Ok, so a laser on earth hits a mirror on the moon and bounces back. On the ATmega128 microprocessor, we use TIMER1 to capture the clock ticks when the laser shot out and the clock ticks when it returned, subtract and get a "distance" in clock ticks. (16MHz clock on ATmega128).
So we are supposed to determine how different can this measured distance be from the actual distance and what can cause it. As well as compute max error in each legal prescaler of TIMER1.
Looking at TIMER1's registers and input capture information in the ATmega128 datasheet I cannot find any kind of percentage error with the input capture. Like, this seems like a conceptual question, yet we are supposed to pull values out of the air and calculate something?
My question is if anyone knows anything of ATmega128, what values are being referred to in determining error from reading distance with timer ticks? My only guess is the error occurs when you use higher and higher prescalers because you lose preciseness when prescalers get larger. But again this is a conceptual answer and don't understand how I would calculate anything.
The counters/prescalars can be assumed to be perfect and will not cause any loss of resolution.
Your original clock source will be a predominant source of errors. If you are using an external clock with a crystal, these are usually good to 50 ppm (part per million) or better. If you are using an internal clock, the error is much higher (1% is not unreasonable for some microcontrollers).
The whole thing gets tricky if you remember your general relativity (you do have a PhD in Physics right?). The earth's rotation and gravity come into play wrt the speed of light and distance...
Related
Well, i want to ask if ADXL345 can be used to detect an Earthquake Occurrence based on its magnitude/intensity level. For more information, I want to used an accelerometer to create a Device that can detect the intensity/magnitude level of an Earthquake.
I have absolutely no experience in this field, but it looks useful and fascinating.
Questions are:
is this device able to detect medium scale earthquakes?
if yes, does anybody did it, available to share experiences?
if no to the previous, is there any guide which explains algorithms, calculations and mechanical plans?
That sensor is not suitable. It has 13 bit resolution at +-16g full range. That gives you a sensitivity of 0.002g for the lsb. In order to detect an earthquake directly below you, you need approx. a few milli-g (e.g. see here), even less for earthquakes with an epicentre elsewhere.
You want a sensor which is much more sensitive by a factor of 100 and probably with more resolution (better ADC), too.
(And you should have been able to do this quick google-search analysis yourself ;) )
Using accelerometers reading tells you nothing about the actual magnitude of the quake itself. It tells you the size of the quake at your location. Combining location and amplitude will give you a 'weighted' measurement, but that's still useless without a calibration curve. Without knowing what acceleration, at a certain distance, corresponds to what magnitude you will be unable to tell what the magnitude is. You can certainly conclude that your measured earthquake has a median amplitude of, say, 2000% of a non-earthquake reading, but you won't be able to turn it into a Richter measurement. To do that you'd need to take some data during earthquakes of known magnitude and then work out how acceleration, distance and magnitude are related for your device. You could alternatively use a scale like the Shindo (just Google it).
I wanted to know if there is any efficient way of finding the distance between 2 devices(a transmitter and a receiver) which is accurate to atleast the order of a couple of inches.
I am basically want to detect the movement of the transmitter from the receiver and how far it has moved from its original position.
I was thinking in terms of using a wireless hotspot/bluetooth connection. I cannot Use some form of audio/medium which can be detected by humans.
Could anybody help me with this?
To my mind, assuming there is no common synchronisation signal between the devices, there are 2 differents way to do this (not really easy):
1. Measure received power : some receivers provide RSSI (Received Signal Strength Indication). RSSI is a measure of how much power you received. If you know the transmitted power, you can estimate the transmission loss (from the transsmission channel) by taking different measure of RSSI at different distance. It will really depends on the channel (environment, frequency, throughput, ..), so don't change it for the measure. Once you got enough points, try to fit it by a curve. You can now predict distance by having RSSI.
2. Measure round trip time : this is called RADAR, and is really more difficult but is the classic way to measure distance and speed. Broadband systems (like WiFi) are better for this kind of measure. By the way you also can do the same with audio for short distances (SONAR), without being detected if you use frequencies higher than 20kHz.
I'm trying to measure the distance an object is pulled along a pulley track using arduino (or Rasp Pi) sensors. I have an object that is manually pulled, from a resting position, on a pulley system and I need to be able to track the distance it travels over one pull.
Example: The object traveled 90% (example) of the total pulley distance for 5/10 pulls.
Example: The object traveled 11.53 ft along the pulley.
See the image below for a visual diagram. I have two ideas, but I'm not an Arduino sensor expert. I'd love input on an elegant solution.
Use an arduino sensor to sense how many times the pulley rotates, and then use an equation to determine the distance.
Use a sensor that senses the distance pulled in cord, maybe each direction, and record that distance. I got this idea from pump sensors, no idea if a comparison exists.
Really just looking for advice on what sensors to use, how to implement them (general), and what type of metrics I could record.
There are at least two ways of doing this:
Use a rotary encoder, which will tell how many rotations the pulley did. There are two disadvantages: line can slip on the pulley, resulting in inaccurate measurement, and there is no way you can now at what actual position the object is. Rotary encoder will give you only position relative to the starting position (unless you only need one rotation of the pulley - then you can use absolute encoders)
Use a distance sensor (ultrasonic, or IR) to measure at what height is the object. That way you will know the exact position of the object, but you may have a problem if your object is too small, has a shape or surface that will impact the measurements, or if the space is limited and a sensor can pick up walls, or other surrounding objects. Also if the objects may swing on the rope, there will probably a problem.
Software implementation for all of these solutions should be pretty simple, just decide on type of sensors - there are plenty of tutorials for all of them.
Both task 1 and 2 can be done easily using a digital rotary encoder at very nominal cost. It can sense direction and distance of travel quite accurately.
In a previous question, I had asked Why can't I simply negate the source time domain amplitude values to produce a destructive noise signal?
One of the posters said that while simply producing a inverses polarity (negated) signal will work in theory, in practice it is not possible
So I am asking, what is the fundamental approach (in a sort of semi technical way) to active noise cancellation?
Secondly, why are most literature on this topic in frequency domain?
It's rather simple.
By the time you send your inverted signal, the noise has already been heard.
You need to look at what frequencies are being generated, and then produce the appropriate inverted signals of those to cancel them out.
Noise cancellation is prediction. Your algorithm has to predict what the sound of the noise will be at some time in the future (that time given by the system and audio time latencies), and then predict what signal will produce the opposite sound at that same point in the future (which your system will distort and delay, so you have to figure in the opposite distortion and delay).
You might be able to use several successive FFTs to determine which frequencies in the noise are not changing, and assume or calculate some probability that they will continue for a short time into the future.
If you know the frequency response curve of the speaker, you might be able to figure out the frequency amplitudes of a signal needed to match some predicted noise spectrum. The phase angle of a sinusoid will change with time. If you know the time delay of your output signal, you might be able to calculate the phase of a sinusoid at some point in the future. If you have a predicted phase of a particular frequency of noise at some time and location, you can add π to that phase angle to estimate the noice-cancelling signal.
If you don't know the frequency response and delay of your system, then you won't know what frequencies, amplitudes or phases of signal to create for cancellation. You might well end up amplifying the noise instead of cancelling it.
It seems that what’s missing is the propagation delay required to intercept and negate a signal. The KISS rule will eventually prove this true. The FFT is a complex calculation and each N iteration will introduce resulting error due to the time required to process the signal. To cancel a sound wave it will need to be intercepted in advance, processed and inverted. Then the time constant of the transducer must. E considered. My experience is that a microphone near the source of “noise” connected by wire and amplification device and transducer near the location where It is to be cancelled.
edit: typo
The basic idea of ANC is to find repetitive sound and play the opposite of it. If the repetitive sound continue to play we'll be able to cancel it. That goes in direct contradiction to to the other answers, but I'll clarify.
Playing the opposite sound means playing it again with a precise power and delay, possibly inverting the waveform. The delay itself varies for each frequency. For example, for a 20Hz sound we have to replay the inverted sound on a precise multiple of 1/20 = 0.05s. For 23Hz, for example, the delay has to be a multiple of 1/23 ~= 0.04347s.
Since any waveform can be produced by sum of sinusoidal, one way of doing it would be to only worry about the N biggest sinusoids, measured in power (square of the amplitudes). For finding the sinusoidal's frequencies and power we use the Fourier Transform, typically with the FFT algorithm.
If we take, for example N=8, it means we are trying to eliminate the 8 most powerfull wave components. For each of them we store:
wave's amplitude
wave's offset, taking the computer's clock as a base.
than we constantly play 8 sinusoids, each on the correct power and with the correct delay. The hard part is what happens next. We need to keep listening to adapt, but now we are listening to the environment sound + our own sound. This algorithm is harder to implement, but conceptually is easier, and one could easily figure out how to do it by himself.
So, contrary to what the other answers say, managing the time delay is critical. Is not possible to create an ANC system without doing it. If you only care about the frequency domain, the only thing you could possibly do is filter those frequencies. On an ANC system this makes not sense.
I am attempting to use output from an accelerometer moving back and forth on a single axis to calculate its current position.
I have tried using Euler integrations, but the velocity and position errors become too large too quickly. After some reading, I am wondering whether RK4 could be applied to this problem to minimise the error?
Cheers,
A.
There is usually some drift in accelerometers. You need a measurement of the actual position to remove static errors.
RK4 might help.
Are you also measuring position, and that's where your assessment of accumulated error comes from?
It might also be due to time increments being too large. Perhaps you've exceeded a stability limit for your system. A finer time step and an implicit integration method might help with those errors.