1

M2SMART PROJECT

M2SmartNewsLetter

Vol.14
Mar 2022
Dr.Usha Dixit Visit Nihon univ on 2nd Mar
SPECIAL TOPICS

It’s been an eventful period(JCC#6, Co-symposium,Ahmedabad trip)!
by Misa Kitagawa (M2Smart Residential Coordinator)
6th JCC conducted online
The 6th Joint Coordination
Committee (JCC) meeting was heldd
online on 17th January 2022.
Prof B S Murty, Director of Indian
Institute of Technology Hyderabad(IITH), gave an encouraging opening
remark that this project is very
important not only for bilateral
corporation but also interdisciplinary
endeavour to apply technologies toreal city. He pointed out that it isneede to bring in change in culture
and discipline is required at all levelsof society (citizens, service provider
such
as
bus
drivers,andadministarions),introducing
theexamples of Indore and Surat.
Dr.Tsutomu Tsuboi(M2Smart Project
Leader) introduced the progress inactivities by SCADL (Smart City
Ahmedabad Development Limited) ,which was one of the destinations of
his trip to Delhi & Ahmedabad(→See page. 2 for details).
Group activities and the situation of“Handbook” writing was shared with
an efforts of each group and some

remarkable results. The momentumof dissemination and publication of“Handbook” was also confirmed.
Lastly, Mr. Kenichi Kono, Japan
International Cooperation Agency(JICA) , and Mr.Masayuki Kamimoto,Japan Science and Technology
Agency (JST) expressed their highexpectation as the project is in thephase of finalization.

Co-Symposium with Friend in
Thailand was held
On 25th Jan, the Co-Symposiumwith “SmarTran4T4 (Smart
Transport Strategy for Realizing
Thailand 4.0)”, another SATREPS
project was held online.
(→See page.4 for details).

Dr. Dixit visit the Nihon University
On 2nd March, Dr. Usha Dixit, acounsellor (S&T) from the Embassyof India, Tokyo, visited Nihon
university, requested through JST. It
was very fruitful as M2Smart project
activities were introduced and insightwere
exchangedamongus.
She gave us encouraging comments
on support in the dessimination of
handbook and possibly in workshops
in Ahmedabad and Hyderabad. In
Ahmedabad in particular, she alsocommented that it might be betterto collaborate with Ahmedabad
University, IIT-Gandhinagar and IIMA
( Indian Institute Of Management –Ahmedabad).

M2SMART NEWSLETTER | VOL.14
SPECIAL TOPICS

Trip to Delhi & Ahmedabad
by Dr.Tsutomu Tsuboi (M2Smart Project Leader)
I would like to report the activities of my trip to India from 1st to 10th December 2021, the first time in almost two years
due to COVID 19 pandemic. What was impressive about this trip was the steady progress made towards smart cities inIndia over the last two years.

SCADL (Smart City Ahmedabad Development Limited)
The most impactful part of the visit was the visit to the Smart City Corporation promoted by the local administration in
Ahmedabad and the presentation given there. Some of the highlights are as follows
1. 7,200 surveillance cameras have been installed in the city to check citizens' compliance with traffic rules and to
charge violators with “Challan(fines)” via their personal smartphones.
2. More than 180 free Wi-Fi hotspots have been set up in key spots in the city, giving citizens unlimited access to highspeeds of 2 megabits per second.
3. More than 1,120 buses are equipped with GPS and used for fleet management.
4. 16,000 LED street lights have been installed on major roads, enabling automatic brightness control and contributing
to the safety of citizens at night.
5. The early morning refuse collection vehicles are equipped with IoT (Internet of Things) devices to control whether
they are properly operating to the designated refuse collection points in the city.
6. Smart public toilets have been set up at 120 locations in the city with a fee (Rs. 1) to provide sanitation services.

At SCADL- A monitor screen at the front of the building constantly shows video from surveillance cameras installed in the city,
and about 50 staff members work behind the screen in three shifts.
What I learned here is the importance of shifting from "MONO(products)" to "KOTO(experience) ", which has become apopular phrase in Japanese business scene in recent years. On the hardware side, it was obvious from this visit that the
major cities in India are making steady progress.

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SPECIAL TOPICS

CEPT university
Since it has been difficult to conduct the series experiments in City of
Ahmedabad due to Covid 19 situation for the past two years, we asked
CEPT University (formerly the Centre for Environmental Planning andTechnology) to undertake part of the work, the discussion about which was
one of the main purpose of this trip.
The visit to CEPT University was made possible by a professor who wasintroduced to us by Ms.Mona Khandhar, Minister of the Indian Embassy inJapan. The Minister, who happens to be from Gujarat, was very
knowledgeable about the city of Ahmedabad in minute detail and helped
us to arrange a visit. The professor kindly welcomed and hosted us for twodays, on 6th and 9th December.

At CEPT Research & Development Foundation (CRDF)

Part of the experiments involves theuse of VMS(The variable message signs)
installed around the city, with the aim of
improving the habits of city bus drivers
passing through the boards by
displaying information on trafficmanners (e.g. keeping a safe distance
between vehicles) on half of the boards.
75 drivers on city bus routes passing by 9
information boards were interviewed
about their opinions of the boards and whether they had led to a change in behaviour,
as is the current fashion. We asked for the cooperation of CEPT University in translating
the local Gujarati language as well as English shown in VMS.

METRO construction site at NIPPON SIGNAL
Visiting Metro construction site operated by Nippon Signal was anotherexcitement. At the moment, Metro is operating between 6 stations, at theedge of the city, with an interval of 20 mins. When we visited the actual
station “Apparel park”, as it we could see a few passengers actually using
the metro, although it was sparse because service time still remains
short(10am - 6pm). At Nippon Signal, we could clearly saw how thepassengers and the train operator aremonitored with the sit uations of train
cars, platform, and ticket gates with highresolution screen . So far, the entire line is
expected to open between June andAugust, which will possibly contributemulti-modalization in the city.

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CO-SYMPOSIUM WITH SMARTRAN4T4

Co-Symposium with “SmarTran4T4 (Smart Transport Strategy for
Realizing Thailand 4.0)”

Co-symposium with like-minded friends in Thailand
by Misa Kitagawa (M2Smart Residential Coordinator)
On 25th Jan, the Co-Symposium with “SmarTran4T4 (Smart Transport Strategy for Realizing Thailand 4.0)”, another
SATREPS project was held online.
The aim of the symposium was to exchange insights and views among of each other, to explore future challenge and tostrengthen research networks.
We are happy to announce that it ended successfully, with the audience of as many as 114 at maximum, including bothproject members and young researchers from 3 countries, JICA, JST and the relevant public and private sectors.
2 projects have a lot in common in the goal, to create a sustainable and eco & people-friendly city by smart transportation
to address the common issues which emerging countries are facing.
We will keep in touch with for future challenge!
https://youtu.be/Gai6zdt6zdE

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5CO-SYMPOSIUM WITH “SMARTRAN4T4”

Introduction of the day's presentations

Estimation of Traffic State by Image Processing in Ahmedabad
by Prof.C. Krishna Mohan (Group 1 Leader), K Naveen Kumar, Sai Harsha Yelleni
Intersections are one of the main sources of congestion and hence, it is important to understand traffic behavior atintersections. Particularly, in developing countries with high vehicle density, mixed traffic type, and lane-less driving
behavior, it is difficult to distinguish between congested and normal traffic behavior. We worked on traffic congestion
state detection and prediction using image processing[3] and deep learning methods[1].

Congestion state is modeled as:
1.
2.
3.

(OF)Open flow (low congestion),
Mild congestion (medium),High congestion.

We performed experiments on traffic congestion state modeling based on vehicle queue length [2] and capacity/densitybased modeling. Capacity is the percentage of roads occupied by vehicles. In a Capacity based model, users should givethe area of interest (AOI), and based on blurring and thresholding, we obtain the binary (black/white) pixels. White pixels
represent the area of the road segment and black pixels are the vehicles.
AOI in our case is half part of the road. It is characterized as a polygon with (x,y) coordinates on the image. The advantageof the polygon is we can choose the AOI as precisely as possible. For the input video, we have to fix the AOI according to
the camera angle. Outputs are video with capacity displayed and a graph for time (of the day) vs capacity. The higher thecapacity of the road is occupied the higher the congestion. Capacity and Level of Service (LOS) are for obtaining
quantitative measures of traffic congestion.

Methodology of Proposed Approach:1.
From the input video, get the edges of the frame.
2.
Blur the frames to get the area that is more filled.
3.
Blur the image by binary thresholding.
4.
Overlap the thresholded image with AOI and count black
pixels/white pixels which give you traffic capacity

OutcomeGiven a video captured by surveillance cameras at various outdoor
locations, our model would process each frame in the video andprovide a real-time high-quality estimation of congestion forfurther analysis to any road scene analysis task, providing better
assistance to capture the scene information.

Figure 1: Capacity model input
and output

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CO-SYMPOSIUM WITH “SMARTRAN4T4”

Time vs Capacity Graph
This Big data of time vs capacity is generated and stored in CSV format, which helps in traffic congestion prediction using
time series analysis.

Figure 2: Real-time graph of capacity at Ahmedabad
junction

Observations••

With the capacity based approach, we can analyze traffic in a particular region.
Real-time capacity measures help in monitoring traffic congestion and help in congestion prediction.

With the help of traffic congestion detection, we can get traffic states in real-time, and with the help of big data of capacity
stored, we get traffic congestion prediction estimates. By doing traffic congestion detection at multiple junctions, we cando traffic optimization to reduce congestion. In the future, we would like to improve the performance of models during
nighttime using image enhancement techniques.

References
1.
2.
3.

Chakraborty, Pranamesh, et al. "Traffic congestion detection from camera images using deep convolutional
neural networks." Transportation Research Record 2672.45 (2018): 222-231.
Shirazi, Mohammad Shokrolah, and Brendan Morris. "Vision-based vehicle queue analysis at junctions." 2015
12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2015.
Wei, Li, and Dai Hong-ying. "Real-time road congestion detection based on image texture analysis." Procedia
engineering 137 (2016): 196-201.

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CO-SYMPOSIUM WITH “SMARTRAN4T4”

Group 2 – Assessment of Mobility and Traffic Emission by Controlling
the Speed at the Intersection under Heterogeneous Traffic Flowby Aditya BETHALA , Prof. Tetsuhiro Ishizaka (Group 2 Leader), Shaik Mohammed Asif Nawaz
One of the Goals of Group 2 has been to study and test the implications of smart applications of traffic control systems, due
to lack of signal timing information and lengthy signal cycle, vehicles wait at the intersection for extended period which
result in unnecessary idling cause inturn resulting in huge amount of fuel wastage
Objectives
The main objective of this study is to evaluate the traffic delay, fuel consumption,and traffic emissions at the intersection by providing upcoming traffic signal
information and required speed to cross the intersection in the green phase underheterogeneous traffic flow and indiscipline lane behavior. The secondary

1

Required speed Rs(speed adjustment criteria )

𝑅𝑠 =

𝐷𝑖
𝑅𝑡 − 𝑇𝑡

Fig2 : Conceptual realization of a working systemobjective is to evaluate the speed control under different speeds (nothing but
different road types like urban streets, arterial roads, rural roads, andHighways/motorways). To conduct this study PTV Vissim microscopic traffic
simulation was employed as an effective and flexible tool to simulate thespeed controlling rule.
Speed Control and Carbon dioxide emission
It is difficult to control the vehicle in the real world and genereate such quality
data. So, in this study, to estimate the impact of the speed control at theFig 1 : Working model flow chart for simulation
upcoming intersection on flow and environment under heterogeneous traffic
flow a PTV VISSIM simulation-based traffic model is used. In the simulation
speed controlling and estimation of CO2 are done by using user-defined attributes and a COM interface following the steps
shown in Fig.1 and Fig.2
Results and inferences
The purpose is to evaluate the efficiency of the speed adjusting by proving the upcoming signal information to improve
mobility and reducing CO2 emission. The results indicated that the speed control has a significant change in CO2.
There is a significant impact in both scenarios if all the vehicles use the speed controlling option.

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CO-SYMPOSIUM WITH “SMARTRAN4T4”

As shown in above Fig.3, the average delay is improved by an average of 80% in the case of scenario ‘A’ and 30% in the case
Scenario "A" Avg Delay (hrs)

16000

Scenario "A" Fuel Consumption(gallons)

Scenario "A" CO2 (grams)

0.7

100

14000

0.6
80

Fuel Consumption

10000

CO2 (g/h)

Avg delay (hrs)

12000

0.5
0.4
0.3

8000
6000

0.2

4000

0.1

40
20

2000

0
30 (km/hr)

A1

60

50 (km/hr)

80 (km/hr)

30 (km/hr)

Speed variants and % of Vehicle share
0

1

25

50

75

0

0

100 (km/hr)

A2

100

1

Scenrio "B"Avg Delay (hrs)16000

50 (km/hr)

30 (km/hr)

80 (km/hr) 100 (km/hr)

0

25

50

75

100

50 (km/hr)

80 (km/hr)

100 (km/hr)

Speed variants and% of Vehicle share

Speed variants and % of Vehicle share

A2

0

25

50

75

100

1
Scenario "B" Fuel Consumption(gallons)

Scenario "B" CO2 (grams)
100

0.7
14000

0.6

Fuel Consumption

10000

CO2(g/h)Avg Delay

80

12000

0.5
0.4
0.3

8000
6000

0.2

4000

0.1

2000

0

0

60
40

20
0

30 (km/hr)

B1
2

50 (km/hr)

80 (km/hr)

25

50

75

50 (km/hr)

80 (km/hr) 100 (km/hr)

50 (km/hr)

80 (km/hr)

100 (km/hr)

Speed variants and% of Vehicle share

Speed variants and % of Vehicle share

Speed variants and % of Vehicle share0

30 (km/hr)
30 (km/hr)

100 (km/hr)

B2

100

2

0

25

50

75

100

B3

0

25

50

75

100

2

Fig3 : Scenario comparison - Average delay (Scenario AvB), Emissions (Scenario AvB) and Fuel Consumption (Scenario AvB)

of scenario ‘B’. Similarly, 50% change in the fuel consumption and CO2 in the case of scenario ‘A’ and 18% change in case of
scenario ‘B’ when compared with the base scenario. In addition, speed control has a positive impact on the other trafficparameters like Vehicle Kilometres Travelled, Vehicles Hours Travelled, Queue length, the average number of stops, andspeed inside the network. The results of this study shows that signal control has significant impact in both the scenarios onmobility, fuel consumption, and CO2-emissions.It is evident that even partial application of speed control shows
improvement but also if all vehicles use only speeding condition there is significant positive impact when compared withbase scenario.
Application of study
As the conception of smart cities is evident, adoption of autonomous traffic control measures can be a stepping stone tomore smart measures to alleviate congestion problem. The real world application tests can be gradually performed toinvestigate other possible influencing factors such as Vehicle2Vehicle and Vechicle2Infrastructure connectivity tounderstand the the study in the real world context.

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9CO-SYMPOSIUM WITH “SMARTRAN4T4”

Data Acquisition by Various Sensing Devices and its Utilization
by Dr.Tsutomu Tsuboi(M2Smart Project Leader)Traffic flow analysis
For these years, we have been accumulating traffic data through various sensing devices installed in Ahmedabad City,
which enable us to understand and summarize the traffic situations.
••••Traffic monitor camera … 29 locations
CCTV… 1 location (Paldi junction)
Monitor camera… 4 locations (Paldi junction)
VMS… 10 locations

By utilizing the obtained data, we could analyze and estimate when will the congestion occurs, and what is the mechanismof congestion.
First, we conducted time-zone analysis and could understand congestion is always occurring in evening, 5-10pm.
Secondly, we focused on the location where congestion were occurring and a closer look at the sight through monitoring
camera led us to the insight that a possible cause of the congestion is the appearance of many small stalls in the evening,where there was space and cars were flowing during the day.

These kind of analysis of traffic situation may
be beneficial for the local government who
tries to address the issue.

Fig1 : Time zone analysis

Image1 : 3D visualization of traffic situation

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CO-SYMPOSIUM WITH “SMARTRAN4T4”

Travel activity analysis and Urban Trip modeling
Meanwhile, personal travel activity data obtained through smartphone App carried by each resident can be utilized toclarify the actual travel route, when and how citizen choose the travel route.
Moreover by combining several different types of data(actural travel route, Origin and Destination travel route, traffic
spatial analysis, traffic mesh analysis, and census data), it is possible to bring to light a multi-layered picture of the realsituation in the city, which may lead more advanced congestion forecasting and urban trip modeling. There was field trail
test in December 2021. The detal analysis is under going. It will be informed at the next NewsLetter.
Driver’s behaviour analysis by VMS
From October to December 2021, survey of driver’s behaviour was conducted in Ahmedabad city with the support of CEPT
university. Main purpose of this survey is to verify the effects of utilizing VMS as a communication tool, and Key question
was “How VMS influence drivers mind and how mind change leading to the travel behaviour?”.
75 bus drivers were divided into 4 groups according to with/without pre-learning and on/off bus route where VMS was
installed. During the survey, various messeages in different languages were displayed on VMS, then bus driver’s behaviouralchange in each group was monitored and compared quantitatively.
In conclusion, the observation strongly supports the effect of learning and VMS messaging to improve driver’s behavior,although it is now at the preliminary phase of study and subject to further analysis.

Impact on greenhouse gases/roadside air pollution
by Prof. Soumya Jana(Group 4 Leader), Anand Kakarla
Developing nations have been witnessing accelerated economic growth and technological advancements in recent years
and contributed to increase in resource usage and depletion. In this backdrop, several such nations have taken smart cities
initiative in an attempt at sustaining the environment while providing desirable citizen services. In this paper, we focus onan essential service, namely, urban mobility, and develop tools to measure its environmental impact, so that the traffic canbe managed in real time subject to appropriate locallyenforced emission limits [1].
At present, desired high-resolution spatio-temporal maps of vehicular pollution are not available. In practice, local traffic
agencies set real-time mobility targets at streets and junctions, while a pollution monitoring agency independently looks at
city-scale contribution of vehicular emission over a much longer time horizon [2]. In particular, the latter usually obtains
aggregate data on fuel consumption, vehicle kilometers travelled and similar quantities over a period, and makes estimationof gross emission levels. Such coarse estimates are clearly inadequate for the present purpose. Indeed, we need alternatives
to gross source modeling because of heterogeneous traffic characteristics in developing cities exhibit high spatiotemporalvariability. In addition, non-traffic sources, such as garbage burning and cooking in close proximity, contribute to on-road
air quality.

TABLE I CLASS-DIFFERENTIATED TVC AND EMISSION ESTIMATES IN A
SPECIFIC HOUR BASED ON BSIV STANDARD
Vehicle Class

2W

3W

4W

Bus

Truck

595

113

717

39

100

Emission Rate (g/km) 0.1

1.2

2.3

5.2

4.7

Total Count

Fig. 1. Preliminary setup at IIT Hyderabad

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CO-SYMPOSIUM WITH “SMARTRAN4T4”

In such circumstances, we propose to complement source measurements and models with emission measurements anddispersion models towards obtaining reliable high-resolution profiles. In this vein, we shall specifically obtain fine-scale dataon (i) class-differentiated traffic alongside suitable driving cycle and emission factor, and (ii) on-road pollution levels. Thus,
we shall rely on not only traffic information collected by the traffic management system, but also pollution information of
the air quality management system.
As the vehicular traffic significantly contributes to the on-road air quality, we assume that estimating the said contribution,
can improve the reliability of the estimating pollution profiles. Specifically, if the emission information is known along withthe ambient meteorology, one can estimate the on-road pollution profiles using dispersion models. Here, the total emissions
on a particular road segment can be obtained from traffic volume estimates which includes total count of different types ofvehicles such as two-wheelers and three-wheelers and the emission rates of each vehicle type.
For this small scale demonstration, we selected a road segment of National Highway (NH) 65 near Indian Institute ofTechnology Hyderabad (IITH) main-gate. With the main objective of obtaining the on-road pollution profile, we placed twoalphasense CO sensors known for their low minimum detection limit, one on road (S2) and one off road (S1) as shown in
Figure 1. The sensors were calibrated against known concentrations of CO at five different levels. For this, we setup The
Infra-Red Traffic Logger (TIRTL) that provides TVC including vehicle flow rate (VFR) and speed of various vehicle types suchas motorised two-wheelers, threewheelers, cars, buses and trucks.
For the generation of spatial CO profile, we only considered the time between 17:00 PM and 18:00 PM and obtained thecorresponding TVC. And, emission limits were set according to Bharat Stage (BS IV) and source emission rate (𝐸𝑠 ) was
calculated. And, to create a pollution profile, AERMOD dispersion model [6] was adopted. Certain meteorologicalparameters necessary for AERMOD were obtained from the IITH weather station. Further, the emission release height was
set to 1 m and line area source with a width of 5 m and length of 600 m was considered. Next, we found the source emission
rate (𝐸𝑚 ) based on CO measurements as follows. Framing an optimization problem, we found the emission rate thatminimizes the mismatch between simulated profile and the measurements made at the corresponding locations of S1 andS2. Next, we took the average of 𝐸𝑠 and 𝐸𝑚 and simulated the corresponding AERMOD profile.
Within the selected hour, obtained TVC from TIRTL and the corresponding emissions set based on BS-IV standard are given
in Table I. Using these values Es was obtained as 5.42 × 10−4 𝑔/𝑠𝑒𝑐/𝑚2 . On the other hand, the average value of
measurements within the selected hour at S1 and S2 were found to be 1.1 ppm and 3.58 ppm. After obtaining all thenecessary parameters for AERMOD dispersion, Em was backcaliculated as 2.89 × 10−3 𝑔/𝑠𝑒𝑐/𝑚2 . Consequently, we
obtained a fused average value of 1.7 × 10−3 𝑔/𝑠𝑒𝑐/𝑚2 . Next,
AERMOD profile was simulated based on the rate estimate. This
profile shows that mean concentration was about 1 ppm along theroad and decreased as we go far away from the the road as
expected. Finally we employed a CO to CO2 correlation factor of
Fig. 2. Source-based pollution profile around NH-65 near IIT
212 (based on emperical data) to obtain CO2 profile.
Hyderabad.

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M2SMART NEWSLETTER | VOL.14
INFORMATIONACKNOWLEDGEMENT FOR RESEACH ASSISTANTS
by Prof. Bheemarjuna Reddy Tamma (M2Smart Co-Project Leader)
The research and development activities of M2Smart project are carried out by passionate research assistants (RAs) under
the guidance of faculty members at IIT Hyderabad and other researchers from Japan. Being interdisplinary nature of work,
we put together 20 RAs from various disciplines to work together as part of this project.
They created novel solutions and technologies for collecting, processing, and providing traffic information in the IITH
campus and Ahmedabad city using various sensing technologies, wireless communication technologies and big dataprocessing techniques,some of which came to fruition in the form of 34 publications(20 conferences, 7 journals, 5workshop papers, 2 posters).
I would like to congratlaute all the scholars for making their research outcomes visible by publishing them in reputed
conferences and journals. I would like to place on record my sincere thanks for the key role that they played in shaping thehandbook of the project and wish them all the best in their future.
Many Thanks to
Mr. Arun Kant Dubey,
Ms. Rachakonda Yashasvi,
Mr. Munagala Venkata Satish Kumar Reddy,
Mr. Thamilselvam B,
Mr. Vineel Abhinav G,
Ms. Anshika Chourasia,
Ms. Amala Sonny,
Mr. Naganaboina Venkata Ramesh,
Mr. Chandrasekhar C,
Ms. Jaya Sharma,
Mr. Kummari Naveen Kumar,
Mr. Anand Kakarla,
Mr. Sai Harsha Yelleni,
Mr. Pabbathi Uday Kumar
and the students who has already graduated…
(listed in no particular order)

UPCOMING SCHEDULE

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PUBLICATION

Journals⚫

Aditya BETHALA，Mohammed Asif Nawaz SHAIK，Tetsuhiro ISHIZAKA andDigvijay Sampatrao PAWAR (2021).
Assessment of Mobility and Traffic Emission by Controlling the Speed at the Intersection under Heterogeneous Traffic
Flow, The 14th International Conference of Eastern Asia Society for Transportation Studies⚫

Tetsuhiro ISHIZAKA, Debaditya Roy, Syo MATSUNOSHITA (2021). Detection and Evaluation of Running Condition
under Mixed Traffic Flow using Social Force Model in India, The 64th Conference of Infrastructure Planning andManagement, JSCE⚫

Kazu Fujieda,Kosuke Tanaka and Tetsuhiro ISHIZAKA (2021). Estimation of Satulation Flow rate under Mixed Traffic
Flow and Analysis of Reduction Factor, The 49th Conference of Research and Technology, Kanto Branch, JSCE⚫

VR Naganaboina, SG Singh(2021). Graphene-CeO2 based flexible gas sensor: Monitoring of low ppm CO gas with highselectivity at room temperature⚫

VR Naganaboina, SG Singh(2022). CdS based Chemiresistor with Schottky Contact: Toxic Gases Detection with
Enhanced Sensitivity and Selectivity at Room Temperature, Sensors and Actuators B: Chemical vol. 357, April 2022,
131421⚫

VR Naganaboina, M Anandkumar, AS Deshpande, SG Singh(2022). Single-Phase High-Entropy Oxide-based
Chemiresistor: Toward Selective and Sensitive Detection of Methane Gas for Real-time Applications, Sensors andActuators B: Chemical vo. 357, April 2022, 131426

Conference
Workshop paper⚫

Priyambada Ambastha, Maunendra Sankar Desarkar(2022). mTransDial: Multilingual Dataset for Transport Domain
Dialog Systems, AI for Transportation workshop at Thirty-Sixth AAAI Conference on Artificial Intelligence⚫

K Naveen Kumar, Digvijay S. Pawar, and C. Krishna Mohan(2022). Open-air Off-street Vehicle Parking Management
System Using Deep Neural Networks: A Case Study

Conference Presentation⚫

Thamilselvam B, Subrahmanyam Kalyanasundaram, M. V. Panduranga Rao(2021). Statistical Model Checking for Traffic
Models, SBMF2021 -24th Brazilian Symposium on Formal Methods⚫

Suhel Sajjan Magdum, Antony Franklin, Bheemarjuna Reddy Tamma(2022). A Cooperative Federated Learning
Mechanism for Collision Avoidance using Cellular and 802.11p based Radios Opportunistically, ANTS 2021 IEEE
International Conference on Advanced Networks and Telecommunications Systems⚫

Kakarla. A, V. S. K. R. Munagala, T. Ishizaka, A. Fukuda, and S. Jana, Spatio-temporal prediction of roadside PM2.5 basedon sparse mobile sensing and traffic information, National Conference on Communications (NCC), 2021⚫

VR Naganaboina, SG Singh, Fabrication of highly selective NO2 gas sensor for low ppm detection, IEEE Conference on
Nanotechnology

M2SmartNewsLetterVol. 14
Mar 2022

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