Connexions

You are here: Home » Content » Results
Content Actions
Lenses

What is a lens?

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...
Affiliated with (?)
This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.
  • This module is included inLens: Rice University ELEC 301 Project Lens
    By: Rice University ELEC 301As a part of collection:"ELEC 301 Projects Fall 2004"

    Click the "Rice University ELEC 301 Projects" link to see all content affiliated with them.

    Rice University ELEC 301 Projects
  • This module is included inLens: Rice University OpenCourseWare
    By: OpenCourseWare ConsortiumAs a part of collections:"Array Signal Processing", "ELEC 301 Projects Fall 2004"

    Click the "Rice University OCW" link to see all content affiliated with them.

    Rice University OCW
Tags

(?)

These tags come from the endorsement, affiliation, and other lenses that include this content.

Results

Module by: Edward Rodriguez, Jeremy Bass, Claiborne McPheeters, James Finnigan

Summary: A discussion of the project results and conclusion.

The tests of the real-world implementation were successful. Using the second laptop, we positioned a 1000 Hz signal at about 40 degrees and a 1600Hz signal at about -25 degrees relative to the axis perpendicular to our array. Both signals were on simultaneously and at equal volume. As you can see from the spectrum of the output of our program, changing the dial to tune to the array to different directions results in the expected behavior.
Tuned towards 40 degrees
1000.JPG
Figure 1
Tuned towards -25 degrees
1600.JPG
Figure 2
These first two figures show that our output signal consists of the two test sinusoids. Tuning the software to look in the appropriate directions shows that the magnitude of the corresponding sinusoid is more powerful than that of the power of the other sinusoid. Focusing on the 1000 Hz sinusoid enhances the power of that sinusoid to about 5 times that of the other sinusoid. Focusing on the 1600 Hz sinusoid gives even better results. The difference in power here is more than 10 times.
Tuned straight ahead
zero.JPG
Figure 3
Tuned towards -83 degrees
none.JPG
Figure 4
When we tune the array to a direction which does not have a source we get scaled down versions of anything close to that angle. For example, when we steer it at the middle we get small versions of the two sinusoids, and when we steer the beam at a direction that's way off, we get much smaller peaks from our two sinusoids.

Conclusion

We were able to demonstrate this processing technique in both simulation and real life. The main problem with our system is the amount of processing that is needed to make this a realtime process. For example, using a 1.6GHz processor we were capturing 1 second of data and taking about 2 seconds to process it. Due to this restriction in processing power, we are only processing a band of spectrum from 800 - 1600 Hz. This is not enough to process voice data. Another problem is that we have to space the sensors closer together in order to sample a higher frequencies because we have to avoid spatial aliasing in addition to temporal aliasing. Therefore the main improvements to this system would be ways to decrease the amount of processing or to use a much more powerful system. Although we have hammered out a good chunk of this project, there is definitely room for improvement in the optimization of processing.

Comments, questions, feedback, criticisms?

Send feedback