Cellular Neural Network
Computer Science Clay Active In SP Posts: 712 Joined: Jan 2009 
25022009, 12:26 AM
Cellular Neural Network 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 CNNUM and is also used in imageprocessing.It can also implement any Boolean functions. ARCHITECTURE OF CNN A standard CNN architecture consists of an m*n rectangular array of cells c(i,j) with Cartesian coordinates (i,j) i=1,2Â¦..M, j=12Â¦...N. A class 1 m*n standard CNN is defined by a m*n rectangular array of cells cij located at site (i,j) i= 1,2 Â¦Â¦.m ,j=1,2,Â¦.n is defined mathematically by (dXij/dt )= Xij + A(I,j,k,l) Ykl + B(i,j,k,l) + Zij Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion



mangalsitm Active In SP Posts: 6 Joined: Nov 2010 
20112010, 12:43 PM
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seminar surveyer Active In SP Posts: 3,541 Joined: Sep 2010 
22112010, 10:16 AM
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seminar ideas Super Moderator Posts: 10,003 Joined: Apr 2012 
02052012, 04:02 PM
Cellular Neural Network
INTRODUCTION CNN.docx (Size: 393.1 KB / Downloads: 30) INTRODUCTION Cellular Neural Network is a revolutionary concept and an experimentally proven new computing paradigm for analog computers. Cellular neural networks (CNN) are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighbouring units only. Typical applications include image processing, analyzing 3D surfaces, solving partial differential equations, reducing nonvisual problems to geometric maps, modeling biological vision and other sensorymotor organs. 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 CNNUM and is also used in image processing. It can also implement any Boolean functions. DIGITAL HARDWARE ACCELERATORS We can emulate analog dynamics by digital hardware accelerators. Emulating large CNN arrays need more computing power. A special hardware accelerator board (HAB) was developed for simulating up to one million pixel arrays with on board memory, with 4 DSP chips. Using hab’s large arrays can be simulated with cheap pc. Actually the DSP is a reduced instruction set (RISC). Processor used for calculating CNN dynamics. New dsp packages host 48 dsp processors in a chip .hence the process numbers are 48 times higher .since for the calculation of CNN dynamics ,a major part of dsp is not used , a special purpose chip (castle) have been developed SIMPLICAL CNN Recently a novel structure has been introduced to implement any Boolean / gray level function of any number of variables .The output is no longer restricted to be binary so that CNNs with gray scale outputs are obtained. Simplical CNNs are implemented using RTDs (resonant tunneling diodes) RTDs are nano electric quantum device is featuring high speed regims and small integration sizes and they can be designed to operate in nano /femto sizes leading to extreme low power designs. In addition they exhibit an intrinsic non linear behaviour which can be exploited in many diverse applications, for instance in frequency multiplier and parity generators the threshold logic gates multi gigahertz ad converters, and multivalued logic applications including multivalued memory design among others. IMPLEMENTATION OF SIMPLICAL R.T.D. C.N.N. The first block is responsible for vertex code generation, whereas the second block is responsible for producing the values of function at the vertices . Finally there is the interpolation state, which is the charge of producing the result VERTEX CODE GENERATOR The vertex code generator is the part of the circuit that receives as input the nine values {u1, u2, …u9}. Corresponding to the inputs associated with central pixel and its 8 neighbours & generates s a code. This code is used to retrieve the value of associated parameter. The key component of vertex code generator is a comparator. One way to implement this is using a single R.T.D in current mode configuration as in the circuit illustrated below. 


