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Preliminaries

Module by: Marco F. Duarte, Ronald DeVore. E-mail the authors

We previously described Shannon's Theorem plus encoding: the Nyquist sampling rate is the minimal required sampling rate to recover the entire class of bandlimited signals. We have seen that this sampling rate may be prohibitively large for broadband signals. We see a way to improve upon this situation: we will pose a different model for the signals which is more restrictive than the assumption that the signals are bandlimited. Fortunately, there are several real world scenarios in which one knows much more information about the signals of interest. For example, they may be written in terms of very few fundamental building blocks (such as sine waves or chirps). This leads us to define new signal classes based on notions of sparsity and seek to determine if we can improve on sampling and encoding in this new setting.

Let us define the general setting for this section. Let XX be a Banach space of functions. The typical examples are X=Lp⁢(R),Lp⁢(Rd),Lp⁢(-T,T)X=Lp⁢(R),Lp⁢(Rd),Lp⁢(-T,T), 1≤p≤∞1≤p≤∞. We denote the norm on XX by ∥⋅∥X∥⋅∥X. We define a dictionary DD as any collection of functions D⊆XD⊆X such that ∥g∥X=1∥g∥X=1 for all g∈Dg∈D, i.e. all the elements of the dictionary are normalized. While the definition is very broad, in practice dictionaries usually have more structure. Some examples include D=BD=B, a basis for XX, such as (i) the Fourier basis on [-π,π][-π,π], (ii) a wavelet basis,1 (iii) redundant families of waveforms of the form ψa,b,σ=e-a⁢(t-b)2⁢ei⁢σ⁢xψa,b,σ=e-a⁢(t-b)2⁢ei⁢σ⁢x, i.e. D={ψa,b,σ}a,b,σD={ψa,b,σ}a,b,σ, and (iv) wavelet packets.

Definition 1

We define the class of nn-sparse signals as Σn:=Σn(D)={s=∑g∈Λcgg,Λ⊆D,♯Λ≤n}Σn:=Σn(D)={s=∑g∈Λcgg,Λ⊆D,♯Λ≤n}. We also say that ss has sparsity nn in DD if s∈Σn⁢(D)s∈Σn⁢(D), i.e. if it can be written as the linear combination of nn functions from DD. We note that ΣnΣn is not a linear space; we instead have Σn+Σn⊆Σ2⁢nΣn+Σn⊆Σ2⁢n.

Footnotes

  1. Wavelet basis form orthonormal systems for L2⁢(I)L2⁢(I).

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Definition of a lens

Lenses

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

What is in a lens?

Lens makers point to 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 member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

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