Solve Problems with Gaussian Random Matrix: Compressed Sensing Senior Project

In summary, compressed sensing is a mathematical technique that reconstructs a sparse signal from a small number of random measurements. It works by using a mathematical algorithm and a key idea that the signal is sparse. A Gaussian random matrix is commonly used in compressed sensing as a measurement matrix. This method can be used to solve problems such as signal reconstruction, data compression, and image reconstruction, offering benefits such as reduced data storage requirements and improved accuracy. It also has applications in various fields such as medical imaging, wireless communication, and remote sensing.
  • #1
giaoduong
2
0
I'm doing my senior project about Compressed sensing, I have some problems with some algorithms.
1)Can anybody help me about Gaussian random matrix, how we can explain it briefly.
2)Does the randn(m,n) building function in Matlab working bases on Probability density function? How does randn work?

Please help me!

Thank you!
Have a nice day!
 
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  • #2
Any body here:((
 

1. What is compressed sensing?

Compressed sensing is a mathematical technique that allows for the reconstruction of a sparse signal from a small number of random measurements. It has various applications in signal processing, data compression, and image processing.

2. How does compressed sensing work?

Compressed sensing works by taking random measurements of a signal and then using a mathematical algorithm to reconstruct the original signal. The key idea is that the signal is sparse, meaning it has a small number of non-zero elements, and thus can be represented by a small number of measurements.

3. What is a Gaussian random matrix?

A Gaussian random matrix is a matrix whose entries are randomly generated from a Gaussian distribution with mean 0 and variance 1. It is commonly used in compressed sensing as a measurement matrix to take random measurements of a signal.

4. How is compressed sensing used to solve problems?

Compressed sensing can be used to solve problems such as signal reconstruction, data compression, and image reconstruction. By taking a small number of random measurements, it allows for the efficient and accurate reconstruction of a signal or image, even if it is sparse or noisy.

5. What are the benefits of using compressed sensing for problem-solving?

Compressed sensing offers several benefits for problem-solving, including reduced data storage requirements, faster data acquisition, and improved accuracy in signal reconstruction. It also has applications in various fields such as medical imaging, wireless communication, and remote sensing.

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