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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.

A new theory known as Compressed Sensing (CS) has recently emerged that can also be categorized as a type of dimensionalityreduction. Like manifold learning, CS is strongly model-based (relying on sparsity in particular).However, unlike many of the standard techniques in dimensionality reduction (such as manifold learning or the JL lemma), the goal ofCS is to maintain a low-dimensional representation of a signal x from which a faithful approximation to x can be recovered. In a sense, this more closely resembles the traditional problem ofdata compression (see Compression ). In CS, however, the encoder requires no a priori knowledge of thesignal structure. Only the decoder uses the model (sparsity) to recover the signal. Wejustify such an approach again using geometric arguments.

Motivation

Consider a signal x R N , and suppose that the basis Ψ provides a K -sparse representation of x

x = Ψ α ,
with α 0 = K . (In this section, we focus on exactly K -sparse signals, though many of the key ideas translate to compressible signals  [link] , [link] . In addition, we note that the CS concepts are also extendable totight frames.)

As we discussed in Compression , the standard procedure for compressing sparse signals, known as transformcoding, is to (i) acquire the full N -sample signal x ; (ii) compute the complete set of transform coefficients α ; (iii) locate the K largest, significant coefficients and discard the (many) small coefficients; (iv) encode the values and locations of the largest coefficients.

This procedure has three inherent inefficiencies: First, for a high-dimensional signal, we must start with a large number ofsamples N . Second, the encoder must compute all N of the transform coefficients α , even though it will discard all but K of them. Third, the encoder must encode the locations of the large coefficients, which requiresincreasing the coding rate since the locations change with each signal.

Incoherent projections

This raises a simple question: For a given signal, is it possible to directly estimate the set of large α ( n ) 's that will not be discarded? While this seems improbable, Candès, Romberg,and Tao  [link] , [link] and Donoho [link] have shown that a reduced set of projections can contain enoughinformation to reconstruct sparse signals. An offshoot of this work, often referred to as Compressed Sensing (CS) [link] , [link] , [link] , [link] , [link] , [link] , [link] , has emerged that builds on this principle.

In CS, we do not measure or encode the K significant α ( n ) directly. Rather, we measure and encode M < N projections y ( m ) = < x , φ m T > of the signal onto a second set of functions { φ m } , m = 1 , 2 , ... , M . In matrix notation, we measure

y = Φ x ,
where y is an M × 1 column vector and the measurement basis matrix Φ is M × N with each row a basis vector φ m . Since M < N , recovery of the signal x from the measurements y is ill-posed in general; however the additional assumption of signal sparsity makes recovery possible and practical.

Questions & Answers

anyone know any internet site where one can find nanotechnology papers?
Damian Reply
research.net
kanaga
Introduction about quantum dots in nanotechnology
Praveena Reply
what does nano mean?
Anassong Reply
nano basically means 10^(-9). nanometer is a unit to measure length.
Bharti
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Damian Reply
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Daniel
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Akash Reply
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Anassong
Do somebody tell me a best nano engineering book for beginners?
s. Reply
there is no specific books for beginners but there is book called principle of nanotechnology
NANO
what is fullerene does it is used to make bukky balls
Devang Reply
are you nano engineer ?
s.
fullerene is a bucky ball aka Carbon 60 molecule. It was name by the architect Fuller. He design the geodesic dome. it resembles a soccer ball.
Tarell
what is the actual application of fullerenes nowadays?
Damian
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.
Tarell
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Abhijith Reply
Mostly, they use nano carbon for electronics and for materials to be strengthened.
Virgil
is Bucky paper clear?
CYNTHIA
carbon nanotubes has various application in fuel cells membrane, current research on cancer drug,and in electronics MEMS and NEMS etc
NANO
so some one know about replacing silicon atom with phosphorous in semiconductors device?
s. Reply
Yeah, it is a pain to say the least. You basically have to heat the substarte up to around 1000 degrees celcius then pass phosphene gas over top of it, which is explosive and toxic by the way, under very low pressure.
Harper
Do you know which machine is used to that process?
s.
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SUYASH Reply
for screen printed electrodes ?
SUYASH
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s. Reply
of graphene you mean?
Ebrahim
or in general
Ebrahim
in general
s.
Graphene has a hexagonal structure
tahir
On having this app for quite a bit time, Haven't realised there's a chat room in it.
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Sanket Reply
what's the easiest and fastest way to the synthesize AgNP?
Damian Reply
China
Cied
types of nano material
abeetha Reply
I start with an easy one. carbon nanotubes woven into a long filament like a string
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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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