NTT Technical Review

March 2018 Vol. 16 No. 3
Feature Articles: Research and Development in Sports Brain Science

Understanding and Shaping the Athlete’s Brain―NTT Sports Brain Science Project―Sports Brain Laboratory for Measuring Athletic Competence and PerformanceTiming Adjustment of Baseball Batters Determined from Motion Analysis of Batting

Virtual Reality-based Sports Training System and Its Application to Baseball

Regular Articles
Branched Optical Fiber Loss Measurement Technology for End-to-end Testing in OpticalAccess Networks

High Precision 12-single-mode-fiber Multi-fiber Push-on Connector for Reference Use inConnector Inspection

Global Standardization Activities


Report on 21st Global Standards Collaboration (GSC-21)

External Awards/Papers Published in Technical Journals and Conference ProceedingsExternal Awards/Papers Published in Technical Journals and Conference Proceedings

Feature Articles: Research and Development in Sports
Brain Science

Understanding and Shaping theAthlete’s Brain—NTT Sports Brain Science
Project—
Makio Kashino
Abstract

In sports, a variety of brain functions hold the key to winning, such as grasping current conditions,strategizing against one’s opponent, and making instantaneous decisions under pressure. Most of thesefunctions, however, are implicit brain functions that the athlete is not even aware of. The NTT Sports
Brain Science project was established in January 2017 to conduct research with the aim of understanding
superior implicit brain functions in top athletes, identifying the factors in winning, and improving theperformance of athletes based on research findings.
Keywords: sports brain science, implicit brain functions, body-mind reading

1. Importance of the brain in sports
What are the necessary conditions for becoming an
outstanding athlete? It goes without saying thatphysical abilities are important such as a resilientbody excelling in muscular strength and cardiopulmonary function and an appropriate form that caneffectively produce power and minimize the risk of
injury. However, to reach even higher levels as an
athlete, physical abilities by themselves are not sufficient. There is also a need for other faculties such as
the ability to grasp match conditions, develop a strategy and make instantaneous decisions, the ability to
predict your opponent’s next move based on current
behavior and take preemptive action, the ability to
deceive and manipulate your opponent, the ability toquickly and flexibly adjust one’s actions even during
complex moves, and the ability to amply demonstrate
one’s strengths while under intense pressure during a
big match. None of these abilities can be achieved
without advanced information processing in thebrain. In general, top athletes are especially proficientin such brain functions.
1

Research in conventional sports science and associated training techniques has led to an accumulation ofsubstantial results from a physical point of view.
However, brain functions in relation to sports are stilllargely unexplored even from a global perspective.
Against this background, NTT launched its Sports
Brain Science project (SBS) in January 2017 as aninterdisciplinary research organization [1]. The purpose of SBS is to understand the brain functions that
support superior performance in top athletes and toactually improve the performance of athletes basedon those scientific findings. Now, with about one year
behind us, we are off to a good start, having gained
the cooperation of top-level athletes and teams centered on baseball and softball. Here, I would like tointroduce the basic concepts and research policy of
SBS.
2. Deciphering implicit brain functions
In sports where athletes play against an opponent,
for example, baseball and martial arts, the timeallowed for grasping ever-changing conditions, making
NTT Technical Review

Feature Articles

Sensing

Analysis
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and eye movements

Fig. 1. Deciphering and adjusting implicit brain functions.

decisions and predictions, and adjusting movements
is exceedingly short, on the order of split seconds. For
the athlete, this is too short a time to recognize orconsciously control something that occurs in thebrain. Out of necessity, important brain functions in
sports with an opponent are for the most part unconscious activities. From the athlete’s perspective, thefeeling is that the body reacts on its own.
These brain functions, however, are different fromspinal cord reflexes and other simple reactions common to everyone. Rather, they are extremely advanced
functions the knowledge of which would not be possible without the results of a massive amount of learning about the behavior of opponents and physicalobjects such as balls and the way the body moves in
response to that behavior. Such brain information
processing affects complex actions while being
unconscious to the athlete, and for this reason, we
refer to different instances of such processing as
implicit brain functions. We have been involved in thebasic (i.e., not directly sports-related) research of
implicit brain functions for many years [2]. The firstobjective of SBS is to clarify how the various elements of these implicit brain functions related tosports performance differ between superior athletes
and regular athletes.
However, implicit brain functions by their very
nature prevent an athlete from being aware of them,Vol. 16 No. 3 Mar. 2018

so asking a top athlete about them is no help in arriving at the truth. It is rare for a person excelling at a
certain skill to be able to accurately put into words
how he or she does it. Even if the skill can be verbally described, it is not uncommon for those words
to describe not what actually occurs but rather to fit
the results observed. This is thought to be one of thefactors behind the difficulty of coaching a specificskill. A great player does not necessarily make a greatcoach.
With this being the case, SBS has been using bodymind reading technology developed at the NTT laboratories with the aim of deciphering the implicit brain
functions of athletes (Fig. 1). The first step in thistechnology is to measure the movement of various
parts of the athlete’s body in action and various biometric signals. Of great importance here is that suchmeasurements be made under conditions as close as
possible to those of a real match without impeding theathlete’s inherent performance. Here, we use multiple
video cameras and wearable inertial sensors to record
body movements and quantify them. We also use
wearable sensors to measure biometric signals suchas brain waves, cardiac potential and heart rate, respiration, and myoelectric potential. Additionally, weuse camera goggles to track eyeball movements.
The second step is to use the data obtained fromthese measurements to extract essential information2

Feature Articles

Actual match

Smart bullpen

Pitcher: from MUSASHIHEAT BEARS

VR

Conventional laboratory

VR: virtual reality

Fig. 2. Four types of experimental environments.

on the physical behavior and brain information processing of athletes. Here, to obtain information on
brain information processing, the brain activity does
not necessarily have to be measured. For example,
eyeball activity (direction of line of sight, fine eye
movements called microsaccades, changes in pupil
diameter, etc.) reflects and changes the internal brain
conditions (surprise at something unexpected, direction and extent of attention, information processing
load, etc.). That is to say, if eyeball activity can beanalyzed, it should be possible to infer to some extent
internal brain conditions at that time [3]. Similarly,changes in heart rate and respiration, for example,
can provide information related to underlying brain
activity more so than the movement of certain parts ofthe body.
To carry out such analysis, we are applying various
artificial intelligence related technologies fromNTT’s corevoTM initiative including image processing, biometric signal processing, and machine learning. In all, these analysis technologies constitutebody-mind reading, which is becoming a powerfultool in the hands of SBS.
Appropriate experimental environments are neededin order to conduct experiments that can capture theessence of sports. In SBS, we divide experimental
environments into four main types (Fig. 2). The firstis an actual match. The stress that accompanies theneed to perform cannot be felt outside of an actual
match. In the case of baseball games, we attach a3

wearable sensor (“hitoe”) under the athlete’s uniform
to capture heart rate, acceleration, and other data, andwe analyze how psychological factors can influence
performance. However, the fact that an actual matchis literally real also means that many factors including accidental occurrences and things that cannot be
identified will be intertwined in a complex manner.
As a result, the experimenter does not have control
over everything in such an experiment.
With this in mind, the second type of experimental
environment is the smart bullpen that we created to
extract at least a portion of the true nature of sports
while enabling a controlled experiment. Details areprovided in another of the Feature Articles [4] in thisissue, but in brief, the smart bullpen is an experimental facility equipped with multiple cameras and various measurement devices in a space such as an indoor
baseball practice area. A key feature of this facility is
the ability to analyze in detail the mutual interaction
among multiple athletes, as in the battle between apitcher and batter in baseball and softball. The
essence of competitive sports lies in the mutual interaction between players. Simply analyzing pitching or
batting will not lead to an understanding of what it
takes to win. The smart bullpen enables the physicalmovements and biometric signals of both the pitcher
and batter to be measured synchronously under competitive conditions while also enabling the behaviorof the ball (velocity, spin, trajectory, etc.) to be simultaneously recorded by specialized radar. An example
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Feature Articles

of an experiment using this smart bullpen is describedin another Feature Article [5] in this issue.
The third type of experimental environment is virtual reality (VR). NTT has developed a VR systemthat enables the user to experience the pitcher’s form
and the ball’s trajectory from the batter’s line of sightin three dimensions [6, 7]. With this system, we areable to create experimental conditions that cannot
technically or ethically be produced in a real environment such as the smart bullpen. For example, a VR
system enables us to manipulate information on thepitcher’s form or ball movement for a certain purposeor to create a dangerous situation such as a ball flying
toward the batter’s head and to then analyze the batter’s response to such situations.
The fourth type of experimental environment is alaboratory for conventional cognitive brain science.
In this environment, we can present precisely
designed visual, auditory, and haptic stimuli andaccurately measure arm and eyeball reactions, brain
activity, and other data. Here, a participant is fixed ina chair facing equipment and asked to deal with very
simple tasks, so this environment is far removed froman actual sports scene. On the other hand, it is geared
to fundamental experiments that enable specific factors to be isolated and analyzed in detail.
As described above, these four types of experimental environments each have their advantages and disadvantages. Which to use should be decided based on
the current research objective.
3. Adjusting implicit brain functions
The second objective of SBS as described above isto actually improve the performance of athletes basedon scientific knowledge related to the implicit brain
functions of top athletes. To this end, there is a needfor some means of adjusting implicit brain functions
in order to improve performance. Conscious controlof implicit brain functions is difficult, and verbalizingthem is likewise hard. As a consequence, giving verbal instructions to athletes can hardly achieve results.
The athlete needs to objectively grasp his or her stateof performance and intuitively understand points tobe corrected or the direction of change. For this reason, we are working at SBS to present information on
movements through sensory feedback by visual orauditory means.
For example, we can use a large-screen display
installed in the smart bullpen to present video of apitcher’s form captured with multi-angle cameras
together with data such as the ball’s speed and spinVol. 16 No. 3 Mar. 2018several seconds after the delivery. In this way, pitchers can check their movement and the result directly
after throwing the ball while their sensation of thepitch still remains. Users have commented that thissystem helps them to become aware of the gap
between subjectivity and objectivity, which is a common problem for athletes, while providing them with
an opportunity to make appropriate corrections.
We have also developed a sonification system thatconverts actions such as pitching a ball into soundbased on information received from wearable myoelectric sensors and acceleration sensors attached tovarious parts of the body (Fig. 3) [8]. While differences in form can be easily understood by video, theamount of applied force and the timing can be easily
understood by sound. This system can therefore help
the athlete to intuitively understand the difference
between a model action and one’s own actions, andthe difference between one’s good form and poor
form. We will continue to research methods of intervention using VR.
4. Future outlook
In SBS, our two objectives of understanding an
athlete’s implicit brain functions and improving performance constitute our basic policy around which
our work revolves. It is a general rule that a big gap
will exist between basic research in the laboratory
and improving athletic performance in the field. To
overcome this gap, we have adopted a style thatenlists the cooperation of top athletes and teams todevelop an awareness of real problems in the field
and uncover seeds for basic research. In this way, we
can promote athlete-centered research and not justresearch for research’s sake. We must strictly avoid
having a negative effect on athletes by simply intervening. To intervene on scientific grounds, a large
volume of high-quality data is needed, and at thesame time, a cooperative relationship with athletes isessential.
Progress in research in SBS will help to systemize
a methodology for winning based on scientificgrounds in relation to brain characteristics. As part ofthis progression, there will no doubt be findings thatdiverge from what has so far been considered common sense. Inside a facility like a smart bullpen, athletes can develop an understanding of their own state,including things that they are not normally aware of,and identify points for improvement. Such a facility
may also help to change one’s behavior or form or
detect signs of injury. In addition, accumulating data4

Feature Articles

Frequency (kHz)

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Upper panels: sound spectrograms, lower panels: amplitude waveforms
0.0

0.2

Fig. 3. Sonification of pitching action.

on many athletes will make it possible to diagnose
individual type, aptitude, level, and other factors. It
will also facilitate coaching oriented to individual
conditions and enable early discovery of talent.
In SBS, we are presently focusing on the sports ofbaseball and softball, but we feel that the knowledge
gained here can be essentially expanded to othersports. Furthermore, we are now targeting only toplevel athletes such as those in professional baseballand the Japanese national softball team or an equivalent level, but in the future, we would like to broaden
our target level to include junior athletes and evendevoted sports fans and the elderly. More details on
our research and progress to date can be found on the
SBS website [9].

[3]

[4]

[5]

[6]

[7]

Acknowledgment
Part of this research received CREST funding(JPMJCR14E4) from the Japan Science and Technology Agency (JST).
References
[1]
[2]

5

Advertisement feature: “Split Seconds Matter – the Brain and Sport,”
Nature, Vol. 549, No. 7670, 2017.
M. Kashino, M. Yoneya, H.-I. Liao, and S. Furukawa, “Reading the
Implicit Mind from the Body,” NTT Technical Review, Vol. 12, No.
11, 2014.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201411fa6.html

[8]

[9]

S. Furukawa, M. Yoneya, H.-I. Liao, and M. Kashino, “The Eyes as an
Indicator of the Mind—A Key Element of Heart-Touching-AI,” NTT
Technical Review, Vol. 14, No. 5, 2016.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201605fa4.html
M. Yamaguchi, “Sports Brain Laboratory for Measuring Athletic
Competence and Performance,” NTT Technical Review, Vol. 16, No.
3, 2018.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201803fa2.html
D. Nasu, “Timing Adjustment of Baseball Batters Determined from
Motion Analysis of Batting,” NTT Technical Review, Vol. 16, No. 3,
2018.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201803fa3.html
T. Kimura, K. Takahashi, D. Mikami, and M. Kashino, “Elucidating
the Brain Processing Mechanisms of Athletes Using Virtual Reality
Technology,” NTT Technical Review, Vol. 14, No. 11, 2016.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201611fa6.html
D. Mikami, K. Takahashi, N. Saijo, M. Isogawa, T. Kimura, and H.
Kimata, “Virtual Reality-based Sports Training System and Its Application to Baseball,” NTT Technical Review, Vol. 16, No. 3, 2018.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201803fa4.html
T. Kimura, T. Mochida, T. Ijiri, and M. Kashino, “Body-mind Sonification to Improve Players Actions in Sports,” NTT Technical Review,Vol. 14, No. 1, 2016.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201601ra1.html
Website of Sports Brain Science project, http://sports-brain.ilab.ntt.
co.jp

Trademark notes

All brand names, product names, and company names that appear in thisarticle are trademarks or registered trademarks of their respective owners.

NTT Technical Review

Feature Articles

Makio Kashino

Senior Distinguished Scientist/Project Manager, Sports Brain Science Project, NTT Communication Science Laboratories.
He received a B.A., M.A., and Ph.D. in psychophysics from the University of Tokyo in 1987,
1989, and 2000. He joined NTT in 1989. From1992 to 1993, he was a visiting scientist at the
University of Wisconsin (Prof. Richard Warren’slaboratory), USA. He is currently a speciallyappointed professor in the School of Engineering, Tokyo Institute of Technology. He has been
investigating functional and neural mechanismsof human cognition, especially auditory perception, cross-modal and sensorimotor interaction,and interpersonal communication through theuse of psychophysical experiments, neuroimaging, physiological recordings, and computationalmodeling.

Vol. 16 No. 3 Mar. 20186

Feature Articles: Research and Development in Sports
Brain Science

Sports Brain Laboratory for
Measuring Athletic Competence
and Performance
Masumi Yamaguchi
Abstract

Collaboration with athletes is essential for carrying out the Sports Brain Science project, a recent NTT
initiative. This project aims to create novel insights into human science as well as to enhance athletic
performance and thus benefit both researchers and athletes. The project is introduced in this article,
focusing on the research facilities.
Keywords: hitoe, baseball, laboratory

1. A laboratory designed to attract athletes
The Sports Brain Science (SBS) project was initiated to create a novel research field by combining
three of NTT’s strengths: the human and brain science being studied at NTT Communication Science
Laboratories; the vital sensing fabric called “hitoe,”developed from bioscience research carried out at
NTT Basic Research Laboratories and first commercialized as sportswear in 2014; and information andcommunication technology, a basic technology ofNTT (Fig. 1).
At the beginning of the project, researchers conducted simple experiments such as measuring electromyograms of men throwing balls to a batting tee
net or putting golf balls on a pad in a small room. In
2015, we began work on a specialized facility dedicated to conducting authentic sports experiments.
This facility was located in an empty space that used
to be the synchrotron radiation (SOR) facility at the
NTT Atsugi R&D Center [1].
The SOR equipment was moved out in 2015. The
total area of the space is 20 m x 30 m with a ceilingheight of 11 m for most of the space. Researchers firstbuilt a baseball pitching mound from clay and concrete blocks. A former professional baseball pitcher,
who is collaborating with us in the project, came tothe laboratory to throw balls from the mound. He
1acknowledged the researchers’ construction effort but
said that the mound was too small for throwing byprofessional players.
Although we realized that the mound should be
extended, we were not up to the task of molding ten
tons of clay into a real pitching mound. When we
discussed this issue with the planning section, thelaboratory director authorized the construction of areal pitching mound, adding that we should create asports laboratory that athletes would want to visit
again and again.
This meant that the laboratory should be planned
not only to facilitate data collection but also to provide an attractive environment for athletes. Because
athletes dedicate much of their lives to improving
their performance, it was necessary to construct a
facility enabling us to carry out experiments on athletes’ performance without hindering their training.
From a basic research point of view, we wanted ourexperiments to have long-term benefits for athletic
events, but we also thought it was important to generate short-term benefits for the athletes participating in
our experiments. The idea of a laboratory that athletes
would want to visit repeatedly was the guiding principle in designing the laboratory and also in planningand conducting the research. The Sports Brain Laboratory (SBL) was completed in March 2016 (Fig. 2),
and we are continuously improving the facilities
NTT Technical Review

Feature Articles

Wearable

Brain science

ICTSportsmeasurement
Neuroscience

Computer vision

ECG/HR
Virtual reality

EMG

Physical/mental feedback

Sports Brain Science project
ECG: electrocardiogram
EMG: electromyogram
HR: heart rate
ICT: information and communication technology

Fig. 1. Elements of the Sports Brain Science project.

Handmade mound

Smart bullpen

Manipulandum

Lighting

Fig. 2. Sports Brain Laboratory.

Vol. 16 No. 3 Mar. 20182

Feature Articles

Fig. 3. Displaying the performance and multi-angle videos.

while using them for various experiments.
2. Smart bullpen
Of particular importance in designing the bullpen
was achieving one that would enable the players—thepitcher and batter—to play seriously. One of the skills
we are focusing on is batting, where we are seeking
to understand the implicit brain function within the
0.5 seconds before the ball arrives at the home plateafter the pitcher releases it. The best way to study thisis to take measurements during real games. However,this is impractical because of the game’s regulations
and field rules. Therefore, we prepared a smart bullpen where the pitcher and batter can play as if on areal field, and many kinds of data can be collected.
High-speed video, pitching speeds, trajectories, andspins of balls obtained by radar, as well as video fromthe front, sides, and top are taken as players play realistically in the smart bullpen (Fig. 3). In addition,
when players wear a motion-sensing suit and “hitoe”
shirt, motion data and biological data such as heart
rate can be measured.
Live video with a delay of a couple of seconds, aswell as pitching performance such as speed and spin,
can be displayed on a large monitor installed in frontof the mound on the left. Normally, such data are notshown to subjects during experiments because theiractions—and thus the experimental results—may beaffected. In our laboratory, however, athletes use the3monitor to check their own motion and performance
during breaks between and after experiments. The
video and measured data are used not only for
research data but are also provided as feedback to theathletes, enabling them to review their motion andperformance.
3. Facilities of SBL
The SBL includes measurement equipment for use
in research on other sports and activities such as golf
and walking. The space within the SBL is limited, so
the configuration of equipment was determined on
the basis of input from every researcher involved. For
example, the golf tee, where two force plates are used
to measure shifts in weight, is located at the rightcorner from the bullpen in order to obtain the longest
shot distance. The area where the ceiling is lower isused as a gym floor for virtual reality experiments(Fig. 4) and for basic biomechanical experiments
done with a treadmill and manipulandum system.
Although the SBL was expertly designed for various experiments, we initially overlooked the importance of lighting. Since the laboratory was alreadybright enough for normal activity, and taking video
was not emphasized, no one recognized the need toenhance the brightness of the room. The importanceof lighting was pointed out by an expert in sports
broadcast lighting who happened to visit our lab withthe designer we consulted. He said that lighting is of
NTT Technical Review

Feature Articles

Fig. 4. Batting experiment using virtual reality.

fundamental importance for the laboratory. This
became evident when we faced a flicker problem
when using the high-speed camera. In most cases, it
was difficult to add support lighting around the bullpen because the balls that are hit fly around the bullpen during experiments. Therefore, we used lightemitting diodes (LEDs) to increase the brightness of
the laboratory light. The LEDs are compatible withthe high-speed camera and make the SBL as bright as
the infield during a professional night baseball game.
We later found that the lighting was important not
only for obtaining good experimental data but also for
ensuring the high quality of the feedback video forathletes. To make the SBL more viable, we are continuously modifying it by taking into account theopinions from various professionals and the demandsof researchers and athletes.
4. Smart sensing with “hitoe”
Another important objective of our project otherthan the basic research of sports brain science is toestablish smart sensing skills using “hitoe” in sports
scenes. The functional material “hitoe” is a conductive fabric that enables us to measure electrocardiograms and electromyograms by using it as a sensor
electrode on human skin. Consequently, electricalsignals are generally sharper and more stable compared to pulse signals. In addition, one of the majoradvantages of “hitoe” is that its hydrophilic property
Vol. 16 No. 3 Mar. 2018enables low-noise measurements even when subjects
are sweating during hard practice.
A remaining important issue, however, is determining how to configure the transmitter and loggers on
the body. It is necessary to configure them so thatthey do not disturb the athlete’s activity. For example,
in the case of C3fit IN-pulse*, a transmitter set at thechest sends data to smartphones via Bluetooth Low
Energy (BLE). We have so far performed heart ratemeasurements at a baseball game in an attempt tovisualize the mental tension of the pitcher using“hitoe” [2]. For these measurements, the pitcher wore
a small smartphone as well as the transmitter because
it was difficult to send the small BLE signal outsideof the playing field. This solution was only applicableto practice matches. Furthermore, the operation of thesmartphone would disturb players, even if the rules of
the game allowed them to wear a smartphone.
To solve this problem, we are developing a systemthat automatically measures the athletes’ biologicalsignals when they are inside the SBL. This is done byhaving them wear “hitoe” smart wear. This system isbased on the same wireless technology used in the
rehabilitation institution introduced in this journal[3]. In the future, we will develop and implement thesame kind of system in a real baseball stadium.
*C3fit IN-pulse: Brand name of a sportswear-type device using“hitoe” supplied by GOLDWIN since 2014. One’s heart rate andelectrocardiographic waveform can be measured by wearing it.

4

Feature Articles

5. Future development
Although measurement tools are now smaller andsmarter than ever, the measurements themselves still
impose a psychological load on some athletes. A
coach of junior high school aged athletes told us thatgetting used to such measurements at a young age isimportant because athletes would then be able toundergo them without any undue psychological stresswhen they had reached the top level in their sport. We
were impressed with his long-term training visionand expectations regarding our scientific approach.
Our goal is to train athletes’ brains for winning, but
this cannot be achieved only by researchers. Collaboration with athletes as well as with coaches and staff
is necessary for our project’s success. By creating theappropriate environments and having a researchvision that makes athletes want to participate, we are
putting forward projects to gain novel insights into

human science and to assist athletes in attaining victory.
References
[1]
[2]

[3]

T. Hosokawa, “Super-ALIS: NTT’s Exploring Tool for ULSIs,” Synchrotron Radiation News, Vol. 6, pp. 16–20, 1993.
T. Kimura, T. Mochida, T. Ijiri, and M. Kashino, “Support for Improving Sports Performance Using Information and Communication Science,” IEICE Communications Society Magazine, Vol. 10, No. 1, pp.
23–28, 2016 (in Japanese).
T. Kondo, Y. Yamato, M. Nakayama, A. Chiba, K Sakaguchi, T. Nishiguchi, T. Masuda, and T. Yoshida, “Natural Sensing with “hitoe”
Functional Material and Initiatives towards Its Applications,” NTT
Technical Review, Vol. 15, No. 9, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201709fa3.html

Trademark notes

All brand names, product names, and company names that appear in thisarticle are trademarks or registered trademarks of their respective owners.

Masumi Yamaguchi

Senior Research Scientist, Molecular and Bio
Science Research Group, NTT Basic ResearchLaboratories, and Sports Brain Science Project,
NTT Communication Science Laboratories.
He received a B.S. in physics from Kanazawa
University in 1998 and an M.S. and Ph.D. inphysics from Kyoto University in 2000 and 2003.
He joined NTT Basic Research Laboratories in
2003. He has been part of the Sports Brain Science project since 2016. He is a member of the
Physical Society of Japan, the Japan Society ofApplied Physics, and the Institute of Electronics,
Information and Communication Engineers.

5

NTT Technical Review

Feature Articles: Research and Development in Sports
Brain Science

Timing Adjustment of BaseballBatters Determined from Motion
Analysis of Batting
Daiki Nasu
Abstract

It takes only 500 ms for a baseball to reach the batter after it leaves the hand of the pitcher. We at NTT
Communication Science Laboratories set ourselves the task of figuring out how information is processedin the mind of skilled hitters, enabling them to respond and hit the baseball within this extraordinarily
brief interval of time. Here, we explore the information processing time structure in the brain of a batter
using motion analysis technology during actual pitcher-batter match-ups.
Keywords: athlete, motion analysis, timing control

1. Challenge of hitting a baseballA baseball batting average of .300 in a season isquite respectable, considering that no player has everreached an average of .400 in the entire history ofprofessional baseball in Japan. A .300 average meansthat the batter safely gets a hit three times for every
ten times at bat. One might initially assume that thepitcher has an obvious advantage, but actually thisreveals a fascinating aspect of the game. The batting
average can be attributed to the distance that separates the pitcher from the batter, traditionally established as 18.44 m. When the ball is thrown at a velocity of 140 km/h, it takes only about 500 ms for the ballto traverse this distance. The batter must determine
within a split second precisely when and where theball is going to cross the plate, then spatially and temporally synchronize his swing to connect with theball.
What sort of processing and control goes on in thebatter’s mind and body to achieve this enormously
difficult motor task has long been a subject of fascination, but the mechanism remains elusive. We exploredthis issue by conducting precise measurements of thebatter’s motions when he was squared off at the platefacing a pitcher. In this article, we share some of ourfindings and reveal the timing structure of batting1

when the batter is facing an actual pitcher [1].
2. Measurement of motion inpitcher-batter match-ups
In batting, there are two critical factors that the hitter must keep in mind: the temporal factor—howlong it takes the ball to reach the plate after it leaves
the hand of the pitcher—and the spatial factor—the
trajectory of the ball as it approaches the plate. Let us
first consider the temporal factor by measuring andanalyzing the batter’s movements when hitting two
types of pitches: straight fastball pitches and offspeed changeup pitches*. A changeup is actually aslow pitch that is meant to deceive the batter into
thinking it is a fastball and thus throw off the batter’stiming.
The data gathered in this study were derived fromtwo former professional baseball players—a pitcher
and a batter—as subjects, both of whom were righthanded. The trials were conducted by having the batter*

Motion was measured in this work using an inertial sensor motion capture suit (MVN Biomech Link manufactured by Xsens)
at a sampling rate of 240 Hz. With the MVN Biomech Link suit,we were able to capture hitting and pitching action in the field
and unrestrained swinging action of the subject without the useof a camera.

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Table 1. Information on typical pitches and bat swings.
Pitch type

Ball speed(km/h)

Time from pitcher’s ballrelease to bat contact(ms)

Swing speed(km/h)

Outcome

Fastball

123.9

496

114.4

Strong hit

Changeup

103.7

579

124.4

Strong hit

Changeup

109.9

533

106.0

Miss

swing and try to hit the balls thrown by the pitcher
from the mound (at a regulation distance of 18.44 mfrom the plate) toward the catcher behind the plate.
The order in which the two types of pitches (changeups or straight fastballs) were thrown was randomly
determined by a computer, so there was no way forthe batter to anticipate which type of pitch was coming next.
The pitcher threw the ball at velocities ranging from125.1 km/h for straight fastballs (standard deviation
2.6 km/h) to 101.3 km/h for changeup pitches (standard deviation 4.5 km/h), so from pitcher’s hand tohit, the ball was in the air for an average of 451 ms forstraight fastballs (standard deviation 10 ms) and for581 ms for changeup pitches (standard deviation 22
ms).
For the purposes of this discussion, we will consider three typical swings as illustrated in Table 1: apower swing that hits a straight fastball, a powerswing that hits an off-speed changeup, and a swingthat misses a changeup pitch.
3. Batting time structure
Let us first consider the batter’s swing motion in
addressing a straight fastball as the basic batting
motion structure in baseball. A heatmap representing
relative speeds of different parts of the batter’s bodyis shown in Fig. 1(a). This heatmap makes it easy torecognize the overall spatiotemporal structure. To
make it easier to see what is going on, the findings arepresented as a ratio of the data to the maximum speedof different parts of the batter’s body to the time of theball release, within the range of analysis (i.e., 1000
ms before and after the pitcher releases the ball). The
red portions in the figure represent faster movement.
In Fig. 1(b), we can see time waveforms and characteristic event times for speeds of different parts ofthe batter’s body: lower limbs, torso, and upper limbs.
The batter estimates the timing as he raises his forward leg in synch with the pitcher’s motion, thensteps out with his forward leg as he shifts his centerVol. 16 No. 3 Mar. 2018of gravity. About 350 ms after the ball has left thepitcher’s hand, the batter plants his forward foot,stops the shift in the center of gravity, and increases
the speed of his upper limbs (i.e., his arms) whilerotating his torso. One can see that a power hit
involves a chainlike transmission of energy thatbegins in the legs and is transmitted to the torso, thento the player’s arms, and finally to the bat in the formof bat head speed [2]. The characteristics of thismovement, the so-called kinetic chain, are not confined to batting but are a fundamental sequential
motion in many hitting and throwing sports. For
example, they also exist in the swinging of a golfclub, smashing the badminton birdie, and othermaneuvers [3].
4. Changeups: when and how to respond?
Since there is no way for the hitter to know inadvance if the next pitch will be a straight fastball oran off-speed changeup, he typically takes the stanceof a player anticipating a fastball [4]. When the batter
begins a swing motion expecting a fastball but thenrealizes the pitch is a changeup, he has to adjust his
timing to fill the additional 90 ms before the ballreaches him, which thus requires a so-called pause(tame).
Once committed to a continuous swing, when andhow is the batter able to adjust his movements? A
comparison of the timing of successful swings for achangeup hit and a fastball hit is shown in Fig. 2. If
one focuses on the velocity waveform of the batter’sarms and the event timing, it is apparent that the batter delays the beginning of his swing when faced witha changeup, which means that the impact timing is
also delayed. When the hitter’s legs and torso turn,one can see that the waveforms are more or less thesame regardless of pitch for approximately the first
300 ms after the ball is released from the pitcher’shand, but a change suddenly occurs as highlighted by
the arrow in Fig. 2. The movement of the player’s
torso region (long dashed lines) abruptly slows when2

Speeds of different parts of body

Feature Articles(Ratio)
1
Upper
limbs

0.8
0.6

Torso

0.4
Lower
limbs

0.2

−1000

−800

−600

−400 −200
0
200
400
600
Time from the pitcher’s ball release (ms)

800

1000

800

1000

0

Speeds of different parts of body (m/s)(a) Heatmap

30
25Upper limbs
Torso
Lower limbs

20
15
10
5
0

Ball release

Raise foot.

Plant foot.

Ball impact

Swing start
−1000

−800

−600

−400 −200
0
200
400
600
Time from the pitcher’s ball release (ms)(b) Time waveforms

Fig. 1. Time structure of baseball batting.

dealing with a fastball but continues at the same
speed when addressing a changeup pitch. Finally, inFig. 3, we observe a downward motion in the movement of the batter’s hips. In other words, the batter
steps out with his forward foot while lowering his
center of gravity; then when responding to the changeup pitch, he continues the downward shift, whichallows him to adjust his foot placement timing anddelay his swing.
In the case of a missed swing, the waveform is practically identical to the straight fastball hit; the batter
stops shifting his torso downward, plants his leading
foot, then goes into a swing with the same timing as
when addressing a straight fastball. Thus committed,
the batter is unable to adjust his timing to the slower
changeup and misses the ball.
The data in Fig. 3 reveal that the batter begins to3adjust his movement for a changeup pitch about 300
ms after the ball leaves the pitcher’s hand. It takes
another 150–200 ms for the batter to physically
respond after visually confirming that the pitch is achangeup. Under the assumption that the batter
obtains visual confirmation of a changeup andresponds within 150 ms, the information needed tomake a decision must be obtained within 150 ms after
the ball leaves the pitcher’s hand, which is equivalent
to a travel distance of about 5 m in the air. Baseballcoaches are fond of telling their players to keep theireyes on the ball until the bat makes contact with theball, but with the limitations of human vision andreaction time, information about the latter half of theball’s trajectory is completely useless.
Although the findings presented here are based ona single former professional baseball player, we
NTT Technical Review

Speeds of different parts of body (m/s)

Feature Articles

30
25
20
15
10

Fastball (Hit)Upper limbs
Torso
Lower limbs
Changeup (Hit)Upper limbs
Torso
Lower limbs
Changeup (Miss)
Upper limbs
Torso
Lower limbs

50
Fastball (Hit)Ball release

Plant foot.

Raise foot.

Ball impact

Swing start

Changeup (Hit)Changeup (Miss)

−1000

−800

−600

−400

−200

0

200

400

600

800

1000

Time from the pitcher’s ball release (ms)Fig. 2. Speeds of different parts of batter’s body and characteristic event timing.

Height of hip (m)

0.8

0.75

0.7
Fastball (Hit)Changeup (Hit)Changeup (Miss)
0.65
−1000

−500
0
500
Time from the pitcher’s ball release (ms)1000

Fig. 3. Height of batter’s hip and ball release timing.

obtained identical results for another former professional baseball player when measured under the same
conditions. In other words, we observed the same
shift in timing as the second player lowered his centerof gravity about 300 ms after the ball left the pitcher’shand. Even more interestingly, we observed the sametiming shift at 300 ms after the ball was released forVol. 16 No. 3 Mar. 2018a woman batter on a professional softball team. Ourfindings reveal that within a time frame of about 500
ms, figuring out how to obtain information enabling
the hitter to make a decision (or from the pitcher’s
perspective, how to prevent the hitter from obtaining
this information) within 150 ms after the ball leaves
the pitcher’s hand—after subtracting the time needed4Feature Articles

for the batter to adjust his stance based on visual confirmation of the pitch (about 150 ms) and the timeneeded for the batter to follow through with a swing(approximately 200 ms)—is critically important indetermining whether the batter actually hits the ballor swings and misses.
5. Future development
This study focused on the critical time frame of apitch lasting about 150 ms after the ball leaves thepitcher’s hand. During this 150-ms window, thepitcher’s pitching form and the trajectory of the ballare important pieces of information, but which ismore important in helping the batter decide how todeal with the approaching pitch is not at all clear. To
help answer this question, we are conducting a study
using virtual reality (VR) technology as part of thisproject. VR gives us the ability to freely manipulate
both the pitching form and the trajectory of the ball,so we can measure the batter’s response in the same
way as having the batter swing at pitches on a baseball field. With the addition of VR, we expect to corroborate the study’s findings.
This study marks an important first step in identifying the mechanism involved in triggering motor tasks
as whole-body movements within extraordinarily
short reaction times of less than 500 ms as faced bybatters on the baseball diamond. Gaining a clear
understanding of how skilled hitters are able to moveand maneuver when they are at the plate will provide
valuable clues as to what to focus on in the future
work in order to really comprehend this split-second
reaction timing. To examine these elements in greater
detail, we are employing VR, scrutinizing the motor

tasks involved in maneuvering the arms, and conducting periodic measurements of brain activity. We areconvinced that this approach will bring us a clearer
understanding of the fundamental mechanism
involved in batting.
We would also note that the analytical approachdescribed here could be readily adapted to the onsite
needs of capturing and assessing the performance ofindividual players. Even the limited task we set ourselves here of dealing with changeup pitches has
produced some interesting results. We are now beginning to understand the different strategies players
have for dealing with changeups, some of which aresuccessful and others less successful, and feeding
those insights back to the teams that we support andwork with. Continuing to build on these efforts will
enable us to directly aid and support the actual gameon the field while at the same time contributing tofurther development of sports and brain science.
References
[1]
[2]
[3]
[4]

D. Nasu, “Time Structure of Baseball Batting during Real Matchup
between Pitcher and Batter,” The Brain and Neural Networks, Vol. 24,
No. 3, pp. 132–137, 2017 (in Japanese).
C. M. Welch, S. A. Banks, F. F. Cook, and P. Draovitch, “Hitting a
Baseball: A Biomechanical Description,” J. Orthop. Sports. Phys.
Ther., Vol. 22, No. 5, pp. 193–201, 1995.
E. Kreighbaum and K. M. Barthels, “Throwlike and Pushlike,” Biomechanics: A Qualitative Approach for Studying Human Movement,
4th ed., pp. 335–348, Allyn and Bacon, Boston, USA, 1995.
R. Cañal-Bruland, M. A. Filius, and R. R.D. Oudejans, “Sitting on a
Fastball,” J. Mot. Behav., Vol. 47, No. 4, pp. 267–270, 2015.

Trademark notes

All brand names, product names, and company names that appear in thisarticle are trademarks or registered trademarks of their respective owners.

Daiki Nasu

Sports Brain Science Project, NTT Communication Science Laboratories.
He received a Ph.D. in medicine from the
Graduate School of Medicine, Osaka University,
in 2014. He joined NTT Communication Science
Laboratories in 2016. He has been engaged inresearch on human motor control and biomechanics in sports. He has recently been focusing
on the relationship between sports performanceskill and brain function in athletes. He is a member of the Society for Neuroscience, the Japan
Society of Physical Education, Health and Sport
Sciences, the Japanese Society of Biomechanics,
and the Japan Society of Baseball Science.

5

NTT Technical Review

Feature Articles: Research and Development in Sports
Brain Science

Virtual Reality-based Sports Training
System and Its Application to
BaseballDan Mikami, Kosuke Takahashi, Naoki Saijo,
Mariko Isogawa, Toshitaka Kimura, and Hideaki Kimata
Abstract

This article introduces a system we are developing that provides a first-person-view experience tousers. It is aimed at preparing athletes to engage in sports competitions by enabling them to experiencegame situations from their own point of view before the game. The use of video has rapidly become
popular in the sports field in various ways such as for scouting the opposing team before the game. Such
videos are usually captured from a position different from that of players while they are playing thegame. Thus, they cannot simulate the game very well. Our system provides a virtual reality-based firstperson-view experience by using information captured or measured from locations that will not disturb
the game. We describe in this article how our system worked in a trial with a professional baseball team.
Keywords: VR, sports training, baseball

1. Introduction
The use of information and communication technology has rapidly permeated into sports in recent
years. One of the most popular uses is statisticalanalysis and its visualization because of the easy correspondence to big-data mining and broadcasting. In
baseball, statistical analysis verifies factors that correlate to victory. One such type of analysis is known
as sabermetrics and is mainly used for evaluation andrecruiting in team operations.
In contrast, our research mainly focuses on supporting individual athletes to help them perform at theirbest. For sports in which players directly confront
each other, for example, baseball, tennis, and soccer,
it is important to not only improve the player’s own
skill but also to help players adjust to their opponents.
We believe that this will be especially important for
professional athletes because of the high skill level of
their competitors.
For athletes to perform at their best in a game or
sport, it is important for them to know their opponent.
1

Video-based scouting has been popularized for thispurpose in recent years. It enables intuitive understanding of the opposition, which words or statistics
cannot easily provide.
However, there are limitations to what can beachieved with the current video-based scouting. One
such limitation is viewpoint. Because the scouting
video is captured from outside of the baseball field,
the viewpoints of the actual game situation and of thescouting video are different. Because of this difference, the scouting video does not fully support athletes in their pre-game or pre-sport preparation.
To address this, we tackled the problem of how to
generate and provide a first-person-view experience
before the game or sport was actually played. We
have mainly focused on the sport of baseball in thisarticle because it is one of the most appropriate ways
in which the technology can be applied.
2. BackgroundIn this section, we briefly explain some key features
NTT Technical Review

Feature Articles

Baseball field

Camera(pitcher’s form)

Trackman (ball trajectory sensor)

VR content authoring
Content
generation

SensorTraining (experience)
HMD

HDMI cablePC

HDMI: high-definition multimedia interface
HMD: head mounted display

Player

PC: personal computer

Fig. 1. Structure of VR-based imagery training system.

of batting in baseball and then discuss the requiredelements of the technology we are developing.
2.1 Features of baseball batting
A baseball game always starts when the pitcher
throws the ball to the first batter. During a game, starting pitchers usually throw about 100 pitches, andbatters typically have about three opportunities at bat.
Therefore, the batters have to adjust to the way thepitcher throws the ball during the game. Radio broadcasters commentating on the game are likely to reporton how batters adjust to the way the pitcher throws
the ball the second or third time they face the pitcher.
Batters can therefore use what they have learned fromvirtual reality (VR)-based technology to improvetheir batting performance.
2.2 Definition of requirements
We held discussions with professional players andcoaches to determine the necessary capabilities of thesystem. We extracted the following four requirements:(1)	The system can provide the ball trajectoryVol. 16 No. 3 Mar. 2018with depth information.
(2)	The system can reproduce the correct ball trajectory at arbitrary positions within the batter’s box.
(3)	The system can be taken to any place and set
up easily.
(4)	The system can be updated with the latest
information about the pitcher.
In the next section, we describe the system we propose that fulfills these requirements.
3. VR-based imagery training system
The system structure is depicted in Fig. 1. The proposed system consists of two phases: a generation
phase and an experience phase. The generation phase
generates an experience in a three-dimensional (3D)
virtual space based on a pitcher’s motion and on balltracking. We use captured videos to represent a
pitcher’s motions, while the ball is depicted by computer graphics (CG) based on the measured 3D ballposition.
The experience phase provides an experience to2

Feature Articles

Pitcher:from MUSASHI HEAT BEARS

Fig. 2. Example snapshot of batter’s view.

users through a head mounted display (HMD). The
position and orientation of the HMD are measured sothat the view that is rendered to the user changes
depending on the user’s posture. We used Oculus Riftas the HMD. Another well-known HMD with position tracking is VIVE, but we selected the Oculus Riftmodel because it is lighter. An example snapshot of abatter’s view wearing the HMD is shown in Fig. 2. In
actual use cases, the HMD displays two different
images to each eye to provide parallax.
The system that we briefly introduce here effectively provides the correct ball trajectory from arbitrary viewpoints, and it can be used in arbitrary
places because the HMD we adopted is highly portable. We describe the generation phase in the next
section. This is the main contribution of this article
from a technical aspect and mainly focuses on how
easily updates can be made by using the latest information.
4. Generation phase
The generation phase updates VR content with thelatest information. This makes it easy even for nonexperts to update operations while maintaining stable
quality. We adopted a hybrid approach that combines
a billboard representation of a pitcher and a CGdrawn ball at a measured 3D position.
4.1 Billboard-based pitcher representation
There are several possible ways to represent a
pitcher in 3D virtual space. In one 3D CG example,
the pitcher’s posture and texture are measured. However, it is difficult to stably update VR content using
this solution because pitchers will almost never per3

mit their posture to be captured with a motion capturesystem. This is to prevent the opposing team fromgetting hold of such content and watching them
before the game.
A well-known method called billboard-based video
representation [1] does not rely on the pitcher’s posture. Roughly speaking, one advantage of this method
is that it mainly makes use of captured videos. Another advantage is that it can easily represent subtle
nuances in pitchers such as changes in their facial
expressions, which are very hard to measure and represent through the use of CG. We used these advantages to provide billboard-based representations ofpitchers. First, we captured a video of the pitcher’s
motions from outside the playing field. We thenplaced a virtual panel (called a billboard) at thepitcher’s location in a virtual stadium depicted by 3D
CG. Finally, we played the captured video on thebillboard. In baseball, the positions of the pitcher andbatter do not significantly change. Thus, the billboard-based representation suits this task.
4.2 Representation of ball trajectory by CG
Unlike a pitcher’s motion, the thrown ball is a significantly difficult target for the billboard approach to
represent. This is because it includes large and abrupt
position changes; a thrown ball moves about 18 m inabout 0.5 s. Therefore, we do not use captured video
directly to represent the ball. Instead, we first measure the ball trajectory and then use CG to render theball. This solution enables us to provide an accurateball trajectory at arbitrary positions from the batter’s
box area.*
However, a severe disadvantage occurs in combining billboard-based pitcher representation and
NTT Technical Review

Feature Articles

Pitcher:from MUSASHI HEAT BEARS(a) Duplication of actual filmed ball and CG rendered ball(b) Filmed ball has been removed

Fig. 3. Duplication of the filmed ball and the CG rendered ball, and the same image after removing the filmed ball.

CG-based ball rendering. That is, there is a duplication between the actually filmed ball and the CG
rendered ball (Fig. 3(a)). To avoid this duplication,
the filmed ball should be removed by image processing. We used the inpainting method proposed by
Isogawa et al. [2] to remove the ball in the filmed
video. We verified that it significantly improves thequality of experience, as shown in Fig. 3(b).
5. Experiments and future direction
We verified the validity of our system with thecooperation of a team in the Nippon Professional
Baseball (NPB) organization from two aspects. The
first was the degree to which NPB players and coaches would accept it. The second was the system’s operability, especially in updating VR content.
5.1 Acceptability
There are various indexes to evaluate batters; one of
the most popular is batting average. However, we
obtained users’ (i.e., batters) subjective evaluations ina system trial conducted throughout a baseball season. This made it hard to make comparisons under acontrolled setting. Furthermore, the task of successfully hitting a baseball is a quite uncertain one; a batter who gets a hit only three times in ten attempts is
considered to be a very good hitter. This makes it very
hard to evaluate the performance of our system on thebasis of an objective score such as batting average.
(1) Depth sensing
Almost all professional baseball players have usedvideos to check out their opponents. However, watching videos on a 2D display does not give sufficientVol. 16 No. 3 Mar. 2018depth information since it provides neither binocularparallax nor motion parallax. In contrast, the system
we propose reproduces 3D ball positions in virtual
space and renders videos depending on the positionand orientation of the HMD. It also renders them forboth the right eye and left eye positions. Thus, it provides an HMD experience with depth cues, that is,binocular parallax and motion parallax. Many athletes who used our system said that with it, they
really felt that the ball was coming from the pitcher.
(2)	Simulation training for adjusting to pitcher’s
motionMany batters say that it is very helpful for them tobe able to see simulations of the opposing pitcher
before the game. This enables them, for example, tocheck on how they will adjust to the pitchers’ motions
based on the changes the pitchers make in the speeds
at which they throw the ball. They feel that this willallow them to make full use of their batting chances,even in their first time at bat.
(3) The system as a communication device
We received a comment from a staff member who
said that using this system made it possible to expect
better communication with players since both coaches and players could get the same ball trajectory.
However, some points arose that need to beaddressed, including the low resolution provided dueto the use of the HMD and the insufficient field ofview for batting. One of the tasks we will tackle in the
future is to carry out a more detailed analysis of the*Ball tracking systems have been rapidly popularized in baseballstadiums. We believe it will be possible to accurately reproduceball trajectories within a few years.

4Frequency of requests for content

Feature Articles

team’s schedule. Sometimes, due to schedule changes
or other factors, we were requested to provide content
within a short time, even within a single day. The
relatively high per-day submission frequency shownin the figure indicates that with our system we wereable to handle such requests appropriately.

15
10
5

6. Summary00 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Number of days taken to fulfill requests

Fig. 4. Elapsed time between receiving a request forcontent from a team and the provision of thecontent.

system. The most important but most difficult thing
we need to achieve is how to objectively measure theperceived correctness of the batter’s experience. We
will need to tackle this point to further develop thesystem.
5.2 Operation ability throughout a season
We conducted a trial of the system throughout the2016 baseball season. The season comprised 143
games, but some of them were lacking in data to create VR content. Nevertheless, we created VR content
for 96 pitchers, who threw an average of 15.6 pitches
per game.
A histogram of the elapsed time between a requestfrom a team for content and the time we returned thecompleted VR content to the team is shown in Fig. 4.
Note that we could have returned the content sooner
than we did if it had been necessary to meet the5This article described a VR-based imagery training
system we are developing and experiments we conducted on it in cooperation with a professional baseball team. We believe that our use of VR will provide
people with advance experience in improving theiractions in the real world. This will help them to makedecisions under severe temporal constraints and thus
will be helpful for various VR developers.
Future tasks will include clearly detailing factors
such as HMD resolution, field of view angles, andsystem delay. This will make it essential to conduct
experiments using athletes as subjects. We willattempt to carry out such experiments in a way thatwill mutually benefit the athletes and ourselves.
References[1][2]

Y. Ohta, I. Kitahara, Y. Kameda, H. Ishikawa, and T. Koyama, “Live
3D Video in Soccer Stadium,” International Journal of Computer
Vision (IJCV), Vol. 75, No. 1, pp. 173–187, 2007.
M. Isogawa, D. Mikami, K. Takahashi, and A. Kojima, “Image andVideo Completion via Feature Reduction and Compensation,” Multimedia Tools and Applications, Vol. 76, No. 7, pp. 9443–9462, 2016.

Trademark notes

All brand names, product names, and company names that appear in thisarticle are trademarks or registered trademarks of their respective owners.

NTT Technical Review

Feature Articles

Dan Mikami

Senior Research Scientist, Visual Media Project, NTT Media Intelligence Laboratories.
He received a B.E. and M.E. from Keio University, Kanagawa, in 2000 and 2002, and a Ph.D.
from the University of Tsukuba in 2012. He
joined NTT in 2002. His current research activities are mainly focused on computer vision andvirtual reality. He was awarded the Meeting on
Image Recognition and Understanding 2009
Excellent Paper Award 2009, the IEICE (Instituteof Electronics, Information and Communication
Engineers) Best Paper Award 2010, the IEICEKIYASU Zen’iti Award 2010, the IPSJ SIG-CDS(Information Processing Society of Japan, Special Interest Group on Consumer Devices &Systems) Excellent Paper Award 2013, and the
IEICE Human Communication Award 2015.

Kosuke Takahashi

Research Scientist, Visual Media Project, NTT
Media Intelligence Laboratories.
He received a B.Sc. in engineering and an
M.Sc. in informatics from Kyoto University in2010 and 2012. He joined NTT Media Intelligence Laboratories in 2012. His research interests include computer vision. He received theBest Open Source Code award Second Prize atthe 2012 IEEE Conference on Computer Vision
and Pattern Recognition.

Mariko Isogawa

Researcher, NTT Media Intelligence Laboratories.
She received her B.S. and M.S. from Osaka
University in 2011 and 2013. She is currently a
researcher at NTT Media Intelligence Laboratories and has been a Ph.D. candidate at Osaka
University since 2016. Her research interests
include computer vision topics such as image
completion and quality assessment.

Toshitaka Kimura

Senior Research Scientist, Human Information
Science Laboratory, NTT Communication Science Laboratories.
He received a Ph.D. in human neurosciencefrom the Graduate School of Arts and Sciences,
the University of Tokyo, in 2003. He joined NTT
Communication Science Laboratories in 2003.
He has been engaged in research on sensorimotor
control and action-perception interaction inhumans. He has recently been focusing on brain
functions in athletes and the use of informationand communication technology in sports. He is amember of SfN, JNS, and IEICE.

Hideaki Kimata

Naoki Saijo

Senior Research Scientist, Sports Brain Science Project, NTT Communication Science
Laboratories.
He received a B.E. and M.E. in mechanicalengineering from Waseda University, Tokyo, in1999 and 2001, and a Ph.D. from Tokyo Instituteof Technology in 2010. He joined NTT in 2001
and has been engaged in research on human
information processing. His research interests
include human sensorimotor mechanisms, especially visuomotor control properties. He is amember of the Society for Neuroscience (SfN),
the Japan Neuroscience Society (JNS), the Society of Instrument and Control Engineers, and
IEICE.

Vol. 16 No. 3 Mar. 2018

Senior Research Engineer, Supervisor, HighReality Visual Communication Group Leader,
NTT Media Intelligence Laboratories.
He received a B.E. and M.E. in applied physics
in 1993 and 1995 and a Ph.D. in electrical engineering in 2006 from Nagoya University, Aichi.
He joined NTT in 1995 and has been researchingand developing a video coding algorithm, visualcommunication systems, and machine learning.
He is a Chief Examiner of IPSJ Special Interest
Group on Audio Visual and Multimedia Information Processing.

6Regular Articles

Branched Optical Fiber Loss
Measurement Technology for
End-to-end Testing in OpticalAccess Networks
Hiroshi Takahashi, Chihiro Kito, Kunihiro Toge,
and Tetsuya Manabe
Abstract

We have developed branched optical fiber loss measurement technology that enables us to measure
branched fiber sections beyond optical splitters from a central office without entering the customer’s
premises. This article provides an overview of the developed technology and its application to theoperation and maintenance of optical access networks.
Keywords: optical access, fiber loss, PON

1. Optical fiber measurement for optical accessnetwork maintenance
The number of fiber-to-the-home (FTTH) subscribers has reached approximately 29.30 million in
Japan, about two-thirds of which—20.05 million—
are with NTT EAST and NTT WEST. It is therefore
important to reduce the cost of operating the hugeamount of optical fiber that this system employs andensure quality over a long period of time.
Passive optical networks (PONs) have been adopted for the majority of optical access networks. A PON
has a topology where multiple customers share optical fibers using an optical splitter. PONs have contributed to achieving economic optical fiber network
construction. On the other hand, they have also
caused complications regarding the operation andmaintenance of optical fiber equipment outside thecentral office, especially maintenance work involving
the use of optical techniques for monitoring, testing,and identifying fibers.
Specifically, the optical signal used for maintenance and launched from a central office is equally1

divided by an optical splitter, so it is necessary to
work from the customer side as well. Optical time
domain reflectometry (OTDR) [1] is used for monitoring optical access networks from the central officeside. However, since all backward scattered lightbeyond the splitter overlaps, it is impossible to perform the health monitoring and fault location in asection of branched optical fiber (Fig. 1(a)). In thiscase, measurement equipment is installed at the customer’s premises to measure branched optical fiber
sections, and the maintenance work requires the customer’s presence. Moreover, redundant staff may bedispatched depending on the cause of the failure.
Additionally, there are similar cases when evaluating the link insertion loss (LIL) from the central
office to the customer’s premises, which is the mostimportant characteristic of optical access networks,
and the connection loss of the optical fiber. Similarly,
when using a standard method to measure light suchas the light source power meter (LSPM) [2] methodor the bidirectional OTDR [3] method, it is necessary
to measure from both the central office and the customer’s premises (Figs. 1(b) and (c)). To evaluate
NTT Technical Review

Regular Articles(a) Health monitoring and fault location(b) Measurement of link insertion loss (LIL)

Worker B
OTDRWorker B

Splitter

Splitter
ONU

OLT

ONU

WorkerA

WorkerA

WorkerA

Splitter

ONU

OTDR

ONU

Faulty?

?
dB

Frequently occurs in heterogeneous
optical fiber connection

Unidirectional OTDR
• Impossible to specify beyond splitter• Measured from customer premises
LS: light source
LSPM: light source power meter
OLT: optical line terminal

Connection point
×

OLT

ONU

LS

ONU

Worker B
OTDR

PM

OLT

ONU

OTDR

(c) Measurement of actual connection loss

LSPM
• Measured from customer premises• Service interruption occurs.

Bidirectional OTDR
• Measured from both central office andcustomer premises• Service interruption occurs.

ONU: optical network unit
PM: power meter

Fig. 1. Conventional optical measurement and its problems.

fiber quality after an investigation and fault repair,these measurements can be easily conducted when
the customer is not present, such as during construction. However, measurements are difficult to perform
when customers must be provided with certain services, for example, PON services.
Therefore, we have worked on developing a new
optical loss measurement technology focusing on thecharacteristics of optical access networks, where one
end of the optical fiber is located at the customer’s
residence, and where an optical splitter is installed.
2. Branched optical fiber loss
measurement technique
A schematic diagram of our branched optical fiber
loss measurement technique is shown in Fig. 2 [4].
This technique involves using the differences inlength between branched fibers resulting from construction and end reflections at a common test light
cut-off filter [3] installed in front of an optical network unit (ONU) to measure existing optical accessnetworks without any additional optical devices. Two
optical pulses are launched into the fiber under test
(FUT) with a time difference Δt and an optical frequency difference Δf. The light pulses have lower andhigher optical frequencies and are respectively called
the probe and pump pulses.
Vol. 16 No. 3 Mar. 2018

The probe pulse is reflected at the far end, and collides with the pump light at a position corresponding
to the time difference Δt, Then a Brillouin interaction
occurs. In this Brillouin interaction, the probe pulse isamplified by the pump pulse. The generated Brillouingain corresponds to the loss experienced by the pumplight until the collision position; therefore, Brillouingain analysis can be used to measure the optical loss.
The probe pulse that obtained the Brillouin gain
returns to the incident side with a time difference corresponding to the branched optical fiber length difference. Therefore, it is possible to acquire the opticalloss data for each branched optical fiber by analyzing
the Brillouin gain for each pulse. Note that it is necessary to set the probe pulse so that it is narrower than
the time difference caused by the branched opticalfiber length difference. In addition, it is possible toacquire the loss distribution data by changing thetime difference Δt.
We describe here the remote and single-end measurement of the LIL [5] and connection loss [6] using
the branched optical fiber loss measurement technique. The LIL is obtained by subtracting the far-end
return loss from the loss that the pulse experienced in
the round-trip. The round-trip insertion loss of theprobe pulse is measured without a pump pulse. The
far-end return loss is obtained with the gain difference given immediately in front of the reflector by2

Regular Articles

Launched pulse
Pump

Probe

 t/2

t

ONU
#1

ONU
ONU

#2
#3

: Collision point: Test light cut-off filter

Detected pulse

Collision point
Brillouin gain
#1

#2

pump

#3

Time difference corresponding tobranched fiber length difference

Reflected probe
Generated Brillouin gain

Reflected probe
Collision of optical pulses

Fig. 2. Schematic diagram of branched optical fiber loss measurement technique.

changing the launch order of the probe and pumppulses. Therefore, this technique can be used to conduct
a remote LIL measurement for each customer.
The connection loss can be measured by changing
both the time difference Δt and the launch order of theprobe/pump pulse. When we launch the pump after
the probe, we can obtain the loss distribution from the
input end, and when we launch the probe after thepump, we can obtain the pseudo loss distribution
from the far end. Therefore, bidirectional measurement can be achieved from a single end.
3. Developed apparatus of branched opticalfiber loss measurement
A photograph and the configuration of the measurement system we developed are shown in Fig. 3. Two
DFB-LDs (distributed feedback laser diodes) wereused as probe/pump sources, whose frequency difference was set at a Brillouin frequency shift of about
10 GHz. The probe/pump lights were launched into
an FUT after being amplified and pulsed by SOAs
(semiconductor optical amplifiers) 1 and 2. The
reflected probe pulses from the FUT were detected at
the PD (photodetector) after removing the pump lightwith an optical filter. This is the first technique capable of measuring optical loss using Brillouin phenomena, and it is the only technique that can measure
the loss distribution beyond an optical splitter.
3

4. Measurement examples with branched fiber
loss measurement apparatus
In this section, we describe some examples of measurements obtained with our branched fiber loss measurement apparatus.
The measurement results for branched opticalfibers obtained with the developed measurement
apparatus and conventional LSPM and OTDR are
shown in Fig. 4. The FUT, which was composed of
six fibers of different lengths, is depicted in Fig. 4(a).
The LIL results obtained with LSPM and the developed apparatus are in Fig. 4(b). It can be seen that theresult with the technique developed for single-end
measurement agrees well with the result measured
using LSPM. The results obtained with unidirectional
OTDR and the developed apparatus are presented in
Figs. 4(c) and (d). They show that the loss distributionof branched fiber sections cannot be measured withunidirectional OTDR. In contrast, it can be seen thatthe developed apparatus can measure individual loss
distributions including loss events.
The connection loss results obtained with bidirectional OTDR and the developed apparatus are shownin Fig. 5. The FUT was composed of optical fibers
with different bending characteristics (G.652D and
G.657A). The results obtained with the developed
apparatus for single-end measurement agree wellwith those obtained with bidirectional OTDR.
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Regular Articles

Pump pulse

FUT

SOA
Pulse
combiner

DFB-LD
Probe pulse
SOA

Pump cut-off
filterDFB-LD

OpticalreceiverSignalprocessor
DFB-LD: distributed feedback laser diodes
SOA: semiconductor optical amplifierFig. 3. Measurement apparatus and configuration.

(a) FUT configuration
Drop
Branch no. (total length)8-SP1029 m4-SP1035 m
1.5 dB
530 m
507 m

1230 mMeasurement
system510 m#2 (2775 m)#1

17.6 ± 0.3

17.9#3 (2777m)#217.4 ± 0.3

17.7

#3

18.7 ± 0.6

19.2#4 (2825 m)#4

17.5 ± 0.4

17.8#5 (3056 m)#5

17.3 ± 0.2

17.9

#6

17.2 ± 0.3

17.1

8-SP
1335 m980 m

1092 m

G.652DG.657A
Test light cut-off filter

1092 m(c) Example of OTDR result (comparison)474 m#6 (3118m)

536 m

Branch #1

–20
–40

–10
–20
–30

0

1000

2000
Distance (m)

–20
–40

3000

4000

–10

1.5 dB loss

–20
–40

500 1000 1500 2000 2500 3000 3500 4000
Distance (m)Branch #6

10
G.657A

G.652D–20–40

Branch #3–10

Branch #5
0–30
500 1000 1500 2000 2500 3000 3500 4000
Distance (m)0–30
500 1000 1500 2000 2500 3000 3500 4000
Distance (m)

10

0

–40

10

–10

Branch #4

10

Branch #2

0–30
500 1000 1500 2000 2500 3000 3500 4000
Distance (m)10Distribution 8-branch splitter
Drop

0

Loss (dB)–108-branch splitter

Feeder20

10

Loss (dB)

30

0

–30

End reflections

Loss (dB)

Loss (dB)#5 #6

4-branch
splitter

40

Loss (dB)

10#4* Continuously monitored for 2 hours(d) Example of individual loss distribution results#1#2, #3 (2-m fiber length difference)
50

LSPM (dB)
BranchDevelopedno.
technology* (dB) (comparison)#1 (2769 m)

Loss (dB)Distribution

Loss (dB)

Feeder(b) Example of LIL results
LIL

0
–10

G.652DG.657A–20–30

500 1000 1500 2000 2500 3000 3500 4000
Distance (m)–40

500 1000 1500 2000 2500 3000 3500 4000
Distance (m)8-branch splitter

Fig. 4. Examples of LIL and loss distribution in branched optical access network.

5. Field demonstration
The configuration and measurement results for afield demonstration using the developed apparatus
Vol. 16 No. 3 Mar. 2018

are shown in Fig. 6. A 4-branch splitter was installedinside a central office, and an 8-branch splitter was
installed in a distribution area near four customers’
premises. FBG (fiber Bragg grating)-based reflectors
4Regular Articles

FUT configuration
Total length: 9620 m

G.652DG.657A
Test light cut-off filter
Connection point

5060 m 1000 m 2560 m 1000 m
Example of connection loss results

Loss (5 dB/Div.)

0.6 dB 0.6 dB
Developed technology

Bidirectional-OTDR(comparison)

0

2000

1.1 dB

0.6 dB 0.5 dB

1.1 dB

4000
6000
Distance (m)8000

10,000Fig. 5. Example of connection loss measurement results with heterogeneous fiber connection.

(a) Configuration of field demonstration

8-branch splitter

Optical coupler
OLT
4-branch
splitter

Central office

Customer premises

Developed
technology

ONU

Manhole: Test light cut-off filter(b) Result with unidirectional OTDR

–10
–20
–30
–40

2200

2400

2600

Branch #2

100

Loss (dB)

Loss (dB)

4 users

2800

0
–10
–20
–30
–40

3000

2200

Distance (m)

3000

–10
–20
–30
–40

2200

2400

2600

2600

2800

3000

2800

3000

Branch #4

10

0

Loss (dB)

1000
2000
Distance (m)2400

Distance (m)Branch #3

10

Loss (dB)

Loss (10 dB/Div.)

Branch #1

108-branch splitter (2070 m)

0(c) Results with developed apparatus

2800

3000

0
–10
–20
–30
–40

Distance (m)

2200

2400

2600

Distance (m)Fig. 6. Field demonstration using developed technique.

with a nominal return loss of 1 dB at 1650 nm were
installed just in front of an ONU. The total length of5

the test optical distribution network (ODN) was about
2.5 km. We performed the measurement from a test
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Regular Articles

coupler installed immediately below the 4-branch
splitter.
Measurement results obtained for a deployed ODN
with OTDR and the developed apparatus are shown in
Figs. 6(b) and (c). The branched fiber sections weresuccessfully visualized with the developed apparatus
in the field.
An OTDR trace of the entire ODN is shown in thegraph in Fig. 6(b). An 8-branch splitter was installed
at 2070 m, and there were four end reflections on thecustomers’ premises. Enlarged traces of the drop fiber
section beyond the 8-branch splitter, which were
measured with the proposed system, are in Fig. 6(c).
The branched fiber sections were successfully visualized. As mentioned above, the developed apparatus
can measure the individual loss distribution for eachcustomer even if the OTDR cannot do so because ofoverlaps from the branched fiber section or becauseof the excessive loss of the splitter.

nected, when there is a large loss near the far end, andwhen there is no branch fiber length difference.
Nevertheless, we believe that a centralized approach,which is a unique and attractive feature of the developed technology, would result in operational applications that overcome the lack of fiber-end accessibility.
The developed technology was commercialized in2017. We now plan to start employing it as a troubleshooting tool where we require a detailed diagnosisof optical access networks.
References
[1]
[2]
[3]
[4]

6. Future perspectives[5]

We introduced branched optical fiber loss measurement technology that enables end-to-end measurement of optical access networks. Since this technology uses end reflection, there are conditions thatcannot be measured depending on the optical accessnetworks, such as when they are completely discon-Vol. 16 No. 3 Mar. 2018[6]

Recommendation ITU-T G.650.3: “Test methods for installed singlemode optical fibre cable links,” 2008.
IEC 61280-4-2: “Fibre-optic communication subsystem basic test
procedures – Part 4-2: Installed cable plant – Single-mode attenuationand optical return loss measurement,” 2014.
Recommendation ITU-T L.301/L.41: “Maintenance wavelength on
fibres carrying signals,” 2000.
H. Takahashi, F. Ito, C. Kito, and K. Toge, “Individual Loss
Distribution Measurement in 32-branched PON Using Pulsed Pumpprobe Brillouin Analysis,” Opt. Exp., Vol. 21, No. 6, pp. 6739–6748,
2013.
K. Toge, H. Takahashi, C. Kito, and T. Manabe, “End-to-end Testingof Optical Distribution Network in Access Networks without Entering
Customer’s Premises,” Proc. of the 66th International Cable &Connectivity Symposium (IWCS), Orlando, FL, USA, Oct. 2017.
H. Takahashi, K. Toge, and F. Ito, “Connection Loss Measurement by
Bi-directional End-reflection Assisted Brillouin Analysis,” J. Lightw.
Technol., Vol. 32, No. 21, p. 4204, 2014.

6

Regular Articles

Hiroshi Takahashi

Research Engineer, Access Media Project,
NTT Access Network Service Systems Laboratories.
He received a B.E. in electrical engineeringfrom Tokyo University of Science in 2008 and an
M.E. in electrical engineering and information
systems from the University of Tokyo in 2010.
He joined NTT Access Network Service Systems
Laboratories in 2010. He has been engaged inresearch on optical reflectometry and opticalfiber measurements. He is a member of the Institute of Electronics, Information and Communication Engineers (IEICE) of Japan.

Chihiro Kito

Research Engineer, Access Media Project,
NTT Access Network Service Systems Laboratories.
He received a B.E. and M.E. in electrical engineering from Toyota Technological Institute,
Aichi, in 2009 and 2011, and a D.E. in electricalengineering from Shimane University in 2017.
He joined NTT Access Network Service Systems
Laboratories in 2011. He has since been involved
in research on optical reflectometry and opticalfiber measurements. He is a member of IEICE.

7

Kunihiro Toge

Senior Research Engineer, Access Media Project, NTT Access Network Service Systems Laboratories.
He received a B.E. and M.E. in mechanicalengineering and a D.E. in electrical engineeringfrom Waseda University, Tokyo, in 1999, 2001,
and 2007. He joined NTT Access Network Service Systems Laboratories in 2001, where he has
been researching optical fiber cable and advanced
optical measurements. He is presently a seniorresearch engineer in a research group developing
advanced optical measurements for fiber network
management and evaluation. He has also been
involved in standardization activities in ITU-T
Study Group 15 since 2011 and has been servingas Rapporteur of Question 17 (maintenance andoperation of optical fibre cable networks) since2012. He received the best paper award at the12th OECC/IOOC in 2007. He is a member of
IEICE.

Tetsuya Manabe

Senior Research Engineer, Supervisor, AccessMedia Project, NTT Access Network Service
Systems Laboratories.
He received a B.E., M.E., and Ph.D. inmechanical engineering from Osaka University
in 1989, 1991, and 1998. He also received an
MBA (Advanced) from the University of Western Australia in 2007. He joined NTT AccessNetwork Service Systems Laboratories in 1991.
His research interests include learning control,
robotics, and mechanical system control. He iscurrently involved in research to improve theefficiency of telecommunication system maintenance. He is a member of IEICE and the Societyof Instrument and Control Engineers.

NTT Technical Review

Regular Articles

High Precision 12-single-mode-fiber
Multi-fiber Push-on Connector for
Reference Use in Connector
Evaluation
Ryo Koyama, Chisato Fukai, Yoshiteru Abe,
Mitsuru Kihara, and Kazunori Katayama
Abstract

The data transfer speed between data communication equipment in datacenters has been increasing inrecent years, and the use of multi-fiber optical links for the communication wiring in such buildings is
expanding. In particular, the 12-fiber multi-fiber push-on (MPO) connector for single-mode fiber is
expected to be widely used in the future, but an accurate evaluation method is necessary for its proper
procurement. We introduce the high precision 12-fiber MPO connector developed as a reference for
MPO connector evaluation.
Keywords: multi-fiber optical connector, reference connector, optical connector evaluation

1. Introduction
The data transfer speed between communicationequipment has been increasing in communication
facilities such as datacenters. Therefore, the use ofmulti-fiber optical links as opposed to single-fiber
optical links is expanding in high speed communication links. Multi-fiber push-on (MPO) connectors, atype of optical connector developed by NTT, are mostwidely used as the interfaces of multi-fiber opticallinks in buildings. An example of the standard wiringof a datacenter is shown in Fig. 1 [1]. In modern datacenters, high speed communication links such as 40
Gbit/s or 100 Gbit/s are widely used between theaggregation switches and spine switches, and theirlink speed continues to increase. Examples of thestandard high speed communication links using the
MPO connector are listed in Table 1 [2–4]. Becauseinter-switch communication at a datacenter is expected to be over 100 Gbit/s in communication speed andover 100 m in wiring length, the 12-single-mode fiber
1

MPO connector is expected to be used more and more
for high-speed communication optical wiring inbuildings.
The structure of the MPO connector is shown in
Fig. 2. The MPO connector consists of a male plug,
which contains pre-inserted guide pins, a female plugwith guide pin holes on the connector endface, and an
adaptor that couples the plugs with a slide lock mechanism. To connect optical fibers, we have to align thepositions of the fiber cores through which signal lightpasses in order to bring the fiber cores in contact witheach other. In the MPO connector plug, the fibers are
precisely arrayed and fixed into a mechanically transferable (MT) ferrule, and when one plug is connected
to another one, the MT ferrule is aligned with the
opposite MT ferrule by using the guide pins and theguide pin holes.
In an actual optical connector connection, it is difficult to perfectly match the positions of the fibercores, and some of the signal light is consequently
lost and not transmitted. This loss (connection loss)
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Optical cable forhigh speed data transfer linkSpineswitchAggregation
switchServers

Fig. 1. Example of cabling for datacenter building.

Table 1. Examples of high speed data transfer links using MPO connector interface.
Data transfer
speed
40 Gbit/s
100 Gbit/s
200 Gbit/s
400 Gbit/s

Specification
nameOptical fiber

Numberof fibers

Distance
range

40GBASE-SR4

Multi-mode fiber

12

150 m

100GBASE-SR4

Multi-mode fiber

12

150 m

100GBASE-PSM

Single-mode fiber

12

2 km

200GBASE-SR4

Multi-mode fiber

12

100 m

200GBASE-DR4

Single-mode fiber

12

500 m

400GBASE-SR16

Multi-mode fiber

32

100 m

400GBASE-DR4

Single-mode fiber

12

500 m

Male plug
AdaptorMT ferrule

Female plug

Guide-pin

Guide-pin hole
Optical fiber end
MT: mechanically transferable

Fig. 2. Structure of MPO connector.

Vol. 16 No. 3 Mar. 20182

Regular Articles

Maximum connection lossof 12-fiber MPO connector (dB)

1

0.5
0.33

0
0

0.5
Maximum error of fiber hole position in MT ferrule (µm)

1Fig. 3. Calculated connection loss in MPO connector.

affects the communication quality, so it is necessary
to evaluate the connection loss. One evaluation method involves using a reference connector. The reference connector should be a highly accurate connector
manufactured with less error than the standardized
ideal design of an optical connector. This method is
widely used because it is accurate based on the standardized design, and it guarantees the interoperabilityof the optical connectors.
However, there are no reference connectors currently available on the market for the MPO connector,
so it is not possible to conduct an accurate evaluation
as done for the conventional single-fiber optical connector. Consequently, there may be uncertainty about
the measured connection loss between, for example,
the manufacturer and the purchaser in the evaluationof the MPO connector. In addition, when the MPO
connector is required to have a connection loss equivalent to the conventional single-mode-fiber opticalconnector, the connection loss of the MPO connector
cannot be guaranteed.
We introduce here a high precision 12-singlemode-fiber MPO connector that we developed as areference connector to achieve accurate evaluation of
the 12-single-mode-fiber MPO connector. Our highprecision MPO connector is the world’s first 12-fiber
MPO connector for single-mode fiber that meets the
specifications of the International ElectrotechnicalCommission (IEC) reference connector.
2. High precision MPO connector
In the connection of optical connectors, any posi3

tional error of the fiber core will mainly affect theconnection loss, so the target position of the fiber coreis specified. However, the actual optical connector
may have a deviation between the fiber core position
and the target position due to a manufacturing error.
The reference connector must therefore be a highprecision connector with small error of the fiber core
position. For example, the IEC specifies that the connection loss between reference connectors shall be no
more than 0.2 dB, which means that the error of thefiber core position should be 0.5 μm or less [5].
There are two difficulties in achieving high precision multi-fiber connectors with a connection loss of
0.2 dB or less. One is the effect of manufacturing
error. As mentioned above, in the MPO connector, thefiber cores are aligned by the MT ferrule, but the MT
ferrule has a manufacturing error of 0.5 μm at maximum.
The connection loss of the MPO connector calculated based on the manufacturing error of the MT
ferrule is given in Fig. 3. The figure shows that theconnection loss of the MPO connector given the
maximum 0.5-μm manufacturing error of the MT ferrule exceeds the target loss of 0.2 dB. It is difficult to
reduce the manufacturing error to less than 0.5 μm
with the current manufacturing technology, so inorder to achieve a 12-core high precision connector,
it is necessary to select connectors with a small
manufacturing error from those manufactured withthe maximum error of 0.5 μm.
The other difficulty lies in measuring the error of
the fiber core positions in the MPO connector. In the
MPO connector, fiber cores with a diameter of about
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Connection loss test
Connection loss test

0.2 dB> 0.2 dB
0.2 dB
Dispose

Selected sample group(a) Assemble connector using MT
ferrule with manufacturing
error of no more than 0.5 µm.

(b) Select a group of samples whose
connection losses are no more than
0.2 dB when mated with each other.

(c) Select connectors whose connection
losses are below 0.2 dB when matedwith the other group samples.

Fig. 4. Selection procedure for high precision MPO connectors.

Ideal position

Y-axisY-axisY-axisSample group 1

Sample group 2

0.5 µmSelected connectors

X-axisX-axisX-axis
0.5 µmSample group 3(a) Distribution area of fiber coresin all manufactured connectors(b) Distribution area of fiber coresin selected sample groups(c) Distribution area of fiber coresin selected high precision MPO
connector

Fig. 5. Example of fiber core position of selected connector samples.

10 μm are lined up in a 4.6-mm-wide area. The fibercore position cannot be measured accurately becauseof the large difference between the observation range
and the size of the fiber core to be measured. For thisreason, it is not possible to select connectors with asmall manufacturing error based on the fiber core
position itself.
The procedure for selecting connectors using connection loss when the actual fiber core position is
unknown is shown in Fig. 4, and an example of thefiber core position in each procedure is shown in
Fig. 5.
First, we manufacture an MPO connector using an
MT ferrule with a manufacturing error of 0.5 μm or
less. The fiber core position of the fabricated connecVol. 16 No. 3 Mar. 2018

tor is distributed around the target position, as shownin Fig. 5(a), due to manufacturing error. Next, asshown in Fig. 4(b), we connect the fabricated MPO
connectors together and select a sample group exhibiting a connection loss of 0.2 dB or less. Throughselection, connectors whose fiber core position is in
the range of 0.5 μm are extracted, although the centerof the fiber core position will deviate from the ideal
position, as shown in Fig. 5(b).
To select connectors with a fiber core position errorof less than 0.5 μm from the ideal position, we prepared three sample groups and selected the connectors that had a connection loss of 0.2 dB when they
were connected to the connectors of the other sample
group. Although the fiber core position of each4

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Connector with similarfiber core positon error

Manufactured connector:
large fiber core position error

Connector with large
fiber core positon error

PASS

FAIL

Connection
loss test

Connection
loss test
Reference connector :very small fiber core
position error

FAIL

PASS

Connector with small
fiber core position error

Connector with small
fiber core position error(a) Random mating test(b) Test with reference connector

Fig. 6. Example of MPO connector evaluation methods.

Table 2. Reference connectors in major optical connectors.
Connector family

Standard

Requirement for
fiber core position error

Requirement forconnection loss

SC connectors

JIS C5973

Below 0.5 mm

Below 0.2 dB

MT connectors

JIS C5981

No requirement

Below 0.2 dB

MPO connectors

JIS C5982

No requirement

Below 0.3 dB

Our developed highprecision MPO connector—

Below 0.5 mm

Below 0.2 dB

JIS: Japanese Industrial Standards
SC: subscriber connector

sample group is unknown, the range where the fibercore can exist can be estimated to determine whether
it is 0.5 μm or less based on the fiber core position of
the connector selected between each of the sample
groups, as shown in Fig. 5(c). We are using this procedure to achieve a high precision MPO connector
with a fiber core position of 0.5 μm or less for the firsttime in the world.
3. Application to MPO connector evaluation
Two widely used optical connector evaluation
methods are depicted in Fig. 6. They are used toevaluate the connection losses and other characteristics of the connector. The random mating test method
is used when connecting manufactured connectors toeach other (Fig. 6(a)). As shown in the figure, therandom mating test is not accurate when the fibercore positions have some deviation. Therefore, even
if the connection loss was confirmed to be less than aspecified value in the random mating test, the connec5

tion loss would not always be less than the specified
value in actual equipment.
In the other method, the manufactured connectors
are tested against a reference connector (Fig. 6(b)).
Because the reference connector has low deviation of
the fiber core position, this method can be performed
accurately as shown in the figure, and interoperability
between connectors is guaranteed.
However, even high precision reference connectors
have a small deviation of the fiber core position. A
comparison between the specifications of the reference connector in major optical connector standards
and the developed high precision MPO connector isgiven in Table 2 [5–8]. The developed high precision
MPO connector has the same deviation of the fibercore position and connection loss as those of the reference connector for single-mode subscriber connectors (SCs). Since the reference connector for a singlemode SC has been utilized in the practical evaluationof single-mode optical connectors for the last few
decades, our developed high precision MPO connector
NTT Technical Review

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References

has a sufficiently small deviation for the reference
connector for practical connector evaluation.
[1]

4. Summary and future work
We introduced our developed 12-single-mode-fiberhigh precision MPO connector to realize accurate
connector evaluation of the 12-fiber MPO connector.
The developed high precision MPO connector is thefirst in the world to meet the specifications of IEC’sreference connector as a 12-fiber MPO connector for
single-mode fibers. The accurate connector evaluation method enables fair competition among connector manufacturers and contributes to the proper procurement of MPO connectors in the NTT Group. We
will continue to research a way to achieve a reference
connector with more than 12 fibers by focusing on thetechnological trends of high speed communication.

Ryo Koyama

[2]
[3]
[4]
[5]

[6]
[7]
[8]

TIA-942: “Telecommunications Infrastructure Standards for Data
Centers,” Rev. 2, 2017.
IEEE 802.3bm: “IEEE Standard for Ethernet – Amendment 3:
Physical Layer Specifications and Management Parameters for 40
Gb/s and 100 Gb/s Operation over Fiber Optic Cables,” 2015.
IEEE 802.3bs: “IEEE Standard for Ethernet – Amendment 10: Media
Access Control Parameters, Physical Layers, and Management
Parameters for 200 Gb/s and 400 Gb/s Operation,” 2017.
100G PSM4 MSA: “100G PSM4 Specification,” Ver. 2.0, 2014.
IEC 61755-2-5: “Fibre optic interconnecting devices and passive
components – Connector optical interfaces – Part 2-5: Connection
parameters of non-dispersion shifted single-mode physicallycontacting fibres – Angled for reference connection applications,”
Ed.1.0, 2015.
JIS C5973: “F04 Type connectors for optical fiber cables (Type SC
connector),” 2014.
JIS C5981: “F12 Type connectors for optical fiber ribbons (MTconnectors),” 2012.
JIS C5982: “F13 type connectors for optical fiber ribbons,” 2010.

Mitsuru Kihara

Senior Research Engineer, Media Utilization
Group, Access Media Project, NTT Access Network Service Systems Laboratories.
He received a B.S. and M.S. in precision engineering from the University of Tokyo in 2001 and2003. In 2003, he joined NTT Access Network
Service Systems Laboratories, where he has been
engaged in research on optical fiber wiring andsplicing techniques for optical access networks.
He is a member of the Institute of Electronics,
Information and Communication Engineers(IEICE).

Senior Research Engineer, Media Utilization
Group, Access Media Project, NTT Access Network Service Systems Laboratories.
He received a B.E. and M.E. in nuclear engineering, and a Dr.Eng. in electronic engineeringfrom Kyushu University, Fukuoka, in 1988,
1990, and 1997. He joined NTT in 1990 andbegan researching optical fiber interconnection
techniques, future network planning, and opticalfiber sensing techniques. He is a member of
IEICE and the Institute of Electrical and Electronics Engineers (IEEE).

Chisato Fukai

Kazunori Katayama

Engineer, Media Utilization Group, AccessMedia Project, NTT Access Network Service
Systems Laboratories.
She received a B.E. and M.E. in functional
materials science and a Ph.D. in materials science from Saitama University in 1999, 2001, and2014. In 2001, she joined NTT Access Network
Service Systems Laboratories, where she has
been researching optical fiber design and related
measurement techniques. She is a member of
IEICE.

Senior Research Engineer, Supervisor andGroup Leader of Media Utilization Group,
Access Media Project, NTT Access Network
Service Systems Laboratories.
He joined NTT Access Network Service Systems Laboratories in 1997, where he engaged inresearch of home area networks. Since 2007, hehas been engaged in research on an intermediatesession control server and optical line switchingsystem and optical fiber cable systems. He is amember of IEICE.

Yoshiteru Abe

Senior Research Engineer, Media Utilization
Group, Access Media Project, NTT Access Network Service Systems Laboratories.
He received a B.E. in electrical engineering, an
M.E. in electronic device engineering, and a Dr.
Eng. in electrical engineering from Kyushu University, Fukuoka, in 1996, 1998, and 2005. He
joined NTT Opto-electronics Laboratories in
1998. His research interests include optical fiberconnectors. He is also acting as Assistant Secretary of SC86B of IEC. He is a member of IEICE.

Vol. 16 No. 3 Mar. 2018

6

Global Standardization Activities

Report on 21st Global Standards
Collaboration (GSC-21)
Hideyuki Iwata
Abstract

The 21st Global Standards Collaboration (GSC-21) was held in Vienna, Austria, on September 26 and27, 2017. Its purpose is to enable standards developing organizations (SDOs) to share information about
the activities of the respective organizations and to accelerate standardization activities by avoiding
duplication. Seventy-six delegates from 11 SDOs attended the meeting and discussed two strategic topics: artificial intelligence and smart cities.
Keywords: Global Standards Collaboration, artificial intelligence, smart cities

1. Overview

2. Activity progress reports from SDOs

The 21st Global Standards Collaboration (GSC-21)
was held at the European Office of the Institute ofElectrical and Electronics Engineers (IEEE) in Vienna, Austria, on September 26 and 27, 2017. The meeting was attended by 76 delegates from 11 standards
developing organizations (SDOs): ARIB (Association of Radio Industries and Businesses) of Japan,
ATIS (Alliance for Telecommunications Industry
Solutions) of the USA, ETSI (European Telecommunications Standards Institute), IEC (International
Electrotechnical Commission), IEEE-SA (IEEEStandards Association), ISO (International Organization for Standardization), ITU (International Telecommunication Union), TIA (Telecommunications
Industry Association) of the USA, TSDSI (Telecommunications Standards Development Society, India),
TTA (Telecommunications Technology Association)of Korea, and TTC (The Telecommunication Technology Committee) of Japan (Photo 1). Delegates
reported on the latest activities and high-priority standardization issues of the respective organizations anddiscussed the two strategic topics of artificial intelligence (AI) and smart cities. They also deliberated on
WRC (World Radiocommunication Conference)-19
Agenda Item 1.12.

The 11 SDOs reported on their latest activities andhigh-priority standardization issues. Issues where
duplication was found were fifth-generation mobile
communications networks (5G), SDN/NFV (software-defined networking and network functions virtualization), Internet of Things (IoT), connectedcards, security, open source software, blockchain, AI,
and smart cities. The last two issues were strategic
topics picked for discussion during this meeting.

1

3. Discussions on strategic topics
A brief description of each discussion topic is givenin this section.
3.1 C
 ommunication technologies and artificial
intelligence in autonomous systems
IEEE-SA served as the facilitator for discussions on
AI, and eight SDOs reported on their activities.
(1)	ATIS Initiatives in Support of Artificial Intelligence and Autonomous Systems (ATIS)
ATIS explained its 2017 White Paper on cybersecurity of connected cars. The paper addresses connecting paths via clouds, as well as security requirements.
ATIS collaborates with Auto-ISAC (Automotive
Information Sharing and Analysis Center).
(2)	Overview of Communications Technologies for
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Global Standardization Activities

Photo 1. Participants of GSC-21.

Autonomous and Connected Vehicles (TTA)
TTA is also applying AI to connected cars. It presented the activities of the 3GPP (3rd Generation
Partnership Project) concerning Cellular V2X, inwhich cars communicate with other vehicles and withroad infrastructure devices such as traffic signals androad signs directly rather than via clouds. In the
future, this technology will lead to automated driving.
(3) ETSI Issues on Artificial Intelligence (ETSI)ETSI described the action programs and impacts ofintroducing AI to various fields: radio access technology; vehicle connectivity; IMT2020; access; x-hauland core network technologies; and network management and control. In February 2017, ETSI ISG-ENI(Industry Specification Group on Experiential Networked Intelligence) began studying applications of
AI to network operations, including 5G network
operation.
(4)	Overview of JTC 1 Activities in the Area ofArtificial Intelligence (IEC)The ISO/IEC Joint Technical Committee 1 (JTC 1)
explained the evolution and definition of AI technology. One year ago, JAG (JTC 1 Advisory Group)
formed a temporary group named JETI (JTC 1 on
Emerging Technology and Innovations). It also called
for rapid standardization of AI and autonomous systems (AS) and began studying them in JTC 1/SC 7(subcommittee 7: software and systems engineering),
SC 34 (document description and processing languages), SC 40 (IT (information technology) manVol. 16 No. 3 Mar. 2018

agement and IT governance) and Working Group(WG) 9 (big data).
(5)	IEEE-SA Initiatives in Artificial Intelligenceand Autonomous Systems (IEEE-SA)
IEEE-SA reported that in 2015 it launched a projectcalled Global Initiatives of Ethical Considerations in
Artificial Intelligence and Autonomous Systems, andthat it was studying applications of AI and AS basedon ethics and policy.
(6)	Communications Technology and Artificial
Intelligence (ISO)ISO presented its analysis of opportunities andchallenges that exist in applying AI to research anddevelopment of aviation/space and defense. It
explained issues relating to the application of AI suchas data quality (specified in ISO 8000), data collection using IoT technology, and the use of blockchain
to ensure data security.
(7) AI for Good Global Summit (ITU)ITU outlined the “AI for GOOD” Global Summit
held in June 2017. It was hosted by ITU and the
XPRIZE Foundation and sponsored by 20 United
Nations organizations. The summit participants discussed how to develop AI that is secure, ethical, andfair in all social aspects.
(8)	Standardization Perspectives for Augmental
Robotics (TSDSI)TSDSI presented the concept, challenges, and standardization issues concerning augmental robotics. It
is studying a codec for expressing tactile sense and2Global Standardization Activities

feeling (IEEE P.1918.1) and time-sensitive networking (IEEE 802.1 WG) targeted at use in securitysensitive businesses. It holds particularly high hopes
for application of AI to remote medicine because
60% of the Indian population does not have access tobasic medical care.
3.2 Smart cities(1)	ATIS Initiatives in Support of Smart Cities(ATIS)ATIS published the ATIS Smart Cities Technology
Roadmap in May 2017. The roadmap presents issues
that ATIS identified in four technical fields: access,platforms, applications, and infrastructure. Issues in
the access field include cloud data, context awareness, next-generation position detection, and privacy
and security management. Platform issues include
AI, machine learning, data collection platforms anddata exchange, and augmented reality (AR) platforms. Issues relating to applications include AR/VR(virtual reality) content and content cooperation.
Infrastructure issues identified were resiliencethrough distribution, facility management, and emergency responses.
(2) Smart City, Achieving Better Life (CCSA)CCSA reported on smart city projects implemented
by Huawei: public services, including sewage andwaste treatment, in Dubai; efficient administration of
local government in Guangzhou; remote medicine in
Kenya; Wi-Fi service in a stadium in Amsterdam;
smart tourism in Dunhuang; and a high-speed Wi-Fi
network at Newcastle University, UK.
(3) ETSI Strategy on Smart City Standards (ETSI)ETSI pointed out that technical studies on smart
cities are advancing and that standardization reduces
the risks faced by individual cities in selecting thetechnologies that they need. It presented its smart city
initiatives and emphasized the need for understanding context in using data, as well as the importance of
security, privacy, and trust.
(4)	IEC - Using a Systems Approach to Develop
Smart City Standards (IEC)
Since a smart city involves complex, large-scale
infrastructure, it is important to ensure interoperability. It is necessary to adopt a systems approach indeveloping smart city standards. IEC formed IEC
SyC (Systems Committee) in 2017 and has beenworking on standardization of smart energy, active
assisted living, and low-voltage direct current for
implementing smart cities.
(5) IEEE Smart Cities (IEEE-SA)
Various IEEE standards were discussed. These3

included standards related to 5G and two smart cityrelated standards: IEEE802.11ax (high efficiency
WLAN (wide local area network)) and IEEE P1931.1(ROOF: real-time onsite operations facilitation),
which is aimed at ensuring IoT interoperability andsecurity in local environments such as houses or otherbuildings.
(6)	ISO Smart and Sustainable Cities Development(ISO)
ISO described standardization activities for ISO/TC 268 (sustainable cities and communities), specifically, a standard concerning city-level issues, andactivities for JTC 1 WG11 (information technology
for smart cities), specifically, a standard concerning
machine-level issues. It also referred to cooperation
between SC 41 (IoT) and SC 27 (security).
(7)	ITU Smart Sustainable Cities and Communities
Initiatives: Towards a Smart Global Vision(ITU)
ITU-T’s smart city activities are undertaken by
U4SSC (United for Smart Sustainable Cities), in
which a number of international organizations are
participating. U4SSC studied key performance indicators for using information and communication
technology (ICT) to achieve smart sustainability andinvestigated shared knowledge and future directions.
(8) Building the Smart City Together (TIA)
The collaboration between NIST (National Institute of Standards and Technology) and Cybercity
Framework, IES-City Framework, US Ignite (anadvanced-wireless nonprofit consortium), and FirstNet was reported.
(9)	India’s SMART Cities Initiative and the Role ofStandardization (TSDSI)
Critical urban issues facing India are transportation, energy, and sewage. It was stated that the definition of a smart city is that it is backed by relevant
standards, that it is secure, reliable, and harmonized,
and that ICT and IoT are essential to its implementation.
(10)	Sustainable Development of IoT-Enabled Smart
Cities in South Korea (TTA)
TTA learned from failures of its past projects thatsustainability and interoperability are important, andit believes that it is necessary to expand the horizontal
platform of oneM2M and to ensure interoperability
via standard interfaces. TTA’s current activities
included a certification service for oneM2M specifications and interworking with other platforms such as
FIWARE.

NTT Technical Review

Global Standardization Activities

4. Future plan
The 22nd meeting will be hosted by ISO/IEC in
Switzerland in March 2019.

Trademark notes

All brand names, product names, and company names that appear in thisarticle are trademarks or registered trademarks of their respective owners.

Hideyuki Iwata

Senior Research Engineer, Supervisor,
Research and Development Planning Department, NTT.
He received a Ph.D. in electrical engineeringfrom Yamagata University in 2011. From 1993 to
2000, he conducted research on high-density andaerial optical fiber cables at NTT Access Network Service Systems Laboratories. Since 2000,
he has been responsible for standardization strategy planning for NTT research and development.
He has been a delegate of International Electrotechnical Commission (IEC) SC 86A (opticalfiber and cable) since 1998 and of the ITU-T
Telecommunication Standardization Advisory
Group since 2003. He is a vice-chair of theExpert Group on Bridging the Standardization
Gap in the Asia-Pacific Telecommunity Standardization Program Forum. In 2004, he received
an award from the IEC Activities Promotion
Committee of Japan for his contributions to standardization work in IEC.

Vol. 16 No. 3 Mar. 20184

External Awards
Chairman’s Award
Winner: Kazutaka Hara, Katsuhisa Taguchi, Tomohiro Taniguchi,
Susumu Nishihara, Kota Asaka, Ken-Ichi Suzuki, Takeshi Arai, andAkihiro Otaka, NTT Access Network Service Systems Laboratories
Date: July 27, 2017
Organization: The Institute of Electronics, Information and Communication Engineers (IEICE) Technical Committee on Communication Systems
For “Characteristic Analysis of Optical Path Penalty for NG-PON2
Coexisting with Other Systems on Same ODN.”
Published as: K. Hara, K. Taguchi, T. Taniguchi, S. Nishihara, K.
Asaka, K. Suzuki, T. Arai, and A. Otaka, “Characteristic Analysis of
Optical Path Penalty for NG-PON2 Coexisting with Other Systems
on Same ODN,” IEICE Tech. Rep., Vol. 116, No. 401, CS2016-75,
pp. 65–70, Jan. 2017.
Encouraging Award
Winner: Ryota Shiina, Toshihito Fujiwara, Tomohiro Taniguchi, andTomoki Sugawa, NTT Access Network Service Systems Laboratories
Date: July 27, 2017
Organization: IEICE Technical Committee on Communication Systems
For “Proposal of The Optical Video Distribution System Using
Digitized-Radio-over-Fiber Transmission.”
As a wired rebroadcasting service, the RF (radio frequency) transmission system with HFC (hybrid fiber coaxial) or FTTH (fiber-tothe-home) has been widely used to transmit broadcasting signals via
DTT (digital terrestrial television) broadcasting, BS (broadcastingsatellite), and CS (communications satellite). Since the RF system
conveys the broadcasting signals without any changes for modulationor signals, it enables customers to utilize existing coaxial cables
which are used commonly in the customer premises environment. In
addition, existing TV (television) sets are utilized as a receiver without an STB (set-top box). However, the RF transmission system hassome problems such as an increase in CAPEX/OPEX due to thelimitation of transmission distance and branches, since the systemneeds a dedicated network constructed only for analog transmission.
In this paper, we propose a novel digital video transmission systemusing DRoF (digitized radio over fiber) technology, which realizes
the digital transmission of the RF-based broadcasting signals while
retaining the existing input/output interface of the RF signals to/from
the transmission network. This system also enables RF-based broadcasting signals to overlay on the exiting digital communication system by sharing the network equipment. We also indicate the transmission rate calculated by an analytical model for the proposed system.
Published as: R. Shiina, T. Fujiwara, T. Taniguchi, and T. Sugawa,“Proposal of The Optical Video Distribution System Using DigitizedRadio-over-Fiber Transmission,” IEICE Tech. Rep., Vol. 116, No.
346, CS2016-52, pp. 39–43, Dec. 2016.
FIT Best Paper Award
Winner: Naoko Kosaka, Akira Koyama, Tsuneko Kura, and Koji
Kishi, NTT Secure Platform Laboratories; Tadayoshi Maruyama andKoichi Takamatsu, 2017 Sapporo Asian Winter Games Organizing
Committee

1Date: December 1, 2017
Organization: Steering Committee of the 16th Forum on Information Technology (FIT2017)
For “Applicability Assessment of Integrated Emergency Management Support System, “KADAN” - Using for Management of Largescale International Sports Tournaments -.”Published as: N. Kosaka, A. Koyama, T. Kura, K. Kishi, T.
Maruyama, and K. Takamatsu, “Applicability Assessment of Integrated Emergency Management Support System, “KADAN” - Using
for Management of Large-scale International Sports Tournaments -,”Proc. of FIT2017, CO-010, Tokyo, Japan, Sept. 2017 (in Japanese).
Best Paper Award
Winner: Mehrdad Kiamari, University of Southern California;
Chenwei Wang, DOCOMO Innovations, Inc.; Salman Avestimehr,
University of Southern California
Date: December 8, 2017
Organization: The Institute of Electrical and Electronics Engineers(IEEE) Global Communications Conference (GLOBECOM) 2017
For “On Heterogeneous Coded Distributed Computing.”
We consider the recently proposed Coded Distributed Computing(CDC) framework that leverages carefully designed redundant computations to enable coding opportunities that substantially reduce thecommunication load of distributed computing. We generalize thisframework to heterogeneous systems where different nodes in thecomputing cluster can have different storage (or processing) capabilities. We provide the information-theoretically optimal data set
placement and coded data shuffling scheme that minimizes the communication load in a cluster with 3 nodes. For clusters with K > 3
nodes, we provide an algorithm description to generalize our coding
ideas to larger networks.
Published as: M. Kiamari, C. Wang, and S. Avestimehr, “On Heterogeneous Coded Distributed Computing,” Proc. of IEEE GLOBECOM 2017, Singapore, Dec. 2017.
IEEE Fellow
Winner: Akira Fujiwara, NTT Basic Research Laboratories
Date: January 1, 2018
Organization: IEEE
For contributions to silicon single-electron devices.
Best Paper Award
Winner: Shun Tobiyama, Yukiko Yamaguchi, Hirokazu Hasegawa,and Hajime Shimada, Nagoya University; Mitsuaki Akiyama andTakeshi Yagi, NTT Secure Platform Laboratories
Date: January 10, 2018
Organization: The 32nd International Conference on Information
Networking (ICOIN 2018)
For “A Method for Estimating Process Maliciousness with
Seq2Seq Model.”Published as: S. Tobiyama, Y. Yamaguchi, H. Hasegawa, H. Shimada, M. Akiyama, and T. Yagi, “A Method for Estimating Process
Maliciousness with Seq2Seq Model,” Proc. of ICOIN 2018, pp.
255–260, Chiang Mai, Thailand, Jan. 2018.

NTT Technical Review

Papers Published in Technical Journals andConference Proceedings
Parsing Expression Grammars with Unordered Choices
N. Chida and K. Kuramitsu
Journal of Information Processing, Vol. 25, pp. 975–982, December 2017.
Parsing expression grammars (PEGs) were formalized by Ford in
2004, and have several pragmatic operators (such as ordered choice
and unlimited lookahead) for better expressing modern programming
language syntax. In addition, PEGs can be parsed in a linear time byusing recursive-descent parsing and memorization. In this way, PEGs
have a lot of positive aspects. On the other hand, it is known thatordered choices defy intuition. They may cause bugs. This is due to apriority of an ordered choice. To avoid this, unordered choices are
required. In this paper, we define a parsing expression grammar with
unordered choices (PEGwUC), an extension of a PEG with unordered choices. By the extension, it is expected that a PEGwUC
includes both a PEG and a context-free grammar (CFG), and thisallows us to write a grammar more intuitively. Furthermore, we show
an algorithm for parsing a PEGwUC. The algorithm runs in a linear
time when a PEGwUC does not include unordered choice and in a
cubic time in worst-case running time.

Auditory Surprise Model Based on Pattern Retrieval fromthe Past Observation
M. Yoneya, H.-I. Liao, S. Furukawa, and M. Kashino
Neuroscience, January 2018.

Vol. 16 No. 3 Mar. 2018

The sensory cortex may adapt to predictable events, focusing
instead on unexpected events or surprise stimuli. Previous studies
modeled the auditory surprise using the joint probability of an incoming stimulus and the recent short stimulus history. However, such an
approach is not applicable to describe a long-term pattern change inauditory sequences, since the joint probability is incomputable due todata sparsity when the window size of the stimulus history increases.
Additionally, ‘‘predictive uncertainty” should be considered to prevent overestimation of surprise, since a violation of expectation
would not evoke a large surprise when the prediction is made with asparse observation. Here, we propose a novel auditory surprise modelthat can detect a deviant sound embedded in long-term patternchanges. Instead of calculating the joint probability, our model uses
the similarity-based pattern retrieval from past observation to predict
the future behavior of auditory sequences. The predictive uncertainty
was expressed as the variance of the prediction distribution, which isinversely correlated with the similarity between the selected past patterns and the recent history. Our model is applicable to any auditory
input since it requires neither exact pattern matching nor any conversion of auditory signals into symbolic forms. We conducted two
experiments to test the applicability of our model. In experiment 1,
we showed that the model could predict the reaction time for detecting the disappearance of tone pips. In experiment 2, we showed thatthe model could predict a pupil size change after the pattern transition
in auditory sequences.

2