I have an ECG signal generated using Simulate ECG VI from BioMedical Toolkit. I need the P-wave amplitudes from this signal. Using peak detection, I will get all the peaks and then I should write an algorithm to extract the P-wave peak. Doing this, I cannot be sure my algorithm will work for a signal which will represent a disease. I there a posibility to do this ?
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I'm using a high accuracy GPS RTK setup to precisely locate a mobile robotic platform in the field (down to 10 cm accuracy). I have also a 9DOF IMU mounted on the platform (9DOF sparkfun IMU Razor).
The Question is, Do I really need to perform a sensor fusion between IMU and GPS like what this ROS node do (http://wiki.ros.org/robot_localization) to estimate the robot pose? or is it just enough to read the Pitch,Yaw,Rotation data from the IMU to know the heading along with the GPS Long,Lat,Alt ?
What cases make it essential to perform this type of fusion ?
Thanks in advance
It is essential to perform fusion because:
1) Roll, Pitch, and Rotation data from the IMU are not perfect, and they will drift over time due to gyro errors. The magnetic field sensor in the IMU module limits this, but crudely. Fusion allows the GPS RTK measurements to be used to continuously estimate the dominant error sources in the IMU and maintain better attitude information.
2) The IMU supports position estimation when GPS-RTK is lost through signal blockage or any other outage, such that the robotic platform is not lost when and if GPS signals are interrupted.
I am doing a project on indoor localisation using fingerprinting. Is it possible to build a system in LabView which can scan the entire spectrum and provide me the RSSI measurements of different types of signals?(say FM, GSM, DVB-T and so on.) In case it has to be done separately, can someone please point me to some resources that would help me to find the RSSIs of say, FM signals? I am new to SDRs and would really appreciate some help. I have used this paper as a reference:
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7444902
There can be no general method. RSSI is inherently signal-specific, and hence, for each signal type, you will need a different estimator. An estimator that estimates the received signal strength of FM broadcasts will only see noise in a DVB-T signal at incredibly high noise power with no stable signal at all, whereas a DVB-T RSSI indicator would only see a slowly moving interfere in an FM signal.
I see you're using the indoor-positioning-system tag. That is a very bad thing: In an indoor scenario, fading is extremely important, and your received signal strength allow absolutely no conclusions on the distance to the transmitter. This is pretty much the definition of the indoor channel, where lots and lots of reflections overlap and interefere, and there's not always a direct line of sight between transmitter and receiver.
I'm afraid you still have quite some theory to read up on.
I don't have much knowledge about microcontrollers. In my project, I need to shift the sine wave. Here, I want to know, if I feed pure sine at port A pin 2. Then, will i get the shifted version of pure sine wave at port B pin 2 . will the following instruction work?
Inialise port A as input and port B as output
call delay
portb=porta
we can generate sine wave using DAC in microcontroller. But, as it is not perfect, it wont meet required conditions.
First of all the input needs to be to an ADC, and the output needs to be from a DAC (or a PWM with appropriate output filtering). It is not clear from your question that the pins you have chosen are appropriate for that.
If you are generating the sine from the DAC, why would you apply it to an input only to output it again? If you need two sine waves shifted in phase, why not simply generate calculated outputs from two DAC or PWM? Either way you need two analogue outputs, but that way you do not need any input. A PWM will need greater analogue filtering than a DAC and is likely to support lower bandwidth, but most microcontroller have more PWMs than DACs.
You cannot simply call a delay than copy port a to port b, that would be simply be a copy of a to b after a delay. You need to take samples from A and place then in a FIFO buffer, then apply the output of the FIFO to B. The length of the FIFO determines the delay.
A microcontroller is not an analogue device, you cannot put in an analogue signal on just any old pin and and transfer that signal to another pin. Most pins are digital GPIO, they except just two states representing 0 or 1. No matter what voltage you apply, it will be interpreted as either high or low.
Rather you will have to use an ADC input, sample at sufficiently high frequency, delay the samples through a FIFO, then apply the delayed samples to a DAC. Reconstruction of a "pure" sine wave from the quantized DAC output requires analogue filtering circuitry. With a filter cut-off lower than half the sampling rate you will recover a reasonably good representation of the original signal (which can be any signal with components below half the sampling frequency - it need not be a sine wave). If you do use a more complex signal, you will need to analogue filter the input to remove components above half the sampling rate to avoid aliasing.
It might be possible to do all that on one chip using a Cypress PSoC, since these are hybrid chips with reconfigurable analogue elements as well as a microcontroller.
How do I feed the waveform pulses into DAQ Assistant to cause a DAQ 6259 board to generate desired voltage pulses?
Using the Simulate Signal express VI I have created a square pulse waveform.
My goal is to allow a LabView user to configure the Frequency and Pulse width using knobs from the GUI as needed in order to generate a desired pulse train. This pulse train should be sent to the DAQ 6259 to generate a voltage pulse train. The voltage pulse train would be captured by an oscilloscope in order to verify its correctness (i.e. the captured pulse train looks exactly like the waveform displayed in the labview GUI).
What is the simplest way this can be achieved? Are there any tutorials that explain how this can be done?
Have you checked out the example finder (Help>Find Examples...)
Hardware Input and Output > DAQmx> Analog Output / Digital Output
There are a bunch of examples in there that will get you 90% of the way
I just bought Digital heart beat rate sensor:
http://www.dealextreme.com/p/digital-heart-beat-rate-sensor-3-5mm-data-port-16009
And I'm looking how I can make application for iOS to work with.
Sensor has 3.5mm jack and I can detect signal with audio framework on iOS.
Can you give me some guidelines how to start with detecting these signals into heart beat rates?
That sensor looks rather like one I have here in my junk box. If so, it generates a voltage signal which depends on the pressure exerted on it by the skin against which it is pressed. If there is a strong pulse at the point of pressure, I see a signal on an oscilloscope which has a component at the pulse rate: so it is at a frequency of around 1-2Hz.
This is WAY below the audio range, and in most audio interfaces would be filtered out before it ever got to the audio in ADC. I don't have a handy iPhone to check this on, but it would be bad design if the audio input did let such frequencies through. And Mr Jobs (R.I.P.) did not approve of bad design!
There is also a lot of interference at other frequencies: mains hum (50Hz here), and at lower frequencies spurious signals from muscle twitches.
To make this work, you would need some sort of signal conditioning. If it was up to me, I would use a high input impedance amplifier, with about a 0.1Hz - 10Hz passband, followed by a voltage to frequency converter. That would give me a tone, which i could set in the audio band, whose frequency varied up & down as the pressure on the sensor changes. That would let me use fairly simple frequency detection software to recover the pressure waveform, which could then be processed using autocorrelation or similar techniques to recover the heartbeat frequency. A DTMF decoder is not the right tool, though.
I did find when I played about with the senor that it was very touchy, responding to almost everything going, and it wouldn't be easy to pick out the heartbeat. Your sensor may be different, though.