Cellular neural

1. CELLULAR NEURAL NETWORK SUMBITTED BY: MUKESH KUMAR M.TECH(2010ECB1026)

2. 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. INTRODUCTION

3. 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. CONT…

4. 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 ARCHITECTURE OF CNN

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6. 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. CNN TEMPLATES

7. The CNN Universal Machine (CNN-UM) is based on a CNN. First programmable analog processor array computer with its own language and operation system whose VLSI implementation has the same computing power as a supercomputer in image processing applications. The extended universal cells of CNN-UM are controlled by global analogic programming unit (GAPU ), which has analog and logic parts: global analog program register, global logic program register, switch configuration register and global analogic control unit. Every cell has analog and logical memory. Universal Machine (CNN-UM)

8. 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|) CHARACTERISTICS OF THE CNN

9. 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 . APPLICATIONS

10. 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. CONCLUSION

11. THANKS