0.8 pm CMOS Implementation of Weighted-Order Statistic Image Filter Based on Cellular Neural NetworkArchitecture  

Oleh:

Kowalski, J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 5 (2003), page 1366-1374.
Topik: STATISTICS; CMOS implementation; weighted - order; statistic; cellular neural network; architecture

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.9 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper, a very large scale integration chip of an analog image weighted - order statistic (WOS) filter based on cellular neural network (CNN) architecture for real - time applications is described. The chip has been implemented in CMOS AMS 0.8 µm technology. CNN - based filter consists of feedforward nonlinear template B operating within the window of 3 × 3 pixels around the central pixel being filtered. The feedforward nonlinear CNN coefficients have been realized using programmable non linear coupler circuits. The WOS filter chip allows for processing of images with 300 pixels horizontal resolution. The resolution can be increased by cascading of the chips. Experimental results of basic circuit building blocks measurements are presented. Functional tests of the chip have been performed using a special test setup for PAL composite video signal processing. Using the setup real images have been filtered by WOS filter chip under test.

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