Compressive sensing approach became very popular in different fields, mainly in the optics (I have this opinion since I am from the optics). In which other fields, except optics, used compressed sensing (relevant references are welcome)?
In your opinion what are the main advantages and disadvantages of this approach?
Is it possible to apply it in your research?
Thank you in advance!
Any digital devices that sense real world signals, such as sensors in our gadgets (microphone, camera, magnetometer, ... etc), employs ADC (analog to digital converter ). The ADC relies on Shannon/Whitaker sampling theorem for a proper sampling. Therefore, when there is a better sampling method, potentially it will revolutionize those devices. That's my opinion.
Any digital devices that sense real world signals, such as sensors in our gadgets (microphone, camera, magnetometer, ... etc), employs ADC (analog to digital converter ). The ADC relies on Shannon/Whitaker sampling theorem for a proper sampling. Therefore, when there is a better sampling method, potentially it will revolutionize those devices. That's my opinion.
Compressive sensing would be useful in WSN , as in this application we can minimize data transmission,power consumption of sensors, perhaps error detection and improve security.
Cheers
Compressive sensing is also proposed for Quantum State Tomography. That is to reconstruct an unknown quantum state (almost pure state in high dimension) with few measurements. Here is the pioneering paper by Noble Laureate David Gross in this field.
http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.105.150401
It might be related to your topics in Optics!.
Later on Candes used the ideas from this work to introduce RPCA (robust PCA).
By the way I have not studied the details and only have superficial knowledge about all the contents I mentioned.
Compressive sensing (CS) is also used in x-ray computed tomography (XCT). Briefly, CS is used in advanced XCT image reconstruction algorithm, which allows less x-ray projection data to be acquired for reliable and accurate image reconstruction. The major advantage of CS based reconstruction is that it leads to radiation dose reduction. The major disadvantage is that such algorithms are typically slow which hampers its utilization in clinics.
Compressed sensing is also used for estimation of sparse multipath channels (e.g., under water communication channels, aeronautical communication channels, etc).
[1] http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5454399&tag=1
[2] http://digital-library.theiet.org/content/journals/10.1049/iet-com.2012.0162
Thank you all for your responses.
What are your opinion about disadvantages of the compressive sensing?
CS has many many advantages like the ones stated above. The only disadvantage i could think of is that more antennas are needed to resolve q signals. From a CS point of view, a q-sparse signal needs at least 2N antennas. Traditional techniques require N antennas.
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