Signal recording parameters (EE)

In summary, to accurately determine the power spectrum of a signal from a high speed pressure sensor below 35 kHz with a resolution of 0.5 Hz, you would need to use a sampling rate that satisfies the criterion of no aliasing and a number of samples that allows for a frequency resolution of 0.5 Hz. This can be determined by combining the minimum sampling rate and the desired frequency resolution. No reconstruction filter is necessary in this case.
  • #1
xzibition8612
142
0

Homework Statement



What (complete) set of sampling and filtering parameters would you choose to record a signal from a high speed pressure sensor if you wanted to accurately determine its power spectrum below 35 kHz with a resolution of 0.5 Hz?

Homework Equations


frequency resolution = sampling rate / number of samples


The Attempt at a Solution



So I guess for the 0.5 Hz frequency resolution, any combination in the above formula giving 0.5 would work? For example 1000 sampling rate, 2000 samples?

The power spectrum would use filter. Since the signal is below 35 kHz, a low-pass filter with cutoff at 35 kHz would work?

Am I doing this correctly? Thx.
 
Physics news on Phys.org
  • #2
The sampling rate must satisfy the criterion of no aliasing. If the frequency spread of interest is 0 - 35 KHz, what is the minimum sampling frequency?

You realize I assume that this is a DFT problem. So how many time samples N would you need to achieve 0.5 Hz resolution of the 0 - 35 KHz signal? It's not the formula you gave above for freq. resolution.

You then get N/2 + 1 numbers representing the cosine component and another N/2 + 1 numbers representing the sine component of each harmonic of the fundamental frequency which is the resolution frequency. So what would be the power in each frequency component 0, 1/NT, 2/NT etc. where 1/T is the sampling frequency?

(You can also get complex frequency components. This is actually easier for determining power for each harmonic.)

I don't see the need for a reconstruction filter if all you want is the power spectrum.
 
  • #3
RETRACT: your formula for frequency resolution is correct. But your sampling rate is way off. If the spectrum is 0 - 35 KHz, what is the minimum sampling rate?

So you can determine N, the number of samples needed, by combining the minimum sampling rate and the desired frequency resolution of 0.5 Hz.
 

FAQ: Signal recording parameters (EE)

1. What is the purpose of signal recording parameters in EEG?

The purpose of signal recording parameters in EEG (electroencephalography) is to control and monitor the recording of electrical activity in the brain. These parameters determine the quality, accuracy, and reliability of the recorded signals, allowing for better interpretation and analysis of the brain's activity.

2. What are the most important signal recording parameters in EEG?

The most important signal recording parameters in EEG include sampling rate, filter settings, impedance, and sensitivity. These parameters affect the frequency range and amplitude of the recorded signals, as well as their noise levels.

3. How does the sampling rate affect the recorded signals in EEG?

The sampling rate determines the number of data points collected per second, which affects the temporal resolution of the recorded signals. A higher sampling rate allows for better detection of fast, transient brain activity, while a lower sampling rate may miss important information.

4. What is the impact of filter settings on EEG signal recording?

The filter settings in EEG determine the frequency range of the recorded signals. Low-pass filters remove high-frequency noise, while high-pass filters remove low-frequency signals. Band-pass filters allow for the selection of specific frequency ranges of interest. Choosing the appropriate filter settings is crucial for accurate interpretation of the signals.

5. How do impedance and sensitivity affect EEG signal recording?

Impedance refers to the resistance of the electrodes and their contact with the skin. High impedance can lead to noise and artifacts in the recorded signals, while low impedance allows for better signal quality. Sensitivity refers to the amplification of the recorded signals. A higher sensitivity can enhance small signals but also increase noise. Finding the optimal balance between impedance and sensitivity is important for high-quality EEG recordings.

Similar threads

Sampling a continuous-time signal, aliasing/Nyquist
Replies
1
Views
2K
Modern Digital Audio: Understanding Dither and Oversampling in Hi-Res Age"
Replies
9
Views
2K
Is my understanding of the Nyquist theorem correct?
Replies
4
Views
1K
Sampling Continuous-Time Signal
Replies
5
Views
2K
Sampling low pass filtered white noise
Replies
2
Views
2K
Oversampling/undersampling of a continuous time signal and preventing aliasing
Replies
1
Views
5K
Problems designing an FIR band pass filter on MATLAB?
Replies
11
Views
2K
Calculating delay b/w signals using correlation
Replies
10
Views
2K
Signal Processing energy conservation
Replies
11
Views
2K
Comparison between ideal, linear and lowpass filter interpolators
Replies
1
Views
2K

Hot Threads

Recent Insights

Back
Top