Measuring CO2 with MQ135


A MQ135 sensor on a small PCB.

The MQ135 is a cheap gas sensor that is primarily intended for detecting the presence of flammable gases. It is marketed as a generalized “air quality” sensor, rather than precision device for measuring the concentration of any specific gas. Nevertheless, according to the data sheet the MQ135 is capable of measuring the concentrations of several gases, one of which is CO2. This makes it an attractive low-cost alternative to more specialized (and more expensive) CO2-specific sensors.

Much has been written about using the MQ135 as a CO2 sensor, mostly focusing on using it together with Arduino. This article will, firstly, discuss the working principles of the MQ135 sensor and how to read the data sheet, and secondly present how to use it with the PSLab.

Theory of operation

The MQ135 consists of a surface covered in a thin layer of SnO2, and a heater resistor which serves to raise the temperature of the SnO2 surface to several hundred degrees Celsius. SnO2 is an n-type semiconductor, in which donor electrons are excited to the conduction band at elevated temperatures. However, SnO2 when exposed to air readily adsorbs oxygen onto its surface. The adsorption reaction consumes electrons from the conduction band to form negatively charged oxygen species. Therefore, SnO2 has low conductivity in clean air. [Shimizu]

In the presence of a flammable gas, the gas will also adsorb onto the sensor surface, where it consumes the adsorbed oxygen species to form CO2 and H2O. This reaction releases the donor electrons back into the conduction band, thereby raising the conductivity of the sensor. By quantifying the conductivity response to the presence of various gases, a thin SnO2 surface can be used to determine the concentration of the gas. [Shimizu]

CO2, notably, is not a flammable gas. Here, the sensing mechanism is different; instead of reacting with adsorbed oxygen directly, CO2 sensing relies on water vapor first reacting with adsorbed oxygen to form adsorbed hydroxide (OH). CO2 then in turn reacts with the adsorbed hydroxide, forming carbonate (CO32-). This process ultimately also returns electrons to the SnO2 conduction band, and therefore results in increased conductivity. [Wang et al.]

SnO2 gas sensors can also detect the presence of reducing gases such as NOx. Here, the sensing mechanism is the opposite; the reducing gas adsorbs onto the sensor surface, consuming additional electrons from the conduction band, and thereby lowers the conductivity further. The MQ135 data sheet does not specify response curves for any reducing gases, so while the sensor should be sensitive to such gases, the user would have to experimentally determine the response curves themselves. [Shimizu]

MQ135 gas response curves

The MQ135 data sheet provides a graph showing how the sensor resistance changes with the concentration of several gases. Figure 1 shows a reproduction of the data sheet graph, where the points have been manually extracted from the data sheet graph using WebPlotDigitizer.

Note that the y-axis is Rs/R0, which is the ratio between the sensor resistance at any given moment (Rs) and the sensor resistance at a specific set of conditions (R0). This is because the sensor resistance depends on the exact amount of active material on the sensor surface, which varies between individual sensors. Therefore, the resistance response must be normalized against a predefined state in order for results from different sensors to be comparable. For the MQ135, this predefined state is 100 ppm ammonia vapor in otherwise clean air at 20 °C and 65% relative humidity. The sensor resistance at this state must be known in order for the curves in Figure 1 to apply. This may seem like a problem, since most people will not be able to reproduce those conditions. Fortunately, there are other ways to derive R0. More on that later.

The responses show clearly linear behavior in loglog-scale, which means that the relationship between resistance and concentration can be expressed as:

RS / R0 = a * cb    (1)

where a and b are constants, and c is gas concentration in ppm.

The curves in the Figure 1 have been fitted using exponential regression, specifically scipy.optimize.curve_fit.

Figure 2: Data sheet gas response values (points) and fitted curves (lines).

Equation 1 gives the sensor resistance as a function of gas concentration, but we are interested in the opposite relation, i.e. gas concentration as a function of sensor resistance. A little elementary algebra gets us there:

c = (1 / a)1/b * (RS / R0)1/b = A * (RS / R0)B    (2)

With that, we can correlate sensor resistance with gas concentration for any of the gases in Figure 1. Now, we need a way to determine the sensor resistance.

MQ135 electrical characteristics

As previously mentioned, the MQ135 behaves as a variable resistor when exposed to different gas concentrations. In order to measure gas concentration using equation 2, we need a way to measure the sensor resistance.

Figure 3: Circuit diagram of a typical sensing circuit using a MQ135. Note the voltage divider in the upper path.

By connecting a resistor in series with the sensor and measuring the voltage between them we can create a voltage divider, as in figure 3. The expression for the output voltage from a voltage divider is:

Vout = RL / (RS + RL) * Vin    (3)

Rearrange to isolate the sensor resistance:

RS = RL * (Vin / Vout – 1)    (4)

Understanding R0

While equation 4 provides a way to measure the sensor resistance, the sensor resistance alone is not enough to determine the gas concentration. This is because every individual sensor has a unique resistance response, based on the amount and distribution of active material on the sensor surface. Therefore, in order to compare the response of one sensor with other sensors, the response must be normalized. R0 is the normalization factor, and is defined as the sensor resistance at a known set of conditions, i.e. a certain temperature, relative, humidity, and most importantly a certain gas concentration. The ratio of the sensor resistance and R0 can be compared between sensors.

The MQ135 data sheet defines R0 as the sensor resistance at 100 ppm ammonia at 20 °C and 65% relative humidity. Of course, it is possible to redefine R0 at some other set of conditions, but if R0 is redefined, the curves in Figure 2 no longer apply! The only situation where it makes sense to redefine R0 is if you have the means to create new response curves for the gas you wish to measure. Otherwise, keeping R0 defined as above is the best available option.

That means we need a way to calculate R0, since most people will not be able to recreate the conditions at which it is defined. By rearranging equation 2, we get:

R0 = RS * (1 / A * c)-1/B    (5)

Since we can measure the sensor resistance, that means the only unknown term in the RHS of equation 5 is the concentration. In other words, if we can measure the sensor resistance in any known gas concentration, we can calculate R0.

As it happens, we do know the concentration of one of the gases in figure 2: Carbon dioxide. The concentration of CO2 in outside air is around 400 ppm (and rising, check for up to date values if you are reading this some years after 2021).

Accounting for temperature and humidity

The sensor response also depends on ambient temperature and humidity. The data sheet specifies the effect on sensor response at four different temperatures and two different humidity levels for ammonia vapor. Figure 4 shows a reproduction of the data sheet graph, plus fitted curves.

Figure 4: Data sheet temperature / humidity corrections (points) and fitter curves (lines).

From figure 4 we can see that the sensor resistance reaches a minimum at some temperature TM and then starts to increase. The reason the resistance decreases as the temperature increases toward TM is that higher surface temperature contributes to faster reaction rates between the gas and the adsorbed oxygen. The increase in sensor resistance at temperatures above TM can be explained by faster re-adsorption of oxygen at higher temperatures, which means more oxygen will cover the sensor surface for the same flammable gas concentration. [Shimizu]

We can also see that sensor resistance seems to decrease with at higher relative humidity. The mechanism for this is less clear. On the one hand, water vapor can adsorb onto the sensor surface, thereby decreasing the oxygen coverage and increasing sensor conductivity. On the other hand, water vapor can heal defective adsorption sites, which increases the surface area available for oxygen adsorption and therefore decreases sensor conductivity. [Wicker et al.] Additionally, recall that the CO2 sensing mechanism is highly dependent on the presence of water. Wang et al. reports a non-linear sensor response to CO2 with respect to humidity, with a response peak at 34% RH. With that in mind, the data sheet humidity dependence graph (which only has two data points and therefore must be assumed to be linear) is of limited utility when measuring CO2.

With that, we have all the pieces we need to use the MQ135 to measure CO2 concentration. Or any other of the gases in figure 2, for that matter. Enough theory, let’s take a look at how to do it in practice using the Pocket Science Lab!

Using the MQ135 with PSLab

If you have just the sensor itself, connect it as shown in figure 5, where the sensor is represented by a SparkFun gas sensor breakout board. CH1 is used to monitor the output voltage of the voltage divider between the sensor and the load resistor (which is 22K in this exaple circuit). Additionally, CH2 is used to monitor the voltage level of the PSLab’s +5V line. It is important that the voltage on the +5V line does not drop below 4.9 V, as the MQ135 will not reach its operating temperature otherwise. The MQ135 heater resistor draws up to 200 mA of current, which may be more power than the USB port powering the PSLab can supply. The PSLab does not regulate the +5V line, so it is up to the USB port to deliver enough current.

Figure 5: Guide to connect MQ135 to PSLab.
Figure 6: A MQ135 mounted on a PCB with a built-in load resistor (highlighted in red), as well as some other circuitry.

If you are using a SNS-MQ135 board, shown in figure 6, simply connect the AO pin to CH1, without the external load resistor. The SNS-MQ135 has its own load resistor, which unfortunately is only 1K. Such a small load resistor means the output voltage on AO will be very small when measuring CO2, which limits measurement accuracy. If possible, you should desolder the built-in SNS-MQ135 load resistor (outlined in red in figure 6), and use an external load resistor as in figure 5 instead. The caveat about the +5V level also applies to the SNS-MQ135.

The MQ135 must be allowed to heat to its operating temperature before it can be used. The data sheet recommends a heating period of at least 24h before use. Additionally, if the sensor hasn’t been used for a long time (or ever, if it is new), it must be allowed to burn in for even longer (>72h) in order to burn off various substances which have reacted with the surface during that time.

After the MQ135 has been allowed to heat up and burn in properly, we can use pslab-python 2.1.0+ to measure the ambient CO2 concentration.

Start by creating a multimeter to measure the voltage level on the +5V line:

>>> import pslab
>>> multimeter = pslab.Multimeter()
>>> multimeter.measure_voltage(“CH2”)

If the voltage is at least 4.9 V, the next step is to determine the sensor’s R0. It is a good idea to use an average value over a period of time (this only needs to be done once for each sensor):

>>> import time
>>> from pslab.external.gas_sensor import MQ135
>>> mq135 = MQ135(gas=”CO2”, r_load=22e3)
>>> r0 = []
>>> for i in range(3600):
>>>     r0.append(mq135.measure_r0(400))
>>>     time.sleep(1)
>>> mq135.r0 = sum(r0) / len(r0)


>>> mq135.measure_concentration()



Shimizu Y. (2014) SnO2 Gas Sensor. In: Kreysa G., Ota K., Savinell R.F. (eds) Encyclopedia of Applied Electrochemistry. Springer, New York, NY.

Wang et al. (2016) CO2-sensing properties and mechanism of nano-SnO2 thick-film sensor, Sensors and Actuators B: Chemical, Volume 227, Pages 73-84, ISSN 0925-4005,

Wicker et al. (2017) Ambient Humidity Influence on CO Detection with SnO2 Gas Sensing Materials. A Combined DRIFTS/DFT Investigation, The Journal of Physical Chemistry C, Volume 121, Number 45, Pages 25064-25073,

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Creating step designs from KiCAD for PSLab

PSLab hardware device is developed using KiCAD. It is an open source PCB designing tool which we can use for free and it has almost all the features needed to build a professional PCB. But it lacks one thing. It cannot generate and export 3D models. In fact there is a 3D viewer in KiCAD but there is no way to export it. When manufacturing PSLab devices, it was required by the manufacturers so that they can have a clear understanding how the components are placed. This step is necessary for them to design the production line.

Before we get started, there are few prerequisites to help us get this done. They are as follows;

  1. FreeCAD: Open source 3D modeling software
  2. KiCAD step up tools: External library to import KiCAD PCB layouts to FreeCAD

You may need to follow installation instructions to install FreeCAD from the link given. Once we are all set, extract the KiCAD Stepup tools. There we can find a set of python libraries and some bash scripts. We can either use the scripts or type commands ourselves. I found scripts having some issues configuring paths.

To fire up FreeCAD with KiCAD stepup tools enabled, type the following command on your console;

$ freecad kicad-StepUp-tools.FCMacro

Make sure the console is pointing to the directory where the FCMacro file is located. This will open up FreeCAD and if you opened it already and saw the opening screen of FreeCAD, you’d notice a whole new toolbar is added.

Here you can see many tools related to import and export step files and 3D models from outside libraries and folders. Each tool is specific;

  • Load-kicad-footprint:

This tool is useful to generate a step file for an individual PCB component, say a resistor into a step file.

  • Export-to-kicad:

There are instances where when we design a custom foot print, and KiCAD doesn’t have the 3D model. In such a case we can export such a model to KiCAD

  • Load-kicad:

This is the tool we are using to export PSLab PCB board to step format. Before we move on to this tool there is one last configuration we have to do. FreeCAD doesn’t know where we have put KiCAD 3D models. This library simply transforms the available 3D models in KiCAD into step files and build the final output combining all of them together as in the featured image of this blog post. To setup the 3D model path, in KiCAD, there is a path configuration option. Copy the path under “KISYS3DMOD”.

Figure 2: Path Configuration dialog box in KiCAD

And paste it into the ini file named “ksu-config.ini” which you can find in home folder.

Figure 3: Place to add 3D model path in ksu-config.ini file

Once that is done, click on the Load-KiCAD tool icon and browse to the repository where the PSLab hardware files are located at. Open the board file and FreeCAD will generate part by part and finally output the complete design. Now we can export the design in plenty of formats such as steps, stl another similar file format and many more.



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Use Travis to extract testing APKs for PSLab

Travis CI is a popular continuous integration tool built to test software and deployments done at GitHub repositories. They provide free plans to open source projects. PSLab Android is using Travis to ensure that all the pull requests made and the merges are build-bug frees. Travis can do this pretty well, but that is not all it can do. It’s a powerful tool and we can think of it as a virtual machine (with high specs) installed in a server for us to utilize efficiently.

There was a need in PSLab development that, if we want to test the functionalities of code at a branch. To fulfil this need, one has to download the branch to his local machine and use gradle to build a new apk. This is a tedious task, not to mention reviewers, even developers wouldn’t like to do this.

If the apk files were generated and in a branch, we can simply download them using a console command.

$ wget<repository>/<branch>/<file with extension>


With the help of Travis integration, we can create a simple script to generate these apks for us. This is done as follows;

Once the Travis build is complete for a triggering event such as a pull request, it will run it’s “after_success” block. We are going to execute a script at this point. Simply add the following code snippet.

  - 'if [ "$TRAVIS_PULL_REQUEST" == "false" ]; then bash <script-name>.sh; fi'


This will run the script you have mentioned using bash. Here we will have the following code snippets in the specified bash script.

First of all we have to define the branch we want to build. This can be done using a variable assignment.



Once the build is complete, there will be new folders in the virtual machine. One of them is the app folder. Inside this folder contains the build folder where all the apk files are generated. So the next step is to copy these apk files to a place of our preference. I am using a folder named apk to make it much sense.

cd apk
\cp -r ../app/build/outputs/apk/*/**.apk .
\cp -r ../app/build/outputs/apk/debug/output.json debug-output.json
\cp -r ../app/build/outputs/apk/release/output.json release-output.json
\cp -r ../ .


Usually, the build folder has following apk files

  • app-debug.apk
  • app-release-unsigned.apk
  • app-release.apk

Release apks are usually signed with a key and it would cause issues while installation. So we have to filter out the debug apk that we usually use for debugging and get it as the output apk. This involves simple file handling operations in Linux and a bit of git.

First of all, rename the apk file so that it will be different from other files.

# Rename apks with dev prefixes
mv app-debug.apk app-dev-debug.apk


Then add and commit them to a specific branch where we want the output from.

git add -A
git commit -am "Travis build pushed [development]"
git push origin apk --force --quiet> /dev/null


Once it is all done, you will have a branch created and updated with the apk files you have defined.


Figure 1: UI of pslab-android apk branch



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I2C communication in PSLab Android

PSLab device is a compact electronic device with a variety of features. One of them is the ability to integrate sensors and get readings from them. One might think that why they should use PSLab device to get sensor readings and they can use some other hardware like Arduino. In those devices, user has to create firmware and need to know how to interface them with correct sampling rates and settings. But with PSLab, they all come in the whole package. User just have to plug the device to his phone and the sensor to device and he’s ready.

The idea of this blog post is to show how this sophisticated process is actually happening. Before that, let me give you a basic introduction on how I2C communication protocol works. I2C protocol is superior to UART and SPI protocols as they are device limited or requires many wires. But with I2C, you can literally connect thousands of sensors with just 4 wires. These wires are labeled as follows;

  • VCC – Power line
  • GND – Ground line
  • SDA – Data line
  • SCL – Signal clock

It is required that the SDA and SCL lines need to be connected to VCC line using two pull up resistors. But that’s just hardware. Let’s move on to learn how I2C communicate.

Here there is this communicating concept called master and slave. To start communication, master issues a global signal like a broadcast to all the devices connected to SCL and SDA lines. This signal contains the address of the slave, master needs to address and get data from. If the slave received this call to him, he will pull down the SDA line to signal the master that he heard him and ready to communicate with him. Here communication means reading or writing data. Then the communication happens and the link between master and slave breaks giving opportunity to other masters and slaves.

One might think this is a slow process. But these signals are transmitted at high frequencies. In PSLab it is at 100 kHz and that is one millisecond.

PSLab library has a special class to handle I2C communication. That is

public class I2C {/**/}


Once this class is initiated, one has to call the start function to start communication. This method requires the address we wish to communicate with and the mode of operation stating if it is a read or write operation

public int start(int address, int rw) throws IOException {
   packetHandler.sendByte((address << 1) | rw & 0xff);
   return (packetHandler.getAcknowledgement() >> 4);


Once the address is sent out, protocol requires us to stop and wait for acknowledgement.

public void wait() throws IOException {


If there are no congestion in the lines such as reading from multiple devices, the acknowledgement will be instantaneous. Once that is complete, we can start communication either byte-wise or bulk-wise

public int send(int data) throws IOException {
   return (packetHandler.getAcknowledgement() >> 4);


As an example, reading sensor values at a given interval can be done using the following method call.

public ArrayList<Byte> read(int length) throws IOException {
   ArrayList<Byte> data = new ArrayList<>();
   for (int i = 0; i < length - 1; i++) {
   return data;


Once we get the data bundle, either we can save them or display in a graph whatever the way it’s convenient.



Continue ReadingI2C communication in PSLab Android

Adding Data Point Markers to OSM

PSLab Android app supports multiple sensors external and internal. Users can view sensor readings and record them into a csv file for later use. They can also have their location embedded into the data set they are recording. Saving the location related to each sensor reading is important. Say in a school experiment teacher can ask the student to measure dust particle concentration in city and inside his house. If the data set didn’t have any reference to the location where it was recorded, it is just incomplete. To facilitate this feature, we have enabled location in data recordings.

Enabling location is just not enough. User should be able to view the locations. Probably on a map itself would be a good idea. With the use of Open Street Maps, we can add markers to specific locations. These markers can be used as a point to show on map where a specific data set had been recorded by the user. This can be implemented further to add additional features such as standardized labels to view which data set is at which location etc.

Figure 1: Markers on Map

Unlike Google Maps API, in Open Street Maps there is no direct implementation to add a marker. But it is not a hard implementation either. We can simply create a class extending map overlays and use that as a base to add markers.

We can start by creating a new class that extends ItemizedOverlay<OverlayItem> class as follows. In this class, it would be wise to have an array list full of markers we are using in the map to modularize the whole markers related tasks into this one class rather than implementing in every place where map is used.

public class MapOverlay extends ItemizedOverlay<OverlayItem> {

    private ArrayList<OverlayItem> overlayItemList = new   ArrayList<OverlayItem>();



Once the class is initiated, have the following methods implemented. The following method will add markers to the array list we have created at the beginning.

public void addItem(GeoPoint p, String title, String snippet){
  OverlayItem newItem = new OverlayItem(title, snippet, p);


This method will be used to handle focusing events related to map markers.

public boolean onSnapToItem(int arg0, int arg1, Point arg2, IMapView arg3){
  return false;


Following method is used by the map itself to generate markers from the marker list.

protected OverlayItem createItem(int arg0) {
  return overlayItemList.get(arg0);


This method is an overriden method we will have to include in the class body.

public int size() {
  return overlayItemList.size();


Once the overlay class is completed, we can move on to actual implementation of a map on Openstreetmap view.

From the main activity where the map is viewed, initiate the marker overlay class and pass the drawable that needs to be shown as the marker to the class constructor as follows:

MapOverlay mapoverlay = null;
Drawable marker=getResources().getDrawable(android.R.drawable.map_hand);
int markerWidth = marker.getIntrinsicWidth();
int markerHeight = marker.getIntrinsicHeight();
marker.setBounds(0, markerHeight, markerWidth, 0);

ResourceProxy resourceProxy = new DefaultResourceProxyImpl(getApplicationContext());
mapoverlay = new MapOverlay(marker, resourceProxy);
GeoPoint point = new GeoPoint(55.75, 37.616667);
mapoverlay.addItem(point, "Russia", "Russia");


We can add as many GeoPoints as we want to markers list and they all will be displayed on the map like this;

Figure 2: Final Output of Markers


Continue ReadingAdding Data Point Markers to OSM

Adding Open Street Maps to PSLab Android

PSLab Android app is an open source app that uses fully open libraries and tools so that the community can use all it’s features without any compromises related to pricing or feature constraints. This will brings us to the topic how to implement a map feature in PSLab Android app without using proprietary tools and libraries. This is really important as now the app is available on Fdroid and they don’t allow apps to have proprietary tools in them if they are published there. In other words, it simply says we cannot use Google Maps APIs no matter how powerful they are in usage.

There is a workaround and that is using Open Street Maps (OSM). OSM is an open source project which is supported by a number of developers all around the globe to develop an open source alternative to Google Maps. It supports plenty of features we need in PSLab Android app as well. Starting from displaying a high resolution map along with caching the places user has viewed, we can add markers to show data points and locations in sensor data logging implementations.

All these features can be made available once we add the following dependencies in gradle build file. Make sure to use the latest version as there will be improvements and bug fixes in each newer version

implementation "org.osmdroid:osmdroid-android:$rootProject.osmVersion"
implementation "org.osmdroid:osmdroid-mapsforge:$rootProject.mapsforgeVersion"
implementation "org.osmdroid:osmdroid-geopackage:$rootProject.geoPackageVersion"


OSM will be functional only after the following permission states were granted.

<uses-permission android:name="android.permission.ACCESS_FINE_LOCATION"/>
<uses-permission android:name="android.permission.INTERNET" />
<uses-permission android:name="android.permission.ACCESS_NETWORK_STATE"  />
<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />


In a view xml file, add the layout org.osmdroid.views.MapView to initiate the map view. There is a known issue in OSM library. That is during the initiation, if the zoom factor is set to a small value, there will be multiple instances of maps as shown in Fig 1. The solution is to have a higher zoom value when the map is loaded.

Figure 1: Multiple map tiles in OSM

Once we initialize the map view inside an activity, a zoom level can be easily set using a map controller as follows;

map = findViewById(;

IMapController mapController = map.getController();
mapController.setZoom((double) 9);
GeoPoint startPoint = new GeoPoint(0.00, 0.00);


After successfully implementing the map view, we can develop the business logic to add markers and descriptions to improve the usability of OS Maps. They will be available in the upcoming blog posts.



Continue ReadingAdding Open Street Maps to PSLab Android

Capturing Position Data with PSLab Android App

PSLab Android app by FOSSASIA can be used to visualize different waveforms, signal levels and patterns. Many of them involve logging data from different instruments. These data sets can be unique and the user might want them to be specific to a location or a time. To facilitate this feature, PSLab Android app offers a feature to save user’s current location along with the data points.

This implementation can be done in two ways. One is to use Google Maps APIs and the other one is to use LocationManager classes provided by Android itself. The first one is more on to proprietary libraries and it will give errors when used in an open source publishing platform like Fdroid as they require all the libraries used in an app to be open. So we have to go with the latter, using LocationManager classes.

As first step, we will have to request permission from the user to allow the app access his current location. This can be easily done by adding a permission tag in the Manifest.xml file.

<uses-permission android:name="android.permission.ACCESS_FINE_LOCATION" />


Since we are not using Google Maps APIs, capturing the current location will take a little while and that can be considered as the only downfall of using this method. We have to constantly check for a location change to capture the data related to current location. This can be easily done by attaching a LocationListener as it will do the very thing for us.

private LocationListener locationListener = new LocationListener() {
   public void onLocationChanged(Location location) {
       locationAvailable = true;

   public void onStatusChanged(String s, int i, Bundle bundle) {/**/}

   public void onProviderEnabled(String s) {/**/}

   public void onProviderDisabled(String s) {
       // TODO: Handle GPS turned on/off situations


In case if the user has turned off GPS in his device, this method wouldn’t work. We will have to request him to turn the feature on using a simple dialog box or a bottom sheet dialog.

We can also customize how frequent the locationlistener should check for a location using another class named LocationManager. This class can be instantiated as follows:

locationManager = (LocationManager) getSystemService(Context.LOCATION_SERVICE);


Then we can easily set the time interval using requestLocationUpdates method. Here I have requested location updates in every one second. That is a quite reasonable rate.

locationManager.requestLocationUpdates(provider, 1000, 1, locationListener);


Once we have set all this up, we can capture the current location assuming that the user has turned on the GPS option from his device settings and the LocationManager class has a new location as we checked earlier.

if (locationAvailable) {
   Location location = locationManager.getLastKnownLocation(LocationManager.GPS_PROVIDER);


Each location will contain details related to its position such as latitudes and longitudes. We can log these data using the CSVLogger class implementation in PSLab Android app and let user have this option while doing his experiments.


  1. An open source implementation :

Google Maps:

Continue ReadingCapturing Position Data with PSLab Android App

Setting up environment to build PSLab Android app using Fdroid Build

Fdroid is a place for open source enthusiasts and developers to host their Free and Open Source Software (FOSS) for free and get more people onboard into their community. In order to host an app in their repository, one has to go through a several steps of builds and tests. This is to ensure that the software provided by them are as quality and safe as they can ever be. They are not allowing proprietary libraries or tools to integrate into any app or they will  be published outside the Fdroid main repository (fdroid-data) so that the users will know what they are downloading.

In a normal Linux computer where we are developing Android apps and have setup Android Studio will not be able to run the build command using:

$ fdroid build -v -l org.fossasia.pslab

The reason behind this is that we have not installed gradle and build tools required by the “fdroid build” because they are not useful in our day today activities for standalone activities. First thing we need to do is, install gradle separately. This will include adding gradle to $PATH as well.

Download the latest gradle version zip file or the version your project is using with the following command. In PSLab Android app, we are using 4.5.1 version and the snippet below include that version.

$ wget

Next step is to install this in a local folder. We can select any path we want, but /opt/ folder is generally used in such scenarios.

sudo mkdir /opt/gradle
sudo unzip -d /opt/gradle

Then we can add gradle to our $PATH variable using the following command:

$ export PATH=$PATH:/opt/gradle/gradle-4.5.1/bin

Now we are all set with gradle settings. Next step is to verify that the fdroid server is properly configured and up to date. When you run the build command after setting up the gradle in PC, it will throw an error similar to “failed to find any output apks”. This causes if the installed fdroid server version is old.

Fdroid server is running on python 3 and it will require some additional libraries pre-installed to properly function.

$ sudo apt-get install vagrant virtualbox git python3-certifi python3-libvirt python3-requestbuilder python3-yaml python3-clint python3-vagrant python3-paramiko python3-pyasn1 python3-pyasn1-modules

Once these libraries are installed, remove the previous instance of fdroidserver by using the following command:

$ sudo apt-get remove fdroidserver

Then we can reinstall the latest version of fdroid server from git using the following command:

$ git clone
export PATH="$PATH:$PWD/fdroidserver"

Now we are all set to do a brand new lint build on our PC to make our app ready to be published in Fdroid repository!


  1. Install gradle :
  2. Gradle versions :
  3. Setting up Fdroid-server :

Installing fdroidserver :

Continue ReadingSetting up environment to build PSLab Android app using Fdroid Build

Using external UART modules to debug PSLab operations

Pocket Science Lab by FOSSASIA is a compact tool that can be used for circuit analytics and debugging. To make things more interesting, this device can be accessed via the user interface using an Android app or also a desktop app. Both these apps use the UART protocol (Universal Asynchronous Receiver-Transmitter) to transmit commands to the PSLab device from mobile phone or PC and receive commands vice versa. The peculiar thing about hardware is that the developer cannot simply log data just like developing and debugging a software program. He needs some kind of an external mechanism or a tool to visualize those data packets travelling through the wires.

Figure 1: UART Interface in PSLab

PSLab has a UART interface extracted out simply for this reason and also to connect external sensors that use the UART protocol. With this, a developer who is debugging any of the Android app or the desktop app can view the command and data packets transmitted between the device and the user end application.

This  requires some additional components. UART interface has two communication related pins: Rx(Receiver) and Tx(Transmitter). We will need to monitor both these pin signals for input and output data packets. It should be kept in mind that PSLab is using 3.3V signals. This voltage level is important to mention here because if someone uses 5V signals on these pins, it will damage the main IC. There are FTDI modules available in market. FTDI stands for Future Technology Devices International which is a company name and their main product is this USB transceiver chip. These chips play a major role in electronic industry due to product reliability and multiple voltage support. PSLab uses 3.3V USB Tx Rx pins and modules other than FTDI wouldn’t support it.

Figure 2: FTDI Module from SparkFun

The module shown in Fig.2 is a FTDI module which you can simply plug in to computer and have a serial monitor interface. There are cheaper versions in shopping websites like eBay or Alibaba and they will also work fine. Both Tx and Rx pins will require two of these modules and connectivity is as follows;

PSLab [Rx Pin] → FTDI Module 1 [Rx Pin]
PSLab [Tx Pin] → FTDI Module 2 [Rx Pin]

This might look strange because everywhere we see a UART module is connected Rx → Tx and Tx → Rx. Notice that our idea is to monitor data packets. Not communicate with PSLab device directly. We want to see if our mobile phone Android app is sending correct commands to PSLab device or not and if PSLab device is transmitting back the expected result or not. This method helped a lot when debugging resistance measurement application in PSLab Android app.

Monitoring these UART data packets can be done simply using a serial monitor. You can either download and install some already built serial monitors or you can simply write a python script as follows which does the trick.

import serial

ser = serial.Serial(

while 1 :
    data = ''
    while ser.inWaiting() > 0:
        data +=

    if data != '':
        print ">>" + data

Once you are getting commands and responses, it will look like debugging a software using console.


  1. PySerial Library :
  2. UART Protocol :
  3. FTDI :

Continue ReadingUsing external UART modules to debug PSLab operations

Building PSLab Android app with Fdroid

Fdroid is a place for open source enthusiasts and developers to host their Free and Open Source Software (FOSS) for free and get more people onboard into their community. Hosting an app in Fdroid is not a fairly easy process just like hosting one in Google Play. We need to perform a set of build checks prior to making a merge request (which is similar to pull request in GitHub) in the fdroid-data GitLab repository. PSLab Android app by FOSSASIA has undergone through all these checks and tests and now ready to be published.

Setting up the fdroid-server and fdroid-data repositories is one thing. Building our app using the tools provided by fdroid is another thing. It will involve quite a few steps to get started. Fdroid requires all the apps need to be built using:

$ fdroid build -v -l org.fossasia.pslab


This will output a set of logs which tell us what went wrong in the builds. The usual one in a first time app is obviously the build is not taking place at all. The reason is our metadata file needs to be changed to initiate a build.

    commit=<commit which has the build mentioned in versioncode>


When a metadata file is initially created, this build is disabled by default and commit is set to “?”. We need to fill in those blanks. Once completed, it will look like the snippet above. There can be many blocks of “Build” can be added to the end of metadata file as we are advancing and upgrading through the app. As an example, the latest PSLab Android app has the following metadata “Build” block:



In case of an update, add another “Build” block and mention the version you want to appear on the Fdroid repository as follows:

Auto Update Mode:Version v%v
Update Check Mode:Tags
Current Version:1.1.5
Current Version Code:7


Once it is all filled, run the build command once again. If you have properly set the environment in your local PC, build will end successfully assuming there were no Java or any other language syntax errors.

It is worth to mention few other facts which are common to Android software projects. Usually the source code is packed in a folder named “app” inside the repository and this is the common scenario if Android Studio builds up the project from scratch. If this “app” folder is one level below the root, that is “android/app”, the build instructions shown above will throw an error as it cannot find the project files.

The reason behind this is we have mentioned “subdir=app” in the metadata file. Change this to “subdir=android/app” and run the build again. The idea is to direct the build to find where the project files are.

Apart from that, the commit can be represented by a tag instead of a long commit hash. As an example, if we had merge commits in PSLab labeled as “v.<versioncode>”, we can simply use “commit=v.1.1.5” instead of the hash code. It is just a matter of readability.

Happy Coding!


  1. Metadata :
  2. PSLab Android app Fdroid :

Continue ReadingBuilding PSLab Android app with Fdroid