### Precision Temperature Measurement

In an effort to resume previous work [A, B, C, D] on developing a crystal oven for radio frequency transmitter / receiver stabilization purposes, the first step for me was to create a device to accurately measure and log temperature. I did this with common, cheap components, and the output is saved to the computer (over 1,000 readings a second). Briefly, I use a LM335 precision temperature sensor (\$0.70 on mouser) which outputs voltage with respect to temperature. It acts like a Zener diode where the breakdown voltage relates to temperature. 2.95V is 295K (Kelvin), which is 22ºC / 71ºF. Note that Kelvin is just ºC + 273.15 (the difference between freezing and absolute zero). My goal was to use the ADC of a microcontroller to measure the output. The problem is that my ADC (one of 6 built into the ATMEL ATMega8 microcontroller) has 10-bit resolution, reporting steps from 0-5V as values from 0-1024. Thus, each step represents 0.0049V (0.49ºC / 0.882ºF). While ~1ºF resolution might be acceptable for some temperature measurement or control applications, I want to see fractions of a degree because radio frequency crystal temperature stabilization is critical. Here’s a video overview.

This is the circuit came up with. My goal was to make it cheaply and what I had on hand. It could certainly be better (more stable, more precise, etc.) but this seems to be working nicely. The idea is that you set the gain (the ratio of R2/R1) to increase your desired resolution (so your 5V of ADC recording spans over just several ºF you’re interested in), then set your “base offset” temperature that will produce 0V. In my design, I adjusted so 0V was room temperature, and 5V (maximum) was body temperature. This way when I touched the sensor, I’d watch temperature rise and fall when I let go.  Component values are very non-critical. LM324 is powered 0V GND and +5V Vcc. I chose to keep things simple and use a single rail power supply. It is worth noting that I ended-up using a 3.5V Zener diode for the positive end of the potentiometer rather than 5V.  If your power supply is well regulated 5V will be no problem, but as I was powering this with USB I decided to go for some extra stability by using a Zener reference.

On the microcontroller side, analog-to-digital measurement is summed-up pretty well in the datasheet. There is a lot of good documentation on the internet about how to get reliable, stable measurements. Decoupling capacitors, reference voltages, etc etc. That’s outside the scope of today’s topic. In my case, the output of the ADC went into the ATMega8 ADC5 (PC5, pin 28). Decoupling capacitors were placed at ARef and AVcc, according to the datasheet. Microcontroller code is at the bottom of this post.

To get the values to the computer, I used the USART capability of my microcontroller and sent ADC readings (at a rate over 1,000 a second) over a USB adapter based on an FTDI FT232 chip. I got e-bay knock-off FTDI evaluation boards which come with a USB cable too (they’re about \$6, free shipping). Yeah, I could have done it cheaper, but this works effortlessly. I don’t use a crystal. I set fuse settings so the MCU runs at 8MHz, and thanks to the nifty online baud rate calculator determined I can use a variety of transfer speeds (up to 38400). At 1MHz (if DIV8 fuse bit is enabled) I’m limited to 4800 baud. Here’s the result, it’s me touching the sensor with my finger (heating it), then letting go.

I spent a while considering fancy ways to send the data (checksums, frame headers, error correction, etc.) but ended-up just sending it old fashioned ASCII characters. I used to care more about speed, but even sending ASCII it can send over a thousand ADC readings a second, which is plenty for me. I ended-up throttling down the output to 10/second because it was just too much to log comfortable for long recordings (like 24 hours). In retrospect, it would have made sense to catch all those numbers and do averaging on the on the PC side.

On the receive side, I have nifty Python with PySerial ready to catch data coming from the microcontroller. It’s decoded, turned to values, and every 1000 receives saves a numpy array as a NPY binary file. I run the project out of my google drive folder, so while I’m at work I can run the plotting program and it loads the NPY file and shows it – today it allowed me to realize that my roomate turned off the air conditioning after I left, because I saw the temperature rising mid-day. The above graph is temperature in my house for the last ~24 hours. That’s about it! Here’s some of the technical stuff.

AVR ATMega8 microcontroller code:

```#define F_CPU 8000000UL
#include <avr/io.h>
#include <util/delay.h>
#include <avr/interrupt.h>

/*
8MHZ: 300,600,1200,2400,4800,9600,14400,19200,38400
1MHZ: 300,600,1200,2400,4800
*/
#define USART_BAUDRATE 38400
#define BAUD_PRESCALE (((F_CPU / (USART_BAUDRATE * 16UL))) - 1)

/*
{
PORTD^=255;
}
*/

void USART_Init(void){
UBRRL = BAUD_PRESCALE;
UBRRH = (BAUD_PRESCALE >> 8);
UCSRB = (1<<TXEN);
UCSRC = (1<<URSEL)|(1<<UCSZ1)|(1<<UCSZ0); // 9N1
}

void USART_Transmit( unsigned char data ){
while ( !( UCSRA & (1<<UDRE)) );
UDR = data;
}

void sendNum(long unsigned int byte){
if (byte==0){
USART_Transmit(48);
}
while (byte){
USART_Transmit(byte%10+48);
byte-=byte%10;
byte/=10;
}

}

}

}

int main(void){
//DDRD=255;
USART_Init();
for(;;){
USART_Transmit('n');
_delay_ms(100);
}
}```

Here is the Python code to receive the data and log it to disk:

```import serial, time
import numpy
ser = serial.Serial("COM15", 38400, timeout=100)

t1=time.time()
lines=0

data=[]

while True:

if "," in line:
line=line.split(",")
for i in range(len(line)):
line[i]=line[i][::-1]
else:
line=[line[::-1]]
temp=int(line[0])
lines+=1
data.append(temp)
print "#",
if lines%1000==999:
numpy.save("DATA.npy",data)
print
print line
print "%d lines in %.02f sec (%.02f vals/sec)"%(lines,
time.time()-t1,lines/(time.time()-t1))```

Here is the Python code to plot the data that has been saved:

```import numpy
import pylab

print data
data=data*.008 #convert to F
xs=numpy.arange(len(data))/9.95  #vals/sec
xs=xs/60.0# minutes
xs=xs/60.0# hours

pylab.plot(xs,data)
pylab.grid(alpha=.5)
pylab.axis([None,None,0*.008,1024*.008])
pylab.ylabel(r'\$Delta\$ Fahrenheit')
pylab.xlabel("hours")
pylab.show()```

If you recreate this project, or have any questions, feel free to email me!

### Realtime image pixelmap from Numpy array data in Qt

WARNING: this project is largely outdated, and some of the modules are no longer supported by modern distributions of Python.

For a more modern, cleaner, and more complete GUI-based viewer of realtime audio data (and the FFT frequency data), check out my Python Real-time Audio Frequency Monitor project.

Consider realtime spectrograph software like QRSS VD.  It’s primary function is to scroll a potentially huge data-rich image across the screen. In Python, this is often easier said than done. If you’re not careful, you can tackle this problem inefficiently and get terrible frame rates (<5FPS) or eat a huge amount of system resources (I get complaints often that QRSS VD takes up a lot of processor resources, and 99% of it is drawing the images).  In the past, I’ve done it at least 4 different ways (one, two, three, four, five). Note that “four” seems to be the absolute fastest option so far. I’ve been keeping an eye out for a while now contemplating the best way to rapidly draw color-mapped 8-bit data in a python program. Now that I’m doing a majority of my graphical development with PyQt and QtDesigner (packaged with PythonXY), I ended-up with a solution that looks like this (plotting random data with a colormap):

Here are the main points of how it’s done, with itallicised lines looped to refresh the data.

1.) in QtDesigner, create a form with a scrollAreaWidget

2.) in QtDesigner, add a label inside the scrollAreaWidget

3.) in code, resize label and also scrollAreaWidgetContents to fit data (disable “widgetResizable”)

4.) in code, create a QImage from a 2D numpy array (dtype=uint8)

5.) in code, set label pixmap to QtGui.QPixmap.fromImage(QImage)

That’s pretty much it! Here are some highlights of my program. Note that the code for the GUI is in a separate file, and must be downloaded from the ZIP provided at the bottom. Hope it helps someone else out there who might want to do something similar!

```import ui_main
import sys
from PyQt4 import QtCore, QtGui

import sys
from PyQt4 import Qt
import PyQt4.Qwt5 as Qwt
from PIL import Image
import numpy
import time

spectroWidth=1000
spectroHeight=1000

a=numpy.random.random(spectroHeight*spectroWidth)*255
a=numpy.reshape(a,(spectroHeight,spectroWidth))
a=numpy.require(a, numpy.uint8, 'C')

COLORTABLE=[]
for i in range(256): COLORTABLE.append(QtGui.qRgb(i/4,i,i/2))

def updateData():
global a
a=numpy.roll(a,-5)
QI=QtGui.QImage(a.data, spectroWidth, spectroHeight, QtGui.QImage.Format_Indexed8)
QI.setColorTable(COLORTABLE)
uimain.label.setPixmap(QtGui.QPixmap.fromImage(QI))

if __name__ == "__main__":
app = QtGui.QApplication(sys.argv)
win_main = ui_main.QtGui.QWidget()
uimain = ui_main.Ui_win_main()
uimain.setupUi(win_main)

# SET UP IMAGE
uimain.IM = QtGui.QImage(spectroWidth, spectroHeight, QtGui.QImage.Format_Indexed8)
uimain.label.setGeometry(QtCore.QRect(0,0,spectroWidth,spectroHeight))
uimain.scrollAreaWidgetContents.setGeometry(QtCore.QRect(0,0,spectroWidth,spectroHeight))

# SET UP RECURRING EVENTS
uimain.timer = QtCore.QTimer()
uimain.timer.start(.1)
win_main.connect(uimain.timer, QtCore.SIGNAL('timeout()'), updateData)

### DISPLAY WINDOWS
win_main.show()
sys.exit(app.exec_())```

### Wireless Microcontroller / PC Interface for \$3.21

Here I demonstrate a dirt-cheap method of transmitting data from any microchip to any PC using \$3.21 in parts.  I’ve had this idea for a while, but finally got it working tonight. On the transmit side, I’m having a an ATMEL AVR microcontroller (ATMega48) transmit data (every number from 0 to 200 over and over) wirelessly using 433mhz wireless modules. The PC receives the data through the microphone port of a sound card, and a cross-platform Python script I wrote decodes the data from the audio and graphs it on the screen. I did something similar back in 2011, but it wasn’t wireless, and the software wasn’t nearly as robust as it is now.

This is a proof-of-concept demonstration, and part of a larger project. I think there’s a need for this type of thing though! It’s unnecessarily hard to transfer data from a MCU to a PC as it is. There’s USB (For AVR V-USB is a nightmare and requires a precise, specific clock speed, DIP chips don’t have native USB, and some PIC DIP chips do but then you have to go through driver hell), USART RS-232 over serial port works (but who has serial ports these days?), or USART over USB RS-232 interface chips (like FTDI FT-232, but surface mount only), but both also require precise, specific clock speeds. Pretend I want to just measure temperature once a minute. Do I really want to etch circuit boards and solder SMT components? Well, kinda, but I don’t like feeling forced to. Some times you just want a no-nonsense way to get some numbers from your microchip to your computer. This project is a funky out-of-the-box alternative to traditional methods, and one that I hope will raise a few eyebrows.

Ultimately, I designed this project to eventually allow multiple “bursting” data transmitters to transmit on the same frequency routinely, thanks to syncing and forced-sync-loss (read on). It’s part of what I’m tongue-in-cheek calling the Scott Harden RF Protocol (SH-RFP). In my goal application, I wish to have about 5 wireless temperature sensors all transmitting data to my PC.  The receive side has some error checking in that it makes sure pulse sizes are intelligent and symmetrical (unlike random noise), and since each number is sent twice (with the second time being in reverse), there’s another layer of error-detection.  This is *NOT* a robust and accurate method to send critical data. It’s a cheap way to send data. It is very range limited, and only is intended to work over a distance of ten or twenty feet. First, let’s see it in action!

The RF modules are pretty simple. At 1.56 on ebay (with free shipping), they’re cheap too! I won’t go into detail documenting the ins and out of these things (that’s done well elsewhere). Briefly, you give them +5V (VCC), 0V (GND), and flip their data pin (ATAD) on and off on the transmitter module, and the receiver module’s DATA pin reflects the same state. The receiver uses a gain circuit which continuously increases gain until signal is detected, so if you’re not transmitting it WILL decode noise and start flipping its output pin. Note that persistent high or low states are prone to noise too, so any protocol you use these things for should have rapid state transitions. It’s also suggested that you maintain an average 50% duty cycle. These modules utilize amplitude shift keying (ASK) to transmit data wirelessly. The graphic below shows what that looks like at the RF level. Transmit and receive is improved by adding a quarter-wavelength vertical antenna to the “ANT” solder pad. At 433MHz, that is about 17cm, so I’m using a 17cm copper wire as an antenna.

Transmitting from the microcontroller is easy as pie! It’s just a matter of copying-in a few lines of C.  It doesn’t rely on USART, SPI, I2C, or any other protocol. Part of why I developed this method is because I often use ATTiny44A which doesn’t have USART for serial interfacing. The “SH-RFP” is easy to implement just by adding a few lines of code. I can handle that.  How does it work? I can define it simply by a few rules:

• Pulses can be one of 3 lengths: A (0), B (1), or C (break).
• Each pulse represents high, then low of that length.

To send a packet:

• prime synchronization by sending ten ABCs
• indicate we’re starting data by sending C.
• for each number you want to send:
• send your number bit by bit (A=0, B=1)
• send your number bit by bit (A=1, B=0)
• indicate number end by sending C.
• tell PC to release the signal by sending ten Cs.

Decoding is the same thing in reverse. I use an eBay sound card at \$1.29 (with free shipping) to get the signal into the PC. Synchronization is required to allow the PC to know that real data (not noise) is starting. Sending the same number twice (once with reversed bit polarity) is a proof-checking mechanisms that lets us throw-out data that isn’t accurate.

From a software side, I’m using PyAudio to collect data from the sound card, and the PythonXY distribution to handle analysis with numpy, scipy, and plotting with QwtPlot, and general GUI functionality with PyQt. I think that’s about everything.

The demonstration interface is pretty self-explanatory. The top-right shows a sample piece of data. The top left is a histogram of the number of samples of each pulse width. A clean signal should have 3 pulses (A=0, B=1, C=break). Note that you’re supposed to look at the peaks to determine the best lengths to tell the software to use to distinguish A, B, and C. This was intentionally not hard-coded because I want to rapidly switch from one microcontroller platform to another which may be operating at a different clock speed, and if all the sudden it’s running 3 times slower it will be no problem to decide on the PC side. Slick, huh? The bottom-left shows data values coming in. The bottom-right graphs those values. Rate reporting lets us know that I’m receiving over 700 good data points a second. That’s pretty cool, especially considering I’m recording at 44,100 Hz.

All source code (C files for an ATMega48 and Python scripts for the GUI) can be viewed here: SHRFP project on GitHub

If you use these concepts, hardware, or ideas in your project, let me know about it! Send me an email showing me your project – I’d love to see it. Good luck!

### Realtime FFT Audio Visualization with Python

WARNING: this project is largely outdated, and some of the modules are no longer supported by modern distributions of Python.

For a more modern, cleaner, and more complete GUI-based viewer of realtime audio data (and the FFT frequency data), check out my Python Real-time Audio Frequency Monitor project.

I’m no stranger to visualizing linear data in the frequency-domain. Between the high definition spectrograph suite I wrote in my first year of dental school (QRSS-VD, which differentiates tones to sub-Hz resolution), to the various scripts over the years (which go into FFT imaginary number theory, linear data signal filtering with python, and real time audio graphing with wckgraph), I’ve tried dozens of combinations of techniques to capture data, analyze it, and display it with Python. Because I’m now branching into making microcontroller devices which measure and transfer analog data to a computer, I need a way to rapidly visualize data obtained in Python. Since my microcontroller device isn’t up and running yet, linear data from a PC microphone will have to do.  Here’s a quick and dirty start-to-finish project anyone can tease apart to figure out how to do some of these not-so-intuitive processes in Python. To my knowledge, this is a cross-platform solution too. For the sound card interaction, it relies on the cross-platform sound card interface library PyAudio. My python distro is 2.7 (python xy), but pythonxy doesn’t [yet] supply PyAudio.

The code behind it is a little jumbled, but it works. For recording, I wrote a class “SwhRecorder” which uses threading to continuously record audio and save it as a numpy array. When the class is loaded and started, your GUI can wait until it sees newAudio become True, then it can grab audio directly, or use fft() to pull the spectral component (which is what I do in the video). Note that my fft() relies on numpy.fft.fft(). The return is a nearly-symmetrical mirror image of the frequency components, which (get ready to cringe mathematicians) I simply split into two arrays, reverse one of them, and add together. To turn this absolute value into dB, I’d take the log10(fft) and multiply it by 20. You know, if you’re into that kind of thing, you should really check out a post I made about FFT theory and analyzing audio data in python.

Here’s the meat of the code. To run it, you should really grab the zip file at the bottom of the page. I’ll start with the recorder class:

```import matplotlib
matplotlib.use('TkAgg') # THIS MAKES IT FAST!
import numpy
import scipy
import struct
import pyaudio
import pylab
import struct

class SwhRecorder:
"""Simple, cross-platform class to record from the microphone."""

def __init__(self):
"""minimal garb is executed when class is loaded."""
self.RATE=48100
self.BUFFERSIZE=2**12 #1024 is a good buffer size
self.secToRecord=.1
self.newAudio=False

def setup(self):
"""initialize sound card."""
#TODO - windows detection vs. alsa or something for linux
#TODO - try/except for sound card selection/initiation

self.buffersToRecord=int(self.RATE*self.secToRecord/self.BUFFERSIZE)
if self.buffersToRecord==0: self.buffersToRecord=1
self.samplesToRecord=int(self.BUFFERSIZE*self.buffersToRecord)
self.chunksToRecord=int(self.samplesToRecord/self.BUFFERSIZE)
self.secPerPoint=1.0/self.RATE

self.p = pyaudio.PyAudio()
self.inStream = self.p.open(format=pyaudio.paInt16,channels=1,
rate=self.RATE,input=True,frames_per_buffer=self.BUFFERSIZE)
self.xsBuffer=numpy.arange(self.BUFFERSIZE)*self.secPerPoint
self.xs=numpy.arange(self.chunksToRecord*self.BUFFERSIZE)*self.secPerPoint
self.audio=numpy.empty((self.chunksToRecord*self.BUFFERSIZE),dtype=numpy.int16)

def close(self):
"""cleanly back out and release sound card."""
self.p.close(self.inStream)

### RECORDING AUDIO ###

def getAudio(self):
"""get a single buffer size worth of audio."""
return numpy.fromstring(audioString,dtype=numpy.int16)

def record(self,forever=True):
"""record secToRecord seconds of audio."""
while True:
for i in range(self.chunksToRecord):
self.audio[i*self.BUFFERSIZE:(i+1)*self.BUFFERSIZE]=self.getAudio()
self.newAudio=True
if forever==False: break

def continuousStart(self):
"""CALL THIS to start running forever."""
self.t.start()

def continuousEnd(self):
"""shut down continuous recording."""

### MATH ###

def downsample(self,data,mult):
"""Given 1D data, return the binned average."""
overhang=len(data)%mult
if overhang: data=data[:-overhang]
data=numpy.reshape(data,(len(data)/mult,mult))
data=numpy.average(data,1)
return data

def fft(self,data=None,trimBy=10,logScale=False,divBy=100):
if data==None:
data=self.audio.flatten()
left,right=numpy.split(numpy.abs(numpy.fft.fft(data)),2)
if logScale:
ys=numpy.multiply(20,numpy.log10(ys))
xs=numpy.arange(self.BUFFERSIZE/2,dtype=float)
if trimBy:
i=int((self.BUFFERSIZE/2)/trimBy)
ys=ys[:i]
xs=xs[:i]*self.RATE/self.BUFFERSIZE
if divBy:
ys=ys/float(divBy)
return xs,ys

### VISUALIZATION ###

def plotAudio(self):
"""open a matplotlib popup window showing audio data."""
pylab.plot(self.audio.flatten())
pylab.show()```

And now here’s the GUI launcher:

```import ui_plot
import sys
import numpy
from PyQt4 import QtCore, QtGui
import PyQt4.Qwt5 as Qwt
from recorder import *

def plotSomething():
if SR.newAudio==False:
return
xs,ys=SR.fft()
c.setData(xs,ys)
uiplot.qwtPlot.replot()
SR.newAudio=False

if __name__ == "__main__":
app = QtGui.QApplication(sys.argv)

win_plot = ui_plot.QtGui.QMainWindow()
uiplot = ui_plot.Ui_win_plot()
uiplot.setupUi(win_plot)
uiplot.btnA.clicked.connect(plotSomething)
#uiplot.btnB.clicked.connect(lambda: uiplot.timer.setInterval(100.0))
#uiplot.btnC.clicked.connect(lambda: uiplot.timer.setInterval(10.0))
#uiplot.btnD.clicked.connect(lambda: uiplot.timer.setInterval(1.0))
c=Qwt.QwtPlotCurve()
c.attach(uiplot.qwtPlot)

uiplot.qwtPlot.setAxisScale(uiplot.qwtPlot.yLeft, 0, 1000)

uiplot.timer = QtCore.QTimer()
uiplot.timer.start(1.0)

win_plot.connect(uiplot.timer, QtCore.SIGNAL('timeout()'), plotSomething)

SR=SwhRecorder()
SR.setup()
SR.continuousStart()

### DISPLAY WINDOWS
win_plot.show()
code=app.exec_()
SR.close()
sys.exit(code)```

Note that by commenting-out the FFT line and using “c.setData(SR.xs,SR.audio)” you can plot linear PCM data to visualize sound waves like this:

Finally, here’s the zip file. It contains everything you need to run the program on your own computer (including the UI scripts which are not written on this page)

If you make a cool project based on this one, I’d love to hear about it. Good luck!

### Realtime Data Plotting in Python

WARNING: this project is largely outdated, and some of the modules are no longer supported by modern distributions of Python.

For a more modern, cleaner, and more complete GUI-based viewer of realtime audio data (and the FFT frequency data), check out my Python Real-time Audio Frequency Monitor project.

I love using python for handing data. Displaying it isn’t always as easy. Python fast to write, and numpy, scipy, and matplotlib are an incredible combination. I love matplotlib for displaying data and use it all the time, but when it comes to realtime data visualization, matplotlib (admittedly) falls behind. Imagine trying to plot sound waves in real time. Matplotlib simply can’t handle it. I’ve recently been making progress toward this end with PyQwt with the Python X,Y distribution. It is a cross-platform solution which should perform identically on Windows, Linux, and MacOS. Here’s an example of what it looks like plotting some dummy data (a sine wave) being transformed with numpy.roll().

How did I do it? Easy. First, I made the GUI with QtDesigner (which comes with Python x,y). I saved the GUI as a .ui file. I then used the pyuic4 command to generate a python script from the .ui file. In reality, I use a little helper script I wrote designed to build .py files from .ui files and start a little “ui.py” file which imports all of the ui classes. It’s overkill for this, but I’ll put it in the ZIP anyway.  Here’s what the GUI looks like in QtDesigner:

After that, I tie everything together in a little script which updates the plot in real time. It takes inputs from button click events and tells a clock (QTimer) how often to update/replot the data. Replotting it involves just rolling it with numpy.roll().  Check it out:

```import ui_plot #this was generated by pyuic4 command
import sys
import numpy
from PyQt4 import QtCore, QtGui
import PyQt4.Qwt5 as Qwt

numPoints=1000
xs=numpy.arange(numPoints)
ys=numpy.sin(3.14159*xs*10/numPoints) #this is our data

def plotSomething():
global ys
ys=numpy.roll(ys,-1)
c.setData(xs, ys)
uiplot.qwtPlot.replot()

if __name__ == "__main__":
app = QtGui.QApplication(sys.argv)
win_plot = ui_plot.QtGui.QMainWindow()
uiplot = ui_plot.Ui_win_plot()
uiplot.setupUi(win_plot)

# tell buttons what to do when clicked
uiplot.btnA.clicked.connect(plotSomething)
uiplot.btnB.clicked.connect(lambda: uiplot.timer.setInterval(100.0))
uiplot.btnC.clicked.connect(lambda: uiplot.timer.setInterval(10.0))
uiplot.btnD.clicked.connect(lambda: uiplot.timer.setInterval(1.0))

# set up the QwtPlot (pay attention!)
c=Qwt.QwtPlotCurve()  #make a curve
c.attach(uiplot.qwtPlot) #attach it to the qwtPlot object
uiplot.timer = QtCore.QTimer() #start a timer (to call replot events)
uiplot.timer.start(100.0) #set the interval (in ms)
win_plot.connect(uiplot.timer, QtCore.SIGNAL('timeout()'), plotSomething)

# show the main window
win_plot.show()
sys.exit(app.exec_())```

I’ll put all the files in a ZIP to help out anyone interested in giving this a shot. Clicking different buttons updates the graph at different speeds. If you make something cool with this concept, let me know! I’d love to see it.

## I got everything running on Windows 7 x64 with the following:

Here’s the “hello world” of microchip programs (it simply blinks an LED). I’ll assume the audience of this page knows the basics of microcontroller programming, so I won’t go into the details. Just note that I’m using an ATMega48 and the LED is on pin 9 (PB6). This file is named “blink.c”.

```#define F_CPU 1000000UL
#include <avr/io.h>
#include <util/delay.h>

int main (void)
{
DDRB = 255;
while(1)
{
PORTB ^= 255;
_delay_ms(500);
}
}```

Here’s how I compiled the code:

```avr-gcc -mmcu=atmega48 -Wall -Os -o blink.elf blink.c

In reality, it is useful to put these commands in a text file and call them “compile.bat”

Here’s how I program the AVR. I used AVRDudess! I’ve been using raw AVRDude for years. It’s a little rough around the edges, but this GUI interface is pretty convenient. I don’t even feel the need to include the command to program it from the command line! If I encourage nothing else by this post, I encourage (a) people to use and support AVRDudess, and (b) AVRDudess to continue developing itself as a product nearly all hobby AVR programmers will use. Thank you 21-year-old Zak Kemble.

And finally, the result. A blinking LED. Up and running programming AVR microcontrollers in 64-bit Windows 7 with an unofficial programmer, and never needing to install bloated AVR Studio software.

### Tenma 72-7750 Multimeter Excellent for RF Engineering

I recently got my hands on a Tenma 72-7750 multimeter. Tenma has a pretty large collection of test equipment and measurement products, including several varieties of hand-held multimeters. The 72-7750 multimeter has the standard measurement modes you’d expect (voltage, current capacitance, resistance, conductivity), but stood out to me because it also measures frequency, temperature, and has RS232 PC connectivity. Currently it’s sale from Newark for under fifty bucks! This is what mine arrived with:

The obvious stuff worked as expected. Auto ranging, (5 ranges of voltage and resistance, 3 of current, 7 of capacitance), accurate measurement, etc. I was, however, impressed with the extra set of test leads they provided – little short ones with gator clips! These are perfect for measuring capacitance, or for clipping onto wires coming out of a breadboard. So many times with my current multimeters I end-up gator-clipping wires to my probes and taking them to what I’m measuring. I’m already in love with the gator clip leads, and know I’ll have a set of these at my bench for the rest of my life.

I was impressed by the frequency measuring ability of this little multimeter! When I read that it could measure up to 60MHz, I was impressed, but also suspected it might be a little flakey. This was not at all the case – the frequency measurement was dead-on at several ranges! With so many of the projects I work on being RF-involved (radio transmitters, radio receivers, modulators, mixers, you name it), I sided with this meter because unlike some of its siblings this one is rated beyond 50Mz. I hooked it up to the frequency synthesizer I built based around an ad9850 direct digital synthesizer and played around. When the synthesizer was set to various frequencies, the multimeter followed it to the digit! Check out the pics of it in action, comparing the LCD screen frequency with that being displayed on the meter:

I also took a closer look at the PC interface. When I looked closely, I noticed it wasn’t an electrical connection – it was an optical one! It has a phototransistor on one end, and a serial connection on the other. I’m no stranger to tossing data around with light (I made something that did this here, which was later featured on Hack-A-Day here). I wondered what the format of the data was, when to my surprise I saw it spelled out in the product manual! (Go Tenma!)  It specifically says “Baud Rate 19230, Start Bit 1 (always 0), Stop bit 1 (always 1), Data bits (7), Parity 1 (odd)”. Although they have their own windows-only software to display/graph readings over time, I’d consider writing Python-based logging software. It should be trivial with python, pySerial, numpy, and matplotlib. Clearly I’m no stranger to graphing things in python 🙂

How does the photo-transistor work without power? I attached my o-scope to the pins and saw nothing when RS232 mode was activated on the multimeter. Presumably, the phototransistor requires a voltage source (albeit low current) to operate. With a little digging on the internet, I realized that the serial port can source power. I probably previously overlooked this because serial devices were a little before my time, but consider serial mice: they must have been supplied power! Joseph Sullivan has a cool write-up on a project which allowed him to achieve bidirectional optical (laser) communication over (and completely powered by) a serial port. With a little testing, I applied 0V to pin 5 (GND), +5V to pin 6 (DSR, data set ready), and looked at the output on pin 3 (PC RX). Sure enough, there were bursts of easy-to-decode RS232 data. Here’s the scheme Joseph came up with to power his laser communication system, which presumably is similar to the one in the multi-meter. (Note, that the cable is missing its “TX” light, but the meter has an “RX” phototransistor. I wonder if this would allow optically-loaded firmware?)

There were a couple other things I found useful. Although I didn’t appreciate it at first, after a few days the backlight grew on me. I’ve been doing experiments with photosensors which require me to turn out the lights in the room, and the backlight saved the day! Also, the meter came with a thermocouple for temperature measurement. It has it’s own “ºC” setting on the dial, and displays human-readable temperature right on the screen. I used to do this with LM334-type thermosensitive current sources but it was always a pain (especially if I had one which output temperature in Kelvin!) I’m not sure exactly what’s inside the one that came with this meter, but the datasheet suggests it can measure -40 through 1,000 C, which certainly will do for my experiments!

All in all, I’m happy with this little guy, and am looking forward to hacking into it a little bit. There may be enough room in the case to add a hacked-together high frequency divider (a decade counter would be fantastic, divided by ten would allow measurement through 500MHz), but I might be over-reaching a bit. Alternatively, a high gain preamplifier would be a neat way to allow the sort probe to serve as an antenna to measure frequency wirelessly, rater than requiring contact. Finally, I’m looking forward to writing software to interface the RS232 output. The ability to measure, record, and display changes in voltage or temperature over time is an important part of designing controller systems. For example, an improved crystal oven is on my list of projects to make. What a perfect way to monitor the temperature and stability of the completed project! Straight out of the box, this multimeter is an excellent tool.

### Fixing Slow Matplotlib in Python(x,y)

I recently migrated to Python(x,y) and noticed my matplotlib graphs are resizing unacceptably slowly when I use the pan/zoom button. I’m quite a fan of numpy, scipy, matplotlib, the python imaging library (PIL), and GUI platforms like Tk/TkInter, pyGTK, and pyQT, but getting them all to play nicely is a sometimes pain. I’m considering migrating entirely to Python(x,y) because, as a single distribution, it’s designed to install all these libraries (and many more) in a compatible way out of the box. However, when I did, I noticed matplotlib graphs would resize, rescale, and drag around the axes very slowly. After a lot of digging on the interweb, I figured out what was going wrong. I’ll show you by plotting 20 random data points the slow way (left) then the fast way (right).

THE PROBLEM: See the difference between the two plots? The one on the left (SLOW!) uses the Qt4Agg backend, which renders the matplotlib plot on a QT4 canvas. This is slower than the one on the right, which uses the more traditional TkAgg backend to draw the plot on a Tk canvas with tkinter (FASTER!). Check out matplotlib’s official description of what a backend is and which ones you can use. When you just install Python and matplotlib, Tk is used by default.

```import numpy
import matplotlib
matplotlib.use('TkAgg') # <-- THIS MAKES IT FAST!
import pylab
pylab.plot(numpy.random.random_integers(0,100,20))
pylab.title("USING: "+matplotlib.get_backend())
pylab.show()```

THE FIX: Tell matplotlib to stop using QT to draw the plot, and let it plot with Tk. This can be done immediately after importing matplotlib, but must be done before importing pylab using the line `matplotlib.use('TkAgg')`. Here’s the full example I used to generate the demonstration plots above. Change TkAgg to Qt4Agg (or comment-out the ‘use’ line if you’re using PythonXY) and you will see performance go down the tube. Alternatively, make a change to the matplotlib rc file to customize default behavior when the package is loaded.

### Simple DIY ECG + Pulse Oximeter (version 2)

UPDATE: An improved ECG design was posted in August, 2016.
Check out: https://www.swharden.com/wp/2016-08-08-diy-ecg-with-1-op-amp/

Of the hundreds of projects I’ve shared over the years, none has attracted more attention than my DIY ECG machine on the cheap posted almost 4 years ago. This weekend I re-visited the project and made something I’m excited to share!  The original project was immensely popular, my first featured article on Hack-A-Day, and today “ECG” still represents the second most searched term by people who land on my site. My gmail account also has had 194 incoming emails from people asking details about the project. A lot of it was by frustrated students trying to recreate the project running into trouble because it was somewhat poorly documented. Clearly, it’s a project that a wide range of people are interested in, and I’m happy to revisit it bringing new knowledge and insight to the project. I will do my best to document it thoroughly so anyone can recreate it!

The goal of this project is to collect heartbeat information on a computer with minimal cost and minimal complexity.  I accomplished this with fewer than a dozen components (all of which can be purchased at RadioShack). It serves both as a light-based heartbeat monitor (similar to a pulse oximeter, though it’s not designed to quantitatively measure blood oxygen saturation), and an electrocardiogram (ECG) to visualize electrical activity generated by heart while it contracts. Let’s jump right to the good part – this is what comes out of the machine:

That’s my actual heartbeat. Cool, right? Before I go into how the circuit works, let’s touch on how we measure heartbeat with ECG vs. light (like a pulse oximeter).  To form a heartbeat, the pacemaker region of the heart (called the SA node, which is near the upper right of the heart) begins to fire and the atria (the two top chambers of the heart) contract. The SA node generates a little electrical shock which stimulated a synchronized contraction. This is exactly what defibrillators do when a heart has stopped beating. When a heart attack is occurring and a patient is undergoing ventricular fibrillation, it means that heart muscle cells are contracting randomly and not in unison, so the heart quivers instead of pumping as an organ. Defibrillators synchronize the heart beat with a sudden rush of current over the heart to reset all of the cells to begin firing at the same time (thanks Ron for requesting a more technical description).  If a current is run over the muscle, the cells (cardiomyocytes) all contract at the same time, and blood moves. The AV node (closer to the center of the heart) in combination with a slow conducting pathway (called the bundle of His) control contraction of the ventricles (the really large chambers at the bottom of the heart), which produce the really large spikes we see on an ECG.  To measure ECG, optimally we’d place electrodes on the surface of the heart. Since that would be painful, we do the best we can by measuring voltage changes (often in the mV range) on the surface of the skin. If we amplify it enough, we can visualize it. Depending on where the pads are placed, we can see different regions of the heart contract by their unique electrophysiological signature. ECG requires sticky pads on your chest and is extremely sensitive to small fluctuations in voltage. Alternatively, a pulse oximeter measures blood oxygenation and can monitor heartbeat by clipping onto a finger tip. It does this by shining light through your finger and measuring how much light is absorbed. This goes up and down as blood is pumped through your finger. If you look at the relationship between absorbency in the red vs. infrared wavelengths, you can infer the oxygenation state of the blood. I’m not doing that today because I’m mostly interested in detecting heart beats.

For operation as a pulse oximeter-type optical heartbeat detector (a photoplethysmograph which produces a photoplethysmogram), I use a bright red LED to shine light through my finger and be detected by a phototransistor (bottom left of the diagram). I talk about how this works in more detail in a previous post. Basically the phototransistor acts like a variable resistor which conducts different amounts of current depending on how much light it sees. This changes the voltage above it in a way that changes with heartbeats. If this small signal is used as the input, this device acts like a pulse oximeter.

For operation as an electrocardiograph (ECG), I attach the (in) directly to a lead on my chest. One of them is grounded (it doesn’t matter which for this circuit – if they’re switched the ECG just looks upside down), and the other is recording. In my original article, I used pennies with wires soldered to them taped to my chest as leads. Today, I’m using fancier sticky pads which are a little more conductive. In either case, one lead goes in the center of your chest, and the other goes to your left side under your arm pit. I like these sticky pads because they stick to my skin better than pennies taped on with electrical tape. I got 100 Nikomed Nikotabs EKG Electrodes 0315 on eBay for \$5.51 with free shipping (score!). Just gator clip to them and you’re good to go!

In both cases, I need to build a device to amplify small signals. This is accomplished with the following circuit. The core of the circuit is an LM324 quad operational amplifier.  These chips are everywhere, and extremely cheap. It looks like Thai Shine sells 10 for \$2.86 (with free shipping). That’s about a quarter each. Nice!  A lot of ECG projects use instrumentation amplifiers like the AD620 (which I have used with fantastic results), but these are expensive (about \$5.00 each). The main difference is that instrumentation amplifiers amplify the difference between two points (which reduces noise and probably makes for a better ECG machine), but for today an operational amplifier will do a good enough job amplifying a small signal with respect to ground. I get around the noise issue by some simple filtering techniques. Let’s take a look at the circuit.

This project utilizes one of the op-amps as a virtual ground. One complaint of using op-amps in simple projects is that they often need + and – voltages. Yeah, this could be done with two 9V batteries to generate +9V and -9V, but I think it’s easier to use a single power source (+ and GND). A way to get around that is to use one of the op-amps as a current source and feed it half of the power supply voltage (VCC), and use the output as a virtual ground (allowing VCC to be your + and 0V GND to be your -). For a good description of how to do this intelligently, read the single supply op amps web page. The caveat is that your signals should remain around VCC/2, which can be done if it is decoupled by feeding it through a series capacitor. The project works at 12V or 5V, but was designed for (and has much better output) at 12V. The remaining 3 op-amps of the LM324 serve three unique functions:

STAGE 1: High gain amplifier. The input signals from either the ECG or pulse oximeter are fed into a chain of 3 opamp stages. The first is a preamplifier. The output is decoupled through a series capacitor to place it near VCC/2, and amplified greatly thanks to the 1.8Mohm negative feedback resistor. Changing this value changes initial gain.

STAGE 2: active low-pass filter. The 10kOhm variable resistor lets you adjust the frequency cutoff. The opamp serves as a unity gain current source / voltage follower that has high input impedance when measuring the output f the low-pass filter and reproduces its voltage with a low impedance output. There’s some more information about active filtering on this page. It’s best to look at the output of this stage and adjust the potentiometer until the 60Hz noise (caused by the AC wiring in the walls) is most reduced while the lower-frequency component of your heartbeat is retained. With the oximeter, virtually no noise gets through. Because the ECG signal is much smaller, this filter has to be less aggressive, and this noise is filtered-out by software (more on this later).

STAGE 3: final amplifier with low-pass filter. It has a gain of ~20 (determined by the ratio of the 1.8kOhm to 100Ohm resistors) and lowpass filtering components are provided by the 22uF capacitor across the negative feedback resistor. If you try to run this circuit at 5V and want more gain (more voltage swing), consider increasing the value of the 1.8kOhm resistor (wit the capacitor removed). Once you have a good gain, add different capacitor values until your signal is left but the noise reduced. For 12V, these values work fine. Let’s see it in action!

Now for the second half – getting it into the computer. The cheapest and easiest way to do this is to simply feed the output into a sound card! A sound card is an analog-to-digital converter (ADC) that everybody has and can sample up to 48 thousand samples a second! (overkill for this application) The first thing you should do is add an output potentiometer to allow you to drop the voltage down if it’s too big for the sound card (in the case of the oximeter) but but also allow full-volume in the case of sensitive measurements (like ECG). Then open-up sound editing software (I like GoldWave for Windows or Audacity for Linux, both of which are free) and record the input. You can do filtering (low-pass filter at 40Hz with a sharp cutoff) to further eliminate any noise that may have sneaked through. Re-sample at 1,000 Hz (1kHz) and save the output as a text file and you’re ready to graph it! Check it out.

Here are the results of some actual data recorded and processed with the method shown in the video. let’s look at the pulse oximeter first.

That looks pretty good, certainly enough for heartbeat detection. There’s obvious room for improvement, but as a proof of concept it’s clearly working. Let’s switch gears and look at the ECG. It’s much more challenging because it’s signal is a couple orders of magnitude smaller than the pulse oximeter, so a lot more noise gets through. Filtering it out offers dramatic improvements!

Here’s the code I used to generate the graphs from the text files that GoldWave saves. It requires Python, Matplotlib (pylab), and Numpy. In my case, I’m using 32-bit 2.6 versions of everything.

```# DIY Sound Card ECG/Pulse Oximeter
# by Scott Harden (2013) http://www.SWHarden.com

import pylab
import numpy

f=open("light.txt")
f.close()

data = numpy.array(raw,dtype=float)
data = data-min(data) #make all points positive
data = data/max(data)*100.0 #normalize
times = numpy.array(range(len(data)))/1000.0
pylab.figure(figsize=(15,5))
pylab.plot(times,data)
pylab.xlabel("Time Elapsed (seconds)")
pylab.ylabel("Amplitude (% max)")
pylab.title("Pulse Oximeter - filtered")
pylab.show()```

Future directions involve several projects I hope to work on soon. First, it would be cool to miniaturize everything with surface mount technology (SMT) to bring these things down to the size of a postage stamp. Second, improved finger, toe, or ear clips (or even taped-on sensors) over long duration would provide a pretty interesting way to analyze heart rate variability or modulation in response to stress, sleep apnea, etc. Instead of feeding the signal into a computer, one could send it to a micro-controller for processing. I’ve made some darn-good progress making multi-channel cross-platform USB option for getting physiology data into a computer, but have some work still to do. Alternatively, this data could be graphed on a graphical LCD for an all-in-one little device that doesn’t require a computer. Yep, lots of possible projects can use this as a starting point.

LET ME KNOW WHAT YOU THINK! If you make this, I’m especially interested to see how it came out. Take pictures of your projects and send them my way! If you make improvements, or take this project further, I’d be happy to link to it on this page. I hope this page describes the project well enough that anyone can recreate it, regardless of electronics experience. Finally, I hope that people are inspired by the cool things that can be done with surprisingly simple electronics. Get out there, be creative, and go build something cool!

### AVR Programming in Linux

It is not difficult to program ATMEL AVR microcontrollers with linux, and I almost exclusively do this because various unofficial (inexpensive) USB AVR programmers are incompatible with modern versions of windows (namely Windows Vista and Windows 7). I am just now setting-up a new computer station in my electronics room (running Ubuntu Linux 12.04), and to make it easy for myself in the future I will document everything I do when I set-up a Linux computer to program microcontrollers.

Install necessary software

`sudo apt-get install gcc-avr avr-libc uisp avrdude`

Connect the AVR programmer
This should be intuitive for anyone who has programmed AVRs before. Visit the datasheet of your MCU, identify pins for VCC (+), GND (-), MOSI, MISO, SCK, and RESET, then connect them to the appropriate pins of your programmer.

Write a simple program in C
I made a file “main.c” and put the following inside. It’s the simplest-case code necessary to make every pin on PORTD (PD0, PD1, …, PD7) turn on and off repeatedly, sufficient to blink an LED.

```#include <avr/io.h>
#include <util/delay.h>

int main (void)
{
DDRD = 255; // MAKE ALL PORT D PINS OUTPUTS

while(1) {
PORTD = 255;_delay_ms(100); // LED ON
PORTD = 0;  _delay_ms(100); // LED OFF
}

return 0;
}
```

Compile the code (generate a HEX file)

```avr-gcc -w -Os -DF_CPU=2000000UL -mmcu=atmega8 -c -o main.o main.c
avr-gcc -w -mmcu=atmega8 main.o -o main
avr-objcopy -O ihex -R .eeprom main main.hex
```

note that the arguments define CPU speed and chip model – this will need to be customized for your application

Program the HEX firmware onto the AVR

```sudo avrdude -F -V -c avrispmkII -p ATmega8 -P usb -U flash:w:main.hex
```

note that this line us customized based on my connection (-P flag, USB in my case) and programmer type (-c flag, AVR ISP mkII in my case)

When this is run, you will see something like this:

```
avrdude: AVR device initialized and ready to accept instructions

Reading | ################################################## | 100% 0.01s

avrdude: Device signature = 0x1e9307
avrdude: NOTE: FLASH memory has been specified, an erase cycle will be performed
To disable this feature, specify the -D option.
avrdude: erasing chip
avrdude: input file main.hex auto detected as Intel Hex
avrdude: writing flash (94 bytes):

Writing | ################################################## | 100% 0.04s

avrdude: 94 bytes of flash written
```