NTT Technical Review

October 2023 Vol. 21 No. 10

Front-line ResearchersTakahiro

Kawabe,SeniorDistinguished Researcher,

NTT Communication

ScienceLaboratories
Rising ResearchersYuka Hashimoto, Distinguished Researcher, NTT Network Service Systems Laboratories

Feature Articles: Designing a Future Where Everyone Can Flourish by Sharing Diverse
Knowledge and Technologies
Design a World Where Everyone Can Flourish by Deciphering the Future of Individuals,
Society, and the Earth—Communication Science That Connects the Past, Present, and Future
through Diverse Knowledge and Technologies

Machine Learning That Reproduces Physical Phenomena from Data

Understanding Mindful Awareness in Mindfulness Meditation—Investigation of thePsychological, Physiological, and Neural Mechanisms of Mindfulness MeditationDilemma between Quantum Speedup and Computational Reliability—Overcoming Errors with
Efficient Verification Methods for Quantum Computing

Global Standardization ActivitiesTechnical Trends in ISO/IEC Joint Technical Committee 1

Practical Field Information about Telecommunication Technologies


A Case Study of Malfunction of Wireless Communication System Caused by Electromagnetic
Disturbance at a Construction Site

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

Front-line Researchers

Information-presentation Technology
That Gives an Illusory Texture of
Objects without Needing to Touch
Them
Takahiro Kawabe
Senior Distinguished Researcher,
NTT Communication Science
Laboratories
Abstract

Head-mounted displays for cross reality (XR) have beenreleased by various companies and are gradually gaining popularity. Such XR displays present three-dimensional stereoscopic
images that take advantage of the characteristics of the humanvisual system and give users a greater sense of reality. However,illusions are not just created by sight; they are the result of a combination of sight with other senses, such as hearing and touch.
Takahiro Kawabe, a senior distinguished researcher at NTT Communication Science Laboratories, is engaged in research on illusions related to the texture of objects
created by the combination of multiple senses. We interviewed him about his research on presenting the
texture of objects for touchless user interfaces, the fusion of psychology and engineering, and his attitude
as a researcher.
Keywords: illusion, texture impression, touchless user interface

Using illusions to manipulate the texture ofobjects without needing to touch them
—What research are you currently conducting?
I’m investigating information-presentation technology that uses illusions. I believe that by using illusions, we can perceptually present information that is
physically difficult to present. In the previous interview (November 2020 issue) [1], I introduced our
information-presentation technologies that use proNTT Technical Review

Vol. 21 No. 10 Oct. 2023

jection mapping, namely, “Hengento,” which gives
an illusion as if a stationary object is moving and“Ukuzo,” which gives an illusion as if a stationary
object is floating in the air. I’m currently working
on two themes: “technology for manipulating the
texture impression of virtual objects for touchlessuser interfaces” and “tangible interfaces and virtualinterfaces.”
Regarding the first theme, I’m investigating methods for more directly manipulating the texture perception of objects that I briefly touched upon in the1Front-line Researchers

previous interview. Together with my research colleagues Takumi Yokosaka and Yusuke Ujitoko, I
studied the problem of how to convey the weight ofan object on a screen, which cannot be touched
directly, to a user when they make a lifting motion. In
an experiment, we moved the stripes displayed on ascreen in synchronization with the user’s lifting
motion. When the speed at which the stripes moved
was decreased, the user felt that the stripes wereheavier, even though no force was actually applied tothe user’s hand. We also found that the direction of
movement of the stripes does not necessarily have tocoincide with the direction of the lifting motion; forexample, the user feels that the stripes are heavier
even if the speed of the stripes moving horizontally isdecreased to match the upward lifting motion (Fig. 1).
I expect that by applying these research results, wewill be able to give a sense of weight to virtual objects
that a user manipulates without needing to touchthem in a manner that creates a highly realistic
impression of the manipulation.
As shown in Fig. 2, when the user holds their handin front of the laptop and moves it left or right, thetextured virtual object in the upper left corner of thescreen appears to stretch. The amount by which thevirtual object is stretched changes in accordance withthe amount by which the user’s hand moves, andmore stretching makes the object feels soft and lessstretching makes it feel hard. If the hand goes out of
the range detected by the camera and the hand movement is not reflected in the virtual object midway
through the movement (the state of being stretchedstops midway through the movement), something felt
strange to the user, but we did not understand thereason for that strange feeling in details. Therefore,
Yusuke Ujitoko and I set an effective range (i.e.,
detection range of the camera) in which the sensing
function works for hand movements that stretch thevirtual object and investigated how the impression of
the virtual object changes in accordance with theeffective range. We found that when the stretching of
the object is stopped midway, the object feels hard,
heavy, and has friction. This result indicates thatengineers who create touchless devices must understand the effect that the effective range of hand movement for manipulating a virtual object has on theimpression of the texture of the object. Conversely, italso indicates that by using the effect of the effective
range, they may be able to provide people with additional texture impressions of an object.
We submitted many papers to academic journals ontechnology for manipulating the texture impression
NTT Technical Review

Vol. 21 No. 10 Oct. 2023

Direction of lifting motion

User’s handMoving stripes

Fig. 1. Illusion of weight.

Object to be stretched

User’s handFig. 2. Changes in the impression of stretching.

of virtual objects for touchless user interfaces, andour latest research has been accepted for publication
in a high-impact-factor journal, the IEEE (Institute ofElectrical and Electronics Engineers) Transactions on
Visualization and Computer Graphics [2]. Under thetheme of moving robots in a touchless manner, I also
started researching causal perception and texture perception when moving objects by moving a cubeshaped robot through hand movements.
Regarding the second theme “tangible interfaces
and virtual interfaces,” using scientific understandingof human characteristics and physical properties, I
aim to develop a next-generation user interface that
supports interaction between people and information.
I started this theme in October 2022 as research on a
new form of user interface that fuses tangible interfaces and virtual interfaces. Tangible interfaces
enable users to access information while touchingreal objects and intuitively interact with information
as an extension of their daily lives. They, however,use real objects, so they are subject to time andphysical constraints. On the contrary, virtual interfaces enable users to access information virtually, so
2Front-line Researchers

time and physical constraints are fewer; however, it ismore difficult for users to interact with information as
an extension of their daily lives because they have towear a head-mounted display or use unfamiliar
devices. Accordingly, I imagine a new user interface
that merges tangible and virtual interfaces by, forexample, adding a tangible interface to the cross reality (XR) approach. I am considering tangible interfaces as one solution to the question of how to provide natural XR experiences for people.
—So, letting users feel the texture of objects in atouchless manner makes it possible to provide themwith a more convenient and enriching experience,right?
We have shown the possibility of presenting textures, such as weight and hardness, of objects bycombining hand movements and video. We are now
entering a research phase of increasing the reality ofthe presentation. I believe that in the future, ourresearch results could be applied to present information in online-shopping and other applications. For
example, you may have heard stories of how the firmness of a pillow or the feel of clothes bought online
differed from what you imagined. Although the use ofvideo can express a feel of a material to some extent,
it has yet to accurately convey the texture to thepotential buyer. Further development of informationpresentation technology that conveys the texture ofobjects will make it possible to correctly convey
information, including texture, to buyers so that they
can buy items that match the ones they imagine. Conveying textures, such as the warmth of skin and feelof palms with family members living in remote locations makes deeper communication possible. By linking texture-manipulation technology using touchlessinterfaces with appropriate visual-expression technology, information presentation using illusions canbe made richer and more elaborate.
Deepening research through the integration of
psychology and engineering—You originally majored in psychology, right?
In Japan, illusions and perception are studied in thefield of psychology. We are currently using a psychological approach to explain the phenomena concerning illusions and perception and applying the phenomena to everyday life through integration withengineering technology. Since psychology is the
NTT Technical Review

Vol. 21 No. 10 Oct. 2023

study of people, psychological experiments taketime, and unlike mechanical systems, people do notalways respond in exactly the same way to the same
stimuli, interactions, etc. It is therefore difficult toreproduce and collect data, and it also takes time tobuild theories using the collected data. Research on
engineering is evolving at a dizzying pace, so how toclose the gap in speeds of these two research fields
will be the key factor in research on illusions andperception. Artificial intelligence (AI) has developed
remarkably, and I believe that by using AI, we canclose this gap.
If it were possible to create a complete humanmodel and analyze it using Digital Twin Computing,which uses AI, I believe the above problem could be
solved. However, people interact with each other andreact differently according to the environment; therefore, it would not be straightforward to create ahuman model by taking all the data and training themodel using them.
In psychology, we analyze elements of human perception separately, for example, visual perception,
auditory perception, and interpersonal perception. I
believe it is possible to use AI to model each element,
and, in fact, such efforts have been made. By fusing
these models, we will be able to explain the full picture of the human mind.
Scientifically understanding the psychologicalcharacteristics of the recipients of information is also
important in the field of user interfaces. I believe thatscientific research concerning user interfaces canonly be completed by fully understanding how thenature of an object to be conveyed can be expressed
via an interface and why it is conveyed to users.
Break the mold and create new values byintegrating the values of others with yourown values—What do you keep in mind as a researcher and what
do you aim to achieve in the future?
I think that researchers tend to get caught up in theirown logic and think that their values are correct when
they immerse themselves in their research. If you
continue to conduct your research while being confined to your own values, you may begin to struggle
as you are not really sure if you are doing the rightthing or hit a wall. I became a group leader three
years ago and have had many opportunities to learn
about the values of others in various ways. Of course,
sometimes I feel that the values others hold do not3Front-line Researchers

match those of mine; regardless, I have recently cometo strongly believe that it is important to understandand recognize the values of others then create newvalues by integrating their values with my own andthat by doing so, I can grow as a person.
For example, the speed of recent academic research
on AI is remarkable, and it is not uncommon for a
problem that could not be solved yesterday to suddenly be solved today. The researcher working onthat problem must look for their next research target.
In that case, some researchers may lose sight of theirgoals because their research field suddenly becomesobsolete or others may shortsightedly set easy targets
in an attempt to stay just slightly ahead of new technologies. Therefore, it is crucial to acquire knowledge in multiple fields, connect a variety of knowledge, and expand your own research area. To that
end, it is important to create new values by merging
the values of others with your own. Even if there is
one answer to a problem, there is often more than one
way to solve that problem. The speed and effectiveness of each solution will differ, and in some cases,
combining several solutions may create synergy. By
combining several solutions, you may also discover
new problems. I believe that learning and combining
knowledge from multiple disciplines to solve problems will naturally expand your research area andyour capability as a researcher will broaden.
I hope to continue to be a researcher, but I want tobe a researcher who can accommodate and combine
diverse values and continue to create and provide
new values. As I create new values, I may look atother areas or themes. I think that even in such a case,I always have something to rely on as a starting point,
and by using that point as a foundation, I can makegreat progress. My foundation and starting point is
psychology, and I intend to absorb various new
knowledge on the basis of psychology. I want tocontinue my research so that I can successfully integrate psychology and engineering and contribute tosociety.

NTT Technical Review

Vol. 21 No. 10 Oct. 2023

—Do you have a message for younger researchers?
I know that your superiors and peers expect a lot
from you. It is, of course, important to meet those
expectations, but also exceed those expectations andbreak the mold. Since meeting expectations means
staying within the range imposed by your superiors
and peers, you will inevitably have less scope andfewer opportunities for growth. I believe that the
moment you exceed those expectations, you will
have more discussions with your superiors and peers.
By having discussions, you can involve the people
around you and move the whole process forward. To
do so, it is necessary to have a foundation, or something to rely on, which may be knowledge including
your own experience, knowledge of others, or accumulation of such knowledge. Let us exceed the
expectations of those around us, go beyond ourimagination, and move forward together.
References[1][2]

T. Kawabe, “Do Your Best to Be Second to None in Achieving What
You Want to Do,” NTT Technical Review, Vol. 18, No. 11, pp. 6–10,
Nov. 2020. https://doi.org/10.53829/ntr202011fr1
T. Kawabe and Y. Ujitoko, “Softness Perception of Visual Objects
Controlled by Touchless Inputs: The Role of Effective Distance ofHand Movements,” IEEE Trans. Vis. Comput. Graph., Mar. 2023.
https://doi.org/10.1109/TVCG.2023.3254522

Interviewee profile
Takahiro Kawabe received a Doctor of
Psychology from Kyushu University, Fukuoka,
in 2005. In 2011, he joined NTT Communication
Science Laboratories, where he studies humanmaterial recognition and cross-modal perception.
He received the 2013 JPA Award for International
Contributions to Psychology: Award forDistinguished Early and Middle Career
Contributions from the Japanese Psychological
Association. He is a member of the Vision
Sciences Society and the Vision Society of Japan.

4

Rising Researchers

Analysis of Complex Data Based on
C*-algebra and Operator Theory to
Explain Unknown Phenomena in
Data Analysis Using Mathematical
Theories
Yuka Hashimoto
Distinguished Researcher, NTT Network
Service Systems Laboratories
Abstract

Current neural networks achieve a high level of accuracy whenconstructing learning models using large amounts of data, butlearning accuracy drops significantly when only a small amountof data can be obtained for learning. Additionally, in the field ofautomating the operation of current information and communication technology (ICT) systems that detect anomalies beforehand,data analysis is performed under a variety of conditions, so there
is a need for analyzing data that behaves in complex ways such as“data with noise” or “mutual interaction between multiple itemsof data and continuous change.” This time, we talked with NTT Distinguished Researcher Yuka Hashimoto to learn about her research in applying advanced mathematical theories such as C*-algebra andoperator theory to diverse social problems including improving the accuracy of data analysis even witha small amount of data and automating the operation of ICT systems.
Keywords: C*-algebra, operator theory, data analysis

Applying advanced mathematical theories todata analysis to solve social problems in diverse
fields including communications—Dr. Hashimoto, what type of technology is “analysis of complex data based on C*-algebra and operator theory”?
Analysis of complex data based on C*-algebra and
NTT Technical Review

Vol. 21 No. 10 Oct. 2023

operator theory is technology for expressing and analyzing complex data using the concept of an “operator” as a generalized matrix. To give some background on the need for this technology, automating
the operation of current information and communication technology (ICT) systems requires the analysisof data that behaves in complex ways such as “datawith noise” or “mutual interaction between multiple
items of data and continuous change.” For example,
5

Rising Researchersin network communication traffic, the mutual interaction of communication traffic at various locations
must be simultaneously considered such as “whentraffic increases at point A, the traffic at point B also
increases.” In addition, when investigating the stateof equipment and devices, different types of data
must be simultaneously considered such as CPU(central processing unit) usage and memory usage.
Moreover, when constructing a learning model using
a current neural network, a large amount of data isneeded, but in actuality, there are cases in which ahighly accurate learning model cannot be constructed
due to an insufficient amount of data, so there is a
growing demand for technology that can perform
analysis with high accuracy even with a small amountof data.
In response to this problem, a mathematical theory
that has been commonly used up to now involves theobserving of a single value (scalar value) such as areal number or complex number. In contrast, analysis
of complex data based on C*-algebra and operator
theory proposes a new framework for data analysis
that uses advanced mathematical theories such as C*algebra and operator theory to integrate (make continuous), abstract, and simplify models or data. If weapply to data analysis the property in which an object
can be considered to have multiple values simultaneously as an extension of conventional theories, multiple values can be extracted from one type of datawhen analyzing data, thereby enabling high-efficiency, high-accuracy data analysis.
My research into this technology of analysis ofcomplex data based on C*-algebra and operator theory came about when I had been toying with the ideathat matrices or mathematical concepts extending
matrices could be used to good effect for analyzing
the relationships among multiple data items. Then, on
being taught about C*-algebra from my co-researchers, I began to apply it in earnest. Mathematical theories used in analysis of complex data based on C*algebra and operator theory are not limited to specificapplication fields. Rather, as a framework, they arevery general in nature, which means that they can be
deployed in a variety of fields beyond communications and ICT systems. I am also studying whether
problems can be solved by using a continuous framework to represent time-series-like changes as in the
propagation of light. For example, in studies that I am
now conducting, I’m using this research to analyze
biological data such as the brain waves and bodytemperature of animals in the natural world. In addition, using data that has been analyzed with highNTT Technical Review

Vol. 21 No. 10 Oct. 2023

accuracy should make it possible to detect anomalies
such as disasters or faults beforehand or to mount
responses to unknown anomalies based on communication traffic and signals. In this way, I am conducting interdisciplinary research in machine learning,mathematics, physics, biology, and quantum mechanics and other fields regardless of the framework ofcommunications. My target here is a comprehensive
approach beyond traditional field boundaries toenhance industry and explain natural and social phenomena (Fig. 1).
—Please explain the specific technique behind analysis of complex data based on C*-algebra and operator theory.
In analysis of complex data based on C*-algebra
and operator theory, data that continuously changes
over time is treated as a mathematical object foranalysis. When analyzing time-series data by conventional means, data at different times are treated asa collection of separate “points.” In contrast, treatingthat data as a mathematical object called a function,that is, as a “line,” enables data analysis on a moreadvanced level.
In addition, high-accuracy analysis even with asmall amount of data becomes possible by finding
values such as eigenvalues or singular values thatcharacterize that data. In existing research, such values have been found by abstracting the data to convert it to mathematical information and to then returnit to discrete values to extract approximate features.
This technique, however, has a drawback in that theproperties of the data are lost and the accuracy of theanalysis drops. In analysis of complex data based on
C*-algebra and operator theory, I have been working
on a technique for analyzing data continuously while
preventing a loss of information as much as possible.
A technical issue here is that the property of being
continuous (infinite dimensional) is completely different from the property of being discrete (finite
dimensional) and that unimaginable concepts fromeach dimension frequently arise. This makes it difficult to analyze data simply by attempting to extend
discrete data to continuous data, so I am studying theapplication of advanced mathematical knowledge todetermine how best to extend present concepts. Handling data in continuous form requires advanced
mathematical knowledge, so at present, I myself canproceed with my research with the help of many
experts.

6

Rising Researchers

Proposed general framework

Real world
Complex data

x(t)

t

High-accuracy output
Degree of anomaly
Short-term anomaly

Convert/input
Abstraction (noinformation loss)
Continuous, simple model
Large amounts of data unnecessary
Mathematically easy to interpretC-algebra theory
Application

Operator theory Interpretation
Continuization/
abstraction

Real world

Analysis

Abstract mathematical world

Long-term anomalytInterpretation of results,
relationshipx(t)Large contribution to output

Application
RelatedExisting neural-network
machine learning modelt

Fig. 1. Overview of analysis of complex data based on C*-algebra and operator theory.

—Are there any difficult points to deal with in yourresearch of analysis of complex data based on
C*-algebra and operator theory?
A major problem is how to link data in the realworld with the theoretical world of mathematics. For
example, in neural-network analysis, neural networks
are studied as a simple structure having a form that iseasy to mathematically describe in theory. However,to completely reproduce an actual and complex neural network, a variety of operations are needed suchas data processing or the cutting of values during theinformation-processing step, which at present is not
fully reflected in neural networks. Improving accuracy by performing such detailed operations has actually been demonstrated by experimental results. My
current aim therefore is to construct a high-accuracy
neural network by newly conducting as many studies
as possible on two key points. These are “what kindof mechanism does a neural network use to operate,”and once that is explained, “how can accuracy befurther improved.” In this regard, given that there aremany cases in data analysis that can be thought of as
simple in theory, I would like to make every effort inthe construction of a technique using theoreticalanalysis and mathematical theories to enable data
analysis while incorporating many realistic settings.
In addition, I pay particular attention to guaranteeing theoretical accuracy in my research. In the case ofneural networks, for example, a data model used in
NTT Technical Review

Vol. 21 No. 10 Oct. 2023

training may fit a certain type of data thereby raising
the accuracy of data analysis, but there are cases inwhich the same model does not fit other types of data
resulting in a drop in accuracy. Of course, in actual
research, this can happen even for results described ina paper. Even if a paper states that “XX% accuracy
was achieved using this data,” it cannot necessarily
be guaranteed that a similar improvement in accuracy
can be expected when actually attempting to use thattechnology with one’s own settings. It often happensthat changing the data changes the results, so to prevent such a problem from occurring, it is necessary toplace importance on theoretically investigating
beforehand whether high-accuracy data analysis canbe performed for all sorts of data. In this way, it iseasy to make this technology attractive even to people accustomed to using technology—I feel that it’s
an important undertaking to get people to understandthat this technology can be used without worry.
Toward research that places importance on
collaboration with other fields while having abroad perspective—Going forward, what is your research vision foranalysis of complex data based on C*-algebra andoperator theory?
My research vision for the future is to apply thistechnology to a variety of fields requiring the analysis
7

Rising Researchers

Enhancing industry
Cloud

Network/server
Resources

Traffic
Traffic A

Time

Time

Traffic A

Time

Facility A

Resource A

Time

Time

Traffic B

Traffic B

Facility
statusResources

Traffic
Resource A

Plant

Time

• Detects anomaly+ identifies facilities contributing to anomaly
and relationship between facility status andproduction status
Revitalization of various fields

Temperature (1 and 2)

Explaining natural phenomena

37
35
33
0

25

50

t

75

100

125

Time

Time

• Detects anomaly+ identifies traffic and resources contributing toanomaly and relationship between traffic

Biological data

Production
status A

Facility B

Time

39

Production
status• Discovers illness+ identifies abnormal function and relationshipbetween functions• Discovers presence/absence of synchronization
between biological organisms+ identifies strength, temporal change, etc. ofsynchronization• Machine learning• Mathematics• Physics• Biology• Quantum mechanics

Leaders cross-field
collaboration

Fig. 2. Application examples of analysis of complex data based on C*-algebra and operator theory.

of complex data, including communication networks,
with the aim of solving many social problems. For
example, given that light changes continuously, I
believe that explaining the properties of light using
the framework of analysis of complex data based on
C*-algebra and operator theory can contribute to thedevelopment of NTT’s Innovative Optical and Wireless Network (IOWN).
In addition, problems analyzed in biology havefeatures similar to those of data analysis in communication networks, so as a new research vision to pursue
from here on, I would like to explain certain animal
properties such as animal sleep in coordination andcooperation with experts in biology. A mathematicalframework used in analysis of complex data based on
C*-algebra and operator theory is very general innature, so I would like to broaden my collaboration
with people in diverse fields. Thinking that I canapply this technology to problems in various fields
and thereby unite issues that up to now have beenstudied separately, my aim is to help find solutions todiverse problems in industry and the natural world(Fig. 2).
—Dr. Hashimoto, please leave us with a message forresearchers, students, and business partners.
In my research of analysis of complex data basedon C*-algebra and operator theory, I have been working to bridge the gap between application and theory
in mathematics. For this reason, there have been
many opportunities to conduct research together witha variety of people and there have been many occasions to share results within the company. In such anenvironment, research being conducted by a small
group of people can be shared with another group in
the manner, for example, of “I wonder if this technology could be used here as well?” In this way, getting
an idea about an application that you have not thoughtof is very beneficial and can also generate a moment

NTT Technical Review

Vol. 21 No. 10 Oct. 2023

8

Rising Researchersof joy in your research activity. Therefore, I place
great importance on pursuing my research whileinteracting with many people.
NTT conducts a wide range of research from basic
research to applied research, and NTT Network Service Systems Laboratories that I belong to works on
both application and theory, which makes it a very
attractive environment. For example, talking with thepeople around me involved in actual development
enables me to learn quickly about their goals andactual problems and to receive comments from anacademic point of view. This helps me to move myresearch forward. To give an example of this, thedepartment that I belong to researches and develops
fundamental technologies for the automation of ICT
system operations, so I am surrounded by colleagues
having a wealth of knowledge and diverse backgrounds, which gives me daily inspiration in myresearch.
Of course, specializing in one field is important fora researcher, and your field should not be ignored. On
the other hand, you may miss many things by focusing excessively on only one field. In research, it isimportant to have a broad perspective, so I try to stay
active without being stuck in one field as much as
possible. To be sure, an environment that promotes
joint research with people in related fields makes it
easy to obtain an understanding of one’s surroundings and to communicate with others, which facilitates research. Yet, by taking a step outside one’s
everyday environment and listening to people inother fields or participating in academic societies infields different than usual, you should be able toobtain new ways of looking at things that you had not

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Vol. 21 No. 10 Oct. 2023

thought about before. I myself, by cooperating withpeople in other fields different from those I had so far
been exposed to, I have noticed mistakes in my own
way of thinking, and when talking to people in otherfields and not being able to communicate at all, I havebecome aware that “I have been thinking in only avery narrow space.” I feel that experiences such asthese are a huge plus for a researcher.
Taking a step forward by oneself into a new area isdifficult, but I think that an interesting world that you
have never seen before waits for you there. I too
would like to broaden my field of view even further
from here on, and to everyone reading this, I would
like to ask you first and foremost to try talking topeople in fields different than your own. This will
open new doors for you!
Interviewee profile
Yuka Hashimoto received her Master’s Degreein science from Keio University in 2018 andentered Nippon Telegraph and Telephone
Corporation (NTT) in the same year. She received
her Ph.D. in science from Keio University in
2022. She has been an NTT Distinguished
Researcher since 2023 engaged in the research of
network operation automation. She received theYonezawa Award from Keio University in 2021,
the Fujiwara Award from Keio University in
2018, and the Encouragement Award at the 78th
National Convention of the Information
Processing Society of Japan (IPSJ) in 2017.

9

Feature Articles: Designing a Future Where Everyone Can
Flourish by Sharing Diverse Knowledge and Technologies

Design a World Where Everyone Can
Flourish by Deciphering the Futureof Individuals, Society, and theEarth—Communication Science That
Connects the Past, Present, andFuture through Diverse Knowledge
and Technologies
Futoshi Naya
Abstract

Since its establishment, NTT Communication Science Laboratories (CS Labs) has been engaged inresearching basic theories that address the essence of human science and information science and creating innovative technologies that will bring about changes in society to achieve communication thatreaches the heart between humans and between humans and computers. This article introduces recent
research activities of CS Labs from the perspective of deciphering individuals, society, and the Earth.
Keywords: communication science, artificial intelligence, brain science

1. Deciphering individuals
In everyday communication, a person can makeinferences about how another person is feeling or
what they are going to do on the basis of their behavior or situation, even if they do not express words or
facial expressions. Many current information andcommunication technology devices require explicit
commands, voice, and gestures, but if these devices
can understand the other person’s state of mind like ahuman, we can expect more natural and smooth communication between humans and machines.
We at NTT Communications Science Laboratories(CS Labs) have been researching mind-reading technology [1], which interprets such latent mental statesfrom unconscious bodily movements and automatic
physiological responses. We are conducting research
NTT Technical Review

Vol. 21 No. 10 Oct. 2023

on estimating various cognitive states of people inreal time by non-invasively and conveniently measuring eye movements and pupil changes, which isinformation that appears on the surface of the body.
Previous research has shown that the pupil of anobserver constricts when they see an attractive face;conversely, when the pupil of an observer constricts
unconsciously by changing the luminance contrast
around the image of the face, the attractiveness of theface they are looking at increases. This is the first
result to show that the constriction of the pupil affects
the judgment of preference, while at the same time
reversing the conventional theory that the pupil
dilates when looking at attractive faces. In anotherstudy, we also found that the pupils’ response to thebrightness of the visual stimulus presented in the direction of the sound to which we are paying attention
10Feature Articles

High arousal
moya moya

shonbori

un unniko nikosukkiriTime
Choose how
you feel now.
niko niko

0.3

doki doki

1. Buy the movie ticket
EEE: waku waku

0.2

sowa sowa
biku biku

2. Advertisements before
the movie starts
EEE: sowa sowa

0.1

Watching an action
movie

3. Soon after the movie
starts (action scenes)
EEE: hara hara
4. During the movie(The hero is in danger)
EEE: gyaa

Enter your current emotional state on
your smartphone.

5. During the movie(The hero escapes
danger)
EEE: huu

yosshayoshi

hara hara
gyaa

waai
waku waku
uki ukiniko nikoshakiinwaa

iki ikizawa zawa

kyun

un un

p piripi
piri
0.0 Unpleasant
ee
gee
oee
ira ira−0.1muka muka haa uun

uttori
Pleasant
hokkorihotoo heto huu
sukkiriyare yare hetokachin−0.2 moya moya
guttarigaanbohgakkuri
uto utoshonbori
−0.3
−0.4

−0.2

Low arousal

0.0

0.2

Experiencing a
massage chair
1. Recline on a chair
EEE: heto heto
2. Turn on the switch
EEE: un un
3. During massage
EEE: huu
4. Massage withmoderate intensity
EEE: uto uto
5. After the massage isfinished
EEE: sukkiri

0.4

Fig. 1. Intuitive recording and visualization of mental and physical states using EEEs.

reflects which sound we are paying attention toamong sounds presented separately to the left andright ears via headphones [2]. This result not only
suggests that the brain mechanism of attention iscommon to some extent between vision and hearing
but also the possibility of developing hearing-support
devices such as hearing aids by automatically judging
and extracting the direction and object of the soundthat the user wants to hear from the pupil response.
The aforementioned mental states, i.e., facial preference and attention, are often felt unconsciously, and
it is difficult for a person to clearly articulate or
become aware of them. An article in this issue introduces the latest research results on mindfulness
meditation’s physiological, psychological, and neuralmechanisms [3], which has attracted attention as apractical method for cultivating the state of being
aware of sensations, emotions, and other experiencesin the present moment without suppression.
2. Deciphering our society
Next, I will introduce research cases to understandvarious social situations caused by various humaninteractions.
To prevent the spread of COVID-19, new lifestyles
such as telework and remote classes are becoming
more common. However, fewer opportunities forface-to-face interaction have led to an increase in thenumber of people who feel isolated, alienated, andanxious with a diminished sense of belonging. To
understand and pursue people’s future well-being,that is, the state of being physically, mentally, and
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socially fulfilled and alive, it is important to clarify
the situations and factors that improve the well-beingof individuals. To clarify this issue from the perspective of the relationship between individuals andteams and society, CS Labs developed a scale to measure personality traits of people toward teams andsociety based on the view of “Self as We” advocatedby Professor Yasuo Deguchi of the Graduate Schoolof Literature at Kyoto University. We also developed
a method and smartphone application to measure thewell-being of individuals and teams in a multifacetedand sustained manner, which was released in 2020[4]. To capture changes in an individual’s subjective
mental and physical states, the smartphone application records intuitive and physical experiences as
embodied emotional expressions (EEEs), which uses
exclamation words, onomatopoeia, and pictograms,
and these records are visualized in two dimensions
using the embodied emotional expression map (Horizontal axis: pleasant/unpleasant; Vertical axis: high/low arousal) (Fig. 1). This makes it possible to capture subjective and ever-changing mental states
before and after various experiences while reducing
the burden on the user. This can also be used by members of a group who are having a shared experience toanalyze the relationship between group well-being
and mood changes such as meeting satisfaction.
We also developed a method for visualizing andsharing values about what improves the well-being ofindividuals. We analyzed 3900 responses to a questionnaire survey from about 1300 people asking themto list 3 things (values) that are important to them.
The results indicated that those values can be classified
11

Feature Articles

into 4 categories: self-related (I), relationships withothers (We), relationships with local communities
and society (Society), and relationships with theworld and nature (Universe). Our Well-being Cards(Fig. 2), which are based on this classification of values, are used in various workshops for elementary
school students as a tool to deepen their understanding of their values and the diversity of the values of
those around them by sharing them with each other.
Our Well-being Cards are available for downloadfrom the web with instructions on how to use them[5].
We have seen a lot of news and articles about ChatGPT, which is a generative artificial intelligence (AI).
At CS Labs, we are conducting somewhat unusual
research using interactive AI technology. We developed a dialogue agent that helps users who have difficulty in directly communicating with others, such as
patients with depression, to disclose their problems
and communicate them to experts [6]. After establishing a trusting relationship through a conversation
with a chatbot that is an interactive agent and allowing both parties to self-disclose, we conducted anexperiment to determine how the trust relationshipbetween users and experts changes when the chatbot
introduces a trusted human expert.
In the experiment, 47 participants chatted with thechatbot for about 15 minutes every day for 4 weeks.
The experimenter interviewed them online for 1 hour.
The participants who only built trust with the chatbot
as Step 1 and those who were later introduced to an
expert by the chatbot as Step 2 were asked to rate
their trust in the chatbot and the expert on a 7-point
scale (1: not at all trustworthy to 7: very trustworthy).
There was no change in the level of trust in the chatbot between the cases in which Step 2 was not conducted and those in which Step 2 was conducted. The
level of trust in the chatbot was as high as 6, whilethat in the expert was significantly higher when Step
2 was conducted. We also found that when the chatbot asked the participants if they could share what
they had disclosed to it with the expert, they weremore likely to be positive and motivated by it. Most
research on interactive agents had focused on therelationship between people and agents, but thisresearch is unique in that it aims at connecting people
through agents. Technology that learns the relationship between people from conversations and situations will become increasingly important, and CS
Labs is conducting research that uses AI to recognize
the purpose of dialogue and dialogue situations frommultimodal information such as audio, image, and
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Fig. 2. Our Well-being Cards.

language information in dialogue scenes [7].
3. Deciphering the Earth (universe)
It is the ultimate goal of science to clarify andreproduce the structure and laws behind all things innature. Advances in sensing technology have led tothe acquisition and distribution of high-definition andhigh-quality image data, for example, observations of
typhoon development by the Geostationary Metrological Satellite Himawari. With the increasing number of natural disasters, expectations for predicting
the future of complex physical phenomena related to
fluids, such as weather and ocean currents, areincreasing by accurately modeling their developmentfrom observation data and reproducing them throughsimulation. Humans have manually discovered thelaws behind physical phenomena (e.g., Newton’slaws of motion, equations of motion, etc.) fromobserved data. However, the more complex the12Feature Articles

phenomenon, the more difficult it is to model, and themore limited it is to design equations manually. The
discovery of such laws and the automatic simulations
that faithfully reproduce phenomena using AI is avery challenging task. In an article in this issue, theresearch of physical simulation based on the datadriven approach is introduced in detail as a machinelearning technology that reproduces physical phenomena only from observation data without manuallydesigning equations in advance [8].
The above is an example of research aimed at modeling physical phenomena. For complex analyses
such as meteorological data, it is necessary to conduct enormous calculations at high speed. There are
increasing expectations for quantum computers,
which are increasingly being reported for practicaluse. However, overcoming quantum errors is anextremely important issue to resolve to put such computers into practical use. Quantum computers can
execute high-speed parallel calculations using quantum bits, which is quantum mechanical superposition
information. However, since errors due to noise arelikely to occur in quantum calculations, it is important to have a technique to verify whether the calculation results of a quantum computer are correct. An
article in this issue describes methods for efficiently
verifying the correctness of quantum-computer calculation results [9].
In terms of understanding the universe, the Greek
philosopher and mathematician Pythagoras preachedaround the 6th century BCE that “All Is Number” andbelieved that everything in the universe could beunderstood by mathematics according to the laws ofnumbers rather than human subjectivity. From thelatest research results of the Institute of Fundamental
Mathematics established in CS Labs in October 2021,
we developed a method for calculating the heat kernel, which is the basic solution for the time evolutionof a mathematical model that describes the minuteinteraction between light and matter, such as atoms,
using an algebraic theory called representation theory[10]. This study also reveals an interesting relationship between the heat kernel and zeta function in the
Riemann hypothesis, a well-known open problem inmodern mathematics.
4. Designing our future
In this article, I have introduced research efforts at
CS Labs from the viewpoint of deciphering individu-

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als, society, and the Earth (universe). In addition toAI, technological progress will continue to accelerate, but we are also being asked about our responsibility and ethics in creating and using technology. We
will deepen our understanding of the diversity of
individuals, societies, and the Earth, which is constantly changing, and pursue the truth of the universe.
We will also design a sustainable and better futuresociety in harmony with individuals, societies, andthe changing global environment and conduct further
research and development toward this goal.
References[1]

M. Kashino, M. Yoneya, H.-I. Liao, and S. Furukawa, “Reading the
Implicit Mind from the Body,” NTT Technical Review, Vol. 12, No.
11, Nov. 2014.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201411fa6.html[2] H.-I. Liao, H. Fujihira, S. Yamagishi, Y.-H. Yang, and S. Furukawa,“Seeing an Auditory Object: Pupillary light Response Reflects Covert
Attention to Auditory Space and Object,” Journal of Cognitive Neuroscience, Vol. 35, No. 2, pp. 276–290, Feb. 2023.
https://doi.org/10.1162/jocn_a_01935[3] M. Fujino, “Understanding Mindful Awareness in Mindfulness Meditation—Investigation of the Psychological, Physiological, and Neural
Mechanisms of Mindfulness Meditation,” NTT Technical Review,Vol. 21, No. 10, pp. 20–25, Oct. 2023.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr202310fa3.html[4] N. Saijo, M. Fujino, A. Murata, Y. Ooishi, and J. Watanabe, “Human
Information Science Research for Understanding and Improving Our
Well-being,” NTT Technical Review, Vol. 20, No. 10, pp. 37–43, Oct.
2022.
https://doi.org/10.53829/ntr202210fa5[5] Website for downloading Our Well-being Cards (in Japanese), https://socialwellbeing.ilab.ntt.co.jp/tool_measure_wellbeingcard.html[6] Y. Lee, N. Yamashita, and Y. Huang, “Designing a Chatbot as aMediator for Promoting Deep Self-Disclosure to a Real Mental
Health Professional,” Proc. of ACM Human-Computer Interaction,
Vol. 4, No. CSCW1, Article no. 31, pp. 1–27, 2020.
https://doi.org/10.1145/3392836[7] Y. Chiba and R. Higashinaka, “Analyzing Variations of Everyday
Japanese Conversations Based on Semantic Labels of Functional
Expressions,” ACM TALLIP, Vol. 22, No. 2, Article no. 52, pp. 1–26,
2022.
https://doi.org/10.1145/3552310[8] Y. Tanaka, “Machine Learning That Reproduces Physical Phenomena
from Data,” NTT Technical Review, Vol. 21, No. 10, pp. 15–19, Oct.
2023.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr202310fa2.html[9] Y. Takeuchi and S. Tani, “Dilemma between Quantum Speedup andComputational Reliability—Overcoming Errors with Efficient Verification Methods for Quantum Computing,” NTT Technical Review,Vol. 21, No. 10, pp. 26–29, Oct. 2023.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr202310fa4.html[10] C. Reyes-Bustos and M. Wakayama, “Heat Kernel for the Quantum
Rabi Model,” Adv. Theor. Math. Phys., Vol. 26, No. 5, pp. 1347–1447,
2022.
https://dx.doi.org/10.4310/ATMP.2022.v26.n5.a8

13

Feature Articles

Futoshi Naya

Vice President and Head of NTT Communication Science Laboratories.
He received a B.E. in electrical engineering,
M.S. in computer science, and Ph.D. in engineering from Keio University, Kanagawa, in 1992,
1994, and 2010. He joined NTT Communication
Science Laboratories in 1994. From 2003 to
2009, he was with Intelligent Robotics and Communication Laboratories, Advanced Telecommunications Research Institute International(ATR). His research interests include communication robots, sensor networks, pattern recognition, data mining in cyber-physical systems, andAI-based tailor-made education support. He is amember of the Institute of Electrical and Electronics Engineers (IEEE), the Society of Instrument and Control Engineers, and the Institute ofElectronics, Information and Communication
Engineers (IEICE).

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14

Feature Articles: Designing a Future Where Everyone Can
Flourish by Sharing Diverse Knowledge and Technologies

Machine Learning That Reproduces
Physical Phenomena from Data
Yusuke Tanaka
Abstract

Machine learning has made remarkable progress and has been used successfully in various applications. Our research goal is to use machine learning to simulate physical phenomena. In this article, I
introduce machine-learning models that can accurately reproduce physical phenomena from observed
data by using prior knowledge of physics. I also discuss the prospects of and value that can be createdwith this research.
Keywords: simulation, physical laws, physics-informed machine learning

1. Machine learning and physics simulation
The dynamics of many physical phenomena aredescribed with differential equations. Traditionally,
experts in various fields have designed equations to
reproduce physical phenomena through observationand theoretical investigation (top of Fig. 1). Solving
these equations can simulate physical phenomena
under various conditions using a computer without
real-world experiments. Physical simulations areused in various real-world applications, such as
weather forecasting and aircraft design. However, thedesign of equations is very costly, and there are limitations in modeling complex phenomena, such as
weather, in the real world.
Advances in information and communication technologies have attracted attention to data-driven
approaches. Machine learning has revealed that various real-world problems can be solved with extremely high accuracy by using large-scale data.
Is it possible to simulate physical phenomena using
machine learning? To answer this question, my
research colleagues and I are studying machinelearning models for accurately reproducing physicalphenomena from observed data. Unlike the conventional approach of handcrafting differential equations
described above, we are developing a model for automatically constructing a highly accurate physics
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simulator from data without the need to design equations for the phenomena (bottom of Fig. 1).
2. Limitations of current
machine-learning models
Behind the data-driven approach for physics simulation was a technological breakthrough: Neuralordinary differential equation (NODE) [1], publishedin 2018. NODE uses observed data to learn the neural-network parameters to represent a phenomenon
without designing an equation (see the orange box“Machine-learning model” in Fig. 1).
Is it possible to simulate physical phenomena by
naively applying NODE to observed data? Unfortunately, achieving a highly accurate simulation is difficult. Figure 2 shows the results of an experimentusing a pendulum as an example of a simple physicalsystem. We provide the dynamics of a pendulum inthe upper center of Fig. 2, with the angle on the horizontal axis and the angular momentum on the verticalaxis. Due to the energy conservation law, the blackdot (i.e., the system’s state) continues to move in thegreen orbit. Given the observed data of such motion(upper right of Fig. 2), the simulation result using
NODE is the lower part of Fig. 2. The simulated trajectory deviates from the true orbit depicted in green.
The simulation accuracy significantly degrades when
15

Feature Articles

Equation

u
u
 3u
−
= −u
t
x
x 3

High cost: difficult to model complex phenomena
Parameteru= ƒ(u ;)t

Our approach:
Machine learning with laws of physics
No equation design required: learning complex
phenomena by using machine learning techniques

Sensing

Machinelearning model

Learning

Data

Physical phenomena

Simulation:
Predict physical phenomena given
any initial condition
Physicalphenomena(Ex. fluids)

Location x

Conventional approach:
Equations are handcrafted.

Time

Significance:
Simulation of physical phenomenapreviously difficult to model forscientific discovery, engineering
design, etc.

Fig. 1. Differences in approaches.

Examples of physical systems and observed data
Dynamics ofPenduluma pendulum
Black dot moves
in green orbit
due to energy
conservation law.

Momentum

Angle

Observed data

Angle
Results of applying a standard neural net
Small amount ofLarge amount of Large noise & small
noise
noise
amount of data
Simulated trajectories(revolving over timefrom blue to red)Fig. 2. Limitations of current machine-learning models.

only a small amount of data containing a large
amount of noise is available.
3. Difficulty in learning
Why did we obtain these results? Machine-learningmodels are known to be highly expressive and havethe potential to accurately model large and complex
physical phenomena. Due to their high expressive
power, it is not easy to identify a model that accurately reproduces a physical phenomenon from thevast search space of machine-learning models (theNTT Technical Review

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gray region in Fig. 3). Learning becomes even moredifficult when only a small amount of data containing
a large amount of noise or missing values is available.
4. Introducing prior knowledge of physics
When data are scarce containing a large amount ofnoise or missing values, it is effective to introduce
prior knowledge that can provide hints to guide
appropriate learning. The research field that aims touse prior knowledge from physics for training iscalled physics-informed machine learning [2] and has
16Feature Articles

Machine-learningmodelsData
Learningu=tƒ(u ;)
Model space satisfying
laws of physics
Model before learning
Narrow down

SearchTrue model

Vast search space of machine-learning models

Fig. 3. Narrowing down search space via prior knowledge.

been gaining attention. It is expected to narrow the
search space and effectively identify models that
accurately reproduce physical phenomena (seeorange region in Fig. 3). The simplest way to introduce prior knowledge is to assume an equation, aswith the conventional approach illustrated in Fig. 1,
and learn the physical parameters (α and β in Fig. 1)
in the equation from the data. However, this approach
narrows the search space too much. It does not takeadvantage of the high expressive power of machinelearning models, making it difficult to apply to complex phenomena that cannot be represented withknown equations. Therefore, we focused on using thelaws of physics as prior knowledge. This enables us
to maintain the expressive power of the machinelearning model while appropriately narrowing thesearch space.
5. Machine-learning model incorporating theenergy conservation law
Throughout history, various physical laws havebeen discovered, including conservation of energy,
conservation of mass, and conservation of momentum. I introduce a case study of incorporating theenergy conservation law, which is generally obeyedin dynamical systems, into a machine-learningmodel. The theory of Hamiltonian mechanics is aconvenient way to describe physical phenomena thatobey the energy conservation law. In Hamiltonianmechanics, instead of designing equations that represent physical phenomena, the energy function of the
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physical system is designed. Once the energy function is determined, the physical dynamics can besystematically obtained in accordance with the Hamilton’s equation. It should be noted that the dynamics
derived from the Hamilton’s equation can be guaranteed to always obey the energy conservation law. The
Hamiltonian neural network (HNN) [3] was proposedto train a model for estimating from data the physicalphenomena that follow the energy conservation law
by replacing the energy function with a neural network. With neural-network-based models, however,
it is implicitly assumed that data of sufficient quality
and quantity are available. When such data areunavailable, the models often over-fit to the data,
resulting in a loss of accuracy. Therefore, we developed a Gaussian-process model [4] incorporating thetheory of Hamiltonian mechanics. The Gaussian process is a machine-learning model advantageous forlearning from a small amount of data with a large
amount of noise. Figure 4 shows a schematic diagram of this model. The model has the following
three features. The first feature is estimating theenergy function by a Gaussian process instead of aneural network. This is expected to avoid overfitting
and estimate the energy function even when theamount of data used for training is small. The second
feature is deriving the dynamics using the Hamilton’s
equation. It can be guaranteed that the Gaussian process will always satisfy the energy conservation law.
The third feature is introducing an observation modelfor noisy data. This enables robust learning even fromnoisy data. Our model enables effective learning of17

Feature Articles

Point 1
Estimate energyfunction using
Gaussian process.

Momentum

Dynamics

Momentum

EnergyAngle

Point 3Introduce
observation
model of noisy
data (red dots).
Angle

Point 2
Dynamics derived from the Hamilton’s equation.
Obtained dynamics conserves energy.

Fig. 4. Our Gaussian-process model.

Our model

Time variation of energy

EnergyMomentum

MomentumBaseline (HNN)

Angle

Angle
Green: True

Blue: Ours

Time
Red: Baseline

Fig. 5. Simulation results.

physical phenomena that obey the energy conservation law, even when data of insufficient quality andquantity are available and enables highly accurate
simulations.
Figure 5 shows the results of simulating the motionof a pendulum using the conventional model (i.e.,
HNN) and our model. From comparing the left andmiddle of Fig. 5, our model is more accurate in simulating the true trajectory (in green). The right of Fig. 5
plots the energy variation over time. The true value(in green) is constant due to the energy conservation
law. Our model captures the energy value more accurately than the conventional model. These results
indicate that our model accurately simulates physicalphenomena while obeying the energy conservationlaw.
6. Prospects and applications
There are still limitations in the input data required
for training and the types of physical phenomena thatcan be handled. We will aim to reproduce more complex phenomena on the basis of more realistic obserNTT Technical Review

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vations for real-world applications. In the research
introduced in this article, we assumed that physicalvariables, such as the angle of a pendulum, can beobserved. However, as physical systems become
more complex, it is not easy to obtain such variables
directly. However, sensor values, images, and videos
related to physical phenomena may be relatively easy
to observe. We need a technology that can reproduce
physical phenomena even when such types of inputs
are given. In addition, complex real-world phenomena, such as meteorological phenomena, are often
represented with a partial differential equation, and it
is desirable to extend the technology to such equations.
As this research field develops, it is expected tohave a variety of applications. For example, weather
forecasting can address environmental issues, such as
climate change, or reproduce phenomena such as
typhoons and tsunamis for disaster prevention. The
simulation of complex weather phenomena in the realworld, which cannot be represented with manuallycrafted equations, is expected to provide more accurate
forecasting. Aircraft, automobiles, semiconductors,
18

Feature Articles

and other products are designed using physics simulation. If physics simulators can be automaticallyacquired from observed data, it will be possible to
make simulators more accurate and efficient whencreating new products. A simulator can also be
applied to artificial-intelligence tasks, such as scene
understanding by robots. For example, when a humansees an image of a messy stack of packages, they caninfer that it may collapse. If robots can learn physicalphenomena using the ideas in this research, we
believe it will be possible for them to avoid risks insuch situations.
This work was supported by Japan Science and Technology Agency (JST), ACT-X Grant Number JPM-

JAX210D, Japan.
References
[1]
[2]

[3]
[4]

R. T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud,“Neural Ordinary Differential Equations,” Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018.
G. E. Karniadakis, I. G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, andL. Yang, “Physics-informed Machine Learning,” Nat. Rev. Phys., Vol.
3, No. 6, pp. 422–440, 2021. https://doi.org/10.1038/s42254-02100314-5
S. Greydanus, M. Dzamba, and J. Yosinski, “Hamiltonian Neural
Networks,” Advances in Neural Information Processing Systems 32(NeurIPS 2019), 2019.
Y. Tanaka, T. Iwata, and N. Ueda, “Symplectic Spectrum GaussianProcesses: Learning Hamiltonians from Noisy and Sparse Data,”
Advances in Neural Information Processing Systems 35 (NeurIPS2022), 2022.

Yusuke Tanaka

Research Scientist, Learning and Intelligent
Systems Research Group, Innovative Communication Laboratory, NTT Communication Science
Laboratories.
He received a B.E. in engineering from Kobe
University in 2011 and M.Inf. and D.Inf. in informatics from Kyoto University in 2013 and 2020.
He joined NTT in 2013. His research interests
include machine learning and data mining. He isa member of the Institute of Electronics, Information and Communication Engineers (IEICE)
and the Japanese Society for Artificial Intelligence (JSAI).

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19

Feature Articles: Designing a Future Where Everyone Can
Flourish by Sharing Diverse Knowledge and Technologies

Understanding Mindful Awarenessin Mindfulness Meditation—
Investigation of the Psychological,
Physiological, and Neural
Mechanisms of Mindfulness
Meditation
Masahiro Fujino
Abstract

Mindfulness meditation, which is used to cultivate a state of awareness of the experience of the present moment as it is (called mindful awareness), has been shown to contribute to enhancing well-being.
There are two types of mindfulness meditation: focused attention meditation and open monitoring
meditation. To understand mindful awareness, our group focused on the psychological, physiological,
and neural mechanisms of open monitoring meditation thought to be involved in mindful awareness.
Keywords: mindfulness, mindful awareness, open monitoring meditation

1. The importance of awareness of the present
moment experience as it is
Mindfulness is the state of receptive attention toand awareness of ongoing events and experiences as
they are. “Receptive attention” means, as opposed tointentionally focusing attention on a specific object,
the spontaneous broadening of attention by letting goof such an intention. “Awareness” means the bringing
to consciousness of previously unaware experiences.
“Experiences” means the sensations, feelings, andthoughts that arise one after another in one’s body andmind. “As they are” means an attitude of not reacting,judging, or inhibiting the experiences within awareness after broadening of attention. We call this kindof awareness mindful awareness in this article.
Many studies have shown that mindful awarenessof negative experiences can alleviate symptoms suchas depression and anxiety [1]. However, it has also
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been reported that people who think they are aware ofnegative experiences as they are may react or suppress them unconsciously, resulting in adverse events
such as increased anxiety or tension [2]. Considering
these studies and reports, it is clear that the key toincreasing effectiveness while reducing adverse
events of mindfulness practice is to be aware of theexperience as it is, that is, mindful awareness. To
understand this mindful awareness, we need to elucidate its mechanism.
2. Focused attention and open
monitoring meditations
There are two types of mindfulness meditation:
focused attention meditation (FAM) and open monitoring meditation (OMM) (Fig. 1). FAM is a methodof intentionally focusing attention on a specificobject [3]. For example, we focus our attention on
20

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breathing, which occurs naturally. However, when
distracting stimuli arise, such as stronger sensations
or emotions, our attention is captured by them. When
we become aware of this, we try to shift attention
back to the specific object. This shifting attention
allows it to be diverted away from the distracting
stimulus. Repeatedly doing this can improve the ability to focus attention on a target object. However,focusing attention on a specific object does notenable us to become aware of experiences that occur
one after another in the range other than the narrow
range of attention.
OMM, however, is a method for maintaining
awareness of the ongoing flow of experiences as theynaturally occur one after another, without selectively
focusing on specific objects [3]. With this method,since there is no specific object, the range of attention
is broadened, and every breath, sensation, and emotion that arises one after the other becomes an objectof awareness. However, when distracting stimuli,
such as stronger sensations or emotions, arise, ourattention is captured by them. When we becomeaware of this, we try to be aware of the stimulus as itis, rather than moving our attention away from it. The
distracting stimulus is then returned to the object ofawareness, and the range of attention that was captured by the stimulus is naturally broadened. By
repeating this process, it becomes possible to maintain awareness of various sensations, feelings, andthoughts as they are, without differentiation between
selected and deselected objects.
Our group has been working to elucidate the psychological, physiological, and neural mechanisms of
OMM to clarify the mechanism of mindful awareness. I describe three of these studies in the following
sections.
3. Study 1: Investigation of the psychologicalmechanism of OMM
When we concentrate on a task, we subconsciously
inhibit distracting stimuli that are irrelevant to thetask, such as visual stimuli. Such inhibition is fatiguing. It is believed that mindfulness meditation reduces such inhibition. However, previous studies havenot confirmed that FAM and OMM as well as mindfulness meditation reduce inhibition to distracting
stimuli at the behavioral level. Therefore, we
designed a cognitive task to assess the degree of inhibition against distracting stimuli and examined theeffects of FAM and OMM on inhibition [4].
The experiment was conducted by dividing 72
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Mindfulness meditation+
FAM
Focusing attention ona specific object

OMM
Maintaining awarenessof the ongoing flowof experiences

Fig. 1. T wo types of meditation methods comprising
mindfulness meditation.

meditation-naïve participants into three groups:
FAM, OMM, and relaxation. A 30-minute intervention was first given using voice instruction [5], followed by a questionnaire to measure the state ofrelaxation, then two tasks were performed (Fig. 2). In
Task 1, 24 face images that included 3 facial expressions of 8 people were presented repeatedly in turn,and the participants were asked to respond as quickly
and accurately as possible to the orientation of theletter presented in the center of the face images. In
this task, the face images were distractors. In Task 2,
participants were asked to rate their attractivenesstoward the eight neutral face images presented in
Task 1 and another eight neutral face images thatwere not presented. Based on previous research [6], if
the face images were not inhibited, the attractivenesstoward the face images that were presented repeatedly would be expected to increase. However, if theface images were inhibited, we would not see such anincrease in attractiveness.
The results indicated that the relaxed group did notincrease their attractiveness toward the face images
they were repeatedly exposed to in Task 1 (Fig. 3).
This indicates that the relaxed group inhibited theface images during Task 1. However, the FAM andOMM groups showed increased attractivenesstoward the face images they were repeatedly exposed
to in Task 1 (Fig. 3). This indicates that, comparedwith the relaxed group, the FAM and OMM groups
reduced inhibition to the face images during Task 1.
Interestingly, in the FAM group, those who weremore relaxed immediately before Task 1 showed lessinhibition to the face images, while those who wereless relaxed showed more inhibition to the face
images (Fig. 4). In contrast, no such relationship wasobserved in the OMM group (Fig. 4). This indicatesthat the attention-regulation strategy of the FAM
21

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Intervention
30-min FAM(24 non-meditators)

Task 1: Discrimination task
Face image is a distractor.

Task 2: Preferencejudgment task

24 images with 3 facial
expressions of 8 people

Attractiveness (1–9)

30-min OMM(24 non-meditators)
30-min relaxation(24 non-meditators)

Questionnaire: Assessing the state of relaxation

Fig. 2. Experimental design for investigating the psychological mechanism of OMM.

Presented face images in Task 1

5.5
*

*

FAM

OMM

Un-presented face images in Task 1

Attractiveness

5.0
4.5
4.0
3.5
3.0
Control

The error bar shows the 95% confidence interval. * p < .05

Fig. 3. Attractiveness to face images.

2.0

r = .48
p = .02

1.5
1.0
0.5
0.0
−0.5
−1.0
−1.5

2.5

3.0

3.5

4.0

4.5

5.0

5.5

State of relaxation before Task 1
FAM

Difference in attractiveness

Difference in attractiveness

2.0

1.5

r = .21
p = .33

1.0
0.5
0.0
−0.5
−1.0
−1.5

2.5

3.0

3.5

4.0

4.5

5.0

5.5

State of relaxation before Task 1
OMM

Fig. 4. T he correlation between the difference in attractiveness between presented and un-presented face images and thestate of relaxation before Task 1.

group was influenced by the relaxation state, whereasthat of the OMM group was not influenced by therelaxation state.

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4. Study 2: Investigation of the physiologicalmechanism of OMM
The popular image of mindfulness meditation is22

Feature Articles

Para-sympathetic
nerve activity

40
30
20

FAM

OMM

*

2.0
1.5
1.0

Cortisol level(pg/ml)*SDNN/rMSSD

rMSSD (ms)50

Cortisol levels

Sympathetic nerve activity

1000
800
600
400

OMM

FAM

**

FAM

OMM

The error bar shows standard deviation. * p < .05, ** p < .01

rMSSD: root mean square of successive differences
SDNN: standard deviation of normal-to-normal intervals

Fig. 5. The effect of FAM and OMM on the autonomic nervous activity and salivary cortisol levels.

that it relaxes the mind. However, in the field of
meditation research, it has been thought that mindful
awareness is not simple relaxation since it is a state ofbeing aware of various sensations, emotions, andthoughts. Therefore, the effects of mindfulness meditation on physiological indices related to relaxationand stress have been investigated, but the results havevaried. The reason for this is thought to be that mindfulness meditation was examined without considering FAM and OMM separately. Therefore, we
focused on them separately and examined theireffects on physiological indices [7].
In the experiment, 41 meditation-naïve participants
were asked to perform 30 minutes of FAM and
OMM, respectively [5], and the effects of each meditation on heart-rate variability before and during
meditation and on cortisol levels in saliva before andafter meditation were examined.
The results indicated that parasympathetic nerve
activity increased during FAM (Fig. 5). This indicates a higher state of relaxation. These results alsosuggest that by focusing attention on a specificobject, the participants were less likely to be distracted by other sensations, emotions, and thoughts,
thus were able to relax. During OMM, interestingly,
sympathetic nerve activity increased while cortisol
levels decreased (Fig. 5). This indicates that the stresslevel was lower despite the higher arousal level.
These results suggest that OMM is not simply a stateof relaxation but may be a state in which the participants were aware of various sensations, emotions,and thoughts but were less able to react to or inhibit
them.

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5. Study 3: Investigation of the neuralmechanism of OMM
What type of activity occurs in the brain during
mindful awareness of various sensations, emotions,and thoughts? Previous studies have not clearlyshown the difference in brain activity during FAM
and OMM. Therefore, we designed an experiment for
extracting brain activity during meditation and identified brain activity during FAM and OMM [8]. This
study was conducted while I was a member of the
Graduate School of Education, Kyoto University.
The experiment included 17 meditation practitioners. Conventional studies comparing brain activity
during FAM and OMM have mainly used a block
design, for example, 6 minutes of FAM, 6 minutes ofrest, and 6 minutes of OMM, during which brain
activity was measured with functional magnetic resonance imaging (fMRI). However, as a meditation
practitioner, I realized that it is difficult to achieve an
optimal meditative state in a 6-minute meditation session. Therefore, we measured brain activity during 6
minutes using fMRI immediately after 1 hour of
meditation in a soundproof room. Because I had feltthat the meditative state continued for a while after
the 1-hour meditation, we divided the days of FAM
and OMM conditions into two separate days.
Functional connectivity analysis, which examines
the correlation between activities of brain regions,
was used for the analysis. Mindfulness meditation is
known to involve a wide range of brain regions
because it involves various cognitive functions suchas attention control, emotion regulation, awareness ofbodily sensations, and change in perspective on the23

Feature Articles(b)
Functional connectivity change

(a)

0.20

r = .52
p = .03

0.10
0.00
−0.10
−0.20
−0.30
−0.40
−0.50
0

500 1000 1500 2000 2500
Total meditation-practice hours

Fig. 6. ( a) The retrosplenial cortex showing reduced functional connectivity with the ventral striatum during OMM. (b) The
correlation between total meditation-practice hours and altered functional connectivity from the resting state to the
OMM state between the right retrosplenial cortex and left ventral striatum.

self. The striatum, which is located below the cerebral cortex, forms multiple different circuits witheach brain region of the cortex and involved in various functions such as movement, attention, emotion,
motivation, learning, and memory. By examiningchanges in functional connectivity between the striatum and each brain region, we identified brain activities specific to FAM and OMM and estimated thefunctions of meditation from these activities.
The results indicated that the functional connectivity between the ventral striatum and visual cortex
increased during FAM while it decreased during
OMM. This functional connectivity is thought to berelated to intentional attention control directed at a
specific object. The results support the conventional
understanding that FAM enhances intentional attention control, while OMM reduces it. Functional connectivity between the ventral striatum and retrosplenial cortex was also reduced during OMM (Fig. 6(a)).
Notably, the longer the lifetime of meditation practice, the greater the decrease in functional connectivity (Fig. 6(b)). This functional connectivity is thought
to be related to the degree to which one is captured bymemories. This suggests that when the participants
were aware of sensations, feelings, and thoughts during OMM, there may have been a decrease in brain
activity that connected these experiences to past
memories.
6. Summary

fulness is the state of receptive attention to andawareness of ongoing events and experiences as theyare. We call this type of awareness mindful awareness. OMM is considered more effective than FAM in
cultivating mindful awareness. We have found thatOMM reduces inhibition to distracting stimuli. Thisis consistent with the understanding that mindful
awareness does not distinguish experiences as thetarget object and other distracting stimuli but rather
receives everything as an object of awareness. In
addition, the physiological parameters of mindful
awareness of these experiences revealed that the participants were not merely relaxed but in a state of low
stress despite their high arousal levels. We were alsoable to identify the possibility that such a low stressstate may be related to not associating their experiences in the present moment with their past memories.
7. Future perspectives
On the basis of these findings, we are currently
developing biomarkers to quantify mindful awareness. We believe that the establishment of such atechnology will contribute to increasing the effectiveness of mindfulness-meditation interventions while
reducing adverse events. Therefore, we are working
to contribute to people’s well-being by elucidating
the mechanisms of mindful awareness and developing technologies by using this understanding.

In light of the three aforementioned studies, let mesummarize the concept of mindful awareness. MindNTT Technical Review

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24

Feature Articles

References
[1]

[2]

[3]
[4]

M. Goyal, S. Singh, E. M. S. Sibinga, N. F. Gould, A. Rowland-Seymour, R. Sharma, Z. Berger, D. Sleicher, D. D. Maron, H. M. Shihab,
P. D. Ranasinghe, S. Linn, S. Saha, E. B. Bass, and J. A. Haythornthwaite, “Meditation Programs for Psychological Stress and Wellbeing: A Systematic Review and Meta-analysis,” JAMA Internal
Medicine, Vol. 174, No. 3, pp. 357–368, 2014. https://doi.org/10.1001/
jamainternmed.2013.13018
N. T. Van Dam, M. K. van Vugt, D. R. Vago, L. Schmalzl, C. D. Saron,
A. Olendzki, T. Meissner, S. W. Lazar, C. E. Kerr, J. Gorchov, K. C.
R. Fox, B. A. Field, W. B. Britton, J. A. Brefczynski-Lewis, and D. E.
Meyer, “Mind the Hype: A Critical Evaluation and Prescriptive
Agenda for Research on Mindfulness and Meditation,” Perspect.
Psychol. Sci., Vol. 13, No. 1, pp. 36–61, 2018. https://doi.org/10.1177/
1745691617709589
A. Lutz, H. A. Slagter, J. D. Dunne, and R. J. Davidson, “Attention
Regulation and Monitoring in Meditation,” Trends Cogn. Sci., Vol. 12,
No. 4, pp. 163–169, 2008. https://doi.org/10.1016/j.tics.2008.01.005
M. Fujino, Y. Ueda, V. Inoue, Y. Ooishi, N. Kitagawa, and M. Nomura, “Evidence of Difference in Emotion Regulation between Focused
Attention Meditation and Open Monitoring Meditation,” Proc. of

[5]

[6]

[7]

[8]

Contemplative Science Symposium (Poster sessions), Fürstenfeldbruck, Germany, 2019.
M. Fujino, Y. Ueda, V. Inoue, J. G. Sanders, S. Murphy-Shigematsu,
and M. Nomura, “Development of Instructions of Short-term Focused
Attention, Insight, and Compassion Meditation for Use in Psychological Experiments,” Japanese Journal of Mindfulness, Vol. 4, No. 1,
pp, 10–33, 2019. https://doi.org/10.51061/jjm.41_3
D. De Vito, N. Al-Aidroos, and M. J. Fenske, “Neural Evidence That
Inhibition Is Linked to the Affective Devaluation of Distractors That
Match the Contents of Working Memory,” Neuropsychologia, Vol.
99, pp, 259–269, 2017. https://doi.org/10.1016/j.neuropsychologia.
2017.03.022
Y. Ooishi, M. Fujino, V. Inoue, M. Nomura, and N. Kitagawa, “Differential Effects of Focused Attention and Open Monitoring Meditation on Autonomic Cardiac Modulation and Cortisol Secretion,”
Front. Physiol., Vol. 12, 675899, July 2021. https://doi.org/10.3389/
fphys.2021.675899
M. Fujino, Y. Ueda, H. Mizuhara, J. Saiki, and M. Nomura, “Open
Monitoring Meditation Reduces the Involvement of Brain Regions
Related to Memory Function,” Sci. Rep., Vol. 8, 9968, 2018. https://doi.org/10.1038/s41598-018-28274-4

Masahiro Fujino

Research Specialist, Human Information Science Laboratory, NTT Communication Science
Laboratories.
He received a BBA in business administrationfrom Kobe University in 2004 and worked for
Sysmex Corporation for 7 years. After starting topractice Buddhism meditation, he left the company and returned to school to understand themechanism of meditation and share its mechanisms and benefits with others. He received aB.A., M.Ed., and Ph.D. in cognitive psychology
in education from Kyoto University in 2014,2016, and 2019. He served as an assistant professor in cognitive psychology in education at
Kyoto University from 2019 to 2020 and at Open
Innovation Institute of Kyoto University from2020 to 2022. In 2022, he joined NTT Communication Science Laboratories to study psychology
and physiology of mindfulness meditation. He is
also actively working to implement meditation
programs in medical and educational settings toenhance the physical and mental well-being ofindividuals. He received the Best ResearchAward at the Annual Japanese Association of
Mindfulness conference multiple times. He is amember of the Japanese Psychological Association and the Japanese Association of Mindfulness.

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Vol. 21 No. 10 Oct. 2023

25

Feature Articles: Designing a Future Where Everyone Can
Flourish by Sharing Diverse Knowledge and Technologies

Dilemma between Quantum Speedup
and Computational Reliability—
Overcoming Errors with Efficient
Verification Methods for Quantum
Computing
Yuki Takeuchi and Seiichiro Tani
Abstract

Quantum computers are expected to solve several problems faster than any classical computer. However, they may sometimes output incorrect answers because they are prone to errors. Therefore, todevelop reliable quantum computers, it is essential to develop methods of verifying whether the outputsof quantum computers are correct. In this article, we introduce our recent research results on our verification methods.
Keywords: quantum computer, cloud quantum computing, quantum information processing

1. Advantages and issues withquantum computers
In 1994, Shor proposed a quantum algorithm that
efficiently factors large integers [1]. It is strongly
believed that factorization is a hard problem for classical computers, and this hardness conjecture is usedas evidence of the security of several modern cryptographic protocols. Shor’s algorithm is a well-known
instance showing the computational advantage ofquantum computers, and since his proposal, quantum
computers have been extensively studied. The quantum computational advantage is known for several
problems, such as the simulation of physical andchemical systems and the approximation of Jones
polynomials. Despite these advantages, quantum
computers face implementation challenges due toenvironmental noise. There are various methods fordesigning qubits; a qubit is the basic unit of information in quantum computers. When superconducting
circuits are used as qubits, for example, errors canoccur due to temporal fluctuations in the resonant
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Vol. 21 No. 10 Oct. 2023

frequency. In factorization, such errors are not significant. This is because the correctness of the output(i.e., the answer to a factorization) can be easily
checked by executing multiplication on a classicalcomputer; hence, it is easy to determine whether
errors have occurred during the quantum computation. As mentioned above, quantum computers canalso be applied to various problems other than factorization. For instance, when using a quantum computer to approximate Jones polynomials, there is no
known classical method for efficiently checking
whether the output is an accurate approximation. In
other words, there is a dilemma caused by quantum
superposition. This enables high-speed calculationsof quantum computers but makes it difficult to verify
the correctness of the outputs. To leverage the highcomputational power of quantum computers, it isnecessary to address the impact of errors and develop
techniques for resolving this dilemma.

26Feature Articles

Table 1. Quantum error correction and verification of quantum computation.
Error correction

Error detection

Quantum error
correction
Verification ofquantum computation(topic of this article)

×(Error prob. must be small.)×

2. Techniques to protect quantum computersfrom errors
This section discusses two techniques to suppress
the impact of errors during quantum computations:quantum error correction and mitigation and verification of quantum computation. Quantum error correction is a technique that detects and corrects errors.
The information of a single qubit is encoded using
multiple physical qubits. Since it is currently challenging to prepare a large number of physical qubits,
the implementation of quantum error correction isstill limited to small-scale experiments. To overcome
this limitation, quantum error mitigation has been
proposed, which suppresses the impact of errors by
repeating small-scale quantum computations insteadof increasing the number of qubits. However, quantum error mitigation is applicable only to limited
tasks, such as calculating expectation values, andgenerally requires an exponential number of executions of quantum computations. To apply quantum
error correction or mitigation, some knowledge about
the errors is required. Several quantum-error-correction protocols and quantum-error-mitigation methods
cannot be used unless the error probability is sufficiently small.
Verification of quantum computation can be usedeven when the error probability is large. However, itcannot correct or mitigate errors; it can only detect
errors. By solving the same problem multiple times
with a quantum computer and verifying each answer,
it is possible to extract the correct answers, i.e., theoutput that is not affected by errors. Therefore, verification can be considered effective for addressing
the impact of errors.
As a summary, quantum error correction can correct errors but is applicable to limited situations withsmall error probabilities. Verification of quantum
computation, however, can be used even when error
probabilities are large but can only detect errors.
Quantum error correction thus compensates for theverification drawbacks and vice versa. Therefore,
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Applicability

Vol. 21 No. 10 Oct. 2023

(Large error prob. is allowed.)both techniques are crucial for developing highly
reliable large-scale quantum computers (see Table 1).
In the following sections, we introduce some of ourresearch results on our methods for verifying quantum computation.
3. Several verification methods
3.1 V erification of measurement-based quantum
computation
There are several models of quantum computing.
One is measurement-based quantum computation(MBQC). In the conventional approach known as thequantum circuit model, quantum computation is
executed by first initializing qubits then applying
quantum gates to them, followed by measurements.
In MBQC, once a specific entangled state* called agraph state is prepared, any quantum computation
can be carried out by sequentially measuring individual qubits. Since the graph state is independent of
the problems to be solved, it can be prepared inadvance before starting to solve the problems. When
we design qubits by using light (more precisely, photons), single-qubit gates and measurements are relatively easy to conduct. However, the implementationof two-qubit operations is challenging and can only
be done probabilistically (in linear optical quantum
computing). In MBQC, two-qubit gates are only
required during the preparation of the graph state.
After starting to solve the problems, only simple
operations, i.e., measurements, are needed. This is asignificant advantage over the quantum circuit
model. Therefore, MBQC has been applied to various
quantum-information-processing tasks such as quantum cryptography and quantum communication.
When a quantum computer is developed in accordance with MBQC, the step of preparing a graph stateis the most error-prone. Therefore, several methods
have been proposed for verifying whether the graph*

Entangled state: A quantum state with quantum correlation. It canbe generated using two or more qubits. It plays an essential rolein various quantum-information-processing tasks.

27

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0
0
0

U1

0
0
0

U1

U2

U3

U3

U2

General quantum circuits

0
0
0

0
0
0

D1 D
2

D1

D3

D3

D2

IQP circuits

Fig. 1. IQP and general quantum circuits.

state is correctly prepared. In 2019, we devised anefficient verification method, which was superior in
efficiency to other verification methods at that time[2]. To achieve this improvement, we were the first to
apply a mathematical technique, which was previously used in quantum key distribution, to verification. Subsequently, we applied our verification
method to quantum sensing [3] and experimentallydemonstrated it in a small-scale optical experiment[4]. These developments indicate a significant impact
and expansion of our research in this field.
3.2 V erification of quantum-random-number
generation
We extended the graph-state verification mentioned
in the previous section to more complex quantum
states called weighted graph states. This extension
enables the verification of a family of quantum circuits called instantaneous quantum polynomial-time(IQP) circuits. This family of circuits can only execute a limited set of computations obtained regardlessof the order of quantum gates to be applied (seeFig. 1), although this property may make the physicalimplementation of IQP circuits easier. Consequently,
the computational power of IQP circuits should be
weaker than that of an ideal full-fledged quantum
computer since the computation on the latter heavily
depends on the order of quantum gates. By using
ideal IQP circuits, however, it is possible to generaterandom numbers that is difficult on classical computers. It is not easy to determine whether the generated
numbers follow an ideal probability distribution or
noisy one that is easily reproducible with classicalcomputers. In 2019, we made it possible to efficiently
check whether the random numbers are correctly
generated by conducting verification of IQP circuits[5].
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3.3 V erification of noisy intermediate-scale quantum computers
Quantum computers with computational capabilities weaker than full-fledged quantum computers,such as IQP circuits, are referred to as non-universal
quantum computers. Some non-universal quantum
computers are currently in use or expected to be
developed in the near future. They are called noisy
intermediate-scale quantum (NISQ) computers,
which are small or medium-scale quantum computerswith inevitable noise. As reviewed in another article
in this journal [6], we proposed a verification method
tailored for NISQ computers [7].
3.4 V erification of quantumness of quantum computers
Our methods introduced above require small-scalequantum measurement devices to execute verification. In other words, these methods verify the outputsof various quantum computers, such as MBQC, IQP
circuits, and NISQ computers, by using anothersmaller quantum computer. To make verification ofquantum computation more practical, it would bedesirable to enable efficient verification using a classical computer. In 2018, Mahadev proposed a classical verification method [8] by using post-quantum
cryptography, which is modern cryptography secure
even against quantum attacks. Her method was a significant breakthrough in the field and was subsequently extended by many researchers. In 2022, byapplying her technique, we also proposed a verification method for verifying the correct preparations andmeasurements of a special quantum state, so-called
magic state [9]. Quantum computation without magicstates (specifically, computations limited to Clifford
unitary operations) can be efficiently simulated withclassical computers. Therefore, verifying magic
28

Feature Articles

states can be used to determine the presence of essential quantum properties in quantum computers.

References[1]

4. Outlook
We proposed various verification methods thatmake several types of quantum computers verifiable.
Further improvements in many directions are necessary for their practical use. A possible direction
would be to improve our methods and apply verification of quantum computation to cloud-quantumcomputing systems. Companies, such as IBM andAmazon, provide cloud-quantum-computing systems, but they lack the verification features for users
to check the correctness of their received results. By
incorporating verification methods into existing systems, users can verify the accuracy of their received
answers for themselves, and the companies providing
the systems can transparently demonstrate the highperformance of their quantum computers. Our goal isto achieve a society where anyone can benefit fromquantum computers from anywhere. Toward thisgoal, we will continually contribute to the development of fundamental technologies in quantum computing.

Yuki Takeuchi

Associate Distinguished Researcher, Computing Theory Research Group, Media Information
Laboratory, NTT Communication Science Laboratories.
He received a Ph.D. in science from Osaka
University in 2018. He joined NTT Communication Science Laboratories as a research associate
the same year and was a researcher from 2019 to2023. Since April 2023, he has been in his current
position and engaged in the theoretical investigation of quantum information and is especiallyinterested in the verifiability of quantum computing. He received IPSJ Computer Science
Research Award for Young Scientists from theInformation Processing Society of Japan (IPSJ)
and Young Scientist Award of the Physical Society of Japan from the Physical Society of Japan.
He is a member of the Physical Society of Japanand IPSJ.

NTT Technical Review

Vol. 21 No. 10 Oct. 2023

[2]

[3]

[4]

[5]

[6]
[7]

[8]
[9]

P. W. Shor, “Polynomial-Time Algorithms for Prime Factorization
and Discrete Logarithms on a Quantum Computer,” SIAM J. Comput., Vol. 26, 1484, 1997. https://doi.org/10.1137/S0097539795293172
Y. Takeuchi, A. Mantri, T. Morimae, A. Mizutani, and J. F. Fitzsimons, “Resource-efficient Verification of Quantum Computing Using
Serfling’s Bound,” npj Quantum Information, Vol. 5, 27, 2019. https://doi.org/10.1038/s41534-019-0142-2
Y. Takeuchi, Y. Matsuzaki, K. Miyanishi, T. Sugiyama, and W. J.
Munro, “Quantum Remote Sensing with Asymmetric Information
Gain,” Phys. Rev. A, Vol. 99, 022325, 2019. https://doi.org/10.1103/
PhysRevA.99.022325
P. Yin, Y. Takeuchi, W.-H. Zhang, Z.-Q. Yin, Y. Matsuzaki, X.-X.
Peng, X.-Y. Xu, J.-S. Xu, J.-S. Tang, Z.-Q. Zhou, G. Chen, C.-F. Li,
and G.-C. Guo, “Experimental Demonstration of Secure Quantum
Remote Sensing,” Phys. Rev. Applied, Vol. 14, 014065, 2020. https://doi.org/10.1103/PhysRevApplied.14.014065
M. Hayashi and Y. Takeuchi, “Verifying Commuting Quantum Computers via Fidelity Estimation of Weighted Graph States,” New J.
Phys., Vol. 21, 093060, 2019. https://doi.org/10.1088/1367-2630/
ab3d88
S. Tani, S. Akibue, and Y. Takeuchi, “Extracting Quantum Power byUsing Algorithms and Their Verification,” NTT Technical Review,Vol. 21, No. 6, pp. 43–47, 2023. https://doi.org/10.53829/ntr202306fa5
Y. Takeuchi, Y. Takahashi, T. Morimae, and S. Tani, “Divide-andconquer Verification Method for Noisy Intermediate-scale Quantum
Computation,” Quantum, Vol. 6, 758, 2022. https://doi.org/10.22331/
q-2022-07-07-758
U. Mahadev, “Classical Verification of Quantum Computations,”
SIAM J. Comput., Vol. 51, 1172, 2022. https://doi.org/10.1137/
20M1371828
A. Mizutani, Y. Takeuchi, R. Hiromasa, Y. Aikawa, and S. Tani,“Computational Self-testing for Entangled Magic States,” Phys. Rev.
A, Vol. 106, L010601, 2022. https://doi.org/10.1103/PhysRevA.106.
L010601

Seiichiro Tani

Distinguished Scientist, Computing Theory
Research Group, Media Information Laboratory,
NTT Communication Science Laboratories.
He received a B.E. in information science from
Kyoto University in 1993 and M.E. and Ph.D. incomputer science from the University of Tokyoin 1995 and 2006. He joined NTT LSI Laboratories in 1995 and moved to NTT Network Innovation Laboratories in 1998. Since 2003, he has
been studying quantum computing theory at NTT
Communication Science Laboratories. He is also
an associate member of the Science Council ofJapan and visiting professor at the Quantum
Computing Unit, International Research Frontiers Initiatives (IRFI), Tokyo Institute of Technology. He was a member of ERATO/SORST
Quantum Computing and Information Project,
Japan Science and Technology Agency (JST)from 2004 to 2009 and visiting researcher at theInstitute for Quantum Computing (IQC), theUniversity of Waterloo, Canada, from 2010 to2011. He received the Institute of Electronics,
Information and Communication Engineers(IEICE) Achievement Award and the IEICE
Information and Systems Society Best Paper
Award. He also received the Maejima Hisoka
Award. He is a member of the Association for
Computing Machinery (ACM), the Institute ofElectrical and Electronics Engineers (IEEE),IEICE, and IPSJ.

29

Global Standardization Activities

Technical Trends in ISO/IEC Joint
Technical Committee 1
Hideaki Yamamoto
Abstract

The International Organization for Standardization (ISO)/International Electrotechnical Commission(IEC) Joint Technical Committee (JTC) 1 is an organization established by ISO and IEC and is responsible for international standardization in the field of information technology. This article introduces theactivities being addressed by subgroups directly under JTC 1, which were identified in Resolutions fromrecent ISO/IEC JTC 1 Plenaries.
Keywords: ISO/IEC JTC 1, Plenary, Advisory Group

1. Introduction

2. Organization of ISO/IEC JTC 1

The International Organization for Standardization(ISO)/International Electrotechnical Commission(IEC) Joint Technical Committee (JTC) 1 [1] wasestablished by ISO [2] and IEC [3] and is responsible
for the international standardization of information
technology. Formerly, ISO/Technical Committee(TC) 97 (established in 1960) and IEC/TC 53 (established in 1961) independently promoted standardization activities in the information-technology field,
resulting in the duplication of certain technical fields
between these organizations [4]. JTC 1 was established in 1987 to solve this problem. The secretariat
of JTC 1 is the American National Standards Institute(ANSI). As of September 2023, JTC 1 comprises 40
P-members (active participants) and 62 O-members(observers).
The Plenary is JTC 1’s highest decision-making
meeting, and the main agenda is as follows:•	Establishment and/or disbandment of Subcommittees (SCs), Working Groups (WGs), andAdvisory Groups (AGs) under JTC 1•	Appointment of JTC 1 Officers•	Revision of operational rules•	Reports on SC activities

The organization of JTC 1 is shown in Fig. 1. The
development of standards is carried out by 23 SCs
and 5 WGs directly under JTC 1. Discussions on
management issues, such as the review of Directives,and on technology issues, such as examination ofissues with a view to development of future standards, are carried out by AGs under JTC 1. JTC 1 hasestablished liaisons with organizations other than
JTC 1, including IEC/TC 65 (Industrial-process measurement, control, and automation), IEC/TC 100(Audio, video, and multimedia systems and equipment), ISO/TC 215 (Health informatics), ISO/TC
307 (Blockchain and distributed ledger technologies), ITU-T (International Telecommunication
Union - Telecommunication Standardization Sector),and Ecma International.
Japan has been in a significant position regarding
the management of JTC 1 as follows:•	P-members in all 23 SCs and 5 WGs directly
under JTC 1•	Chairs of SC 2 (Character code), SC 23 (Digital
storage media), SC 28 (Office equipment) andCommittee Managers of SC 2, SC 23, SC 28, SC
29 (Media encoding), SC 34 (Document description and processing language)•	Approximately 90 Project Editors (as of the endof March 2023)

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30

Global Standardization Activities

ISO/IEC JTC 1

Information technology

JAG*1

JTC 1 Advisory GroupAG 1

Communications

AG 2

JTC 1 Emerging Technology and Innovation (JETI)SC 2

Coded character sets

SC 6

Telecommunications and information exchange between
systems

SC 7

Software and systems engineering

SC 17

Cards and security devices for personal identification

SC 22

Programming languages, their environments and system
software interfaces

SC 23

Digitally recorded media for information interchange andstorage

AG 14

Systems Integration Facilitation (SIF)

AG 15

Standards and Regulations

SC 24

Computer graphics, image processing and environmental
data representation

AG 19

Coordination with ISO TC 20/SC 16 on Unmanned
Aircraft Systems (UAS)SC 25

Interconnection of information technology equipment

AG 20

Coordination with ISO/TC 268/SC 1 on Smart
Community Infrastructures

SC 27

Information security, cybersecurity and privacy
protection

AG 21

JTC 1 strategic direction

SC 28

Office equipment

AHG 4Collaboration Across Domains

SC 29

Coding of audio, picture, multimedia and hypermedia
information

AHG 5

JTC 1 Standards Made Freely Available

SC 31

Automatic identification and data capture techniques

AHG 7

Supplement alignment

SC 32

Data management and interchange

WG 11

Smart Cities

SC 34

Document description and processing languages

WG 12

3D Printing and scanning

SC 35

User interfaces

WG 13

Trustworthiness

SC 36

Information technology for learning, education andtraining

WG 14

Quantum information technology

SC 37

Biometrics

JTC 1 vocabulary

SC 38

Cloud computing and distributed platforms

SC 39

Sustainability, IT and data centres

SC 40

IT service management and IT governance

SC 41Internet of things and digital twin

SC 42

Artificial intelligence

SC 43

Brain-computer interfaces

WG 15*1 Although JAG exists as an organization, the JAG meeting was not scheduled to be held after
the JTC 1 Stockholm Plenary in 2018. A management-related meeting body and a meeting bodyfor conducting research on emerging technologies were established and reorganized directly
under JTC 1.

(As of September 2023)

Fig. 1. Organization of ISO/IEC JTC 1.

•	Hosts of JTC 1 Plenary (4 times)
JTC 1 Plenary has been held twice a year (spring
and fall) since 2019. The two most recent JTC 1 Plenaries were held in Tokyo (Japan) in November 2022
and in Paestum (Italy) in May 2023, both in the format of “face-to-face mode with some remote participants”*1. At both Plenaries, Japan has greatly contributed to steering JTC 1 by proposals through morethan a dozen contributions, participation in the Drafting Committee and hosting Tokyo Plenary*2, which
was the first face-to-face JTC 1 Plenary during the
COVID-19 pandemic.
The following sections introduce several trends in
JTC 1 subgroups and activities for JTC 1 in the Japanese National Body (NB)*3.
3. Latest trends in JTC 1 subgroups
3.1 SC on information technology for smart cities
In JTC 1, WG 11 addresses standardization items
related to information technology for smart cities. To
NTT Technical Review

Vol. 21 No. 10 Oct. 2023

coordinate activities related to smart community
infrastructure between ISO TC 268/SC 1 (Smart
community infrastructures) and JTC 1, AG 20 was
established according to the Resolution of the JTC 1
virtual Plenary in May 2022. At the JTC 1 Paestum
Plenary in May 2023, Chinese NB submitted a contribution describing that a new SC should be established for smart cities, which was addressed by WG*1 “Face-to-face mode with some remote participants” is the nameof a meeting format that uses both face-to-face and virtual modes,
as defined in Standing Document (SD) N 19 (Meetings) of ISO/IEC JTC 1. Although this SD defines a similar meeting format“hybrid mode,” there are differences between these two formats,such as the maximum meeting hours per day and whether remoteparticipants have the right to pause the meetings when they lose
remote connectivity.
*2 When preparing to host JTC 1 Plenary in Okayama (Japan) in
November 2020, the format of the Plenary changed to virtual dueto the COVID-19 pandemic. Japan therefore re-invited the Plenary in November 2022.
*3 NB: ISO uses the term MB (Member Body), and IEC uses NC(National Committee). As ISO/IEC JTC 1 is common to both organizations, it is called NB.

31

Global Standardization Activities

Table 1. Examples of WG/AG/AHG under ISO/IEC JTC 1.
Theme

ToR

Quantum Information Technology(JTC 1/WG 14)

JTC 1 Strategic Direction(JTC 1/AG 21)

Collaboration Across Domains(JTC 1/AHG 4)

1. Serve as a focus of and proponent for
JTC 1’s standardization program on
Quantum Information Technology.
Identify gaps and opportunities in
Quantum Information Technology
standardization;
2. Develop and maintain a list of existing
Quantum Information Technology
standards produced and standards
development projects underway in
ISO/TCs, IEC/TCs, JTC 1 and otherorganizations;
3. Develop deliverables in the area of
Quantum Information Technology;
4. As a systems integration entity,
maintain relationships with other ISO
and IEC TCs and other organizations
that are involved in Quantum
Technology standardization.

1. Identify stakeholders, assess market
and technology evolution;
2. Perform a Strengths/Weaknesses/
Opportunities/Threats (SWOT)
analysis of JTC 1, including reference
to ICT standardization outside of
JTC 1;
3. Assess JTC 1 Subcommittee activity(critical mass, technological maturity);
4. Assess intensity of collaborative work(within JTC 1, with ISO and IEC TCs
and with external SDOs);
5. Prepare recommendations including• JTC 1 governance and operations• JTC 1 system work andcollaborations• Optimal structure

1. Review ITTF’s draft revision of SD 15(JTC 1 N16195), as well as the UScomments on the ITTF draft (JTC 1
N16247), and propose any further
changes that might be necessary by
the 1st deadline (18 September 2023)
for the November 2023 JTC 1 Plenary.

(As of September 2023)
Source: Approved Resolutions of the 14-18 November 2022 JTC 1 Plenary in Tokyo (ISO/IEC JTC 1 N 16159) and Resolutions of the 8-12
May 2023 JTC 1 Plenary in Paestum, Italy (ISO/IEC JTC 1 N 16364)
SDO: standards developing organization
ITTF: Information Technology Task Force
SD: standing document

11. During the Plenary, there was a great deal of discussion resulting in no consensus, and the decision
was scheduled on approval of Resolutions on the lastday. Japanese NB expressed its opposition because ofconcerns that the scope of the new SC would overlap
with that of other organizations related to smart cities.
As a result of the vote, the establishment of the 44th
SC was approved by a majority of the P-memberspresent at the Plenary. This new SC will be chaired byHeng Qian (China), Convenor of WG 11, and WG 11will disband when the new SC is inaugurated. Notethat this new SC has been approved by the JTC 1
level at the time of writing this manuscript and it will
be inaugurated after approval by the votes of the management boards of ISO and IEC (ISO Technical Management Board and IEC Standardization Management Board).
3.2 WG on quantum information technology
The standardization activities of quantum information technology at JTC 1 are initiated by the Study
Group (SG) on quantum computing, approved at JTC
1 Stockholm Plenary in November 2018. Taking over
its activities as SG, WG 14 was established according
to Resolution of JTC 1 virtual Plenary in June 2020,
starting the development of standards on quantum
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Vol. 21 No. 10 Oct. 2023

computing. At the JTC 1 Tokyo Plenary in November
2022, Preliminary Work Item (PWI) on quantum
simulation was approved, and its structure was discussed. It was agreed to expand the activities of theexisting WG 14 because it would be inefficient toestablish a single WG specific to quantum simulation. The title of WG 14 was renamed from “Quantum computing” to “Quantum information technology,” and its Terms of Reference (ToR) was alsochanged to support a wider range of quantum information technologies. This WG is chaired by Hong
Yang (China) and its ToR is indicated in Table 1.
3.3 AG on JTC 1 strategic direction
AG 21 was established in response to a contribution
submitted by Canadian NB to the JTC 1 Tokyo Plenary in November 2022. The purpose of this contribution is to determine JTC 1’s optimal structure,
because JTC 1 has not reviewed its structure, governance, or mode of operation in the light of its current
mission for more than 20 years although there havebeen major changes in not only the information andcommunication technology (ICT) market and technical landscape but also in the way ICT is used inour global society. This AG is chaired by Norbert
Bensalem (France) and its ToR is indicated in Table 1.
32

Global Standardization Activities

3.4 Ad Hoc Group on collaboration across domains
Ad Hoc Group (AHG) 4 was established in
response to a Japanese NB’s contribution for JTC 1
virtual Plenary in May 2021. The purpose of this contribution is to propose a restructure of WGs addressing application-layer areas, including the restructuring of WG 11 and WG 12. Although the Plenary
could not reach a consensus, it decided to continue
this discussion by establishing an AHG. Several proposals were contributed by AHG 4 to JTC 1 Plenaries, including monitoring of liaison activities, longterm plan for across-domain activities and transfer of
AHG 4 to AG, which were not adopted as Resolutions. This AHG is chaired by Jacqui Taylor (UK) and
its ToR is indicated in Table 1.
4. Activities in Japanese NB
In Japan, SCs under JTC 1 and WGs/AGs/AHGs
directly under JTC 1 are mainly operated by Information Technology Standards Commission of Japan(ITSCJ)*4 [5]. Each mirror subcommittee for WG 11through WG 15 directly under JTC 1 has been established in ITSCJ to share the status of deliberations
and address international ballots. Since 2018, JTC 1

Subgroup Subcommittee has been active in ITSCJ,
which comprehensively addresses the issues that theAGs are required to participate in as NB. JTC 1 Subgroup Subcommittee shares deliberations of each AG
and discusses how to deal with AGs. This Subcommittee also addresses standardization items for SC 43(Brain-computer interfaces).
In addition, Subcommittee for Directives is active
in ITSCJ, as Japanese companies and organizations
other than the members of the subcommittee mentioned above are highly interested in ISO/IEC Directives.
5. Future Plenaries
The upcoming Plenaries will be held in Berlin(Germany) in November 2023 and Australia (city to
be determined) in May 2024.
References
[1]
[2]
[3]
[4]
[5]

JTC 1, https://jtc1info.org/
ISO, https://www.iso.org/home.html
IEC, https://www.iec.ch/homepage
Information Processing Society of Japan (IPSJ), “60-year History,”
2020 (in Japanese), https://www.ipsj.or.jp/60anv/60nenshi/
IPSJ/ITSCJ, https://itscj.ipsj.or.jp/english/index.html

Hideaki Yamamoto

Senior Research Engineer, NTT Social Informatics Laboratories.
He received a B.E. and M.E. in electronicengineering from Osaka University in 1994 and1996. He joined NTT in 1996, where he has been
engaged in research and development (R&D) of
smart card systems, in security design of information and communication systems, in standardization activities including ISO/IEC JTC 1 andJIS (Japan Industrial Standard), and in risk management for NTT R&D’s information and communication systems.
He received the FY 2011 Industrial Standardization Award from the Ministry of Economy,
Trade and Industry for his outstanding contributions to activities regarding ISO/IEC SC 17/WG
8 (contactless cards). He is a member of theboard of directors of ITSCJ and Vice Chairperson of Task Force for wireless card systems in the
Association of Radio Industries and Business ofJapan. He served as Chair of the November 2022
JTC 1 Tokyo meeting preparatory committee.
He has been registered as a Professional Engineer, Japan (P.E.Jp) in electrical and electronics
engineering since 2011 and in engineering management since 2015. He has been registered as an
APEC Engineer (Electrical) and International
Professional Engineer (IntPE) since 2015. He isa member of the Institute of Electronics, Information and Communication Engineers, the Japan
Society of Applied Physics, the Information
Processing Society of Japan, and the Institutionof Professional Engineers, Japan.

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Vol. 21 No. 10 Oct. 2023

*4 SC 17, SC 28, SC 35/WG 1 (Keyboards, methods and devices related to input and its feedback), SC 35/WG 2 (Graphical user interface and interaction), SC 35/WG 4 (User interfaces for mobile
and wearable devices) and SC 35/WG 6 (User interfaces accessibility) are operated by the Japan Business Machine and Information System Industries Association (JBMIA). SC 25/WG 3 (Customer premises cabling), WGs under SC 31 and SC 39 are operated by Japan Electronics and Information Technology Industries
Association (JEITA).

33

Practical Field Information about
Telecommunication Technologies
Practical Field Information about Telecommunication Technologies

A Case Study of Malfunction ofWireless Communication System
Caused by Electromagnetic
Disturbance at a Construction Site
Technical Assistance and Support Center, NTT EAST
Abstract

In this article, we explain a case study of a malfunction of a wireless communication system caused
by electromagnetic disturbance at a construction site and countermeasures to prevent such a malfunction.
This is the seventy-eighth article in a series on telecommunication technologies.
Keywords: electromagnetic disturbance, inverter, isolation transformer, conducted disturbance1. Introduction
Various construction units require high-capacity
and low latency wireless communication for remote
video monitoring and remote control. An alternatingcurrent (AC) motor is widely used to control AC
frequencies by using an inverter. Inside the inverter,
the AC power source is converted to a direct-current(DC) power source, then a high-speed switching
operation is carried out to convert the DC powersource to an AC power source with the desired frequency. This switching operation becomes a cause ofelectromagnetic disturbance and may affect theoperation of surrounding telecommunication units.
In this article, a case study of a malfunction of awireless communication system caused by electromagnetic disturbance generated by a motor, the causeof the malfunction, and countermeasures aredescribed.

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Vol. 21 No. 10 Oct. 2023

2. A case study of malfunction of
telecommunication unit in high-capacity wirelesscommunication system
At a construction site, a customer reported that awireless communication system has been rebooting
on weekdays. Therefore, Technical Assistance andSupport Center (TASC), NTT EAST was asked fortechnical assistance.
2.1 E
 quipment configuration and malfunction
status
The configuration of the wireless communication
system is shown in Fig. 1. The wireless communication system has control and radio units. The control
unit is located in a telecommunication box, and theradio unit is located outdoors. The radio unit has optical, power, and ground lines. Each line is connectedto the control unit, uninterruptible power supply(UPS), and earth bar. The UPS is powered by a temporary power supply through the power supply fortelecommunication. A temporary power supply also
supplies power to the site office, various construction
34

Practical Field Information about Telecommunication Technologies

Radio unit

Voltage measurement
Current measurement

Power line
Ground line
Optical line
Temporarypower supply

Telecommunication box
Earth barControlunit

To site office,construction units,
and lighting, etc.
Power supply fortelecommunication

UPS

Fig. 1. Configuration of units and components composing wireless communication system.

Input connector
Maximum input voltage
Measurement voltage range

Frequency bandwidth• Original connector• BNC connector
5 V peakTwo ranges• Range 1: –30 to 0 dBV• Range 2: –50 to –20 dBV
100 Hz to 30 MHz
Separated into 7 bands• 1-kHz band: 500 Hz to 3 kHz• 15-kHz band: 7.5 to 22.5 kHz• 70-kHz band: 5 to 105 kHz• 250-kHz band: 125 to 375 kHz• 1-MHz band: 0.5 to 1.5 MHz• 3-MHz band: 1.5 to 4.5 MHz• 20-MHz band: 10 to 30 MHz

BNC: Bayonet Neill Concelman

Fig. 2. Specifications and image of Noise Search Tester.

units, and outdoor lighting.
Regarding the status of the malfunction, the radio
unit repeatedly rebooted at certain hours of the day
during which a conveyor belt operated for transporting sand at the construction site.
2.2 Results of field survey
There are two types of electromagnetic disturbancethat affect telecommunication equipment. One isradiation disturbance that propagates through the air.
The other is conducted disturbance that propagates
through wired cables. In this construction site case,
we focused on measuring conducted disturbanceNTT Technical Review

Vol. 21 No. 10 Oct. 2023

because it seems to have an effect by the belt conveyor. To investigate the effect of conducted disturbance, we measured the common mode voltage andcurrent of a conducted disturbance on the power line
and ground line, as shown in Fig. 1.
To detect the malfunction, we first measured thecommon mode voltage using Noise Search Tester [1]developed by TASC. This tester can be easily
installed on site with a contactless voltage probe(Fig. 2). The screen graphically displays commonmode-voltage levels in the 1-kHz, 15-kHz, 70-kHz,
250-kHz, 1-MHz, 3-MHz, and 20-MHz frequency
bands.
35

Practical Field Information about Telecommunication Technologies

Frequency band of
maximum voltage level

15 kHz (7.5 to 22.5 kHz)Measured voltage value

More than 0 dBV

Screenshot of Noise Search Tester

Measurement setting

Fig. 3. Results of measuring common mode voltage using Noise Search Tester.

(Photo source: Yokogawa Electric
Corporation website) [2](Photo source: TOYO
Corporation website) [3]

Fig. 4. Photograph of oscilloscope (left) and current probe (right).

The screenshot of Noise Search Tester measuringcommon mode voltage is shown in Fig. 3. We confirmed that the highest common-mode-voltage levelexceeded 0 dBV in the 15-kHz band, as shown in thewhite circle in Fig. 3.
Next, we used an oscilloscope and current and voltage probes to measure the disturbance in detail(Fig. 4). To clarify the relationship between the conducted disturbance and operation of the radio unit, wemeasured the common mode voltage and current during radio anomalies (when a reboot occurs) and normal conditions, i.e., when the conveyor belt is operating and when it stops.
The measurement results are shown in Fig. 5. During the reboot period, it was found that the common
mode voltage, which is the voltage between the coldand earth of the power line (Cold–E), was 323 Vp-p
and common mode current on the power line was 2.7
Ap-p (Fig. 5(1)). The common mode current on the
NTT Technical Review

Vol. 21 No. 10 Oct. 2023

ground line was 1.2 Ap-p. At the same location, during the non-reboot period, the values were 38 Vp-p,
25 mAp-p, and 11 mAp-p, respectively (Fig. 5(2)).
These results indicate that a large common mode
voltage and current were generated during a reboot.
Comparing the common mode current in the powerline (Fig. 5(b)) and in the ground line (Fig. 5(c)), we
confirmed that the common mode current on the
power line is larger than that on the ground line.
To clarify the frequency of the disturbance, we analyzed the common mode voltage during the reboot
and non-reboot periods using the fast Fourier transform (FFT) method. As shown in Fig. 6, the analysis
results indicate that the disturbance had a wide frequency range of 2 kHz to 1 MHz.
2.3 Estimation of malfunction cause
The above results indicate that (i) the radio unit
reboots when the conveyor belt is operated and (ii)36Practical Field Information about Telecommunication Technologies[V] 200(a) Common mode
voltage (Cold–E)
−200
[A] 2
(b) Common modecurrent (power line)−2[A] 2(c) Common modecurrent (ground line)−2323 Vp-p

38 Vp-p

2.7 Ap-p

25 mAp-p

1.2 Ap-p

0Time

11 mAp-p

200 0
[ms]

(1) Reboot period

200
[ms]

Time

(2) Non-reboot period

Fig. 5. Results of measured common mode voltage and current.

[dBV] 30

• Window function (rectangular)

Reboot period0Common−30
mode voltage−70 Non-reboot period
1
10

100
Frequency

1000

[kHz]

Fig. 6. Results of FFT analysis of common mode voltage on power line.

high-level common mode voltage and current appear
on the power and ground lines during the operation of
the conveyor belt. Therefore, we estimated that the
conducted disturbance generated from the motor of
the conveyor belt caused the reboot of the radio unit.
2.4 Countermeasures and Results
Countermeasures against the effects of conducted
disturbance on the wireless communication system
include the following:(1)	Isolate or maintain disturbance sources:
We must isolate or remove the disturbancesources from the same power supply of theradio unit. If the disturbance sources could not
be isolated or removed, we must maintain the
disturbance sources.
(2)	Filter conducted disturbance: If we could
not remove the disturbance sources, weshould consider installing a noise filter, isolation transformer, etc. on the telecommunication, power, and ground lines of the paths ofthe conducted disturbance for filtering it.
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Vol. 21 No. 10 Oct. 2023

Table 1. Characteristics of noise-cut transformer.
Characteristics ofnoise-cut transformer
Frequency bandwidth
Common mode rejection

DC to 30 MHz
Above 40 dB(3)	
Replace telecommunication units: Replace
disturbance-affected units that are more resistant to disturbance.
In this case study, it was difficult to apply disturbance countermeasures for the conveyor belt. Since
the radio unit is specified in this construction site, wecannot replace it. Therefore, we decided to install anoise-cut transformer (the characteristics of which
are shown in Table 1) in the power line connecting to
the radio unit, which is the path of the disturbance.
The measured common mode current after installation of the noise-cut transformer is shown in Fig. 7.
This result indicates that the common mode current in
the power line (measured at the input and output sides
37

Practical Field Information about Telecommunication Technologies[A] 2
Commonmode current−2[A] 2Commonmode current

Input side of transformer

3 Ap-p

Output side of transformer 0.3 Ap-p

−2
0

Time

200
[ms]

Fig. 7. Results of measuring common mode current after installation of noise-cut transformer.

of the transformer) during operation of the conveyor
belt decreased from 3 to 0.3 Ap-p. As a result of thenoise-cut transformer action, the radio unit no longer
had rebooting issues.
3. Conclusion
A case study of a malfunction of the radio unit of awireless communication system caused by electromagnetic disturbance at a construction site and countermeasures to prevent the malfunction were
described. Though inverters are used to maintainefficiency of the power system at construction sites
and in photovoltaic power systems, they can be asource of electromagnetic disturbance that affects
telecommunication systems. It is important to investigate the inverters before installation and take necessary countermeasures against such disturbance. At
TASC, we will continue to play a role in reducing

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Vol. 21 No. 10 Oct. 2023

maintenance operation in the field by providing theknowledge we have accumulated. To reduce malfunctions caused by electromagnetic disturbance,
radio, induction, and lightning and improve the reliability of telecommunication services, the EMC
Engineering Group of TASC will continue to engage
in technical cooperation, development, and dissemination of technology through technical seminars andother activities.
References
[1]

[2]
[3]

Technical Assistance and Support Center, NTT EAST, “Case Study of
Audible Noise on Analog Leased Lines Caused by Electromagnetic
Disturbance,” NTT Technical Review, Vol. 20, No. 10, pp. 87–91,
Oct. 2022. https://doi.org/10.53829/ntr202210pf1
The website of Yokogawa Electric Corporation,
https://tmi.yokogawa.com/jp/solutions/products/oscilloscopes/
digital-and-mixed-signal-oscilloscopes/dlm3000-mso-series/
The website of TOYO Corporation,
https://www.toyo.co.jp/emc/products/detail/id=1178

38

External Awards
IEEE VTS Tokyo/Japan Chapter 2023 Student Paper
Award
Winner: Ei Tanaka, Tohoku University (Co-authors: Yuichi
Kawamoto, Tohoku University; Nei Kato, Tohoku University;
Masashi Iwabuchi, NTT Access Network Service Systems Laboratories; Riku Ohmiya, NTT Access Network Service Systems Laboratories; Tomoki Murakami, NTT Access Network Service Systems
Laboratories)
Date: June 21, 2023
Organization: Institute of Electrical and Electronics Engineers
Vehicular Technology Society (IEEE VTS) Tokyo/Japan Chapter
For “Exploiting Reflection Direction Variation for Phase Control
in Multiple Simultaneous IRS Links.”
Published as: E. Tanaka, Y. Kawamoto, N. Kato, M. Iwabuchi, R.
Ohmiya, and T. Murakami, “Exploiting Reflection Direction Variation for Phase Control in Multiple Simultaneous IRS Links,” Proc. of2023 IEEE 97th Vehicular Technology Conference (VTC2023Spring), Florence, Italy, Mar. 2023.

MVE Award
Winners: Motohiro Makiguchi, NTT Human Informatics Laboratories; Ayaka Sano, NTT Human Informatics Laboratories; Takahiro
Matsumoto, NTT Human Informatics Laboratories; Rika Mochizuki,
NTT Human Informatics Laboratories; Ryuji Yamamoto, NTT
Human Informatics Laboratories
Date: July 13, 2023
Organization: The Institute of Electronics, Information and Communication Engineers (IEICE) Technical Committiee on Media
Experience Virtual Environment (MVE)
For “Basic Study on Pointing Methods in Mirror-Transcending
Aerial imaging.”
Published as: M. Makiguchi, A. Sano, T. Matsumoto, R. Mochizuki,
and R. Yamamoto, “Basic Study on Pointing Methods in MirrorTranscending Aerial imaging,” MVE meeting, MVE2022-118, Mar.
2023.

Papers Published in Technical Journals andConference Proceedings
Velocity and Confinement of Edge Plasmons in HgTebased Two-dimensional Topological Insulators
A. Gourmelon, E. Frigerio, H. Kamata, L. Lunczer, A. Denis, P.
Morfin, M. Rosticher, J. Berroir, G. Fève, B. Plaçais, H. Buhmann, L.
W. Molenkamp, and E. Bocquillon
Physical Review B, Vol. 108, No. 3, 035405, July 2023.
High-frequency transport in the edge states of the quantum spin
Hall (QSH) effect has rarely been explored, though it could cast lighton the scattering mechanisms taking place therein. Here we report on
the measurement of the plasmon velocity in topological HgTe quantum wells both in the QSH and quantum Hall (QH) regimes, using

NTT Technical Review

Vol. 21 No. 10 Oct. 2023

harmonic GHz excitations and phase-resolved detection. We observe
low plasmon velocities in both regimes, with, in particular, largetransverse widths in the QH regime despite a sharp edge confinement
profile. We ascribe these observations to the prominent influence of
charge puddles forming in the vicinity of edge channels. Together
with other recent works, it suggests that puddles play an essential rolein the edge state physics and probably constitute a main hurdle on theway to clean and robust edge transport.

39