DIY Project: Create a Tracking App Part 1

The discussion about mobile phone location tracking of people and tracing back to potential transmissions is one of the hot topics at moment. In Germany we could expect an app officially being launched towards end of April. I attempt to go through the technical considerations by myself. A hands-on coding excursion with Android to use Bluetooth to scan nearby devices and exchange data with them.

The most basic requirements for a tracking app to be successful:

  • A person need to posses and carry a switched-on mobile (smart) phone.
  • The phone must have GPS and Bluetooth feature and both being enabled.
  • The location need to be recorded as fine-grain as possible. Use of GPS is mandatory, the celldata is way too coarse (see previous post). Though we might consider to skip location completely and rely on the paring of fingerprints solely, depending on the approach.
  • Approach 1: We record the location and time of a device (aka person) and transmit the data to a server immediately and try to match data with other devices on the server. Hard to implement in a GDPR compliant way and users most likely wont buy in.
  • Approach 2: We record the location and time on the device and any digital fingerprint of devices nearby. This anonymous pairings we transmit to the server. Once one device is flagged as infected, the server can flag any other device “paired” previously and push (or pull) a notification to the impacted devices. This way most data remains on the device. A more GDPR compliant way of solving this. Some details need to be worked out though in regards of matching and informing the respective user.
  • Approah 3: Even better if we could rely solely on the fingerprint of nearby devices and the timestamp.
  • The more user we have in the system, the bigger the impact and the chance to trace and inform and potentially stop spreading further.
  • We must have a mean to report an infection and inform affected other users (and still stay within the boundaries of GDPR).

Before walking into the Bluetooth space, some facts:

  • The not-for-profit organisation Bluetooth Special Interest Group (SIG) is responsible for thedevelopment of Bluetooth standards since 1998. (Wikipedia)
  • There is a regular update to the Bluetooth standard, by January this year SIG released version 5.2. It takes time for the hardware manufacturers to adopt the newer standards.
  • We need to distinguish between Bluetooth Classic and Bluetooth Low Energy (BLE). BLE was introduced with version 4 and supported by Android 4.3.
  • Bluetooth Classic is designed for continous short distance two-way data transfer at a speed of up to 5 Mbps (2.1 Mbps with Bluetooth 4). BLE was made to work with other devices at a lower speed and greater distance.
  • Android 8.0 onwards support Bluetooth 5 which is a significant milestone for Bluetooth technology in terms of range, speed and power consumption.
  • It is not possible to programmatically check the supported Bluetooth version in Android, though you can check if BLE is available on the phone.
  • The MAC address of the Bluetooth adapter is fixed and can’t be changed (except for rooted phones). This way it becomes the digital fingerprint.

Are we running out of MAC addresses ?

MAC addresses (used by ethernet, wifi and bluetooth adapters), as per IEEE 802 definition, have 48 bits (6×8 bytes).
Sample AC:07:5F:F8:2F:44
This would result in some 281 trillion (2^48) possible combinations, but the first 3 bytes are reserved to identify the hardware manufacturer. For above sample AC:07:5F it is Huawei. The remaining 3 bytes are used as unique identifier, resulting in only 16 million (2^24) unique devices. Quite likely this number would be used up more or less quickly by a big manufacturer. In reality we also could have 16 million unique manufacturer ID’s, Huawei owns about 600 of these, giving a total of currently 10 billion devices. We need to consider this numbers when we talk about unique fingerprints (MAC), though it is unlikely at a country level to have duplicates. In Germany we have ~83 million citizens and about 142 million mobile phones from different manufacturers, small chance that two persons (actually using the tracking app) will have the same MAC address.
You can check/download the identifiers here.

Lets get started with some coding..

Basic: Android to list paired devices

Before we jump into the more complex discovering, pairing and communication between devices (using threads,) we start with the basics. Lets enumerate the paired devices.

Required Permission

At minimum access to coarse location (since Android 6) is needed since Bluetooth can be used to derive the users location. I skip the code to request the permission, only location access being a critical permission. (complete code will be pusblished at the end).

<uses-permission android:name="android.permission.BLUETOOTH"/>
<uses-permission android:name="android.permission.BLUETOOTH_ADMIN"/>
<uses-permission android:name="android.permission.ACCESS_COARSE_LOCATION" />

Check and Activate Bluetooth Adapter

public class MainActivity extends AppCompatActivity {

    private static final String TAG = "bt.MainActivity";
    private BluetoothAdapter bAdapter = BluetoothAdapter.getDefaultAdapter();

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);

        checkAndRequestPermissions();

        if(bAdapter==null){
            Log.i(TAG,"Bluetooth not supported.");
        } else {
            Log.i(TAG,"Bluetooth supported.");
			
	        if(bAdapter.isEnabled()){
				Log.i(TAG,"Bluetooth enabled.");
				if (!getPackageManager().hasSystemFeature(PackageManager.FEATURE_BLUETOOTH_LE))
					Log.i(TAG, "BLE not supported.");
				else
					Log.i(TAG, "BLE supported.");
			} else {
				Log.i(TAG,"Bluetooth not enabled.");
				startActivityForResult(new Intent(BluetoothAdapter.ACTION_REQUEST_ENABLE),1);
			}
        }
    }
..

List existing pairings

Quite simple to iterate through the existing pairings and list their name and MAC Address

private void showPairedDevices(){
	Set<BluetoothDevice> pairedDevices = bAdapter.getBondedDevices();
	if (pairedDevices.size() > 0) {
		for (BluetoothDevice device : pairedDevices) {
			String deviceName = device.getName();
			String deviceMAC = device.getAddress();
			Log.i(TAG,"Device: " + deviceName + "," + deviceMAC);
		}
	}
}
I/bt.MainActivity: Device: HUAWEI P20,AC:07:5F:XX:XX:XX
I/bt.MainActivity: Device: moto x4,0C:CB:85:XX:XX:XX

In the next post we will discover nearby Bluetooth devices and setting up a communication channel between two devices.

Stay tuned for more tracking..

References

Image by Brian Merrill from Pixabay

Daily Tech Observations

As the pandemic crisis continues, more discussion, data exchange and research is happening and progressing in the digital space. I wont mention the massive increase of security threads here (reference info at Trendmicro), but rather look at the non-malicious activities.

PEPP-PT

The PEPP-PT (Pan-European Privacy-Preserving Proximity Tracing) project around a number of prominent research institutes across Europe is working on a proximity-based solution utilising BLE technology embedded in mobile phones. It will be in line with GDPR regulations and to be used on a voluntary base. It is supposed to track and report your whereabouts adn nearby other app users to a server anonymously only, and inform you when you have been close to an infected person, all that without using personal information, which is the key concern of many parties. A key element for the success of such a solution is the penetration factor. It need to build up a database with a significant number of users and traces. Instead of releasing yet another app, they try to piggyback into existing apps, such as NINA (an app to publish and warn about local dangerous incidents in Germany). It has not been published yet, I assume the technical field test was successful is reported, still they have to sort out the communication channels in the case of an infected user.

COVID-19 Apps

There are no new apps in the Google Playstore since my last post, though I have to correct the app I mentioned previously, TraceTogether, only appears for Singapore based accounts. In the German Playstore we see two apps, the app “COVID-19” transmits the status of a COVID test to the respective user, only reducing the need to physically visit a place to retrieve the results. The other app, Coronika, tries to assist individuals to trace their locations and contacts.

Google to hand over anonymous location data

Google and the other big players are in active talks in various countries with the respective authorities about releasing data, either aggregated or anonymous or both. Depends very much on the local regulations. In the context of stopping the pandemy this would provide valuable insights. Aggregated data can help to identify streams of persons or hotspots of too many people in the same area or similar. If anonymization alone is good enough to protect personal data, I would question, the trace that everyone leaves with an Android phone (location services enabled) would easily allow to identify an individual or a small group, you just look at regularly visited places to identify someone’s home or office etc.

You know you can not only see your traces in Google Maps but also export the data (as well delete it permanently if you want) with the Take-Out feature?

Your Timeline – Google Maps
Take Out – Personal Google Maps Data

You are looking for some well formatted data to play with ? Download your own location data and have some hands-on datascience exercise. Easy to request and download, all nicely packaged in self-explaining JSON formatted monthly files.

Stay tuned and safe !

DIY Tracking and Tracing

In the current situation to trace people with an infectious disease is key and quite some manual Sherlock Holmes style work to find the traces of a patient in a certain region and who her/she/it met and potentially infected.

Technology could be at help here. GDPR is protecting personal data and the wherabout’s of a person falls into this category, but GDPR describes the current situation.

Reference (eur-lex.europa.eu)

  • Article 46
    The processing of personal data should also be regarded to be lawful where it is necessary to protect an interest which is essential for the life of the data subject or that of another natural person. Processing of personal data based on the vital interest of another natural person should in principle take place only where the processing cannot be manifestly based on another legal basis. Some types of processing may serve both important grounds of public interest and the vital interests of the data subject as for instance when processing is necessary for humanitarian purposes, including for monitoring epidemics and their spread or in situations of humanitarian emergencies, in particular in situations of natural and man-made disasters.
  • Article 6 (d) – Lawfulness of processing
    processing is necessary in order to protect the vital interests of the data subject or of another natural person;

Some data (geolocation data) has been provided by German Telekom of its mobile customers to the RKI for people movement research, no individual identifiable data though. Please note it is a small subset and anonymized, there are quite a number of social meida posts and comments informing wrongly.

This is the right thing to do in this situation, the data exist and can speed up the containment. Though it need to be ensured that the date is not used for other purposes or beyond the crisis (as long personal data is stored). Unfortunately this could serve as reference for more (meta-)data harvesting by authorities in future without immediate purpose.

Google and Apple would be in the best position to track down individuals and find the cross roads of tracks and potential infection clusters etc. Most of the people have Android or iOS phones and even not all of them have GPS enabled, the devices still log into the cell towers.

In an earlier project I created an app that is recording the cell tower information while you are on the move. (The app is not in the playstore due to GDPR). Using the recorded cell tower info and matching with the opencellid celltower geolocations I created a map of my own movements in Python using the Folium library.

The geolocation data of the individual towers coming from opencellid is not always 100% accurate on the spot but certainly good enough to to estimate the track. Individual points are too coarse, in this sample dataset, created when I was driving along the highway (red line) the phone connected to various celltowers along the way, even to towers further away from my route. Conclusion: Only the complete dataset can help datascientists to estimate my track and potential contact points with other people.

It will be much harder to track down an individual in a urban environment with 100’s of cell towers, see OpenCellid sample for Frankfurt. I would guess that is the reason RKI only tries to identify streams of people between places.

In China or Israel the technology is already used to pin down individuals. I leave it to you to comment. In Europe, in the interest of personal data protection, an innovative approach would be to (continue to) trace (yourself) but allow the individual to be alerted or verify against an open dataset to be informed about clusters etc. Though, as mentioned before, at some stage the authoroities should make use of the data as long it matches laws and is purposeful.

Further Reading: Wired Magazine

Android and Speech Recognition (2)

In part 2 about speech recognition we do the reverse, instead od openly calling Google Search or integrating the speech recognition intent (prominently showing the Google logo/splash screen) we call the same recognition method in a headless way, making the experience more seamless. To make it menaingful in the aviation context, we will request BA flight information (through IAG Webservices) through audio.

(Please note, the below code snippets are incomplete and only highlighting the key methods, the complete sourcecode you find at Github, see link at the end of the post.)

Calling the speech recognizer

sr = SpeechRecognizer.createSpeechRecognizer(this);
sr.setRecognitionListener(new listener());

Intent intent = new Intent(RecognizerIntent.ACTION_RECOGNIZE_SPEECH);
intent.putExtra(RecognizerIntent.EXTRA_LANGUAGE_MODEL,RecognizerIntent.LANGUAGE_MODEL_FREE_FORM);

intent.putExtra(RecognizerIntent.EXTRA_MAX_RESULTS,10);
sr.startListening(intent);

Implementation of the speech recognition class

class listener implements RecognitionListener
{
	public void onResults(Bundle results)
	{
		ArrayList data = results.getStringArrayList(SpeechRecognizer.RESULTS_RECOGNITION);
		StringBuffer result = new StringBuffer();
		for (int i = 0; i < data.size(); i++)
		{
			Log.d(TAG, "result " + data.get(i));
			result.append(i + ":" + data.get(i) + "\n");
		}
		textViewResult.setText(result);
	}
}

With this approach we get the usual 2 acoustic sounds signalling the begin of the speech recognition phase and the end (after a short time out).

If we need to create a hands-free user experience, avoiding the user to touch the screen, we will make use of the play or call button that you usually find on headsets. We can capture the event that gets fired when pressing these hardware buttons too.

Capture hardware button events

@Override
public boolean onKeyDown(int keyCode, KeyEvent event) {
	Log.v(TAG, event.toString());
	if(keyCode == KeyEvent.KEYCODE_HEADSETHOOK || keyCode==KeyEvent.KEYCODE_MEDIA_PLAY){
		Log.i(TAG, event.toString());
		listenHeadless();
		return true;
	}
	return super.onKeyDown(keyCode, event);
}

Text-To-Speech

The missing link to the hands-in-the-pocket experience is the audio response by the app through our headset. We will add the standard Android Text-To-Speech (TTS) implementation.

textToSpeech = new TextToSpeech(getApplicationContext(),null);
...
textToSpeech.setPitch(1.0f);
textToSpeech.setSpeechRate(0.80f);
int speechStatus = textToSpeech.speak(textToSpeak, TextToSpeech.QUEUE_FLUSH, null, null);

if (speechStatus == TextToSpeech.ERROR) {
	Log.e("TTS", "Error in converting Text to Speech!");
}

A remark about text to speech engines, the default speech engines are not very pleasant to listen to, for an application speaking to users repeatedly or over a long time I recommend to look for commercial TTS engines (eg. Nuance, Cereproc,..). Check out the sound samples at the end of the post. TTS engines are usually produced per language. For a multi-lingual application you need to cater for every language.

Adding Webservice

To make this sample app more business relevant (airport related), we use the spoken text to request for flight data. For this purpose I will reuse the BA webservice call that was used in an earlier post. Do not forget to add permission for internet access to our app.

We straight away hit the first challenge here, we receive a text version of the spoken request. As we wont implement a semantic or contextual speech recognition we have to apply regular expression in order to attempt to find the key elements such as fligh number and date. The human requests the information in dozens of possible variations plus the varying interpretations by the speech-to-text engine, some listed below in the regex test screen (link).
To keep it simple we allow the user to request flight info for one particular British Airways flight number between yesterday and tomorrow. We will look for a 2 character 1..4 number combination in the text, plus using the keyword yesterday/tomorrow/today (or no statement representing today).

regular expression to identify flight number

To push the scenario further we can let the TTS engine read the response to the user, after selecting relevant information from the JSON formatted WS response.

Soundsamples

Sourcecode

References

Android and Speech Recognition (1)

Speech recognition, the translation of spoken language to text, as an interdisciplinary subfield of computational linguistics finds its roots and first steps as early as 1952 with Audrey, a device that could recognize numerical digits, created in the Bell Labs, and the 1960’s when IBM developed the Shoebox, a machine that could recognize and arithmetic and sent the result to an attache printer (I highly recommend the video in the IBM archive). A lot of research happened in the field by IBM, DARPA, CMU but it would take more than 2 decades before products hit the shelf to be used by a wider audience. In 1981 it took up to 100 minutes to decode 30 seconds of spoken text (see the Sarasota Business Journal article).

The first time I started working with commerical products in this field was in the mid-end 1990’s with Dragon Dictate and IBM Via Voice, the engine had to be trained for a specific speaker in a 30min+ training session. Once you had passed the training you could achieve decent results when talking to Word using a plugin, the experience was not quite real-time, as you saw the text magically being typed few seconds after you said it. The product also allowed saying commands to control Windows to open or close windows, starting applications and similar simple tasks.

Fast forward to today, and you find speech recognition in quite some consumer applications, most prominently in the assistance area with products like Amazon Alexa, Apple Siri, Google Assistant dominating the market. If we look at the Gartner Hype Cycle 2019, reaching the Plateau of Productivity “Speech Recognition is less than two years to mainstream adoption and is predicted to deliver the most significant transformational benefits of all technologies on the Hype Cycle.” [Quote].

For many simple use-cases or applications a conversational model nor a semantic interpretation is required, we can focus on recognizing keywords. I will discuss pro and cons later in this series.

In this first part I am demonstrating how simple it is to integrate speech recognition in Android Apps. To put this into an aviation context, lets pretend we search for a flight by calling the flightnumber.

We can start with an empty activity skeleton application in Android Studio.
Required is the permission to record audio.

String requiredPermission = Manifest.permission.RECORD_AUDIO;

We need to call the Android Speech by calling the respective intent.

import android.speech.RecognizerIntent;
..
Intent i = new Intent(RecognizerIntent.ACTION_RECOGNIZE_SPEECH);
i.putExtra(RecognizerIntent.EXTRA_LANGUAGE_MODEL, RecognizerIntent.LANGUAGE_MODEL_FREE_FORM);
i.putExtra(RecognizerIntent.EXTRA_LANGUAGE, Locale.getDefault());
i.putExtra(RecognizerIntent.EXTRA_PROMPT, "Say something");
..
startActivityForResult(i, 100);

Handle the result of the activity response

@Override
protected void onActivityResult(int requestCode, int resultCode, Intent data) {
	super.onActivityResult(requestCode, resultCode, data);
	if (requestCode == 100) {
		if (resultCode == RESULT_OK && null != data) {
			ArrayList res = data.getStringArrayListExtra(RecognizerIntent.EXTRA_RESULTS);
			Log.d("TTS", res.toString());
		}
	}
}
The default Google style audio input.
Result for “Flight LH 778”

The longer the statement we speak the more results we get back in the text array.

Result for “Departure Information for flight LH 778”

We could defer the search directly into the Google Search by changing the code to another action. Here I have to ask a complete question and cannot just say the flight number. The request is diverted to the web search and wont return to the application. So this is more completeness but not helpful for our use-case.

Intent i = new Intent(RecognizerIntent.ACTION_WEB_SEARCH);
i.putExtra(RecognizerIntent.EXTRA_LANGUAGE_MODEL, RecognizerIntent.LANGUAGE_MODEL_WEB_SEARCH);
i.putExtra(RecognizerIntent.EXTRA_LANGUAGE, Locale.getDefault());

In the next part we will omit the Google standard screen and read the audio input directly. We will also look at the further processing challenges, as well add speech synthesis.

Stay tuned !

METAR and TAF Webservice

ozedf

I am regularly exploring the Web Service space in the aviation domain looking for new datasets and feeds. I just stumbled upon CheckWX.com collecting weather messages from various sources and redistribute them. Thanks to Henry who is operating the page (it is not run by a company) we have access to big pool of weather messages via an API. The WS use is free, as long you wont hit the service with more than 2000 calls a day.

METAR (Meteorological Terminal Aviation Routine Weather Report) is a weather forecast in a standard message format, usually used by pilots for the pre-flight briefing. The standard defined by ICAO. The data is collected by weather observation stations.
TAF (Terminal aerodrome forecast), similar to METAR focus on aerodromes and provides a forecast for the next ~24 hours, usually every 6 hours.

You find more information in the ICAO reference “Manual of Aeronautical Meteorological Practice” or in the Airforce “Aircrew Quick Reference to the METAR and TAF Codes”.

Lets build a 5min Android app, similar to the BA Webservice client in a previous post. Thanks to the OkHttp library we get data nicely JSON formatted with a few lines of code. We just need to pass the airportcode (ICAO) to get back the current METAR info (plus some extra info about the airport).

private void callWXweatherService(String airportCode) {

	OkHttpClient httpClient = new OkHttpClient();
	String callURL = "https://api.checkwx.com/metar/" + airportCode + "/decoded";

	Request request = new Request.Builder().url(callURL)
			.addHeader("Content-Type", "application/json")
			.addHeader("X-API-KEY", "{your own API key}")
			.build();

	httpClient.newCall(request).enqueue(new Callback() {
		@Override
		public void onFailure(Call call, IOException e) {
			System.out.println("WS Call failed (1):" + e.getMessage());
		}

		@Override
		public void onResponse(Call call, Response response) {
			ResponseBody responseBody = response.body();
			if (!response.isSuccessful()) {
				final String errRep = "WS Call failed (2):" + response.toString();
			} else {
				try {
					System.out.println("Response " + response.toString());
					String str = new String(responseBody.bytes());
					final JSONObject svcresponse = new JSONObject(str);
					int spacesToIndentEachLevel = 2;
					final String prettyPrintString = svcresponse.toString(spacesToIndentEachLevel);

				} catch (Exception e) {
					e.printStackTrace();
				}
			}
		}
	});
}

The API documentation: api.checkwx.com

screenshotcheckwx

If I have time I will create a better readable gui and integrate with Google Maps.

Disclaimer: This discussion, datamodel and sourcecode or application is for study purpose solely. It does not reflect or replicate any existing commercial product. It is also not fit for or meant for production.

Please note, weather data from CheckWX is not a substitute for an official weather briefing.

Airport Simulator on Android

Dont rise your expectation to high on this topic for now. Before jumping into the serious use cases for airport process and environment simulations, as well the gamification of enterprise environments, I will review some simulator apps available on Android and PC in a rather humorous way.

I just love simulators, it is amazing to see how current 3D and physic engines running on today’s powerful and affordable hardware can execute real-time simulations that were only possible in well equipped research labs 2 decades ago. I am experimenting with various 3D engines like Unity and CryEngine, both freely available as personal or educational version or for indie games.

OK, lets have a look at simulators available for the Android mobile platform. If you search for the term simulator you will get thousands of apps, and there is a simulator for almost everything and anything you never heard of before, though the majority is plain crap and only exists because Unity makes it so easy to create a game by clicking-together some assets and adjusting some properties, almost without the need to code. Most of them are just badly made and often only serve the purpose to bombard you with ads once installed or running.

We find 100’s of car, truck, train simulation apps, plus dozens of somewhat strange apps to simulate dogs, cats, dinosaurs, Fishing, Fork Lifts, shark attacks, Miami Crime, swimming trains, flying boats and tuk tuk’s and endless more objects.

Finetuning our search towards the term airport (vehicle) simulator we still reveal dozens of results. Here we have a selection of flying, airport construction and all kind of driving around the airport tasks. The majority is made with Unity engine and the free assets, one reason why most of these apps display the same assets like cars, trucks and planes.

AirportSimulator_13

Selection of simulator apps

Most of them have in common: Well rendered icons and preview images in the play store to catch your interest, generous with requested permissions and truckloads of ads.
Surprisingly all of them have downloads of well beyond 100.000 ! Guess we don’t know the uninstall-rate though, but if you look at the user comments you know.

AirportSimulator_30

Permissions galore inclusive to run as service during startup, snooping accounts and location, downloading files.

Here some actual screenshots of the often weird gameplay or scenery I found.

AirportSimulator_9

Factory Airport ? Not much room to maneuver. Where are taxiways, positions, gates ? But 3 Towers !

AirportSimulator_28

If your are short of pushbacks, go for a regular truck as fallback !

AirportSimulator_29

Personal pickup service for MIB passengers in Area 51 ?

AirportSimulator_3

Taking off from a construction site with speed limits and stop signs. Here the tower also a bit too close to the runway maybe.

AirportSimulator_19

Disembarking Zombie passengers walking down from the service staircase to the waiting Cobus(?)

AirportSimulator_11

Driving a heavy duty tow truck between the planes parked on the grass. Nothing else to do. Maybe one of these defunc airline desert airplane parking grounds ?

AirportSimulator_25

This time we drive an airport security car in a totally static airport. Someone forgot to add tarmac lines here ?

AirportSimulator_7

This plane looks like the airport fire brigade drill setup. But in this app all planes look like this model (front part is mocking a war train from the 1930’s). Not so standard cargo handling either.

AirportSimulator_32

Interesting, floating mobile stairs supported by a RC wreck. Walls at the tarmac ?

AirportSimulator_33

Winter wonderland. Random assets like palms, lamp posts and others stuff thrown at the scene. Big Christmas trees at the end of the runway… Walls again.

AirportSimulator_34

Gives the term Greenfield Airport a more genuine meaning ! Randomly appearing zombie passengers again. Leave the boarding gate through the window ?

AirportSimulator_35

Oh yes, that is a smart way to avoid a potential IP conflict with a famous airline ! Bonus: Floating bridge and you fly with a 1:5 scale plane ?

AirportSimulator_36

Smart parking, what to say ? Smart as in stacking cars on the back of a truck or smart as in placing this thing at the end of the runway ?

Conclusion: Dont expect anything when downloading these simulators, other than uninstalling it again and potentially being spied or bombarded with ads. None of the app I tried even remotely comes close to any real operations. These are games – confirmed – nothing else.

In the next blog entry we will look at Simulator Software available for Windows PC’s.

Stay tuned.