How would I find the percent of mechanical energy lost to thermal energy due to friction if a 3.250g bullet enters a wooden block with a velocity of 80.00 m/s, and if it exits the block with a velocity of 20.00 m/s?
Thank-you!
You can do this using the conservation of energy. "Non-Conservative Force" only means that use won't get the kinetic energy back by moving the bullet backwards.
Your system consists of the wooden block and the bullet. There is nothing else that can exchange energy so the whole energy is always the sum of
the kinetic energy of the bullet (=m v^2 / 2)
the thermal energy of the bullet
the kinetic energy of the block
the thermal energy of the block
At first everything but the kinetic energy of the bullet is zero.
If your block is very heavy and is not accelerated by the bullet, its kinetic energy will stay =0. So all the lost kinetic energy must have been converted to thermal energy.
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I have a vehicle with a tracker installed. The device has a gps syst, 3-axis accelerometer, 3-axis magnetomet and a gyromeyet. Is it possible to determine by how much the vehicle rolled back on a slope or hill. Using gps angle wasn't an option as the angle given for short backward movement isn't always reliable. Can accelerometer be used in such a scenario??
You're right that the GPS angle (heading) will not help you in a single-antennae setup. On its own a GPS receiver needs a minimum distance of movement to determine heading.
A simple GPS receiver, when used without GPS corrections (which is the case for off-the-shelf GPS devices and mobile phones/tablets), has a minimum ~5 meter accuracy. That's why a short backward movement will not yield the desired results.
In construction/mining applications, there is often a fixed GPS base station nearby that broadcasts GPS corrections, which allows vehicle-mounted GPS receiver to apply corrections, reduce error and ultimately get centimeter-level accuracy.
So in conclusion, your 3-axis accelerometer will likely be the only sensor that you can rely on until your vehicle has rolled back at least 5 meters.
If your accelerometer is sensitive enough, you'll get measurable sensor values. However, if you rollback is very slow, where the G forces are almost imperceptible to the accelerometer, then you're out of luck.
This is assuming that you want near real-time detection of vehicle rollback.
Currently I am working on this project to provide the layout of a smart street light system with energy saving function based on sensor network for energy management. The proposal is an autonomous-distributed-controlled light system, in which the lights turn on before pedestrians come and turn off or reduce power when there is no one by means of a distributed-installed sensor network.
I will be adding a few things to the project for energy reduction but what I need to know is how do I perform the simulation to show that this approach would reduce energy consumption?
If a theoretical system is adiabatic and can't exchange heat with it's surroundings, is the exterior surface theoretically the same temperature as the surroundings and is the interior surface theoretically the same temperature as the the system itself? and if so, how does this not break the definition of heat as energy that flows from something at a hotter temperature to something at a lower temperature?
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.
I am currently creating an application which signals readiness to other devices using a high frequency sound.
(transmitter): A device will produce a short burst of sound of around 20khz.
(receiver): Another device will be listening for a sound at this frequency at a small distance from the transmitter(10m approx) The device recieves audio data from a microphone
The background noise will be fairly loud, varying from around 0 - 10khz(about human speech range), and would be produced by a small crowd of people.
I need the receiving device to be able to detect the 20khz sound, separated from the noise,
and know the time at which it was received.
Any help with an appropriate algorithm, a library, or even better, code in C or
Objc to detect this high frequency sound would be greatly appreciated.
20 kHz may be pushing it, as (a) most sound cards have low pass (anti aliassing) filters at 18 - 20 kHz and (b) most speakers and microphones tend to have a poor response at 20 kHz. You might want to consider say 15 kHz ?
The actual detection part should be easy - just implement a narrow band pass filter at the tone frequency, rectify the output and low pass filter (e.g. 10 Hz).
You may want to look into FFT (Fast Fourier Transform). This algorithm will allow you to analyse a waveform and convert it to the frequency spectrum for further analysis.
If this is for Mac OS or iOS, I'd start looking into Core Audio's Audio Units.
1 Here's Apple's Core Audio Overview.
2 Some AudioUnits for Mac OS
3 Or for iOS AudioUnit Hosting
A sound with that high frequency will not travel at all with the iphone speaker.