Signal Processing energy conservation

In summary: Good. Replace the summation with an integral, since the problem states that all frequencies in the band are present. Let's assume that they have equal strength, so that A(\omega)=A_0 for 20π≤ω≤400π. For line-of-sight propagation in a homogeneous medium, furthermore, A = B. Your integral then takes on a simpler form that you should be able to evaluate for power and compare to the power in the incident wave.$u(x,t)=\int_{-\infty}^{\infty}A(\omega )d\omega \,\,\,\,\,\,\,\,\,\,
  • #1
MathOamtiX
6
0

Homework Statement



Two receivers, d=5 [meters] apart, are recording an air wave signal.
The air wave travels in v=330 [m/sec] and coming from one side of the receivers.
The air wave contains all the frequencies between 10 [Hz] and 200 [Hz].

a) If we sum up the recorded signal from the two receivers, does all the energy from the original wave will be conserved ? explain.

b) The sampling rate in the receivers is Δt=0.004 [Sec], sketch the amplitude spectrum of the sum series.

Homework Equations



[itex]$\omega =2\pi f$[/itex] ,

[itex]\[\sum\limits_{n=0}^{N-1}{|}{{x}_{n}}{{|}^{2}}=\frac{1}{N}\sum\limits_{k=0}^{N-1}{|}{{X}_{k}}{{|}^{2}}\][/itex] (Parseval Theorem)

and maybe the DFT of sin/cos functions

The Attempt at a Solution



a) I know that the starting point should be the distance and the velocity.
so we get t=v/d and from that the frequency can be calculated somehow.
also the wave is a combination of known sin waves, so after DFT it transfers into a delta function.

b) Here i don't even know how start, since nothing is implied about the amplitude !
 
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  • #2
Hint: Since you are summing the outputs of two sensors, you must account for the frequency-dependent phase shift between them.
 
  • #3
Thank you! but...

marcusl said:
Hint: Since you are summing the outputs of two sensors, you must account for the frequency-dependent phase shift between them.

You are right, i calculated the phase change between the receivers, i got 66 [Hz].
but how this affects the summation ?
 
  • #4
Your Nyquist frequency is less than 200 Hz.
 
  • #5
Thank you! but...

.Scott said:
Your Nyquist frequency is less than 200 Hz.

How did you compute the Nyquist frequency in this ?
In order to calculate the Nyquist frequency i need the sampling time interval of the receivers no ?
 
  • #6
MathOamtiX said:
How did you compute the Nyquist frequency in this ?
In order to calculate the Nyquist frequency i need the sampling time interval of the receivers no ?
The sampling period is given in part b of the problem as 0.004sec. The inverse of that, 250Hz is the sampling rate. The Nyquist - well, you can take it from there.
 
  • #7
Thank you

.Scott said:
The sampling period is given in part b of the problem as 0.004sec. The inverse of that, 250Hz is the sampling rate. The Nyquist - well, you can take it from there.

Thank you,
but I'm not sure that i can use the given information in part b for calculations in part a ...

and as for part b, how can i get anything about the amplitude ?
 
  • #8
Part a is, in part, a spatial question. Draw a picture of a wave impinging on your sensor array and notice the difference in phase. A plane wave has the form [itex]A(\omega)=A_0 \exp\left[i(\omega t - \vec{k}\cdot\vec{r})\right][/itex] where the wavevector [itex]\vec{k}[/itex] points along the direction of propagation with [itex]|\vec{k}|=2\pi/\lambda[/itex]. Perform the summation, using the positions [itex]\vec{r}[/itex] of your sensors, before calculating the power.
 
  • #9
marcusl said:
Part a is, in part, a spatial question. Draw a picture of a wave impinging on your sensor array and notice the difference in phase. A plane wave has the form [itex]A(\omega)=A_0 \exp\left[i(\omega t - \vec{k}\cdot\vec{r})\right][/itex] where the wavevector [itex]\vec{k}[/itex] points along the direction of propagation with [itex]|\vec{k}|=2\pi/\lambda[/itex]. Perform the summation, using the positions [itex]\vec{r}[/itex] of your sensors, before calculating the power.

Ok, i think i got part A:

In order to conserve all the frequencies, there must be at least 2 samples per 1 wave length. so the spatial Nyquist is then: [itex]${{k}_{\max }}=\frac{1}{2\Delta x}=0.1$[/itex]

and on the other hand:

[itex]$k=f/v\to {{k}_{\max }}=200/330\sim 0.6$ [/itex]

so we won't get all the energy, is that right ??

but I'm still struggling with part b, since nothing is said about the amplitude,
 
  • #10
No, part a has nothing whatever to do with digital sampling. Have you studied anything about wave propagation? Can you visit your TA or professor? It will be easier to get this explained face to face and with a whiteboard handy.
 
  • #11
marcusl said:
No, part a has nothing whatever to do with digital sampling. Have you studied anything about wave propagation? Can you visit your TA or professor? It will be easier to get this explained face to face and with a whiteboard handy.

My Professor is unavailable, and there is no TA.
i know the basics of wave propagation but i just don't understand how to perform the summation.
Following your steps i get:

[itex]$u(x,t)=\sum\limits_{\omega =20\pi }^{400\pi }{[A(\omega )~{{e}^{i(\omega t)}}+B(\omega )~{{e}^{i(5k-\omega t)}}}]\,\,$[/itex]
 
  • #12
Good. Replace the summation with an integral, since the problem states that all frequencies in the band are present. Let's assume that they have equal strength, so that [itex]A(\omega)=A_0[/itex] for 20π≤ω≤400π. For line-of-sight propagation in a homogeneous medium, furthermore, A = B. Your integral then takes on a simpler form that you should be able to evaluate for power and compare to the power in the incident wave.
 

1. What is signal processing energy conservation?

Signal processing energy conservation is the practice of using techniques and algorithms to reduce the amount of energy required for signal processing tasks. This can include reducing the amount of data that needs to be processed, optimizing the use of resources, and implementing energy-efficient algorithms.

2. Why is signal processing energy conservation important?

Signal processing energy conservation is important because it can help reduce energy consumption and costs, increase the lifespan of electronic devices, and minimize the environmental impact of signal processing technologies. It also allows for more efficient use of limited resources in mobile and wireless devices.

3. What are some techniques used for signal processing energy conservation?

Some techniques used for signal processing energy conservation include data compression, resource allocation optimization, and the use of low-power hardware and software components. Other methods include adaptive algorithms, energy-efficient routing protocols, and dynamic voltage and frequency scaling.

4. How does signal processing energy conservation impact real-world applications?

Signal processing energy conservation has a significant impact on real-world applications, especially in the areas of wireless communication, multimedia processing, and internet-of-things devices. It allows for longer battery life, faster data transmission, and more reliable performance, making these applications more practical and efficient.

5. What are the challenges in implementing signal processing energy conservation?

Some challenges in implementing signal processing energy conservation include balancing energy efficiency with performance, finding the optimal trade-off between energy consumption and quality of service, and adapting to changing environments and user needs. Additionally, there may be limitations in hardware and software capabilities, as well as the need for specialized knowledge and skills in developing energy-efficient algorithms and systems.

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