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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tsai, R.H. Sheu, B.J. Wang, M.Y. Jen, S.H. |
| Copyright Year | 1997 |
| Description | Author affiliation: Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA (Tsai, R.H.) |
| Abstract | The architecture and the components design of a compact VLSI model based on the extension of the cellular neural network paradigm is described. Face and image recognition is an important function in future smart, portable multimedia systems. However, it involves many complicated mathematical operations that requires extremely large computing power. Since most operations are in two-dimensional format, the cellular neural network paradigm can be extended for selective tasks in face recognition function. Compact neural network is a suitable computing architecture. It uses the state-constrained neuron model that prevents the state variables becoming unbounded. The clear advantage is that the network will converge to proper solutions quickly. In addition, the hardware annealing technique can be applied to the compact neural network architecture so that the optimized solution can be quickly obtained without the drawback of conventional simulated annealing searching complexity and time penalty. |
| Starting Page | 733 |
| Ending Page | 736 |
| File Size | 345384 |
| Page Count | 4 |
| File Format | |
| ISBN | 078033583X |
| DOI | 10.1109/ISCAS.1997.608985 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1997-06-12 |
| Publisher Place | Hong Kong |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Cellular neural networks Computer architecture Power system modeling Neural networks Simulated annealing Very large scale integration Image recognition Multimedia systems Face recognition Computer networks |
| Content Type | Text |
| Resource Type | Article |
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