A non-trivial example of exploiting extra assumptions about the signal is given by the recent field of compressed sensing, which allows for full reconstruction with a sub-Nyquist sampling rate. Specifically, this applies to signals that are sparse (or compressible) in some domain. As an example, compressed sensing deals with signals that may have a low over-all bandwidth (say, the "effective" bandwidth "EB"), but the frequency locations are unknown, rather than all together in a single band, so that the passband technique does not apply. In other words, the frequency spectrum is sparse. Traditionally, the necessary sampling rate is thus 2"B". Using compressed sensing techniques, the signal could be perfectly reconstructed if it is sampled at a rate slightly lower than 2"EB". With this approach, reconstruction is no longer given by a formula, but instead by the solution to a linear optimization program.