Category Archives: project

Wifi-based trilateration on Android

404px-Sea_island_surveyTriangulation offers a way to locate yourself in space.  Cartographers in the 1600s originally used the technique to measure things like the height of the cliff, which would be too impractical to measure directly.  Later, triangulation evolved into an early navigation system when Dutch mathematician Willebrord Snell discovered three points can be used to locate a point on a map.

While triangulation uses angles to locate points, trilateration uses lateral distances.  If we know the positions of three points P1P2, and P3, as well as our distance from each of the points, r1r2, and r3; we can look at the overlapping circles formed to estimate where we are relative to the three points. We can even extend the technique to 3D, finding the intersecting region of spheres surrounding the points.

In this project, I’d like to show how we can use the Wifi signal strength, in dB, to approximate distance from a wireless access point (AP) or router.  Once we have this distance, we can create a circle surrounding an AP to show possible locations we might occupy.  In the next part of the project, I plan to show how we can use three APs to estimate our position in a plane using concepts of trilateration. (Note: I haven’t had time to implement this, but you can use this Wiki article to implement it yourself).

Trilateration using 3 access points providing a very precise position (a) and a rougher estimate (b)

Trilateration using 3 access points providing a very precise position (a) and a rougher estimate (b)

Determining distance from decibel level

There’s a useful concept in physics that lets us mathematically relate the signal level in dB to a real-world distance.  Free-space path loss (FSPL) characterizes how the wireless signal degrades over distance (following an inverse square law):

Screen Shot 2013-07-05 at 2.36.07 PM

The constant there, 92.45, varies depending on the units you’re using for other measurements (right now it’s using GHz for frequency and km for distance).  For my application I used the recommended constant -27.55, which treats frequency in MHz and distance in meters (m).  We can re-arrange the equation to solve for d, in Java:

public double calculateDistance(double levelInDb, double freqInMHz)    {
   double exp = (27.55 - (20 * Math.log10(freqInMHz)) + Math.abs(levelInDb)) / 20.0;
   return Math.pow(10.0, exp);

Now, there are few drawbacks to this rough approximation:

  1. FSPL explicitly requires “free space” for calculation, while most Wifi signals are obstructed by walls and other materials.
  2. Ideally, we will want to sample the signal strength many times (10+) to account for varying interference.

Problem (1) will be resolved in the future by using the signal-to-noise ratio to more accurately estimate (that sounds like an oxymoron) obstructions to the wifi signal.  Problem (2) can be implemented in code by sampling many times and computing the average signal level.

Using the above code along with Android’s WifiManager and ScanResult classes, I can print out our final measurements:

WifiManager wifi = (WifiManager) getSystemService(Context.WIFI_SERVICE);

registerReceiver(new BroadcastReceiver()
	public void onReceive(Context c, Intent intent) 
		results = wifi.getScanResults();
		for (ScanResult s : results)	{
			DecimalFormat df = new DecimalFormat("#.##");
			Log.d(TAG, s.BSSID + ": " + s.level + ", d: " + 
					df.format(calculateDistance((double)s.level, s.frequency)) + "m");
}, new IntentFilter(WifiManager.SCAN_RESULTS_AVAILABLE_ACTION)); 


And we can get back data that appears to be correct when moving further away from my test router (MAC address: 84:1b:5e:2c:76:f2):

[Image lost during host transition, but basically just showed how the distance increased]

Quickie: Which way does gravity point?


Everyone knows a compass always points north, and most people know it’s because of magnetic fields present on Earth’s surface.  There’s another force here on Earth directed to a central point, and that’s gravity.  Humans are quite adept at sensing gravity thanks to equilibrioception, where  fluid contained in structures in our inner ear provide feedback to help us stay balanced.

But machines, too, can detect gravity thanks to the simple accelerometer.  Already present in most smartphones today, accelerometers react to gravity with tiny springs, creating a voltage difference that we can measure and turn into meaningful units.

On Android, we can easily read the accelerometer data:

SensorManager sensorManager = (SensorManager) getSystemService(Context.SENSOR_SERVICE);
Sensor accel = sensorManager.getDefaultSensor(Sensor.TYPE_ACCELEROMETER);
sensorManager.registerListener(this, accel, SensorManager.SENSOR_DELAY_NORMAL);


public void onSensorChanged(SensorEvent event) {
	float x, y, z;
	x = event.values[0];
	y = event.values[1];
	z = event.values[2];

Using accelerometers to emulate human’s perception of gravity

I’d like to show how we can use an Android phone (even my dusty old Droid Eris) to visualize the force of gravity.  To save time, we’re only going to use two dimensions, x and y, but the technique used here can easily be extended into 3D.

Let’s represent gravity the same way students in a high school physics class would — with an arrow pointing down.  The goal would be the ability to rotate the phone (changing the x and y position), while still having that arrow point down, illustrating the direction of gravity.

The first thing we’ll need to do is convert the rectangular coordinates given to us (x and y) to a polar system (r, θ), where extracting an angle is much easier.

Thinking back to high school geometry, the inverse tangent will provide that angle directly.  Java has a built-in method, atan2(), which even gracefully handles the divide-by-zero case when x = 0. Because the image rotation I’m using is based on degrees (more on that in a moment), we can convert the radian angle to a common degree (0-360°).

double theta = Math.atan2(y, x);
double degree = ((theta * -180.0) / 3.14159) + 180;  // +180 to keep 0 on the right

That gives us the degree rotation of the phone in 2D.  We’re almost there.  To determine the degree that we would like the gravity arrow to point, we need to offset that degree, modulo 360 to keep us within the range (0-360°):

float rotateDegree = (float) ((degree + 270.0) % 360.0);

Now it’s just a matter of re-drawing the arrow image on the screen.  Android offers some fancy animation techniques, but for this quickie project, I chose to use a matrix rotation:

Matrix matrix = new Matrix();
Bitmap rotated = Bitmap.createBitmap(myImg, 0, 0, myImg.getWidth(), myImg.getHeight(),matrix, true);

With that code in place, we can finally visualize the force of gravity, at least in two dimensions:

If you are interested, you can find more educational video presentations on YouTube promoted by The Marketing Heaven.
This project was a quick one (writing this blog entry actually took longer than the code itself), but I think it’s important to show how we can figuratively “teach” a device a human trait and give them a new skill.  For instance, with a faster refresh rate and perhaps a little more accuracy, a robot can use this technique to keep itself balanced, much like humans use information from gravitational forces to stay balanced.

Github available here.

CS530 Visualization Projects

This is a collection of projects I created for CS 530: Introduction to Computer Visualization. Each project required an HTML writeup, so I figured it would be easiest to keep a collection of links here…

Project 1: First Steps with VTK

shapeimage_2To get acquainted with the Visualization Toolkit (VTK), we used bathymetry (sea depth) and topography information from NASA to visualize the earth in a few different ways. We also implemented a Sea Rise simulation that shows how land masses on Earth change as the sea level rises.

LINK to project 1

Project 2: Color Mapping

shapeimage_3This project focused on choosing the right color maps to visualize different types of data. The two types of data we looked at were MRI scans and a topographical map of the western U.S. With these data sets, we were tasked with creating appropriate color maps in both continuous and discrete styles.

LINK to project 2

Project 3: Isosurfaces

shapeimage_4Isosurfacing allows the medical industry to convert 2-dimensional slices, such as the CT slices used in this project, to 3-dimensional surface in space. This project explored different techniques of building isosurfaces and mapping colors to them.

LINK to project 3

Project 4: Direct Volume Rendering

shapeimage_5Although isosurfacing can generate a surface in 3D, the medical industry often uses raycast volume rendering instead because it better reflects the ambiguity and imprecision of the measurement. Rather than creating a geometry from data, volume rendering uses rays emitted from the object, adding opacity and color along the way. This project dealt with two data sets, the CT scan from the previous project, and vorticity surrounding a delta wing on an aircraft.

LINK to project 4

Project 4 Bonus: Multidimensional Transfer Function

shapeimage_6Using the programs created for the last project, I added a 2nd component to rendering using the gradient magnitude files generated from the same data set.

LINK to project 4 bonus

Project 5: Vector Field/Flow Visualization

p5_t2_h_sThis final project explored vector field visualization of velocity data surrounding a delta wing dataset. I visualize the vector field in different ways: plane slices showing the velocity data with arrow glyphs, streamlines, stream tubes, and a stream surface. Finally, I present the streamlines with the isosurface that makes up the magnitude of the vortices for reference.

LINK to project 5

Wind Turbine Analysis



For our final project for CS 59000: Embedded Systems, a partner and I implemented several tests on a small-scale wind turbine using the Texas Instruments MSP430 board. We use the Analog to Digital Converter (ADC) to gather information on voltage generated by the turbine and rotations per minute calculated with the help of an optical tachometer. We then send these values to a Java-based user interface to report in real-time on an attached computer.

For the final part of our project, we designed a wind turbine stand on springs that we can use, along with the MSP430, to measure accelerometer data from the wind turbine under stress. We also send the real-time data to the user interface on an attached computer.


Power Coefficient (Cp)

10ee302faa559afbeabbf9f6e403151a (Wiki link)

We measured the following characteristics of the wind turbine at LOW fan speed:

  • AT = 0.134614 m2
  • V3 = (2.101 m/sec)3 = 9.275
  • ρ = 1.2041 kg/m3 at 20°C (from Wikipedia)

The average voltage reported by our program at LOW fan speed was 2.304 volts. Resistance was set at 330 Ω.

Using these values, we found the power coefficient, Cp, to be:

Cp = 0.00929 or 0.01

Tip-Speed Ratio

(Wiki Link)

This part of the project required the use of the optical tachometer connected to the MSP430 board. The tachometer will output a high value when no blade blocks the beam, and a low value (close to zero) when a blade is in front. We read this information and convert the rate at which blades are passing in the beam to compute a rotations per minute (RPM) value.

The average RPM we measured at a given time was: 55 RPM

We measured the radius of a blade, and found R = 20.7 cm or 0.207 meters.

At LOW fan speed, the velocity of wind was recorded as V = 2.101 m/sec * 60 s= 126.06 m/min.

Using these values, we found the Tip-Speed Ratio to be:
λ = .567 rotations

Accelerometer Data


We constructed a special stand for the wind turbine that allows the turbine and MSP board to move in unison, while still being flexible to allow natural movement due to the wind.

For this part of the project, we modified the provided Java program to also display accelerometer data in the X- and Y-axes. We track and record this data in real-time, which gives some insight into how the wind turbine is moving as the speed and direction of wind changes.

Although we are not able to give a unit for these values, the magnitude of change can indicate what is happening in the physical system. For instance, when we see X values change from near-zero to negative, we know that stress is being placed in the wind turbine in the negative X direction (see diagram below — blue values represent negative readings).

Screen Shot 2013-01-24 at 12.12.48 AM

Arduino Web Server


Winter break means plenty of time to toy around with something new. I’m not sure what inspired this project, perhaps the ethernet driver we designed for our Operating Systems course, but I’ve decided to explore the field of embedded networking. And you can’t get much more embedded than a 16 MHz Arduino Uno with 32K of memory.


I want to create an Arduino-based web server, but with a few twists, because the idea already exists and has been implemented. The first link points to Lady Ada’s quick and dirty Arduino file server, which can serve up character-based files stored on micro SD. The second link offers a more functional server called Webduino, which claims to offer image support (ie. binary transfers). However, reading through the code, it looks like the developer took the easy way out by re-encoding a PNG as hex values, and then sending those values byte-by-byte over the network. That’s not image support! Also, both implementations seem to suffer from the limitation that only one client can connect at a time.

Because the Arduino has no formal notion of threads, it would make sense that multiple clients just won’t work. But I’ve been reading up on a project called Protothreads, which adds the most basic threading you can imagine. No separate stacks. No pre-emptive scheduling. Just a way to give the appearance that two computations are concurrent. I’m hoping that I can use protothreading to allow multiple clients to connect.

Additionally, it would be nice to find a way to do binary transfers. Glancing at the EthernetClient and EthernetServer API, it looks like they’re both set up for byte transfers. I wonder if there’s a way I can trick it into sending binary information. We’ll see.

Update – 26 January 2012:

I found an easy way (untested) to get the Arduino to send non-text content over the EthernetClient interface. When a client requests a file of a certain type, say, PNG, you can send a response indicating that you will be sending PNG binary data byte by byte as follows:

client.println("HTTP/1.1 200 OK");
client.println("Content-Type: image/png");

I hope to test this technique soon. Admittedly, I still have a long way to go on this project, but other projects (iPhone app, stay tuned) keep arising.

Caterpillar iPhone App

shapeimage_1I was hired to add functionality to an existing iPhone app for Caterpillar’s tree harvesting service. The goal is to give Caterpillar the ability to track and record data about their equipment during use.

Data is stored in an Entity model using SQL Lite and the iPhone’s Core Data framework. The app follows a walk-through model, where a user proceeds to add data to a “study” (represented by an entity in the database) and record measurements for that study. The user can also view a history of all studies on the device.

01_1 cat_new_1

MultiType Eclipse Plugin


As part of this team project for my CS 307 software engineering course, we developed a collaborative editor plug-in for the Eclipse environment. Users are able to share source code files with other users, who can view changes made to a document by other users in real-time, and make their own viewable modifications.

The plug-in integrates seamlessly with the Eclipse IDE, and features an independent server that can be run on any port-forwarded connection. Future versions will support additional editors, with more customizable features.

Update (5/7/11)

Test-drive the plug-in and browse the source code at our project homepage, hosted on Google Code here.

Arduino-controlled Robotic Arm with Android Interface


Project hosted on Google Code [LINK].

Continuing my trend of Arduino projects, I decided to toy around with this robotic arm I got for Christmas. The arm is not the best construction, using DC motors instead of servos, but with Adafruit’s motor/stepper/servo shield kit, I think I can make it somewhat more useful.

First to go is the flimsy plastic controller. The motor shield allows up to four of the of five motor joints to be controlled with the Arduino. I want to use this opportunity to explore the Java serial libraries to communicate with the Arduino over the Internet in a Java applet *or* purchase an Arduino ethernet shield and send commands directly to the device (see below)

I suppose, as an overall goal, I would like to be able to feed my fish while away with the use of a webcam and this robotic arm.

Completed Robotic Arm connection to motor

IMAG0138_1 IMAG0135_1

This setup allows control of the maximum of four motors.  I’d rather have everything integrated into one cable, but the supplied cable didn’t have enough pins to handle 4 motors and power and ground.  That’s why I added the red/black leads shown above v.  Here’s how everything looks hooked up to the freshly soldered motor shield:



Right now the Arduino is powered by USB, and the motors are powered by the arm’s original DC power source — 4 D batteries. This can easily be changed to have the arm run off of a DC adapter.

Next up: Due to the number of pins that the motor shield requires, I won’t be able to use an ethernet shield with this project. So, Java-based serial communication it is!

Update 4/16/11

I wrote a Java application with SWT to handle the serial communication with the Arduino’s motor controller. Essentially, it relays messages over the serial line about what motor to enable. The “speed” of the motor is an illusion by using a custom TimerTask to repeatedly send requests to enable the motor (every 10ms) and then sleep the thread for a duration between each task interval, until the button is no longer pressed. This solution makes the robotic arm much more useful as it can now make very controlled movements (<1mm).

Here is a screen shot of the user interface:



Update 9/7/12

I’ve started working on this project again! I decided to clean up some of the code and create an Android app that can control the arm wirelessly (as long as it’s connected to a host PC).


Project hosted on Google Code [LINK].

Arduino OBD-II Interface


For my CS 497 Spring 2011 course, I worked on an independent study project creating an Arduino-based OBD-II (vehicle onboard diagnostic) interface written in C.

First, I assembled an interface between an Arduino Uno and the ISO9141 bus that most vehicles prior to 2008 use. An open-source project called OBDuino describes how to assemble such an interface, available here. Second, I wrote software for the Arduino to poll information from the vehicle’s engine control unit (ECU), such as instantaneous information like RPM and vehicle speed, and also diagnostic feedback, and display it in real time on an attached LCD screen. Third, I added an Arduino data-logging shield to allow the capturing of long-term data from a vehicle in a spreadsheet-ready format.

Download the presentation I gave on this project.

Download the source code used for this project.

Update – 3/27/11

Since my last progress update, I have written software for the Arduino to:

  • Interface with my automobile’s ISO-9141 bus, and
  • Begin polling for parameter IDs (PIDs)

Before the Arduino can begin polling data from the automobile’s ECU, it must first establish a serial connection. The initialization sequence was adapted from ISO standard 9141-2:1994, available at the Engineering library. Once started, it sends address 0x33 to the ECU at 5-baud to “bit-bang” the ECU and establish a serial connection. The Arduino then switches into normal serial communication at 10.4kbps and waits for a return address of 0x55 from the ECU followed by two keywords. The Arduino sends back the inverse of the second keyword and, upon a response of 0xCC (the inverse of 0x33) from the ECU, the Arduino displays “Init. Success!” on its LCD screen. Initialization usually takes a few seconds to complete, and the connection must be re-initialized if the ECU is not polled for data within 5 seconds.

Screen shot 2011-03-27 at 11.12.07 PM


Displayed on the LCD screen above are instantaneous readings from the four PIDs currently polled by the Arduino. Clockwise from the top-left corner, these include:

  • Engine RPM
  • Vehicle speed
  • Mass airflow (MAF) sensor, which can be used to calculate instantaneous MPG
  • Engine coolant temperature

Values update every 200ms on the LCD. Polling for PIDs involves sending a byte-encoded message to the ECU requesting a value for a PID defined in the SAE J1979 standard. The ECU then responds with a byte-encoded value that the Arduino can decode and extract information from to display on screen. Currently, the system only polls for these four PIDs, but will be expanded to poll for several more.

Upcoming Tasks

In addition to adding more PIDs to poll, I will mainly be focusing on implementing a data logger shield to hold data captured by the Arduino and display in spreadsheet form on a computer.

The data logger shield sits on top of the Arduino unit, and uses pins not used by the LCD or serial communication. The shield also contains a real-time clock to add a timestamp next to data read from the ECU. Data is stored on a 2GB SD card.



The ability to log large amounts of data from a vehicle demonstrates the usefulness for this system to offer diagnostic capabilities with a high degree of precision and temporal resolution. Implementing and programming this data logger shield, as well as graphically displaying spreadsheet data, should consume the remainder of the time allotted for myproject.

Update – 4/12/11

I’ve added the data logging shield and a way to compute MPG from the MAF (mass air flow) and VSS (vehicle speed sensor) PIDs.

MPG = VSS * ( 1 / (MAF * .0889))

Here are some Excel graphs plotted from data over a 20 minute period (844 data points). Click for larger graphs.



CityBus iPhone App


CityBus iPhone is currently in development as part of a Purdue ACM SigApp application. As of now we have a tab-based interface with one tab displaying a MapKit UI with route overlays and the other tab with a UITableView representing the route selections. Certain stops can also be turned ‘on’ via annotations. Touching a bus stop annotation opens a UIWebView to display live data from the CityBus website on a certain stop visit collectiveray.

The latest version requires iOS 4.0+ and uses the latest API for MKPolylines to draw overlays on the map.

Visit the project page here.