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This collection reviews fundamental concepts underlying the use of concise models for signal processing. Topics are presented from a geometric perspective and include low-dimensional linear, sparse, and manifold-based signal models, approximation, compression, dimensionality reduction, and Compressed Sensing.


In characterizing a given problem in signal processing, one is often able to specify a model for the signals to be processed. This model may distinguish (either statistically or deterministically)classes of interesting signals from uninteresting ones, typical signals from anomalies, information from noise, etc.

Very commonly, models in signal processing deal with some notion of structure, constraint, or conciseness. Roughly speaking, one often believes that a signal has “few degrees of freedom”relative to the size of the signal. This notion of conciseness is a very powerful assumption, and it suggests the potential for dramatic gains via algorithms that capture and exploit the true underlyingstructure of the signal.

In these modules, we survey three common examples of concise models: linear models, sparse nonlinear models, and manifold-based models. In each case, we discuss an important phenomenon:the conciseness of the model corresponds to a low-dimensional geometric structure along which the signals of interest tend to cluster. This low-dimensional geometry again has important implicationsin the understanding and the development of efficient algorithms for signal processing.

We discuss this low-dimensional geometry in several contexts, including projecting a signal onto the model class (i.e., forming a concise approximation to a signal), encoding such an approximation(i.e., data compression), and reducing the dimensionality of signals and data sets. We conclude with an important and emerging application area known as Compressed Sensing (CS), which is a novel methodfor data acquisition that relies on concise models and builds upon strong geometric principles. We discuss CS in its traditional, sparsity-based context and also discuss extensions of CS to otherconcise models such as manifolds.

General mathematical preliminaries

Signal notation

We will treat signals as real- or complex-valued functions having domains that are either discrete (and finite) or continuous (andeither compact or infinite). Each of these assumptions will be made clear as needed. As a generalrule, however, we will use x to denote a discrete signal in R N and f to denote a function over a continuousdomain D . We also commonly refer to these as discrete- or continuous- time signals, though the domain need not actually be temporal in nature.

Lp and lp norms

As measures for signal energy, fidelity, or sparsity, we will employ the L p and p norms. For continuous-time functions, the L p norm is defined as

f L p ( D ) = D | f | p 1 / p , p ( 0 , ) ,
and for discrete-time functions, the p norm is defined as
x p = ( i = 1 N | x ( i ) | p ) 1 / p , p ( 0 , ) , max i = 1 , , N | x ( i ) | , p = , i = 1 N 1 x ( i ) 0 , p = 0 ,
where 1 denotes the indicator function. (While we often refer to these measures as “norms,” they actually do not meetthe technical criteria for norms when p < 1 .)

Linear algebra

Let A be a real-valued M × N matrix. We denote the nullspace of A as N ( A ) (note that N ( A ) is a linear subspace of R N ), and we denote the transpose of A as A T .

We call A an orthoprojector from R N to R M if it has orthonormal rows. From such a matrix we call A T A the corresponding orthogonal projection operator onto the M -dimensional subspace of R N spanned by the rows of A .

Questions & Answers

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Bob Reply
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Introduction about quantum dots in nanotechnology
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s. Reply
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Devang Reply
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what is the actual application of fullerenes nowadays?
That is a great question Damian. best way to answer that question is to Google it. there are hundreds of applications for buck minister fullerenes, from medical to aerospace. you can also find plenty of research papers that will give you great detail on the potential applications of fullerenes.
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Abhijith Reply
Mostly, they use nano carbon for electronics and for materials to be strengthened.
is Bucky paper clear?
carbon nanotubes has various application in fuel cells membrane, current research on cancer drug,and in electronics MEMS and NEMS etc
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s. Reply
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Do you know which machine is used to that process?
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s. Reply
of graphene you mean?
or in general
in general
Graphene has a hexagonal structure
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Source:  OpenStax, Concise signal models. OpenStax CNX. Sep 14, 2009 Download for free at http://cnx.org/content/col10635/1.4
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