The National Science Experiment (NSE) is an island-wide outdoor science experiment carried out by Singapore students. Themed “Step Out for Science”, students get to track their carbon footprint, travel mobility patterns, amount of time they spend indoors and outdoors, and more.
The experiment involves Singapore students carrying a specially designed sensor, named the SENSG, to collect data on their daily travel as well as data from the environment. This data is transferred wirelessly to a central online portal from which these students (and their teachers) can log in to view the results, including the aggregated data of students from all over Singapore who are taking part.
Through this experiment, students learn about the Internet of Things and Big Data as the portal will provide the knowledge and tools to teach them to read and analyse the information, create graphs, interpret visualisations, and compare trends. Teachers will also be able to leverage the data to develop interesting physics lessons and teach concepts such as humidity, linear kinematics and pendulum motion through hypotheses testing and hands-on experiments.
The NSE is organised by the National Research Foundation Singapore, in partnership with the Ministry of Education, Singapore University of Technology and Design, Science Centre Singapore, Agency for Science, Technology and Research (A*STAR), Singapore Land Authority and OneMap Singapore, to excite young Singaporeans in science and technology.
Technology sponsors of NSE include A*STAR’s Institute of High Performance Computing, the Government Technology Agency of Singapore, [email protected], NVIDIA, SAP, Dassault Systèmes, Skyhook and National Supercomputing Centre Singapore. The logistics sponsor of NSE is Singapore Post.
NSE Big Data Challenge
About NSE National Science Experiment
Highlights of NSE 2016 include the NSE Big Data Challenge where students come up with innovative applications of data collected during their experiments, and the Geo-mood tagging feature which will allow students to associate places with their well-being at a particular time.
The whole project for 2015 took place from September to November, and it aims to reach out to over 250,000 young Singaporean students. At the end of the experiment, a series of data visualisations will be produced for exhibition at Science Centre’s newly re-opened digital planetarium. This exhibition will showcase how each student’s individual efforts contribute towards a national map of our youths’ travel patterns and Singapore’s urban outdoor environment.
Each team will be given a unique password and PIN to access their data through ModStore. After which, IHPC researchers will demonstrate the use of ModStore for sample analyses. There will also be explanation on the variables collected and the type of analyses that can be generated using the online platform. Students are encouraged to bring their own laptop so that they can try out the ModStore on the spot .
The preparatory workshops are open to schools which have participated in NSE 2015 and 2016. Participating schools can send up to 12 students and two teachers to attend the workshop. For schools which did not participate in NSE, they can also send up to two teachers to attend the workshop.
Winners of NSE 2016 Big Data Challenge
Secondary School Category
Winner: NUS High School of Mathematics and Science Team 1
- Leong Song Zhu Owen
- Ong Yong Chein
- Saravanan Yukesh Ragavendar
- Tay Kai Jun
2nd Prize: Maris Stella High School Team 3
- Gan Jia Jian
- Chan Dar Shyang
- Koh Huai, Edan
- Teo Hao Zhi
3rd Prize: Eastview Secondary School Team 1
- Huang Chao Hung
- Benedine Tay Hui Qing
- Jarry Goh Yu Xian
- Natasha Lydia Selvan
Consolation Prize: Maris Stella High School Team 1
- Ang En Ren, Ariel
- Gavin Goh Jun Chong
- Lim Xi, Perry
- Tan Hsien Wen
Consolation Prize: Swiss Cottage Secondary School Team 2
- Lim Kiat Sen Jaron
- Tan Xuan Qi Rachel
- Ang Bin Heng
- Chen Jun Hua
Winner: Singapore Polytechnic Team 1
- Bryan Cheong Teng Yue
- Bryston Chang Wa Jie
- Paing Khantt Lin
- James Pang Jun Jie
2nd Prize: ITE College West Team 4
- Neo Zhen Cheng
- Yap Jun Lin
- Lim Zi Xiang
- Koh Zhan Wah Christien
3rd Prize: Nanyang Polytechnic Team 1
- Ngo Wei An
- Noel Sung Tze Xuan
- Andre Ang Peng Ren
- Tan Zhen Wen
Consolation Prize: Nanyang Polytechnic Team 4
- Mohamed Izzat Khair Bin Mohamed Noor
- Lim Dao Yong
- Chang Jesslyn
- Darrell Ong Zheng Dao
Consolation Prize: Ngee Ann Polytechnic Team 3
- Pang Biao Yi
- Aaron Sng Li Wen
- Soon Qing Rong
- Wong Wen Kang
Consolation Prize: ITE College West Team 3
- Mohammad Fitri B Mohd Ridwi
- Adam Muhammad Harith B Zakba
- Hanaffie B Bakri
- Muhammad Faruq Bin Jumadi
Participating Primary Schools
- Ai Tong School
- Anchor Green Primary School
- Anderson Primary School
- Anglo-Chinese School (Primary)
- Balestier Hill Primary School
- CHIJ St Nicholas Girls’ School (Primary)
- Dazhong Primary School
- De La Salle School
- East Spring Primary School
- Endeavour Primary School
- Fernvale Primary School
- First Toa Payoh Primary School
- Fuhua Primary School
- Henry Park Primary School
- Jing Shan Primary School
- Juying Primary School
- Kheng Cheng School
- Kranji Primary School
- Lakeside Primary School
- Lianhua Primary School
- MacPherson Primary School
- Maha Bodhi School
- Marsiling Primary School
- Naval Base Primary School
- New Town Primary School
- North Spring Primary School
- North Vista Primary School
- Northoaks Primary School
- Princess Elizabeth Primary School
- Punggol Green Primary School
- Queenstown Primary School
- Raffles Girls’ Primary School
- Singapore Chinese Girls’ Primary School
- Temasek Primary School
- Townsville Primary School
- Unity Primary School
- Zhenghua Primary School
- Zhonghua Primary School
- Anglican High School
- Bartley Secondary School
- Catholic High School
- Cedar Girls’ Secondary School
- CHIJ Katong Convent
- CHIJ St Nicholas Girls’ School
- CHIJ St Theresa’s Convent
- Clementi Town Secondary School
- Compassvale Secondary School
- Crest Secondary School
- East View Secondary School
- Fuchun Secondary School
- Holy Innocents’ High School
- Hwa Chong Institution
- Jurongville Secondary School
- Kent Ridge Secondary School
- Kranji Secondary School
- Maris Stella High School
- Outram Secondary School
- Northbrooks Secondary School
- Paya Lebar Methodist Girls’ School
- Peicai Secondary School
- Ping Yi Secondary School
- Pioneer Secondary School
- Sengkang Secondary School
- Serangoon Garden Secondary School
- Serangoon Secondary School
- Springfield Secondary School
- St Joseph’s Institution
- Swiss Cottage Secondary School
- Tanjong Katong Girls’ School
- Westwood Secondary School
- Yishun Secondary School
- Yuying Secondary School
What is SENSg
The device, named “SENSg” (pronounced “SENSE-SG”) measures and stores data on motion, temperature, humidity, atmospheric pressure, light intensity and sound pressure levels, which are correlated to the sensor’s location. The device uses Wi-Fi signals to localise itself, and periodically uploads sensor data to a secured database if it is in range of a known access point. The sensor data is anonymous, and stored securely in the cloud.
The SENSg device was designed and developed by researchers from the Singapore University of Technology and Design (SUTD) as a “Laboratory on a Lanyard” to enable Singapore’s National Science Experiment. It offers several unique and original features:
- Lowest cost in class: they are designed as the lowest-cost multi-functional sensor available so that a large number can be deployed throughout Singapore over a long period of experiments;
- Radio localisation: they use their Wi-Fi radios not only to move data from the device, but also to ‘sniff’ Wi-Fi hotspots in order to determine their location; and
- Rapid-prototyping: they use an open compilation toolchain to allow students to learn how to code, experiment, sense, and build their own applications on the SENSg devices.
The SUTD team worked hard to create this unique, Singaporean technology, and hope that you enjoy experimenting with the devices in the National Experiment and beyond!
What kinds of data can SENSg collect?
Electronic humidity sensors can be broadly divided into three categories: capacitive, resistive and thermal conductivity. The SENSg devices contain capacitive humidity sensors. The accuracy of the relative humidity reading is strongly dependant on temperature, since temperature is in the formula used to calculate its value. As such, we have designed the SENSg devices with multiple perforations to increase ventilation, and mounted the sensor in a way to minimise heat transfer from other electronic sensors.
For more information, you can read: http://en.wikipedia.org/wiki/Relative_humidity
The SENSg device’s pressure sensor consists of a piezo-resistive sensor. Change in pressure results in a change in electrical resistivity of the sensor, and altering current or voltage readout which is correlated to pressure readings. The measured pressure allows for estimation of the altitude, i.e. the height above sea level you are at.
For more information, you can read: http://en.wikipedia.org/wiki/Atmospheric_pressure
The SENSg device contains an infrared (IR) thermometer that provides non-contact temperature sensing. It measures the temperature of an object by sensing the infrared radiation emitted by the object (which is given by Planck’s law) and converting the voltage generated to a digital reading of the temperature. The lens covering the IR thermometer sensor requires uses special materials to minimise reflection and absorption by the lens material.
For more information, you can read: http://www.sensorsmag.com/sensors-mag/demystifying-thermopile-ir-temp-sensors-13157
The microphone in the SENSg device converts acoustic (sound) pressure waves to electrical signals, and provides the Sound Pressure Level readings in decibels. Typical ambient noise in Singapore measured at night is around 55 decibels (dB), reaching up to around 80 dB in the day. The SENSg devices use special capacitive micro electromechanical systems (MEMS) microphones to measure sound pressure level for measuring city noise.
For more information, you can read: http://en.wikipedia.org/wiki/Microphone#MEMS_microphone
The SENSg devices contain a photodiode to measure light. The light which shines on the photodiode is first passed through a specially designed filter to ensure that reads light in the visible range similar to what is perceived by the human eye, and generates a small amount of current which is used to measure light. Similar to the IR thermometer sensor, the light sensor must to be mounted behind a clear lens to ensure accurate light intensity measurements. Photodiodes are often used in applications such as street lighting control, back-light calibration, etc where light must be adjusted according to how humans perceive light.
For more information, you can read: http://en.wikipedia.org/wiki/Photodiode
The SENSg device contains a MEMS Inertial Measurement Unit (IMU) which combines a 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer in the same chip. This chip also comes with a built-in pedometer function to fuse sensor data and provide the step count reading.
The accelerometer measures acceleration forces; these forces may be static (gravitational force) or dynamic such as those generated through motion. A MEMS accelerometer can be thought of as a tiny vibrating bridge, where the changes in vibration are used to estimate force.
A gyroscope is a spinning wheel or disc in which the axis of rotation is free to assume any orientation. A MEMS accelerometer uses a tiny, specially designed vibrating structure which measures angular velocity about the X-, Y- and Z- Axes, also known as the roll-, pitch- and yaw- axes respectively.
The magnetometer detects the earth’s magnetic field and is used to compensate for orientation drift.
For more information, you can read: https://en.wikipedia.org/wiki/Inertial_measurement_unit
Counting steps is most often performed by measuring the magnitude of acceleration along a particular axis (x,y,or z). If the acceleration is greater than a specific amount, a step is registered. Analog measurement devices use simple technologies such as a weight attached to a spring which is tuned to bounce against a switch if a step is taken. Popular devices such as the ‘FitBit’ which exist for measuring personal activity use MEMS accelerometers such as the one in the SENSg device. By watching the acceleration cross thresholds according to some rules, accurate step counting can be performed.
For more details on the types of algorithms which can be applied, you can read: http://www.analog.com/library/analogdialogue/archives/44-06/pedometer.html
Whether a student is indoors or outdoors is defined by:
- Air conditioned indoor spaces versus open air spaces e.g. offices, malls and hospitals
- Covered indoor spaces (roved, with windows doors and ventilation but no air conditioning) versus open air spaces e.g. classrooms with ceiling fans, MRT stations and underground parking garages
The algorithms which we are developing use the differences in relative humidity and light between indoor and outdoor spaces in the different cases to differentiate between them. Of course, time of the day is taking into consideration when using the light algorithm.
A student’s mode of transportation is determined by:
- Whether a person is on foot, in a motorised mode of transport, or on a train
- Whether a person is in a motorised form of mass transit or a light-duty vehicle
All of the machine learning should happen on the device, since we are limited in terms of how much data we can send back to our more powerful webservers. This is a big challenge, and is solved with the help of decision trees which have been designed based on data collected across a set of travel modes before the National Experiment. Nevertheless, it would be great if students can log into this NSE web portal and check if our artificial intelligence got it right – you will have a chance to tell us if we guessed your travel mode correctly.
The planet’s atmosphere is being changed by “Anthropogenic carbon dioxide emissions” which is a big way of saying it is being changed by people burning fossil fuels (mostly for energy, but also to clear land).
As the planet’s atmosphere absorbs more carbon dioxide (CO2), it traps more of the sun’s energy in what is known as the ‘greenhouse effect’.
This then causes the planet’s temperature to change, causing storms, rising sea levels, and floods/droughts – all of which harm people and wildlife.
Check out http://www.explainthatstuff.com/globalwarmingforkids.html to learn more.
Tips for reducing personal CO2 footprint:
- Swap old incandescent light bulbs for the new compact fluorescent lights (CFLs). They use only 25% as much electricity to give the same light. They last ten times longer.
- Turn off lights, TVs, computers, when you do not need them.
- Unplug! Any electronic gadget you can turn on with a remote (TV, DVD player, Nintendo, Xbox) uses power even when it is “off.” Appliances with a digital clock (like a coffee maker) or a power adapter (like a laptop computer) also suck power like a sneaky vampire. Plug these kinds of things into a surge protector or power strip that has an on/off switch. Then you can shut off all the power without unplugging each gadget.
- There are even power strips that glow to show you how much power is going through them, and power strips you can control from your computer or iPhone.
- Turn up the thermostat on the air conditioning when it’s hot. Use fans if you’re still hot. They use much less power.
- Turn down the thermostat on the heating when it’s cold. Sweaters, blankets, and socks are good for you and better for the planet.
- Walk or ride your bike instead of taking a car everywhere. Even a 2-mile car trip puts 2 pounds of CO2 into the atmosphere!
- If you must ride, carpool.
- Stay out of the drive thru! When you go to a fast-food place, ask your driver to park the car and let you walk inside, rather than sitting in a line of cars with the engine running and polluting.