Final cnn shruthi gali


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Final cnn shruthi gali

  1. 1. Biological Neurons Artificial Neurons
  2. 2. CELLULAR NEURAL NETWORKS G. Shruthi 06B61A0521 Under the Guidance of Rajashree sutrawe Assoc. Prof CSE-Dept & HOD of IT Dept
  3. 3. OVERVIEW <ul><li>Introduction </li></ul><ul><li>History of CNN </li></ul><ul><li>Architecture of CNN </li></ul><ul><li>Applications </li></ul><ul><li>Advantages </li></ul><ul><li>Conclusion </li></ul><ul><li>Future Scope </li></ul><ul><li>Reference </li></ul>
  4. 4. <ul><li>Why Neural Networks? </li></ul><ul><li>Some tasks can be done effortlessly by humans but are hard by conventional paradigms. </li></ul><ul><li>Examples: </li></ul><ul><li>Pattern recognition (old friends, hand-written characters) </li></ul><ul><li>Content addressable recall (associative reasoning) </li></ul><ul><li>Approximate, common sense reasoning (driving, playing piano, baseball player) </li></ul>INTRODUCTION
  5. 5. <ul><li>What is Neural Networks? </li></ul><ul><li>It is an interconnection of artificial neurons. </li></ul><ul><li>A set of nodes (units, neurons, processing elements) </li></ul><ul><ul><ul><li>Each node has input and output. </li></ul></ul></ul><ul><ul><ul><li>Each node performs a simple computation by its </li></ul></ul></ul><ul><ul><ul><li>node function. </li></ul></ul></ul><ul><ul><ul><li>Connectivity gives the structure/architecture of </li></ul></ul></ul><ul><ul><ul><li>the net. </li></ul></ul></ul><ul><ul><ul><li>What can be computed by a NN is primarily </li></ul></ul></ul><ul><ul><ul><li>determined by the connections and their </li></ul></ul></ul><ul><ul><ul><li>weights. </li></ul></ul></ul>
  6. 6. <ul><li>Why Cellular Neural Networks? </li></ul><ul><li>The major disadvantage of neural networks </li></ul><ul><li>implementations is the number of interconnections </li></ul><ul><li>between neurons. </li></ul><ul><li>In order to reduce the number of interconnections Chua & </li></ul><ul><li>Yang proposed Cellular Neural Networks where neurons </li></ul><ul><li>were only connected to other neurons within a certain </li></ul><ul><li>neighborhood. </li></ul>
  7. 7. <ul><li>What is Cellular Neural Networks? </li></ul><ul><li>A Cellular Neural Network (CNN), also known as Cellular </li></ul><ul><li>Nonlinear Network, is an array of dynamical systems </li></ul><ul><li>or coupled networks with local connections only. </li></ul><ul><li>The network is an ensemble of spatially arranged cells, </li></ul><ul><li>where each cell is itself a dynamical system that is locally </li></ul><ul><li>coupled to its neighboring cells within some prescribed </li></ul><ul><li>sphere. </li></ul>
  8. 8. History of CNN <ul><li>In 1988 papers from Leon Chua & Lin Yang introduced the </li></ul><ul><li>concept of the Cellular Neural Network. </li></ul><ul><li>In 1993 Tamas Roska & Leon Chua’s article has introduced the </li></ul><ul><li>first analog CNN processor for the engineering research </li></ul><ul><li>community. </li></ul><ul><li>One of the best review about CNN & its types and definitions </li></ul><ul><li>was given by Valerio Cimagalli & Marco Balsi. </li></ul>
  9. 9. Architecture of CNN <ul><li>This is a two-dimensional cellular neural network. </li></ul><ul><li>The squares are the circuit units called cells. </li></ul><ul><li>The links between the cells indicate that there are interactions </li></ul><ul><li>between the linked cells. </li></ul>
  10. 10. <ul><li>The basic circuit unit of CNN is called a cell. </li></ul><ul><li>It contains linear & non-linear elements, which typicallyare linear capacitors, linear resistors, controlled sources & independent sources </li></ul><ul><li>Consider an M X N cellular neural network, having M X N cells arranged in M rows and N columns. </li></ul><ul><li>We call the cell on the i 'th row and the j’th column, </li></ul><ul><li>cell (i, j), and denote it by C(i, j) as in the above figure. </li></ul><ul><li>Now let us define what we mean by a neighborhood of C(i, j). </li></ul>
  11. 11. <ul><li>The r-neighborhood of a cell C(i, j) , in a cellular neural network is shown as </li></ul>Definition: r-neighborhood <ul><li>This figure shows 3 neighborhoods of the same cell with r=1,2 and 3 respectively. </li></ul>
  12. 12. <ul><li>It lends itself to local, low-level & processor intensive operations. </li></ul><ul><li>Feature Extraction </li></ul><ul><li>Contrast Enhancement </li></ul><ul><li>Image Compression </li></ul><ul><li>Image Encoding </li></ul><ul><li>Image Decoding </li></ul><ul><li>Image Segmentation </li></ul><ul><li>Image Stabilization </li></ul><ul><li>Image Fusion </li></ul><ul><li>Image In painting </li></ul><ul><li>Pattern Recognition </li></ul><ul><li>Resolution Enhancement </li></ul><ul><li>Optical Flow </li></ul><ul><li>Moving Object Detection </li></ul><ul><li>Axis of Symmetry Detection </li></ul><ul><li>Multi-target Tracking </li></ul>Applications
  13. 13. <ul><li>CNN processors can achieve upto 50,000 frames per second & for certain applications such as missile tracking, flash detection & sparkling plug diagnostics these processors have outperformed a conventional super computer. </li></ul><ul><li>CNN processors have implemented a real-time system that replicates mammalian retinas, validating that the original CNN modeled correct aspects of biological neural networks used to perform. </li></ul>
  14. 14. <ul><li>The Reconstructions Of Damaged Images Using Cellular Neural Network Model </li></ul>Experimental results CNN Approach (a) Original image (40% defects) (b) Restored image. <ul><li>The experimental results are obtained by using the &quot;CadetWin&quot; (CNN Application Development Environment and Toolkit under Windows) </li></ul>
  15. 15. Figure: Experimental results in different noise conditions with CNN.
  16. 16. Advantages <ul><li>Processing Speed </li></ul><ul><li>Flexibility </li></ul><ul><li>Versatile in Nature </li></ul><ul><li>Dynamical Behavior </li></ul><ul><li>Less Cost </li></ul><ul><li>Easier to Integrate </li></ul><ul><li>Processors Utility </li></ul><ul><li>Robustness </li></ul><ul><li>Speed </li></ul>
  17. 17. CONCLUSION CNN processors have been implemented using current technology and there are plans to implement CNN processors into future technologies. They include the necessary interfaces for programming and interfacing, and have been implemented in a variety of systems and provide value to their users. Researchers are also transitioning CNN processors into emerging technologies.
  18. 18. Future Scope One potential future application of CNN microprocessors is to combine them with the DNA microarrays to allow for a near- real time DNA analysis of hundreds of thousands of different DNA sequences.
  19. 19. REFERENCES <ul><li>L. O. Chua and L. Yang, &quot;Cellular neural networks: Applications,“ IEEE Trans. Circuits Syst., pp. 1273-1290, this issue. </li></ul><ul><li>J. J. Hopfield, &quot;Neural networks and physical systems with emergent computational abilities,&quot; Proc. Natl. Acad. Sei. USA., vol. 79,pp. 2554-2558, 1982. </li></ul><ul><li>J. J. Hopfield and D. W. Tank &quot;'Neural' computation of decisions in optimization problems,&quot; Biol. Cybern., vol. 52, pp. 141-152,1985. </li></ul><ul><li>I. Szatmari, P. Foldesy, C. Rekeczky and A. Zarandy, “Image Processing Library for the Aladdin Computer”, Int’l Workshop on Cellular Neural Networks and Their Applications </li></ul><ul><li>D. Balya, G, Tímar, G. Cserey, and T. Roska , “A New Computational Model for CNN-UMs and its Computational Complexity”, Int’l Workshop on Cellular Neural Networks and Their Applications </li></ul><ul><li> </li></ul>
  20. 20. Thank You
  21. 21. Queries..?