

will have very few citations. The resolution be-

tween well performing algorithms is blurred due to

the distribution of citations followed by the power

ONE OF OUR OBJECTIVES

WAS TO EXTRACT A CAUSAL

RELATIONSHIP BETWEEN EACH

PREDICTION PARAMETER AND

THE NUMBER OF CITATIONS.


law. We addressed this problem by employing clas-

sification instead of prediction. Another challenge

was how to select a correct classification algorithm.

There exists already a huge number of widely used

classification procedures; it is tempting to add to

that collection and develop yet another classifi-

cation technique. However, it is evident that goes

into an area of diminishing returns. Our analysis

showed that different classification algorithms best

perform on different data sets. Therefore, we de-

cided to look for better predictions elsewhere, i.e.

in identifying best predictors. Of course, identifica-

tion of essential predictors was critical to our work.

Our procedure for finding accurate prediction pa-

rameters followed two dimensions. The first was

looking for predictors that are accurate by them-

selves. The second was to identify principal com-

ponents, i.e. predictors that predict well when they

are used simultaneously in combinations. Finally

(and maybe most significantly), one of our objec-

tives was to extract a causal relationship between

each prediction parameter and the number of cita-

tions. A firm grasp of this knowledge may in fact be

more important than the actual prediction of the

number of citations for a given paper.

Put all together, we generated prediction re-

sults based on a generic framework that we de-

veloped. We call this framework CiteCast. It is fed

with publication network data sets as inputs, and

it terminates when the prediction accuracy cannot

be improved. During the process, it selects one ma-

chine learning (ML) algorithm and its parameters

to obtain the best prediction results as outputs. In

our case, for a given paper, the goal was to predict

citation counts one year following publication. We

used only information available at publication time

(zero citation history) as predictive features for an

individual data set, extracted from the feature ex-

traction scheme. We solved this citation prediction

problem as a multi-classification problem using a

set of thresholds, where thresholds can be obtained

from the distribution of citation counts in the data

FIGURE 2

BLOCK DIAGRAM OF OUR

CITATION PREDICTION

FRAMEWORK.


set; a training set was a set of pre-classified sam-

ples. Each sample as a paper instance consisted of

a feature sample. The training set was augmented

with a class vector which represents the class to

which each sample belonged. Using a classifier

with multi-classes, we obtained citation prediction

results as to which class the predicted citations be-

longed. Importantly, we defined prediction accura-

cy to be the number of correctly classified instances

divided by the total number of instances.

For the features, we employed three terms: h-

index, g-index, and e-index, which are closely relat-

ed in that h-index is a basis of g-index and e-index.

The g-index is defined as the highest number g of

papers that together have received g2 or more ci-

tations. From this definition it is already clear that

g = h”. In contrast to the h-index, the g-index gives

more weight to highly cited papers. The e-index on

the other hand tries to represent excess citations

that the h-index ignores.

To create this data shifting technique, we

made progress only after clearly understanding

these relationships. The key technique that we

found was to select features based on a linear cor-

relation coefficient (CC) c, while a regression coef-

ficient is heavily utilized in the literature for feature

selection. The reason is that a CC-based approach

is shown to be less sensitive to the year of predic-

tion in our experiments. Let us denote fi and fj to

be an individual feature. The higher |c| we get, the

more correlation between a selected feature and a

predicting target.

When we had gotten about halfway along, I

mentioned at one point to Potkonjak, “Isn’t this

exciting?” and he gave me a knowing look. Further

work began yielding accurate results to the point

that research about published papers warranted it-

self contributing a paper and sharing the progress

we had made.

Now, where things stand today on this work is

that no matter what data sets are used, we can au-

tomatically extract a set of predictive features from

the data set. The framework builds a classifier with

multi-classes using a set of thresholds determined

from the distribution of citations, selects then one

ML algorithm to predict future citations, identifies

the top n features by one of the ranking algorithms,

and finally tunes parameters of the ML algorithm to

obtain the best results at the end. The framework

depicted in Figure 2 runs until it obtains a best-per-

forming ML algorithm with proper parameter set-

tings. Although we used features with correlation

coefficients less than 0.5, a combination of the top

n features performs well with 82.5% of accuracy in

tenth year predictions. We also applied it to the data

set from the database field, thus generating 83.5%

prediction accuracy in the same setting. Interest-

ingly, the e-index turns out to be a very important

factor in the database field, while the h-index plays

an important role in different data sets.

As an interesting application to our methods,

we applied it to the prediction problem of an au-

WE DEVELOPED CITECAST,

A FRAMEWORK THAT IS FED

WITH PUBLICATION NETWORK

DATA SETS AS INPUTS, AND IT

TERMINATES WHEN PREDIC-

TION ACCURACY CANNOT BE

IMPROVED.

c f fn f f f f

n f f n f fi j

i j i j

i i j j

( , ) =− ( )( )

( ) − ( ) ( ) − ( )∑ ∑ ∑

∑ ∑ ∑ ∑22

22

(1)


thor’s future ranking, based on the same data set

used in the citation prediction problem. A co-author

graph presents an equally interesting relationship

between authors. The implication illustrated in Fig-

ure 3 is that, for an edge connected more closely

to a targeted person, the more publications the

connected person has co-authored with the target.

For instance, David E. Culler has had 308 publica-

tions in total as of 2010, and among them twenty-

three papers are with his collaborator Eric Brewer

at UC Berkeley. Such a relationship from the graph

has garnered a great deal of attention in academia,

because quantifying the quality of an individual’s

research outcomes is important to evaluate his per-

formance and make choices about supporting fu-

ture directions. The problem to solve in more detail

concerns what to measure and, more importantly,

how to evaluate the predictive nature of the quanti-

tative metrics used.

For this reason, predicting the future rank

of individual authors is important because it can

provide a powerful new method for assessing the

quality of research outcomes in advance. Faster

identification of promising individuals can draw at-

tention in the early stages of one’s career and pro-

vide potential benefits on promotion, tenure, and

new funding opportunities. We introduce RankCast

based on the same framework used in CiteCast.

A typical approach to author rank is to cal-

culate and rank it via the number of citations for

each author. A widely accepted measure, the impact

factor can be derived by the number of citations di-

FIGURE 3

A CO-AUTHOR NETWORK FOR

DAVID E. CULLER AT UC BERKE-

LEY WHO HAS 308 PUBLICATIONS

AND 283 CO-AUTHORS.


PREDICTING THE FUTURE

RANK OF INDIVIDUAL AUTHORS

IS IMPORTANT BECAUSE IT

CAN PROVIDE A POWERFUL

NEW METHOD FOR ASSESSING

THE QUALITY OF RESEARCH

OUTCOMES IN ADVANCE.


vided by the number of publications for a given pe-

riod of time. By contrast, we define an author rank

in three different ways using the h-index, g-index,

or e-index, respectively. Now, we can define a no-

tion of author ranking as an author rank which can

be calculated by one of the bibliometric measures.

We found that the best feature for the author

ranking prediction is the h-index, which can help

achieve over 80 percent prediction accuracy. This

implies that authors who have a higher h-index at

one point in time are more likely to be important

in the future. The results additionally show that

features obtained from authors’ social networks do

not help much in predicting author ranking.

In summary, techniques for predicting future

citations have had a fundamental difficulty ever

since Price introduced the first quantitative stud-

ies in citation networks in 1965. Many researchers

have studied patterns of citation in scientific pub-

lications. Fu and Aliferis studied three prediction

models in their state-of-the-art work. They used bi-

omedical publications within a horizon of ten years

based only on predictive information available at

the time of publication. Their work is different from

our CiteCast in that our approach is to predict fu-

ture citations with bibliometric features based on

correlation coefficients which are more reliable in

prediction than are regression coefficients used by

Fu and Aliferis. The clear benefit of our framework

is its generic applicability to data sets. As an exam-

ple given above, we successfully applied it to an

author-ranking prediction problem. Although the

approach in FutureRank is similar to our RankCast

in that it utilizes the co-authorship network and

the time of article publication to predict future cita-

tions, RankCast has clear benefits in terms of pre-

diction performance as well as in the method itself.

Likewise, no matter what data sets are used as in-

puts and what prediction algorithms are used, the

framework can be easily applied to different types

of problems generating high prediction accuracy.

REFERENCES[1] D. Feitelson, and U. Yovel, “Predictive ranking of computer sci-

entists using CiteSeer data,” Journal of Documentation, vol.

60(1), pp. 44–61, 2004.

[2] M. E. J. Newman, “Power laws, Pareto distributions and Zipf’s

law,” Contemporary Physics, vol. 46, pp. 323–351, 2005.

[3] J. E. Hirsch, “Does the h-index have predictive power?,” PNAS,

vol. 104(49), pp. 19193–19198, 2007.

[4] L. Egghe, “Theory and practice of the g-index,” Scientometrics,

vol. 69(1), pp. 131–152, 2006.

[5] C-T Zhang, “The e-Index, Complementing the h-Index for Ex-

cess Citations,” PLoS ONE, vol. 4(5), 2009.

[6] L. D. Fu, and C. F. Aliferis, “Using content-based and bibliomet-

ric features for machine learning models to predict citation

counts in the biomedical literature,” Scientometrics, vol.

85(1), pp. 257–270, 2010.

[7] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and

I. H. Witten, “The WEKA Data Mining Software: An Update;

SIGKDD Explorations,” vol. 11(1), pp. 10–18, 2009.

[8] C. Lokker, and K. A. McKibbon, “Prediction of citation counts

for clinical articles at two years using data available within

three weeks of publication: Retrospective cohort study,”

BMJ, 2008.

[9] N. L. Geller, J. S. de Cani, and R. E. Davis, “Life time-citation

rates: a mathematical model to compare scientists’ work,”

Journal of the American Society for Information Science,

vol. 32(1), pp. 3–15, 1981.

[10] R. Van Noorden, “The Trials of New Carbon,” Nature , vol. 469,

pp. 14–16, 2011.

[11] Microsoft Academic Search. http://academic.research.micro-

soft.com.

For further reading: http://bit.ly/jDa7Te(accepted for publication in the International Conference on Microelectronic Systems Education (MSE), June, 2011)

[email protected]


WELL...

COURTESY OF TUMBLR:

http://tumblr.com/xjgsp2v2

What the customer described.

What got budgeted.What the

engineer designed.

How manufacturing installed it.

What marketing advertised.

What got documented.

What the customer finally received.

What the customer was billed for.

What the customer actually wanted.


Here at the UCLA Henry Samueli School of Engineering and Applied Sci-

ence, we are committed to providing a world-class education to our remarkable

students, so that they may one day become productive engineers and leaders

capable of addressing society’s most pressing needs and continue our tradition

of engineering excellence.

At the same time, our distinguished UCLA Engineering faculty are lead-

ing efforts to realize innovative and potentially paradigm-shifting concepts that

will address some of the grand challenges of the 21st century. These include re-

search in areas that could transform people’s lives including renewable energy

and energy efficiency; clean water; personalized healthcare; wireless network-

ing, sustainability and cyber security, just to name a few.

Together, the school’s exceptional faculty, students, and alumni have

helped place UCLA Engineering amongst the best engineering schools in the

country.

Recent rankings by the National Research Council of graduate engineer-

ing programs placed three UCLA Engineering programs in the top 10, and seven

in the top 20. This indicates the excellence in breadth and depth throughout the

school and in our seven academic departments.

In addition, our Ph.D. production per faculty member continues to be

amongst the very highest in the nation with 164 doctorate degrees awarded in

2010. We continue to enroll some of the brightest undergraduates in the coun-

try, receiving more than 11,400 applications for the fall 2011 freshman class.

The average weighted GPA of those admitted were 4.36, and their average SAT

score was 2142 out of 2400.

Of course, these numbers are just general indicators of the contribu-

tions of many individuals and their accomplishments. This inaugural issue of

the student-produced Bruin Innovation & Technology magazine spotlights just

a few of our talented UCLA Engineering students and their work. I hope read-

ers, especially those throughout the UCLA community, will come away with a

sense of shared pride in the work these engineering students are doing, and be

inspired by them as well.

One final note, congratulations to all those associated with this new mag-

azine for their foresight and perseverance in bringing it to fruition. I wish this

new publication great success and look forward to it becoming a tradition at

UCLA Engineering.

Sincerely,

Vijay K. DhirDean

65 Years of Driving Innovation

Documents

Bruin Innovation & Technology - Volume 1, Spring 2011