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SUMBITTED BY:
MUKESH KUMAR
M.TECH(2010ECB1026)
Cellular Neural Network is a revolutionary concept
and an experimentally proven new computing
paradigm for analog computers. Looking at the
technological advancement in the last 50 years ;
we see the first revolution which led to pc
industry in 1980’s, second revolution led to
internet industry in 1990’s cheap sensors & mems
arrays in desired forms of artificial eyes, nose, ears
etc. this third revolution owes due to C.N.N. This
technology is implemented using CNN-UM. and
is also used in image processing. It can also
implement any Boolean functions.
Cellular neural networks (CNN) are a regular, single
or multi-layer, parallel computing paradigm similar
to neural networks, with the difference that
communication is allowed between neighbouring
units only.
processing structures with analog nonlinear dynamic
units (cells).
Each cell is made up of linear capacitor, non linear
voltage controlled current source, resistive linear
circuit element.
Cellular neural network (CNN) is a locally connected,
analog processor array which has two or more
dimensions. A standard CNN architecture consists of an
M × N rectangular array of cells C(i, j) with Cartesian
coordinate (i, j), where i = 1..M, j = 1..N
The state of a cell depends on inter-connection weights
between the cell and its neighbours. These parameters
are expressed in the form of the template.
The CNN Universal Machine (CNN-UM) is based on aCNN.
First programmable analog processor array computer withits own language and operation system whose VLSIimplementation has the same computing power as asupercomputer in image processing applications.
The extended universal cells of CNN-UM are controlled byglobal analogic programming unit (GAPU), which hasanalog and logic parts: global analog program register,global logic program register, switch configuration registerand global analogic control unit. Every cell has analog andlogical memory.
The CNN can be defined as an M x N type array of identical
cells arranged in a rectangular grid. Each cell is locally
connected to its 8 nearest surrounding neighbors.
Each cell is characterized by uij, yij and vij being the input,
the output and the state variable of the cell respectively.
The output is related to the state by the nonlinear equation:
yij = f(vij) = 0.5 (| vij + 1| – |vij – 1|)
High speed target recognition, tracking.
Real time visual inspection of manufacturing processes.
Cheap sensors and mems arrays are in the desired forms of artificial eyes, nose, ears, taste & realization of telepathy.
Intelligent vision capable of recognition of context-sensitive & moving scenes as well as applications requiring real time fusing of multiple modalities such as multi spectral images involving infrared, long wave-infrared and polarized lights.
PDE based in modern image processing techniques are
becoming most challenging & important for analogic
C.N.N. computers. A major challenge yet not solved by
any existing technology is to build analogic adaptive
sensor computer where sensing & computing
understanding are fully integrated on a chip.
THANKS